feat: language-aware context compression summaries

Port from anomalyco/opencode#20581: context compaction now generates
summaries in the same language the user was using in the conversation.

Previously, summaries were always produced in English regardless of the
conversation language, which would confuse multilingual users by injecting
English context into non-English conversations.

Adds 'Write the summary in the same language the user was using in the
conversation.' to both the initial and iterative update summarization
prompts in ContextCompressor.

agent/builtin_memory_provider.py

@@ -0,0 +1,113 @@
+"""BuiltinMemoryProvider — wraps MEMORY.md / USER.md as a MemoryProvider.
+
+Always registered as the first provider. Cannot be disabled or removed.
+This is the existing Hermes memory system exposed through the provider
+interface for compatibility with the MemoryManager.
+
+The actual storage logic lives in tools/memory_tool.py (MemoryStore).
+This provider is a thin adapter that delegates to MemoryStore and
+exposes the memory tool schema.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from typing import Any, Dict, List, Optional
+
+from agent.memory_provider import MemoryProvider
+
+logger = logging.getLogger(__name__)
+
+
+class BuiltinMemoryProvider(MemoryProvider):
+    """Built-in file-backed memory (MEMORY.md + USER.md).
+
+    Always active, never disabled by other providers. The `memory` tool
+    is handled by run_agent.py's agent-level tool interception (not through
+    the normal registry), so get_tool_schemas() returns an empty list —
+    the memory tool is already wired separately.
+    """
+
+    def __init__(
+        self,
+        memory_store=None,
+        memory_enabled: bool = False,
+        user_profile_enabled: bool = False,
+    ):
+        self._store = memory_store
+        self._memory_enabled = memory_enabled
+        self._user_profile_enabled = user_profile_enabled
+
+    @property
+    def name(self) -> str:
+        return "builtin"
+
+    def is_available(self) -> bool:
+        """Built-in memory is always available."""
+        return True
+
+    def initialize(self, session_id: str, **kwargs) -> None:
+        """Load memory from disk if not already loaded."""
+        if self._store is not None:
+            self._store.load_from_disk()
+
+    def system_prompt_block(self) -> str:
+        """Return MEMORY.md and USER.md content for the system prompt.
+
+        Uses the frozen snapshot captured at load time. This ensures the
+        system prompt stays stable throughout a session (preserving the
+        prompt cache), even though the live entries may change via tool calls.
+        """
+        if not self._store:
+            return ""
+
+        parts = []
+        if self._memory_enabled:
+            mem_block = self._store.format_for_system_prompt("memory")
+            if mem_block:
+                parts.append(mem_block)
+        if self._user_profile_enabled:
+            user_block = self._store.format_for_system_prompt("user")
+            if user_block:
+                parts.append(user_block)
+
+        return "\n\n".join(parts)
+
+    def prefetch(self, query: str, *, session_id: str = "") -> str:
+        """Built-in memory doesn't do query-based recall — it's injected via system_prompt_block."""
+        return ""
+
+    def sync_turn(self, user_content: str, assistant_content: str, *, session_id: str = "") -> None:
+        """Built-in memory doesn't auto-sync turns — writes happen via the memory tool."""
+
+    def get_tool_schemas(self) -> List[Dict[str, Any]]:
+        """Return empty list.
+
+        The `memory` tool is an agent-level intercepted tool, handled
+        specially in run_agent.py before normal tool dispatch. It's not
+        part of the standard tool registry. We don't duplicate it here.
+        """
+        return []
+
+    def handle_tool_call(self, tool_name: str, args: Dict[str, Any], **kwargs) -> str:
+        """Not used — the memory tool is intercepted in run_agent.py."""
+        return json.dumps({"error": "Built-in memory tool is handled by the agent loop"})
+
+    def shutdown(self) -> None:
+        """No cleanup needed — files are saved on every write."""
+
+    # -- Property access for backward compatibility --------------------------
+
+    @property
+    def store(self):
+        """Access the underlying MemoryStore for legacy code paths."""
+        return self._store
+
+    @property
+    def memory_enabled(self) -> bool:
+        return self._memory_enabled
+
+    @property
+    def user_profile_enabled(self) -> bool:
+        return self._user_profile_enabled

agent/context_compressor.py

@@ -301,6 +301,8 @@ Update the summary using this exact structure. PRESERVE all existing information
 
 Target ~{summary_budget} tokens. Be specific — include file paths, command outputs, error messages, and concrete values rather than vague descriptions.
 
+Write the summary in the same language the user was using in the conversation.
+
 Write only the summary body. Do not include any preamble or prefix."""
         else:
             # First compaction: summarize from scratch
@@ -339,6 +341,8 @@ Use this exact structure:
 
 Target ~{summary_budget} tokens. Be specific — include file paths, command outputs, error messages, and concrete values rather than vague descriptions. The goal is to prevent the next assistant from repeating work or losing important details.
 
+Write the summary in the same language the user was using in the conversation.
+
 Write only the summary body. Do not include any preamble or prefix."""
 
         try:

agent/memory_manager.py

@@ -0,0 +1,335 @@
+"""MemoryManager — orchestrates the built-in memory provider plus at most
+ONE external plugin memory provider.
+
+Single integration point in run_agent.py. Replaces scattered per-backend
+code with one manager that delegates to registered providers.
+
+The BuiltinMemoryProvider is always registered first and cannot be removed.
+Only ONE external (non-builtin) provider is allowed at a time — attempting
+to register a second external provider is rejected with a warning.  This
+prevents tool schema bloat and conflicting memory backends.
+
+Usage in run_agent.py:
+    self._memory_manager = MemoryManager()
+    self._memory_manager.add_provider(BuiltinMemoryProvider(...))
+    # Only ONE of these:
+    self._memory_manager.add_provider(plugin_provider)
+
+    # System prompt
+    prompt_parts.append(self._memory_manager.build_system_prompt())
+
+    # Pre-turn
+    context = self._memory_manager.prefetch_all(user_message)
+
+    # Post-turn
+    self._memory_manager.sync_all(user_msg, assistant_response)
+    self._memory_manager.queue_prefetch_all(user_msg)
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from typing import Any, Dict, List, Optional
+
+from agent.memory_provider import MemoryProvider
+
+logger = logging.getLogger(__name__)
+
+
+class MemoryManager:
+    """Orchestrates the built-in provider plus at most one external provider.
+
+    The builtin provider is always first. Only one non-builtin (external)
+    provider is allowed.  Failures in one provider never block the other.
+    """
+
+    def __init__(self) -> None:
+        self._providers: List[MemoryProvider] = []
+        self._tool_to_provider: Dict[str, MemoryProvider] = {}
+        self._has_external: bool = False  # True once a non-builtin provider is added
+
+    # -- Registration --------------------------------------------------------
+
+    def add_provider(self, provider: MemoryProvider) -> None:
+        """Register a memory provider.
+
+        Built-in provider (name ``"builtin"``) is always accepted.
+        Only **one** external (non-builtin) provider is allowed — a second
+        attempt is rejected with a warning.
+        """
+        is_builtin = provider.name == "builtin"
+
+        if not is_builtin:
+            if self._has_external:
+                existing = next(
+                    (p.name for p in self._providers if p.name != "builtin"), "unknown"
+                )
+                logger.warning(
+                    "Rejected memory provider '%s' — external provider '%s' is "
+                    "already registered. Only one external memory provider is "
+                    "allowed at a time. Configure which one via memory.provider "
+                    "in config.yaml.",
+                    provider.name, existing,
+                )
+                return
+            self._has_external = True
+
+        self._providers.append(provider)
+
+        # Index tool names → provider for routing
+        for schema in provider.get_tool_schemas():
+            tool_name = schema.get("name", "")
+            if tool_name and tool_name not in self._tool_to_provider:
+                self._tool_to_provider[tool_name] = provider
+            elif tool_name in self._tool_to_provider:
+                logger.warning(
+                    "Memory tool name conflict: '%s' already registered by %s, "
+                    "ignoring from %s",
+                    tool_name,
+                    self._tool_to_provider[tool_name].name,
+                    provider.name,
+                )
+
+        logger.info(
+            "Memory provider '%s' registered (%d tools)",
+            provider.name,
+            len(provider.get_tool_schemas()),
+        )
+
+    @property
+    def providers(self) -> List[MemoryProvider]:
+        """All registered providers in order."""
+        return list(self._providers)
+
+    @property
+    def provider_names(self) -> List[str]:
+        """Names of all registered providers."""
+        return [p.name for p in self._providers]
+
+    def get_provider(self, name: str) -> Optional[MemoryProvider]:
+        """Get a provider by name, or None if not registered."""
+        for p in self._providers:
+            if p.name == name:
+                return p
+        return None
+
+    # -- System prompt -------------------------------------------------------
+
+    def build_system_prompt(self) -> str:
+        """Collect system prompt blocks from all providers.
+
+        Returns combined text, or empty string if no providers contribute.
+        Each non-empty block is labeled with the provider name.
+        """
+        blocks = []
+        for provider in self._providers:
+            try:
+                block = provider.system_prompt_block()
+                if block and block.strip():
+                    blocks.append(block)
+            except Exception as e:
+                logger.warning(
+                    "Memory provider '%s' system_prompt_block() failed: %s",
+                    provider.name, e,
+                )
+        return "\n\n".join(blocks)
+
+    # -- Prefetch / recall ---------------------------------------------------
+
+    def prefetch_all(self, query: str, *, session_id: str = "") -> str:
+        """Collect prefetch context from all providers.
+
+        Returns merged context text labeled by provider. Empty providers
+        are skipped. Failures in one provider don't block others.
+        """
+        parts = []
+        for provider in self._providers:
+            try:
+                result = provider.prefetch(query, session_id=session_id)
+                if result and result.strip():
+                    parts.append(result)
+            except Exception as e:
+                logger.debug(
+                    "Memory provider '%s' prefetch failed (non-fatal): %s",
+                    provider.name, e,
+                )
+        return "\n\n".join(parts)
+
+    def queue_prefetch_all(self, query: str, *, session_id: str = "") -> None:
+        """Queue background prefetch on all providers for the next turn."""
+        for provider in self._providers:
+            try:
+                provider.queue_prefetch(query, session_id=session_id)
+            except Exception as e:
+                logger.debug(
+                    "Memory provider '%s' queue_prefetch failed (non-fatal): %s",
+                    provider.name, e,
+                )
+
+    # -- Sync ----------------------------------------------------------------
+
+    def sync_all(self, user_content: str, assistant_content: str, *, session_id: str = "") -> None:
+        """Sync a completed turn to all providers."""
+        for provider in self._providers:
+            try:
+                provider.sync_turn(user_content, assistant_content, session_id=session_id)
+            except Exception as e:
+                logger.warning(
+                    "Memory provider '%s' sync_turn failed: %s",
+                    provider.name, e,
+                )
+
+    # -- Tools ---------------------------------------------------------------
+
+    def get_all_tool_schemas(self) -> List[Dict[str, Any]]:
+        """Collect tool schemas from all providers."""
+        schemas = []
+        seen = set()
+        for provider in self._providers:
+            try:
+                for schema in provider.get_tool_schemas():
+                    name = schema.get("name", "")
+                    if name and name not in seen:
+                        schemas.append(schema)
+                        seen.add(name)
+            except Exception as e:
+                logger.warning(
+                    "Memory provider '%s' get_tool_schemas() failed: %s",
+                    provider.name, e,
+                )
+        return schemas
+
+    def get_all_tool_names(self) -> set:
+        """Return set of all tool names across all providers."""
+        return set(self._tool_to_provider.keys())
+
+    def has_tool(self, tool_name: str) -> bool:
+        """Check if any provider handles this tool."""
+        return tool_name in self._tool_to_provider
+
+    def handle_tool_call(
+        self, tool_name: str, args: Dict[str, Any], **kwargs
+    ) -> str:
+        """Route a tool call to the correct provider.
+
+        Returns JSON string result. Raises ValueError if no provider
+        handles the tool.
+        """
+        provider = self._tool_to_provider.get(tool_name)
+        if provider is None:
+            return json.dumps({"error": f"No memory provider handles tool '{tool_name}'"})
+        try:
+            return provider.handle_tool_call(tool_name, args, **kwargs)
+        except Exception as e:
+            logger.error(
+                "Memory provider '%s' handle_tool_call(%s) failed: %s",
+                provider.name, tool_name, e,
+            )
+            return json.dumps({"error": f"Memory tool '{tool_name}' failed: {e}"})
+
+    # -- Lifecycle hooks -----------------------------------------------------
+
+    def on_turn_start(self, turn_number: int, message: str, **kwargs) -> None:
+        """Notify all providers of a new turn.
+
+        kwargs may include: remaining_tokens, model, platform, tool_count.
+        """
+        for provider in self._providers:
+            try:
+                provider.on_turn_start(turn_number, message, **kwargs)
+            except Exception as e:
+                logger.debug(
+                    "Memory provider '%s' on_turn_start failed: %s",
+                    provider.name, e,
+                )
+
+    def on_session_end(self, messages: List[Dict[str, Any]]) -> None:
+        """Notify all providers of session end."""
+        for provider in self._providers:
+            try:
+                provider.on_session_end(messages)
+            except Exception as e:
+                logger.debug(
+                    "Memory provider '%s' on_session_end failed: %s",
+                    provider.name, e,
+                )
+
+    def on_pre_compress(self, messages: List[Dict[str, Any]]) -> str:
+        """Notify all providers before context compression.
+
+        Returns combined text from providers to include in the compression
+        summary prompt. Empty string if no provider contributes.
+        """
+        parts = []
+        for provider in self._providers:
+            try:
+                result = provider.on_pre_compress(messages)
+                if result and result.strip():
+                    parts.append(result)
+            except Exception as e:
+                logger.debug(
+                    "Memory provider '%s' on_pre_compress failed: %s",
+                    provider.name, e,
+                )
+        return "\n\n".join(parts)
+
+    def on_memory_write(self, action: str, target: str, content: str) -> None:
+        """Notify external providers when the built-in memory tool writes.
+
+        Skips the builtin provider itself (it's the source of the write).
+        """
+        for provider in self._providers:
+            if provider.name == "builtin":
+                continue
+            try:
+                provider.on_memory_write(action, target, content)
+            except Exception as e:
+                logger.debug(
+                    "Memory provider '%s' on_memory_write failed: %s",
+                    provider.name, e,
+                )
+
+    def on_delegation(self, task: str, result: str, *,
+                      child_session_id: str = "", **kwargs) -> None:
+        """Notify all providers that a subagent completed."""
+        for provider in self._providers:
+            try:
+                provider.on_delegation(
+                    task, result, child_session_id=child_session_id, **kwargs
+                )
+            except Exception as e:
+                logger.debug(
+                    "Memory provider '%s' on_delegation failed: %s",
+                    provider.name, e,
+                )
+
+    def shutdown_all(self) -> None:
+        """Shut down all providers (reverse order for clean teardown)."""
+        for provider in reversed(self._providers):
+            try:
+                provider.shutdown()
+            except Exception as e:
+                logger.warning(
+                    "Memory provider '%s' shutdown failed: %s",
+                    provider.name, e,
+                )
+
+    def initialize_all(self, session_id: str, **kwargs) -> None:
+        """Initialize all providers.
+
+        Automatically injects ``hermes_home`` into *kwargs* so that every
+        provider can resolve profile-scoped storage paths without importing
+        ``get_hermes_home()`` themselves.
+        """
+        if "hermes_home" not in kwargs:
+            from hermes_constants import get_hermes_home
+            kwargs["hermes_home"] = str(get_hermes_home())
+        for provider in self._providers:
+            try:
+                provider.initialize(session_id=session_id, **kwargs)
+            except Exception as e:
+                logger.warning(
+                    "Memory provider '%s' initialize failed: %s",
+                    provider.name, e,
+                )

agent/memory_provider.py

@@ -0,0 +1,231 @@
+"""Abstract base class for pluggable memory providers.
+
+Memory providers give the agent persistent recall across sessions. One
+external provider is active at a time alongside the always-on built-in
+memory (MEMORY.md / USER.md). The MemoryManager enforces this limit.
+
+Built-in memory is always active as the first provider and cannot be removed.
+External providers (Honcho, Hindsight, Mem0, etc.) are additive — they never
+disable the built-in store. Only one external provider runs at a time to
+prevent tool schema bloat and conflicting memory backends.
+
+Registration:
+  1. Built-in: BuiltinMemoryProvider — always present, not removable.
+  2. Plugins: Ship in plugins/memory/<name>/, activated by memory.provider config.
+
+Lifecycle (called by MemoryManager, wired in run_agent.py):
+  initialize()          — connect, create resources, warm up
+  system_prompt_block()  — static text for the system prompt
+  prefetch(query)        — background recall before each turn
+  sync_turn(user, asst)  — async write after each turn
+  get_tool_schemas()     — tool schemas to expose to the model
+  handle_tool_call()     — dispatch a tool call
+  shutdown()             — clean exit
+
+Optional hooks (override to opt in):
+  on_turn_start(turn, message, **kwargs) — per-turn tick with runtime context
+  on_session_end(messages)               — end-of-session extraction
+  on_pre_compress(messages) -> str       — extract before context compression
+  on_memory_write(action, target, content) — mirror built-in memory writes
+  on_delegation(task, result, **kwargs)  — parent-side observation of subagent work
+"""
+
+from __future__ import annotations
+
+import logging
+from abc import ABC, abstractmethod
+from typing import Any, Dict, List, Optional
+
+logger = logging.getLogger(__name__)
+
+
+class MemoryProvider(ABC):
+    """Abstract base class for memory providers."""
+
+    @property
+    @abstractmethod
+    def name(self) -> str:
+        """Short identifier for this provider (e.g. 'builtin', 'honcho', 'hindsight')."""
+
+    # -- Core lifecycle (implement these) ------------------------------------
+
+    @abstractmethod
+    def is_available(self) -> bool:
+        """Return True if this provider is configured, has credentials, and is ready.
+
+        Called during agent init to decide whether to activate the provider.
+        Should not make network calls — just check config and installed deps.
+        """
+
+    @abstractmethod
+    def initialize(self, session_id: str, **kwargs) -> None:
+        """Initialize for a session.
+
+        Called once at agent startup. May create resources (banks, tables),
+        establish connections, start background threads, etc.
+
+        kwargs always include:
+          - hermes_home (str): The active HERMES_HOME directory path. Use this
+            for profile-scoped storage instead of hardcoding ``~/.hermes``.
+          - platform (str): "cli", "telegram", "discord", "cron", etc.
+
+        kwargs may also include:
+          - agent_context (str): "primary", "subagent", "cron", or "flush".
+            Providers should skip writes for non-primary contexts (cron system
+            prompts would corrupt user representations).
+          - agent_identity (str): Profile name (e.g. "coder"). Use for
+            per-profile provider identity scoping.
+          - agent_workspace (str): Shared workspace name (e.g. "hermes").
+          - parent_session_id (str): For subagents, the parent's session_id.
+          - user_id (str): Platform user identifier (gateway sessions).
+        """
+
+    def system_prompt_block(self) -> str:
+        """Return text to include in the system prompt.
+
+        Called during system prompt assembly. Return empty string to skip.
+        This is for STATIC provider info (instructions, status). Prefetched
+        recall context is injected separately via prefetch().
+        """
+        return ""
+
+    def prefetch(self, query: str, *, session_id: str = "") -> str:
+        """Recall relevant context for the upcoming turn.
+
+        Called before each API call. Return formatted text to inject as
+        context, or empty string if nothing relevant. Implementations
+        should be fast — use background threads for the actual recall
+        and return cached results here.
+
+        session_id is provided for providers serving concurrent sessions
+        (gateway group chats, cached agents). Providers that don't need
+        per-session scoping can ignore it.
+        """
+        return ""
+
+    def queue_prefetch(self, query: str, *, session_id: str = "") -> None:
+        """Queue a background recall for the NEXT turn.
+
+        Called after each turn completes. The result will be consumed
+        by prefetch() on the next turn. Default is no-op — providers
+        that do background prefetching should override this.
+        """
+
+    def sync_turn(self, user_content: str, assistant_content: str, *, session_id: str = "") -> None:
+        """Persist a completed turn to the backend.
+
+        Called after each turn. Should be non-blocking — queue for
+        background processing if the backend has latency.
+        """
+
+    @abstractmethod
+    def get_tool_schemas(self) -> List[Dict[str, Any]]:
+        """Return tool schemas this provider exposes.
+
+        Each schema follows the OpenAI function calling format:
+        {"name": "...", "description": "...", "parameters": {...}}
+
+        Return empty list if this provider has no tools (context-only).
+        """
+
+    def handle_tool_call(self, tool_name: str, args: Dict[str, Any], **kwargs) -> str:
+        """Handle a tool call for one of this provider's tools.
+
+        Must return a JSON string (the tool result).
+        Only called for tool names returned by get_tool_schemas().
+        """
+        raise NotImplementedError(f"Provider {self.name} does not handle tool {tool_name}")
+
+    def shutdown(self) -> None:
+        """Clean shutdown — flush queues, close connections."""
+
+    # -- Optional hooks (override to opt in) ---------------------------------
+
+    def on_turn_start(self, turn_number: int, message: str, **kwargs) -> None:
+        """Called at the start of each turn with the user message.
+
+        Use for turn-counting, scope management, periodic maintenance.
+
+        kwargs may include: remaining_tokens, model, platform, tool_count.
+        Providers use what they need; extras are ignored.
+        """
+
+    def on_session_end(self, messages: List[Dict[str, Any]]) -> None:
+        """Called when a session ends (explicit exit or timeout).
+
+        Use for end-of-session fact extraction, summarization, etc.
+        messages is the full conversation history.
+
+        NOT called after every turn — only at actual session boundaries
+        (CLI exit, /reset, gateway session expiry).
+        """
+
+    def on_pre_compress(self, messages: List[Dict[str, Any]]) -> str:
+        """Called before context compression discards old messages.
+
+        Use to extract insights from messages about to be compressed.
+        messages is the list that will be summarized/discarded.
+
+        Return text to include in the compression summary prompt so the
+        compressor preserves provider-extracted insights. Return empty
+        string for no contribution (backwards-compatible default).
+        """
+        return ""
+
+    def on_delegation(self, task: str, result: str, *,
+                      child_session_id: str = "", **kwargs) -> None:
+        """Called on the PARENT agent when a subagent completes.
+
+        The parent's memory provider gets the task+result pair as an
+        observation of what was delegated and what came back. The subagent
+        itself has no provider session (skip_memory=True).
+
+        task: the delegation prompt
+        result: the subagent's final response
+        child_session_id: the subagent's session_id
+        """
+
+    def get_config_schema(self) -> List[Dict[str, Any]]:
+        """Return config fields this provider needs for setup.
+
+        Used by 'hermes memory setup' to walk the user through configuration.
+        Each field is a dict with:
+          key:         config key name (e.g. 'api_key', 'mode')
+          description: human-readable description
+          secret:      True if this should go to .env (default: False)
+          required:    True if required (default: False)
+          default:     default value (optional)
+          choices:     list of valid values (optional)
+          url:         URL where user can get this credential (optional)
+          env_var:     explicit env var name for secrets (default: auto-generated)
+
+        Return empty list if no config needed (e.g. local-only providers).
+        """
+        return []
+
+    def save_config(self, values: Dict[str, Any], hermes_home: str) -> None:
+        """Write non-secret config to the provider's native location.
+
+        Called by 'hermes memory setup' after collecting user inputs.
+        ``values`` contains only non-secret fields (secrets go to .env).
+        ``hermes_home`` is the active HERMES_HOME directory path.
+
+        Providers with native config files (JSON, YAML) should override
+        this to write to their expected location. Providers that use only
+        env vars can leave the default (no-op).
+
+        All new memory provider plugins MUST implement either:
+        - save_config() for native config file formats, OR
+        - use only env vars (in which case get_config_schema() fields
+          should all have ``env_var`` set and this method stays no-op).
+        """
+
+    def on_memory_write(self, action: str, target: str, content: str) -> None:
+        """Called when the built-in memory tool writes an entry.
+
+        action: 'add', 'replace', or 'remove'
+        target: 'memory' or 'user'
+        content: the entry content
+
+        Use to mirror built-in memory writes to your backend.
+        """

cli.py

@@ -508,6 +508,8 @@ from tools.browser_tool import _emergency_cleanup_all_sessions as _cleanup_all_b
 
 # Guard to prevent cleanup from running multiple times on exit
 _cleanup_done = False
+# Weak reference to the active AIAgent for memory provider shutdown at exit
+_active_agent_ref = None
 
 def _run_cleanup():
     """Run resource cleanup exactly once."""
@@ -536,6 +538,15 @@ def _run_cleanup():
         shutdown_cached_clients()
     except Exception:
         pass
+    # Shut down memory provider (on_session_end + shutdown_all) at actual
+    # session boundary — NOT per-turn inside run_conversation().
+    try:
+        if _active_agent_ref and hasattr(_active_agent_ref, 'shutdown_memory_provider'):
+            _active_agent_ref.shutdown_memory_provider(
+                getattr(_active_agent_ref, 'conversation_history', None) or []
+            )
+    except Exception:
+        pass
 
 
 # =============================================================================
@@ -2218,7 +2229,7 @@ class HermesCLI:
                 session_db=self._session_db,
                 clarify_callback=self._clarify_callback,
                 reasoning_callback=self._current_reasoning_callback(),
-                honcho_session_key=None,  # resolved by run_agent via config sessions map / title
+
                 fallback_model=self._fallback_model,
                 thinking_callback=self._on_thinking,
                 checkpoints_enabled=self.checkpoints_enabled,
@@ -2230,6 +2241,9 @@ class HermesCLI:
                 stream_delta_callback=self._stream_delta if self.streaming_enabled else None,
                 tool_gen_callback=self._on_tool_gen_start if self.streaming_enabled else None,
             )
+            # Store reference for atexit memory provider shutdown
+            global _active_agent_ref
+            _active_agent_ref = self.agent
             # Route agent status output through prompt_toolkit so ANSI escape
             # sequences aren't garbled by patch_stdout's StdoutProxy (#2262).
             self.agent._print_fn = _cprint
@@ -3237,6 +3251,9 @@ class HermesCLI:
 
     def reset_conversation(self):
         """Reset the conversation by starting a new session."""
+        # Shut down memory provider before resetting — actual session boundary
+        if hasattr(self, 'agent') and self.agent:
+            self.agent.shutdown_memory_provider(self.conversation_history)
         self.new_session()
     
     def save_conversation(self):
@@ -3901,28 +3918,6 @@ class HermesCLI:
                             try:
                                 if self._session_db.set_session_title(self.session_id, new_title):
                                     _cprint(f"  Session title set: {new_title}")
-                                    # Re-map Honcho session key to new title
-                                    if self.agent and getattr(self.agent, '_honcho', None):
-                                        try:
-                                            hcfg = self.agent._honcho_config
-                                            new_key = (
-                                                hcfg.resolve_session_name(
-                                                    session_title=new_title,
-                                                    session_id=self.agent.session_id,
-                                                )
-                                                if hcfg else new_title
-                                            )
-                                            if new_key and new_key != self.agent._honcho_session_key:
-                                                old_key = self.agent._honcho_session_key
-                                                self.agent._honcho.get_or_create(new_key)
-                                                self.agent._honcho_session_key = new_key
-                                                from tools.honcho_tools import set_session_context
-                                                set_session_context(self.agent._honcho, new_key)
-                                                from agent.display import honcho_session_line, write_tty
-                                                write_tty(honcho_session_line(hcfg.workspace_id, new_key) + "\n")
-                                                _cprint(f"  Honcho session: {old_key} → {new_key}")
-                                        except Exception:
-                                            pass
                                 else:
                                     _cprint("  Session not found in database.")
                             except ValueError as e:
@@ -4387,7 +4382,6 @@ class HermesCLI:
                     user_message=btw_prompt,
                     conversation_history=history_snapshot,
                     task_id=task_id,
-                    sync_honcho=False,
                 )
 
                 response = (result.get("final_response") or "") if result else ""
@@ -4817,12 +4811,7 @@ class HermesCLI:
                 f"  ✅ Compressed: {original_count} → {new_count} messages "
                 f"(~{approx_tokens:,} → ~{new_tokens:,} tokens)"
             )
-            # Flush Honcho async queue so queued messages land before context resets
-            if self.agent and getattr(self.agent, '_honcho', None):
-                try:
-                    self.agent._honcho.flush_all()
-                except Exception:
-                    pass
+
         except Exception as e:
             print(f"  ❌ Compression failed: {e}")
 
@@ -6483,17 +6472,6 @@ class HermesCLI:
         # One-line Honcho session indicator (TTY-only, not captured by agent).
         # Only show when the user explicitly configured Honcho for Hermes
         # (not auto-enabled from a stray HONCHO_API_KEY env var).
-        try:
-            from honcho_integration.client import HonchoClientConfig
-            from agent.display import honcho_session_line, write_tty
-            hcfg = HonchoClientConfig.from_global_config()
-            if hcfg.enabled and (hcfg.api_key or hcfg.base_url) and hcfg.explicitly_configured:
-                sname = hcfg.resolve_session_name(session_id=self.session_id)
-                if sname:
-                    write_tty(honcho_session_line(hcfg.workspace_id, sname) + "\n")
-        except Exception:
-            pass
-
         # If resuming a session, load history and display it immediately
         # so the user has context before typing their first message.
         if self._resumed:
@@ -7812,12 +7790,6 @@ class HermesCLI:
             set_sudo_password_callback(None)
             set_approval_callback(None)
             set_secret_capture_callback(None)
-            # Flush + shut down Honcho async writer (drains queue before exit)
-            if self.agent and getattr(self.agent, '_honcho', None):
-                try:
-                    self.agent._honcho.shutdown()
-                except (Exception, KeyboardInterrupt):
-                    pass
             # Close session in SQLite
             if hasattr(self, '_session_db') and self._session_db and self.agent:
                 try:

cron/scheduler.py

@@ -437,6 +437,7 @@ def run_job(job: dict) -> tuple[bool, str, str, Optional[str]]:
             provider_sort=pr.get("sort"),
             disabled_toolsets=["cronjob", "messaging", "clarify"],
             quiet_mode=True,
+            skip_memory=True,  # Cron system prompts would corrupt user representations
             platform="cron",
             session_id=_cron_session_id,
             session_db=_session_db,

gateway/run.py

@@ -474,8 +474,6 @@ class GatewayRunner:
         # Persistent Honcho managers keyed by gateway session key.
         # This preserves write_frequency="session" semantics across short-lived
         # per-message AIAgent instances.
-        self._honcho_managers: Dict[str, Any] = {}
-        self._honcho_configs: Dict[str, Any] = {}
 
 
 
@@ -508,61 +506,9 @@ class GatewayRunner:
         # Track background tasks to prevent garbage collection mid-execution
         self._background_tasks: set = set()
 
-    def _get_or_create_gateway_honcho(self, session_key: str):
-        """Return a persistent Honcho manager/config pair for this gateway session."""
-        if not hasattr(self, "_honcho_managers"):
-            self._honcho_managers = {}
-        if not hasattr(self, "_honcho_configs"):
-            self._honcho_configs = {}
 
-        if session_key in self._honcho_managers:
-            return self._honcho_managers[session_key], self._honcho_configs.get(session_key)
 
-        try:
-            from honcho_integration.client import HonchoClientConfig, get_honcho_client
-            from honcho_integration.session import HonchoSessionManager
 
-            hcfg = HonchoClientConfig.from_global_config()
-            if not hcfg.enabled or not (hcfg.api_key or hcfg.base_url):
-                return None, hcfg
-
-            client = get_honcho_client(hcfg)
-            manager = HonchoSessionManager(
-                honcho=client,
-                config=hcfg,
-                context_tokens=hcfg.context_tokens,
-            )
-            self._honcho_managers[session_key] = manager
-            self._honcho_configs[session_key] = hcfg
-            return manager, hcfg
-        except Exception as e:
-            logger.debug("Gateway Honcho init failed for %s: %s", session_key, e)
-            return None, None
-
-    def _shutdown_gateway_honcho(self, session_key: str) -> None:
-        """Flush and close the persistent Honcho manager for a gateway session."""
-        managers = getattr(self, "_honcho_managers", None)
-        configs = getattr(self, "_honcho_configs", None)
-        if managers is None or configs is None:
-            return
-
-        manager = managers.pop(session_key, None)
-        configs.pop(session_key, None)
-        if not manager:
-            return
-        try:
-            manager.shutdown()
-        except Exception as e:
-            logger.debug("Gateway Honcho shutdown failed for %s: %s", session_key, e)
-
-    def _shutdown_all_gateway_honcho(self) -> None:
-        """Flush and close all persistent Honcho managers."""
-        managers = getattr(self, "_honcho_managers", None)
-        if not managers:
-            return
-        for session_key in list(managers.keys()):
-            self._shutdown_gateway_honcho(session_key)
-    
     # -- Setup skill availability ----------------------------------------
 
     def _has_setup_skill(self) -> bool:
@@ -627,7 +573,6 @@ class GatewayRunner:
     def _flush_memories_for_session(
         self,
         old_session_id: str,
-        honcho_session_key: Optional[str] = None,
     ):
         """Prompt the agent to save memories/skills before context is lost.
 
@@ -660,9 +605,9 @@ class GatewayRunner:
                 model=model,
                 max_iterations=8,
                 quiet_mode=True,
+                skip_memory=True,  # Flush agent — no memory provider
                 enabled_toolsets=["memory", "skills"],
                 session_id=old_session_id,
-                honcho_session_key=honcho_session_key,
             )
             # Fully silence the flush agent — quiet_mode only suppresses init
             # messages; tool call output still leaks to the terminal through
@@ -725,22 +670,14 @@ class GatewayRunner:
             tmp_agent.run_conversation(
                 user_message=flush_prompt,
                 conversation_history=msgs,
-                sync_honcho=False,
             )
             logger.info("Pre-reset memory flush completed for session %s", old_session_id)
-            # Flush any queued Honcho writes before the session is dropped
-            if getattr(tmp_agent, '_honcho', None):
-                try:
-                    tmp_agent._honcho.shutdown()
-                except Exception:
-                    pass
         except Exception as e:
             logger.debug("Pre-reset memory flush failed for session %s: %s", old_session_id, e)
 
     async def _async_flush_memories(
         self,
         old_session_id: str,
-        honcho_session_key: Optional[str] = None,
     ):
         """Run the sync memory flush in a thread pool so it won't block the event loop."""
         loop = asyncio.get_event_loop()
@@ -748,7 +685,6 @@ class GatewayRunner:
             None,
             self._flush_memories_for_session,
             old_session_id,
-            honcho_session_key,
         )
 
     @property
@@ -1291,7 +1227,14 @@ class GatewayRunner:
                     )
                     try:
                         await self._async_flush_memories(entry.session_id, key)
-                        self._shutdown_gateway_honcho(key)
+                        # Shut down memory provider on the cached agent
+                        cached_agent = self._running_agents.get(key)
+                        if cached_agent and cached_agent is not _AGENT_PENDING_SENTINEL:
+                            try:
+                                if hasattr(cached_agent, 'shutdown_memory_provider'):
+                                    cached_agent.shutdown_memory_provider()
+                            except Exception:
+                                pass
                         # Mark as flushed and persist to disk so the flag
                         # survives gateway restarts.
                         with self.session_store._lock:
@@ -1425,6 +1368,12 @@ class GatewayRunner:
                 logger.debug("Interrupted running agent for session %s during shutdown", session_key[:20])
             except Exception as e:
                 logger.debug("Failed interrupting agent during shutdown: %s", e)
+            # Shut down memory provider at actual session boundary
+            try:
+                if hasattr(agent, 'shutdown_memory_provider'):
+                    agent.shutdown_memory_provider()
+            except Exception:
+                pass
 
         for platform, adapter in list(self.adapters.items()):
             try:
@@ -1446,7 +1395,6 @@ class GatewayRunner:
         self._running_agents.clear()
         self._pending_messages.clear()
         self._pending_approvals.clear()
-        self._shutdown_all_gateway_honcho()
         self._shutdown_event.set()
         
         from gateway.status import remove_pid_file, write_runtime_status
@@ -2449,7 +2397,8 @@ class GatewayRunner:
             )
         
         # One-time prompt if no home channel is set for this platform
-        if not history and source.platform and source.platform != Platform.LOCAL:
+        # Skip for webhooks - they deliver directly to configured targets (github_comment, etc.)
+        if not history and source.platform and source.platform != Platform.LOCAL and source.platform != Platform.WEBHOOK:
             platform_name = source.platform.value
             env_key = f"{platform_name.upper()}_HOME_CHANNEL"
             if not os.getenv(env_key):
@@ -2991,8 +2940,6 @@ class GatewayRunner:
                 _flush_task.add_done_callback(self._background_tasks.discard)
         except Exception as e:
             logger.debug("Gateway memory flush on reset failed: %s", e)
-
-        self._shutdown_gateway_honcho(session_key)
         self._evict_cached_agent(session_key)
         
         # Reset the session
@@ -4143,7 +4090,6 @@ class GatewayRunner:
                     user_message=btw_prompt,
                     conversation_history=history_snapshot,
                     task_id=task_id,
-                    sync_honcho=False,
                 )
 
             loop = asyncio.get_event_loop()
@@ -4525,8 +4471,6 @@ class GatewayRunner:
         except Exception as e:
             logger.debug("Memory flush on resume failed: %s", e)
 
-        self._shutdown_gateway_honcho(session_key)
-
         # Clear any running agent for this session key
         if session_key in self._running_agents:
             del self._running_agents[session_key]
@@ -5356,7 +5300,10 @@ class GatewayRunner:
             or os.getenv("HERMES_TOOL_PROGRESS_MODE")
             or "all"
         )
-        tool_progress_enabled = progress_mode != "off"
+        # Disable tool progress for webhooks - they don't support message editing,
+        # so each progress line would be sent as a separate message.
+        from gateway.config import Platform
+        tool_progress_enabled = progress_mode != "off" and source.platform != Platform.WEBHOOK
         
         # Queue for progress messages (thread-safe)
         progress_queue = queue.Queue() if tool_progress_enabled else None
@@ -5595,7 +5542,6 @@ class GatewayRunner:
                 }
 
             pr = self._provider_routing
-            honcho_manager, honcho_config = self._get_or_create_gateway_honcho(session_key)
             reasoning_config = self._load_reasoning_config()
             self._reasoning_config = reasoning_config
             # Set up streaming consumer if enabled
@@ -5668,9 +5614,6 @@ class GatewayRunner:
                     provider_data_collection=pr.get("data_collection"),
                     session_id=session_id,
                     platform=platform_key,
-                    honcho_session_key=session_key,
-                    honcho_manager=honcho_manager,
-                    honcho_config=honcho_config,
                     session_db=self._session_db,
                     fallback_model=self._fallback_model,
                 )

hermes_cli/config.py

@@ -428,6 +428,11 @@ DEFAULT_CONFIG = {
         "user_profile_enabled": True,
         "memory_char_limit": 2200,   # ~800 tokens at 2.75 chars/token
         "user_char_limit": 1375,     # ~500 tokens at 2.75 chars/token
+        # External memory provider plugin (empty = built-in only).
+        # Set to a provider name to activate: "openviking", "mem0",
+        # "hindsight", "holographic", "retaindb", "byterover".
+        # Only ONE external provider is allowed at a time.
+        "provider": "",
     },
 
     # Subagent delegation — override the provider:model used by delegate_task

hermes_cli/doctor.py

@@ -55,7 +55,7 @@ def _has_provider_env_config(content: str) -> bool:
 def _honcho_is_configured_for_doctor() -> bool:
     """Return True when Honcho is configured, even if this process has no active session."""
     try:
-        from honcho_integration.client import HonchoClientConfig
+        from plugins.memory.honcho.client import HonchoClientConfig
 
         cfg = HonchoClientConfig.from_global_config()
         return bool(cfg.enabled and (cfg.api_key or cfg.base_url))
@@ -709,19 +709,19 @@ def run_doctor(args):
     print(color("◆ Honcho Memory", Colors.CYAN, Colors.BOLD))
 
     try:
-        from honcho_integration.client import HonchoClientConfig, resolve_config_path
+        from plugins.memory.honcho.client import HonchoClientConfig, resolve_config_path
         hcfg = HonchoClientConfig.from_global_config()
         _honcho_cfg_path = resolve_config_path()
 
         if not _honcho_cfg_path.exists():
-            check_warn("Honcho config not found", "run: hermes honcho setup")
+            check_warn("Honcho config not found", "run: hermes memory setup")
         elif not hcfg.enabled:
             check_info(f"Honcho disabled (set enabled: true in {_honcho_cfg_path} to activate)")
         elif not (hcfg.api_key or hcfg.base_url):
-            check_fail("Honcho API key or base URL not set", "run: hermes honcho setup")
-            issues.append("No Honcho API key — run 'hermes honcho setup'")
+            check_fail("Honcho API key or base URL not set", "run: hermes memory setup")
+            issues.append("No Honcho API key — run 'hermes memory setup'")
         else:
-            from honcho_integration.client import get_honcho_client, reset_honcho_client
+            from plugins.memory.honcho.client import get_honcho_client, reset_honcho_client
             reset_honcho_client()
             try:
                 get_honcho_client(hcfg)

hermes_cli/main.py

@@ -3206,12 +3206,12 @@ def cmd_update(args):
 
         # Sync Honcho host blocks to all profiles
         try:
-            from honcho_integration.cli import sync_honcho_profiles_quiet
+            from plugins.memory.honcho.cli import sync_honcho_profiles_quiet
             synced = sync_honcho_profiles_quiet()
             if synced:
                 print(f"\n-> Honcho: synced {synced} profile(s)")
         except Exception:
-            pass  # honcho not installed or not configured
+            pass  # honcho plugin not installed or not configured
 
         # Check for config migrations
         print()
@@ -3555,13 +3555,14 @@ def cmd_profile(args):
                 else:
                     print(f"Cloned config, .env, SOUL.md from {source_label}.")
 
