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Author SHA1 Message Date
Teknium
8daa1c1702 docs: add Supermemory to memory providers docs, env vars, CLI reference
- Add full Supermemory section to memory-providers.md with config table,
  tools, setup instructions, and key features
- Update provider count from 7 to 8 across memory.md and memory-providers.md
- Add SUPERMEMORY_API_KEY to environment-variables.md
- Add Supermemory to integrations/providers.md optional API keys table
- Add supermemory to cli-commands.md provider list
- Add Supermemory to profile isolation section (config file providers)
2026-04-06 21:54:42 -07:00
Teknium
a2f4252b42 fix: use get_hermes_home() for profile-scoped storage, fix README
- Replace hardcoded os.path.expanduser('~/.hermes') with
  get_hermes_home() from hermes_constants for profile isolation
- Fix README echo command quoting error
2026-04-06 21:50:37 -07:00
Hermes Agent
818d0359e4 chore(memory): remove supermemory PR scaffolding 2026-04-06 20:56:28 -07:00
Hermes Agent
a839111438 fix(memory): clean up supermemory provider threads 2026-04-06 20:56:28 -07:00
Hermes Agent
eba886478a docs(memory): add Supermemory PR draft and cleanup 2026-04-06 20:56:28 -07:00
Hermes Agent
336bca4fa8 feat(memory): add Supermemory memory provider 2026-04-06 20:56:28 -07:00
9 changed files with 1037 additions and 6 deletions

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# Supermemory Memory Provider
Semantic long-term memory with profile recall, semantic search, explicit memory tools, and session-end conversation ingest.
## Requirements
- `pip install supermemory`
- Supermemory API key from [supermemory.ai](https://supermemory.ai)
## Setup
```bash
hermes memory setup # select "supermemory"
```
Or manually:
```bash
hermes config set memory.provider supermemory
echo 'SUPERMEMORY_API_KEY=your-key-here' >> ~/.hermes/.env
```
## Config
Config file: `$HERMES_HOME/supermemory.json`
| Key | Default | Description |
|-----|---------|-------------|
| `container_tag` | `hermes` | Container tag used for search and writes |
| `auto_recall` | `true` | Inject relevant memory context before turns |
| `auto_capture` | `true` | Store cleaned user-assistant turns after each response |
| `max_recall_results` | `10` | Max recalled items to format into context |
| `profile_frequency` | `50` | Include profile facts on first turn and every N turns |
| `capture_mode` | `all` | Skip tiny or trivial turns by default |
| `entity_context` | built-in default | Extraction guidance passed to Supermemory |
| `api_timeout` | `5.0` | Timeout for SDK and ingest requests |
## Tools
| Tool | Description |
|------|-------------|
| `supermemory_store` | Store an explicit memory |
| `supermemory_search` | Search memories by semantic similarity |
| `supermemory_forget` | Forget a memory by ID or best-match query |
| `supermemory_profile` | Retrieve persistent profile and recent context |
## Behavior
When enabled, Hermes can:
- prefetch relevant memory context before each turn
- store cleaned conversation turns after each completed response
- ingest the full session on session end for richer graph updates
- expose explicit tools for search, store, forget, and profile access

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"""Supermemory memory plugin using the MemoryProvider interface.
Provides semantic long-term memory with profile recall, semantic search,
explicit memory tools, cleaned turn capture, and session-end conversation ingest.
"""
from __future__ import annotations
import json
import logging
import os
import re
import threading
import urllib.error
import urllib.request
from datetime import datetime, timezone
from pathlib import Path
from typing import Any, Dict, List, Optional
from agent.memory_provider import MemoryProvider
logger = logging.getLogger(__name__)
_DEFAULT_CONTAINER_TAG = "hermes"
_DEFAULT_MAX_RECALL_RESULTS = 10
_DEFAULT_PROFILE_FREQUENCY = 50
_DEFAULT_CAPTURE_MODE = "all"
_DEFAULT_API_TIMEOUT = 5.0
_MIN_CAPTURE_LENGTH = 10
_MAX_ENTITY_CONTEXT_LENGTH = 1500
_CONVERSATIONS_URL = "https://api.supermemory.ai/v4/conversations"
_TRIVIAL_RE = re.compile(
r"^(ok|okay|thanks|thank you|got it|sure|yes|no|yep|nope|k|ty|thx|np)\.?$",
re.IGNORECASE,
)
_CONTEXT_STRIP_RE = re.compile(
r"<supermemory-context>[\s\S]*?</supermemory-context>\s*", re.DOTALL
)
_CONTAINERS_STRIP_RE = re.compile(
r"<supermemory-containers>[\s\S]*?</supermemory-containers>\s*", re.DOTALL
)
_DEFAULT_ENTITY_CONTEXT = (
"User-assistant conversation. Format: [role: user]...[user:end] and "
"[role: assistant]...[assistant:end].\n\n"
"Only extract things useful in future conversations. Most messages are not worth remembering.\n\n"
"Remember lasting personal facts, preferences, routines, tools, ongoing projects, working context, "
"and explicit requests to remember something.\n\n"
"Do not remember temporary intents, one-time tasks, assistant actions, implementation details, or in-progress status.\n\n"
"When in doubt, store less."
