diff --git a/plugins/hindsight-memory/__init__.py b/plugins/hindsight-memory/__init__.py
new file mode 100644
index 00000000000..2d5a058c1fc
--- /dev/null
+++ b/plugins/hindsight-memory/__init__.py
@@ -0,0 +1,304 @@
+"""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 ~/.hindsight/config.json (written by the original setup wizard).
+"""
+
+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 ~/.hindsight/config.json, falling back to env vars."""
+    from pathlib import Path
+    config_path = Path.home() / ".hindsight" / "config.json"
+
+    if config_path.exists():
+        try:
+            return json.loads(config_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
+
+    @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 _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) -> 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) -> 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) -> None:
+        combined = f"User: {user_content}\nAssistant: {assistant_content}"
+        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)
+
+    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:
+        if self._prefetch_thread and self._prefetch_thread.is_alive():
+            self._prefetch_thread.join(timeout=5.0)
+
+
+def register(ctx) -> None:
+    """Register Hindsight as a memory provider plugin."""
+    ctx.register_memory_provider(HindsightMemoryProvider())
diff --git a/plugins/hindsight-memory/plugin.yaml b/plugins/hindsight-memory/plugin.yaml
new file mode 100644
index 00000000000..6d4f12e58e8
--- /dev/null
+++ b/plugins/hindsight-memory/plugin.yaml
@@ -0,0 +1,8 @@
+name: hindsight-memory
+version: 1.0.0
+description: >
+  Long-term memory via Hindsight — knowledge graph with entity resolution,
+  multi-strategy retrieval (semantic + BM25 + graph + temporal), and
+  cross-encoder reranking. Cloud or local mode.
+requires_env:
+  - HINDSIGHT_API_KEY
diff --git a/plugins/mem0-memory/__init__.py b/plugins/mem0-memory/__init__.py
new file mode 100644
index 00000000000..c21a24e20a4
--- /dev/null
+++ b/plugins/mem0-memory/__init__.py
@@ -0,0 +1,286 @@
+"""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
+from pathlib import Path
+from typing import Any, Dict, List
+
+from agent.memory_provider import MemoryProvider
+
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# Config
+# ---------------------------------------------------------------------------
+
+def _load_config() -> dict:
+    """Load config from $HERMES_HOME/mem0.json or env vars."""
+    hermes_home = os.environ.get("HERMES_HOME", os.path.expanduser("~/.hermes"))
+    config_path = Path(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 (+150ms)."
+    ),
+    "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"],
+    },
+}
+
+CONTEXT_SCHEMA = {
+    "name": "mem0_context",
+    "description": (
+        "Deep retrieval with forced reranking. Use when you need the most "
+        "relevant memories for a specific topic."
+    ),
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "query": {"type": "string", "description": "What to search for."},
+        },
+        "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._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
+
+    @property
+    def name(self) -> str:
+        return "mem0"
+
+    def is_available(self) -> bool:
+        cfg = _load_config()
+        return bool(cfg.get("api_key"))
+
+    def _get_client(self):
+        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 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) -> 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) -> None:
+        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)
+            except Exception as e:
+                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) -> None:
+        """Send the turn to Mem0 for server-side fact extraction."""
+        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)
+        except Exception as e:
+            logger.warning("Mem0 sync 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:
+        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)
+                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:
+                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,
+                )
+                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:
+                return json.dumps({"error": f"Search failed: {e}"})
+
+        elif tool_name == "mem0_context":
+            query = args.get("query", "")
+            if not query:
+                return json.dumps({"error": "Missing required parameter: query"})
+            try:
+                results = client.search(
+                    query=query, user_id=self._user_id,
+                    rerank=True, top_k=5,
+                )
+                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:
+                return json.dumps({"error": f"Context retrieval 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,
+                )
+                return json.dumps({"result": "Fact stored."})
+            except Exception as e:
+                return json.dumps({"error": f"Failed to store: {e}"})
+
+        return json.dumps({"error": f"Unknown tool: {tool_name}"})
+
+    def shutdown(self) -> None:
+        if self._prefetch_thread and self._prefetch_thread.is_alive():
+            self._prefetch_thread.join(timeout=5.0)
+        self._client = None
+
+
+def register(ctx) -> None:
+    """Register Mem0 as a memory provider plugin."""
+    ctx.register_memory_provider(Mem0MemoryProvider())
diff --git a/plugins/mem0-memory/plugin.yaml b/plugins/mem0-memory/plugin.yaml
new file mode 100644
index 00000000000..2a0ff01100d
--- /dev/null
+++ b/plugins/mem0-memory/plugin.yaml
@@ -0,0 +1,7 @@
+name: mem0-memory
+version: 1.0.0
+description: >
+  Long-term memory via Mem0 Platform — server-side LLM fact extraction,
+  semantic search with reranking, and automatic deduplication.
+requires_env:
+  - MEM0_API_KEY