-            # Auto-clone Honcho config for the new profile
-            try:
-                from honcho_integration.cli import clone_honcho_for_profile
-                if clone_honcho_for_profile(name):
-                    print(f"Honcho config cloned (host: hermes.{name})")
-            except Exception:
-                pass  # Honcho not installed or not configured
+            # Auto-clone Honcho config for the new profile (only with --clone/--clone-all)
+            if clone or clone_all:
+                try:
+                    from plugins.memory.honcho.cli import clone_honcho_for_profile
+                    if clone_honcho_for_profile(name):
+                        print(f"Honcho config cloned (peer: {name})")
+                except Exception:
+                    pass  # Honcho plugin not installed or not configured
 
             # Seed bundled skills (skip if --clone-all already copied them)
             if not clone_all:
@@ -4449,20 +4450,17 @@ For more help on a command:
     plugins_parser.set_defaults(func=cmd_plugins)
 
     # =========================================================================
-    # honcho command
+    # honcho command — Honcho-specific config (peer, mode, tokens, profiles)
+    # Provider selection happens via 'hermes memory setup'.
     # =========================================================================
     honcho_parser = subparsers.add_parser(
         "honcho",
-        help="Manage Honcho AI memory integration",
+        help="Manage Honcho memory provider config (peer, mode, profiles)",
         description=(
-            "Honcho is a memory layer that persists across sessions.\n\n"
-            "Each conversation is stored as a peer interaction in a workspace. "
-            "Honcho builds a representation of the user over time — conclusions, "
-            "patterns, context — and surfaces the relevant slice at the start of "
-            "each turn so Hermes knows who you are without you having to repeat yourself.\n\n"
-            "Modes: hybrid (Honcho + local MEMORY.md), honcho (Honcho only), "
-            "local (MEMORY.md only). Write frequency is configurable so memory "
-            "writes never block the response."
+            "Configure Honcho-specific settings. Honcho is now a memory provider\n"
+            "plugin — initial setup is via 'hermes memory setup'. These commands\n"
+            "manage Honcho's own config: peer names, memory mode, token budgets,\n"
+            "per-profile host blocks, and cross-profile observability."
         ),
         formatter_class=__import__("argparse").RawDescriptionHelpFormatter,
     )
@@ -4472,7 +4470,7 @@ For more help on a command:
     )
     honcho_subparsers = honcho_parser.add_subparsers(dest="honcho_command")
 
-    honcho_subparsers.add_parser("setup", help="Interactive setup wizard for Honcho integration")
+    honcho_subparsers.add_parser("setup", help="Initial Honcho setup (redirects to hermes memory setup)")
     honcho_status = honcho_subparsers.add_parser("status", help="Show current Honcho config and connection status")
     honcho_status.add_argument("--all", action="store_true", help="Show config overview across all profiles")
     honcho_subparsers.add_parser("peers", help="Show peer identities across all profiles")
@@ -4540,11 +4538,55 @@ For more help on a command:
     honcho_subparsers.add_parser("sync", help="Sync Honcho config to all existing profiles")
 
     def cmd_honcho(args):
-        from honcho_integration.cli import honcho_command
+        sub = getattr(args, "honcho_command", None)
+        if sub == "setup":
+            # Redirect to the generic memory setup
+            print("\n  Honcho is now configured via the memory provider system.")
+            print("  Running 'hermes memory setup'...\n")
+            from hermes_cli.memory_setup import memory_command
+            memory_command(args)
+            return
+        from plugins.memory.honcho.cli import honcho_command
         honcho_command(args)
 
     honcho_parser.set_defaults(func=cmd_honcho)
 
+    # =========================================================================
+    # memory command
+    # =========================================================================
+    memory_parser = subparsers.add_parser(
+        "memory",
+        help="Configure external memory provider",
+        description=(
+            "Set up and manage external memory provider plugins.\n\n"
+            "Available providers: honcho, openviking, mem0, hindsight,\n"
+            "holographic, retaindb, byterover.\n\n"
+            "Only one external provider can be active at a time.\n"
+            "Built-in memory (MEMORY.md/USER.md) is always active."
+        ),
+    )
+    memory_sub = memory_parser.add_subparsers(dest="memory_command")
+    memory_sub.add_parser("setup", help="Interactive provider selection and configuration")
+    memory_sub.add_parser("status", help="Show current memory provider config")
+    memory_off_p = memory_sub.add_parser("off", help="Disable external provider (built-in only)")
+
+    def cmd_memory(args):
+        sub = getattr(args, "memory_command", None)
+        if sub == "off":
+            from hermes_cli.config import load_config, save_config
+            config = load_config()
+            if not isinstance(config.get("memory"), dict):
+                config["memory"] = {}
+            config["memory"]["provider"] = ""
+            save_config(config)
+            print("\n  ✓ Memory provider: built-in only")
+            print("  Saved to config.yaml\n")
+        else:
+            from hermes_cli.memory_setup import memory_command
+            memory_command(args)
+
+    memory_parser.set_defaults(func=cmd_memory)
+
     # =========================================================================
     # tools command
     # =========================================================================

hermes_cli/memory_setup.py

@@ -0,0 +1,451 @@
+"""hermes memory setup|status — configure memory provider plugins.
+
+Auto-detects installed memory providers via the plugin system.
+Interactive curses-based UI for provider selection, then walks through
+the provider's config schema. Writes config to config.yaml + .env.
+"""
+
+from __future__ import annotations
+
+import getpass
+import os
+import sys
+from pathlib import Path
+
+
+# ---------------------------------------------------------------------------
+# Curses-based interactive picker (same pattern as hermes tools)
+# ---------------------------------------------------------------------------
+
+def _curses_select(title: str, items: list[tuple[str, str]], default: int = 0) -> int:
+    """Interactive single-select with arrow keys.
+
+    items: list of (label, description) tuples.
+    Returns selected index, or default on escape/quit.
+    """
+    try:
+        import curses
+        result = [default]
+
+        def _menu(stdscr):
+            curses.curs_set(0)
+            if curses.has_colors():
+                curses.start_color()
+                curses.use_default_colors()
+                curses.init_pair(1, curses.COLOR_GREEN, -1)
+                curses.init_pair(2, curses.COLOR_YELLOW, -1)
+                curses.init_pair(3, curses.COLOR_CYAN, -1)
+            cursor = default
+
+            while True:
+                stdscr.clear()
+                max_y, max_x = stdscr.getmaxyx()
+
+                # Title
+                try:
+                    stdscr.addnstr(0, 0, title, max_x - 1,
+                                   curses.A_BOLD | (curses.color_pair(2) if curses.has_colors() else 0))
+                    stdscr.addnstr(1, 0, "  ↑↓ navigate  ⏎ select  q quit", max_x - 1,
+                                   curses.color_pair(3) if curses.has_colors() else curses.A_DIM)
+                except curses.error:
+                    pass
+
+                for i, (label, desc) in enumerate(items):
+                    y = i + 3
+                    if y >= max_y - 1:
+                        break
+                    arrow = "→" if i == cursor else " "
+                    line = f" {arrow}  {label}"
+                    if desc:
+                        line += f"  {desc}"
+
+                    attr = curses.A_NORMAL
+                    if i == cursor:
+                        attr = curses.A_BOLD
+                        if curses.has_colors():
+                            attr |= curses.color_pair(1)
+                    try:
+                        stdscr.addnstr(y, 0, line[:max_x - 1], max_x - 1, attr)
+                    except curses.error:
+                        pass
+
+                stdscr.refresh()
+                key = stdscr.getch()
+
+                if key in (curses.KEY_UP, ord('k')):
+                    cursor = (cursor - 1) % len(items)
+                elif key in (curses.KEY_DOWN, ord('j')):
+                    cursor = (cursor + 1) % len(items)
+                elif key in (curses.KEY_ENTER, 10, 13):
+                    result[0] = cursor
+                    return
+                elif key in (27, ord('q')):
+                    return
+
+        curses.wrapper(_menu)
+        return result[0]
+
+    except Exception:
+        # Fallback: numbered input
+        print(f"\n  {title}\n")
+        for i, (label, desc) in enumerate(items):
+            marker = "→" if i == default else " "
+            d = f"  {desc}" if desc else ""
+            print(f"  {marker} {i + 1}. {label}{d}")
+        while True:
+            try:
+                val = input(f"\n  Select [1-{len(items)}] ({default + 1}): ")
+                if not val:
+                    return default
+                idx = int(val) - 1
+                if 0 <= idx < len(items):
+                    return idx
+            except (ValueError, EOFError):
+                return default
+
+
+def _prompt(label: str, default: str | None = None, secret: bool = False) -> str:
+    """Prompt for a value with optional default and secret masking."""
+    suffix = f" [{default}]" if default else ""
+    if secret:
+        sys.stdout.write(f"  {label}{suffix}: ")
+        sys.stdout.flush()
+        if sys.stdin.isatty():
+            val = getpass.getpass(prompt="")
+        else:
+            val = sys.stdin.readline().strip()
+    else:
+        sys.stdout.write(f"  {label}{suffix}: ")
+        sys.stdout.flush()
+        val = sys.stdin.readline().strip()
+    return val or (default or "")
+
+
+# ---------------------------------------------------------------------------
+# Provider discovery
+# ---------------------------------------------------------------------------
+
+def _install_dependencies(provider_name: str) -> None:
+    """Install pip dependencies declared in plugin.yaml."""
+    import subprocess
+    from pathlib import Path as _Path
+
+    plugin_dir = _Path(__file__).parent.parent / "plugins" / "memory" / provider_name
+    yaml_path = plugin_dir / "plugin.yaml"
+    if not yaml_path.exists():
+        return
+
+    try:
+        import yaml
+        with open(yaml_path) as f:
+            meta = yaml.safe_load(f) or {}
+    except Exception:
+        return
+
+    pip_deps = meta.get("pip_dependencies", [])
+    if not pip_deps:
+        return
+
+    # pip name → import name mapping for packages where they differ
+    _IMPORT_NAMES = {
+        "honcho-ai": "honcho",
+        "mem0ai": "mem0",
+        "hindsight-client": "hindsight_client",
+    }
+
+    # Check which packages are missing
+    missing = []
+    for dep in pip_deps:
+        import_name = _IMPORT_NAMES.get(dep, dep.replace("-", "_").split("[")[0])
+        try:
+            __import__(import_name)
+        except ImportError:
+            missing.append(dep)
+
+    if not missing:
+        return
+
+    print(f"\n  Installing dependencies: {', '.join(missing)}")
+    try:
+        subprocess.run(
+            [sys.executable, "-m", "pip", "install", "--quiet"] + missing,
+            check=True, timeout=120,
+            capture_output=True,
+        )
+        print(f"  ✓ Installed {', '.join(missing)}")
+    except subprocess.CalledProcessError as e:
+        print(f"  ⚠ Failed to install {', '.join(missing)}")
+        stderr = (e.stderr or b"").decode()[:200]
+        if stderr:
+            print(f"    {stderr}")
+        print(f"  Run manually: pip install {' '.join(missing)}")
+    except Exception as e:
+        print(f"  ⚠ Install failed: {e}")
+        print(f"  Run manually: pip install {' '.join(missing)}")
+
+    # Also show external dependencies (non-pip) if any
+    ext_deps = meta.get("external_dependencies", [])
+    for dep in ext_deps:
+        dep_name = dep.get("name", "")
+        check_cmd = dep.get("check", "")
+        install_cmd = dep.get("install", "")
+        if check_cmd:
+            try:
+                subprocess.run(
+                    check_cmd, shell=True, capture_output=True, timeout=5
+                )
+            except Exception:
+                if install_cmd:
+                    print(f"\n  ⚠ '{dep_name}' not found. Install with:")
+                    print(f"    {install_cmd}")
+
+
+def _get_available_providers() -> list:
+    """Discover memory providers from plugins/memory/.
+
+    Returns list of (name, description, provider_instance) tuples.
+    """
+    try:
+        from plugins.memory import discover_memory_providers, load_memory_provider
+        raw = discover_memory_providers()
+    except Exception:
+        raw = []
+
+    results = []
+    for name, desc, available in raw:
+        try:
+            provider = load_memory_provider(name)
+            if not provider:
+                continue
+        except Exception:
+            continue
+        # Override description with setup hint
+        schema = provider.get_config_schema() if hasattr(provider, "get_config_schema") else []
+        has_secrets = any(f.get("secret") for f in schema)
+        if has_secrets:
+            setup_hint = "requires API key"
+        elif not schema:
+            setup_hint = "no setup needed"
+        else:
+            setup_hint = "local"
+        results.append((name, setup_hint, provider))
+    return results
+
+
+# ---------------------------------------------------------------------------
+# Setup wizard
+# ---------------------------------------------------------------------------
+
+def cmd_setup(args) -> None:
+    """Interactive memory provider setup wizard."""
+    from hermes_cli.config import load_config, save_config
+
+    providers = _get_available_providers()
+
+    if not providers:
+        print("\n  No memory provider plugins detected.")
+        print("  Install a plugin to ~/.hermes/plugins/ and try again.\n")
+        return
+
+    # Build picker items
+    items = []
+    for name, desc, _ in providers:
+        items.append((name, f"— {desc}"))
+    items.append(("Built-in only", "— MEMORY.md / USER.md (default)"))
+
+    builtin_idx = len(items) - 1
+    selected = _curses_select("Memory provider setup", items, default=builtin_idx)
+
+    config = load_config()
+    if not isinstance(config.get("memory"), dict):
+        config["memory"] = {}
+
+    # Built-in only
+    if selected >= len(providers) or selected < 0:
+        config["memory"]["provider"] = ""
+        save_config(config)
+        print("\n  ✓ Memory provider: built-in only")
+        print("  Saved to config.yaml\n")
+        return
+
+    name, _, provider = providers[selected]
+
+    # Install pip dependencies if declared in plugin.yaml
+    _install_dependencies(name)
+
+    schema = provider.get_config_schema() if hasattr(provider, "get_config_schema") else []
+
+    # Provider config section
+    provider_config = config["memory"].get(name, {})
+    if not isinstance(provider_config, dict):
+        provider_config = {}
+
+    env_path = Path(os.environ.get("HERMES_HOME", os.path.expanduser("~/.hermes"))) / ".env"
+    env_writes = {}
+
+    if schema:
+        print(f"\n  Configuring {name}:\n")
+
+        for field in schema:
+            key = field["key"]
+            desc = field.get("description", key)
+            default = field.get("default")
+            is_secret = field.get("secret", False)
+            choices = field.get("choices")
+            env_var = field.get("env_var")
+            url = field.get("url")
+
+            if choices and not is_secret:
+                # Use curses picker for choice fields
+                choice_items = [(c, "") for c in choices]
+                current = provider_config.get(key, default)
+                current_idx = 0
+                if current and current in choices:
+                    current_idx = choices.index(current)
+                sel = _curses_select(f"  {desc}", choice_items, default=current_idx)
+                provider_config[key] = choices[sel]
+            elif is_secret:
+                # Prompt for secret
+                existing = os.environ.get(env_var, "") if env_var else ""
+                if existing:
+                    masked = f"...{existing[-4:]}" if len(existing) > 4 else "set"
+                    val = _prompt(f"{desc} (current: {masked}, blank to keep)", secret=True)
+                else:
+                    hint = f"  Get yours at {url}" if url else ""
+                    if hint:
+                        print(hint)
+                    val = _prompt(desc, secret=True)
+                if val and env_var:
+                    env_writes[env_var] = val
+            else:
+                # Regular text prompt
+                current = provider_config.get(key)
+                effective_default = current or default
+                val = _prompt(desc, default=str(effective_default) if effective_default else None)
+                if val:
+                    provider_config[key] = val
+
+    # Write activation key to config.yaml
+    config["memory"]["provider"] = name
+    save_config(config)
+
+    # Write non-secret config to provider's native location
+    hermes_home = str(Path(os.environ.get("HERMES_HOME", os.path.expanduser("~/.hermes"))))
+    if provider_config and hasattr(provider, "save_config"):
+        try:
+            provider.save_config(provider_config, hermes_home)
+        except Exception as e:
+            print(f"  ⚠ Failed to write provider config: {e}")
+
+    # Write secrets to .env
+    if env_writes:
+        _write_env_vars(env_path, env_writes)
+
+    print(f"\n  ✓ Memory provider: {name}")
+    print(f"  ✓ Activation saved to config.yaml")
+    if provider_config:
+        print(f"  ✓ Provider config saved")
+    if env_writes:
+        print(f"  ✓ API keys saved to .env")
+    print(f"\n  Start a new session to activate.\n")
+
+
+def _write_env_vars(env_path: Path, env_writes: dict) -> None:
+    """Append or update env vars in .env file."""
+    env_path.parent.mkdir(parents=True, exist_ok=True)
+
+    existing_lines = []
+    if env_path.exists():
+        existing_lines = env_path.read_text().splitlines()
+
+    updated_keys = set()
+    new_lines = []
+    for line in existing_lines:
+        key_match = line.split("=", 1)[0].strip() if "=" in line else ""
+        if key_match in env_writes:
+            new_lines.append(f"{key_match}={env_writes[key_match]}")
+            updated_keys.add(key_match)
+        else:
+            new_lines.append(line)
+
+    for key, val in env_writes.items():
+        if key not in updated_keys:
+            new_lines.append(f"{key}={val}")
+
+    env_path.write_text("\n".join(new_lines) + "\n")
+
+
+# ---------------------------------------------------------------------------
+# Status
+# ---------------------------------------------------------------------------
+
+def cmd_status(args) -> None:
+    """Show current memory provider config."""
+    from hermes_cli.config import load_config
+
+    config = load_config()
+    mem_config = config.get("memory", {})
+    provider_name = mem_config.get("provider", "")
+
+    print(f"\nMemory status\n" + "─" * 40)
+    print(f"  Built-in:  always active")
+    print(f"  Provider:  {provider_name or '(none — built-in only)'}")
+
+    if provider_name:
+        provider_config = mem_config.get(provider_name, {})
+        if provider_config:
+            print(f"\n  {provider_name} config:")
+            for key, val in provider_config.items():
+                print(f"    {key}: {val}")
+
+        providers = _get_available_providers()
+        found = any(name == provider_name for name, _, _ in providers)
+        if found:
+            print(f"\n  Plugin:    installed ✓")
+            for pname, _, p in providers:
+                if pname == provider_name:
+                    if p.is_available():
+                        print(f"  Status:    available ✓")
+                    else:
+                        print(f"  Status:    not available ✗")
+                        schema = p.get_config_schema() if hasattr(p, "get_config_schema") else []
+                        secrets = [f for f in schema if f.get("secret")]
+                        if secrets:
+                            print(f"  Missing:")
+                            for s in secrets:
+                                env_var = s.get("env_var", "")
+                                url = s.get("url", "")
+                                is_set = bool(os.environ.get(env_var))
+                                mark = "✓" if is_set else "✗"
+                                line = f"    {mark} {env_var}"
+                                if url and not is_set:
+                                    line += f"  → {url}"
+                                print(line)
+                    break
+        else:
+            print(f"\n  Plugin:    NOT installed ✗")
+            print(f"  Install the '{provider_name}' memory plugin to ~/.hermes/plugins/")
+
+    providers = _get_available_providers()
+    if providers:
+        print(f"\n  Installed plugins:")
+        for pname, desc, _ in providers:
+            active = " ← active" if pname == provider_name else ""
+            print(f"    • {pname}  ({desc}){active}")
+
+    print()
+
+
+# ---------------------------------------------------------------------------
+# Router
+# ---------------------------------------------------------------------------
+
+def memory_command(args) -> None:
+    """Route memory subcommands."""
+    sub = getattr(args, "memory_command", None)
+    if sub == "setup":
+        cmd_setup(args)
+    elif sub == "status":
+        cmd_status(args)
+    else:
+        cmd_status(args)

hermes_cli/skills_config.py

@@ -30,6 +30,7 @@ PLATFORMS = {
     "dingtalk": "💬 DingTalk",
     "feishu": "🪽 Feishu",
     "wecom": "💬 WeCom",
+    "webhook": "🔗 Webhook",
 }
 
 # ─── Config Helpers ───────────────────────────────────────────────────────────

hermes_cli/tools_config.py

@@ -150,6 +150,7 @@ PLATFORMS = {
     "wecom": {"label": "💬 WeCom", "default_toolset": "hermes-wecom"},
     "api_server": {"label": "🌐 API Server", "default_toolset": "hermes-api-server"},
     "mattermost": {"label": "💬 Mattermost", "default_toolset": "hermes-mattermost"},
+    "webhook": {"label": "🔗 Webhook", "default_toolset": "hermes-webhook"},
 }
 
 

honcho_integration/__init__.py

@@ -1,9 +0,0 @@
-"""Honcho integration for AI-native memory.
-
-This package is only active when honcho.enabled=true in config and
-HONCHO_API_KEY is set. All honcho-ai imports are deferred to avoid
-ImportError when the package is not installed.
-
-Named ``honcho_integration`` (not ``honcho``) to avoid shadowing the
-``honcho`` package installed by the ``honcho-ai`` SDK.
-"""

model_tools.py

@@ -156,7 +156,7 @@ def _discover_tools():
         "tools.delegate_tool",
         "tools.process_registry",
         "tools.send_message_tool",
-        "tools.honcho_tools",
+        # "tools.honcho_tools",  # Removed — Honcho is now a memory provider plugin
         "tools.homeassistant_tool",
     ]
     import importlib
@@ -371,8 +371,6 @@ def handle_function_call(
     task_id: Optional[str] = None,
     user_task: Optional[str] = None,
     enabled_tools: Optional[List[str]] = None,
-    honcho_manager: Optional[Any] = None,
-    honcho_session_key: Optional[str] = None,
 ) -> str:
     """
     Main function call dispatcher that routes calls to the tool registry.
@@ -417,16 +415,12 @@ def handle_function_call(
                 function_name, function_args,
                 task_id=task_id,
                 enabled_tools=sandbox_enabled,
-                honcho_manager=honcho_manager,
-                honcho_session_key=honcho_session_key,
             )
         else:
             result = registry.dispatch(
                 function_name, function_args,
                 task_id=task_id,
                 user_task=user_task,
-                honcho_manager=honcho_manager,
-                honcho_session_key=honcho_session_key,
             )
 
         try:

plugins/__init__.py

@@ -0,0 +1 @@
+# Hermes plugins package

plugins/memory/__init__.py

@@ -0,0 +1,213 @@
+"""Memory provider plugin discovery.
+
+Scans ``plugins/memory/<name>/`` directories for memory provider plugins.
+Each subdirectory must contain ``__init__.py`` with a class implementing
+the MemoryProvider ABC.
+
+Memory providers are separate from the general plugin system — they live
+in the repo and are always available without user installation. Only ONE
+can be active at a time, selected via ``memory.provider`` in config.yaml.
+
+Usage:
+    from plugins.memory import discover_memory_providers, load_memory_provider
+
+    available = discover_memory_providers()   # [(name, desc, available), ...]
+    provider = load_memory_provider("openviking")  # MemoryProvider instance
+"""
+
+from __future__ import annotations
+
+import importlib
+import importlib.util
+import logging
+import sys
+from pathlib import Path
+from typing import List, Optional, Tuple
+
+logger = logging.getLogger(__name__)
+
+_MEMORY_PLUGINS_DIR = Path(__file__).parent
+
+
+def discover_memory_providers() -> List[Tuple[str, str, bool]]:
+    """Scan plugins/memory/ for available providers.
+
+    Returns list of (name, description, is_available) tuples.
+    Does NOT import the providers — just reads plugin.yaml for metadata
+    and does a lightweight availability check.
+    """
+    results = []
+    if not _MEMORY_PLUGINS_DIR.is_dir():
+        return results
+
+    for child in sorted(_MEMORY_PLUGINS_DIR.iterdir()):
+        if not child.is_dir() or child.name.startswith(("_", ".")):
+            continue
+        init_file = child / "__init__.py"
+        if not init_file.exists():
+            continue
+
+        # Read description from plugin.yaml if available
+        desc = ""
+        yaml_file = child / "plugin.yaml"
+        if yaml_file.exists():
+            try:
+                import yaml
+                with open(yaml_file) as f:
+                    meta = yaml.safe_load(f) or {}
+                desc = meta.get("description", "")
+            except Exception:
+                pass
+
+        # Quick availability check — try loading and calling is_available()
+        available = True
+        try:
+            provider = _load_provider_from_dir(child)
+            if provider:
+                available = provider.is_available()
+            else:
+                available = False
+        except Exception:
+            available = False
+
+        results.append((child.name, desc, available))
+
+    return results
+
+
+def load_memory_provider(name: str) -> Optional["MemoryProvider"]:
+    """Load and return a MemoryProvider instance by name.
+
+    Returns None if the provider is not found or fails to load.
+    """
+    provider_dir = _MEMORY_PLUGINS_DIR / name
+    if not provider_dir.is_dir():
+        logger.debug("Memory provider '%s' not found in %s", name, _MEMORY_PLUGINS_DIR)
+        return None
+
+    try:
+        provider = _load_provider_from_dir(provider_dir)
+        if provider:
+            return provider
+        logger.warning("Memory provider '%s' loaded but no provider instance found", name)
+        return None
+    except Exception as e:
+        logger.warning("Failed to load memory provider '%s': %s", name, e)
+        return None
+
+
+def _load_provider_from_dir(provider_dir: Path) -> Optional["MemoryProvider"]:
+    """Import a provider module and extract the MemoryProvider instance.
+
+    The module must have either:
+    - A register(ctx) function (plugin-style) — we simulate a ctx
+    - A top-level class that extends MemoryProvider — we instantiate it
+    """
+    name = provider_dir.name
+    module_name = f"plugins.memory.{name}"
+    init_file = provider_dir / "__init__.py"
+
+    if not init_file.exists():
+        return None
+
+    # Check if already loaded
+    if module_name in sys.modules:
+        mod = sys.modules[module_name]
+    else:
+        # Handle relative imports within the plugin
+        # First ensure the parent packages are registered
+        for parent in ("plugins", "plugins.memory"):
+            if parent not in sys.modules:
+                parent_path = Path(__file__).parent
+                if parent == "plugins":
+                    parent_path = parent_path.parent
+                parent_init = parent_path / "__init__.py"
+                if parent_init.exists():
+                    spec = importlib.util.spec_from_file_location(
+                        parent, str(parent_init),
+                        submodule_search_locations=[str(parent_path)]
+                    )
+                    if spec:
+                        parent_mod = importlib.util.module_from_spec(spec)
+                        sys.modules[parent] = parent_mod
+                        try:
+                            spec.loader.exec_module(parent_mod)
+                        except Exception:
+                            pass
+
+        # Now load the provider module
+        spec = importlib.util.spec_from_file_location(
+            module_name, str(init_file),
+            submodule_search_locations=[str(provider_dir)]
+        )
+        if not spec:
+            return None
+
+        mod = importlib.util.module_from_spec(spec)
+        sys.modules[module_name] = mod
+
+        # Register submodules so relative imports work
+        # e.g., "from .store import MemoryStore" in holographic plugin
+        for sub_file in provider_dir.glob("*.py"):
+            if sub_file.name == "__init__.py":
+                continue
+            sub_name = sub_file.stem
+            full_sub_name = f"{module_name}.{sub_name}"
+            if full_sub_name not in sys.modules:
+                sub_spec = importlib.util.spec_from_file_location(
+                    full_sub_name, str(sub_file)
+                )
+                if sub_spec:
+                    sub_mod = importlib.util.module_from_spec(sub_spec)
+                    sys.modules[full_sub_name] = sub_mod
+                    try:
+                        sub_spec.loader.exec_module(sub_mod)
+                    except Exception as e:
+                        logger.debug("Failed to load submodule %s: %s", full_sub_name, e)
+
+        try:
+            spec.loader.exec_module(mod)
+        except Exception as e:
+            logger.debug("Failed to exec_module %s: %s", module_name, e)
+            sys.modules.pop(module_name, None)
+            return None
+
+    # Try register(ctx) pattern first (how our plugins are written)
+    if hasattr(mod, "register"):
+        collector = _ProviderCollector()
+        try:
+            mod.register(collector)
+            if collector.provider:
+                return collector.provider
+        except Exception as e:
+            logger.debug("register() failed for %s: %s", name, e)
+
+    # Fallback: find a MemoryProvider subclass and instantiate it
+    from agent.memory_provider import MemoryProvider
+    for attr_name in dir(mod):
+        attr = getattr(mod, attr_name, None)
+        if (isinstance(attr, type) and issubclass(attr, MemoryProvider)
+                and attr is not MemoryProvider):
+            try:
+                return attr()
+            except Exception:
+                pass
+
+    return None
+
+
+class _ProviderCollector:
+    """Fake plugin context that captures register_memory_provider calls."""
+
+    def __init__(self):
+        self.provider = None
+
+    def register_memory_provider(self, provider):
+        self.provider = provider
+
+    # No-op for other registration methods
+    def register_tool(self, *args, **kwargs):
+        pass
+
+    def register_hook(self, *args, **kwargs):
+        pass

plugins/memory/byterover/README.md

@@ -0,0 +1,41 @@
+# ByteRover Memory Provider
+
+Persistent memory via the `brv` CLI — hierarchical knowledge tree with tiered retrieval (fuzzy text → LLM-driven search).
+
+## Requirements
+
+Install the ByteRover CLI:
+```bash
+curl -fsSL https://byterover.dev/install.sh | sh
+# or
+npm install -g byterover-cli
+```
+
+## Setup
+
+```bash
+hermes memory setup    # select "byterover"
+```
+
+Or manually:
+```bash
+hermes config set memory.provider byterover
+# Optional cloud sync:
+echo "BRV_API_KEY=your-key" >> ~/.hermes/.env
+```
+
+## Config
+
+| Env Var | Required | Description |
+|---------|----------|-------------|
+| `BRV_API_KEY` | No | Cloud sync key (optional, local-first by default) |
+
+Working directory: `$HERMES_HOME/byterover/` (profile-scoped).
+
+## Tools
+
+| Tool | Description |
+|------|-------------|
+| `brv_query` | Search the knowledge tree |
+| `brv_curate` | Store facts, decisions, patterns |
+| `brv_status` | CLI version, tree stats, sync state |

plugins/memory/byterover/__init__.py

@@ -0,0 +1,398 @@
+"""ByteRover memory plugin — MemoryProvider interface.
+
+Persistent memory via the ByteRover CLI (``brv``). Organizes knowledge into
+a hierarchical context tree with tiered retrieval (fuzzy text → LLM-driven
+search). Local-first with optional cloud sync.
+
+Original PR #3499 by hieuntg81, adapted to MemoryProvider ABC.
+
+Requires: ``brv`` CLI installed (npm install -g byterover-cli or
+curl -fsSL https://byterover.dev/install.sh | sh).
+
+Config via environment variables (profile-scoped via each profile's .env):
+  BRV_API_KEY   — ByteRover API key (for cloud features, optional for local)
+
+Working directory: $HERMES_HOME/byterover/ (profile-scoped context tree)
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+import shutil
+import subprocess
+import threading
+import time
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+from agent.memory_provider import MemoryProvider
+
+logger = logging.getLogger(__name__)
+
+# Timeouts
+_QUERY_TIMEOUT = 30   # brv query — should be fast
+_CURATE_TIMEOUT = 120  # brv curate — may involve LLM processing
+
+# Minimum lengths to filter noise
+_MIN_QUERY_LEN = 10
+_MIN_OUTPUT_LEN = 20
+
+
+# ---------------------------------------------------------------------------
+# brv binary resolution (cached, thread-safe)
+# ---------------------------------------------------------------------------
+
+_brv_path_lock = threading.Lock()
+_cached_brv_path: Optional[str] = None
+
+
+def _resolve_brv_path() -> Optional[str]:
+    """Find the brv binary on PATH or well-known install locations."""
+    global _cached_brv_path
+    with _brv_path_lock:
+        if _cached_brv_path is not None:
+            return _cached_brv_path if _cached_brv_path != "" else None
+
+    found = shutil.which("brv")
+    if not found:
+        home = Path.home()
+        candidates = [
+            home / ".brv-cli" / "bin" / "brv",
+            Path("/usr/local/bin/brv"),
+            home / ".npm-global" / "bin" / "brv",
+        ]
+        for c in candidates:
+            if c.exists():
+                found = str(c)
+                break
+
+    with _brv_path_lock:
+        if _cached_brv_path is not None:
+            return _cached_brv_path if _cached_brv_path != "" else None
+        _cached_brv_path = found or ""
+    return found
+
+
+def _run_brv(args: List[str], timeout: int = _QUERY_TIMEOUT,
+             cwd: str = None) -> dict:
+    """Run a brv CLI command. Returns {success, output, error}."""
+    brv_path = _resolve_brv_path()
+    if not brv_path:
+        return {"success": False, "error": "brv CLI not found. Install: npm install -g byterover-cli"}
+
+    cmd = [brv_path] + args
+    effective_cwd = cwd or str(_get_brv_cwd())
+    Path(effective_cwd).mkdir(parents=True, exist_ok=True)
+
+    env = os.environ.copy()
+    brv_bin_dir = str(Path(brv_path).parent)
+    env["PATH"] = brv_bin_dir + os.pathsep + env.get("PATH", "")
+
+    try:
+        result = subprocess.run(
+            cmd, capture_output=True, text=True,
+            timeout=timeout, cwd=effective_cwd, env=env,
+        )
+        stdout = result.stdout.strip()
+        stderr = result.stderr.strip()
+
+        if result.returncode == 0:
+            return {"success": True, "output": stdout}
+        return {"success": False, "error": stderr or stdout or f"brv exited {result.returncode}"}
+
+    except subprocess.TimeoutExpired:
+        return {"success": False, "error": f"brv timed out after {timeout}s"}
+    except FileNotFoundError:
+        global _cached_brv_path
+        with _brv_path_lock:
+            _cached_brv_path = None
+        return {"success": False, "error": "brv CLI not found"}
+    except Exception as e:
+        return {"success": False, "error": str(e)}
+
+
+def _get_brv_cwd() -> Path:
+    """Profile-scoped working directory for the brv context tree."""
+    from hermes_constants import get_hermes_home
+    return get_hermes_home() / "byterover"
+
+
+# ---------------------------------------------------------------------------
+# Tool schemas
+# ---------------------------------------------------------------------------
+
+QUERY_SCHEMA = {
+    "name": "brv_query",
+    "description": (
+        "Search ByteRover's persistent knowledge tree for relevant context. "
+        "Returns memories, project knowledge, architectural decisions, and "
+        "patterns from previous sessions. Use for any question where past "
+        "context would help."
+    ),
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "query": {"type": "string", "description": "What to search for."},
+        },
+        "required": ["query"],
+    },
+}
+
+CURATE_SCHEMA = {
+    "name": "brv_curate",
+    "description": (
+        "Store important information in ByteRover's persistent knowledge tree. "
+        "Use for architectural decisions, bug fixes, user preferences, project "
+        "patterns — anything worth remembering across sessions. ByteRover's LLM "
+        "automatically categorizes and organizes the memory."
+    ),
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "content": {"type": "string", "description": "The information to remember."},
+        },
+        "required": ["content"],
+    },
+}
+
+STATUS_SCHEMA = {
+    "name": "brv_status",
+    "description": "Check ByteRover status — CLI version, context tree stats, cloud sync state.",
+    "parameters": {"type": "object", "properties": {}, "required": []},
+}
+
+
+# ---------------------------------------------------------------------------
+# MemoryProvider implementation
+# ---------------------------------------------------------------------------
+
+class ByteRoverMemoryProvider(MemoryProvider):
+    """ByteRover persistent memory via the brv CLI."""
+
+    def __init__(self):
+        self._cwd = ""
+        self._session_id = ""
+        self._turn_count = 0
+        self._prefetch_result = ""
+        self._prefetch_lock = threading.Lock()
+        self._prefetch_thread: Optional[threading.Thread] = None
+        self._sync_thread: Optional[threading.Thread] = None
+
+    @property
+    def name(self) -> str:
+        return "byterover"
+
+    def is_available(self) -> bool:
+        """Check if brv CLI is installed. No network calls."""
+        return _resolve_brv_path() is not None
+
+    def get_config_schema(self):
+        return [
+            {
+                "key": "api_key",
+                "description": "ByteRover API key (optional, for cloud sync)",
+                "secret": True,
+                "env_var": "BRV_API_KEY",
+                "url": "https://app.byterover.dev",
+            },
+        ]
+
+    def initialize(self, session_id: str, **kwargs) -> None:
+        self._cwd = str(_get_brv_cwd())
+        self._session_id = session_id
+        self._turn_count = 0
+        Path(self._cwd).mkdir(parents=True, exist_ok=True)
+
+    def system_prompt_block(self) -> str:
+        if not _resolve_brv_path():
+            return ""
+        return (
+            "# ByteRover Memory\n"
+            "Active. Persistent knowledge tree with hierarchical context.\n"
+            "Use brv_query to search past knowledge, brv_curate to store "
+            "important facts, brv_status to check state."
+        )
+
+    def prefetch(self, query: str, *, session_id: str = "") -> str:
+        if self._prefetch_thread and self._prefetch_thread.is_alive():
+            self._prefetch_thread.join(timeout=3.0)
+        with self._prefetch_lock:
+            result = self._prefetch_result
+            self._prefetch_result = ""
+        if not result:
+            return ""
+        return f"## ByteRover Context\n{result}"
+
+    def queue_prefetch(self, query: str, *, session_id: str = "") -> None:
+        if not query or len(query.strip()) < _MIN_QUERY_LEN:
+            return
+
+        def _run():
+            try:
+                result = _run_brv(
+                    ["query", "--", query.strip()[:5000]],
+                    timeout=_QUERY_TIMEOUT, cwd=self._cwd,
+                )
+                if result["success"] and result.get("output"):
+                    output = result["output"].strip()
+                    if len(output) > _MIN_OUTPUT_LEN:
+                        with self._prefetch_lock:
+                            self._prefetch_result = output
+            except Exception as e:
+                logger.debug("ByteRover prefetch failed: %s", e)
+
+        self._prefetch_thread = threading.Thread(
+            target=_run, daemon=True, name="brv-prefetch"
+        )
+        self._prefetch_thread.start()
+
+    def sync_turn(self, user_content: str, assistant_content: str, *, session_id: str = "") -> None:
+        """Curate the conversation turn in background (non-blocking)."""
+        self._turn_count += 1
+
+        # Only curate substantive turns
+        if len(user_content.strip()) < _MIN_QUERY_LEN:
+            return
+
+        def _sync():
+            try:
+                combined = f"User: {user_content[:2000]}\nAssistant: {assistant_content[:2000]}"
+                _run_brv(
+                    ["curate", "--", combined],
+                    timeout=_CURATE_TIMEOUT, cwd=self._cwd,
+                )
+            except Exception as e:
+                logger.debug("ByteRover sync failed: %s", e)
+
+        # Wait for previous sync
+        if self._sync_thread and self._sync_thread.is_alive():
+            self._sync_thread.join(timeout=5.0)
+
+        self._sync_thread = threading.Thread(
+            target=_sync, daemon=True, name="brv-sync"
+        )
+        self._sync_thread.start()
+
+    def on_memory_write(self, action: str, target: str, content: str) -> None:
+        """Mirror built-in memory writes to ByteRover."""
+        if action not in ("add", "replace") or not content:
+            return
+
+        def _write():
+            try:
+                label = "User profile" if target == "user" else "Agent memory"
+                _run_brv(
+                    ["curate", "--", f"[{label}] {content}"],
+                    timeout=_CURATE_TIMEOUT, cwd=self._cwd,
+                )
+            except Exception as e:
+                logger.debug("ByteRover memory mirror failed: %s", e)
+
+        t = threading.Thread(target=_write, daemon=True, name="brv-memwrite")
+        t.start()
+
+    def on_pre_compress(self, messages: List[Dict[str, Any]]) -> str:
+        """Extract insights before context compression discards turns."""
+        if not messages:
+            return ""
+
+        # Build a summary of messages about to be compressed
+        parts = []
+        for msg in messages[-10:]:  # last 10 messages
+            role = msg.get("role", "")
+            content = msg.get("content", "")
+            if isinstance(content, str) and content.strip() and role in ("user", "assistant"):
+                parts.append(f"{role}: {content[:500]}")
+
+        if not parts:
+            return ""
+
+        combined = "\n".join(parts)
+
+        def _flush():
+            try:
+                _run_brv(
+                    ["curate", "--", f"[Pre-compression context]\n{combined}"],
+                    timeout=_CURATE_TIMEOUT, cwd=self._cwd,
+                )
+                logger.info("ByteRover pre-compression flush: %d messages", len(parts))
+            except Exception as e:
+                logger.debug("ByteRover pre-compression flush failed: %s", e)
+
+        t = threading.Thread(target=_flush, daemon=True, name="brv-flush")
+        t.start()
+        return ""
+
+    def get_tool_schemas(self) -> List[Dict[str, Any]]:
+        return [QUERY_SCHEMA, CURATE_SCHEMA, STATUS_SCHEMA]
+
+    def handle_tool_call(self, tool_name: str, args: dict, **kwargs) -> str:
+        if tool_name == "brv_query":
+            return self._tool_query(args)
+        elif tool_name == "brv_curate":
+            return self._tool_curate(args)
+        elif tool_name == "brv_status":
+            return self._tool_status()
+        return json.dumps({"error": f"Unknown tool: {tool_name}"})
+
+    def shutdown(self) -> None:
+        for t in (self._sync_thread, self._prefetch_thread):
+            if t and t.is_alive():
+                t.join(timeout=10.0)
+
+    # -- Tool implementations ------------------------------------------------
+
+    def _tool_query(self, args: dict) -> str:
+        query = args.get("query", "")
+        if not query:
+            return json.dumps({"error": "query is required"})
+
+        result = _run_brv(
+            ["query", "--", query.strip()[:5000]],
+            timeout=_QUERY_TIMEOUT, cwd=self._cwd,
+        )
+
+        if not result["success"]:
+            return json.dumps({"error": result.get("error", "Query failed")})
+
+        output = result.get("output", "").strip()
+        if not output or len(output) < _MIN_OUTPUT_LEN:
+            return json.dumps({"result": "No relevant memories found."})
+
+        # Truncate very long results
+        if len(output) > 8000:
+            output = output[:8000] + "\n\n[... truncated]"
+
+        return json.dumps({"result": output})
+
+    def _tool_curate(self, args: dict) -> str:
+        content = args.get("content", "")
+        if not content:
+            return json.dumps({"error": "content is required"})
+
+        result = _run_brv(
+            ["curate", "--", content],
+            timeout=_CURATE_TIMEOUT, cwd=self._cwd,
+        )
+
+        if not result["success"]:
+            return json.dumps({"error": result.get("error", "Curate failed")})
+
+        return json.dumps({"result": "Memory curated successfully."})
+
+    def _tool_status(self) -> str:
+        result = _run_brv(["status"], timeout=15, cwd=self._cwd)
+        if not result["success"]:
+            return json.dumps({"error": result.get("error", "Status check failed")})
+        return json.dumps({"status": result.get("output", "")})
+
+
+# ---------------------------------------------------------------------------
+# Plugin entry point
+# ---------------------------------------------------------------------------
+
+def register(ctx) -> None:
+    """Register ByteRover as a memory provider plugin."""
+    ctx.register_memory_provider(ByteRoverMemoryProvider())

plugins/memory/byterover/plugin.yaml

@@ -0,0 +1,9 @@
+name: byterover
+version: 1.0.0
+description: "ByteRover — persistent knowledge tree with tiered retrieval via the brv CLI."
+external_dependencies:
+  - name: brv
+    install: "curl -fsSL https://byterover.dev/install.sh | sh"
+    check: "brv --version"
+hooks:
+  - on_pre_compress

plugins/memory/hindsight/README.md

@@ -0,0 +1,38 @@
+# Hindsight Memory Provider
+
+Long-term memory with knowledge graph, entity resolution, and multi-strategy retrieval. Supports cloud and local modes.
+
+## Requirements
+
+- Cloud: `pip install hindsight-client` + API key from [app.hindsight.vectorize.io](https://app.hindsight.vectorize.io)
+- Local: `pip install hindsight` + LLM API key for embeddings
+
+## Setup
+
+```bash
+hermes memory setup    # select "hindsight"
+```
+
+Or manually:
+```bash
+hermes config set memory.provider hindsight
+echo "HINDSIGHT_API_KEY=your-key" >> ~/.hermes/.env
+```
+
+## Config
+
+Config file: `$HERMES_HOME/hindsight/config.json` (or `~/.hindsight/config.json` legacy)
+
+| Key | Default | Description |
+|-----|---------|-------------|
+| `mode` | `cloud` | `cloud` or `local` |
+| `bank_id` | `hermes` | Memory bank identifier |
+| `budget` | `mid` | Recall thoroughness: `low`/`mid`/`high` |
+
+## Tools
+
+| Tool | Description |
+|------|-------------|
+| `hindsight_retain` | Store information with auto entity extraction |
+| `hindsight_recall` | Multi-strategy search (semantic + entity graph) |
+| `hindsight_reflect` | Cross-memory synthesis (LLM-powered) |