)
def _default_config() -> dict:
return {
"container_tag": _DEFAULT_CONTAINER_TAG,
"auto_recall": True,
"auto_capture": True,
"max_recall_results": _DEFAULT_MAX_RECALL_RESULTS,
"profile_frequency": _DEFAULT_PROFILE_FREQUENCY,
"capture_mode": _DEFAULT_CAPTURE_MODE,
"entity_context": _DEFAULT_ENTITY_CONTEXT,
"api_timeout": _DEFAULT_API_TIMEOUT,
}
def _sanitize_tag(raw: str) -> str:
tag = re.sub(r"[^a-zA-Z0-9_]", "_", raw or "")
tag = re.sub(r"_+", "_", tag)
return tag.strip("_") or _DEFAULT_CONTAINER_TAG
def _clamp_entity_context(text: str) -> str:
if not text:
return _DEFAULT_ENTITY_CONTEXT
text = text.strip()
return text[:_MAX_ENTITY_CONTEXT_LENGTH]
def _as_bool(value: Any, default: bool) -> bool:
if isinstance(value, bool):
return value
if isinstance(value, str):
lowered = value.strip().lower()
if lowered in ("true", "1", "yes", "y", "on"):
return True
if lowered in ("false", "0", "no", "n", "off"):
return False
return default
def _load_supermemory_config(hermes_home: str) -> dict:
config = _default_config()
config_path = Path(hermes_home) / "supermemory.json"
if config_path.exists():
try:
raw = json.loads(config_path.read_text(encoding="utf-8"))
if isinstance(raw, dict):
config.update({k: v for k, v in raw.items() if v is not None})
except Exception:
logger.debug("Failed to parse %s", config_path, exc_info=True)
config["container_tag"] = _sanitize_tag(str(config.get("container_tag", _DEFAULT_CONTAINER_TAG)))
config["auto_recall"] = _as_bool(config.get("auto_recall"), True)
config["auto_capture"] = _as_bool(config.get("auto_capture"), True)
try:
config["max_recall_results"] = max(1, min(20, int(config.get("max_recall_results", _DEFAULT_MAX_RECALL_RESULTS))))
except Exception:
config["max_recall_results"] = _DEFAULT_MAX_RECALL_RESULTS
try:
config["profile_frequency"] = max(1, min(500, int(config.get("profile_frequency", _DEFAULT_PROFILE_FREQUENCY))))
except Exception:
config["profile_frequency"] = _DEFAULT_PROFILE_FREQUENCY
config["capture_mode"] = "everything" if config.get("capture_mode") == "everything" else "all"
config["entity_context"] = _clamp_entity_context(str(config.get("entity_context", _DEFAULT_ENTITY_CONTEXT)))
try:
config["api_timeout"] = max(0.5, min(15.0, float(config.get("api_timeout", _DEFAULT_API_TIMEOUT))))
except Exception:
config["api_timeout"] = _DEFAULT_API_TIMEOUT
return config
def _save_supermemory_config(values: dict, hermes_home: str) -> None:
config_path = Path(hermes_home) / "supermemory.json"
existing = {}
if config_path.exists():
try:
raw = json.loads(config_path.read_text(encoding="utf-8"))
if isinstance(raw, dict):
existing = raw
except Exception:
existing = {}
existing.update(values)
config_path.write_text(json.dumps(existing, indent=2, sort_keys=True) + "\n", encoding="utf-8")
def _detect_category(text: str) -> str:
lowered = text.lower()
if re.search(r"prefer|like|love|hate|want", lowered):
return "preference"
if re.search(r"decided|will use|going with", lowered):
return "decision"
if re.search(r"\bis\b|\bare\b|\bhas\b|\bhave\b", lowered):
return "fact"
return "other"
def _format_relative_time(iso_timestamp: str) -> str:
try:
dt = datetime.fromisoformat(iso_timestamp.replace("Z", "+00:00"))
now = datetime.now(timezone.utc)
seconds = (now - dt).total_seconds()
if seconds < 1800:
return "just now"
if seconds < 3600:
return f"{int(seconds / 60)}m ago"
if seconds < 86400:
return f"{int(seconds / 3600)}h ago"
if seconds < 604800:
return f"{int(seconds / 86400)}d ago"
if dt.year == now.year:
return dt.strftime("%d %b")
return dt.strftime("%d %b %Y")
except Exception:
return ""
def _deduplicate_recall(static_facts: list, dynamic_facts: list, search_results: list) -> tuple[list, list, list]:
seen = set()
out_static, out_dynamic, out_search = [], [], []
for fact in static_facts or []:
if fact and fact not in seen:
seen.add(fact)
out_static.append(fact)
for fact in dynamic_facts or []:
if fact and fact not in seen:
seen.add(fact)
out_dynamic.append(fact)
for item in search_results or []:
memory = item.get("memory", "")
if memory and memory not in seen:
seen.add(memory)
out_search.append(item)
return out_static, out_dynamic, out_search
def _format_prefetch_context(static_facts: list, dynamic_facts: list, search_results: list, max_results: int) -> str:
statics, dynamics, search = _deduplicate_recall(static_facts, dynamic_facts, search_results)
statics = statics[:max_results]
dynamics = dynamics[:max_results]
search = search[:max_results]
if not statics and not dynamics and not search:
return ""
sections = []
if statics:
sections.append("## User Profile (Persistent)\n" + "\n".join(f"- {item}" for item in statics))
if dynamics:
sections.append("## Recent Context\n" + "\n".join(f"- {item}" for item in dynamics))
if search:
lines = []
for item in search:
memory = item.get("memory", "")
if not memory:
continue