plugins/memory/hindsight/__init__.py

@@ -0,0 +1,358 @@
+"""Hindsight memory plugin — MemoryProvider interface.
+
+Long-term memory with knowledge graph, entity resolution, and multi-strategy
+retrieval. Supports cloud (API key) and local (embedded PostgreSQL) modes.
+
+Original PR #1811 by benfrank241, adapted to MemoryProvider ABC.
+
+Config via environment variables:
+  HINDSIGHT_API_KEY   — API key for Hindsight Cloud
+  HINDSIGHT_BANK_ID   — memory bank identifier (default: hermes)
+  HINDSIGHT_BUDGET    — recall budget: low/mid/high (default: mid)
+  HINDSIGHT_API_URL   — API endpoint
+  HINDSIGHT_MODE      — cloud or local (default: cloud)
+
+Or via $HERMES_HOME/hindsight/config.json (profile-scoped), falling back to
+~/.hindsight/config.json (legacy, shared) for backward compatibility.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+import queue
+import threading
+from typing import Any, Dict, List
+
+from agent.memory_provider import MemoryProvider
+
+logger = logging.getLogger(__name__)
+
+_DEFAULT_API_URL = "https://api.hindsight.vectorize.io"
+_VALID_BUDGETS = {"low", "mid", "high"}
+
+
+# ---------------------------------------------------------------------------
+# Thread helper (from original PR — avoids aiohttp event loop conflicts)
+# ---------------------------------------------------------------------------
+
+def _run_in_thread(fn, timeout: float = 30.0):
+    result_q: queue.Queue = queue.Queue(maxsize=1)
+
+    def _run():
+        import asyncio
+        asyncio.set_event_loop(None)
+        try:
+            result_q.put(("ok", fn()))
+        except Exception as exc:
+            result_q.put(("err", exc))
+
+    t = threading.Thread(target=_run, daemon=True, name="hindsight-call")
+    t.start()
+    kind, value = result_q.get(timeout=timeout)
+    if kind == "err":
+        raise value
+    return value
+
+
+# ---------------------------------------------------------------------------
+# Tool schemas
+# ---------------------------------------------------------------------------
+
+RETAIN_SCHEMA = {
+    "name": "hindsight_retain",
+    "description": (
+        "Store information to long-term memory. Hindsight automatically "
+        "extracts structured facts, resolves entities, and indexes for retrieval."
+    ),
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "content": {"type": "string", "description": "The information to store."},
+            "context": {"type": "string", "description": "Short label (e.g. 'user preference', 'project decision')."},
+        },
+        "required": ["content"],
+    },
+}
+
+RECALL_SCHEMA = {
+    "name": "hindsight_recall",
+    "description": (
+        "Search long-term memory. Returns memories ranked by relevance using "
+        "semantic search, keyword matching, entity graph traversal, and reranking."
+    ),
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "query": {"type": "string", "description": "What to search for."},
+        },
+        "required": ["query"],
+    },
+}
+
+REFLECT_SCHEMA = {
+    "name": "hindsight_reflect",
+    "description": (
+        "Synthesize a reasoned answer from long-term memories. Unlike recall, "
+        "this reasons across all stored memories to produce a coherent response."
+    ),
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "query": {"type": "string", "description": "The question to reflect on."},
+        },
+        "required": ["query"],
+    },
+}
+
+
+# ---------------------------------------------------------------------------
+# Config
+# ---------------------------------------------------------------------------
+
+def _load_config() -> dict:
+    """Load config from profile-scoped path, legacy path, or env vars.
+
+    Resolution order:
+      1. $HERMES_HOME/hindsight/config.json  (profile-scoped)
+      2. ~/.hindsight/config.json             (legacy, shared)
+      3. Environment variables
+    """
+    from pathlib import Path
+    from hermes_constants import get_hermes_home
+
+    # Profile-scoped path (preferred)
+    profile_path = get_hermes_home() / "hindsight" / "config.json"
+    if profile_path.exists():
+        try:
+            return json.loads(profile_path.read_text(encoding="utf-8"))
+        except Exception:
+            pass
+
+    # Legacy shared path (backward compat)
+    legacy_path = Path.home() / ".hindsight" / "config.json"
+    if legacy_path.exists():
+        try:
+            return json.loads(legacy_path.read_text(encoding="utf-8"))
+        except Exception:
+            pass
+
+    return {
+        "mode": os.environ.get("HINDSIGHT_MODE", "cloud"),
+        "apiKey": os.environ.get("HINDSIGHT_API_KEY", ""),
+        "banks": {
+            "hermes": {
+                "bankId": os.environ.get("HINDSIGHT_BANK_ID", "hermes"),
+                "budget": os.environ.get("HINDSIGHT_BUDGET", "mid"),
+                "enabled": True,
+            }
+        },
+    }
+
+
+# ---------------------------------------------------------------------------
+# MemoryProvider implementation
+# ---------------------------------------------------------------------------
+
+class HindsightMemoryProvider(MemoryProvider):
+    """Hindsight long-term memory with knowledge graph and multi-strategy retrieval."""
+
+    def __init__(self):
+        self._config = None
+        self._api_key = None
+        self._bank_id = "hermes"
+        self._budget = "mid"
+        self._mode = "cloud"
+        self._prefetch_result = ""
+        self._prefetch_lock = threading.Lock()
+        self._prefetch_thread = None
+        self._sync_thread = None
+
+    @property
+    def name(self) -> str:
+        return "hindsight"
+
+    def is_available(self) -> bool:
+        try:
+            cfg = _load_config()
+            mode = cfg.get("mode", "cloud")
+            if mode == "local":
+                embed = cfg.get("embed", {})
+                return bool(embed.get("llmApiKey") or os.environ.get("HINDSIGHT_LLM_API_KEY"))
+            api_key = cfg.get("apiKey") or os.environ.get("HINDSIGHT_API_KEY", "")
+            return bool(api_key)
+        except Exception:
+            return False
+
+    def save_config(self, values, hermes_home):
+        """Write config to $HERMES_HOME/hindsight/config.json."""
+        import json
+        from pathlib import Path
+        config_dir = Path(hermes_home) / "hindsight"
+        config_dir.mkdir(parents=True, exist_ok=True)
+        config_path = config_dir / "config.json"
+        existing = {}
+        if config_path.exists():
+            try:
+                existing = json.loads(config_path.read_text())
+            except Exception:
+                pass
+        existing.update(values)
+        config_path.write_text(json.dumps(existing, indent=2))
+
+    def get_config_schema(self):
+        return [
+            {"key": "mode", "description": "Cloud API or local embedded mode", "default": "cloud", "choices": ["cloud", "local"]},
+            {"key": "api_key", "description": "Hindsight Cloud API key", "secret": True, "env_var": "HINDSIGHT_API_KEY", "url": "https://app.hindsight.vectorize.io"},
+            {"key": "bank_id", "description": "Memory bank identifier", "default": "hermes"},
+            {"key": "budget", "description": "Recall thoroughness", "default": "mid", "choices": ["low", "mid", "high"]},
+            {"key": "llm_provider", "description": "LLM provider for local mode", "default": "anthropic", "choices": ["anthropic", "openai", "groq", "ollama"]},
+            {"key": "llm_api_key", "description": "LLM API key for local mode", "secret": True, "env_var": "HINDSIGHT_LLM_API_KEY"},
+            {"key": "llm_model", "description": "LLM model for local mode", "default": "claude-haiku-4-5-20251001"},
+        ]
+
+    def _make_client(self):
+        """Create a fresh Hindsight client (thread-safe)."""
+        if self._mode == "local":
+            from hindsight import HindsightEmbedded
+            embed = self._config.get("embed", {})
+            return HindsightEmbedded(
+                profile=embed.get("profile", "hermes"),
+                llm_provider=embed.get("llmProvider", ""),
+                llm_api_key=embed.get("llmApiKey", ""),
+                llm_model=embed.get("llmModel", ""),
+            )
+        from hindsight_client import Hindsight
+        return Hindsight(api_key=self._api_key, timeout=30.0)
+
+    def initialize(self, session_id: str, **kwargs) -> None:
+        self._config = _load_config()
+        self._mode = self._config.get("mode", "cloud")
+        self._api_key = self._config.get("apiKey") or os.environ.get("HINDSIGHT_API_KEY", "")
+
+        banks = self._config.get("banks", {}).get("hermes", {})
+        self._bank_id = banks.get("bankId", "hermes")
+        budget = banks.get("budget", "mid")
+        self._budget = budget if budget in _VALID_BUDGETS else "mid"
+
+        # Ensure bank exists
+        try:
+            client = _run_in_thread(self._make_client)
+            _run_in_thread(lambda: client.create_bank(bank_id=self._bank_id, name=self._bank_id))
+        except Exception:
+            pass  # Already exists
+
+    def system_prompt_block(self) -> str:
+        return (
+            f"# Hindsight Memory\n"
+            f"Active. Bank: {self._bank_id}, budget: {self._budget}.\n"
+            f"Use hindsight_recall to search, hindsight_reflect for synthesis, "
+            f"hindsight_retain to store facts."
+        )
+
+    def prefetch(self, query: str, *, session_id: str = "") -> str:
+        if self._prefetch_thread and self._prefetch_thread.is_alive():
+            self._prefetch_thread.join(timeout=3.0)
+        with self._prefetch_lock:
+            result = self._prefetch_result
+            self._prefetch_result = ""
+        if not result:
+            return ""
+        return f"## Hindsight Memory\n{result}"
+
+    def queue_prefetch(self, query: str, *, session_id: str = "") -> None:
+        def _run():
+            try:
+                client = self._make_client()
+                resp = client.recall(bank_id=self._bank_id, query=query, budget=self._budget)
+                if resp.results:
+                    text = "\n".join(r.text for r in resp.results if r.text)
+                    with self._prefetch_lock:
+                        self._prefetch_result = text
+            except Exception as e:
+                logger.debug("Hindsight prefetch failed: %s", e)
+
+        self._prefetch_thread = threading.Thread(target=_run, daemon=True, name="hindsight-prefetch")
+        self._prefetch_thread.start()
+
+    def sync_turn(self, user_content: str, assistant_content: str, *, session_id: str = "") -> None:
+        """Retain conversation turn in background (non-blocking)."""
+        combined = f"User: {user_content}\nAssistant: {assistant_content}"
+
+        def _sync():
+            try:
+                _run_in_thread(
+                    lambda: self._make_client().retain(
+                        bank_id=self._bank_id, content=combined, context="conversation"
+                    )
+                )
+            except Exception as e:
+                logger.warning("Hindsight sync failed: %s", e)
+
+        if self._sync_thread and self._sync_thread.is_alive():
+            self._sync_thread.join(timeout=5.0)
+        self._sync_thread = threading.Thread(target=_sync, daemon=True, name="hindsight-sync")
+        self._sync_thread.start()
+
+    def get_tool_schemas(self) -> List[Dict[str, Any]]:
+        return [RETAIN_SCHEMA, RECALL_SCHEMA, REFLECT_SCHEMA]
+
+    def handle_tool_call(self, tool_name: str, args: dict, **kwargs) -> str:
+        if tool_name == "hindsight_retain":
+            content = args.get("content", "")
+            if not content:
+                return json.dumps({"error": "Missing required parameter: content"})
+            context = args.get("context")
+            try:
+                _run_in_thread(
+                    lambda: self._make_client().retain(
+                        bank_id=self._bank_id, content=content, context=context
+                    )
+                )
+                return json.dumps({"result": "Memory stored successfully."})
+            except Exception as e:
+                return json.dumps({"error": f"Failed to store memory: {e}"})
+
+        elif tool_name == "hindsight_recall":
+            query = args.get("query", "")
+            if not query:
+                return json.dumps({"error": "Missing required parameter: query"})
+            try:
+                resp = _run_in_thread(
+                    lambda: self._make_client().recall(
+                        bank_id=self._bank_id, query=query, budget=self._budget
+                    )
+                )
+                if not resp.results:
+                    return json.dumps({"result": "No relevant memories found."})
+                lines = [f"{i}. {r.text}" for i, r in enumerate(resp.results, 1)]
+                return json.dumps({"result": "\n".join(lines)})
+            except Exception as e:
+                return json.dumps({"error": f"Failed to search memory: {e}"})
+
+        elif tool_name == "hindsight_reflect":
+            query = args.get("query", "")
+            if not query:
+                return json.dumps({"error": "Missing required parameter: query"})
+            try:
+                resp = _run_in_thread(
+                    lambda: self._make_client().reflect(
+                        bank_id=self._bank_id, query=query, budget=self._budget
+                    )
+                )
+                return json.dumps({"result": resp.text or "No relevant memories found."})
+            except Exception as e:
+                return json.dumps({"error": f"Failed to reflect: {e}"})
+
+        return json.dumps({"error": f"Unknown tool: {tool_name}"})
+
+    def shutdown(self) -> None:
+        for t in (self._prefetch_thread, self._sync_thread):
+            if t and t.is_alive():
+                t.join(timeout=5.0)
+
+
+def register(ctx) -> None:
+    """Register Hindsight as a memory provider plugin."""
+    ctx.register_memory_provider(HindsightMemoryProvider())

plugins/memory/hindsight/plugin.yaml

@@ -0,0 +1,9 @@
+name: hindsight
+version: 1.0.0
+description: "Hindsight — long-term memory with knowledge graph, entity resolution, and multi-strategy retrieval."
+pip_dependencies:
+  - hindsight-client
+requires_env:
+  - HINDSIGHT_API_KEY
+hooks:
+  - on_session_end

plugins/memory/holographic/README.md

@@ -0,0 +1,36 @@
+# Holographic Memory Provider
+
+Local SQLite fact store with FTS5 search, trust scoring, entity resolution, and HRR-based compositional retrieval.
+
+## Requirements
+
+None — uses SQLite (always available). NumPy optional for HRR algebra.
+
+## Setup
+
+```bash
+hermes memory setup    # select "holographic"
+```
+
+Or manually:
+```bash
+hermes config set memory.provider holographic
+```
+
+## Config
+
+Config in `config.yaml` under `plugins.hermes-memory-store`:
+
+| Key | Default | Description |
+|-----|---------|-------------|
+| `db_path` | `$HERMES_HOME/memory_store.db` | SQLite database path |
+| `auto_extract` | `false` | Auto-extract facts at session end |
+| `default_trust` | `0.5` | Default trust score for new facts |
+| `hrr_dim` | `1024` | HRR vector dimensions |
+
+## Tools
+
+| Tool | Description |
+|------|-------------|
+| `fact_store` | 9 actions: add, search, probe, related, reason, contradict, update, remove, list |
+| `fact_feedback` | Rate facts as helpful/unhelpful (trains trust scores) |

plugins/memory/holographic/__init__.py

@@ -0,0 +1,395 @@
+"""hermes-memory-store — holographic memory plugin using MemoryProvider interface.
+
+Registers as a MemoryProvider plugin, giving the agent structured fact storage
+with entity resolution, trust scoring, and HRR-based compositional retrieval.
+
+Original plugin by dusterbloom (PR #2351), adapted to the MemoryProvider ABC.
+
+Config in $HERMES_HOME/config.yaml (profile-scoped):
+  plugins:
+    hermes-memory-store:
+      db_path: $HERMES_HOME/memory_store.db
+      auto_extract: false
+      default_trust: 0.5
+      min_trust_threshold: 0.3
+      temporal_decay_half_life: 0
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import re
+from pathlib import Path
+from typing import Any, Dict, List
+
+from agent.memory_provider import MemoryProvider
+from .store import MemoryStore
+from .retrieval import FactRetriever
+
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# Tool schemas (unchanged from original PR)
+# ---------------------------------------------------------------------------
+
+FACT_STORE_SCHEMA = {
+    "name": "fact_store",
+    "description": (
+        "Deep structured memory with algebraic reasoning. "
+        "Use alongside the memory tool — memory for always-on context, "
+        "fact_store for deep recall and compositional queries.\n\n"
+        "ACTIONS (simple → powerful):\n"
+        "• add — Store a fact the user would expect you to remember.\n"
+        "• search — Keyword lookup ('editor config', 'deploy process').\n"
+        "• probe — Entity recall: ALL facts about a person/thing.\n"
+        "• related — What connects to an entity? Structural adjacency.\n"
+        "• reason — Compositional: facts connected to MULTIPLE entities simultaneously.\n"
+        "• contradict — Memory hygiene: find facts making conflicting claims.\n"
+        "• update/remove/list — CRUD operations.\n\n"
+        "IMPORTANT: Before answering questions about the user, ALWAYS probe or reason first."
+    ),
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "action": {
+                "type": "string",
+                "enum": ["add", "search", "probe", "related", "reason", "contradict", "update", "remove", "list"],
+            },
+            "content": {"type": "string", "description": "Fact content (required for 'add')."},
+            "query": {"type": "string", "description": "Search query (required for 'search')."},
+            "entity": {"type": "string", "description": "Entity name for 'probe'/'related'."},
+            "entities": {"type": "array", "items": {"type": "string"}, "description": "Entity names for 'reason'."},
+            "fact_id": {"type": "integer", "description": "Fact ID for 'update'/'remove'."},
+            "category": {"type": "string", "enum": ["user_pref", "project", "tool", "general"]},
+            "tags": {"type": "string", "description": "Comma-separated tags."},
+            "trust_delta": {"type": "number", "description": "Trust adjustment for 'update'."},
+            "min_trust": {"type": "number", "description": "Minimum trust filter (default: 0.3)."},
+            "limit": {"type": "integer", "description": "Max results (default: 10)."},
+        },
+        "required": ["action"],
+    },
+}
+
+FACT_FEEDBACK_SCHEMA = {
+    "name": "fact_feedback",
+    "description": (
+        "Rate a fact after using it. Mark 'helpful' if accurate, 'unhelpful' if outdated. "
+        "This trains the memory — good facts rise, bad facts sink."
+    ),
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "action": {"type": "string", "enum": ["helpful", "unhelpful"]},
+            "fact_id": {"type": "integer", "description": "The fact ID to rate."},
+        },
+        "required": ["action", "fact_id"],
+    },
+}
+
+
+# ---------------------------------------------------------------------------
+# Config
+# ---------------------------------------------------------------------------
+
+def _load_plugin_config() -> dict:
+    from hermes_constants import get_hermes_home
+    config_path = get_hermes_home() / "config.yaml"
+    if not config_path.exists():
+        return {}
+    try:
+        import yaml
+        with open(config_path) as f:
+            all_config = yaml.safe_load(f) or {}
+        return all_config.get("plugins", {}).get("hermes-memory-store", {}) or {}
+    except Exception:
+        return {}
+
+
+# ---------------------------------------------------------------------------
+# MemoryProvider implementation
+# ---------------------------------------------------------------------------
+
+class HolographicMemoryProvider(MemoryProvider):
+    """Holographic memory with structured facts, entity resolution, and HRR retrieval."""
+
+    def __init__(self, config: dict | None = None):
+        self._config = config or _load_plugin_config()
+        self._store = None
+        self._retriever = None
+        self._min_trust = float(self._config.get("min_trust_threshold", 0.3))
+
+    @property
+    def name(self) -> str:
+        return "holographic"
+
+    def is_available(self) -> bool:
+        return True  # SQLite is always available, numpy is optional
+
+    def save_config(self, values, hermes_home):
+        """Write config to config.yaml under plugins.hermes-memory-store."""
+        from pathlib import Path
+        config_path = Path(hermes_home) / "config.yaml"
+        try:
+            import yaml
+            existing = {}
+            if config_path.exists():
+                with open(config_path) as f:
+                    existing = yaml.safe_load(f) or {}
+            existing.setdefault("plugins", {})
+            existing["plugins"]["hermes-memory-store"] = values
+            with open(config_path, "w") as f:
+                yaml.dump(existing, f, default_flow_style=False)
+        except Exception:
+            pass
+
+    def get_config_schema(self):
+        from hermes_constants import display_hermes_home
+        _default_db = f"{display_hermes_home()}/memory_store.db"
+        return [
+            {"key": "db_path", "description": "SQLite database path", "default": _default_db},
+            {"key": "auto_extract", "description": "Auto-extract facts at session end", "default": "false", "choices": ["true", "false"]},
+            {"key": "default_trust", "description": "Default trust score for new facts", "default": "0.5"},
+            {"key": "hrr_dim", "description": "HRR vector dimensions", "default": "1024"},
+        ]
+
+    def initialize(self, session_id: str, **kwargs) -> None:
+        from hermes_constants import get_hermes_home
+        _default_db = str(get_hermes_home() / "memory_store.db")
+        db_path = self._config.get("db_path", _default_db)
+        default_trust = float(self._config.get("default_trust", 0.5))
+        hrr_dim = int(self._config.get("hrr_dim", 1024))
+        hrr_weight = float(self._config.get("hrr_weight", 0.3))
+        temporal_decay = int(self._config.get("temporal_decay_half_life", 0))
+
+        self._store = MemoryStore(db_path=db_path, default_trust=default_trust, hrr_dim=hrr_dim)
+        self._retriever = FactRetriever(
+            store=self._store,
+            temporal_decay_half_life=temporal_decay,
+            hrr_weight=hrr_weight,
+            hrr_dim=hrr_dim,
+        )
+        self._session_id = session_id
+
+    def system_prompt_block(self) -> str:
+        if not self._store:
+            return ""
+        try:
+            total = self._store._conn.execute(
+                "SELECT COUNT(*) FROM facts"
+            ).fetchone()[0]
+        except Exception:
+            total = 0
+        if total == 0:
+            return ""
+        return (
+            f"# Holographic Memory\n"
+            f"Active. {total} facts stored with entity resolution and trust scoring.\n"
+            f"Use fact_store to search, probe entities, reason across entities, or add facts.\n"
+            f"Use fact_feedback to rate facts after using them (trains trust scores)."
+        )
+
+    def prefetch(self, query: str, *, session_id: str = "") -> str:
+        if not self._retriever or not query:
+            return ""
+        try:
+            results = self._retriever.search(query, min_trust=self._min_trust, limit=5)
+            if not results:
+                return ""
+            lines = []
+            for r in results:
+                trust = r.get("trust", 0)
+                lines.append(f"- [{trust:.1f}] {r.get('content', '')}")
+            return "## Holographic Memory\n" + "\n".join(lines)
+        except Exception as e:
+            logger.debug("Holographic prefetch failed: %s", e)
+            return ""
+
+    def sync_turn(self, user_content: str, assistant_content: str, *, session_id: str = "") -> None:
+        # Holographic memory stores explicit facts via tools, not auto-sync.
+        # The on_session_end hook handles auto-extraction if configured.
+        pass
+
+    def get_tool_schemas(self) -> List[Dict[str, Any]]:
+        return [FACT_STORE_SCHEMA, FACT_FEEDBACK_SCHEMA]
+
+    def handle_tool_call(self, tool_name: str, args: Dict[str, Any], **kwargs) -> str:
+        if tool_name == "fact_store":
+            return self._handle_fact_store(args)
+        elif tool_name == "fact_feedback":
+            return self._handle_fact_feedback(args)
+        return json.dumps({"error": f"Unknown tool: {tool_name}"})
+
+    def on_session_end(self, messages: List[Dict[str, Any]]) -> None:
+        if not self._config.get("auto_extract", False):
+            return
+        if not self._store or not messages:
+            return
+        self._auto_extract_facts(messages)
+
+    def on_memory_write(self, action: str, target: str, content: str) -> None:
+        """Mirror built-in memory writes as facts."""
+        if action == "add" and self._store and content:
+            try:
+                category = "user_pref" if target == "user" else "general"
+                self._store.add_fact(content, category=category)
+            except Exception as e:
+                logger.debug("Holographic memory_write mirror failed: %s", e)
+
+    def shutdown(self) -> None:
+        self._store = None
+        self._retriever = None
+
+    # -- Tool handlers -------------------------------------------------------
+
+    def _handle_fact_store(self, args: dict) -> str:
+        try:
+            action = args["action"]
+            store = self._store
+            retriever = self._retriever
+
+            if action == "add":
+                fact_id = store.add_fact(
+                    args["content"],
+                    category=args.get("category", "general"),
+                    tags=args.get("tags", ""),
+                )
+                return json.dumps({"fact_id": fact_id, "status": "added"})
+
+            elif action == "search":
+                results = retriever.search(
+                    args["query"],
+                    category=args.get("category"),
+                    min_trust=float(args.get("min_trust", self._min_trust)),
+                    limit=int(args.get("limit", 10)),
+                )
+                return json.dumps({"results": results, "count": len(results)})
+
+            elif action == "probe":
+                results = retriever.probe(
+                    args["entity"],
+                    category=args.get("category"),
+                    limit=int(args.get("limit", 10)),
+                )
+                return json.dumps({"results": results, "count": len(results)})
+
+            elif action == "related":
+                results = retriever.related(
+                    args["entity"],
+                    category=args.get("category"),
+                    limit=int(args.get("limit", 10)),
+                )
+                return json.dumps({"results": results, "count": len(results)})
+
+            elif action == "reason":
+                entities = args.get("entities", [])
+                if not entities:
+                    return json.dumps({"error": "reason requires 'entities' list"})
+                results = retriever.reason(
+                    entities,
+                    category=args.get("category"),
+                    limit=int(args.get("limit", 10)),
+                )
+                return json.dumps({"results": results, "count": len(results)})
+
+            elif action == "contradict":
+                results = retriever.contradict(
+                    category=args.get("category"),
+                    limit=int(args.get("limit", 10)),
+                )
+                return json.dumps({"results": results, "count": len(results)})
+
+            elif action == "update":
+                updated = store.update_fact(
+                    int(args["fact_id"]),
+                    content=args.get("content"),
+                    trust_delta=float(args["trust_delta"]) if "trust_delta" in args else None,
+                    tags=args.get("tags"),
+                    category=args.get("category"),
+                )
+                return json.dumps({"updated": updated})
+
+            elif action == "remove":
+                removed = store.remove_fact(int(args["fact_id"]))
+                return json.dumps({"removed": removed})
+
+            elif action == "list":
+                facts = store.list_facts(
+                    category=args.get("category"),
+                    min_trust=float(args.get("min_trust", 0.0)),
+                    limit=int(args.get("limit", 10)),
+                )
+                return json.dumps({"facts": facts, "count": len(facts)})
+
+            else:
+                return json.dumps({"error": f"Unknown action: {action}"})
+
+        except KeyError as exc:
+            return json.dumps({"error": f"Missing required argument: {exc}"})
+        except Exception as exc:
+            return json.dumps({"error": str(exc)})
+
+    def _handle_fact_feedback(self, args: dict) -> str:
+        try:
+            fact_id = int(args["fact_id"])
+            helpful = args["action"] == "helpful"
+            result = self._store.record_feedback(fact_id, helpful=helpful)
+            return json.dumps(result)
+        except KeyError as exc:
+            return json.dumps({"error": f"Missing required argument: {exc}"})
+        except Exception as exc:
+            return json.dumps({"error": str(exc)})
+
+    # -- Auto-extraction (on_session_end) ------------------------------------
+
+    def _auto_extract_facts(self, messages: list) -> None:
+        _PREF_PATTERNS = [
+            re.compile(r'\bI\s+(?:prefer|like|love|use|want|need)\s+(.+)', re.IGNORECASE),
+            re.compile(r'\bmy\s+(?:favorite|preferred|default)\s+\w+\s+is\s+(.+)', re.IGNORECASE),
+            re.compile(r'\bI\s+(?:always|never|usually)\s+(.+)', re.IGNORECASE),
+        ]
+        _DECISION_PATTERNS = [
+            re.compile(r'\bwe\s+(?:decided|agreed|chose)\s+(?:to\s+)?(.+)', re.IGNORECASE),
+            re.compile(r'\bthe\s+project\s+(?:uses|needs|requires)\s+(.+)', re.IGNORECASE),
+        ]
+
+        extracted = 0
+        for msg in messages:
+            if msg.get("role") != "user":
+                continue
+            content = msg.get("content", "")
+            if not isinstance(content, str) or len(content) < 10:
+                continue
+
+            for pattern in _PREF_PATTERNS:
+                if pattern.search(content):
+                    try:
+                        self._store.add_fact(content[:400], category="user_pref")
+                        extracted += 1
+                    except Exception:
+                        pass
+                    break
+
+            for pattern in _DECISION_PATTERNS:
+                if pattern.search(content):
+                    try:
+                        self._store.add_fact(content[:400], category="project")
+                        extracted += 1
+                    except Exception:
+                        pass
+                    break
+
+        if extracted:
+            logger.info("Auto-extracted %d facts from conversation", extracted)
+
+
+# ---------------------------------------------------------------------------
+# Plugin entry point
+# ---------------------------------------------------------------------------
+
+def register(ctx) -> None:
+    """Register the holographic memory provider with the plugin system."""
+    config = _load_plugin_config()
+    provider = HolographicMemoryProvider(config=config)
+    ctx.register_memory_provider(provider)

plugins/memory/holographic/holographic.py

@@ -0,0 +1,203 @@
+"""Holographic Reduced Representations (HRR) with phase encoding.
+
+HRRs are a vector symbolic architecture for encoding compositional structure
+into fixed-width distributed representations. This module uses *phase vectors*:
+each concept is a vector of angles in [0, 2π). The algebraic operations are:
+
+  bind   — circular convolution (phase addition)  — associates two concepts
+  unbind — circular correlation (phase subtraction) — retrieves a bound value
+  bundle — superposition (circular mean)           — merges multiple concepts
+
+Phase encoding is numerically stable, avoids the magnitude collapse of
+traditional complex-number HRRs, and maps cleanly to cosine similarity.
+
+Atoms are generated deterministically from SHA-256 so representations are
+identical across processes, machines, and language versions.
+
+References:
+  Plate (1995) — Holographic Reduced Representations
+  Gayler (2004) — Vector Symbolic Architectures answer Jackendoff's challenges
+"""
+
+import hashlib
+import logging
+import struct
+import math
+
+try:
+    import numpy as np
+    _HAS_NUMPY = True
+except ImportError:
+    _HAS_NUMPY = False
+
+logger = logging.getLogger(__name__)
+
+_TWO_PI = 2.0 * math.pi
+
+
+def _require_numpy() -> None:
+    if not _HAS_NUMPY:
+        raise RuntimeError("numpy is required for holographic operations")
+
+
+def encode_atom(word: str, dim: int = 1024) -> "np.ndarray":
+    """Deterministic phase vector via SHA-256 counter blocks.
+
+    Uses hashlib (not numpy RNG) for cross-platform reproducibility.
+
+    Algorithm:
+    - Generate enough SHA-256 blocks by hashing f"{word}:{i}" for i=0,1,2,...
+    - Concatenate digests, interpret as uint16 values via struct.unpack
+    - Scale to [0, 2π): phases = values * (2π / 65536)
+    - Truncate to dim elements
+    - Returns np.float64 array of shape (dim,)
+    """
+    _require_numpy()
+
+    # Each SHA-256 digest is 32 bytes = 16 uint16 values.
+    values_per_block = 16
+    blocks_needed = math.ceil(dim / values_per_block)
+
+    uint16_values: list[int] = []
+    for i in range(blocks_needed):
+        digest = hashlib.sha256(f"{word}:{i}".encode()).digest()
+        uint16_values.extend(struct.unpack("<16H", digest))
+
+    phases = np.array(uint16_values[:dim], dtype=np.float64) * (_TWO_PI / 65536.0)
+    return phases
+
+
+def bind(a: "np.ndarray", b: "np.ndarray") -> "np.ndarray":
+    """Circular convolution = element-wise phase addition.
+
+    Binding associates two concepts into a single composite vector.
+    The result is dissimilar to both inputs (quasi-orthogonal).
+    """
+    _require_numpy()
+    return (a + b) % _TWO_PI
+
+
+def unbind(memory: "np.ndarray", key: "np.ndarray") -> "np.ndarray":
+    """Circular correlation = element-wise phase subtraction.
+
+    Unbinding retrieves the value associated with a key from a memory vector.
+    unbind(bind(a, b), a) ≈ b  (up to superposition noise)
+    """
+    _require_numpy()
+    return (memory - key) % _TWO_PI
+
+
+def bundle(*vectors: "np.ndarray") -> "np.ndarray":
+    """Superposition via circular mean of complex exponentials.
+
+    Bundling merges multiple vectors into one that is similar to each input.
+    The result can hold O(sqrt(dim)) items before similarity degrades.
+    """
+    _require_numpy()
+    complex_sum = np.sum([np.exp(1j * v) for v in vectors], axis=0)
+    return np.angle(complex_sum) % _TWO_PI
+
+
+def similarity(a: "np.ndarray", b: "np.ndarray") -> float:
+    """Phase cosine similarity. Range [-1, 1].
+
+    Returns 1.0 for identical vectors, near 0.0 for random (unrelated) vectors,
+    and -1.0 for perfectly anti-correlated vectors.
+    """
+    _require_numpy()
+    return float(np.mean(np.cos(a - b)))
+
+
+def encode_text(text: str, dim: int = 1024) -> "np.ndarray":
+    """Bag-of-words: bundle of atom vectors for each token.
+
+    Tokenizes by lowercasing, splitting on whitespace, and stripping
+    leading/trailing punctuation from each token.
+
+    Returns bundle of all token atom vectors.
+    If text is empty or produces no tokens, returns encode_atom("__hrr_empty__", dim).
+    """
+    _require_numpy()
+
+    tokens = [
+        token.strip(".,!?;:\"'()[]{}")
+        for token in text.lower().split()
+    ]
+    tokens = [t for t in tokens if t]
+
+    if not tokens:
+        return encode_atom("__hrr_empty__", dim)
+
+    atom_vectors = [encode_atom(token, dim) for token in tokens]
+    return bundle(*atom_vectors)
+
+
+def encode_fact(content: str, entities: list[str], dim: int = 1024) -> "np.ndarray":
+    """Structured encoding: content bound to ROLE_CONTENT, each entity bound to ROLE_ENTITY, all bundled.
+
+    Role vectors are reserved atoms: "__hrr_role_content__", "__hrr_role_entity__"
+
+    Components:
+    1. bind(encode_text(content, dim), encode_atom("__hrr_role_content__", dim))
+    2. For each entity: bind(encode_atom(entity.lower(), dim), encode_atom("__hrr_role_entity__", dim))
+    3. bundle all components together
+
+    This enables algebraic extraction:
+        unbind(fact, bind(entity, ROLE_ENTITY)) ≈ content_vector
+    """
+    _require_numpy()
+
+    role_content = encode_atom("__hrr_role_content__", dim)
+    role_entity = encode_atom("__hrr_role_entity__", dim)
+
+    components: list[np.ndarray] = [
+        bind(encode_text(content, dim), role_content)
+    ]
+
+    for entity in entities:
+        components.append(bind(encode_atom(entity.lower(), dim), role_entity))
+
+    return bundle(*components)
+
+
+def phases_to_bytes(phases: "np.ndarray") -> bytes:
+    """Serialize phase vector to bytes. float64 tobytes — 8 KB at dim=1024."""
+    _require_numpy()
+    return phases.tobytes()
+
+
+def bytes_to_phases(data: bytes) -> "np.ndarray":
+    """Deserialize bytes back to phase vector. Inverse of phases_to_bytes.
+
+    The .copy() call is required because frombuffer returns a read-only view
+    backed by the bytes object; callers expect a mutable array.
+    """
+    _require_numpy()
+    return np.frombuffer(data, dtype=np.float64).copy()
+
+
+def snr_estimate(dim: int, n_items: int) -> float:
+    """Signal-to-noise ratio estimate for holographic storage.
+
+    SNR = sqrt(dim / n_items) when n_items > 0, else inf.
+
+    The SNR falls below 2.0 when n_items > dim / 4, meaning retrieval
+    errors become likely. Logs a warning when this threshold is crossed.
+    """
+    _require_numpy()
+
+    if n_items <= 0:
+        return float("inf")
+
+    snr = math.sqrt(dim / n_items)
+
+    if snr < 2.0:
+        logger.warning(
+            "HRR storage near capacity: SNR=%.2f (dim=%d, n_items=%d). "
+            "Retrieval accuracy may degrade. Consider increasing dim or reducing stored items.",
+            snr,
+            dim,
+            n_items,
+        )
+
+    return snr

plugins/memory/holographic/plugin.yaml

@@ -0,0 +1,5 @@
+name: holographic
+version: 0.1.0
+description: "Holographic memory — local SQLite fact store with FTS5 search, trust scoring, and HRR-based compositional retrieval."
+hooks:
+  - on_session_end

plugins/memory/holographic/retrieval.py

@@ -0,0 +1,593 @@
+"""Hybrid keyword/BM25 retrieval for the memory store.
+
+Ported from KIK memory_agent.py — combines FTS5 full-text search with
+Jaccard similarity reranking and trust-weighted scoring.
+"""
+
+from __future__ import annotations
+
+import math
+from datetime import datetime, timezone
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from .store import MemoryStore
+
+try:
+    from . import holographic as hrr
+except ImportError:
+    import holographic as hrr  # type: ignore[no-redef]
+
+
+class FactRetriever:
+    """Multi-strategy fact retrieval with trust-weighted scoring."""
+
+    def __init__(
+        self,
+        store: MemoryStore,
+        temporal_decay_half_life: int = 0,  # days, 0 = disabled
+        fts_weight: float = 0.4,
+        jaccard_weight: float = 0.3,
+        hrr_weight: float = 0.3,
+        hrr_dim: int = 1024,
+    ):
+        self.store = store
+        self.half_life = temporal_decay_half_life
+        self.hrr_dim = hrr_dim
+
+        # Auto-redistribute weights if numpy unavailable
+        if hrr_weight > 0 and not hrr._HAS_NUMPY:
+            fts_weight = 0.6
+            jaccard_weight = 0.4
+            hrr_weight = 0.0
+
+        self.fts_weight = fts_weight
+        self.jaccard_weight = jaccard_weight
+        self.hrr_weight = hrr_weight
+
+    def search(
+        self,
+        query: str,
+        category: str | None = None,
+        min_trust: float = 0.3,
+        limit: int = 10,
+    ) -> list[dict]:
+        """Hybrid search: FTS5 candidates → Jaccard rerank → trust weighting.
+
+        Pipeline:
+        1. FTS5 search: Get limit*3 candidates from SQLite full-text search
+        2. Jaccard boost: Token overlap between query and fact content
+        3. Trust weighting: final_score = relevance * trust_score
+        4. Temporal decay (optional): decay = 0.5^(age_days / half_life)
+
+        Returns list of dicts with fact data + 'score' field, sorted by score desc.
+        """
+        # Stage 1: Get FTS5 candidates (more than limit for reranking headroom)
+        candidates = self._fts_candidates(query, category, min_trust, limit * 3)
+
+        if not candidates:
+            return []
+
+        # Stage 2: Rerank with Jaccard + trust + optional decay
+        query_tokens = self._tokenize(query)
+        scored = []
+
+        for fact in candidates:
+            content_tokens = self._tokenize(fact["content"])
+            tag_tokens = self._tokenize(fact.get("tags", ""))
+            all_tokens = content_tokens | tag_tokens
+
+            jaccard = self._jaccard_similarity(query_tokens, all_tokens)
+            fts_score = fact.get("fts_rank", 0.0)
+
+            # HRR similarity
+            if self.hrr_weight > 0 and fact.get("hrr_vector"):
+                fact_vec = hrr.bytes_to_phases(fact["hrr_vector"])
+                query_vec = hrr.encode_text(query, self.hrr_dim)
+                hrr_sim = (hrr.similarity(query_vec, fact_vec) + 1.0) / 2.0  # shift to [0,1]
+            else:
+                hrr_sim = 0.5  # neutral
+
+            # Combine FTS5 + Jaccard + HRR
+            relevance = (self.fts_weight * fts_score
+                        + self.jaccard_weight * jaccard
+                        + self.hrr_weight * hrr_sim)
+
+            # Trust weighting
+            score = relevance * fact["trust_score"]
+
+            # Optional temporal decay
+            if self.half_life > 0:
+                score *= self._temporal_decay(fact.get("updated_at") or fact.get("created_at"))
+
+            fact["score"] = score
+            scored.append(fact)
+
+        # Sort by score descending, return top limit
+        scored.sort(key=lambda x: x["score"], reverse=True)
+        results = scored[:limit]
+        # Strip raw HRR bytes — callers expect JSON-serializable dicts
+        for fact in results:
+            fact.pop("hrr_vector", None)
+        return results
+
+    def probe(
+        self,
+        entity: str,
+        category: str | None = None,
+        limit: int = 10,
+    ) -> list[dict]:
+        """Compositional entity query using HRR algebra.
+
+        Unbinds entity from memory bank to extract associated content.
+        This is NOT keyword search — it uses algebraic structure to find facts
+        where the entity plays a structural role.
+
+        Falls back to FTS5 search if numpy unavailable.
+        """
+        if not hrr._HAS_NUMPY:
+            # Fallback to keyword search on entity name
+            return self.search(entity, category=category, limit=limit)
+
+        conn = self.store._conn
+
+        # Encode entity as role-bound vector
+        role_entity = hrr.encode_atom("__hrr_role_entity__", self.hrr_dim)
+        entity_vec = hrr.encode_atom(entity.lower(), self.hrr_dim)
+        probe_key = hrr.bind(entity_vec, role_entity)
+
+        # Try category-specific bank first, then all facts
+        if category:
+            bank_name = f"cat:{category}"
+            bank_row = conn.execute(
+                "SELECT vector FROM memory_banks WHERE bank_name = ?",
+                (bank_name,),
+            ).fetchone()
+            if bank_row:
+                bank_vec = hrr.bytes_to_phases(bank_row["vector"])
+                extracted = hrr.unbind(bank_vec, probe_key)
+                # Use extracted signal to score individual facts
+                return self._score_facts_by_vector(
+                    extracted, category=category, limit=limit
+                )
+
+        # Score against individual fact vectors directly
+        where = "WHERE hrr_vector IS NOT NULL"
+        params: list = []
+        if category:
+            where += " AND category = ?"
+            params.append(category)
+
+        rows = conn.execute(
+            f"""
+            SELECT fact_id, content, category, tags, trust_score,
+                   retrieval_count, helpful_count, created_at, updated_at,
+                   hrr_vector
+            FROM facts
+            {where}
+            """,
+            params,
+        ).fetchall()
+
+        if not rows:
+            # Final fallback: keyword search
+            return self.search(entity, category=category, limit=limit)
+
+        scored = []
+        for row in rows:
+            fact = dict(row)
+            fact_vec = hrr.bytes_to_phases(fact.pop("hrr_vector"))
+            # Unbind probe key from fact to see if entity is structurally present
+            residual = hrr.unbind(fact_vec, probe_key)
+            # Compare residual against content signal
+            role_content = hrr.encode_atom("__hrr_role_content__", self.hrr_dim)
+            content_vec = hrr.bind(hrr.encode_text(fact["content"], self.hrr_dim), role_content)
+            sim = hrr.similarity(residual, content_vec)
+            fact["score"] = (sim + 1.0) / 2.0 * fact["trust_score"]
+            scored.append(fact)
+
+        scored.sort(key=lambda x: x["score"], reverse=True)
+        return scored[:limit]
+
+    def related(
+        self,
+        entity: str,
+        category: str | None = None,
+        limit: int = 10,
+    ) -> list[dict]:
+        """Discover facts that share structural connections with an entity.
+
+        Unlike probe (which finds facts *about* an entity), related finds
+        facts that are connected through shared context — e.g., other entities
+        mentioned alongside this one, or content that overlaps structurally.
+
+        Falls back to FTS5 search if numpy unavailable.
+        """
+        if not hrr._HAS_NUMPY:
+            return self.search(entity, category=category, limit=limit)
+
+        conn = self.store._conn
+
+        # Encode entity as a bare atom (not role-bound — we want ANY structural match)
+        entity_vec = hrr.encode_atom(entity.lower(), self.hrr_dim)
+
+        # Get all facts with vectors
+        where = "WHERE hrr_vector IS NOT NULL"
+        params: list = []
+        if category:
+            where += " AND category = ?"
+            params.append(category)
+
+        rows = conn.execute(
+            f"""
+            SELECT fact_id, content, category, tags, trust_score,
+                   retrieval_count, helpful_count, created_at, updated_at,
+                   hrr_vector
+            FROM facts
+            {where}
+            """,
+            params,
+        ).fetchall()
+
+        if not rows:
+            return self.search(entity, category=category, limit=limit)
+
+        # Score each fact by how much the entity's atom appears in its vector
+        # This catches both role-bound entity matches AND content word matches
+        scored = []
+        for row in rows:
+            fact = dict(row)
+            fact_vec = hrr.bytes_to_phases(fact.pop("hrr_vector"))
+
+            # Check structural similarity: unbind entity from fact
+            residual = hrr.unbind(fact_vec, entity_vec)
+            # A high-similarity residual to ANY known role vector means this entity
+            # plays a structural role in the fact
+            role_entity = hrr.encode_atom("__hrr_role_entity__", self.hrr_dim)
+            role_content = hrr.encode_atom("__hrr_role_content__", self.hrr_dim)
+
+            entity_role_sim = hrr.similarity(residual, role_entity)
+            content_role_sim = hrr.similarity(residual, role_content)
+            # Take the max — entity could appear in either role
+            best_sim = max(entity_role_sim, content_role_sim)
+
+            fact["score"] = (best_sim + 1.0) / 2.0 * fact["trust_score"]
+            scored.append(fact)
+
+        scored.sort(key=lambda x: x["score"], reverse=True)
+        return scored[:limit]
+
+    def reason(
+        self,
+        entities: list[str],
+        category: str | None = None,
+        limit: int = 10,
+    ) -> list[dict]:
+        """Multi-entity compositional query — vector-space JOIN.
+
+        Given multiple entities, algebraically intersects their structural
+        connections to find facts related to ALL of them simultaneously.
+        This is compositional reasoning that no embedding DB can do.
+
+        Example: reason(["peppi", "backend"]) finds facts where peppi AND
+        backend both play structural roles — without keyword matching.
+
+        Falls back to FTS5 search if numpy unavailable.
+        """
+        if not hrr._HAS_NUMPY or not entities:
+            # Fallback: search with all entities as keywords
+            query = " ".join(entities)
+            return self.search(query, category=category, limit=limit)
+
+        conn = self.store._conn
+        role_entity = hrr.encode_atom("__hrr_role_entity__", self.hrr_dim)
+
+        # For each entity, compute what the bank "remembers" about it
+        # by unbinding entity+role from each fact vector
+        entity_residuals = []
+        for entity in entities:
+            entity_vec = hrr.encode_atom(entity.lower(), self.hrr_dim)
+            probe_key = hrr.bind(entity_vec, role_entity)
+            entity_residuals.append(probe_key)
+
+        # Get all facts with vectors
+        where = "WHERE hrr_vector IS NOT NULL"
+        params: list = []
+        if category:
+            where += " AND category = ?"
+            params.append(category)
+
+        rows = conn.execute(
+            f"""
+            SELECT fact_id, content, category, tags, trust_score,
+                   retrieval_count, helpful_count, created_at, updated_at,
+                   hrr_vector
+            FROM facts
+            {where}
+            """,
+            params,
+        ).fetchall()
+
+        if not rows:
+            query = " ".join(entities)
+            return self.search(query, category=category, limit=limit)
+
+        # Score each fact by how much EACH entity is structurally present.
+        # A fact scores high only if ALL entities have structural presence
+        # (AND semantics via min, vs OR which would use mean/max).
+        role_content = hrr.encode_atom("__hrr_role_content__", self.hrr_dim)
+
+        scored = []
+        for row in rows:
+            fact = dict(row)
+            fact_vec = hrr.bytes_to_phases(fact.pop("hrr_vector"))
+
+            entity_scores = []
+            for probe_key in entity_residuals:
+                residual = hrr.unbind(fact_vec, probe_key)
+                sim = hrr.similarity(residual, role_content)
+                entity_scores.append(sim)
+
+            min_sim = min(entity_scores)
+            fact["score"] = (min_sim + 1.0) / 2.0 * fact["trust_score"]
+            scored.append(fact)
+
+        scored.sort(key=lambda x: x["score"], reverse=True)
+        return scored[:limit]
+
+    def contradict(
+        self,
+        category: str | None = None,
+        threshold: float = 0.3,
+        limit: int = 10,
+    ) -> list[dict]:
+        """Find potentially contradictory facts via entity overlap + content divergence.
+
+        Two facts contradict when they share entities (same subject) but have
+        low content-vector similarity (different claims). This is automated
+        memory hygiene — no other memory system does this.
+
+        Returns pairs of facts with a contradiction score.
+        Falls back to empty list if numpy unavailable.
+        """
+        if not hrr._HAS_NUMPY:
+            return []
+
+        conn = self.store._conn
+
+        # Get all facts with vectors and their linked entities
+        where = "WHERE f.hrr_vector IS NOT NULL"
+        params: list = []
+        if category:
+            where += " AND f.category = ?"
+            params.append(category)
+
+        rows = conn.execute(
+            f"""
+            SELECT f.fact_id, f.content, f.category, f.tags, f.trust_score,
+                   f.created_at, f.updated_at, f.hrr_vector
+            FROM facts f
+            {where}
+            """,
+            params,
+        ).fetchall()
+
+        if len(rows) < 2:
+            return []
+
+        # Guard against O(n²) explosion on large fact stores.
+        # At 500 facts, that's ~125K comparisons — acceptable.
+        # Above that, only check the most recently updated facts.
+        _MAX_CONTRADICT_FACTS = 500
+        if len(rows) > _MAX_CONTRADICT_FACTS:
+            rows = sorted(rows, key=lambda r: r["updated_at"] or r["created_at"], reverse=True)
+            rows = rows[:_MAX_CONTRADICT_FACTS]
+
+        # Build entity sets per fact
+        fact_entities: dict[int, set[str]] = {}
+        for row in rows:
+            fid = row["fact_id"]
+            entity_rows = conn.execute(
+                """
+                SELECT e.name FROM entities e
+                JOIN fact_entities fe ON fe.entity_id = e.entity_id
+                WHERE fe.fact_id = ?
+                """,
+                (fid,),
+            ).fetchall()
+            fact_entities[fid] = {r["name"].lower() for r in entity_rows}
+
+        # Compare all pairs: high entity overlap + low content similarity = contradiction
+        facts = [dict(r) for r in rows]
+        contradictions = []
+
+        for i in range(len(facts)):
+            for j in range(i + 1, len(facts)):
+                f1, f2 = facts[i], facts[j]
+                ents1 = fact_entities.get(f1["fact_id"], set())
+                ents2 = fact_entities.get(f2["fact_id"], set())
+
+                if not ents1 or not ents2:
+                    continue
+
+                # Entity overlap (Jaccard)
+                entity_overlap = len(ents1 & ents2) / len(ents1 | ents2) if (ents1 | ents2) else 0.0
+
+                if entity_overlap < 0.3:
+                    continue  # Not enough entity overlap to be contradictory
+
+                # Content similarity via HRR vectors
+                v1 = hrr.bytes_to_phases(f1["hrr_vector"])
+                v2 = hrr.bytes_to_phases(f2["hrr_vector"])
+                content_sim = hrr.similarity(v1, v2)
+
+                # High entity overlap + low content similarity = potential contradiction
+                # contradiction_score: higher = more contradictory
+                contradiction_score = entity_overlap * (1.0 - (content_sim + 1.0) / 2.0)
+
+                if contradiction_score >= threshold:
+                    # Strip hrr_vector from output (not JSON serializable)
+                    f1_clean = {k: v for k, v in f1.items() if k != "hrr_vector"}
+                    f2_clean = {k: v for k, v in f2.items() if k != "hrr_vector"}
+                    contradictions.append({
+                        "fact_a": f1_clean,
+                        "fact_b": f2_clean,
+                        "entity_overlap": round(entity_overlap, 3),
+                        "content_similarity": round(content_sim, 3),
+                        "contradiction_score": round(contradiction_score, 3),
+                        "shared_entities": sorted(ents1 & ents2),
+                    })
+
+        contradictions.sort(key=lambda x: x["contradiction_score"], reverse=True)
+        return contradictions[:limit]
+
+    def _score_facts_by_vector(
+        self,
+        target_vec: "np.ndarray",
+        category: str | None = None,
+        limit: int = 10,
+    ) -> list[dict]:
+        """Score facts by similarity to a target vector."""
+        conn = self.store._conn
+
+        where = "WHERE hrr_vector IS NOT NULL"
+        params: list = []
+        if category:
+            where += " AND category = ?"
+            params.append(category)
+
+        rows = conn.execute(
+            f"""
+            SELECT fact_id, content, category, tags, trust_score,
+                   retrieval_count, helpful_count, created_at, updated_at,
+                   hrr_vector
+            FROM facts
+            {where}
+            """,
+            params,
+        ).fetchall()
+
+        scored = []
+        for row in rows:
+            fact = dict(row)
+            fact_vec = hrr.bytes_to_phases(fact.pop("hrr_vector"))
+            sim = hrr.similarity(target_vec, fact_vec)
+            fact["score"] = (sim + 1.0) / 2.0 * fact["trust_score"]
+            scored.append(fact)
+
+        scored.sort(key=lambda x: x["score"], reverse=True)
+        return scored[:limit]
+
+    def _fts_candidates(
+        self,
+        query: str,
+        category: str | None,
+        min_trust: float,
+        limit: int,
+    ) -> list[dict]:
+        """Get raw FTS5 candidates from the store.
+
+        Uses the store's database connection directly for FTS5 MATCH
+        with rank scoring. Normalizes FTS5 rank to [0, 1] range.
+        """
+        conn = self.store._conn
+
+        # Build query - FTS5 rank is negative (lower = better match)
+        # We need to join facts_fts with facts to get all columns
+        params: list = []
+        where_clauses = ["facts_fts MATCH ?"]
+        params.append(query)
+
+        if category:
+            where_clauses.append("f.category = ?")
+            params.append(category)
+
+        where_clauses.append("f.trust_score >= ?")
+        params.append(min_trust)
+
+        where_sql = " AND ".join(where_clauses)
+
+        sql = f"""
+            SELECT f.*, facts_fts.rank as fts_rank_raw
+            FROM facts_fts
+            JOIN facts f ON f.fact_id = facts_fts.rowid
+            WHERE {where_sql}
+            ORDER BY facts_fts.rank
+            LIMIT ?
+        """
+        params.append(limit)
+
+        try:
+            rows = conn.execute(sql, params).fetchall()
+        except Exception:
+            # FTS5 MATCH can fail on malformed queries — fall back to empty
+            return []
+
+        if not rows:
+            return []
+
+        # Normalize FTS5 rank: rank is negative, lower = better
+        # Convert to positive score in [0, 1] range
+        raw_ranks = [abs(row["fts_rank_raw"]) for row in rows]
+        max_rank = max(raw_ranks) if raw_ranks else 1.0
+        max_rank = max(max_rank, 1e-6)  # avoid div by zero
+
+        results = []
+        for row, raw_rank in zip(rows, raw_ranks):
+            fact = dict(row)
+            fact.pop("fts_rank_raw", None)
+            fact["fts_rank"] = raw_rank / max_rank  # normalize to [0, 1]
+            results.append(fact)
+
+        return results
+
+    @staticmethod
+    def _tokenize(text: str) -> set[str]:
+        """Simple whitespace tokenization with lowercasing.
+
+        Strips common punctuation. No stemming/lemmatization (Phase 1).
+        """
+        if not text:
+            return set()
+        # Split on whitespace, lowercase, strip punctuation
+        tokens = set()
+        for word in text.lower().split():
+            cleaned = word.strip(".,;:!?\"'()[]{}#@<>")
+            if cleaned:
+                tokens.add(cleaned)
+        return tokens
+
+    @staticmethod
+    def _jaccard_similarity(set_a: set, set_b: set) -> float:
+        """Jaccard similarity coefficient: |A ∩ B| / |A ∪ B|."""
+        if not set_a or not set_b:
+            return 0.0
+        intersection = len(set_a & set_b)
+        union = len(set_a | set_b)
+        return intersection / union if union > 0 else 0.0
+
+    def _temporal_decay(self, timestamp_str: str | None) -> float:
+        """Exponential decay: 0.5^(age_days / half_life_days).
+
+        Returns 1.0 if decay is disabled or timestamp is missing.
+        """
+        if not self.half_life or not timestamp_str:
+            return 1.0
+
+        try:
+            if isinstance(timestamp_str, str):
+                # Parse ISO format timestamp from SQLite
+                ts = datetime.fromisoformat(timestamp_str.replace("Z", "+00:00"))
+            else:
+                ts = timestamp_str
+
+            if ts.tzinfo is None:
+                ts = ts.replace(tzinfo=timezone.utc)
+
+            age_days = (datetime.now(timezone.utc) - ts).total_seconds() / 86400
+            if age_days < 0:
+                return 1.0
+
+            return math.pow(0.5, age_days / self.half_life)
+        except (ValueError, TypeError):
+            return 1.0

plugins/memory/holographic/store.py

@@ -0,0 +1,575 @@
+"""
+SQLite-backed fact store with entity resolution and trust scoring.
+Single-user Hermes memory store plugin.
+"""
+
+import re
+import sqlite3
+import threading
+from datetime import datetime
+from pathlib import Path
+
+try:
+    from . import holographic as hrr
+except ImportError:
+    import holographic as hrr  # type: ignore[no-redef]
+
+_SCHEMA = """
+CREATE TABLE IF NOT EXISTS facts (
+    fact_id         INTEGER PRIMARY KEY AUTOINCREMENT,
+    content         TEXT NOT NULL UNIQUE,
+    category        TEXT DEFAULT 'general',
+    tags            TEXT DEFAULT '',
+    trust_score     REAL DEFAULT 0.5,
+    retrieval_count INTEGER DEFAULT 0,
+    helpful_count   INTEGER DEFAULT 0,
+    created_at      TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    updated_at      TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    hrr_vector      BLOB
+);
+
+CREATE TABLE IF NOT EXISTS entities (
+    entity_id   INTEGER PRIMARY KEY AUTOINCREMENT,
+    name        TEXT NOT NULL,
+    entity_type TEXT DEFAULT 'unknown',
+    aliases     TEXT DEFAULT '',
+    created_at  TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+CREATE TABLE IF NOT EXISTS fact_entities (
+    fact_id   INTEGER REFERENCES facts(fact_id),
+    entity_id INTEGER REFERENCES entities(entity_id),
+    PRIMARY KEY (fact_id, entity_id)
+);
+
+CREATE INDEX IF NOT EXISTS idx_facts_trust    ON facts(trust_score DESC);
+CREATE INDEX IF NOT EXISTS idx_facts_category ON facts(category);
+CREATE INDEX IF NOT EXISTS idx_entities_name  ON entities(name);
+
+CREATE VIRTUAL TABLE IF NOT EXISTS facts_fts
+    USING fts5(content, tags, content=facts, content_rowid=fact_id);
+
+CREATE TRIGGER IF NOT EXISTS facts_ai AFTER INSERT ON facts BEGIN
+    INSERT INTO facts_fts(rowid, content, tags)
+        VALUES (new.fact_id, new.content, new.tags);
+END;
+
+CREATE TRIGGER IF NOT EXISTS facts_ad AFTER DELETE ON facts BEGIN
+    INSERT INTO facts_fts(facts_fts, rowid, content, tags)
+        VALUES ('delete', old.fact_id, old.content, old.tags);
+END;
+
+CREATE TRIGGER IF NOT EXISTS facts_au AFTER UPDATE ON facts BEGIN
+    INSERT INTO facts_fts(facts_fts, rowid, content, tags)
+        VALUES ('delete', old.fact_id, old.content, old.tags);
+    INSERT INTO facts_fts(rowid, content, tags)
+        VALUES (new.fact_id, new.content, new.tags);
+END;
+
+CREATE TABLE IF NOT EXISTS memory_banks (
+    bank_id    INTEGER PRIMARY KEY AUTOINCREMENT,
+    bank_name  TEXT NOT NULL UNIQUE,
+    vector     BLOB NOT NULL,
+    dim        INTEGER NOT NULL,
+    fact_count INTEGER DEFAULT 0,
+    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+"""
+
+# Trust adjustment constants
+_HELPFUL_DELTA   =  0.05
+_UNHELPFUL_DELTA = -0.10
+_TRUST_MIN       =  0.0
+_TRUST_MAX       =  1.0
+
+# Entity extraction patterns
+_RE_CAPITALIZED  = re.compile(r'\b([A-Z][a-z]+(?:\s+[A-Z][a-z]+)+)\b')
+_RE_DOUBLE_QUOTE = re.compile(r'"([^"]+)"')
+_RE_SINGLE_QUOTE = re.compile(r"'([^']+)'")
+_RE_AKA          = re.compile(
+    r'(\w+(?:\s+\w+)*)\s+(?:aka|also known as)\s+(\w+(?:\s+\w+)*)',
+    re.IGNORECASE,
+)
+
+
+def _clamp_trust(value: float) -> float:
+    return max(_TRUST_MIN, min(_TRUST_MAX, value))
+
+
+class MemoryStore:
+    """SQLite-backed fact store with entity resolution and trust scoring."""
+
+    def __init__(
+        self,
+        db_path: "str | Path | None" = None,
+        default_trust: float = 0.5,
+        hrr_dim: int = 1024,
+    ) -> None:
+        if db_path is None:
+            from hermes_constants import get_hermes_home
+            db_path = str(get_hermes_home() / "memory_store.db")
+        self.db_path = Path(db_path).expanduser()
+        self.db_path.parent.mkdir(parents=True, exist_ok=True)
+        self.default_trust = _clamp_trust(default_trust)
+        self.hrr_dim = hrr_dim
+        self._hrr_available = hrr._HAS_NUMPY
+        self._conn: sqlite3.Connection = sqlite3.connect(
+            str(self.db_path),
+            check_same_thread=False,
+            timeout=10.0,
+        )
+        self._lock = threading.RLock()
+        self._conn.row_factory = sqlite3.Row
+        self._init_db()
+
+    # ------------------------------------------------------------------
+    # Initialisation
+    # ------------------------------------------------------------------
+
+    def _init_db(self) -> None:
+        """Create tables, indexes, and triggers if they do not exist. Enable WAL mode."""
+        self._conn.execute("PRAGMA journal_mode=WAL")
+        self._conn.executescript(_SCHEMA)
+        # Migrate: add hrr_vector column if missing (safe for existing databases)
+        columns = {row[1] for row in self._conn.execute("PRAGMA table_info(facts)").fetchall()}
+        if "hrr_vector" not in columns:
+            self._conn.execute("ALTER TABLE facts ADD COLUMN hrr_vector BLOB")
+        self._conn.commit()
+
+    # ------------------------------------------------------------------
+    # Public API
+    # ------------------------------------------------------------------
+
+    def add_fact(
+        self,
+        content: str,
+        category: str = "general",
+        tags: str = "",
+    ) -> int:
+        """Insert a fact and return its fact_id.
+
+        Deduplicates by content (UNIQUE constraint). On duplicate, returns
+        the existing fact_id without modifying the row. Extracts entities from
+        the content and links them to the fact.
+        """
+        with self._lock:
+            content = content.strip()
+            if not content:
+                raise ValueError("content must not be empty")
+
+            try:
+                cur = self._conn.execute(
+                    """
+                    INSERT INTO facts (content, category, tags, trust_score)
+                    VALUES (?, ?, ?, ?)
+                    """,
+                    (content, category, tags, self.default_trust),
+                )
+                self._conn.commit()
+                fact_id: int = cur.lastrowid  # type: ignore[assignment]
+            except sqlite3.IntegrityError:
+                # Duplicate content — return existing id
+                row = self._conn.execute(
+                    "SELECT fact_id FROM facts WHERE content = ?", (content,)
+                ).fetchone()
+                return int(row["fact_id"])
+
+            # Entity extraction and linking
+            for name in self._extract_entities(content):
+                entity_id = self._resolve_entity(name)
+                self._link_fact_entity(fact_id, entity_id)
+
+            # Compute HRR vector after entity linking
+            self._compute_hrr_vector(fact_id, content)
+            self._rebuild_bank(category)
+
+            return fact_id
+
+    def search_facts(
+        self,
+        query: str,
+        category: str | None = None,
+        min_trust: float = 0.3,
+        limit: int = 10,
+    ) -> list[dict]:
+        """Full-text search over facts using FTS5.
+
+        Returns a list of fact dicts ordered by FTS5 rank, then trust_score
+        descending. Also increments retrieval_count for matched facts.
+        """
+        with self._lock:
+            query = query.strip()
+            if not query:
+                return []
+
+            params: list = [query, min_trust]
+            category_clause = ""
+            if category is not None:
+                category_clause = "AND f.category = ?"
+                params.append(category)
+            params.append(limit)
+
+            sql = f"""
+                SELECT f.fact_id, f.content, f.category, f.tags,
+                       f.trust_score, f.retrieval_count, f.helpful_count,
+                       f.created_at, f.updated_at
+                FROM facts f
+                JOIN facts_fts fts ON fts.rowid = f.fact_id
+                WHERE facts_fts MATCH ?
+                  AND f.trust_score >= ?
+                  {category_clause}
+                ORDER BY fts.rank, f.trust_score DESC
+                LIMIT ?
+            """
+
+            rows = self._conn.execute(sql, params).fetchall()
+            results = [self._row_to_dict(r) for r in rows]
+
+            if results:
+                ids = [r["fact_id"] for r in results]
+                placeholders = ",".join("?" * len(ids))
+                self._conn.execute(
+                    f"UPDATE facts SET retrieval_count = retrieval_count + 1 WHERE fact_id IN ({placeholders})",
+                    ids,
+                )
+                self._conn.commit()
+
+            return results
+
+    def update_fact(
+        self,
+        fact_id: int,
+        content: str | None = None,
+        trust_delta: float | None = None,
+        tags: str | None = None,
+        category: str | None = None,
+    ) -> bool:
+        """Partially update a fact. Trust is clamped to [0, 1].
+
+        Returns True if the row existed, False otherwise.
+        """
+        with self._lock:
+            row = self._conn.execute(
+                "SELECT fact_id, trust_score FROM facts WHERE fact_id = ?", (fact_id,)
+            ).fetchone()
+            if row is None:
+                return False
+
+            assignments: list[str] = ["updated_at = CURRENT_TIMESTAMP"]
+            params: list = []
+
+            if content is not None:
+                assignments.append("content = ?")
+                params.append(content.strip())
+            if tags is not None:
+                assignments.append("tags = ?")
+                params.append(tags)
+            if category is not None:
+                assignments.append("category = ?")
+                params.append(category)
+            if trust_delta is not None:
+                new_trust = _clamp_trust(row["trust_score"] + trust_delta)
+                assignments.append("trust_score = ?")
+                params.append(new_trust)
+
+            params.append(fact_id)
+            self._conn.execute(
+                f"UPDATE facts SET {', '.join(assignments)} WHERE fact_id = ?",
+                params,
+            )
+            self._conn.commit()
+
+            # If content changed, re-extract entities
+            if content is not None:
+                self._conn.execute(
+                    "DELETE FROM fact_entities WHERE fact_id = ?", (fact_id,)
+                )
+                for name in self._extract_entities(content):
+                    entity_id = self._resolve_entity(name)
+                    self._link_fact_entity(fact_id, entity_id)
+                self._conn.commit()
+
+            # Recompute HRR vector if content changed
+            if content is not None:
+                self._compute_hrr_vector(fact_id, content)
+            # Rebuild bank for relevant category
+            cat = category or self._conn.execute(
+                "SELECT category FROM facts WHERE fact_id = ?", (fact_id,)
+            ).fetchone()["category"]
+            self._rebuild_bank(cat)
+
+            return True
+
+    def remove_fact(self, fact_id: int) -> bool:
+        """Delete a fact and its entity links. Returns True if the row existed."""
+        with self._lock:
+            row = self._conn.execute(
+                "SELECT fact_id, category FROM facts WHERE fact_id = ?", (fact_id,)
+            ).fetchone()
+            if row is None:
+                return False
+
+            self._conn.execute(
+                "DELETE FROM fact_entities WHERE fact_id = ?", (fact_id,)
+            )
+            self._conn.execute("DELETE FROM facts WHERE fact_id = ?", (fact_id,))
+            self._conn.commit()
+            self._rebuild_bank(row["category"])
+            return True
+
+    def list_facts(
+        self,
+        category: str | None = None,
+        min_trust: float = 0.0,
+        limit: int = 50,
+    ) -> list[dict]:
+        """Browse facts ordered by trust_score descending.
+
+        Optionally filter by category and minimum trust score.
+        """
+        with self._lock:
+            params: list = [min_trust]
+            category_clause = ""
+            if category is not None:
+                category_clause = "AND category = ?"
+                params.append(category)
+            params.append(limit)
+
+            sql = f"""
+                SELECT fact_id, content, category, tags, trust_score,
+                       retrieval_count, helpful_count, created_at, updated_at
+                FROM facts
+                WHERE trust_score >= ?
+                  {category_clause}
+                ORDER BY trust_score DESC
+                LIMIT ?
+            """
+            rows = self._conn.execute(sql, params).fetchall()
+            return [self._row_to_dict(r) for r in rows]
+
+    def record_feedback(self, fact_id: int, helpful: bool) -> dict:
+        """Record user feedback and adjust trust asymmetrically.
+
+        helpful=True  -> trust += 0.05, helpful_count += 1
+        helpful=False -> trust -= 0.10
+
+        Returns a dict with fact_id, old_trust, new_trust, helpful_count.
+        Raises KeyError if fact_id does not exist.
+        """
+        with self._lock:
+            row = self._conn.execute(
+                "SELECT fact_id, trust_score, helpful_count FROM facts WHERE fact_id = ?",
+                (fact_id,),
+            ).fetchone()
+            if row is None:
+                raise KeyError(f"fact_id {fact_id} not found")
+
+            old_trust: float = row["trust_score"]
+            delta = _HELPFUL_DELTA if helpful else _UNHELPFUL_DELTA
+            new_trust = _clamp_trust(old_trust + delta)
+
+            helpful_increment = 1 if helpful else 0
+            self._conn.execute(
+                """
+                UPDATE facts
+                SET trust_score    = ?,
+                    helpful_count  = helpful_count + ?,
+                    updated_at     = CURRENT_TIMESTAMP
+                WHERE fact_id = ?
+                """,
+                (new_trust, helpful_increment, fact_id),
+            )
+            self._conn.commit()
+
+            return {
+                "fact_id":      fact_id,
+                "old_trust":    old_trust,
+                "new_trust":    new_trust,
+                "helpful_count": row["helpful_count"] + helpful_increment,
+            }
+
+    # ------------------------------------------------------------------
+    # Entity helpers
+    # ------------------------------------------------------------------
+
+    def _extract_entities(self, text: str) -> list[str]:
+        """Extract entity candidates from text using simple regex rules.
+
+        Rules applied (in order):
+        1. Capitalized multi-word phrases  e.g. "John Doe"
+        2. Double-quoted terms             e.g. "Python"
+        3. Single-quoted terms             e.g. 'pytest'
+        4. AKA patterns                    e.g. "Guido aka BDFL" -> two entities
+
+        Returns a deduplicated list preserving first-seen order.
+        """
+        seen: set[str] = set()
+        candidates: list[str] = []
+
+        def _add(name: str) -> None:
+            stripped = name.strip()
+            if stripped and stripped.lower() not in seen:
+                seen.add(stripped.lower())
+                candidates.append(stripped)
+
+        for m in _RE_CAPITALIZED.finditer(text):
+            _add(m.group(1))
+
+        for m in _RE_DOUBLE_QUOTE.finditer(text):
+            _add(m.group(1))
+
+        for m in _RE_SINGLE_QUOTE.finditer(text):
+            _add(m.group(1))
+
+        for m in _RE_AKA.finditer(text):
+            _add(m.group(1))
+            _add(m.group(2))
+
+        return candidates
+
+    def _resolve_entity(self, name: str) -> int:
+        """Find an existing entity by name or alias (case-insensitive) or create one.
+
+        Returns the entity_id.
+        """
+        # Exact name match
+        row = self._conn.execute(
+            "SELECT entity_id FROM entities WHERE name LIKE ?", (name,)
+        ).fetchone()
+        if row is not None:
+            return int(row["entity_id"])
+
+        # Search aliases — aliases stored as comma-separated; use LIKE with % boundaries
+        alias_row = self._conn.execute(
+            """
+            SELECT entity_id FROM entities
+            WHERE ',' || aliases || ',' LIKE '%,' || ? || ',%'
+            """,
+            (name,),
+        ).fetchone()
+        if alias_row is not None:
+            return int(alias_row["entity_id"])
+
+        # Create new entity
+        cur = self._conn.execute(
+            "INSERT INTO entities (name) VALUES (?)", (name,)
+        )
+        self._conn.commit()
+        return int(cur.lastrowid)  # type: ignore[return-value]
+
+    def _link_fact_entity(self, fact_id: int, entity_id: int) -> None:
+        """Insert into fact_entities, silently ignore if the link already exists."""
+        self._conn.execute(
+            """
+            INSERT OR IGNORE INTO fact_entities (fact_id, entity_id)
+            VALUES (?, ?)
+            """,
+            (fact_id, entity_id),
+        )
+        self._conn.commit()
+
+    def _compute_hrr_vector(self, fact_id: int, content: str) -> None:
+        """Compute and store HRR vector for a fact. No-op if numpy unavailable."""
+        with self._lock:
+            if not self._hrr_available:
+                return
+
+            # Get entities linked to this fact
+            rows = self._conn.execute(
+                """
+                SELECT e.name FROM entities e
+                JOIN fact_entities fe ON fe.entity_id = e.entity_id
+                WHERE fe.fact_id = ?
+                """,
+                (fact_id,),
+            ).fetchall()
+            entities = [row["name"] for row in rows]
+
+            vector = hrr.encode_fact(content, entities, self.hrr_dim)
+            self._conn.execute(
+                "UPDATE facts SET hrr_vector = ? WHERE fact_id = ?",
+                (hrr.phases_to_bytes(vector), fact_id),
+            )
+            self._conn.commit()
+
+    def _rebuild_bank(self, category: str) -> None:
+        """Full rebuild of a category's memory bank from all its fact vectors."""
+        with self._lock:
+            if not self._hrr_available:
+                return
+
+            bank_name = f"cat:{category}"
+            rows = self._conn.execute(
+                "SELECT hrr_vector FROM facts WHERE category = ? AND hrr_vector IS NOT NULL",
+                (category,),
+            ).fetchall()
+
+            if not rows:
+                self._conn.execute("DELETE FROM memory_banks WHERE bank_name = ?", (bank_name,))
+                self._conn.commit()
+                return
+
+            vectors = [hrr.bytes_to_phases(row["hrr_vector"]) for row in rows]
+            bank_vector = hrr.bundle(*vectors)
+            fact_count = len(vectors)
+
+            # Check SNR
+            hrr.snr_estimate(self.hrr_dim, fact_count)
+
+            self._conn.execute(
+                """
+                INSERT INTO memory_banks (bank_name, vector, dim, fact_count, updated_at)
+                VALUES (?, ?, ?, ?, CURRENT_TIMESTAMP)
+                ON CONFLICT(bank_name) DO UPDATE SET
+                    vector = excluded.vector,
+                    dim = excluded.dim,
+                    fact_count = excluded.fact_count,
+                    updated_at = excluded.updated_at
+                """,
+                (bank_name, hrr.phases_to_bytes(bank_vector), self.hrr_dim, fact_count),
+            )
+            self._conn.commit()
+
+    def rebuild_all_vectors(self, dim: int | None = None) -> int:
+        """Recompute all HRR vectors + banks from text. For recovery/migration.
+
+        Returns the number of facts processed.
+        """
+        with self._lock:
+            if not self._hrr_available:
+                return 0
+
+            if dim is not None:
+                self.hrr_dim = dim
+
+            rows = self._conn.execute(
+                "SELECT fact_id, content, category FROM facts"
+            ).fetchall()
+
+            categories: set[str] = set()
+            for row in rows:
+                self._compute_hrr_vector(row["fact_id"], row["content"])
+                categories.add(row["category"])
+
+            for category in categories:
+                self._rebuild_bank(category)
+
+            return len(rows)
+
+    # ------------------------------------------------------------------
+    # Utilities
+    # ------------------------------------------------------------------
+
+    def _row_to_dict(self, row: sqlite3.Row) -> dict:
+        """Convert a sqlite3.Row to a plain dict."""
+        return dict(row)
+
+    def close(self) -> None:
+        """Close the database connection."""
+        self._conn.close()
+
+    def __enter__(self) -> "MemoryStore":
+        return self
+
+    def __exit__(self, *_: object) -> None:
+        self.close()

plugins/memory/honcho/README.md

@@ -0,0 +1,35 @@
+# Honcho Memory Provider
+
+AI-native cross-session user modeling with dialectic Q&A, semantic search, peer cards, and persistent conclusions.
+
+## Requirements
+
+- `pip install honcho-ai`
+- Honcho API key from [app.honcho.dev](https://app.honcho.dev)
+
+## Setup
+
+```bash
+hermes memory setup    # select "honcho"
+```
+
+Or manually:
+```bash
+hermes config set memory.provider honcho
+echo "HONCHO_API_KEY=your-key" >> ~/.hermes/.env
+```
+
+## Config
+
+Config file: `$HERMES_HOME/honcho.json` (or `~/.honcho/config.json` legacy)
+
+Existing Honcho users: your config and data are preserved. Just set `memory.provider: honcho`.
+
+## Tools
+
+| Tool | Description |
+|------|-------------|
+| `honcho_profile` | User's peer card — key facts, no LLM |
+| `honcho_search` | Semantic search over stored context |
+| `honcho_context` | LLM-synthesized answer from memory |
+| `honcho_conclude` | Write a fact about the user to memory |

plugins/memory/honcho/__init__.py

@@ -0,0 +1,355 @@
+"""Honcho memory plugin — MemoryProvider for Honcho AI-native memory.
+
+Provides cross-session user modeling with dialectic Q&A, semantic search,
+peer cards, and persistent conclusions via the Honcho SDK. Honcho provides AI-native cross-session user
+modeling with dialectic Q&A, semantic search, peer cards, and conclusions.
+
+The 4 tools (profile, search, context, conclude) are exposed through
+the MemoryProvider interface.
+
+Config: Uses the existing Honcho config chain:
+  1. $HERMES_HOME/honcho.json (profile-scoped)
+  2. ~/.honcho/config.json (legacy global)
+  3. Environment variables
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import threading
+from typing import Any, Dict, List, Optional
+
+from agent.memory_provider import MemoryProvider
+
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# Tool schemas (moved from tools/honcho_tools.py)
+# ---------------------------------------------------------------------------
+
+PROFILE_SCHEMA = {
+    "name": "honcho_profile",
+    "description": (
+        "Retrieve the user's peer card from Honcho — a curated list of key facts "
+        "about them (name, role, preferences, communication style, patterns). "
+        "Fast, no LLM reasoning, minimal cost. "
+        "Use this at conversation start or when you need a quick factual snapshot."
+    ),
+    "parameters": {"type": "object", "properties": {}, "required": []},
+}
+
+SEARCH_SCHEMA = {
+    "name": "honcho_search",
+    "description": (
+        "Semantic search over Honcho's stored context about the user. "
+        "Returns raw excerpts ranked by relevance — no LLM synthesis. "
+        "Cheaper and faster than honcho_context. "
+        "Good when you want to find specific past facts and reason over them yourself."
+    ),
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "query": {
+                "type": "string",
+                "description": "What to search for in Honcho's memory.",
+            },
+            "max_tokens": {
+                "type": "integer",
+                "description": "Token budget for returned context (default 800, max 2000).",
+            },
+        },
+        "required": ["query"],
+    },
+}
+
+CONTEXT_SCHEMA = {
+    "name": "honcho_context",
+    "description": (
+        "Ask Honcho a natural language question and get a synthesized answer. "
+        "Uses Honcho's LLM (dialectic reasoning) — higher cost than honcho_profile or honcho_search. "
+        "Can query about any peer: the user (default) or the AI assistant."
+    ),
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "query": {
+                "type": "string",
+                "description": "A natural language question.",
+            },
+            "peer": {
+                "type": "string",
+                "description": "Which peer to query about: 'user' (default) or 'ai'.",
+            },
+        },
+        "required": ["query"],
+    },
+}
+
+CONCLUDE_SCHEMA = {
+    "name": "honcho_conclude",
+    "description": (
+        "Write a conclusion about the user back to Honcho's memory. "
+        "Conclusions are persistent facts that build the user's profile. "
+        "Use when the user states a preference, corrects you, or shares "
+        "something to remember across sessions."
+    ),
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "conclusion": {
+                "type": "string",
+                "description": "A factual statement about the user to persist.",
+            }
+        },
+        "required": ["conclusion"],
+    },
+}
+
+
+# ---------------------------------------------------------------------------
+# MemoryProvider implementation
+# ---------------------------------------------------------------------------
+
+class HonchoMemoryProvider(MemoryProvider):
+    """Honcho AI-native memory with dialectic Q&A and persistent user modeling."""
+
+    def __init__(self):
+        self._manager = None   # HonchoSessionManager
+        self._config = None    # HonchoClientConfig
+        self._session_key = ""
+        self._prefetch_result = ""
+        self._prefetch_lock = threading.Lock()
+        self._prefetch_thread: Optional[threading.Thread] = None
+        self._sync_thread: Optional[threading.Thread] = None
+
+    @property
+    def name(self) -> str:
+        return "honcho"
+
+    def is_available(self) -> bool:
+        """Check if Honcho is configured. No network calls."""
+        try:
+            from plugins.memory.honcho.client import HonchoClientConfig
+            cfg = HonchoClientConfig.from_global_config()
+            return cfg.enabled and bool(cfg.api_key or cfg.base_url)
+        except Exception:
+            return False
+
+    def save_config(self, values, hermes_home):
+        """Write config to $HERMES_HOME/honcho.json (Honcho SDK native format)."""
+        import json
+        from pathlib import Path
+        config_path = Path(hermes_home) / "honcho.json"
+        existing = {}
+        if config_path.exists():
+            try:
+                existing = json.loads(config_path.read_text())
+            except Exception:
+                pass
+        existing.update(values)
+        config_path.write_text(json.dumps(existing, indent=2))
+
+    def get_config_schema(self):
+        return [
+            {"key": "api_key", "description": "Honcho API key", "secret": True, "env_var": "HONCHO_API_KEY", "url": "https://app.honcho.dev"},
+            {"key": "base_url", "description": "Honcho base URL", "default": "https://api.honcho.dev"},
+        ]
+
+    def initialize(self, session_id: str, **kwargs) -> None:
+        """Initialize Honcho session manager."""
+        try:
+            from plugins.memory.honcho.client import HonchoClientConfig, get_honcho_client
+            from plugins.memory.honcho.session import HonchoSessionManager
+
+            cfg = HonchoClientConfig.from_global_config()
+            if not cfg.enabled or not (cfg.api_key or cfg.base_url):
+                logger.debug("Honcho not configured — plugin inactive")
+                return
+
+            self._config = cfg
+            client = get_honcho_client(cfg)
+            self._manager = HonchoSessionManager(
+                honcho=client,
+                config=cfg,
+                context_tokens=cfg.context_tokens,
+            )
+
+            # Build session key from kwargs or session_id
+            platform = kwargs.get("platform", "cli")
+            user_id = kwargs.get("user_id", "")
+            if user_id:
+                self._session_key = f"{platform}:{user_id}"
+            else:
+                self._session_key = session_id
+
+        except ImportError:
+            logger.debug("honcho-ai package not installed — plugin inactive")
+        except Exception as e:
+            logger.warning("Honcho init failed: %s", e)
+            self._manager = None
+
+    def system_prompt_block(self) -> str:
+        if not self._manager or not self._session_key:
+            return ""
+        return (
+            "# Honcho Memory\n"
+            "Active. AI-native cross-session user modeling.\n"
+            "Use honcho_profile for a quick factual snapshot, "
+            "honcho_search for raw excerpts, honcho_context for synthesized answers, "
+            "honcho_conclude to save facts about the user."
+        )
+
+    def prefetch(self, query: str, *, session_id: str = "") -> str:
+        """Return prefetched dialectic context from background thread."""
+        if self._prefetch_thread and self._prefetch_thread.is_alive():
+            self._prefetch_thread.join(timeout=3.0)
+        with self._prefetch_lock:
+            result = self._prefetch_result
+            self._prefetch_result = ""
+        if not result:
+            return ""
+        return f"## Honcho Context\n{result}"
+
+    def queue_prefetch(self, query: str, *, session_id: str = "") -> None:
+        """Fire a background dialectic query for the upcoming turn."""
+        if not self._manager or not self._session_key or not query:
+            return
+
+        def _run():
+            try:
+                result = self._manager.dialectic_query(
+                    self._session_key, query, peer="user"
+                )
+                if result and result.strip():
+                    with self._prefetch_lock:
+                        self._prefetch_result = result
+            except Exception as e:
+                logger.debug("Honcho prefetch failed: %s", e)
+
+        self._prefetch_thread = threading.Thread(
+            target=_run, daemon=True, name="honcho-prefetch"
+        )
+        self._prefetch_thread.start()
+
+    def sync_turn(self, user_content: str, assistant_content: str, *, session_id: str = "") -> None:
+        """Record the conversation turn in Honcho (non-blocking)."""
+        if not self._manager or not self._session_key:
+            return
+
+        def _sync():
+            try:
+                session = self._manager.get_or_create_session(self._session_key)
+                session.add_message("user", user_content[:4000])
+                session.add_message("assistant", assistant_content[:4000])
+                # Flush to Honcho API
+                self._manager._flush_session(session)
+            except Exception as e:
+                logger.debug("Honcho sync_turn failed: %s", e)
+
+        if self._sync_thread and self._sync_thread.is_alive():
+            self._sync_thread.join(timeout=5.0)
+        self._sync_thread = threading.Thread(
+            target=_sync, daemon=True, name="honcho-sync"
+        )
+        self._sync_thread.start()
+
+    def on_memory_write(self, action: str, target: str, content: str) -> None:
+        """Mirror built-in user profile writes as Honcho conclusions."""
+        if action != "add" or target != "user" or not content:
+            return
+        if not self._manager or not self._session_key:
+            return
+
+        def _write():
+            try:
+                self._manager.create_conclusion(self._session_key, content)
+            except Exception as e:
+                logger.debug("Honcho memory mirror failed: %s", e)
+
+        t = threading.Thread(target=_write, daemon=True, name="honcho-memwrite")
+        t.start()
+
+    def on_session_end(self, messages: List[Dict[str, Any]]) -> None:
+        """Flush all pending messages to Honcho on session end."""
+        if not self._manager:
+            return
+        # Wait for pending sync
+        if self._sync_thread and self._sync_thread.is_alive():
+            self._sync_thread.join(timeout=10.0)
+        try:
+            self._manager.flush_all()
+        except Exception as e:
+            logger.debug("Honcho session-end flush failed: %s", e)
+
+    def get_tool_schemas(self) -> List[Dict[str, Any]]:
+        return [PROFILE_SCHEMA, SEARCH_SCHEMA, CONTEXT_SCHEMA, CONCLUDE_SCHEMA]
+
+    def handle_tool_call(self, tool_name: str, args: dict, **kwargs) -> str:
+        if not self._manager or not self._session_key:
+            return json.dumps({"error": "Honcho is not active for this session."})
+
+        try:
+            if tool_name == "honcho_profile":
+                card = self._manager.get_peer_card(self._session_key)
+                if not card:
+                    return json.dumps({"result": "No profile facts available yet."})
+                return json.dumps({"result": card})
+
+            elif tool_name == "honcho_search":
+                query = args.get("query", "")
+                if not query:
+                    return json.dumps({"error": "Missing required parameter: query"})
+                max_tokens = min(int(args.get("max_tokens", 800)), 2000)
+                result = self._manager.search_context(
+                    self._session_key, query, max_tokens=max_tokens
+                )
+                if not result:
+                    return json.dumps({"result": "No relevant context found."})
+                return json.dumps({"result": result})
+
+            elif tool_name == "honcho_context":
+                query = args.get("query", "")
+                if not query:
+                    return json.dumps({"error": "Missing required parameter: query"})
+                peer = args.get("peer", "user")
+                result = self._manager.dialectic_query(
+                    self._session_key, query, peer=peer
+                )
+                return json.dumps({"result": result or "No result from Honcho."})
+
+            elif tool_name == "honcho_conclude":
+                conclusion = args.get("conclusion", "")
+                if not conclusion:
+                    return json.dumps({"error": "Missing required parameter: conclusion"})
+                ok = self._manager.create_conclusion(self._session_key, conclusion)
+                if ok:
+                    return json.dumps({"result": f"Conclusion saved: {conclusion}"})
+                return json.dumps({"error": "Failed to save conclusion."})
+
+            return json.dumps({"error": f"Unknown tool: {tool_name}"})
+
+        except Exception as e:
+            logger.error("Honcho tool %s failed: %s", tool_name, e)
+            return json.dumps({"error": f"Honcho {tool_name} failed: {e}"})
+
+    def shutdown(self) -> None:
+        for t in (self._prefetch_thread, self._sync_thread):
+            if t and t.is_alive():
+                t.join(timeout=5.0)
+        # Flush any remaining messages
+        if self._manager:
+            try:
+                self._manager.flush_all()
+            except Exception:
+                pass
+
+
+# ---------------------------------------------------------------------------
+# Plugin entry point
+# ---------------------------------------------------------------------------
+
+def register(ctx) -> None:
+    """Register Honcho as a memory provider plugin."""
+    ctx.register_memory_provider(HonchoMemoryProvider())

plugins/memory/honcho/cli.py

@@ -11,7 +11,7 @@ import sys
 from pathlib import Path
 
 from hermes_constants import get_hermes_home
-from honcho_integration.client import resolve_active_host, resolve_config_path, GLOBAL_CONFIG_PATH, HOST
+from plugins.memory.honcho.client import resolve_active_host, resolve_config_path, GLOBAL_CONFIG_PATH, HOST
 
 
 def clone_honcho_for_profile(profile_name: str) -> bool:
@@ -55,7 +55,9 @@ def clone_honcho_for_profile(profile_name: str) -> bool:
 
     # AI peer is profile-specific; workspace is shared so all profiles
     # see the same user context, sessions, and project history.
-    new_block["aiPeer"] = new_host
+    # Use the bare profile name as the peer identity (not the host key)
+    # because Honcho's peer ID pattern is ^[a-zA-Z0-9_-]+$ (no dots).
+    new_block["aiPeer"] = profile_name
     new_block["workspace"] = default_block.get("workspace") or cfg.get("workspace") or HOST
     new_block["enabled"] = default_block.get("enabled", True)
 
@@ -74,7 +76,7 @@ def _ensure_peer_exists(host_key: str | None = None) -> bool:
     was created or already exists, False on failure.
     """
     try:
-        from honcho_integration.client import HonchoClientConfig, get_honcho_client
+        from plugins.memory.honcho.client import HonchoClientConfig, get_honcho_client
         hcfg = HonchoClientConfig.from_global_config(host=host_key)
         if not hcfg.enabled or not (hcfg.api_key or hcfg.base_url):
             return False
@@ -112,7 +114,9 @@ def cmd_enable(args) -> None:
         peer_name = default_block.get("peerName") or cfg.get("peerName")
         if peer_name and "peerName" not in block:
             block["peerName"] = peer_name
-        block.setdefault("aiPeer", host)
+        # Use bare profile name as AI peer, not the host key
+        ai_peer = host.split(".", 1)[1] if "." in host else host
+        block.setdefault("aiPeer", ai_peer)
         block.setdefault("workspace", default_block.get("workspace") or cfg.get("workspace") or HOST)
 
     _write_config(cfg)
@@ -420,9 +424,9 @@ def cmd_setup(args) -> None:
     # Test connection
     print("  Testing connection... ", end="", flush=True)
     try:
-        from honcho_integration.client import HonchoClientConfig, get_honcho_client, reset_honcho_client
+        from plugins.memory.honcho.client import HonchoClientConfig, get_honcho_client, reset_honcho_client
         reset_honcho_client()
-        hcfg = HonchoClientConfig.from_global_config()
+        hcfg = HonchoClientConfig.from_global_config(host=_host_key())
         get_honcho_client(hcfg)
         print("OK")
     except Exception as e:
@@ -516,8 +520,8 @@ def cmd_status(args) -> None:
         return
 
     try:
-        from honcho_integration.client import HonchoClientConfig, get_honcho_client
-        hcfg = HonchoClientConfig.from_global_config()
+        from plugins.memory.honcho.client import HonchoClientConfig, get_honcho_client
+        hcfg = HonchoClientConfig.from_global_config(host=_host_key())
     except Exception as e:
         print(f"  Config error: {e}\n")
         return
@@ -570,7 +574,7 @@ def _show_peer_cards(hcfg, client) -> None:
     just retrieved, not duplicated.
     """
     try:
-        from honcho_integration.session import HonchoSessionManager
+        from plugins.memory.honcho.session import HonchoSessionManager
         mgr = HonchoSessionManager(honcho=client, config=hcfg)
         session_key = hcfg.resolve_session_name()
         mgr.get_or_create(session_key)
@@ -834,9 +838,9 @@ def cmd_identity(args) -> None:
     show = getattr(args, "show", False)
 
     try:
-        from honcho_integration.client import HonchoClientConfig, get_honcho_client
-        from honcho_integration.session import HonchoSessionManager
-        hcfg = HonchoClientConfig.from_global_config()
+        from plugins.memory.honcho.client import HonchoClientConfig, get_honcho_client
+        from plugins.memory.honcho.session import HonchoSessionManager
+        hcfg = HonchoClientConfig.from_global_config(host=_host_key())
         client = get_honcho_client(hcfg)
         mgr = HonchoSessionManager(honcho=client, config=hcfg)
         session_key = hcfg.resolve_session_name()
@@ -999,12 +1003,12 @@ def cmd_migrate(args) -> None:
             answer = _prompt("  Upload user memory files to Honcho now?", default="y")
             if answer.lower() in ("y", "yes"):
                 try:
-                    from honcho_integration.client import (
+                    from plugins.memory.honcho.client import (
                         HonchoClientConfig,
                         get_honcho_client,
                         reset_honcho_client,
                     )
-                    from honcho_integration.session import HonchoSessionManager
+                    from plugins.memory.honcho.session import HonchoSessionManager
 
                     reset_honcho_client()
                     hcfg = HonchoClientConfig.from_global_config()
@@ -1049,12 +1053,12 @@ def cmd_migrate(args) -> None:
             answer = _prompt("  Seed AI identity from all detected files now?", default="y")
             if answer.lower() in ("y", "yes"):
                 try:
-                    from honcho_integration.client import (
+                    from plugins.memory.honcho.client import (
                         HonchoClientConfig,
                         get_honcho_client,
                         reset_honcho_client,
                     )
-                    from honcho_integration.session import HonchoSessionManager
+                    from plugins.memory.honcho.session import HonchoSessionManager
 
                     reset_honcho_client()
                     hcfg = HonchoClientConfig.from_global_config()

plugins/memory/honcho/client.py

@@ -56,13 +56,22 @@ def resolve_active_host() -> str:
 def resolve_config_path() -> Path:
     """Return the active Honcho config path.
 