similarity = item.get("similarity")
updated = item.get("updated_at") or item.get("updatedAt") or ""
prefix_bits = []
rel = _format_relative_time(updated)
if rel:
prefix_bits.append(f"[{rel}]")
if similarity is not None:
try:
prefix_bits.append(f"[{round(float(similarity) * 100)}%]")
except Exception:
pass
prefix = " ".join(prefix_bits)
lines.append(f"- {prefix} {memory}".strip())
if lines:
sections.append("## Relevant Memories\n" + "\n".join(lines))
if not sections:
return ""
intro = (
"The following is background context from long-term memory. Use it silently when relevant. "
"Do not force memories into the conversation."
)
body = "\n\n".join(sections)
return f"<supermemory-context>\n{intro}\n\n{body}\n</supermemory-context>"
def _clean_text_for_capture(text: str) -> str:
text = _CONTEXT_STRIP_RE.sub("", text or "")
text = _CONTAINERS_STRIP_RE.sub("", text)
return text.strip()
def _is_trivial_message(text: str) -> bool:
return bool(_TRIVIAL_RE.match((text or "").strip()))
class _SupermemoryClient:
def __init__(self, api_key: str, timeout: float, container_tag: str):
from supermemory import Supermemory
self._api_key = api_key
self._container_tag = container_tag
self._timeout = timeout
self._client = Supermemory(api_key=api_key, timeout=timeout, max_retries=0)
def add_memory(self, content: str, metadata: Optional[dict] = None, *, entity_context: str = "") -> dict:
kwargs = {
"content": content.strip(),
"container_tags": [self._container_tag],
}
if metadata:
kwargs["metadata"] = metadata
if entity_context:
kwargs["entity_context"] = _clamp_entity_context(entity_context)
result = self._client.documents.add(**kwargs)
return {"id": getattr(result, "id", "")}
def search_memories(self, query: str, *, limit: int = 5) -> list[dict]:
response = self._client.search.memories(q=query, container_tag=self._container_tag, limit=limit)
results = []
for item in (getattr(response, "results", None) or []):
results.append({
"id": getattr(item, "id", ""),
"memory": getattr(item, "memory", "") or "",
"similarity": getattr(item, "similarity", None),
"updated_at": getattr(item, "updated_at", None) or getattr(item, "updatedAt", None),
"metadata": getattr(item, "metadata", None),
})
return results
def get_profile(self, query: Optional[str] = None) -> dict:
kwargs = {"container_tag": self._container_tag}
if query:
kwargs["q"] = query
response = self._client.profile(**kwargs)
profile_data = getattr(response, "profile", None)
search_data = getattr(response, "search_results", None) or getattr(response, "searchResults", None)
static = getattr(profile_data, "static", []) or [] if profile_data else []
dynamic = getattr(profile_data, "dynamic", []) or [] if profile_data else []
raw_results = getattr(search_data, "results", None) or search_data or []
search_results = []
if isinstance(raw_results, list):
for item in raw_results:
if isinstance(item, dict):
search_results.append(item)
else:
search_results.append({
"memory": getattr(item, "memory", ""),
"updated_at": getattr(item, "updated_at", None) or getattr(item, "updatedAt", None),
"similarity": getattr(item, "similarity", None),
})
return {"static": static, "dynamic": dynamic, "search_results": search_results}
def forget_memory(self, memory_id: str) -> None:
self._client.memories.forget(container_tag=self._container_tag, id=memory_id)
def forget_by_query(self, query: str) -> dict:
results = self.search_memories(query, limit=5)
if not results:
return {"success": False, "message": "No matching memory found to forget."}
target = results[0]
memory_id = target.get("id", "")
if not memory_id:
return {"success": False, "message": "Best matching memory has no id."}
self.forget_memory(memory_id)
preview = (target.get("memory") or "")[:100]
return {"success": True, "message": f'Forgot: "{preview}"', "id": memory_id}
def ingest_conversation(self, session_id: str, messages: list[dict]) -> None:
payload = json.dumps({
"conversationId": session_id,
"messages": messages,
"containerTags": [self._container_tag],
}).encode("utf-8")
req = urllib.request.Request(
_CONVERSATIONS_URL,
data=payload,
headers={
"Authorization": f"Bearer {self._api_key}",
"Content-Type": "application/json",
},
method="POST",
)
with urllib.request.urlopen(req, timeout=self._timeout + 3):
return
STORE_SCHEMA = {
"name": "supermemory_store",
"description": "Store an explicit memory for future recall.",
"parameters": {
"type": "object",
"properties": {
"content": {"type": "string", "description": "The memory content to store."},
"metadata": {"type": "object", "description": "Optional metadata attached to the memory."},
},
"required": ["content"],
},
}
SEARCH_SCHEMA = {
"name": "supermemory_search",
"description": "Search long-term memory by semantic similarity.",
"parameters": {
"type": "object",
"properties": {
"query": {"type": "string", "description": "What to search for."},