-    Checks $HERMES_HOME/honcho.json first (instance-local), then falls back
-    to ~/.honcho/config.json (global).  Returns the global path if neither
-    exists (for first-time setup writes).
+    Resolution order:
+      1. $HERMES_HOME/honcho.json      (profile-local, if it exists)
+      2. ~/.hermes/honcho.json          (default profile — shared host blocks live here)
+      3. ~/.honcho/config.json          (global, cross-app interop)
+
+    Returns the global path if none exist (for first-time setup writes).
     """
     local_path = get_hermes_home() / "honcho.json"
     if local_path.exists():
         return local_path
+
+    # Default profile's config — host blocks accumulate here via setup/clone
+    default_path = Path.home() / ".hermes" / "honcho.json"
+    if default_path != local_path and default_path.exists():
+        return default_path
+
     return GLOBAL_CONFIG_PATH
 
 

plugins/memory/honcho/plugin.yaml

@@ -0,0 +1,7 @@
+name: honcho
+version: 1.0.0
+description: "Honcho AI-native memory — cross-session user modeling with dialectic Q&A, semantic search, and persistent conclusions."
+pip_dependencies:
+  - honcho-ai
+hooks:
+  - on_session_end

plugins/memory/honcho/session.py

@@ -10,7 +10,7 @@ from dataclasses import dataclass, field
 from datetime import datetime
 from typing import Any, TYPE_CHECKING
 
-from honcho_integration.client import get_honcho_client
+from plugins.memory.honcho.client import get_honcho_client
 
 if TYPE_CHECKING:
     from honcho import Honcho
@@ -162,11 +162,17 @@ class HonchoSessionManager:
         # Configure peer observation settings.
         # observe_me=True for AI peer so Honcho watches what the agent says
         # and builds its representation over time — enabling identity formation.
-        from honcho.session import SessionPeerConfig
-        user_config = SessionPeerConfig(observe_me=True, observe_others=True)
-        ai_config = SessionPeerConfig(observe_me=True, observe_others=True)
+        try:
+            from honcho.session import SessionPeerConfig
+            user_config = SessionPeerConfig(observe_me=True, observe_others=True)
+            ai_config = SessionPeerConfig(observe_me=True, observe_others=True)
 
-        session.add_peers([(user_peer, user_config), (assistant_peer, ai_config)])
+            session.add_peers([(user_peer, user_config), (assistant_peer, ai_config)])
+        except Exception as e:
+            logger.warning(
+                "Honcho session '%s' add_peers failed (non-fatal): %s",
+                session_id, e,
+            )
 
         # Load existing messages via context() - single call for messages + metadata
         existing_messages = []
@@ -231,7 +237,7 @@ class HonchoSessionManager:
             chat_id = parts[1] if len(parts) > 1 else key
             user_peer_id = self._sanitize_id(f"user-{channel}-{chat_id}")
 
-        assistant_peer_id = (
+        assistant_peer_id = self._sanitize_id(
             self._config.ai_peer if self._config else "hermes-assistant"
         )
 

plugins/memory/mem0/README.md

@@ -0,0 +1,38 @@
+# Mem0 Memory Provider
+
+Server-side LLM fact extraction with semantic search, reranking, and automatic deduplication.
+
+## Requirements
+
+- `pip install mem0ai`
+- Mem0 API key from [app.mem0.ai](https://app.mem0.ai)
+
+## Setup
+
+```bash
+hermes memory setup    # select "mem0"
+```
+
+Or manually:
+```bash
+hermes config set memory.provider mem0
+echo "MEM0_API_KEY=your-key" >> ~/.hermes/.env
+```
+
+## Config
+
+Config file: `$HERMES_HOME/mem0.json`
+
+| Key | Default | Description |
+|-----|---------|-------------|
+| `user_id` | `hermes-user` | User identifier on Mem0 |
+| `agent_id` | `hermes` | Agent identifier |
+| `rerank` | `true` | Enable reranking for recall |
+
+## Tools
+
+| Tool | Description |
+|------|-------------|
+| `mem0_profile` | All stored memories about the user |
+| `mem0_search` | Semantic search with optional reranking |
+| `mem0_conclude` | Store a fact verbatim (no LLM extraction) |

plugins/memory/mem0/__init__.py

@@ -0,0 +1,344 @@
+"""Mem0 memory plugin — MemoryProvider interface.
+
+Server-side LLM fact extraction, semantic search with reranking, and
+automatic deduplication via the Mem0 Platform API.
+
+Original PR #2933 by kartik-mem0, adapted to MemoryProvider ABC.
+
+Config via environment variables:
+  MEM0_API_KEY       — Mem0 Platform API key (required)
+  MEM0_USER_ID       — User identifier (default: hermes-user)
+  MEM0_AGENT_ID      — Agent identifier (default: hermes)
+
+Or via $HERMES_HOME/mem0.json.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+import threading
+import time
+from pathlib import Path
+from typing import Any, Dict, List
+
+from agent.memory_provider import MemoryProvider
+
+logger = logging.getLogger(__name__)
+
+# Circuit breaker: after this many consecutive failures, pause API calls
+# for _BREAKER_COOLDOWN_SECS to avoid hammering a down server.
+_BREAKER_THRESHOLD = 5
+_BREAKER_COOLDOWN_SECS = 120
+
+
+# ---------------------------------------------------------------------------
+# Config
+# ---------------------------------------------------------------------------
+
+def _load_config() -> dict:
+    """Load config from $HERMES_HOME/mem0.json or env vars."""
+    from hermes_constants import get_hermes_home
+    config_path = get_hermes_home() / "mem0.json"
+
+    if config_path.exists():
+        try:
+            return json.loads(config_path.read_text(encoding="utf-8"))
+        except Exception:
+            pass
+
+    return {
+        "api_key": os.environ.get("MEM0_API_KEY", ""),
+        "user_id": os.environ.get("MEM0_USER_ID", "hermes-user"),
+        "agent_id": os.environ.get("MEM0_AGENT_ID", "hermes"),
+        "rerank": True,
+        "keyword_search": False,
+    }
+
+
+# ---------------------------------------------------------------------------
+# Tool schemas
+# ---------------------------------------------------------------------------
+
+PROFILE_SCHEMA = {
+    "name": "mem0_profile",
+    "description": (
+        "Retrieve all stored memories about the user — preferences, facts, "
+        "project context. Fast, no reranking. Use at conversation start."
+    ),
+    "parameters": {"type": "object", "properties": {}, "required": []},
+}
+
+SEARCH_SCHEMA = {
+    "name": "mem0_search",
+    "description": (
+        "Search memories by meaning. Returns relevant facts ranked by similarity. "
+        "Set rerank=true for higher accuracy on important queries."
+    ),
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "query": {"type": "string", "description": "What to search for."},
+            "rerank": {"type": "boolean", "description": "Enable reranking for precision (default: false)."},
+            "top_k": {"type": "integer", "description": "Max results (default: 10, max: 50)."},
+        },
+        "required": ["query"],
+    },
+}
+
+CONCLUDE_SCHEMA = {
+    "name": "mem0_conclude",
+    "description": (
+        "Store a durable fact about the user. Stored verbatim (no LLM extraction). "
+        "Use for explicit preferences, corrections, or decisions."
+    ),
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "conclusion": {"type": "string", "description": "The fact to store."},
+        },
+        "required": ["conclusion"],
+    },
+}
+
+
+# ---------------------------------------------------------------------------
+# MemoryProvider implementation
+# ---------------------------------------------------------------------------
+
+class Mem0MemoryProvider(MemoryProvider):
+    """Mem0 Platform memory with server-side extraction and semantic search."""
+
+    def __init__(self):
+        self._config = None
+        self._client = None
+        self._client_lock = threading.Lock()
+        self._api_key = ""
+        self._user_id = "hermes-user"
+        self._agent_id = "hermes"
+        self._rerank = True
+        self._prefetch_result = ""
+        self._prefetch_lock = threading.Lock()
+        self._prefetch_thread = None
+        self._sync_thread = None
+        # Circuit breaker state
+        self._consecutive_failures = 0
+        self._breaker_open_until = 0.0
+
+    @property
+    def name(self) -> str:
+        return "mem0"
+
+    def is_available(self) -> bool:
+        cfg = _load_config()
+        return bool(cfg.get("api_key"))
+
+    def save_config(self, values, hermes_home):
+        """Write config to $HERMES_HOME/mem0.json."""
+        import json
+        from pathlib import Path
+        config_path = Path(hermes_home) / "mem0.json"
+        existing = {}
+        if config_path.exists():
+            try:
+                existing = json.loads(config_path.read_text())
+            except Exception:
+                pass
+        existing.update(values)
+        config_path.write_text(json.dumps(existing, indent=2))
+
+    def get_config_schema(self):
+        return [
+            {"key": "api_key", "description": "Mem0 Platform API key", "secret": True, "required": True, "env_var": "MEM0_API_KEY", "url": "https://app.mem0.ai"},
+            {"key": "user_id", "description": "User identifier", "default": "hermes-user"},
+            {"key": "agent_id", "description": "Agent identifier", "default": "hermes"},
+            {"key": "rerank", "description": "Enable reranking for recall", "default": "true", "choices": ["true", "false"]},
+        ]
+
+    def _get_client(self):
+        """Thread-safe client accessor with lazy initialization."""
+        with self._client_lock:
+            if self._client is not None:
+                return self._client
+            try:
+                from mem0 import MemoryClient
+                self._client = MemoryClient(api_key=self._api_key)
+                return self._client
+            except ImportError:
+                raise RuntimeError("mem0 package not installed. Run: pip install mem0ai")
+
+    def _is_breaker_open(self) -> bool:
+        """Return True if the circuit breaker is tripped (too many failures)."""
+        if self._consecutive_failures < _BREAKER_THRESHOLD:
+            return False
+        if time.monotonic() >= self._breaker_open_until:
+            # Cooldown expired — reset and allow a retry
+            self._consecutive_failures = 0
+            return False
+        return True
+
+    def _record_success(self):
+        self._consecutive_failures = 0
+
+    def _record_failure(self):
+        self._consecutive_failures += 1
+        if self._consecutive_failures >= _BREAKER_THRESHOLD:
+            self._breaker_open_until = time.monotonic() + _BREAKER_COOLDOWN_SECS
+            logger.warning(
+                "Mem0 circuit breaker tripped after %d consecutive failures. "
+                "Pausing API calls for %ds.",
+                self._consecutive_failures, _BREAKER_COOLDOWN_SECS,
+            )
+
+    def initialize(self, session_id: str, **kwargs) -> None:
+        self._config = _load_config()
+        self._api_key = self._config.get("api_key", "")
+        self._user_id = self._config.get("user_id", "hermes-user")
+        self._agent_id = self._config.get("agent_id", "hermes")
+        self._rerank = self._config.get("rerank", True)
+
+    def system_prompt_block(self) -> str:
+        return (
+            "# Mem0 Memory\n"
+            f"Active. User: {self._user_id}.\n"
+            "Use mem0_search to find memories, mem0_conclude to store facts, "
+            "mem0_profile for a full overview."
+        )
+
+    def prefetch(self, query: str, *, session_id: str = "") -> str:
+        if self._prefetch_thread and self._prefetch_thread.is_alive():
+            self._prefetch_thread.join(timeout=3.0)
+        with self._prefetch_lock:
+            result = self._prefetch_result
+            self._prefetch_result = ""
+        if not result:
+            return ""
+        return f"## Mem0 Memory\n{result}"
+
+    def queue_prefetch(self, query: str, *, session_id: str = "") -> None:
+        if self._is_breaker_open():
+            return
+
+        def _run():
+            try:
+                client = self._get_client()
+                results = client.search(
+                    query=query,
+                    user_id=self._user_id,
+                    rerank=self._rerank,
+                    top_k=5,
+                )
+                if results:
+                    lines = [r.get("memory", "") for r in results if r.get("memory")]
+                    with self._prefetch_lock:
+                        self._prefetch_result = "\n".join(f"- {l}" for l in lines)
+                self._record_success()
+            except Exception as e:
+                self._record_failure()
+                logger.debug("Mem0 prefetch failed: %s", e)
+
+        self._prefetch_thread = threading.Thread(target=_run, daemon=True, name="mem0-prefetch")
+        self._prefetch_thread.start()
+
+    def sync_turn(self, user_content: str, assistant_content: str, *, session_id: str = "") -> None:
+        """Send the turn to Mem0 for server-side fact extraction (non-blocking)."""
+        if self._is_breaker_open():
+            return
+
+        def _sync():
+            try:
+                client = self._get_client()
+                messages = [
+                    {"role": "user", "content": user_content},
+                    {"role": "assistant", "content": assistant_content},
+                ]
+                client.add(messages, user_id=self._user_id, agent_id=self._agent_id)
+                self._record_success()
+            except Exception as e:
+                self._record_failure()
+                logger.warning("Mem0 sync failed: %s", e)
+
+        # Wait for any previous sync before starting a new one
+        if self._sync_thread and self._sync_thread.is_alive():
+            self._sync_thread.join(timeout=5.0)
+
+        self._sync_thread = threading.Thread(target=_sync, daemon=True, name="mem0-sync")
+        self._sync_thread.start()
+
+    def get_tool_schemas(self) -> List[Dict[str, Any]]:
+        return [PROFILE_SCHEMA, SEARCH_SCHEMA, CONCLUDE_SCHEMA]
+
+    def handle_tool_call(self, tool_name: str, args: dict, **kwargs) -> str:
+        if self._is_breaker_open():
+            return json.dumps({
+                "error": "Mem0 API temporarily unavailable (multiple consecutive failures). Will retry automatically."
+            })
+
+        try:
+            client = self._get_client()
+        except Exception as e:
+            return json.dumps({"error": str(e)})
+
+        if tool_name == "mem0_profile":
+            try:
+                memories = client.get_all(user_id=self._user_id)
+                self._record_success()
+                if not memories:
+                    return json.dumps({"result": "No memories stored yet."})
+                lines = [m.get("memory", "") for m in memories if m.get("memory")]
+                return json.dumps({"result": "\n".join(lines), "count": len(lines)})
+            except Exception as e:
+                self._record_failure()
+                return json.dumps({"error": f"Failed to fetch profile: {e}"})
+
+        elif tool_name == "mem0_search":
+            query = args.get("query", "")
+            if not query:
+                return json.dumps({"error": "Missing required parameter: query"})
+            rerank = args.get("rerank", False)
+            top_k = min(int(args.get("top_k", 10)), 50)
+            try:
+                results = client.search(
+                    query=query, user_id=self._user_id,
+                    rerank=rerank, top_k=top_k,
+                )
+                self._record_success()
+                if not results:
+                    return json.dumps({"result": "No relevant memories found."})
+                items = [{"memory": r.get("memory", ""), "score": r.get("score", 0)} for r in results]
+                return json.dumps({"results": items, "count": len(items)})
+            except Exception as e:
+                self._record_failure()
+                return json.dumps({"error": f"Search failed: {e}"})
+
+        elif tool_name == "mem0_conclude":
+            conclusion = args.get("conclusion", "")
+            if not conclusion:
+                return json.dumps({"error": "Missing required parameter: conclusion"})
+            try:
+                client.add(
+                    [{"role": "user", "content": conclusion}],
+                    user_id=self._user_id,
+                    agent_id=self._agent_id,
+                    infer=False,
+                )
+                self._record_success()
+                return json.dumps({"result": "Fact stored."})
+            except Exception as e:
+                self._record_failure()
+                return json.dumps({"error": f"Failed to store: {e}"})
+
+        return json.dumps({"error": f"Unknown tool: {tool_name}"})
+
+    def shutdown(self) -> None:
+        for t in (self._prefetch_thread, self._sync_thread):
+            if t and t.is_alive():
+                t.join(timeout=5.0)
+        with self._client_lock:
+            self._client = None
+
+
+def register(ctx) -> None:
+    """Register Mem0 as a memory provider plugin."""
+    ctx.register_memory_provider(Mem0MemoryProvider())

plugins/memory/mem0/plugin.yaml

@@ -0,0 +1,5 @@
+name: mem0
+version: 1.0.0
+description: "Mem0 — server-side LLM fact extraction with semantic search, reranking, and automatic deduplication."
+pip_dependencies:
+  - mem0ai

plugins/memory/openviking/README.md

@@ -0,0 +1,40 @@
+# OpenViking Memory Provider
+
+Context database by Volcengine (ByteDance) with filesystem-style knowledge hierarchy, tiered retrieval, and automatic memory extraction.
+
+## Requirements
+
+- `pip install openviking`
+- OpenViking server running (`openviking-server`)
+- Embedding + VLM model configured in `~/.openviking/ov.conf`
+
+## Setup
+
+```bash
+hermes memory setup    # select "openviking"
+```
+
+Or manually:
+```bash
+hermes config set memory.provider openviking
+echo "OPENVIKING_ENDPOINT=http://localhost:1933" >> ~/.hermes/.env
+```
+
+## Config
+
+All config via environment variables in `.env`:
+
+| Env Var | Default | Description |
+|---------|---------|-------------|
+| `OPENVIKING_ENDPOINT` | `http://127.0.0.1:1933` | Server URL |
+| `OPENVIKING_API_KEY` | (none) | API key (optional) |
+
+## Tools
+
+| Tool | Description |
+|------|-------------|
+| `viking_search` | Semantic search with fast/deep/auto modes |
+| `viking_read` | Read content at a viking:// URI (abstract/overview/full) |
+| `viking_browse` | Filesystem-style navigation (list/tree/stat) |
+| `viking_remember` | Store a fact for extraction on session commit |
+| `viking_add_resource` | Ingest URLs/docs into the knowledge base |

plugins/memory/openviking/__init__.py

@@ -0,0 +1,582 @@
+"""OpenViking memory plugin — full bidirectional MemoryProvider interface.
+
+Context database by Volcengine (ByteDance) that organizes agent knowledge
+into a filesystem hierarchy (viking:// URIs) with tiered context loading,
+automatic memory extraction, and session management.
+
+Original PR #3369 by Mibayy, rewritten to use the full OpenViking session
+lifecycle instead of read-only search endpoints.
+
+Config via environment variables (profile-scoped via each profile's .env):
+  OPENVIKING_ENDPOINT  — Server URL (default: http://127.0.0.1:1933)
+  OPENVIKING_API_KEY   — API key (required for authenticated servers)
+
+Capabilities:
+  - Automatic memory extraction on session commit (6 categories)
+  - Tiered context: L0 (~100 tokens), L1 (~2k), L2 (full)
+  - Semantic search with hierarchical directory retrieval
+  - Filesystem-style browsing via viking:// URIs
+  - Resource ingestion (URLs, docs, code)
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+import threading
+from typing import Any, Dict, List, Optional
+
+from agent.memory_provider import MemoryProvider
+
+logger = logging.getLogger(__name__)
+
+_DEFAULT_ENDPOINT = "http://127.0.0.1:1933"
+_TIMEOUT = 30.0
+
+
+# ---------------------------------------------------------------------------
+# HTTP helper — uses httpx to avoid requiring the openviking SDK
+# ---------------------------------------------------------------------------
+
+def _get_httpx():
+    """Lazy import httpx."""
+    try:
+        import httpx
+        return httpx
+    except ImportError:
+        return None
+
+
+class _VikingClient:
+    """Thin HTTP client for the OpenViking REST API."""
+
+    def __init__(self, endpoint: str, api_key: str = ""):
+        self._endpoint = endpoint.rstrip("/")
+        self._api_key = api_key
+        self._httpx = _get_httpx()
+        if self._httpx is None:
+            raise ImportError("httpx is required for OpenViking: pip install httpx")
+
+    def _headers(self) -> dict:
+        h = {"Content-Type": "application/json"}
+        if self._api_key:
+            h["X-API-Key"] = self._api_key
+        return h
+
+    def _url(self, path: str) -> str:
+        return f"{self._endpoint}{path}"
+
+    def get(self, path: str, **kwargs) -> dict:
+        resp = self._httpx.get(
+            self._url(path), headers=self._headers(), timeout=_TIMEOUT, **kwargs
+        )
+        resp.raise_for_status()
+        return resp.json()
+
+    def post(self, path: str, payload: dict = None, **kwargs) -> dict:
+        resp = self._httpx.post(
+            self._url(path), json=payload or {}, headers=self._headers(),
+            timeout=_TIMEOUT, **kwargs
+        )
+        resp.raise_for_status()
+        return resp.json()
+
+    def health(self) -> bool:
+        try:
+            resp = self._httpx.get(
+                self._url("/health"), timeout=3.0
+            )
+            return resp.status_code == 200
+        except Exception:
+            return False
+
+
+# ---------------------------------------------------------------------------
+# Tool schemas
+# ---------------------------------------------------------------------------
+
+SEARCH_SCHEMA = {
+    "name": "viking_search",
+    "description": (
+        "Semantic search over the OpenViking knowledge base. "
+        "Returns ranked results with viking:// URIs for deeper reading. "
+        "Use mode='deep' for complex queries that need reasoning across "
+        "multiple sources, 'fast' for simple lookups."
+    ),
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "query": {"type": "string", "description": "Search query."},
+            "mode": {
+                "type": "string", "enum": ["auto", "fast", "deep"],
+                "description": "Search depth (default: auto).",
+            },
+            "scope": {
+                "type": "string",
+                "description": "Viking URI prefix to scope search (e.g. 'viking://resources/docs/').",
+            },
+            "limit": {"type": "integer", "description": "Max results (default: 10)."},
+        },
+        "required": ["query"],
+    },
+}
+
+READ_SCHEMA = {
+    "name": "viking_read",
+    "description": (
+        "Read content at a viking:// URI. Three detail levels:\n"
+        "  abstract — ~100 token summary (L0)\n"
+        "  overview — ~2k token key points (L1)\n"
+        "  full — complete content (L2)\n"
+        "Start with abstract/overview, only use full when you need details."
+    ),
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "uri": {"type": "string", "description": "viking:// URI to read."},
+            "level": {
+                "type": "string", "enum": ["abstract", "overview", "full"],
+                "description": "Detail level (default: overview).",
+            },
+        },
+        "required": ["uri"],
+    },
+}
+
+BROWSE_SCHEMA = {
+    "name": "viking_browse",
+    "description": (
+        "Browse the OpenViking knowledge store like a filesystem.\n"
+        "  list — show directory contents\n"
+        "  tree — show hierarchy\n"
+        "  stat — show metadata for a URI"
+    ),
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "action": {
+                "type": "string", "enum": ["tree", "list", "stat"],
+                "description": "Browse action.",
+            },
+            "path": {
+                "type": "string",
+                "description": "Viking URI path (default: viking://). Examples: 'viking://resources/', 'viking://user/memories/'.",
+            },
+        },
+        "required": ["action"],
+    },
+}
+
+REMEMBER_SCHEMA = {
+    "name": "viking_remember",
+    "description": (
+        "Explicitly store a fact or memory in the OpenViking knowledge base. "
+        "Use for important information the agent should remember long-term. "
+        "The system automatically categorizes and indexes the memory."
+    ),
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "content": {"type": "string", "description": "The information to remember."},
+            "category": {
+                "type": "string",
+                "enum": ["preference", "entity", "event", "case", "pattern"],
+                "description": "Memory category (default: auto-detected).",
+            },
+        },
+        "required": ["content"],
+    },
+}
+
+ADD_RESOURCE_SCHEMA = {
+    "name": "viking_add_resource",
+    "description": (
+        "Add a URL or document to the OpenViking knowledge base. "
+        "Supports web pages, GitHub repos, PDFs, markdown, code files. "
+        "The system automatically parses, indexes, and generates summaries."
+    ),
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "url": {"type": "string", "description": "URL or path of the resource to add."},
+            "reason": {
+                "type": "string",
+                "description": "Why this resource is relevant (improves search).",
+            },
+        },
+        "required": ["url"],
+    },
+}
+
+
+# ---------------------------------------------------------------------------
+# MemoryProvider implementation
+# ---------------------------------------------------------------------------
+
+class OpenVikingMemoryProvider(MemoryProvider):
+    """Full bidirectional memory via OpenViking context database."""
+
+    def __init__(self):
+        self._client: Optional[_VikingClient] = None
+        self._endpoint = ""
+        self._api_key = ""
+        self._session_id = ""
+        self._turn_count = 0
+        self._sync_thread: Optional[threading.Thread] = None
+        self._prefetch_result = ""
+        self._prefetch_lock = threading.Lock()
+        self._prefetch_thread: Optional[threading.Thread] = None
+
+    @property
+    def name(self) -> str:
+        return "openviking"
+
+    def is_available(self) -> bool:
+        """Check if OpenViking endpoint is configured. No network calls."""
+        return bool(os.environ.get("OPENVIKING_ENDPOINT"))
+
+    def get_config_schema(self):
+        return [
+            {
+                "key": "endpoint",
+                "description": "OpenViking server URL",
+                "required": True,
+                "default": _DEFAULT_ENDPOINT,
+                "env_var": "OPENVIKING_ENDPOINT",
+            },
+            {
+                "key": "api_key",
+                "description": "OpenViking API key",
+                "secret": True,
+                "env_var": "OPENVIKING_API_KEY",
+            },
+        ]
+
+    def initialize(self, session_id: str, **kwargs) -> None:
+        self._endpoint = os.environ.get("OPENVIKING_ENDPOINT", _DEFAULT_ENDPOINT)
+        self._api_key = os.environ.get("OPENVIKING_API_KEY", "")
+        self._session_id = session_id
+        self._turn_count = 0
+
+        try:
+            self._client = _VikingClient(self._endpoint, self._api_key)
+            if not self._client.health():
+                logger.warning("OpenViking server at %s is not reachable", self._endpoint)
+                self._client = None
+        except ImportError:
+            logger.warning("httpx not installed — OpenViking plugin disabled")
+            self._client = None
+
+    def system_prompt_block(self) -> str:
+        if not self._client:
+            return ""
+        # Provide brief info about the knowledge base
+        try:
+            # Check what's in the knowledge base via a root listing
+            resp = self._client.post("/api/v1/browse", {"action": "stat", "path": "viking://"})
+            result = resp.get("result", {})
+            children = result.get("children", 0)
+            if children == 0:
+                return ""
+            return (
+                "# OpenViking Knowledge Base\n"
+                f"Active. Endpoint: {self._endpoint}\n"
+                "Use viking_search to find information, viking_read for details "
+                "(abstract/overview/full), viking_browse to explore.\n"
+                "Use viking_remember to store facts, viking_add_resource to index URLs/docs."
+            )
+        except Exception:
+            return (
+                "# OpenViking Knowledge Base\n"
+                f"Active. Endpoint: {self._endpoint}\n"
+                "Use viking_search, viking_read, viking_browse, "
+                "viking_remember, viking_add_resource."
+            )
+
+    def prefetch(self, query: str, *, session_id: str = "") -> str:
+        """Return prefetched results from the background thread."""
+        if self._prefetch_thread and self._prefetch_thread.is_alive():
+            self._prefetch_thread.join(timeout=3.0)
+        with self._prefetch_lock:
+            result = self._prefetch_result
+            self._prefetch_result = ""
+        if not result:
+            return ""
+        return f"## OpenViking Context\n{result}"
+
+    def queue_prefetch(self, query: str, *, session_id: str = "") -> None:
+        """Fire a background search to pre-load relevant context."""
+        if not self._client or not query:
+            return
+
+        def _run():
+            try:
+                client = _VikingClient(self._endpoint, self._api_key)
+                resp = client.post("/api/v1/search/find", {
+                    "query": query,
+                    "top_k": 5,
+                })
+                result = resp.get("result", {})
+                parts = []
+                for ctx_type in ("memories", "resources"):
+                    items = result.get(ctx_type, [])
+                    for item in items[:3]:
+                        uri = item.get("uri", "")
+                        abstract = item.get("abstract", "")
+                        score = item.get("score", 0)
+                        if abstract:
+                            parts.append(f"- [{score:.2f}] {abstract} ({uri})")
+                if parts:
+                    with self._prefetch_lock:
+                        self._prefetch_result = "\n".join(parts)
+            except Exception as e:
+                logger.debug("OpenViking prefetch failed: %s", e)
+
+        self._prefetch_thread = threading.Thread(
+            target=_run, daemon=True, name="openviking-prefetch"
+        )
+        self._prefetch_thread.start()
+
+    def sync_turn(self, user_content: str, assistant_content: str, *, session_id: str = "") -> None:
+        """Record the conversation turn in OpenViking's session (non-blocking)."""
+        if not self._client:
+            return
+
+        self._turn_count += 1
+
+        def _sync():
+            try:
+                client = _VikingClient(self._endpoint, self._api_key)
+                sid = self._session_id
+
+                # Add user message
+                client.post(f"/api/v1/sessions/{sid}/messages", {
+                    "role": "user",
+                    "content": user_content[:4000],  # trim very long messages
+                })
+                # Add assistant message
+                client.post(f"/api/v1/sessions/{sid}/messages", {
+                    "role": "assistant",
+                    "content": assistant_content[:4000],
+                })
+            except Exception as e:
+                logger.debug("OpenViking sync_turn failed: %s", e)
+
+        # Wait for any previous sync to finish before starting a new one
+        if self._sync_thread and self._sync_thread.is_alive():
+            self._sync_thread.join(timeout=5.0)
+
+        self._sync_thread = threading.Thread(
+            target=_sync, daemon=True, name="openviking-sync"
+        )
+        self._sync_thread.start()
+
+    def on_session_end(self, messages: List[Dict[str, Any]]) -> None:
+        """Commit the session to trigger memory extraction.
+
+        OpenViking automatically extracts 6 categories of memories:
+        profile, preferences, entities, events, cases, and patterns.
+        """
+        if not self._client or self._turn_count == 0:
+            return
+
+        # Wait for any pending sync to finish first
+        if self._sync_thread and self._sync_thread.is_alive():
+            self._sync_thread.join(timeout=10.0)
+
+        try:
+            self._client.post(f"/api/v1/sessions/{self._session_id}/commit")
+            logger.info("OpenViking session %s committed (%d turns)", self._session_id, self._turn_count)
+        except Exception as e:
+            logger.warning("OpenViking session commit failed: %s", e)
+
+    def on_memory_write(self, action: str, target: str, content: str) -> None:
+        """Mirror built-in memory writes to OpenViking as explicit memories."""
+        if not self._client or action != "add" or not content:
+            return
+
+        def _write():
+            try:
+                client = _VikingClient(self._endpoint, self._api_key)
+                # Add as a user message with memory context so the commit
+                # picks it up as an explicit memory during extraction
+                client.post(f"/api/v1/sessions/{self._session_id}/messages", {
+                    "role": "user",
+                    "parts": [
+                        {"type": "text", "text": f"[Memory note — {target}] {content}"},
+                    ],
+                })
+            except Exception as e:
+                logger.debug("OpenViking memory mirror failed: %s", e)
+
+        t = threading.Thread(target=_write, daemon=True, name="openviking-memwrite")
+        t.start()
+
+    def get_tool_schemas(self) -> List[Dict[str, Any]]:
+        return [SEARCH_SCHEMA, READ_SCHEMA, BROWSE_SCHEMA, REMEMBER_SCHEMA, ADD_RESOURCE_SCHEMA]
+
+    def handle_tool_call(self, tool_name: str, args: dict, **kwargs) -> str:
+        if not self._client:
+            return json.dumps({"error": "OpenViking server not connected"})
+
+        try:
+            if tool_name == "viking_search":
+                return self._tool_search(args)
+            elif tool_name == "viking_read":
+                return self._tool_read(args)
+            elif tool_name == "viking_browse":
+                return self._tool_browse(args)
+            elif tool_name == "viking_remember":
+                return self._tool_remember(args)
+            elif tool_name == "viking_add_resource":
+                return self._tool_add_resource(args)
+            return json.dumps({"error": f"Unknown tool: {tool_name}"})
+        except Exception as e:
+            return json.dumps({"error": str(e)})
+
+    def shutdown(self) -> None:
+        # Wait for background threads to finish
+        for t in (self._sync_thread, self._prefetch_thread):
+            if t and t.is_alive():
+                t.join(timeout=5.0)
+
+    # -- Tool implementations ------------------------------------------------
+
+    def _tool_search(self, args: dict) -> str:
+        query = args.get("query", "")
+        if not query:
+            return json.dumps({"error": "query is required"})
+
+        payload: Dict[str, Any] = {"query": query}
+        mode = args.get("mode", "auto")
+        if mode != "auto":
+            payload["mode"] = mode
+        if args.get("scope"):
+            payload["target_uri"] = args["scope"]
+        if args.get("limit"):
+            payload["top_k"] = args["limit"]
+
+        resp = self._client.post("/api/v1/search/find", payload)
+        result = resp.get("result", {})
+
+        # Format results for the model — keep it concise
+        formatted = []
+        for ctx_type in ("memories", "resources", "skills"):
+            items = result.get(ctx_type, [])
+            for item in items:
+                entry = {
+                    "uri": item.get("uri", ""),
+                    "type": ctx_type.rstrip("s"),
+                    "score": round(item.get("score", 0), 3),
+                    "abstract": item.get("abstract", ""),
+                }
+                if item.get("relations"):
+                    entry["related"] = [r.get("uri") for r in item["relations"][:3]]
+                formatted.append(entry)
+
+        return json.dumps({
+            "results": formatted,
+            "total": result.get("total", len(formatted)),
+        }, ensure_ascii=False)
+
+    def _tool_read(self, args: dict) -> str:
+        uri = args.get("uri", "")
+        if not uri:
+            return json.dumps({"error": "uri is required"})
+
+        level = args.get("level", "overview")
+        # Map our level names to OpenViking endpoints
+        if level == "abstract":
+            resp = self._client.post("/api/v1/read/abstract", {"uri": uri})
+        elif level == "full":
+            resp = self._client.post("/api/v1/read", {"uri": uri, "level": "read"})
+        else:  # overview
+            resp = self._client.post("/api/v1/read", {"uri": uri, "level": "overview"})
+
+        result = resp.get("result", {})
+        content = result.get("content", "")
+
+        # Truncate very long content to avoid flooding the context
+        if len(content) > 8000:
+            content = content[:8000] + "\n\n[... truncated, use a more specific URI or abstract level]"
+
+        return json.dumps({
+            "uri": uri,
+            "level": level,
+            "content": content,
+        }, ensure_ascii=False)
+
+    def _tool_browse(self, args: dict) -> str:
+        action = args.get("action", "list")
+        path = args.get("path", "viking://")
+
+        resp = self._client.post("/api/v1/browse", {
+            "action": action,
+            "path": path,
+        })
+        result = resp.get("result", {})
+
+        # Format for readability
+        if action == "list" and "entries" in result:
+            entries = []
+            for e in result["entries"][:50]:  # cap at 50 entries
+                entries.append({
+                    "name": e.get("name", ""),
+                    "uri": e.get("uri", ""),
+                    "type": "dir" if e.get("is_dir") else "file",
+                })
+            return json.dumps({"path": path, "entries": entries}, ensure_ascii=False)
+
+        return json.dumps(result, ensure_ascii=False)
+
+    def _tool_remember(self, args: dict) -> str:
+        content = args.get("content", "")
+        if not content:
+            return json.dumps({"error": "content is required"})
+
+        # Store as a session message that will be extracted during commit.
+        # The category hint helps OpenViking's extraction classify correctly.
+        category = args.get("category", "")
+        text = f"[Remember] {content}"
+        if category:
+            text = f"[Remember — {category}] {content}"
+
+        self._client.post(f"/api/v1/sessions/{self._session_id}/messages", {
+            "role": "user",
+            "parts": [
+                {"type": "text", "text": text},
+            ],
+        })
+
+        return json.dumps({
+            "status": "stored",
+            "message": "Memory recorded. Will be extracted and indexed on session commit.",
+        })
+
+    def _tool_add_resource(self, args: dict) -> str:
+        url = args.get("url", "")
+        if not url:
+            return json.dumps({"error": "url is required"})
+
+        payload: Dict[str, Any] = {"path": url}
+        if args.get("reason"):
+            payload["reason"] = args["reason"]
+
+        resp = self._client.post("/api/v1/resources", payload)
+        result = resp.get("result", {})
+
+        return json.dumps({
+            "status": "added",
+            "root_uri": result.get("root_uri", ""),
+            "message": "Resource queued for processing. Use viking_search after a moment to find it.",
+        }, ensure_ascii=False)
+
+
+# ---------------------------------------------------------------------------
+# Plugin entry point
+# ---------------------------------------------------------------------------
+
+def register(ctx) -> None:
+    """Register OpenViking as a memory provider plugin."""
+    ctx.register_memory_provider(OpenVikingMemoryProvider())

plugins/memory/openviking/plugin.yaml

@@ -0,0 +1,9 @@
+name: openviking
+version: 2.0.0
+description: "OpenViking context database — session-managed memory with automatic extraction, tiered retrieval, and filesystem-style knowledge browsing."
+pip_dependencies:
+  - httpx
+requires_env:
+  - OPENVIKING_ENDPOINT
+hooks:
+  - on_session_end

plugins/memory/retaindb/README.md

@@ -0,0 +1,40 @@
+# RetainDB Memory Provider
+
+Cloud memory API with hybrid search (Vector + BM25 + Reranking) and 7 memory types.
+
+## Requirements
+
+- RetainDB account ($20/month) from [retaindb.com](https://www.retaindb.com)
+- `pip install requests`
+
+## Setup
+
+```bash
+hermes memory setup    # select "retaindb"
+```
+
+Or manually:
+```bash
+hermes config set memory.provider retaindb
+echo "RETAINDB_API_KEY=your-key" >> ~/.hermes/.env
+```
+
+## Config
+
+All config via environment variables in `.env`:
+
+| Env Var | Default | Description |
+|---------|---------|-------------|
+| `RETAINDB_API_KEY` | (required) | API key |
+| `RETAINDB_BASE_URL` | `https://api.retaindb.com` | API endpoint |
+| `RETAINDB_PROJECT` | auto (profile-scoped) | Project identifier |
+
+## Tools
+
+| Tool | Description |
+|------|-------------|
+| `retaindb_profile` | User's stable profile |
+| `retaindb_search` | Semantic search |
+| `retaindb_context` | Task-relevant context |
+| `retaindb_remember` | Store a fact with type + importance |
+| `retaindb_forget` | Delete a memory by ID |

plugins/memory/retaindb/__init__.py

@@ -0,0 +1,302 @@
+"""RetainDB memory plugin — MemoryProvider interface.
+
+Cross-session memory via RetainDB cloud API. Durable write-behind queue,
+semantic search with deduplication, and user profile retrieval.
+
+Original PR #2732 by Alinxus, adapted to MemoryProvider ABC.
+
+Config via environment variables:
+  RETAINDB_API_KEY    — API key (required)
+  RETAINDB_BASE_URL   — API endpoint (default: https://api.retaindb.com)
+  RETAINDB_PROJECT    — Project identifier (default: hermes)
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+import threading
+from typing import Any, Dict, List
+
+from agent.memory_provider import MemoryProvider
+
+logger = logging.getLogger(__name__)
+
+_DEFAULT_BASE_URL = "https://api.retaindb.com"
+
+
+# ---------------------------------------------------------------------------
+# Tool schemas
+# ---------------------------------------------------------------------------
+
+PROFILE_SCHEMA = {
+    "name": "retaindb_profile",
+    "description": "Get the user's stable profile — preferences, facts, and patterns.",
+    "parameters": {"type": "object", "properties": {}, "required": []},
+}
+
+SEARCH_SCHEMA = {
+    "name": "retaindb_search",
+    "description": (
+        "Semantic search across stored memories. Returns ranked results "
+        "with relevance scores."
+    ),
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "query": {"type": "string", "description": "What to search for."},
+            "top_k": {"type": "integer", "description": "Max results (default: 8, max: 20)."},
+        },
+        "required": ["query"],
+    },
+}
+
+CONTEXT_SCHEMA = {
+    "name": "retaindb_context",
+    "description": "Synthesized 'what matters now' context block for the current task.",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "query": {"type": "string", "description": "Current task or question."},
+        },
+        "required": ["query"],
+    },
+}
+
+REMEMBER_SCHEMA = {
+    "name": "retaindb_remember",
+    "description": "Persist an explicit fact or preference to long-term memory.",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "content": {"type": "string", "description": "The fact to remember."},
+            "memory_type": {
+                "type": "string",
+                "enum": ["preference", "fact", "decision", "context"],
+                "description": "Category (default: fact).",
+            },
+            "importance": {
+                "type": "number",
+                "description": "Importance 0-1 (default: 0.5).",
+            },
+        },
+        "required": ["content"],
+    },
+}
+
+FORGET_SCHEMA = {
+    "name": "retaindb_forget",
+    "description": "Delete a specific memory by ID.",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "memory_id": {"type": "string", "description": "Memory ID to delete."},
+        },
+        "required": ["memory_id"],
+    },
+}
+
+
+# ---------------------------------------------------------------------------
+# MemoryProvider implementation
+# ---------------------------------------------------------------------------
+
+class RetainDBMemoryProvider(MemoryProvider):
+    """RetainDB cloud memory with write-behind queue and semantic search."""
+
+    def __init__(self):
+        self._api_key = ""
+        self._base_url = _DEFAULT_BASE_URL
+        self._project = "hermes"
+        self._user_id = ""
+        self._prefetch_result = ""
+        self._prefetch_lock = threading.Lock()
+        self._prefetch_thread = None
+        self._sync_thread = None
+
+    @property
+    def name(self) -> str:
+        return "retaindb"
+
+    def is_available(self) -> bool:
+        return bool(os.environ.get("RETAINDB_API_KEY"))
+
+    def get_config_schema(self):
+        return [
+            {"key": "api_key", "description": "RetainDB API key", "secret": True, "required": True, "env_var": "RETAINDB_API_KEY", "url": "https://retaindb.com"},
+            {"key": "base_url", "description": "API endpoint", "default": "https://api.retaindb.com"},
+            {"key": "project", "description": "Project identifier", "default": "hermes"},
+        ]
+
+    def _headers(self) -> dict:
+        return {
+            "Authorization": f"Bearer {self._api_key}",
+            "Content-Type": "application/json",
+        }
+
+    def _api(self, method: str, path: str, **kwargs):
+        """Make an API call to RetainDB."""
+        import requests
+        url = f"{self._base_url}{path}"
+        resp = requests.request(method, url, headers=self._headers(), timeout=30, **kwargs)
+        resp.raise_for_status()
+        return resp.json()
+
+    def initialize(self, session_id: str, **kwargs) -> None:
+        self._api_key = os.environ.get("RETAINDB_API_KEY", "")
+        self._base_url = os.environ.get("RETAINDB_BASE_URL", _DEFAULT_BASE_URL)
+        self._user_id = kwargs.get("user_id", "default")
+        self._session_id = session_id
+
+        # Derive profile-scoped project name so different profiles don't
+        # share server-side memory.  Explicit RETAINDB_PROJECT always wins.
+        explicit_project = os.environ.get("RETAINDB_PROJECT")
+        if explicit_project:
+            self._project = explicit_project
+        else:
+            hermes_home = kwargs.get("hermes_home", "")
+            profile_name = os.path.basename(hermes_home) if hermes_home else ""
+            # Default profile (~/.hermes) → "hermes"; named profiles → "hermes-<name>"
+            if profile_name and profile_name != ".hermes":
+                self._project = f"hermes-{profile_name}"
+            else:
+                self._project = "hermes"
+
+    def system_prompt_block(self) -> str:
+        return (
+            "# RetainDB Memory\n"
+            f"Active. Project: {self._project}.\n"
+            "Use retaindb_search to find memories, retaindb_remember to store facts, "
+            "retaindb_profile for a user overview, retaindb_context for task-relevant context."
+        )
+
+    def prefetch(self, query: str, *, session_id: str = "") -> str:
+        if self._prefetch_thread and self._prefetch_thread.is_alive():
+            self._prefetch_thread.join(timeout=3.0)
+        with self._prefetch_lock:
+            result = self._prefetch_result
+            self._prefetch_result = ""
+        if not result:
+            return ""
+        return f"## RetainDB Memory\n{result}"
+
+    def queue_prefetch(self, query: str, *, session_id: str = "") -> None:
+        def _run():
+            try:
+                data = self._api("POST", "/v1/recall", json={
+                    "project": self._project,
+                    "query": query,
+                    "user_id": self._user_id,
+                    "top_k": 5,
+                })
+                results = data.get("results", [])
+                if results:
+                    lines = [r.get("content", "") for r in results if r.get("content")]
+                    with self._prefetch_lock:
+                        self._prefetch_result = "\n".join(f"- {l}" for l in lines)
+            except Exception as e:
+                logger.debug("RetainDB prefetch failed: %s", e)
+
+        self._prefetch_thread = threading.Thread(target=_run, daemon=True, name="retaindb-prefetch")
+        self._prefetch_thread.start()
+
+    def sync_turn(self, user_content: str, assistant_content: str, *, session_id: str = "") -> None:
+        """Ingest conversation turn in background (non-blocking)."""
+        def _sync():
+            try:
+                self._api("POST", "/v1/ingest", json={
+                    "project": self._project,
+                    "user_id": self._user_id,
+                    "session_id": self._session_id,
+                    "messages": [
+                        {"role": "user", "content": user_content},
+                        {"role": "assistant", "content": assistant_content},
+                    ],
+                })
+            except Exception as e:
+                logger.warning("RetainDB sync failed: %s", e)
+
+        if self._sync_thread and self._sync_thread.is_alive():
+            self._sync_thread.join(timeout=5.0)
+        self._sync_thread = threading.Thread(target=_sync, daemon=True, name="retaindb-sync")
+        self._sync_thread.start()
+
+    def get_tool_schemas(self) -> List[Dict[str, Any]]:
+        return [PROFILE_SCHEMA, SEARCH_SCHEMA, CONTEXT_SCHEMA, REMEMBER_SCHEMA, FORGET_SCHEMA]
+
+    def handle_tool_call(self, tool_name: str, args: dict, **kwargs) -> str:
+        try:
+            if tool_name == "retaindb_profile":
+                data = self._api("GET", f"/v1/profile/{self._project}/{self._user_id}")
+                return json.dumps(data)
+
+            elif tool_name == "retaindb_search":
+                query = args.get("query", "")
+                if not query:
+                    return json.dumps({"error": "query is required"})
+                data = self._api("POST", "/v1/search", json={
+                    "project": self._project,
+                    "user_id": self._user_id,
+                    "query": query,
+                    "top_k": min(int(args.get("top_k", 8)), 20),
+                })
+                return json.dumps(data)
+
+            elif tool_name == "retaindb_context":
+                query = args.get("query", "")
+                if not query:
+                    return json.dumps({"error": "query is required"})
+                data = self._api("POST", "/v1/recall", json={
+                    "project": self._project,
+                    "user_id": self._user_id,
+                    "query": query,
+                    "top_k": 5,
+                })
+                return json.dumps(data)
+
+            elif tool_name == "retaindb_remember":
+                content = args.get("content", "")
+                if not content:
+                    return json.dumps({"error": "content is required"})
+                data = self._api("POST", "/v1/remember", json={
+                    "project": self._project,
+                    "user_id": self._user_id,
+                    "content": content,
+                    "memory_type": args.get("memory_type", "fact"),
+                    "importance": float(args.get("importance", 0.5)),
+                })
+                return json.dumps(data)
+
+            elif tool_name == "retaindb_forget":
+                memory_id = args.get("memory_id", "")
+                if not memory_id:
+                    return json.dumps({"error": "memory_id is required"})
+                data = self._api("DELETE", f"/v1/memory/{memory_id}")
+                return json.dumps(data)
+
+            return json.dumps({"error": f"Unknown tool: {tool_name}"})
+        except Exception as e:
+            return json.dumps({"error": str(e)})
+
+    def on_memory_write(self, action: str, target: str, content: str) -> None:
+        if action == "add":
+            try:
+                self._api("POST", "/v1/remember", json={
+                    "project": self._project,
+                    "user_id": self._user_id,
+                    "content": content,
+                    "memory_type": "preference" if target == "user" else "fact",
+                })
+            except Exception as e:
+                logger.debug("RetainDB memory bridge failed: %s", e)
+
+    def shutdown(self) -> None:
+        for t in (self._prefetch_thread, self._sync_thread):
+            if t and t.is_alive():
+                t.join(timeout=5.0)
+
+
+def register(ctx) -> None:
+    """Register RetainDB as a memory provider plugin."""
+    ctx.register_memory_provider(RetainDBMemoryProvider())

plugins/memory/retaindb/plugin.yaml

@@ -0,0 +1,7 @@
+name: retaindb
+version: 1.0.0
+description: "RetainDB — cloud memory API with hybrid search and 7 memory types."
+pip_dependencies:
+  - requests
+requires_env:
+  - RETAINDB_API_KEY

pyproject.toml

@@ -105,7 +105,7 @@ hermes-acp = "acp_adapter.entry:main"
 py-modules = ["run_agent", "model_tools", "toolsets", "batch_runner", "trajectory_compressor", "toolset_distributions", "cli", "hermes_constants", "hermes_state", "hermes_time", "rl_cli", "utils"]
 
 [tool.setuptools.packages.find]
-include = ["agent", "tools", "tools.*", "hermes_cli", "gateway", "gateway.*", "cron", "honcho_integration", "acp_adapter"]
+include = ["agent", "tools", "tools.*", "hermes_cli", "gateway", "gateway.*", "cron", "acp_adapter", "plugins", "plugins.*"]
 
 [tool.pytest.ini_options]
 testpaths = ["tests"]

run_agent.py

@@ -103,12 +103,6 @@ from agent.trajectory import (
 )
 from utils import atomic_json_write, env_var_enabled
 
-HONCHO_TOOL_NAMES = {
-    "honcho_context",
-    "honcho_profile",
-    "honcho_search",
-    "honcho_conclude",
-}
 
 
 class _SafeWriter:
@@ -221,9 +215,6 @@ _PARALLEL_SAFE_TOOLS = frozenset({
     "ha_get_state",
     "ha_list_entities",
     "ha_list_services",
-    "honcho_context",
-    "honcho_profile",
-    "honcho_search",
     "read_file",
     "search_files",
     "session_search",
@@ -340,46 +331,15 @@ def _paths_overlap(left: Path, right: Path) -> bool:
     return left_parts[:common_len] == right_parts[:common_len]
 
 
-def _inject_honcho_turn_context(content, turn_context: str):
-    """Append Honcho recall to the current-turn user message without mutating history.
 
-    The returned content is sent to the API for this turn only. Keeping Honcho
-    recall out of the system prompt preserves the stable cache prefix while
-    still giving the model continuity context.
-    """
-    if not turn_context:
-        return content
+_SURROGATE_RE = re.compile(r'[\ud800-\udfff]')
 
-    note = (
-        "[System note: The following Honcho memory was retrieved from prior "
-        "sessions. It is continuity context for this turn only, not new user "
-        "input.]\n\n"
-        f"{turn_context}"
-    )
-
-    if isinstance(content, list):
-        return list(content) + [{"type": "text", "text": note}]
-
-    text = "" if content is None else str(content)
-    if not text.strip():
-        return note
-    return f"{text}\n\n{note}"
-
-
-# Budget warning text patterns injected by _get_budget_warning().
 _BUDGET_WARNING_RE = re.compile(
     r"\[BUDGET(?:\s+WARNING)?:\s+Iteration\s+\d+/\d+\..*?\]",
     re.DOTALL,
 )
 
 
-# Regex to match lone surrogate code points (U+D800..U+DFFF).
-# These are invalid in UTF-8 and cause UnicodeEncodeError when the OpenAI SDK
-# serialises messages to JSON.  Common source: clipboard paste from Google Docs
-# or other rich-text editors on some platforms.
-_SURROGATE_RE = re.compile(r'[\ud800-\udfff]')
-
-
 def _sanitize_surrogates(text: str) -> str:
     """Replace lone surrogate code points with U+FFFD (replacement character).
 
@@ -507,9 +467,6 @@ class AIAgent:
         skip_context_files: bool = False,
         skip_memory: bool = False,
         session_db=None,
-        honcho_session_key: str = None,
-        honcho_manager=None,
-        honcho_config=None,
         iteration_budget: "IterationBudget" = None,
         fallback_model: Dict[str, Any] = None,
         credential_pool=None,
@@ -556,10 +513,6 @@ class AIAgent:
             skip_context_files (bool): If True, skip auto-injection of SOUL.md, AGENTS.md, and .cursorrules
                 into the system prompt. Use this for batch processing and data generation to avoid
                 polluting trajectories with user-specific persona or project instructions.
-            honcho_session_key (str): Session key for Honcho integration (e.g., "telegram:123456" or CLI session_id).
-                When provided and Honcho is enabled in config, enables persistent cross-session user modeling.
-            honcho_manager: Optional shared HonchoSessionManager owned by the caller.
-            honcho_config: Optional HonchoClientConfig corresponding to honcho_manager.
         """
         _install_safe_stdio()
 
@@ -1070,75 +1023,80 @@ class AIAgent:
             except Exception:
                 pass  # Memory is optional -- don't break agent init
         
-        # Honcho AI-native memory (cross-session user modeling)
-        # Reads $HERMES_HOME/honcho.json (instance) or ~/.honcho/config.json (global).
-        self._honcho = None  # HonchoSessionManager | None
-        self._honcho_session_key = honcho_session_key
-        self._honcho_config = None  # HonchoClientConfig | None
-        self._honcho_exit_hook_registered = False
+
+
+        # Memory provider plugin (external — one at a time, alongside built-in)
+        # Reads memory.provider from config to select which plugin to activate.
+        self._memory_manager = None
         if not skip_memory:
             try:
-                if honcho_manager is not None:
-                    hcfg = honcho_config or getattr(honcho_manager, "_config", None)
-                    self._honcho_config = hcfg
-                    if hcfg and self._honcho_should_activate(hcfg):
-                        self._honcho = honcho_manager
-                        self._activate_honcho(
-                            hcfg,
-                            enabled_toolsets=enabled_toolsets,
-                            disabled_toolsets=disabled_toolsets,
-                            session_db=session_db,
-                        )
-                else:
-                    from honcho_integration.client import HonchoClientConfig, get_honcho_client
-                    hcfg = HonchoClientConfig.from_global_config()
-                    self._honcho_config = hcfg
-                    if self._honcho_should_activate(hcfg):
-                        from honcho_integration.session import HonchoSessionManager
-                        client = get_honcho_client(hcfg)
-                        self._honcho = HonchoSessionManager(
-                            honcho=client,
-                            config=hcfg,
-                            context_tokens=hcfg.context_tokens,
-                        )
-                        self._activate_honcho(
-                            hcfg,
-                            enabled_toolsets=enabled_toolsets,
-                            disabled_toolsets=disabled_toolsets,
-                            session_db=session_db,
-                        )
+                _mem_provider_name = mem_config.get("provider", "") if mem_config else ""
+
+                # Auto-migrate: if Honcho was actively configured (enabled +
+                # credentials) but memory.provider is not set, activate the
+                # honcho plugin automatically.  Just having the config file
+                # is not enough — the user may have disabled Honcho or the
+                # file may be from a different tool.
+                if not _mem_provider_name:
+                    try:
+                        from plugins.memory.honcho.client import HonchoClientConfig as _HCC
+                        _hcfg = _HCC.from_global_config()
+                        if _hcfg.enabled and (_hcfg.api_key or _hcfg.base_url):
+                            _mem_provider_name = "honcho"
+                            # Persist so this only auto-migrates once
+                            try:
+                                from hermes_cli.config import load_config as _lc, save_config as _sc
+                                _cfg = _lc()
+                                _cfg.setdefault("memory", {})["provider"] = "honcho"
+                                _sc(_cfg)
+                            except Exception:
+                                pass
+                            if not self.quiet_mode:
+                                print("  ✓ Auto-migrated Honcho to memory provider plugin.")
+                                print("    Your config and data are preserved.\n")
+                    except Exception:
+                        pass
+
+                if _mem_provider_name:
+                    from agent.memory_manager import MemoryManager as _MemoryManager
+                    from plugins.memory import load_memory_provider as _load_mem
+                    self._memory_manager = _MemoryManager()
+                    _mp = _load_mem(_mem_provider_name)
+                    if _mp and _mp.is_available():
+                        self._memory_manager.add_provider(_mp)
+                    if self._memory_manager.providers:
+                        from hermes_constants import get_hermes_home as _ghh
+                        _init_kwargs = {
+                            "session_id": self.session_id,
+                            "platform": platform or "cli",
+                            "hermes_home": str(_ghh()),
+                            "agent_context": "primary",
+                        }
+                        # Profile identity for per-profile provider scoping
+                        try:
+                            from hermes_cli.profiles import get_active_profile_name
+                            _profile = get_active_profile_name()
+                            _init_kwargs["agent_identity"] = _profile
+                            _init_kwargs["agent_workspace"] = "hermes"
+                        except Exception:
+                            pass
+                        self._memory_manager.initialize_all(**_init_kwargs)
+                        logger.info("Memory provider '%s' activated", _mem_provider_name)
                     else:
-                        if not hcfg.enabled:
-                            logger.debug("Honcho disabled in global config")
-                        elif not (hcfg.api_key or hcfg.base_url):
-                            logger.debug("Honcho enabled but no API key or base URL configured")
-                        else:
-                            logger.debug("Honcho enabled but missing API key or disabled in config")
-            except Exception as e:
-                logger.warning("Honcho init failed — memory disabled: %s", e)
-                print(f"  Honcho init failed: {e}")
-                print("  Run 'hermes honcho setup' to reconfigure.")
-                self._honcho = None
+                        logger.debug("Memory provider '%s' not found or not available", _mem_provider_name)
+                        self._memory_manager = None
+            except Exception as _mpe:
+                logger.warning("Memory provider plugin init failed: %s", _mpe)
+                self._memory_manager = None
 
-        # Tools are initially discovered before Honcho activation. If Honcho
-        # stays inactive, remove any stale honcho_* tools from prior process state.
-        if not self._honcho:
-            self._strip_honcho_tools_from_surface()
-
-        # Gate local memory writes based on per-peer memory modes.
-        # AI peer governs MEMORY.md; user peer governs USER.md.
-        # "honcho" = Honcho only, disable local writes.
-        if self._honcho_config and self._honcho:
-            _hcfg = self._honcho_config
-            _agent_mode = _hcfg.peer_memory_mode(_hcfg.ai_peer)
-            _user_mode = _hcfg.peer_memory_mode(_hcfg.peer_name or "user")
-            if _agent_mode == "honcho":
-                self._memory_flush_min_turns = 0
-                self._memory_enabled = False
-                logger.debug("peer %s memory_mode=honcho: local MEMORY.md writes disabled", _hcfg.ai_peer)
-            if _user_mode == "honcho":
-                self._user_profile_enabled = False
-                logger.debug("peer %s memory_mode=honcho: local USER.md writes disabled", _hcfg.peer_name or "user")
+        # Inject memory provider tool schemas into the tool surface
+        if self._memory_manager and self.tools is not None:
+            for _schema in self._memory_manager.get_all_tool_schemas():
+                _wrapped = {"type": "function", "function": _schema}
+                self.tools.append(_wrapped)
+                _tname = _schema.get("name", "")
+                if _tname:
+                    self.valid_tool_names.add(_tname)
 
         # Skills config: nudge interval for skill creation reminders
         self._skill_nudge_interval = 10
@@ -2382,6 +2340,23 @@ class AIAgent:
         self._interrupt_requested = False
         self._interrupt_message = None
         _set_interrupt(False)
+
+    def shutdown_memory_provider(self, messages: list = None) -> None:
+        """Shut down the memory provider — call at actual session boundaries.
+
+        This calls on_session_end() then shutdown_all() on the memory
+        manager. NOT called per-turn — only at CLI exit, /reset, gateway
+        session expiry, etc.
+        """
+        if self._memory_manager:
+            try:
+                self._memory_manager.on_session_end(messages or [])
+            except Exception:
+                pass
+            try:
+                self._memory_manager.shutdown_all()
+            except Exception:
+                pass
     
     def _hydrate_todo_store(self, history: List[Dict[str, Any]]) -> None:
         """
@@ -2420,228 +2395,14 @@ class AIAgent:
         """Check if an interrupt has been requested."""
         return self._interrupt_requested
 
-    # ── Honcho integration helpers ──
 
-    def _honcho_should_activate(self, hcfg) -> bool:
-        """Return True when Honcho should be active.
 
-        Self-hosted Honcho may be configured with a base_url and no API key,
-        so activation should accept either credential style.
-        """
-        if not hcfg or not hcfg.enabled:
-            return False
-        if not (hcfg.api_key or hcfg.base_url):
-            return False
-        return True
 
-    def _strip_honcho_tools_from_surface(self) -> None:
-        """Remove Honcho tools from the active tool surface."""
-        if not self.tools:
-            self.valid_tool_names = set()
-            return
 
-        self.tools = [
-            tool for tool in self.tools
-            if tool.get("function", {}).get("name") not in HONCHO_TOOL_NAMES
-        ]
-        self.valid_tool_names = {
-            tool["function"]["name"] for tool in self.tools
-        } if self.tools else set()
 
-    def _activate_honcho(
-        self,
-        hcfg,
-        *,
-        enabled_toolsets: Optional[List[str]],
-        disabled_toolsets: Optional[List[str]],
-        session_db,
-    ) -> None:
-        """Finish Honcho setup once a session manager is available."""
-        if not self._honcho:
-            return
 
-        if not self._honcho_session_key:
-            session_title = None
-            if session_db is not None:
-                try:
-                    session_title = session_db.get_session_title(self.session_id or "")
-                except Exception:
-                    pass
-            self._honcho_session_key = (
-                hcfg.resolve_session_name(
-                    session_title=session_title,
-                    session_id=self.session_id,
-                )
-                or "hermes-default"
-            )
 
-        honcho_sess = self._honcho.get_or_create(self._honcho_session_key)
-        if not honcho_sess.messages:
-            try:
-                from hermes_cli.config import get_hermes_home
 
-                mem_dir = str(get_hermes_home() / "memories")
-                self._honcho.migrate_memory_files(
-                    self._honcho_session_key,
-                    mem_dir,
-                )
-            except Exception as exc:
-                logger.debug("Memory files migration failed (non-fatal): %s", exc)
-
-        from tools.honcho_tools import set_session_context
-
-        set_session_context(self._honcho, self._honcho_session_key)
-
-        # Rebuild tool surface after Honcho context injection. Tool availability
-        # is check_fn-gated and may change once session context is attached.
-        self.tools = get_tool_definitions(
-            enabled_toolsets=enabled_toolsets,
-            disabled_toolsets=disabled_toolsets,
-            quiet_mode=True,
-        )
-        self.valid_tool_names = {
-            tool["function"]["name"] for tool in self.tools
-        } if self.tools else set()
-
-        if hcfg.recall_mode == "context":
-            self._strip_honcho_tools_from_surface()
-            if not self.quiet_mode:
-                print("  Honcho active — recall_mode: context (Honcho tools hidden)")
-        else:
-            if not self.quiet_mode:
-                print(f"  Honcho active — recall_mode: {hcfg.recall_mode}")
-
-        logger.info(
-            "Honcho active (session: %s, user: %s, workspace: %s, "
-            "write_frequency: %s, memory_mode: %s)",
-            self._honcho_session_key,
-            hcfg.peer_name,
-            hcfg.workspace_id,
-            hcfg.write_frequency,
-            hcfg.memory_mode,
-        )
-
-        recall_mode = hcfg.recall_mode
-        if recall_mode != "tools":
-            try:
-                ctx = self._honcho.get_prefetch_context(self._honcho_session_key)
-                if ctx:
-                    self._honcho.set_context_result(self._honcho_session_key, ctx)
-                    logger.debug("Honcho context pre-warmed for first turn")
-            except Exception as exc:
-                logger.debug("Honcho context prefetch failed (non-fatal): %s", exc)
-
-        self._register_honcho_exit_hook()
-
-    def _register_honcho_exit_hook(self) -> None:
-        """Register a process-exit flush hook without clobbering signal handlers."""
-        if self._honcho_exit_hook_registered or not self._honcho:
-            return
-
-        honcho_ref = weakref.ref(self._honcho)
-
-        def _flush_honcho_on_exit():
-            manager = honcho_ref()
-            if manager is None:
-                return
-            try:
-                manager.flush_all()
-            except (Exception, KeyboardInterrupt) as exc:
-                logger.debug("Honcho flush on exit failed (non-fatal): %s", exc)
-
-        atexit.register(_flush_honcho_on_exit)
-        self._honcho_exit_hook_registered = True
-
-    def _queue_honcho_prefetch(self, user_message: str) -> None:
-        """Queue turn-end Honcho prefetch so the next turn can consume cached results."""
-        if not self._honcho or not self._honcho_session_key:
-            return
-
-        recall_mode = (self._honcho_config.recall_mode if self._honcho_config else "hybrid")
-        if recall_mode == "tools":
-            return
-
-        try:
-            self._honcho.prefetch_context(self._honcho_session_key, user_message)
-            self._honcho.prefetch_dialectic(self._honcho_session_key, user_message or "What were we working on?")
-        except Exception as exc:
-            logger.debug("Honcho background prefetch failed (non-fatal): %s", exc)
-
-    def _honcho_prefetch(self, user_message: str) -> str:
-        """Assemble the first-turn Honcho context from the pre-warmed cache."""
-        if not self._honcho or not self._honcho_session_key:
-            return ""
-        try:
-            parts = []
-
-            ctx = self._honcho.pop_context_result(self._honcho_session_key)
-            if ctx:
-                rep = ctx.get("representation", "")
-                card = ctx.get("card", "")
-                if rep:
-                    parts.append(f"## User representation\n{rep}")
-                if card:
-                    parts.append(card)
-                ai_rep = ctx.get("ai_representation", "")
-                ai_card = ctx.get("ai_card", "")
-                if ai_rep:
-                    parts.append(f"## AI peer representation\n{ai_rep}")
-                if ai_card:
-                    parts.append(ai_card)
-
-            dialectic = self._honcho.pop_dialectic_result(self._honcho_session_key)
-            if dialectic:
-                parts.append(f"## Continuity synthesis\n{dialectic}")
-
-            if not parts:
-                return ""
-            header = (
-                "# Honcho Memory (persistent cross-session context)\n"
-                "Use this to answer questions about the user, prior sessions, "
-                "and what you were working on together. Do not call tools to "
-                "look up information that is already present here.\n"
-            )
-            return header + "\n\n".join(parts)
-        except Exception as e:
-            logger.debug("Honcho prefetch failed (non-fatal): %s", e)
-            return ""
-
-    def _honcho_save_user_observation(self, content: str) -> str:
-        """Route a memory tool target=user add to Honcho.
-
-        Sends the content as a user peer message so Honcho's reasoning
-        model can incorporate it into the user representation.
-        """
-        if not content or not content.strip():
-            return json.dumps({"success": False, "error": "Content cannot be empty."})
-        try:
-            session = self._honcho.get_or_create(self._honcho_session_key)
-            session.add_message("user", f"[observation] {content.strip()}")
-            self._honcho.save(session)
-            return json.dumps({
-                "success": True,
-                "target": "user",
-                "message": "Saved to Honcho user model.",
-            })
-        except Exception as e:
-            logger.debug("Honcho user observation failed: %s", e)
-            return json.dumps({"success": False, "error": f"Honcho save failed: {e}"})
-
-    def _honcho_sync(self, user_content: str, assistant_content: str) -> None:
-        """Sync the user/assistant message pair to Honcho."""
-        if not self._honcho or not self._honcho_session_key:
-            return
-        try:
-            session = self._honcho.get_or_create(self._honcho_session_key)
-            session.add_message("user", user_content)
-            session.add_message("assistant", assistant_content)
-            self._honcho.save(session)
-            logger.info("Honcho sync queued for session %s (%d messages)",
-                        self._honcho_session_key, len(session.messages))
-        except Exception as e:
-            logger.warning("Honcho sync failed: %s", e)
-            if not self.quiet_mode:
-                print(f"  Honcho write failed: {e}")
 
     def _build_system_prompt(self, system_message: str = None) -> str:
         """
@@ -2671,8 +2432,8 @@ class AIAgent:
         if not _soul_loaded:
             # Fallback to hardcoded identity
             _ai_peer_name = (
-                self._honcho_config.ai_peer
-                if self._honcho_config and self._honcho_config.ai_peer != "hermes"
+                None
+                if False
                 else None
             )
             if _ai_peer_name:
@@ -2728,59 +2489,7 @@ class AIAgent:
                 if "gemini" in _model_lower or "gemma" in _model_lower:
                     prompt_parts.append(GOOGLE_MODEL_OPERATIONAL_GUIDANCE)
 
-        # Honcho CLI awareness: tell Hermes about its own management commands
         # so it can refer the user to them rather than reinventing answers.
-        if self._honcho and self._honcho_session_key:
-            hcfg = self._honcho_config
-            mode = hcfg.memory_mode if hcfg else "hybrid"
-            freq = hcfg.write_frequency if hcfg else "async"
-            recall_mode = hcfg.recall_mode if hcfg else "hybrid"
-            honcho_block = (
-                "# Honcho memory integration\n"
-                f"Active. Session: {self._honcho_session_key}. "
-                f"Mode: {mode}. Write frequency: {freq}. Recall: {recall_mode}.\n"
-            )
-            if recall_mode == "context":
-                honcho_block += (
-                    "Honcho context is injected into this system prompt below. "
-                    "All memory retrieval comes from this context — no Honcho tools "
-                    "are available. Answer questions about the user, prior sessions, "
-                    "and recent work directly from the Honcho Memory section.\n"
-                )
-            elif recall_mode == "tools":
-                honcho_block += (
-                    "Honcho tools:\n"
-                    "  honcho_context <question>           — ask Honcho a question, LLM-synthesized answer\n"
-                    "  honcho_search <query>                   — semantic search, raw excerpts, no LLM\n"
-                    "  honcho_profile                          — user's peer card, key facts, no LLM\n"
-                    "  honcho_conclude <conclusion>            — write a fact about the user to memory\n"
-                )
-            else:  # hybrid
-                honcho_block += (
-                    "Honcho context (user representation, peer card, and recent session summary) "
-                    "is injected into this system prompt below. Use it to answer continuity "
-                    "questions ('where were we?', 'what were we working on?') WITHOUT calling "
-                    "any tools. Only call Honcho tools when you need information beyond what is "
-                    "already present in the Honcho Memory section.\n"
-                    "Honcho tools:\n"
-                    "  honcho_context <question>           — ask Honcho a question, LLM-synthesized answer\n"
-                    "  honcho_search <query>                   — semantic search, raw excerpts, no LLM\n"
-                    "  honcho_profile                          — user's peer card, key facts, no LLM\n"
-                    "  honcho_conclude <conclusion>            — write a fact about the user to memory\n"
-                )
-            honcho_block += (
-                "Management commands (refer users here instead of explaining manually):\n"
-                "  hermes honcho status                    — show full config + connection\n"
-                "  hermes honcho mode [hybrid|honcho]       — show or set memory mode\n"
-                "  hermes honcho tokens [--context N] [--dialectic N] — show or set token budgets\n"
-                "  hermes honcho peer [--user NAME] [--ai NAME] [--reasoning LEVEL]\n"
-                "  hermes honcho sessions                  — list directory→session mappings\n"
-                "  hermes honcho map <name>                — map cwd to a session name\n"
-                "  hermes honcho identity [<file>] [--show] — seed or show AI peer identity\n"
-                "  hermes honcho migrate                   — migration guide from openclaw-honcho\n"
-                "  hermes honcho setup                     — full interactive wizard"
-            )
-            prompt_parts.append(honcho_block)
 
         # Note: ephemeral_system_prompt is NOT included here. It's injected at
         # API-call time only so it stays out of the cached/stored system prompt.
@@ -2792,12 +2501,21 @@ class AIAgent:
                 mem_block = self._memory_store.format_for_system_prompt("memory")
                 if mem_block:
                     prompt_parts.append(mem_block)
-            # USER.md is always included when enabled -- Honcho prefetch is additive.
+            # USER.md is always included when enabled.
             if self._user_profile_enabled:
                 user_block = self._memory_store.format_for_system_prompt("user")
                 if user_block:
                     prompt_parts.append(user_block)
 
+        # External memory provider system prompt block (additive to built-in)
+        if self._memory_manager:
+            try:
+                _ext_mem_block = self._memory_manager.build_system_prompt()
+                if _ext_mem_block:
+                    prompt_parts.append(_ext_mem_block)
+            except Exception:
+                pass
+
         has_skills_tools = any(name in self.valid_tool_names for name in ['skills_list', 'skill_view', 'skill_manage'])
         if has_skills_tools:
             avail_toolsets = {
@@ -5607,10 +5325,6 @@ class AIAgent:
             return
         if "memory" not in self.valid_tool_names or not self._memory_store:
             return
-        # honcho-only agent mode: skip local MEMORY.md flush
-        _hcfg = getattr(self, '_honcho_config', None)
-        if _hcfg and _hcfg.peer_memory_mode(_hcfg.ai_peer) == "honcho":
-            return
         effective_min = min_turns if min_turns is not None else self._memory_flush_min_turns
         if self._user_turn_count < effective_min:
             return
@@ -5734,8 +5448,6 @@ class AIAgent:
                             old_text=args.get("old_text"),
                             store=self._memory_store,
                         )
-                        if self._honcho and flush_target == "user" and args.get("action") == "add":
-                            self._honcho_save_user_observation(args.get("content", ""))
                         if not self.quiet_mode:
                             print(f"  🧠 Memory flush: saved to {args.get('target', 'memory')}")
                     except Exception as e:
@@ -5761,6 +5473,13 @@ class AIAgent:
         # Pre-compression memory flush: let the model save memories before they're lost
         self.flush_memories(messages, min_turns=0)
 
+        # Notify external memory provider before compression discards context
+        if self._memory_manager:
+            try:
+                self._memory_manager.on_pre_compress(messages)
+            except Exception:
+                pass
+
         compressed = self.context_compressor.compress(messages, current_tokens=approx_tokens)
 
         todo_snapshot = self._todo_store.format_for_injection()
@@ -5887,10 +5606,19 @@ class AIAgent:
                 old_text=function_args.get("old_text"),
                 store=self._memory_store,
             )
-            # Also send user observations to Honcho when active
-            if self._honcho and target == "user" and function_args.get("action") == "add":
-                self._honcho_save_user_observation(function_args.get("content", ""))
+            # Bridge: notify external memory provider of built-in memory writes
+            if self._memory_manager and function_args.get("action") in ("add", "replace"):
+                try:
+                    self._memory_manager.on_memory_write(
+                        function_args.get("action", ""),
+                        target,
+                        function_args.get("content", ""),
+                    )
+                except Exception:
+                    pass
             return result
+        elif self._memory_manager and self._memory_manager.has_tool(function_name):
+            return self._memory_manager.handle_tool_call(function_name, function_args)
         elif function_name == "clarify":
             from tools.clarify_tool import clarify_tool as _clarify_tool
             return _clarify_tool(
@@ -5912,8 +5640,6 @@ class AIAgent:
             return handle_function_call(
                 function_name, function_args, effective_task_id,
                 enabled_tools=list(self.valid_tool_names) if self.valid_tool_names else None,
-                honcho_manager=self._honcho,
-                honcho_session_key=self._honcho_session_key,
             )
 
     def _execute_tool_calls_concurrent(self, assistant_message, messages: list, effective_task_id: str, api_call_count: int = 0) -> None:
@@ -6237,9 +5963,6 @@ class AIAgent:
                     old_text=function_args.get("old_text"),
                     store=self._memory_store,
                 )
-                # Also send user observations to Honcho when active
-                if self._honcho and target == "user" and function_args.get("action") == "add":
-                    self._honcho_save_user_observation(function_args.get("content", ""))
                 tool_duration = time.time() - tool_start_time
                 if self.quiet_mode:
                     self._vprint(f"  {_get_cute_tool_message_impl('memory', function_args, tool_duration, result=function_result)}")
@@ -6299,8 +6022,6 @@ class AIAgent:
                     function_result = handle_function_call(
                         function_name, function_args, effective_task_id,
                         enabled_tools=list(self.valid_tool_names) if self.valid_tool_names else None,
-                        honcho_manager=self._honcho,
-                        honcho_session_key=self._honcho_session_key,
                     )
                     _spinner_result = function_result
                 except Exception as tool_error:
@@ -6318,8 +6039,6 @@ class AIAgent:
                     function_result = handle_function_call(
                         function_name, function_args, effective_task_id,
                         enabled_tools=list(self.valid_tool_names) if self.valid_tool_names else None,
-                        honcho_manager=self._honcho,
-                        honcho_session_key=self._honcho_session_key,
                     )
                 except Exception as tool_error:
                     function_result = f"Error executing tool '{function_name}': {tool_error}"
@@ -6633,7 +6352,6 @@ class AIAgent:
         task_id: str = None,
         stream_callback: Optional[callable] = None,
         persist_user_message: Optional[str] = None,
-        sync_honcho: bool = True,
     ) -> Dict[str, Any]:
         """
         Run a complete conversation with tool calling until completion.
@@ -6649,8 +6367,7 @@ class AIAgent:
             persist_user_message: Optional clean user message to store in
                 transcripts/history when user_message contains API-only
                 synthetic prefixes.
-            sync_honcho: When False, skip writing the final synthetic turn back
-                to Honcho or queuing follow-up prefetch work.
+                    or queuing follow-up prefetch work.
 
         Returns:
             Dict: Complete conversation result with final response and message history
@@ -6734,7 +6451,6 @@ class AIAgent:
         self._user_turn_count += 1
 
         # Preserve the original user message (no nudge injection).
-        # Honcho should receive the actual user input, not system nudges.
         original_user_message = persist_user_message if persist_user_message is not None else user_message
 
         # Track memory nudge trigger (turn-based, checked here).
@@ -6749,27 +6465,6 @@ class AIAgent:
                 _should_review_memory = True
                 self._turns_since_memory = 0
 
-        # Honcho prefetch consumption:
-        # - First turn: bake into cached system prompt (stable for the session).
-        # - Later turns: attach recall to the current-turn user message at
-        #   API-call time only (never persisted to history / session DB).
-        #
-        # This keeps the system-prefix cache stable while still allowing turn N
-        # to consume background prefetch results from turn N-1.
-        self._honcho_context = ""
-        self._honcho_turn_context = ""
-        _recall_mode = (self._honcho_config.recall_mode if self._honcho_config else "hybrid")
-        if self._honcho and self._honcho_session_key and _recall_mode != "tools":
-            try:
-                prefetched_context = self._honcho_prefetch(original_user_message)
-                if prefetched_context:
-                    if not conversation_history:
-                        self._honcho_context = prefetched_context
-                    else:
-                        self._honcho_turn_context = prefetched_context
-            except Exception as e:
-                logger.debug("Honcho prefetch failed (non-fatal): %s", e)
-
         # Add user message
         user_msg = {"role": "user", "content": user_message}
         messages.append(user_msg)
@@ -6807,13 +6502,6 @@ class AIAgent:
             else:
                 # First turn of a new session — build from scratch.
                 self._cached_system_prompt = self._build_system_prompt(system_message)
-                # Bake Honcho context into the prompt so it's stable for
-                # the entire session (not re-fetched per turn).
-                if self._honcho_context:
-                    self._cached_system_prompt = (
-                        self._cached_system_prompt + "\n\n" + self._honcho_context
-                    ).strip()
-
                 # Plugin hook: on_session_start
                 # Fired once when a brand-new session is created (not on
                 # continuation).  Plugins can use this to initialise
@@ -6935,7 +6623,18 @@ class AIAgent:
         
         # Clear any stale interrupt state at start
         self.clear_interrupt()
-        
+
+        # External memory provider: prefetch once before the tool loop.
+        # Reuse the cached result on every iteration to avoid re-calling
+        # prefetch_all() on each tool call (10 tool calls = 10x latency + cost).
+        _ext_prefetch_cache = ""
+        if self._memory_manager:
+            try:
+                _query = user_message if isinstance(user_message, str) else ""
+                _ext_prefetch_cache = self._memory_manager.prefetch_all(_query) or ""
+            except Exception:
+                pass
+
         while api_call_count < self.max_iterations and self.iteration_budget.remaining > 0:
             # Reset per-turn checkpoint dedup so each iteration can take one snapshot
             self._checkpoint_mgr.new_turn()
@@ -6984,10 +6683,11 @@ class AIAgent:
             for idx, msg in enumerate(messages):
                 api_msg = msg.copy()
 
-                if idx == current_turn_user_idx and msg.get("role") == "user" and self._honcho_turn_context:
-                    api_msg["content"] = _inject_honcho_turn_context(
-                        api_msg.get("content", ""), self._honcho_turn_context
-                    )
+                # External memory provider prefetch: inject cached recalled context
+                if idx == current_turn_user_idx and msg.get("role") == "user" and _ext_prefetch_cache:
+                    _base = api_msg.get("content", "")
+                    if isinstance(_base, str):
+                        api_msg["content"] = _base + "\n\n" + _ext_prefetch_cache
 
                 # For ALL assistant messages, pass reasoning back to the API
                 # This ensures multi-turn reasoning context is preserved
@@ -7016,8 +6716,8 @@ class AIAgent:
 
             # Build the final system message: cached prompt + ephemeral system prompt.
             # Ephemeral additions are API-call-time only (not persisted to session DB).
-            # Honcho later-turn recall is intentionally kept OUT of the system prompt
-            # so the stable cache prefix remains unchanged.
+            # External recall context is injected into the user message, not the system
+            # prompt, so the stable cache prefix remains unchanged.
             effective_system = active_system_prompt or ""
             if self.ephemeral_system_prompt:
                 effective_system = (effective_system + "\n\n" + self.ephemeral_system_prompt).strip()
@@ -8730,10 +8430,6 @@ class AIAgent:
         # Persist session to both JSON log and SQLite
         self._persist_session(messages, conversation_history)
 
-        # Sync conversation to Honcho for user modeling
-        if final_response and not interrupted and sync_honcho:
-            self._honcho_sync(original_user_message, final_response)
-            self._queue_honcho_prefetch(original_user_message)
 
         # Plugin hook: post_llm_call
         # Fired once per turn after the tool-calling loop completes.
@@ -8807,6 +8503,14 @@ class AIAgent:
             _should_review_skills = True
             self._iters_since_skill = 0
 
+        # External memory provider: sync the completed turn + queue next prefetch
+        if self._memory_manager and final_response and user_message:
+            try:
+                self._memory_manager.sync_all(user_message, final_response)
+                self._memory_manager.queue_prefetch_all(user_message)
+            except Exception:
+                pass
+
         # Background memory/skill review — runs AFTER the response is delivered
         # so it never competes with the user's task for model attention.
         if final_response and not interrupted and (_should_review_memory or _should_review_skills):
@@ -8819,6 +8523,13 @@ class AIAgent:
             except Exception:
                 pass  # Background review is best-effort
 
+        # Note: Memory provider on_session_end() + shutdown_all() are NOT
+        # called here — run_conversation() is called once per user message in
+        # multi-turn sessions. Shutting down after every turn would kill the
+        # provider before the second message. Actual session-end cleanup is
+        # handled by the CLI (atexit / /reset) and gateway (session expiry /
+        # _reset_session).
+
         # Plugin hook: on_session_end
         # Fired at the very end of every run_conversation call.
         # Plugins can use this for cleanup, flushing buffers, etc.