"limit": {"type": "integer", "description": "Maximum results to return, 1 to 20."},
},
"required": ["query"],
},
}
FORGET_SCHEMA = {
"name": "supermemory_forget",
"description": "Forget a memory by exact id or by best-match query.",
"parameters": {
"type": "object",
"properties": {
"id": {"type": "string", "description": "Exact memory id to delete."},
"query": {"type": "string", "description": "Query used to find the memory to forget."},
},
},
}
PROFILE_SCHEMA = {
"name": "supermemory_profile",
"description": "Retrieve persistent profile facts and recent memory context.",
"parameters": {
"type": "object",
"properties": {
"query": {"type": "string", "description": "Optional query to focus the profile response."},
},
},
}
class SupermemoryMemoryProvider(MemoryProvider):
def __init__(self):
self._config = _default_config()
self._api_key = ""
self._client: Optional[_SupermemoryClient] = None
self._container_tag = _DEFAULT_CONTAINER_TAG
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
self._write_thread: Optional[threading.Thread] = None
self._auto_recall = True
self._auto_capture = True
self._max_recall_results = _DEFAULT_MAX_RECALL_RESULTS
self._profile_frequency = _DEFAULT_PROFILE_FREQUENCY
self._capture_mode = _DEFAULT_CAPTURE_MODE
self._entity_context = _DEFAULT_ENTITY_CONTEXT
self._api_timeout = _DEFAULT_API_TIMEOUT
self._hermes_home = ""
self._write_enabled = True
self._active = False
@property
def name(self) -> str:
return "supermemory"
def is_available(self) -> bool:
api_key = os.environ.get("SUPERMEMORY_API_KEY", "")
if not api_key:
return False
try:
__import__("supermemory")
return True
except Exception:
return False
def get_config_schema(self):
return [
{"key": "api_key", "description": "Supermemory API key", "secret": True, "required": True, "env_var": "SUPERMEMORY_API_KEY", "url": "https://supermemory.ai"},
{"key": "container_tag", "description": "Container tag for reads and writes", "default": _DEFAULT_CONTAINER_TAG},
{"key": "auto_recall", "description": "Enable automatic recall before each turn", "default": "true", "choices": ["true", "false"]},
{"key": "auto_capture", "description": "Enable automatic capture after each completed turn", "default": "true", "choices": ["true", "false"]},
{"key": "max_recall_results", "description": "Maximum recalled items to inject", "default": str(_DEFAULT_MAX_RECALL_RESULTS)},
{"key": "profile_frequency", "description": "Include profile facts on first turn and every N turns", "default": str(_DEFAULT_PROFILE_FREQUENCY)},
{"key": "capture_mode", "description": "Capture mode", "default": _DEFAULT_CAPTURE_MODE, "choices": ["all", "everything"]},
{"key": "entity_context", "description": "Extraction guidance passed to Supermemory", "default": _DEFAULT_ENTITY_CONTEXT},
{"key": "api_timeout", "description": "Timeout in seconds for SDK and ingest calls", "default": str(_DEFAULT_API_TIMEOUT)},
]
def save_config(self, values, hermes_home):
sanitized = dict(values or {})
if "container_tag" in sanitized:
sanitized["container_tag"] = _sanitize_tag(str(sanitized["container_tag"]))
if "entity_context" in sanitized:
sanitized["entity_context"] = _clamp_entity_context(str(sanitized["entity_context"]))
_save_supermemory_config(sanitized, hermes_home)
def initialize(self, session_id: str, **kwargs) -> None:
from hermes_constants import get_hermes_home
self._hermes_home = kwargs.get("hermes_home") or str(get_hermes_home())
self._session_id = session_id
self._turn_count = 0
self._config = _load_supermemory_config(self._hermes_home)
self._api_key = os.environ.get("SUPERMEMORY_API_KEY", "")
self._container_tag = self._config["container_tag"]
self._auto_recall = self._config["auto_recall"]
self._auto_capture = self._config["auto_capture"]
self._max_recall_results = self._config["max_recall_results"]
self._profile_frequency = self._config["profile_frequency"]
self._capture_mode = self._config["capture_mode"]
self._entity_context = self._config["entity_context"]
self._api_timeout = self._config["api_timeout"]
agent_context = kwargs.get("agent_context", "")
self._write_enabled = agent_context not in ("cron", "flush", "subagent")
self._active = bool(self._api_key)
self._client = None
if self._active:
try:
self._client = _SupermemoryClient(
api_key=self._api_key,
timeout=self._api_timeout,
container_tag=self._container_tag,
)
except Exception:
logger.warning("Supermemory initialization failed", exc_info=True)
self._active = False
self._client = None
def on_turn_start(self, turn_number: int, message: str, **kwargs) -> None:
self._turn_count = max(turn_number, 0)
def system_prompt_block(self) -> str:
if not self._active:
return ""
return (
"# Supermemory\n"
f"Active. Container: {self._container_tag}.\n"
"Use supermemory_search, supermemory_store, supermemory_forget, and supermemory_profile for explicit memory operations."