skills/research/ml-paper-writing/SKILL.md

@@ -1,940 +0,0 @@
----
-name: ml-paper-writing
-description: Write publication-ready ML/AI papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. Includes LaTeX templates, reviewer guidelines, and citation verification workflows.
-version: 1.0.0
-author: Orchestra Research
-license: MIT
-dependencies: [semanticscholar, arxiv, habanero, requests]
-metadata:
-  hermes:
-    tags: [Academic Writing, NeurIPS, ICML, ICLR, ACL, AAAI, COLM, LaTeX, Paper Writing, Citations, Research]
-
----
-
-# ML Paper Writing for Top AI Conferences
-
-Expert-level guidance for writing publication-ready papers targeting **NeurIPS, ICML, ICLR, ACL, AAAI, and COLM**. This skill combines writing philosophy from top researchers (Nanda, Farquhar, Karpathy, Lipton, Steinhardt) with practical tools: LaTeX templates, citation verification APIs, and conference checklists.
-
-## Core Philosophy: Collaborative Writing
-
-**Paper writing is collaborative, but Claude should be proactive in delivering drafts.**
-
-The typical workflow starts with a research repository containing code, results, and experimental artifacts. Claude's role is to:
-
-1. **Understand the project** by exploring the repo, results, and existing documentation
-2. **Deliver a complete first draft** when confident about the contribution
-3. **Search literature** using web search and APIs to find relevant citations
-4. **Refine through feedback cycles** when the scientist provides input
-5. **Ask for clarification** only when genuinely uncertain about key decisions
-
-**Key Principle**: Be proactive. If the repo and results are clear, deliver a full draft. Don't block waiting for feedback on every section—scientists are busy. Produce something concrete they can react to, then iterate based on their response.
-
----
-
-## ⚠️ CRITICAL: Never Hallucinate Citations
-
-**This is the most important rule in academic writing with AI assistance.**
-
-### The Problem
-AI-generated citations have a **~40% error rate**. Hallucinated references—papers that don't exist, wrong authors, incorrect years, fabricated DOIs—are a serious form of academic misconduct that can result in desk rejection or retraction.
-
-### The Rule
-**NEVER generate BibTeX entries from memory. ALWAYS fetch programmatically.**
-
-| Action | ✅ Correct | ❌ Wrong |
-|--------|-----------|----------|
-| Adding a citation | Search API → verify → fetch BibTeX | Write BibTeX from memory |
-| Uncertain about a paper | Mark as `[CITATION NEEDED]` | Guess the reference |
-| Can't find exact paper | Note: "placeholder - verify" | Invent similar-sounding paper |
-
-### When You Can't Verify a Citation
-
-If you cannot programmatically verify a citation, you MUST:
-
-```latex
-% EXPLICIT PLACEHOLDER - requires human verification
-\cite{PLACEHOLDER_author2024_verify_this}  % TODO: Verify this citation exists
-```
-
-**Always tell the scientist**: "I've marked [X] citations as placeholders that need verification. I could not confirm these papers exist."
-
-### Recommended: Install Exa MCP for Paper Search
-
-For the best paper search experience, install **Exa MCP** which provides real-time academic search:
-
-**Claude Code:**
-```bash
-claude mcp add exa -- npx -y mcp-remote "https://mcp.exa.ai/mcp"
-```
-
-**Cursor / VS Code** (add to MCP settings):
-```json
-{
-  "mcpServers": {
-    "exa": {
-      "type": "http",
-      "url": "https://mcp.exa.ai/mcp"
-    }
-  }
-}
-```
-
-Exa MCP enables searches like:
-- "Find papers on RLHF for language models published after 2023"
-- "Search for transformer architecture papers by Vaswani"
-- "Get recent work on sparse autoencoders for interpretability"
-
-Then verify results with Semantic Scholar API and fetch BibTeX via DOI.
-
----
-
-## Workflow 0: Starting from a Research Repository
-
-When beginning paper writing, start by understanding the project:
-
-```
-Project Understanding:
-- [ ] Step 1: Explore the repository structure
-- [ ] Step 2: Read README, existing docs, and key results
-- [ ] Step 3: Identify the main contribution with the scientist
-- [ ] Step 4: Find papers already cited in the codebase
-- [ ] Step 5: Search for additional relevant literature
-- [ ] Step 6: Outline the paper structure together
-- [ ] Step 7: Draft sections iteratively with feedback
-```
-
-**Step 1: Explore the Repository**
-
-```bash
-# Understand project structure
-ls -la
-find . -name "*.py" | head -20
-find . -name "*.md" -o -name "*.txt" | xargs grep -l -i "result\|conclusion\|finding"
-```
-
-Look for:
-- `README.md` - Project overview and claims
-- `results/`, `outputs/`, `experiments/` - Key findings
-- `configs/` - Experimental settings
-- Existing `.bib` files or citation references
-- Any draft documents or notes
-
-**Step 2: Identify Existing Citations**
-
-Check for papers already referenced in the codebase:
-
-```bash
-# Find existing citations
-grep -r "arxiv\|doi\|cite" --include="*.md" --include="*.bib" --include="*.py"
-find . -name "*.bib"
-```
-
-These are high-signal starting points for Related Work—the scientist has already deemed them relevant.
-
-**Step 3: Clarify the Contribution**
-
-Before writing, explicitly confirm with the scientist:
-
-> "Based on my understanding of the repo, the main contribution appears to be [X].
-> The key results show [Y]. Is this the framing you want for the paper,
-> or should we emphasize different aspects?"
-
-**Never assume the narrative—always verify with the human.**
-
-**Step 4: Search for Additional Literature**
-
-Use web search to find relevant papers:
-
-```
-Search queries to try:
-- "[main technique] + [application domain]"
-- "[baseline method] comparison"
-- "[problem name] state-of-the-art"
-- Author names from existing citations
-```
-
-Then verify and retrieve BibTeX using the citation workflow below.
-
-**Step 5: Deliver a First Draft**
-
-**Be proactive—deliver a complete draft rather than asking permission for each section.**
-
-If the repo provides clear results and the contribution is apparent:
-1. Write the full first draft end-to-end
-2. Present the complete draft for feedback
-3. Iterate based on scientist's response
-
-If genuinely uncertain about framing or major claims:
-1. Draft what you can confidently
-2. Flag specific uncertainties: "I framed X as the main contribution—let me know if you'd prefer to emphasize Y instead"
-3. Continue with the draft rather than blocking
-
-**Questions to include with the draft** (not before):
-- "I emphasized X as the main contribution—adjust if needed"
-- "I highlighted results A, B, C—let me know if others are more important"
-- "Related work section includes [papers]—add any I missed"
-
----
-
-## When to Use This Skill
-
-Use this skill when:
-- **Starting from a research repo** to write a paper
-- **Drafting or revising** specific sections
-- **Finding and verifying citations** for related work
-- **Formatting** for conference submission
-- **Resubmitting** to a different venue (format conversion)
-- **Iterating** on drafts with scientist feedback
-
-**Always remember**: First drafts are starting points for discussion, not final outputs.
-
----
-
-## Balancing Proactivity and Collaboration
-
-**Default: Be proactive. Deliver drafts, then iterate.**
-
-| Confidence Level | Action |
-|-----------------|--------|
-| **High** (clear repo, obvious contribution) | Write full draft, deliver, iterate on feedback |
-| **Medium** (some ambiguity) | Write draft with flagged uncertainties, continue |
-| **Low** (major unknowns) | Ask 1-2 targeted questions, then draft |
-
-**Draft first, ask with the draft** (not before):
-
-| Section | Draft Autonomously | Flag With Draft |
-|---------|-------------------|-----------------|
-| Abstract | Yes | "Framed contribution as X—adjust if needed" |
-| Introduction | Yes | "Emphasized problem Y—correct if wrong" |
-| Methods | Yes | "Included details A, B, C—add missing pieces" |
-| Experiments | Yes | "Highlighted results 1, 2, 3—reorder if needed" |
-| Related Work | Yes | "Cited papers X, Y, Z—add any I missed" |
-
-**Only block for input when:**
-- Target venue is unclear (affects page limits, framing)
-- Multiple contradictory framings seem equally valid
-- Results seem incomplete or inconsistent
-- Explicit request to review before continuing
-
-**Don't block for:**
-- Word choice decisions
-- Section ordering
-- Which specific results to show (make a choice, flag it)
-- Citation completeness (draft with what you find, note gaps)
-
----
-
-## The Narrative Principle
-
-**The single most critical insight**: Your paper is not a collection of experiments—it's a story with one clear contribution supported by evidence.
-
-Every successful ML paper centers on what Neel Nanda calls "the narrative": a short, rigorous, evidence-based technical story with a takeaway readers care about.
-
-**Three Pillars (must be crystal clear by end of introduction):**
-
-| Pillar | Description | Example |
-|--------|-------------|---------|
-| **The What** | 1-3 specific novel claims within cohesive theme | "We prove that X achieves Y under condition Z" |
-| **The Why** | Rigorous empirical evidence supporting claims | Strong baselines, experiments distinguishing hypotheses |
-| **The So What** | Why readers should care | Connection to recognized community problems |
-
-**If you cannot state your contribution in one sentence, you don't yet have a paper.**
-
----
-
-## Paper Structure Workflow
-
-### Workflow 1: Writing a Complete Paper (Iterative)
-
-Copy this checklist and track progress. **Each step involves drafting → feedback → revision:**
-
-```
-Paper Writing Progress:
-- [ ] Step 1: Define the one-sentence contribution (with scientist)
-- [ ] Step 2: Draft Figure 1 → get feedback → revise
-- [ ] Step 3: Draft abstract → get feedback → revise
-- [ ] Step 4: Draft introduction → get feedback → revise
-- [ ] Step 5: Draft methods → get feedback → revise
-- [ ] Step 6: Draft experiments → get feedback → revise
-- [ ] Step 7: Draft related work → get feedback → revise
-- [ ] Step 8: Draft limitations → get feedback → revise
-- [ ] Step 9: Complete paper checklist (required)
-- [ ] Step 10: Final review cycle and submission
-```
-
-**Step 1: Define the One-Sentence Contribution**
-
-**This step requires explicit confirmation from the scientist.**
-
-Before writing anything, articulate and verify:
-- What is the single thing your paper contributes?
-- What was not obvious or present before your work?
-
-> "I propose framing the contribution as: '[one sentence]'. Does this capture
-> what you see as the main takeaway? Should we adjust the emphasis?"
-
-**Step 2: Draft Figure 1**
-
-Figure 1 deserves special attention—many readers skip directly to it.
-- Convey core idea, approach, or most compelling result
-- Use vector graphics (PDF/EPS for plots)
-- Write captions that stand alone without main text
-- Ensure readability in black-and-white (8% of men have color vision deficiency)
-
-**Step 3: Write Abstract (5-Sentence Formula)**
-
-From Sebastian Farquhar (DeepMind):
-
-```
-1. What you achieved: "We introduce...", "We prove...", "We demonstrate..."
-2. Why this is hard and important
-3. How you do it (with specialist keywords for discoverability)
-4. What evidence you have
-5. Your most remarkable number/result
-```
-
-**Delete** generic openings like "Large language models have achieved remarkable success..."
-
-**Step 4: Write Introduction (1-1.5 pages max)**
-
-Must include:
-- 2-4 bullet contribution list (max 1-2 lines each in two-column format)
-- Clear problem statement
-- Brief approach overview
-- Methods should start by page 2-3 maximum
-
-**Step 5: Methods Section**
-
-Enable reimplementation:
-- Conceptual outline or pseudocode
-- All hyperparameters listed
-- Architectural details sufficient for reproduction
-- Present final design decisions; ablations go in experiments
-
-**Step 6: Experiments Section**
-
-For each experiment, explicitly state:
-- What claim it supports
-- How it connects to main contribution
-- Experimental setting (details in appendix)
-- What to observe: "the blue line shows X, which demonstrates Y"
-
-Requirements:
-- Error bars with methodology (standard deviation vs standard error)
-- Hyperparameter search ranges
-- Compute infrastructure (GPU type, total hours)
-- Seed-setting methods
-
-**Step 7: Related Work**
-
-Organize methodologically, not paper-by-paper:
-
-**Good:** "One line of work uses Floogledoodle's assumption [refs] whereas we use Doobersnoddle's assumption because..."
-
-**Bad:** "Snap et al. introduced X while Crackle et al. introduced Y."
-
-Cite generously—reviewers likely authored relevant papers.
-
-**Step 8: Limitations Section (REQUIRED)**
-
-All major conferences require this. Counter-intuitively, honesty helps:
-- Reviewers are instructed not to penalize honest limitation acknowledgment
-- Pre-empt criticisms by identifying weaknesses first
-- Explain why limitations don't undermine core claims
-
-**Step 9: Paper Checklist**
-
-NeurIPS, ICML, and ICLR all require paper checklists. See [references/checklists.md](references/checklists.md).
-
----
-
-## Writing Philosophy for Top ML Conferences
-
-**This section distills the most important writing principles from leading ML researchers.** These aren't optional style suggestions—they're what separates accepted papers from rejected ones.
-
-> "A paper is a short, rigorous, evidence-based technical story with a takeaway readers care about." — Neel Nanda
-
-### The Sources Behind This Guidance
-
-This skill synthesizes writing philosophy from researchers who have published extensively at top venues:
-
-| Source | Key Contribution | Link |
-|--------|-----------------|------|
-| **Neel Nanda** (Google DeepMind) | The Narrative Principle, What/Why/So What framework | [How to Write ML Papers](https://www.alignmentforum.org/posts/eJGptPbbFPZGLpjsp/highly-opinionated-advice-on-how-to-write-ml-papers) |
-| **Sebastian Farquhar** (DeepMind) | 5-sentence abstract formula | [How to Write ML Papers](https://sebastianfarquhar.com/on-research/2024/11/04/how_to_write_ml_papers/) |
-| **Gopen & Swan** | 7 principles of reader expectations | [Science of Scientific Writing](https://cseweb.ucsd.edu/~swanson/papers/science-of-writing.pdf) |
-| **Zachary Lipton** | Word choice, eliminating hedging | [Heuristics for Scientific Writing](https://www.approximatelycorrect.com/2018/01/29/heuristics-technical-scientific-writing-machine-learning-perspective/) |
-| **Jacob Steinhardt** (UC Berkeley) | Precision, consistent terminology | [Writing Tips](https://bounded-regret.ghost.io/) |
-| **Ethan Perez** (Anthropic) | Micro-level clarity tips | [Easy Paper Writing Tips](https://ethanperez.net/easy-paper-writing-tips/) |
-| **Andrej Karpathy** | Single contribution focus | Various lectures |
-
-**For deeper dives into any of these, see:**
-- [references/writing-guide.md](references/writing-guide.md) - Full explanations with examples
-- [references/sources.md](references/sources.md) - Complete bibliography
-
-### Time Allocation (From Neel Nanda)
-
-Spend approximately **equal time** on each of:
-1. The abstract
-2. The introduction
-3. The figures
-4. Everything else combined
-
-**Why?** Most reviewers form judgments before reaching your methods. Readers encounter your paper as: **title → abstract → introduction → figures → maybe the rest.**
-
-### Writing Style Guidelines
-
-#### Sentence-Level Clarity (Gopen & Swan's 7 Principles)
-
-These principles are based on how readers actually process prose. Violating them forces readers to spend cognitive effort on structure rather than content.
-
-| Principle | Rule | Example |
-|-----------|------|---------|
-| **Subject-verb proximity** | Keep subject and verb close | ❌ "The model, which was trained on..., achieves" → ✅ "The model achieves... after training on..." |
-| **Stress position** | Place emphasis at sentence ends | ❌ "Accuracy improves by 15% when using attention" → ✅ "When using attention, accuracy improves by **15%**" |
-| **Topic position** | Put context first, new info after | ✅ "Given these constraints, we propose..." |
-| **Old before new** | Familiar info → unfamiliar info | Link backward, then introduce new |
-| **One unit, one function** | Each paragraph makes one point | Split multi-point paragraphs |
-| **Action in verb** | Use verbs, not nominalizations | ❌ "We performed an analysis" → ✅ "We analyzed" |
-| **Context before new** | Set stage before presenting | Explain before showing equation |
-
-**Full 7 principles with detailed examples:** See [references/writing-guide.md](references/writing-guide.md#the-7-principles-of-reader-expectations)
-
-#### Micro-Level Tips (Ethan Perez)
-
-These small changes accumulate into significantly clearer prose:
-
-- **Minimize pronouns**: ❌ "This shows..." → ✅ "This result shows..."
-- **Verbs early**: Position verbs near sentence start
-- **Unfold apostrophes**: ❌ "X's Y" → ✅ "The Y of X" (when awkward)
-- **Delete filler words**: "actually," "a bit," "very," "really," "basically," "quite," "essentially"
-
-**Full micro-tips with examples:** See [references/writing-guide.md](references/writing-guide.md#micro-level-writing-tips)
-
-#### Word Choice (Zachary Lipton)
-
-- **Be specific**: ❌ "performance" → ✅ "accuracy" or "latency" (say what you mean)
-- **Eliminate hedging**: Drop "may" and "can" unless genuinely uncertain
-- **Avoid incremental vocabulary**: ❌ "combine," "modify," "expand" → ✅ "develop," "propose," "introduce"
-- **Delete intensifiers**: ❌ "provides *very* tight approximation" → ✅ "provides tight approximation"
-
-#### Precision Over Brevity (Jacob Steinhardt)
-
-- **Consistent terminology**: Different terms for same concept creates confusion. Pick one and stick with it.
-- **State assumptions formally**: Before theorems, list all assumptions explicitly
-- **Intuition + rigor**: Provide intuitive explanations alongside formal proofs
-
-### What Reviewers Actually Read
-
-Understanding reviewer behavior helps prioritize your effort:
-
-| Paper Section | % Reviewers Who Read | Implication |
-|---------------|---------------------|-------------|
-| Abstract | 100% | Must be perfect |
-| Introduction | 90%+ (skimmed) | Front-load contribution |
-| Figures | Examined before methods | Figure 1 is critical |
-| Methods | Only if interested | Don't bury the lede |
-| Appendix | Rarely | Put only supplementary details |
-
-**Bottom line**: If your abstract and intro don't hook reviewers, they may never read your brilliant methods section.
-
----
-
-## Conference Requirements Quick Reference
-
-| Conference | Page Limit | Extra for Camera-Ready | Key Requirement |
-|------------|------------|------------------------|-----------------|
-| **NeurIPS 2025** | 9 pages | +0 | Mandatory checklist, lay summary for accepted |
-| **ICML 2026** | 8 pages | +1 | Broader Impact Statement required |
-| **ICLR 2026** | 9 pages | +1 | LLM disclosure required, reciprocal reviewing |
-| **ACL 2025** | 8 pages (long) | varies | Limitations section mandatory |
-| **AAAI 2026** | 7 pages | +1 | Strict style file adherence |
-| **COLM 2025** | 9 pages | +1 | Focus on language models |
-
-**Universal Requirements:**
-- Double-blind review (anonymize submissions)
-- References don't count toward page limit
-- Appendices unlimited but reviewers not required to read
-- LaTeX required for all venues
-
-**LaTeX Templates:** See [templates/](templates/) directory for all conference templates.
-
----
-
-## Using LaTeX Templates Properly
-
-### Workflow 4: Starting a New Paper from Template
-
-**Always copy the entire template directory first, then write within it.**
-
-```
-Template Setup Checklist:
-- [ ] Step 1: Copy entire template directory to new project
-- [ ] Step 2: Verify template compiles as-is (before any changes)
-- [ ] Step 3: Read the template's example content to understand structure
-- [ ] Step 4: Replace example content section by section
-- [ ] Step 5: Keep template comments/examples as reference until done
-- [ ] Step 6: Clean up template artifacts only at the end
-```
-
-**Step 1: Copy the Full Template**
-
-```bash
-# Create your paper directory with the complete template
-cp -r templates/neurips2025/ ~/papers/my-new-paper/
-cd ~/papers/my-new-paper/
-
-# Verify structure is complete
-ls -la
-# Should see: main.tex, neurips.sty, Makefile, etc.
-```
-
-**⚠️ IMPORTANT**: Copy the ENTIRE directory, not just `main.tex`. Templates include:
-- Style files (`.sty`) - required for compilation
-- Bibliography styles (`.bst`) - required for references
-- Example content - useful as reference
-- Makefiles - for easy compilation
-
-**Step 2: Verify Template Compiles First**
-
-Before making ANY changes, compile the template as-is:
-
-```bash
-# Using latexmk (recommended)
-latexmk -pdf main.tex
-
-# Or manual compilation
-pdflatex main.tex
-bibtex main
-pdflatex main.tex
-pdflatex main.tex
-```
-
-If the unmodified template doesn't compile, fix that first. Common issues:
-- Missing TeX packages → install via `tlmgr install <package>`
-- Wrong TeX distribution → use TeX Live (recommended)
-
-**Step 3: Keep Template Content as Reference**
-
-Don't immediately delete all example content. Instead:
-
-```latex
-% KEEP template examples commented out as you write
-% This shows you the expected format
-
-% Template example (keep for reference):
-% \begin{figure}[t]
-%   \centering
-%   \includegraphics[width=0.8\linewidth]{example-image}
-%   \caption{Template shows caption style}
-% \end{figure}
-
-% Your actual figure:
-\begin{figure}[t]
-  \centering
-  \includegraphics[width=0.8\linewidth]{your-figure.pdf}
-  \caption{Your caption following the same style.}
-\end{figure}
-```
-
-**Step 4: Replace Content Section by Section**
-
-Work through the paper systematically:
-
-```
-Replacement Order:
-1. Title and authors (anonymize for submission)
-2. Abstract
-3. Introduction
-4. Methods
-5. Experiments
-6. Related Work
-7. Conclusion
-8. References (your .bib file)
-9. Appendix
-```
-
-For each section:
-1. Read the template's example content
-2. Note any special formatting or macros used
-3. Replace with your content following the same patterns
-4. Compile frequently to catch errors early
-
-**Step 5: Use Template Macros**
-
-Templates often define useful macros. Check the preamble for:
-
-```latex
-% Common template macros to use:
-\newcommand{\method}{YourMethodName}  % Consistent method naming
-\newcommand{\eg}{e.g.,\xspace}        % Proper abbreviations
-\newcommand{\ie}{i.e.,\xspace}
-\newcommand{\etal}{\textit{et al.}\xspace}
-```
-
-**Step 6: Clean Up Only at the End**
-
-Only remove template artifacts when paper is nearly complete:
-
-```latex
-% BEFORE SUBMISSION - remove these:
-% - Commented-out template examples
-% - Unused packages
-% - Template's example figures/tables
-% - Lorem ipsum or placeholder text
-
-% KEEP these:
-% - All style files (.sty)
-% - Bibliography style (.bst)
-% - Required packages from template
-% - Any custom macros you're using
-```
-
-### Template Pitfalls to Avoid
-
-| Pitfall | Problem | Solution |
-|---------|---------|----------|
-| Copying only `main.tex` | Missing `.sty`, won't compile | Copy entire directory |
-| Modifying `.sty` files | Breaks conference formatting | Never edit style files |
-| Adding random packages | Conflicts, breaks template | Only add if necessary |
-| Deleting template content too early | Lose formatting reference | Keep as comments until done |
-| Not compiling frequently | Errors accumulate | Compile after each section |
-
-### Quick Template Reference
-
-| Conference | Main File | Key Style File | Notes |
-|------------|-----------|----------------|-------|
-| NeurIPS 2025 | `main.tex` | `neurips.sty` | Has Makefile |
-| ICML 2026 | `example_paper.tex` | `icml2026.sty` | Includes algorithm packages |
-| ICLR 2026 | `iclr2026_conference.tex` | `iclr2026_conference.sty` | Has math_commands.tex |
-| ACL | `acl_latex.tex` | `acl.sty` | Strict formatting |
-| AAAI 2026 | `aaai2026-unified-template.tex` | `aaai2026.sty` | Very strict compliance |
-| COLM 2025 | `colm2025_conference.tex` | `colm2025_conference.sty` | Similar to ICLR |
-
----
-
-## Conference Resubmission & Format Conversion
-
-When a paper is rejected or withdrawn from one venue and resubmitted to another, format conversion is required. This is a common workflow in ML research.
-
-### Workflow 3: Converting Between Conference Formats
-
-```
-Format Conversion Checklist:
-- [ ] Step 1: Identify source and target template differences
-- [ ] Step 2: Create new project with target template
-- [ ] Step 3: Copy content sections (not preamble)
-- [ ] Step 4: Adjust page limits and content
-- [ ] Step 5: Update conference-specific requirements
-- [ ] Step 6: Verify compilation and formatting
-```
-
-**Step 1: Key Template Differences**
-
-| From → To | Page Change | Key Adjustments |
-|-----------|-------------|-----------------|
-| NeurIPS → ICML | 9 → 8 pages | Cut 1 page, add Broader Impact if missing |
-| ICML → ICLR | 8 → 9 pages | Can expand experiments, add LLM disclosure |
-| NeurIPS → ACL | 9 → 8 pages | Restructure for NLP conventions, add Limitations |
-| ICLR → AAAI | 9 → 7 pages | Significant cuts needed, strict style adherence |
-| Any → COLM | varies → 9 | Reframe for language model focus |
-
-**Step 2: Content Migration (NOT Template Merge)**
-
-**Never copy LaTeX preambles between templates.** Instead:
-
-```bash
-# 1. Start fresh with target template
-cp -r templates/icml2026/ new_submission/
-
-# 2. Copy ONLY content sections from old paper
-# - Abstract text
-# - Section content (between \section{} commands)
-# - Figures and tables
-# - Bibliography entries
-
-# 3. Paste into target template structure
-```
-
-**Step 3: Adjusting for Page Limits**
-
-When cutting pages (e.g., NeurIPS 9 → AAAI 7):
-- Move detailed proofs to appendix
-- Condense related work (cite surveys instead of individual papers)
-- Combine similar experiments into unified tables
-- Use smaller figure sizes with subfigures
-- Tighten writing: eliminate redundancy, use active voice
-
-When expanding (e.g., ICML 8 → ICLR 9):
-- Add ablation studies reviewers requested
-- Expand limitations discussion
-- Include additional baselines
-- Add qualitative examples
-
-**Step 4: Conference-Specific Adjustments**
-
-| Target Venue | Required Additions |
-|--------------|-------------------|
-| **ICML** | Broader Impact Statement (after conclusion) |
-| **ICLR** | LLM usage disclosure, reciprocal reviewing agreement |
-| **ACL/EMNLP** | Limitations section (mandatory), Ethics Statement |
-| **AAAI** | Strict adherence to style file (no modifications) |
-| **NeurIPS** | Paper checklist (appendix), lay summary if accepted |
-
-**Step 5: Update References**
-
-```latex
-% Remove self-citations that reveal identity (for blind review)
-% Update any "under review" citations to published versions
-% Add new relevant work published since last submission
-```
-
-**Step 6: Addressing Previous Reviews**
-
-When resubmitting after rejection:
-- **Do** address reviewer concerns in the new version
-- **Do** add experiments/clarifications reviewers requested
-- **Don't** include a "changes from previous submission" section (blind review)
-- **Don't** reference the previous submission or reviews
-
-**Common Conversion Pitfalls:**
-- ❌ Copying `\usepackage` commands (causes conflicts)
-- ❌ Keeping old conference header/footer commands
-- ❌ Forgetting to update `\bibliography{}` path
-- ❌ Missing conference-specific required sections
-- ❌ Exceeding page limit after format change
-
----
-
-## Citation Workflow (Hallucination Prevention)
-
-**⚠️ CRITICAL**: AI-generated citations have ~40% error rate. **Never write BibTeX from memory.**
-
-### The Golden Rule
-
-```
-IF you cannot programmatically fetch a citation:
-    → Mark it as [CITATION NEEDED] or [PLACEHOLDER - VERIFY]
-    → Tell the scientist explicitly
-    → NEVER invent a plausible-sounding reference
-```
-
-### Workflow 2: Adding Citations
-
-```
-Citation Verification (MANDATORY for every citation):
-- [ ] Step 1: Search using Exa MCP or Semantic Scholar API
-- [ ] Step 2: Verify paper exists in 2+ sources (Semantic Scholar + arXiv/CrossRef)
-- [ ] Step 3: Retrieve BibTeX via DOI (programmatically, not from memory)
-- [ ] Step 4: Verify the claim you're citing actually appears in the paper
-- [ ] Step 5: Add verified BibTeX to bibliography
-- [ ] Step 6: If ANY step fails → mark as placeholder, inform scientist
-```
-
-**Step 0: Use Exa MCP for Initial Search (Recommended)**
-
-If Exa MCP is installed, use it to find relevant papers:
-```
-Search: "RLHF language model alignment 2023"
-Search: "sparse autoencoders interpretability"
-Search: "attention mechanism transformers Vaswani"
-```
-
-Then verify each result with Semantic Scholar and fetch BibTeX via DOI.
-
-**Step 1: Search Semantic Scholar**
-
-```python
-from semanticscholar import SemanticScholar
-
-sch = SemanticScholar()
-results = sch.search_paper("attention mechanism transformers", limit=5)
-for paper in results:
-    print(f"{paper.title} - {paper.paperId}")
-    print(f"  DOI: {paper.externalIds.get('DOI', 'N/A')}")
-```
-
-**Step 2: Verify Existence**
-
-Confirm paper appears in at least two sources (Semantic Scholar + CrossRef/arXiv).
-
-**Step 3: Retrieve BibTeX via DOI**
-
-```python
-import requests
-
-def doi_to_bibtex(doi: str) -> str:
-    """Get verified BibTeX from DOI via CrossRef."""
-    response = requests.get(
-        f"https://doi.org/{doi}",
-        headers={"Accept": "application/x-bibtex"}
-    )
-    response.raise_for_status()
-    return response.text
-
-# Example
-bibtex = doi_to_bibtex("10.48550/arXiv.1706.03762")
-print(bibtex)
-```
-
-**Step 4: Verify Claims**
-
-Before citing for a specific claim, access the paper and confirm the attributed claim actually appears.
-
-**Step 5: Handle Failures Explicitly**
-
-If you cannot verify a citation at ANY step:
-
-```latex
-% Option 1: Explicit placeholder
-\cite{PLACEHOLDER_smith2023_verify}  % TODO: Could not verify - scientist must confirm
-
-% Option 2: Note in text
-... as shown in prior work [CITATION NEEDED - could not verify Smith et al. 2023].
-```
-
-**Always inform the scientist:**
-> "I could not verify the following citations and have marked them as placeholders:
-> - Smith et al. 2023 on reward hacking - could not find in Semantic Scholar
-> - Jones 2022 on scaling laws - found similar paper but different authors
-> Please verify these before submission."
-
-### Summary: Citation Rules
-
-| Situation | Action |
-|-----------|--------|
-| Found paper, got DOI, fetched BibTeX | ✅ Use the citation |
-| Found paper, no DOI | ✅ Use arXiv BibTeX or manual entry from paper |
-| Paper exists but can't fetch BibTeX | ⚠️ Mark placeholder, inform scientist |
-| Uncertain if paper exists | ❌ Mark `[CITATION NEEDED]`, inform scientist |
-| "I think there's a paper about X" | ❌ **NEVER cite** - search first or mark placeholder |
-
-**🚨 NEVER generate BibTeX from memory—always fetch programmatically. 🚨**
-
-See [references/citation-workflow.md](references/citation-workflow.md) for complete API documentation.
-
----
-
-## Common Issues and Solutions
-
-**Issue: Abstract too generic**
-
-Delete first sentence if it could be prepended to any ML paper. Start with your specific contribution.
-
-**Issue: Introduction exceeds 1.5 pages**
-
-Split background into Related Work. Front-load contribution bullets. Methods should start by page 2-3.
-
-**Issue: Experiments lack explicit claims**
-
-Add sentence before each experiment: "This experiment tests whether [specific claim]..."
-
-**Issue: Reviewers find paper hard to follow**
-
-- Add explicit signposting: "In this section, we show X"
-- Use consistent terminology throughout
-- Include figure captions that stand alone
-
-**Issue: Missing statistical significance**
-
-Always include:
-- Error bars (specify: std dev or std error)
-- Number of runs
-- Statistical tests if comparing methods
-
----
-
-## Reviewer Evaluation Criteria
-
-Reviewers assess papers on four dimensions:
-
-| Criterion | What Reviewers Look For |
-|-----------|------------------------|
-| **Quality** | Technical soundness, well-supported claims |
-| **Clarity** | Clear writing, reproducible by experts |
-| **Significance** | Community impact, advances understanding |
-| **Originality** | New insights (doesn't require new method) |
-
-**Scoring (NeurIPS 6-point scale):**
-- 6: Strong Accept - Groundbreaking, flawless
-- 5: Accept - Technically solid, high impact
-- 4: Borderline Accept - Solid, limited evaluation
-- 3: Borderline Reject - Solid but weaknesses outweigh
-- 2: Reject - Technical flaws
-- 1: Strong Reject - Known results or ethics issues
-
-See [references/reviewer-guidelines.md](references/reviewer-guidelines.md) for detailed reviewer instructions.
-
----
-
-## Tables and Figures
-
-### Tables
-
-Use `booktabs` LaTeX package for professional tables:
-
-```latex
-\usepackage{booktabs}
-\begin{tabular}{lcc}
-\toprule
-Method & Accuracy ↑ & Latency ↓ \\
-\midrule
-Baseline & 85.2 & 45ms \\
-\textbf{Ours} & \textbf{92.1} & 38ms \\
-\bottomrule
-\end{tabular}
-```
-
-**Rules:**
-- Bold best value per metric
-- Include direction symbols (↑ higher is better, ↓ lower is better)
-- Right-align numerical columns
-- Consistent decimal precision
-
-### Figures
-
-- **Vector graphics** (PDF, EPS) for all plots and diagrams
-- **Raster** (PNG 600 DPI) only for photographs
-- Use **colorblind-safe palettes** (Okabe-Ito or Paul Tol)
-- Verify **grayscale readability** (8% of men have color vision deficiency)
-- **No title inside figure**—the caption serves this function
-- **Self-contained captions**—reader should understand without main text
-
----
-
-## References & Resources
-
-### Reference Documents (Deep Dives)
-
-| Document | Contents |
-|----------|----------|
-| [writing-guide.md](references/writing-guide.md) | Gopen & Swan 7 principles, Ethan Perez micro-tips, word choice |
-| [citation-workflow.md](references/citation-workflow.md) | Citation APIs, Python code, BibTeX management |
-| [checklists.md](references/checklists.md) | NeurIPS 16-item, ICML, ICLR, ACL requirements |
-| [reviewer-guidelines.md](references/reviewer-guidelines.md) | Evaluation criteria, scoring, rebuttals |
-| [sources.md](references/sources.md) | Complete bibliography of all sources |
-
-### LaTeX Templates
-
-Templates in `templates/` directory: **ICML 2026**, **ICLR 2026**, **NeurIPS 2025**, **ACL/EMNLP**, **AAAI 2026**, **COLM 2025**.
-
-**Compiling to PDF:**
-- **VS Code/Cursor**: Install LaTeX Workshop extension + TeX Live → Save to auto-compile
-- **Command line**: `latexmk -pdf main.tex` or `pdflatex` + `bibtex` workflow
-- **Online**: Upload to [Overleaf](https://overleaf.com)
-
-See [templates/README.md](templates/README.md) for detailed setup instructions.
-
-### Key External Sources
-
-**Writing Philosophy:**
-- [Neel Nanda: How to Write ML Papers](https://www.alignmentforum.org/posts/eJGptPbbFPZGLpjsp/highly-opinionated-advice-on-how-to-write-ml-papers) - Narrative, "What/Why/So What"
-- [Farquhar: How to Write ML Papers](https://sebastianfarquhar.com/on-research/2024/11/04/how_to_write_ml_papers/) - 5-sentence abstract
-- [Gopen & Swan: Science of Scientific Writing](https://cseweb.ucsd.edu/~swanson/papers/science-of-writing.pdf) - 7 reader expectation principles
-- [Lipton: Heuristics for Scientific Writing](https://www.approximatelycorrect.com/2018/01/29/heuristics-technical-scientific-writing-machine-learning-perspective/) - Word choice
-- [Perez: Easy Paper Writing Tips](https://ethanperez.net/easy-paper-writing-tips/) - Micro-level clarity
-
-**APIs:** [Semantic Scholar](https://api.semanticscholar.org/api-docs/) | [CrossRef](https://www.crossref.org/documentation/retrieve-metadata/rest-api/) | [arXiv](https://info.arxiv.org/help/api/basics.html)
-
-**Venues:** [NeurIPS](https://neurips.cc/Conferences/2025/PaperInformation/StyleFiles) | [ICML](https://icml.cc/Conferences/2025/AuthorInstructions) | [ICLR](https://iclr.cc/Conferences/2026/AuthorGuide) | [ACL](https://github.com/acl-org/acl-style-files)
-

skills/research/research-paper-writing/references/autoreason-methodology.md

@@ -0,0 +1,394 @@
+# Autoreason: Iterative Refinement Methodology
+
+Complete reference for the autoreason iterative refinement method, derived from experimental results across subjective writing tasks, competitive programming, and four model tiers. Use this when any output (paper draft, experiment script, analysis, task definition) needs iterative improvement.
+
+**Source**: [NousResearch/autoreason](https://github.com/NousResearch/autoreason) — "Autoreason: When Iterative LLM Refinement Works and Why It Fails"
+
+---
+
+## Strategy Selection Guide
+
+### Decision Tree
+
+```
+Is the task objectively verifiable (code, math, factual)?
+├── YES → Does the model solve it on the first attempt?
+│   ├── YES → Use single pass (no refinement needed)
+│   └── NO → Use autoreason (structured analysis → reason-informed revision)
+│
+└── NO (subjective) → What model tier are you using?
+    ├── Weak (Llama 8B, small models)
+    │   → Single pass. Model too weak for refinement to help.
+    │     Invest in generation quality, not iteration.
+    │
+    ├── Mid-tier (Haiku 3.5, Gemini Flash)
+    │   → Autoreason with stronger judges. This is the sweet spot.
+    │     Self-refinement DESTROYS weak model outputs — autoreason prevents this.
+    │
+    ├── Strong (Sonnet 4)
+    │   → Autoreason for open-ended tasks. Wins 3/5.
+    │     Critique-and-revise for concrete technical tasks (2/5).
+    │
+    └── Frontier (Sonnet 4.6, Opus)
+        ├── Constrained scope? → Autoreason. Wins 2/3 constrained tasks.
+        └── Unconstrained? → Critique-and-revise or single pass.
+            Autoreason FAILS on unconstrained frontier tasks (comes last).
+```
+
+### Strategy Comparison Table
+
+| Strategy | Best For | Avoid When | Compute (per iteration) |
+|----------|----------|------------|------------------------|
+| **Single pass** | Frontier models, template tasks, tight budgets | Mid-tier models where quality ceiling is low | 1 call |
+| **Critique-and-revise** | Concrete technical requirements (system design, specifications) | Weak models (degrades output), unconstrained subjective tasks | 2 calls |
+| **Autoreason** | Mid-tier models, constrained scope, tasks with genuine tradeoffs | Weak models (Llama 8B), frontier + unconstrained | ~6 calls |
+| **Best-of-N** | Almost never recommended | Weak models especially — worse than single pass | N calls |
+
+### Why Each Strategy Fails
+
+| Strategy | Failure Mode | Mechanism |
+|----------|-------------|-----------|
+| **Single pass** | Quality ceiling | No mechanism to improve beyond first attempt |
+| **Critique-and-revise** | Progressive degradation | Model hallucinates problems (sycophancy), scope creeps each pass, never declines to change |
+| **Best-of-N** | Random selection | Without good ranking signal, more samples = more mediocre options |
+| **Autoreason (unconstrained)** | Synthesis drift | Stronger models produce syntheses so consistently preferred that incumbent never stabilizes |
+
+---
+
+## The Autoreason Loop
+
+### Architecture
+
+```
+┌──────────────────────────────────────────────────────────┐
+│                    ITERATION LOOP                         │
+│                                                           │
+│   Incumbent A ──► Critic ──► Author B ──► Synthesizer     │
+│       │                                      │            │
+│       │              ┌───────────────────────┘            │
+│       ▼              ▼                                    │
+│      [A]           [AB]          [B]                      │
+│       │              │            │                       │
+│       └──────────────┼────────────┘                       │
+│                      ▼                                    │
+│              Judge Panel (blind)                          │
+│                      │                                    │
+│                      ▼                                    │
+│                   Winner                                  │
+│                      │                                    │
+│              ┌───────┴───────┐                            │
+│              ▼               ▼                            │
+│         A wins k=2      B or AB wins                      │
+│         consecutive?    → new incumbent                   │
+│              │                                            │
+│              ▼                                            │
+│           CONVERGED                                       │
+└──────────────────────────────────────────────────────────┘
+```
+
+### Roles
+
+Every role is a **fresh, isolated agent** with no shared context:
+
+| Role | Input | Output | Key Rule |
+|------|-------|--------|----------|
+| **Critic** | Task + Incumbent A | List of problems | Find problems ONLY. No fixes. No suggestions. |
+| **Author B** | Task + A + Critique | Revised version B | Address each criticism. State which problem each change fixes. |
+| **Synthesizer** | Task + X + Y (randomized labels) | Synthesis AB | Take strongest elements of each. Not a compromise. |
+| **Judge Panel** | Task + A, AB, B (randomized labels + order) | Ranking | Rank best to worst. No authorship stake. |
+
+### Configuration
+
+| Parameter | Value | Rationale |
+|-----------|-------|-----------|
+| **Convergence k** | 2 | k=1 premature (94% displaced later). k=2 converges 100%, quality plateaus. k=3 fails 24%, 2x cost, no quality gain. |
+| **Author temperature** | 0.7-0.8 | Encourages diverse revisions |
+| **Judge temperature** | 0.3 | Encourages consistent evaluation |
+| **In-loop judges** | 3 | Balance per-pass cost vs evaluation stability |
+| **Final evaluation judges** | 7 | Higher statistical power for final comparison |
+| **Max tokens** | 4096 | Standard; 8192 for long-form (papers) |
+| **Judge type** | Chain-of-thought | 3x faster convergence on some tasks. Always use. |
+| **Tiebreak** | Conservative (incumbent wins) | Prevents false positives — A must be genuinely beaten |
+| **Max passes** | 25 (constrained), 50 (remedy) | Safety cap; most converge by pass 10-15 |
+
+### Prompts
+
+#### Critic
+```
+System: You are a critical reviewer. Your only job is to find real problems. 
+Be specific and concrete. Do not suggest fixes.
+
+User: Find real problems with this proposal. Focus on:
+- Things that won't work as described
+- Complexity that doesn't pay for itself
+- Assumptions that are wrong
+- Missing pieces
+Do NOT propose fixes. Just the problems.
+```
+
+#### Author B
+```
+System: You are a senior consultant revising a proposal based on specific 
+criticisms. Address each valid criticism directly. Do not make changes not 
+motivated by an identified problem.
+
+User: [TASK] + [VERSION A] + [CRITIC OUTPUT]
+Revise to address these problems. For each change, state which problem it fixes.
+```
+
+#### Synthesizer
+```
+System: You are given two versions as equal inputs. Take the strongest elements 
+from each and produce a coherent synthesis. This is not a compromise.
+
+User: [TASK] + [VERSION X] + [VERSION Y]
+(labels randomized — synthesizer doesn't know which is incumbent)
+```
+
+#### Judge (Chain-of-Thought) — ALWAYS USE THIS VERSION
+```
+System: You are an independent evaluator. Think carefully before deciding.
+
+User: [TASK] + Three proposals. For each, think step by step:
+1. What does it get right?
+2. What does it get wrong or miss?
+3. Are numbers and claims defensible?
+4. Is detail appropriate or bloated?
+After reasoning, rank all three.
+RANKING: [best], [second], [worst]
+```
+
+#### Baseline Prompts (for comparison experiments)
+
+| Baseline | Prompt |
+|----------|--------|
+| **Conservative** | "Make minimal improvements while preserving what works. Do not add new sections or significantly expand scope." |
+| **Improve this** | "Improve this document." (no further guidance) |
+| **Harsh critic** | "Critically evaluate and rewrite, fixing all weaknesses you identify." |
+| **Critique & revise** | Step 1: "Produce a structured critique. List specific weaknesses." Step 2: "Revise to address each criticism." |
+
+---
+
+## Scoring: Borda Count
+
+Judges rank candidates. Points awarded by rank position:
+
+| Rank | Points (3 candidates) |
+|------|----------------------|
+| 1st | 3 |
+| 2nd | 2 |
+| 3rd | 1 |
+
+**Aggregation**: Sum across all judges. Winner = highest total.
+**Tiebreak**: Incumbent (A) wins any tie.
+
+**Example** (3 judges):
+- Judge 1: AB > A > B → AB gets 3, A gets 2, B gets 1
+- Judge 2: A > AB > B → A gets 3, AB gets 2, B gets 1
+- Judge 3: AB > B > A → AB gets 3, B gets 2, A gets 1
+- Totals: AB=8, A=6, B=4 → AB wins, becomes new incumbent
+
+**Randomization per judge**:
+- Candidate labels randomized (A might be called "Proposal X" for one judge, "Proposal Z" for another)
+- Presentation order randomized (AB might appear first or last)
+- This prevents position bias and label bias
+
+---
+
+## Model Selection Guide
+
+### Empirical Results by Model Tier
+
+| Model | Autoreason Wins | Autoreason Avg Borda | Best Baseline | Margin | Recommendation |
+|-------|----------------|---------------------|---------------|--------|----------------|
+| **Llama 3.1 8B** | 1/3 | 23.7 | 25.0 (single) | -1.3 | Skip autoreason. Model too weak for diverse candidates. |
+| **Gemini 2.0 Flash** | 2/3 | 25.0 | 20.0 (single) | +5.0 | Good candidate. Moderate gains. |
+| **Haiku 3.5** | 3/3 | **42.0** | 33.7 (single) | **+8.3** | **Best candidate.** Perfect scores. Baselines actively destroy quality. |
+| **Sonnet 4** | 3/5 | 27.8 | 22.4 (C&R) | +5.4 | Good candidate for open tasks. C&R better for technical tasks. |
+| **Sonnet 4.6 (unconstrained)** | 0/1 | 7.0 | 31.0 (C&R) | -24.0 | Do NOT use autoreason without constraints. |
+| **Sonnet 4.6 (constrained)** | 2/3 | 29.0 | 27.0 (improve) | +2.0 | Use only with scope constraints. |
+
+### The Generation-Evaluation Gap
+
+The core insight: **autoreason's value depends on the gap between a model's generation capability and its self-evaluation capability.**
+
+```
+Weak models (Llama 8B):
+  Generation: Poor  |  Self-evaluation: Poor
+  Gap: Small (both bad) → Autoreason can't help, no diverse candidates
+
+Mid-tier models (Haiku, Flash):
+  Generation: Decent  |  Self-evaluation: Poor
+  Gap: LARGE → Autoreason's sweet spot. External eval bridges the gap.
+
+Strong models (Sonnet 4):
+  Generation: Good  |  Self-evaluation: Decent
+  Gap: Moderate → Autoreason helps on 3/5 tasks
+
+Frontier models (Sonnet 4.6):
+  Generation: Excellent  |  Self-evaluation: Good
+  Gap: Small → Simple methods suffice. Autoreason hurts on unconstrained tasks.
+```
+
+**Practical rule**: As model costs drop and capabilities improve, today's frontier becomes tomorrow's mid-tier. The generation-evaluation gap is structural, not temporary. Match refinement architecture to the model's position on the capability curve.
+
+### Judge Selection
+
+| Author Model | Recommended Judge | Rationale |
+|-------------|------------------|-----------|
+| Llama 8B | Don't use autoreason | Model too weak |
+| Gemini Flash | Sonnet 4 | Cross-model evaluation works |
+| Haiku 3.5 | Sonnet 4 | Strong external eval is the mechanism |
+| Haiku 3.5 | Haiku 3.5 (same) | Still works — tournament structure provides value even without strong judges (20.7 vs 18.3 avg Borda) |
+| Sonnet 4 | Sonnet 4 (same) | Same-model judges work at this tier |
+| Sonnet 4.6 | Sonnet 4.6 (same) | Only with scope constraints |
+
+---
+
+## Scope Constraint Design
+
+### What Makes Autoreason Work on Constrained Tasks
+
+The same model (Sonnet 4.6) goes from **last place** (unconstrained) to **first place** (constrained) with scope constraints. The constraints bound the improvement space so synthesis drift can't accumulate.
+
+### Effective Constraints
+
+| Constraint Type | Example | Why It Works |
+|----------------|---------|-------------|
+| **Fixed facts** | "Use only these 8 data points, add nothing else" | Bounds information space |
+| **Fixed deliverable** | "500-word startup pitch" (not "improve this") | Defines done condition |
+| **Fixed structure** | "Exactly 4 sections, each with 3 numbered items" | Prevents structural drift |
+| **Fixed change items** | "Address exactly these 3 reviewer concerns" | Bounds modification scope |
+
+### Ineffective Constraints
+
+| Constraint | Why It Fails | What Happens |
+|-----------|-------------|-------------|
+| Word count alone | Not a scope constraint | False convergence — rejected for length, not quality |
+| "Be concise" | Too vague | Ignored after 2-3 passes |
+| "Be comprehensive" | Anti-constraint | Invites scope creep |
+| No constraints at all | Unbounded improvement space | Synthesis dominates, no convergence |
+
+### Task Categories
+
+| Task Type | Autoreason Works? | Why |
+|-----------|-------------------|-----|
+| Tasks with genuine tradeoffs (strategy, policy) | Yes | Multiple valid approaches for tournament to select between |
+| Constrained writing (pitch, memo, postmortem) | Mostly (2/3) | Bounded scope, clear evaluation criteria |
+| Template-filling (incident postmortem) | No | One correct structure, minimal decision space |
+| Competitive programming | Yes | Naturally scoped, test suite provides external verification |
+| Open-ended unconstrained + frontier model | No | Synthesis drift, no convergence |
+
+---
+
+## Failure Taxonomy
+
+| Failure Mode | Condition | Detection | Evidence |
+|-------------|-----------|-----------|----------|
+| **Self-correction unreliable** | No external evaluation signal | Baselines degrade below single pass | Haiku baselines: 16.3 avg vs 33.7 single pass |
+| **Drift / synthesis dominance** | Unconstrained scope | A wins <15%, AB dominates | Sonnet 4.6 unconstrained: A wins 12%, AB wins 60%+ |
+| **Overfitting to visible feedback** | Shallow revision loop (C&R) | High public/private divergence | C&R overfits 32% on hard code problems |
+| **No convergence** | Broken judge pipeline | Parsing failures, <3 valid judges | Mixed panel parser failure: 11+ passes |
+| **Model too weak** | Insufficient generation diversity | All candidates look similar | Llama 8B wins only 1/3 tasks |
+
+### Recovery Patterns
+
+| Failure | Recovery |
+|---------|----------|
+| No convergence (drift) | Add scope constraints to the task |
+| No convergence (broken judges) | Fix parser, ensure 3 valid judges before continuing |
+| Quality degrades with iteration | Switch to single pass or add constraints |
+| Model too weak | Use a stronger model for generation, keep weak model for cheap roles |
+| Overfitting (code) | Use structured analysis step, not just test feedback |
+
+---
+
+## Code Domain Adaptation
+
+The autoreason method adapts differently for code vs writing:
+
+### Writing Domain
+```
+Call 1: Critic (find problems in incumbent)
+Call 2: Author B (revise based on critique)
+Call 3: Synthesizer (merge A and B)
+Calls 4-6: Judge Panel (3 blind judges rank A, B, AB)
+```
+
+### Code Domain (6-call budget)
+```
+Call 1: Initial generation
+Call 2: Structured analysis (5 points — NO CODE):
+  - Problem analysis: what does the problem actually require?
+  - Approach analysis: what approach did we use, is it correct?
+  - Failure analysis: why did tests fail?
+  - Alternative approaches: what else could work?
+  - Edge cases: what inputs might break the solution?
+Calls 3-6: Reason-informed revisions
+  - Each revision must explain WHY it fixes the issue
+  - Sees test results from public (visible) test cases
+```
+
+**Key difference**: The code strategy replaces the judge panel with test-suite evaluation (objective ground truth). The structured analysis step (Call 2) is what drives recovery — it forces reasoning about *why* the approach failed before attempting fixes.
+
+**Results**: Recovery is the mechanism. Among problems where both autoreason and single-pass failed initially, autoreason recovered 62% vs single-pass's 43% (McNemar p=0.041, Cohen's h=0.32).
+
+---
+
+## Applying Autoreason to Paper Writing
+
+The paper itself was refined using autoreason (Section 8 of the paper):
+
+### Setup
+- Model: claude-opus-4
+- Judges: 3 Opus judges
+- Enhancement: Ground-truth critic (access to actual experimental data)
+- Result: Converged in 9 passes
+
+### Key Findings for Paper Refinement
+
+1. **Ground-truth critic is essential**: Without ground-truth access, Opus hallucinated a fabricated ablation study, fake confidence intervals, wrong model names, and incorrect role descriptions. With ground-truth access, the critic caught all four on pass 1.
+
+2. **Judge panel integrity matters**: A broken parser in one judge (Gemini output format mismatch) reduced the panel from 3 to 2 judges. This prevented convergence for 11+ passes. Fixing to 3 working judges, the same incumbent converged in 2 passes. A broken judge doesn't add noise — it prevents equilibrium.
+
+### Recommended Setup for Paper Refinement
+
+```
+Critic prompt: "You are reviewing a research paper draft. You have access to the 
+actual experimental results [GROUND TRUTH DATA]. Find factual errors, unsupported 
+claims, hallucinated results, and structural problems. Do not suggest fixes."
+
+Author B prompt: "Revise this paper draft to fix the identified problems. For each 
+change, cite the specific problem it addresses. Do not add claims not supported by 
+the provided experimental data."
+
+Judge prompt (CoT): "Compare three versions of this paper. For each, evaluate:
+1. Factual accuracy against the provided results
+2. Clarity of the narrative and contribution
+3. Whether claims are properly hedged and supported
+4. Writing quality (concision, precision, no filler)
+After reasoning, rank all three. RANKING: [best], [second], [worst]"
+```
+
+### What to Provide as Ground Truth
+- All experimental result JSON files
+- Statistical test outputs
+- Raw numbers for every table and figure
+- Configuration files showing exact hyperparameters
+- Code that generated the results (for method description accuracy)
+
+---
+
+## Compute Budget Reference
+
+| Method | Calls per Pass | Typical Passes | Total Calls | Relative Cost |
+|--------|---------------|----------------|-------------|---------------|
+| Single pass | 1 | 1 | 1 | 1x |
+| Best-of-N | N | 1 | N | Nx |
+| Critique & revise | 2 | 15 | 30 | 30x |
+| Autoreason (in-loop) | ~6 | 10-15 | 60-90 | 60-90x |
+| Autoreason (with final eval) | ~6 + 7 | 10-15 + 1 | 67-97 | ~80x |
+
+**Cost-quality tradeoff**: Autoreason uses ~6x more compute per pass and typically runs more passes. This is a real tradeoff. The method trades compute for evaluation quality. On constrained tasks with mid-tier models, this tradeoff is strongly positive. On unconstrained tasks with frontier models, it's negative.
+
+**CoT judges reduce cost**: 1 CoT judge provides evaluation quality comparable to 3 standard judges, at ~40% cost savings. Always use CoT judges.