)
def prefetch(self, query: str, *, session_id: str = "") -> str:
if not self._active or not self._auto_recall or not self._client or not query.strip():
return ""
try:
profile = self._client.get_profile(query=query[:200])
include_profile = self._turn_count <= 1 or (self._turn_count % self._profile_frequency == 0)
context = _format_prefetch_context(
static_facts=profile["static"] if include_profile else [],
dynamic_facts=profile["dynamic"] if include_profile else [],
search_results=profile["search_results"],
max_results=self._max_recall_results,
)
return context
except Exception:
logger.debug("Supermemory prefetch failed", exc_info=True)
return ""
def sync_turn(self, user_content: str, assistant_content: str, *, session_id: str = "") -> None:
if not self._active or not self._auto_capture or not self._write_enabled or not self._client:
return
clean_user = _clean_text_for_capture(user_content)
clean_assistant = _clean_text_for_capture(assistant_content)
if not clean_user or not clean_assistant:
return
if self._capture_mode == "all":
if len(clean_user) < _MIN_CAPTURE_LENGTH or len(clean_assistant) < _MIN_CAPTURE_LENGTH:
return
if _is_trivial_message(clean_user):
return
content = (
f"[role: user]\n{clean_user}\n[user:end]\n\n"
f"[role: assistant]\n{clean_assistant}\n[assistant:end]"
)
metadata = {"source": "hermes", "type": "conversation_turn"}
def _run():
try:
self._client.add_memory(content, metadata=metadata, entity_context=self._entity_context)
except Exception:
logger.debug("Supermemory sync_turn failed", exc_info=True)
if self._sync_thread and self._sync_thread.is_alive():
self._sync_thread.join(timeout=2.0)
self._sync_thread = None
self._sync_thread = threading.Thread(target=_run, daemon=True, name="supermemory-sync")
self._sync_thread.start()
def on_session_end(self, messages: List[Dict[str, Any]]) -> None:
if not self._active or not self._write_enabled or not self._client or not self._session_id:
return
cleaned = []
for message in messages or []:
role = message.get("role")
if role not in ("user", "assistant"):
continue
content = _clean_text_for_capture(str(message.get("content", "")))
if content:
cleaned.append({"role": role, "content": content})
if not cleaned:
return
if len(cleaned) == 1 and len(cleaned[0].get("content", "")) < 20:
return
try:
self._client.ingest_conversation(self._session_id, cleaned)
except urllib.error.HTTPError:
logger.warning("Supermemory session ingest failed", exc_info=True)
except Exception:
logger.warning("Supermemory session ingest failed", exc_info=True)
def on_memory_write(self, action: str, target: str, content: str) -> None:
if not self._active or not self._write_enabled or not self._client:
return
if action != "add" or not (content or "").strip():
return
def _run():
try:
self._client.add_memory(
content.strip(),
metadata={"source": "hermes_memory", "target": target, "type": "explicit_memory"},
entity_context=self._entity_context,
)
except Exception:
logger.debug("Supermemory on_memory_write failed", exc_info=True)
if self._write_thread and self._write_thread.is_alive():
self._write_thread.join(timeout=2.0)
self._write_thread = None
self._write_thread = threading.Thread(target=_run, daemon=False, name="supermemory-memory-write")
self._write_thread.start()
def shutdown(self) -> None:
for attr_name in ("_prefetch_thread", "_sync_thread", "_write_thread"):
thread = getattr(self, attr_name, None)
if thread and thread.is_alive():
thread.join(timeout=5.0)
setattr(self, attr_name, None)
def get_tool_schemas(self) -> List[Dict[str, Any]]:
return [STORE_SCHEMA, SEARCH_SCHEMA, FORGET_SCHEMA, PROFILE_SCHEMA]
def _tool_store(self, args: dict) -> str:
content = str(args.get("content") or "").strip()
if not content:
return json.dumps({"error": "content is required"})
metadata = args.get("metadata") or {}
if not isinstance(metadata, dict):
metadata = {}
metadata.setdefault("type", _detect_category(content))
metadata["source"] = "hermes_tool"
try:
result = self._client.add_memory(content, metadata=metadata, entity_context=self._entity_context)
preview = content[:80] + ("..." if len(content) > 80 else "")
return json.dumps({"saved": True, "id": result.get("id", ""), "preview": preview})
except Exception as exc:
return json.dumps({"error": f"Failed to store memory: {exc}"})
def _tool_search(self, args: dict) -> str:
query = str(args.get("query") or "").strip()
if not query:
return json.dumps({"error": "query is required"})
try:
limit = max(1, min(20, int(args.get("limit", 5) or 5)))
except Exception:
limit = 5
try:
results = self._client.search_memories(query, limit=limit)
formatted = []
for item in results:
entry = {"id": item.get("id", ""), "content": item.get("memory", "")}
if item.get("similarity") is not None:
try:
entry["similarity"] = round(float(item["similarity"]) * 100)
except Exception:
pass
formatted.append(entry)
return json.dumps({"results": formatted, "count": len(formatted)})
except Exception as exc:
return json.dumps({"error": f"Search failed: {exc}"})
def _tool_forget(self, args: dict) -> str:
memory_id = str(args.get("id") or "").strip()
query = str(args.get("query") or "").strip()
if not memory_id and not query:
return json.dumps({"error": "Provide either id or query"})
try:
if memory_id:
self._client.forget_memory(memory_id)
return json.dumps({"forgotten": True, "id": memory_id})
return json.dumps(self._client.forget_by_query(query))
except Exception as exc:
return json.dumps({"error": f"Forget failed: {exc}"})
def _tool_profile(self, args: dict) -> str:
query = str(args.get("query") or "").strip() or None
try:
profile = self._client.get_profile(query=query)
sections = []
if profile["static"]:
sections.append("## User Profile (Persistent)\n" + "\n".join(f"- {item}" for item in profile["static"]))
if profile["dynamic"]:
sections.append("## Recent Context\n" + "\n".join(f"- {item}" for item in profile["dynamic"]))
return json.dumps({
"profile": "\n\n".join(sections),
"static_count": len(profile["static"]),
"dynamic_count": len(profile["dynamic"]),
})
except Exception as exc:
return json.dumps({"error": f"Profile failed: {exc}"})
def handle_tool_call(self, tool_name: str, args: Dict[str, Any], **kwargs) -> str:
if not self._active or not self._client:
return json.dumps({"error": "Supermemory is not configured"})
if tool_name == "supermemory_store":
return self._tool_store(args)
if tool_name == "supermemory_search":
return self._tool_search(args)
if tool_name == "supermemory_forget":
return self._tool_forget(args)
if tool_name == "supermemory_profile":
return self._tool_profile(args)
return json.dumps({"error": f"Unknown tool: {tool_name}"})
def register(ctx):
ctx.register_memory_provider(SupermemoryMemoryProvider())

View File

@@ -0,0 +1,5 @@
name: supermemory
version: 1.0.0
description: "Supermemory semantic long-term memory with profile recall, semantic search, explicit memory tools, and session ingest."
pip_dependencies:
- supermemory

View File

@@ -0,0 +1,257 @@
import json
import threading
import pytest
from plugins.memory.supermemory import (
SupermemoryMemoryProvider,
_clean_text_for_capture,
_format_prefetch_context,
_load_supermemory_config,
_save_supermemory_config,
)
class FakeClient:
def __init__(self, api_key: str, timeout: float, container_tag: str):
self.api_key = api_key
self.timeout = timeout
self.container_tag = container_tag
self.add_calls = []
self.search_results = []
self.profile_response = {"static": [], "dynamic": [], "search_results": []}
self.ingest_calls = []
self.forgotten_ids = []
self.forget_by_query_response = {"success": True, "message": "Forgot"}
def add_memory(self, content, metadata=None, *, entity_context=""):
self.add_calls.append({
"content": content,
"metadata": metadata,
"entity_context": entity_context,
})
return {"id": "mem_123"}
def search_memories(self, query, *, limit=5):
return self.search_results
def get_profile(self, query=None):
return self.profile_response
def forget_memory(self, memory_id):
self.forgotten_ids.append(memory_id)
def forget_by_query(self, query):
return self.forget_by_query_response
def ingest_conversation(self, session_id, messages):
self.ingest_calls.append({"session_id": session_id, "messages": messages})
@pytest.fixture
def provider(monkeypatch, tmp_path):
monkeypatch.setenv("SUPERMEMORY_API_KEY", "test-key")
monkeypatch.setattr("plugins.memory.supermemory._SupermemoryClient", FakeClient)
p = SupermemoryMemoryProvider()
p.initialize("session-1", hermes_home=str(tmp_path), platform="cli")
return p
def test_is_available_false_without_api_key(monkeypatch):
monkeypatch.delenv("SUPERMEMORY_API_KEY", raising=False)
p = SupermemoryMemoryProvider()
assert p.is_available() is False
def test_is_available_false_when_import_missing(monkeypatch):
monkeypatch.setenv("SUPERMEMORY_API_KEY", "test-key")
import builtins
real_import = builtins.__import__
def fake_import(name, *args, **kwargs):
if name == "supermemory":
raise ImportError("missing")
return real_import(name, *args, **kwargs)
monkeypatch.setattr(builtins, "__import__", fake_import)
p = SupermemoryMemoryProvider()
assert p.is_available() is False