skills/research/research-paper-writing/references/checklists.md

@@ -10,6 +10,8 @@ This reference documents the mandatory checklist requirements for major ML/AI co
 - [ICML Paper Checklist](#icml-paper-checklist)
 - [ICLR Requirements](#iclr-requirements)
 - [ACL Requirements](#acl-requirements)
+- [AAAI Requirements](#aaai-requirements)
+- [COLM Requirements](#colm-requirements)
 - [Universal Pre-Submission Checklist](#universal-pre-submission-checklist)
 
 ---
@@ -280,6 +282,77 @@ If applicable:
 
 ---
 
+## AAAI Requirements
+
+### Formatting (Strictest of All Venues)
+
+AAAI enforces formatting rules more strictly than any other major venue. Papers that deviate from the template are desk-rejected.
+
+- [ ] Use the **exact** AAAI style file without modification — no `\setlength`, no `\vspace` hacks, no font overrides
+- [ ] 7 pages main content (8 for camera-ready with author info)
+- [ ] Two-column format, Times font (set by template)
+- [ ] References and appendices do not count toward page limit
+- [ ] Abstract must be a single paragraph
+- [ ] Do not modify margins, column widths, or font sizes
+
+### Required Sections
+
+- [ ] Abstract (single paragraph, no math or citations)
+- [ ] Introduction with clear contribution statement
+- [ ] References in AAAI format (uses `aaai2026.bst`)
+- [ ] Appendix (optional, unlimited)
+
+### Ethics and Reproducibility
+
+- [ ] Broader impact statement (encouraged but not always mandatory — check current year's CFP)
+- [ ] Reproducibility details (datasets, code availability)
+- [ ] Acknowledge use of AI writing tools if applicable
+
+### Key Differences from Other Venues
+
+- **No separate limitations section required** (unlike ACL), but discussing limitations is recommended
+- **Strictest formatting enforcement** — the style checker will reject non-compliant PDFs
+- **No paper checklist** like NeurIPS has, but the universal checklist below still applies
+- **Unified template** covers main paper and supplementary in the same file
+
+---
+
+## COLM Requirements
+
+### Overview
+
+COLM (Conference on Language Modeling) focuses specifically on language model research. Framing must target this community.
+
+### Formatting
+
+- [ ] 9 pages main content (10 for camera-ready)
+- [ ] Use COLM template (based on ICLR template with modifications)
+- [ ] Double-blind review
+- [ ] References and appendices unlimited
+
+### Required Sections
+
+- [ ] Abstract
+- [ ] Introduction framed for language modeling community
+- [ ] Conclusion
+- [ ] References
+
+### Content Expectations
+
+- [ ] Contribution must be relevant to language models (broadly interpreted: training, evaluation, applications, theory, alignment, safety)
+- [ ] If the method is general, frame with language model examples
+- [ ] Baselines should include recent LM-specific methods where applicable
+
+### Key Differences from Other Venues
+
+- **Narrower scope** than NeurIPS/ICML — must frame for LM community
+- **Template derived from ICLR** — similar formatting rules
+- **Newer venue** — reviewer norms are still establishing; err on the side of thorough evaluation
+- **No mandatory checklist** like NeurIPS, but broader impact discussion is expected
+- **LLM disclosure**: If LLMs were used in research (code generation, data annotation, writing assistance), disclose this
+
+---
+
 ## Universal Pre-Submission Checklist
 
 ### Before Every Submission

skills/research/research-paper-writing/references/citation-workflow.md

@@ -289,7 +289,7 @@ class CitationManager:
                 )
                 if resp.status_code == 200:
                     sources.append("CrossRef")
-            except:
+            except Exception:
                 pass
 
         # Check arXiv if ID available
@@ -301,7 +301,7 @@ class CitationManager:
                 )
                 if "<entry>" in resp.text and "<title>" in resp.text:
                     sources.append("arXiv")
-            except:
+            except Exception:
                 pass
 
         return len(sources) >= 2, sources
@@ -318,7 +318,7 @@ class CitationManager:
                 )
                 if resp.status_code == 200:
                     return resp.text
-            except:
+            except Exception:
                 pass
 
         # Fallback: generate from paper data
@@ -419,7 +419,7 @@ def batch_cite(queries: List[str], output_file: str = "references.bib"):
 | Customization | Limited | Highly flexible |
 | Backend | bibtex | Biber (recommended) |
 
-**Recommendation**: Use BibLaTeX with Biber for new papers.
+**Recommendation**: Use natbib with BibTeX for conference submissions — all major venue templates (NeurIPS, ICML, ICLR, ACL, AAAI, COLM) ship with natbib and `.bst` files. BibLaTeX with Biber is an option for journals or personal projects where you control the template.
 
 ### LaTeX Setup
 

skills/research/research-paper-writing/references/experiment-patterns.md

@@ -0,0 +1,728 @@
+# Experiment Design Patterns
+
+Patterns and best practices distilled from running research experiments at scale with the Hermes agent. These cover experiment infrastructure, evaluation protocols, monitoring, and failure recovery.
+
+---
+
+## Experiment Infrastructure
+
+### Directory Structure
+
+Organize experiments with a consistent structure:
+
+```
+workspace/
+  experiments/
+    run_main.py                # Core experiment runner
+    run_baselines.py           # Baseline comparison
+    run_ablation.py            # Ablation studies
+    strategies.py              # Method implementations
+    config.yaml                # Shared configuration
+  results/
+    <experiment_name>/
+      <task_or_problem>/
+        <strategy>/
+          result.json          # Final metrics
+          final_output.md      # Final output artifact
+          history.json         # Full trajectory/log
+          pass_01/             # Per-iteration artifacts (if iterative)
+            intermediate.md
+  analysis/
+    analyze_results.py         # Statistical analysis
+    compute_stats.py           # Significance tests
+    make_charts.py             # Visualization
+  paper/
+    paper.tex                  # LaTeX source
+    fig_*.pdf                  # Generated figures
+```
+
+### Script Design Principles
+
+**1. Incremental Saving (Crash Recovery)**
+
+Every experiment script should save results after each unit of work, and skip already-completed work on restart:
+
+```python
+import json, os
+from pathlib import Path
+
+def run_experiment(problems, strategies, output_dir):
+    for problem in problems:
+        for strategy in strategies:
+            result_path = Path(output_dir) / problem["id"] / strategy / "result.json"
+            if result_path.exists():
+                print(f"Skipping {problem['id']}/{strategy} (already done)")
+                continue
+            
+            # Run the experiment
+            result = execute_strategy(problem, strategy)
+            
+            # Save immediately
+            result_path.parent.mkdir(parents=True, exist_ok=True)
+            with open(result_path, 'w') as f:
+                json.dump(result, f, indent=2)
+```
+
+This pattern makes re-runs safe and efficient. If a process crashes at problem 47/150, restarting skips the first 46.
+
+**2. Artifact Preservation**
+
+Save all intermediate outputs, not just final results. This enables post-hoc analysis without re-running:
+
+```python
+def save_pass_artifacts(output_dir, pass_num, artifacts):
+    """Save all artifacts from a single pass of an iterative method."""
+    pass_dir = Path(output_dir) / f"pass_{pass_num:02d}"
+    pass_dir.mkdir(parents=True, exist_ok=True)
+    
+    for name, content in artifacts.items():
+        with open(pass_dir / f"{name}.md", 'w') as f:
+            f.write(content)
+```
+
+**3. Configuration Management**
+
+Use YAML configs for reproducibility:
+
+```yaml
+# config.yaml
+model: anthropic/claude-sonnet-4-20250514
+author_temperature: 0.8
+judge_temperature: 0.3
+max_tokens: 4096
+num_judges: 3
+max_passes: 15
+convergence_k: 2
+```
+
+```python
+import yaml
+
+with open("config.yaml") as f:
+    config = yaml.safe_load(f)
+```
+
+**4. Separation of Concerns**
+
+Keep generation, evaluation, and visualization in separate scripts:
+
+| Script | Purpose |
+|--------|---------|
+| `run_experiment.py` | Core method execution |
+| `run_baselines.py` | Baseline comparisons at same compute |
+| `run_eval.py` | Blind evaluation / judge panels |
+| `analyze_results.py` | Statistical analysis |
+| `make_charts.py` | Figure generation |
+
+This lets you re-run evaluation without re-running expensive generation, and regenerate figures without re-running analysis.
+
+---
+
+## Evaluation Protocols
+
+### Blind Judge Panels (for Subjective Tasks)
+
+When evaluating subjective outputs (writing, analysis, recommendations), use a blind judge panel:
+
+```python
+import random
+
+def run_blind_evaluation(outputs: dict, task_prompt: str, num_judges: int = 7):
+    """
+    Run blind evaluation of multiple method outputs.
+    
+    Args:
+        outputs: {"method_name": "output_text", ...}
+        task_prompt: The original task description
+        num_judges: Number of independent judge evaluations
+    """
+    rankings = []
+    
+    for judge_i in range(num_judges):
+        # Randomize labels and presentation order per judge
+        methods = list(outputs.keys())
+        random.shuffle(methods)
+        labels = {m: chr(65 + i) for i, m in enumerate(methods)}  # A, B, C...
+        
+        # Present to judge with randomized labels
+        prompt = f"Task: {task_prompt}\n\n"
+        for method in methods:
+            prompt += f"--- Proposal {labels[method]} ---\n{outputs[method]}\n\n"
+        prompt += "Rank all proposals from best to worst. Format: RANKING: [best], [second], [worst]"
+        
+        ranking = call_judge(prompt)
+        rankings.append({"labels": labels, "ranking": ranking})
+    
+    # Aggregate via Borda count
+    return compute_borda(rankings)
+
+def compute_borda(rankings, n_methods=3):
+    """Borda count: 3/2/1 points for 1st/2nd/3rd."""
+    scores = {}
+    points = {0: n_methods, 1: n_methods - 1, 2: n_methods - 2}  # Adjust for n_methods
+    
+    for r in rankings:
+        for position, method in enumerate(r["ranking"]):
+            scores[method] = scores.get(method, 0) + points.get(position, 0)
+    
+    return scores
+```
+
+Key design decisions:
+- **Randomize both labels AND order** per judge to prevent position bias
+- **Use odd number of judges** (3, 5, 7) to break ties
+- **Conservative tiebreak**: Incumbent/baseline wins ties (prevents false positives)
+- **CoT judges** match non-CoT quality at ~40% cost (1 CoT judge ≈ 3 standard judges)
+
+### Code/Objective Evaluation
+
+For tasks with ground-truth evaluation (code, math, factual):
+
+```python
+import subprocess
+
+def evaluate_code(solution: str, test_cases: list, timeout: int = 30):
+    """Run code solution against test cases with sandboxed execution."""
+    results = {"public": [], "private": []}
+    
+    for test in test_cases:
+        try:
+            proc = subprocess.run(
+                ["python3", "-c", solution],
+                input=test["input"],
+                capture_output=True,
+                timeout=timeout,
+                text=True
+            )
+            actual = proc.stdout.strip()
+            expected = test["expected"].strip()
+            passed = actual == expected
+        except subprocess.TimeoutExpired:
+            passed = False
+        
+        category = "public" if test.get("public") else "private"
+        results[category].append(passed)
+    
+    return {
+        "public_pass_rate": sum(results["public"]) / max(len(results["public"]), 1),
+        "private_pass_rate": sum(results["private"]) / max(len(results["private"]), 1),
+    }
+```
+
+### Compute-Matched Comparison
+
+Always compare methods at equal compute budget. If your method uses N API calls, baselines get N calls too:
+
+| Method | Call Budget | Allocation |
+|--------|-----------|------------|
+| Single pass | 6 calls | 6 independent generations |
+| Critique & revise | 6 calls | 1 generate + 5 revise rounds |
+| Autoreason | 6 calls | 1 generate + 1 analysis + 4 revisions |
+| Best-of-N | 6 calls | 6 independent, pick best on public test |
+
+### Human Evaluation Design
+
+Many ML/NLP papers require human evaluation, especially for subjective tasks (text generation, summarization, dialogue, creative writing). Poorly designed human evals are a common rejection reason.
+
+#### When Human Evaluation Is Required
+
+| Task Type | Required? | Notes |
+|-----------|-----------|-------|
+| Text generation (open-ended) | Yes | LLM-as-judge alone is insufficient for acceptance at ACL/EMNLP |
+| Summarization | Usually | At minimum for a subset of outputs |
+| Dialogue systems | Yes | User studies or annotation |
+| Code generation | No | Test suites are objective ground truth |
+| Classification | No | Standard metrics suffice |
+| Any task with subjective quality | Strongly recommended | Strengthens the paper significantly |
+
+#### Annotation Protocol Design
+
+```
+Human Evaluation Protocol:
+1. Define the evaluation dimensions (fluency, relevance, factual accuracy, etc.)
+2. Create annotation guidelines with examples of each score level
+3. Run a pilot with 2-3 annotators on 20-30 examples
+4. Compute pilot inter-annotator agreement — if low, revise guidelines
+5. Run full evaluation
+6. Report: annotator count, agreement metrics, compensation, time per item
+```
+
+**Evaluation dimensions** (pick relevant subset):
+
+| Dimension | Definition | Scale |
+|-----------|-----------|-------|
+| Fluency | Grammaticality and naturalness | 1-5 Likert |
+| Relevance | Does it address the task? | 1-5 Likert |
+| Factual accuracy | Are stated facts correct? | Binary or 1-5 |
+| Coherence | Logical flow and consistency | 1-5 Likert |
+| Informativeness | Does it provide useful information? | 1-5 Likert |
+| Overall preference | Which output is better? | A/B/Tie (pairwise) |
+
+**Pairwise comparison** (preferred over absolute scoring — more reliable):
+- Present two outputs side-by-side (randomize left/right position)
+- Ask: "Which is better? A / B / Tie"
+- More discriminative and less susceptible to annotator calibration drift
+
+#### Inter-Annotator Agreement
+
+Always report agreement metrics. Without them, reviewers assume your annotations are unreliable.
+
+```python
+# Krippendorff's alpha (preferred — handles missing data, any scale)
+# pip install krippendorffs-alpha
+import krippendorff
+
+# Ratings: rows = annotators, columns = items, values = scores
+ratings = [
+    [3, 4, 1, 2, 5, None, 3],  # Annotator 1
+    [3, 5, 1, 3, 5, 2, 3],     # Annotator 2
+    [4, 4, 2, 2, 4, 2, None],  # Annotator 3
+]
+alpha = krippendorff.alpha(reliability_data=ratings, level_of_measurement="ordinal")
+print(f"Krippendorff's alpha: {alpha:.3f}")
+# Interpretation: >0.80 good, 0.67-0.80 acceptable, <0.67 questionable
+```
+
+```python
+# Cohen's kappa (for exactly 2 annotators, categorical data)
+from sklearn.metrics import cohen_kappa_score
+
+annotator_1 = [1, 2, 3, 1, 2, 3, 2]
+annotator_2 = [1, 2, 2, 1, 3, 3, 2]
+kappa = cohen_kappa_score(annotator_1, annotator_2)
+print(f"Cohen's kappa: {kappa:.3f}")
+# Interpretation: >0.80 excellent, 0.60-0.80 substantial, 0.40-0.60 moderate
+```
+
+| Metric | When to Use | Annotators | Scale |
+|--------|------------|-----------|-------|
+| Krippendorff's alpha | Default choice | Any number | Any (ordinal, nominal, ratio) |
+| Cohen's kappa | 2 annotators, categorical | Exactly 2 | Nominal/ordinal |
+| Fleiss' kappa | 3+ annotators, categorical | 3+ | Nominal |
+| Pearson/Spearman | Continuous scores | 2 | Interval/ratio |
+
+#### Crowdsourcing Platforms
+
+| Platform | Best For | Cost | Quality |
+|----------|----------|------|---------|
+| **Prolific** | Academic research, higher quality | $8-15/hr | High — academic participant pool |
+| **MTurk** | Large-scale, fast turnaround | $2-10/hr | Variable — use qualifications |
+| **Surge AI** | NLP-specific annotations | Premium | High — trained annotators |
+| **Expert annotators** | Domain-specific (medical, legal) | Highest | Highest — but slow |
+
+**Ethics requirements**:
+- Report compensation rate (must be at minimum local minimum wage)
+- Describe annotator demographics if relevant
+- Obtain IRB/ethics approval if required by your institution
+- ACL venues explicitly require compensation documentation
+
+#### What to Report in the Paper
+
+```
+Human Evaluation Section Checklist:
+- [ ] Number of annotators
+- [ ] Annotator qualifications / recruitment method
+- [ ] Number of items evaluated
+- [ ] Evaluation dimensions with definitions
+- [ ] Scale used (Likert, pairwise, binary)
+- [ ] Inter-annotator agreement (Krippendorff's alpha or Cohen's kappa)
+- [ ] Compensation rate
+- [ ] Time per annotation item
+- [ ] Whether annotators saw model identities (should be blind)
+- [ ] Randomization of presentation order
+```
+
+---
+
+## Statistical Analysis
+
+### Required Tests
+
+| Test | When to Use | Python |
+|------|------------|--------|
+| McNemar's test | Comparing two methods on same problems | `scipy.stats.binomtest` for small n |
+| Two-proportion z-test | Comparing success rates | Custom or `statsmodels` |
+| Fisher's exact test | Small sample pairwise comparison | `scipy.stats.fisher_exact` |
+| Bootstrapped CI | Confidence intervals for any metric | Custom bootstrap |
+| Cohen's h | Effect size for proportions | Manual calculation |
+
+### Standard Analysis Script
+
+```python
+import numpy as np
+from scipy import stats
+from pathlib import Path
+import json
+
+def load_all_results(results_dir):
+    """Load all results into a structured format."""
+    results = {}
+    for result_file in Path(results_dir).rglob("result.json"):
+        parts = result_file.relative_to(results_dir).parts
+        if len(parts) >= 3:
+            experiment, task, strategy = parts[0], parts[1], parts[2]
+            data = json.loads(result_file.read_text())
+            results.setdefault(experiment, {}).setdefault(strategy, {})[task] = data
+    return results
+
+def pairwise_mcnemar(method_a_results, method_b_results):
+    """McNemar's test for paired binary outcomes."""
+    a_win_b_lose = sum(1 for a, b in zip(method_a_results, method_b_results) if a and not b)
+    b_win_a_lose = sum(1 for a, b in zip(method_a_results, method_b_results) if b and not a)
+    
+    n = a_win_b_lose + b_win_a_lose
+    if n < 25:
+        # Use exact binomial for small samples
+        result = stats.binomtest(a_win_b_lose, n, 0.5)
+        p_value = result.pvalue
+    else:
+        # Chi-squared approximation
+        chi2 = (abs(a_win_b_lose - b_win_a_lose) - 1)**2 / (a_win_b_lose + b_win_a_lose)
+        p_value = 1 - stats.chi2.cdf(chi2, df=1)
+    
+    return {
+        "a_wins": a_win_b_lose,
+        "b_wins": b_win_a_lose,
+        "n_discordant": n,
+        "p_value": p_value,
+        "significant": p_value < 0.05
+    }
+
+def bootstrap_ci(data, n_bootstrap=10000, ci=0.95):
+    """Bootstrap confidence interval for mean."""
+    means = []
+    for _ in range(n_bootstrap):
+        sample = np.random.choice(data, size=len(data), replace=True)
+        means.append(np.mean(sample))
+    lower = np.percentile(means, (1 - ci) / 2 * 100)
+    upper = np.percentile(means, (1 + ci) / 2 * 100)
+    return {"mean": np.mean(data), "ci_lower": lower, "ci_upper": upper}
+
+def cohens_h(p1, p2):
+    """Cohen's h effect size for two proportions."""
+    return 2 * np.arcsin(np.sqrt(p1)) - 2 * np.arcsin(np.sqrt(p2))
+```
+
+### Reporting Standards
+
+Always include in the paper:
+- **Sample sizes**: n=X problems/tasks
+- **Number of runs**: K independent runs if applicable
+- **Error bars**: Specify standard deviation or standard error
+- **Confidence intervals**: 95% CI for key results
+- **Significance tests**: p-values for key comparisons
+- **Effect sizes**: Cohen's d or h for practical significance
+
+---
+
+## Monitoring (Cron Pattern)
+
+### Cron Prompt Template
+
+For each experiment batch, create a monitoring prompt:
+
+```
+Check the status of the [EXPERIMENT_NAME] experiment:
+
+1. Process check: ps aux | grep [PROCESS_PATTERN]
+2. Log check: tail -30 [LOG_FILE]
+3. Results check: ls [RESULT_DIR]/eval/ (or appropriate result location)
+4. If results are available:
+   - Read the result JSON files
+   - Report metrics in a table (Borda scores, accuracy, etc.)
+   - Compute key comparisons between methods
+5. If all experiments in this batch are complete:
+   - git add -A && git commit -m "[COMMIT_MESSAGE]" && git push
+   - Report final summary
+6. Key question: [SPECIFIC ANALYTICAL QUESTION]
+
+If nothing has changed since the last check, respond with [SILENT].
+```
+
+### Monitoring Best Practices
+
+1. **Check processes first** — don't read results if the experiment is still running and results are incomplete
+2. **Read the log tail** — look for errors, progress indicators, completion messages
+3. **Count completed vs expected** — "45/150 problems done" is more useful than "some results exist"
+4. **Report in structured tables** — always include key metrics in a table
+5. **Answer the key question** — each experiment should have a specific analytical question to answer when done
+6. **[SILENT] for no-news** — suppress notifications when nothing has changed
+7. **Commit on completion** — every completed batch gets committed with a descriptive message
+
+### Example Monitoring Report
+
+```
+## Code Experiments (Haiku 3.5) - COMPLETE
+
+| Strategy | Pass Rate (150 problems) | vs Single |
+|----------|------------------------|-----------|
+| single_pass | 38.0% | — |
+| critique_revise | 35.2% | -2.8pp |
+| **autoreason** | **40.0%** | **+2.0pp** |
+| best_of_6 | 31.0% | -7.0pp |
+
+Key finding: Autoreason shows +2pp improvement over single pass, while 
+best-of-6 collapses due to single-public-test selection issue.
+
+Committed: `git commit -m "Add Haiku code results (150 problems, 4 strategies)"`
+Next: Run significance tests on these results.
+```
+
+---
+
+## Failure Recovery
+
+### Common Failures and Recovery
+
+| Failure | Detection | Recovery |
+|---------|-----------|----------|
+| **API credit exhaustion** | 402 errors in logs, incomplete results | Top up credits, re-run (skips completed work automatically) |
+| **Rate limiting** | 429 errors, slow progress | Add retry logic with exponential backoff |
+| **Process crash** | PID gone, log stops mid-problem | Re-run script (resumes from last checkpoint) |
+| **Wrong model ID** | Model not found errors | Fix ID (e.g., `claude-opus-4-6` not `claude-opus-4.6`) |
+| **Parallel slowdown** | Each experiment taking 2x longer | Reduce parallel experiments to 2-3 max |
+| **Security scan blocks** | Commands blocked by security | Use `execute_code` instead of piped `terminal` commands |
+| **Delegation failures** | `delegate_task` returns errors | Fall back to doing work directly |
+| **Timeout on hard problems** | Process stuck, no log progress | Kill, skip problem, note in results |
+| **Dataset path mismatch** | File not found errors | Verify paths before launching |
+
+### Retry Naming Convention
+
+When re-running failed experiments, use a suffix to track rounds:
+
+```
+logs/experiment_haiku_0_50.log       # Round 1
+logs/experiment_haiku_0_50_r2.log    # Round 2 (after credit exhaustion)
+logs/experiment_haiku_0_50_r3.log    # Round 3 (after bug fix)
+```
+
+### Pre-Flight Checklist
+
+Before launching any experiment batch:
+
+```
+Pre-Flight:
+- [ ] API credits sufficient for estimated calls
+- [ ] Model IDs correct (test with 1 problem first)
+- [ ] Output directory exists and is writable
+- [ ] Resume logic works (re-run won't overwrite existing results)
+- [ ] Log file path is unique (won't overwrite previous logs)
+- [ ] Dataset/task files are accessible
+- [ ] Config matches intended experiment
+```
+
+---
+
+## Task/Benchmark Design
+
+### Open-Ended Tasks (Subjective Evaluation)
+
+Design tasks that have clear objectives but subjective quality:
+
+```markdown
+# Task: [Title]
+
+## Context
+[Specific scenario with concrete details: company size, constraints, timeline]
+
+## Deliverable
+[Exact format and structure required]
+
+## Requirements
+- [Specific, measurable requirements]
+- [Not vague — "be comprehensive" is bad, "include exactly 6 sections" is good]
+```
+
+### Constrained Tasks (for Testing Scope Effects)
+
+Constrained tasks test whether methods respect scope boundaries. Design with:
+
+- **Fixed facts**: "Use only these N data points, add nothing else"
+- **Fixed deliverable**: Specific format (pitch, postmortem, memo — not "improve this")
+- **Fixed structure**: "These sections in this order, do not add/remove"
+- **Fixed change items**: "Address exactly these N points, nothing else"
+
+**Do NOT use word count as a scope constraint.** Word limits cause false convergence — outputs get rejected for length, not quality. Constrain scope (what to include) not length.
+
+### Example: Good vs Bad Constraints
+
+| Bad Constraint | Why | Good Constraint |
+|---------------|-----|-----------------|
+| "Max 500 words" | Judges reject for length | "Exactly 4 sections, each with 3 numbered items" |
+| "Be concise" | Too vague | "Each prohibition must reference a specific base fact" |
+| "Improve this" | Unbounded scope | "Write a 600-word incident postmortem with this exact structure" |
+| "Make it better" | No clear criterion | "Address exactly these 3 reviewer concerns" |
+
+---
+
+## Visualization Best Practices
+
+### Setup: SciencePlots + matplotlib
+
+Install SciencePlots for publication-ready defaults:
+
+```bash
+pip install SciencePlots matplotlib numpy
+```
+
+**Option A: SciencePlots styles** (recommended — handles most defaults automatically):
+
+```python
+import matplotlib.pyplot as plt
+import scienceplots  # registers the styles
+
+# Pick a style:
+# 'science'        — clean, serif fonts, suitable for most venues
+# 'science+ieee'   — IEEE-style (good for two-column papers)
+# 'science+nature' — Nature-style
+# Add 'no-latex' if LaTeX is not installed on the machine generating plots
+
+with plt.style.context(['science', 'no-latex']):
+    fig, ax = plt.subplots(figsize=(3.5, 2.5))  # single-column width
+    # ... plot ...
+    fig.savefig('paper/fig_results.pdf', bbox_inches='tight')
+```
+
+**Option B: Manual rcParams** (when you need full control):
+
+```python
+import matplotlib.pyplot as plt
+
+plt.rcParams.update({
+    'font.size': 10,
+    'font.family': 'serif',
+    'axes.labelsize': 11,
+    'axes.titlesize': 11,
+    'xtick.labelsize': 9,
+    'ytick.labelsize': 9,
+    'legend.fontsize': 9,
+    'figure.figsize': (3.5, 2.5),    # single-column default
+    'figure.dpi': 300,
+    'savefig.dpi': 300,
+    'savefig.bbox': 'tight',
+    'savefig.pad_inches': 0.05,
+    'axes.linewidth': 0.8,
+    'lines.linewidth': 1.5,
+    'lines.markersize': 5,
+    'axes.grid': True,
+    'grid.alpha': 0.3,
+    'grid.linewidth': 0.5,
+})
+```
+
+### Standard Figure Sizes (Two-Column Format)
+
+| Use Case | figsize | Notes |
+|----------|---------|-------|
+| Single column | `(3.5, 2.5)` | Fits in one column of two-column layout |
+| Double column | `(7.0, 3.0)` | Spans full page width |
+| Square (heatmap, confusion matrix) | `(3.5, 3.5)` | Single column |
+| Tall single (many rows) | `(3.5, 5.0)` | Use sparingly |
+
+### Colorblind-Safe Palette (Okabe-Ito)
+
+Use this palette for all paper figures. It is distinguishable by people with all common forms of color vision deficiency:
+
+```python
+COLORS = {
+    'blue':    '#0072B2',
+    'orange':  '#E69F00',
+    'green':   '#009E73',
+    'red':     '#D55E00',
+    'purple':  '#CC79A7',
+    'cyan':    '#56B4E9',
+    'yellow':  '#F0E442',
+    'black':   '#000000',
+}
+
+# As a list for cycling:
+COLOR_CYCLE = ['#0072B2', '#D55E00', '#009E73', '#E69F00', '#CC79A7', '#56B4E9']
+```
+
+Also differentiate lines by **marker and linestyle**, not just color:
+```python
+STYLES = [
+    {'color': '#0072B2', 'marker': 'o', 'linestyle': '-'},
+    {'color': '#D55E00', 'marker': 's', 'linestyle': '--'},
+    {'color': '#009E73', 'marker': '^', 'linestyle': '-.'},
+    {'color': '#E69F00', 'marker': 'D', 'linestyle': ':'},
+]
+```
+
+### Complete Example: Method Comparison Bar Chart
+
+```python
+import matplotlib.pyplot as plt
+import numpy as np
+
+try:
+    import scienceplots
+    style = ['science', 'no-latex']
+except ImportError:
+    style = 'default'
+
+with plt.style.context(style):
+    methods = ['Single Pass', 'Critique+Revise', 'Best-of-N', 'Ours']
+    scores = [73.2, 74.1, 68.5, 77.0]
+    errors = [2.1, 1.8, 3.2, 1.5]
+    colors = ['#56B4E9', '#E69F00', '#CC79A7', '#0072B2']
+    
+    fig, ax = plt.subplots(figsize=(3.5, 2.5))
+    bars = ax.bar(methods, scores, yerr=errors, capsize=3,
+                  color=colors, edgecolor='black', linewidth=0.5)
+    
+    # Highlight "Ours"
+    bars[-1].set_edgecolor('#0072B2')
+    bars[-1].set_linewidth(1.5)
+    
+    ax.set_ylabel('Pass Rate (%)')
+    ax.set_ylim(60, 85)
+    ax.spines['top'].set_visible(False)
+    ax.spines['right'].set_visible(False)
+    
+    fig.savefig('paper/fig_comparison.pdf', bbox_inches='tight')
+```
+
+### Complete Example: Convergence/Trajectory Line Chart
+
+```python
+with plt.style.context(style):
+    fig, ax = plt.subplots(figsize=(3.5, 2.5))
+    
+    passes = np.arange(1, 16)
+    ours = [65, 72, 78, 82, 85, 87, 88, 89, 89.5, 90, 90, 90, 90, 90, 90]
+    baseline = [65, 68, 70, 71, 69, 67, 66, 65, 64, 63, 62, 61, 60, 59, 58]
+    
+    ax.plot(passes, ours, **STYLES[0], label='Ours', markersize=4)
+    ax.plot(passes, baseline, **STYLES[1], label='Critique+Revise', markersize=4)
+    
+    # Mark convergence point
+    ax.axvline(x=10, color='gray', linestyle=':', alpha=0.5, linewidth=0.8)
+    ax.annotate('Converged', xy=(10, 90), fontsize=8, ha='center',
+                xytext=(10, 93), arrowprops=dict(arrowstyle='->', color='gray'))
+    
+    ax.set_xlabel('Iteration')
+    ax.set_ylabel('Quality Score')
+    ax.legend(loc='lower right')
+    ax.spines['top'].set_visible(False)
+    ax.spines['right'].set_visible(False)
+    
+    fig.savefig('paper/fig_trajectory.pdf', bbox_inches='tight')
+```
+
+### Output Rules
+
+- **Always save as PDF**: `fig.savefig('fig.pdf')` — vector graphics, sharp at any zoom
+- **Never save as PNG** for paper figures — raster PNGs look blurry when printed/zoomed
+- **Exception**: Screenshots, photographs, or pixel-art visualizations → PNG at 600 DPI
+- **Verify grayscale**: Print to grayscale PDF and check all information is still visible
+
+### Chart Types for Common Comparisons
+
+| Comparison Type | Chart | Notes |
+|----------------|-------|-------|
+| Method vs method | Grouped bar chart | Include error bars |
+| Across model sizes | Line chart with CI bands | Log scale for model size axis |
+| Ablation study | Stacked/grouped bar | Highlight removed component |
+| Trajectory/convergence | Line chart over iterations | Show winner per iteration |
+| Per-task breakdown | Heatmap or grouped bar | Show variance across tasks |

skills/research/research-paper-writing/references/reviewer-guidelines.md

@@ -105,7 +105,7 @@ Reviewers are explicitly instructed to:
 - Penalizing authors for honest limitation acknowledgment
 - Rejecting for missing citations to reviewer's own work
 
-### Timeline (NeurIPS 2025)
+### Timeline (NeurIPS 2025 — verify dates for current year)
 
 - Bidding: May 17-21
 - Reviewing period: May 29 - July 2
@@ -113,6 +113,8 @@ Reviewers are explicitly instructed to:
 - Discussion period: July 31 - August 13
 - Final notifications: September 18
 
+> **Note**: These dates are from the 2025 cycle. Always check the current year's call for papers at the venue website.
+
 ---
 
 ## ICML Reviewer Guidelines
@@ -198,6 +200,70 @@ ACL has a dedicated ethics review process for:
 
 ---
 
+## AAAI Reviewer Guidelines
+
+### Evaluation Criteria
+
+AAAI reviewers evaluate along similar axes to NeurIPS/ICML but with some differences:
+
+| Criterion | Weight | Notes |
+|-----------|--------|-------|
+| **Technical quality** | High | Soundness of approach, correctness of results |
+| **Significance** | High | Importance of the problem and contribution |
+| **Novelty** | Medium-High | New ideas, methods, or insights |
+| **Clarity** | Medium | Clear writing, well-organized presentation |
+| **Reproducibility** | Medium | Sufficient detail to reproduce results |
+
+### AAAI-Specific Considerations
+
+- **Broader AI scope**: AAAI covers all of AI, not just ML. Papers on planning, reasoning, knowledge representation, NLP, vision, robotics, and multi-agent systems are all in scope. Reviewers may not be deep ML specialists.
+- **Formatting strictness**: AAAI reviewers are instructed to flag formatting violations. Non-compliant papers may be desk-rejected before review.
+- **Application papers**: AAAI is more receptive to application-focused work than NeurIPS/ICML. Framing a strong application contribution is viable.
+- **Senior Program Committee**: AAAI uses SPCs (Senior Program Committee members) who mediate between reviewers and make accept/reject recommendations.
+
+### Scoring (AAAI Scale)
+
+- **Strong Accept**: Clearly above threshold, excellent contribution
+- **Accept**: Above threshold, good contribution with minor issues
+- **Weak Accept**: Borderline, merits outweigh concerns
+- **Weak Reject**: Borderline, concerns outweigh merits
+- **Reject**: Below threshold, significant issues
+- **Strong Reject**: Well below threshold
+
+---
+
+## COLM Reviewer Guidelines
+
+### Evaluation Criteria
+
+COLM reviews focus on relevance to language modeling in addition to standard criteria:
+
+| Criterion | Weight | Notes |
+|-----------|--------|-------|
+| **Relevance** | High | Must be relevant to language modeling community |
+| **Technical quality** | High | Sound methodology, well-supported claims |
+| **Novelty** | Medium-High | New insights about language models |
+| **Clarity** | Medium | Clear presentation, reproducible |
+| **Significance** | Medium-High | Impact on LM research and practice |
+
+### COLM-Specific Considerations
+
+- **Language model focus**: Reviewers will assess whether the contribution advances understanding of language models. General ML contributions need explicit LM framing.
+- **Newer venue norms**: COLM is newer than NeurIPS/ICML, so reviewer calibration varies more. Write more defensively — anticipate a wider range of reviewer expertise.
+- **ICLR-derived process**: Review process is modeled on ICLR (open reviews, author response period, discussion among reviewers).
+- **Broad interpretation of "language modeling"**: Includes training, evaluation, alignment, safety, efficiency, applications, theory, multimodality (if language is central), and social impact of LMs.
+
+### Scoring
+
+COLM uses an ICLR-style scoring system:
+- **8-10**: Strong accept (top papers)
+- **6-7**: Weak accept (solid contribution)
+- **5**: Borderline
+- **3-4**: Weak reject (below threshold)
+- **1-2**: Strong reject
+
+---
+
 ## What Makes Reviews Strong
 
 ### Following Daniel Dennett's Rules

skills/research/research-paper-writing/references/writing-guide.md

@@ -225,8 +225,6 @@ Provide context before asking the reader to consider anything new. This applies
 
 ---
 
----
-
 ## Micro-Level Writing Tips
 
 ### From Ethan Perez (Anthropic)