def test_load_and_save_config_round_trip(tmp_path):
_save_supermemory_config({"container_tag": "demo-tag", "auto_capture": False}, str(tmp_path))
cfg = _load_supermemory_config(str(tmp_path))
assert cfg["container_tag"] == "demo_tag"
assert cfg["auto_capture"] is False
assert cfg["auto_recall"] is True
def test_clean_text_for_capture_strips_injected_context():
text = "hello\n<supermemory-context>ignore me</supermemory-context>\nworld"
assert _clean_text_for_capture(text) == "hello\nworld"
def test_format_prefetch_context_deduplicates_overlap():
result = _format_prefetch_context(
static_facts=["Jordan prefers short answers"],
dynamic_facts=["Jordan prefers short answers", "Uses Hermes"],
search_results=[{"memory": "Uses Hermes", "similarity": 0.9}],
max_results=10,
)
assert result.count("Jordan prefers short answers") == 1
assert result.count("Uses Hermes") == 1
assert "<supermemory-context>" in result
def test_prefetch_includes_profile_on_first_turn(provider):
provider._client.profile_response = {
"static": ["Jordan prefers short answers"],
"dynamic": ["Current project is Supermemory provider"],
"search_results": [{"memory": "Working on Hermes memory provider", "similarity": 0.88}],
}
provider.on_turn_start(1, "start")
result = provider.prefetch("what am I working on?")
assert "User Profile (Persistent)" in result
assert "Recent Context" in result
assert "Relevant Memories" in result
def test_prefetch_skips_profile_between_frequency(provider):
provider._client.profile_response = {
"static": ["Jordan prefers short answers"],
"dynamic": ["Current project is Supermemory provider"],
"search_results": [{"memory": "Working on Hermes memory provider", "similarity": 0.88}],
}
provider.on_turn_start(2, "next")
result = provider.prefetch("what am I working on?")
assert "Relevant Memories" in result
assert "User Profile (Persistent)" not in result
def test_sync_turn_skips_trivial_message(provider):
provider.sync_turn("ok", "sure", session_id="session-1")
assert provider._client.add_calls == []
def test_sync_turn_persists_cleaned_exchange(provider):
provider.sync_turn(
"Please remember this\n<supermemory-context>ignore</supermemory-context>",
"Got it, storing the context",
session_id="session-1",
)
provider._sync_thread.join(timeout=1)
assert len(provider._client.add_calls) == 1
content = provider._client.add_calls[0]["content"]
assert "ignore" not in content
assert "[role: user]" in content
assert "[role: assistant]" in content
def test_on_session_end_ingests_clean_messages(provider):
messages = [
{"role": "system", "content": "skip"},
{"role": "user", "content": "hello"},
{"role": "assistant", "content": "hi there"},
]
provider.on_session_end(messages)
assert len(provider._client.ingest_calls) == 1
payload = provider._client.ingest_calls[0]
assert payload["session_id"] == "session-1"
assert payload["messages"] == [
{"role": "user", "content": "hello"},
{"role": "assistant", "content": "hi there"},
]
def test_on_memory_write_tracks_thread(provider):
provider.on_memory_write("add", "memory", "Jordan likes concise docs")
assert provider._write_thread is not None
provider._write_thread.join(timeout=1)
assert len(provider._client.add_calls) == 1
assert provider._client.add_calls[0]["metadata"]["type"] == "explicit_memory"
def test_shutdown_joins_and_clears_threads(provider, monkeypatch):
started = threading.Event()
release = threading.Event()
def slow_add_memory(content, metadata=None, *, entity_context=""):
started.set()
release.wait(timeout=1)
provider._client.add_calls.append({
"content": content,
"metadata": metadata,
"entity_context": entity_context,
})
return {"id": "mem_slow"}
monkeypatch.setattr(provider._client, "add_memory", slow_add_memory)
provider.sync_turn(
"Please remember this request in long-term memory",
"Absolutely, I will keep that in long-term memory.",
session_id="session-1",
)
assert started.wait(timeout=1)
assert provider._sync_thread is not None
started.clear()
provider.on_memory_write("add", "memory", "Jordan likes concise docs")
assert started.wait(timeout=1)
assert provider._write_thread is not None
release.set()
provider.shutdown()
assert provider._sync_thread is None
assert provider._write_thread is None
assert provider._prefetch_thread is None
assert len(provider._client.add_calls) == 2
def test_store_tool_returns_saved_payload(provider):
result = json.loads(provider.handle_tool_call("supermemory_store", {"content": "Jordan likes concise docs"}))
assert result["saved"] is True
assert result["id"] == "mem_123"
def test_search_tool_formats_results(provider):
provider._client.search_results = [
{"id": "m1", "memory": "Jordan likes concise docs", "similarity": 0.92}
]
result = json.loads(provider.handle_tool_call("supermemory_search", {"query": "concise docs"}))
assert result["count"] == 1
assert result["results"][0]["similarity"] == 92
def test_forget_tool_by_id(provider):
result = json.loads(provider.handle_tool_call("supermemory_forget", {"id": "m1"}))
assert result == {"forgotten": True, "id": "m1"}
assert provider._client.forgotten_ids == ["m1"]
def test_forget_tool_by_query(provider):
provider._client.forget_by_query_response = {"success": True, "message": "Forgot one", "id": "m7"}
result = json.loads(provider.handle_tool_call("supermemory_forget", {"query": "that thing"}))
assert result["success"] is True
assert result["id"] == "m7"
def test_profile_tool_formats_sections(provider):
provider._client.profile_response = {
"static": ["Jordan prefers concise docs"],
"dynamic": ["Working on Supermemory provider"],
"search_results": [],
}
result = json.loads(provider.handle_tool_call("supermemory_profile", {}))
assert result["static_count"] == 1
assert result["dynamic_count"] == 1
assert "User Profile (Persistent)" in result["profile"]
def test_handle_tool_call_returns_error_when_unconfigured(monkeypatch):
monkeypatch.delenv("SUPERMEMORY_API_KEY", raising=False)
p = SupermemoryMemoryProvider()
result = json.loads(p.handle_tool_call("supermemory_search", {"query": "x"}))
assert "error" in result

View File

@@ -846,6 +846,7 @@ You can switch between providers at any time with `hermes model` — no restart
| OpenAI TTS + voice transcription | [OpenAI](https://platform.openai.com/api-keys) | `VOICE_TOOLS_OPENAI_KEY` |
| RL Training | [Tinker](https://tinker-console.thinkingmachines.ai/) + [WandB](https://wandb.ai/) | `TINKER_API_KEY`, `WANDB_API_KEY` |
| Cross-session user modeling | [Honcho](https://honcho.dev/) | `HONCHO_API_KEY` |
| Semantic long-term memory | [Supermemory](https://supermemory.ai) | `SUPERMEMORY_API_KEY` |
### Self-Hosting Firecrawl

View File

@@ -383,7 +383,7 @@ Subcommands:
hermes memory <subcommand>
```
Set up and manage external memory provider plugins. Available providers: honcho, openviking, mem0, hindsight, holographic, retaindb, byterover. Only one external provider can be active at a time. Built-in memory (MEMORY.md/USER.md) is always active.
Set up and manage external memory provider plugins. Available providers: honcho, openviking, mem0, hindsight, holographic, retaindb, byterover, supermemory. Only one external provider can be active at a time. Built-in memory (MEMORY.md/USER.md) is always active.
Subcommands:

View File

@@ -98,6 +98,7 @@ For native Anthropic auth, Hermes prefers Claude Code's own credential files whe
| `GITHUB_TOKEN` | GitHub token for Skills Hub (higher API rate limits, skill publish) |
| `HONCHO_API_KEY` | Cross-session user modeling ([honcho.dev](https://honcho.dev/)) |
| `HONCHO_BASE_URL` | Base URL for self-hosted Honcho instances (default: Honcho cloud). No API key required for local instances |
| `SUPERMEMORY_API_KEY` | Semantic long-term memory with profile recall and session ingest ([supermemory.ai](https://supermemory.ai)) |
| `TINKER_API_KEY` | RL training ([tinker-console.thinkingmachines.ai](https://tinker-console.thinkingmachines.ai/)) |
| `WANDB_API_KEY` | RL training metrics ([wandb.ai](https://wandb.ai/)) |
| `DAYTONA_API_KEY` | Daytona cloud sandboxes ([daytona.io](https://daytona.io/)) |

View File

@@ -1,12 +1,12 @@
---
sidebar_position: 4
title: "Memory Providers"
description: "External memory provider plugins — Honcho, OpenViking, Mem0, Hindsight, Holographic, RetainDB, ByteRover"
description: "External memory provider plugins — Honcho, OpenViking, Mem0, Hindsight, Holographic, RetainDB, ByteRover, Supermemory"
---
# Memory Providers
Hermes Agent ships with 7 external memory provider plugins that give the agent persistent, cross-session knowledge beyond the built-in MEMORY.md and USER.md. Only **one** external provider can be active at a time — the built-in memory is always active alongside it.
Hermes Agent ships with 8 external memory provider plugins that give the agent persistent, cross-session knowledge beyond the built-in MEMORY.md and USER.md. Only **one** external provider can be active at a time — the built-in memory is always active alongside it.
## Quick Start
@@ -20,7 +20,7 @@ Or set manually in `~/.hermes/config.yaml`:
```yaml
memory:
provider: openviking # or honcho, mem0, hindsight, holographic, retaindb, byterover
provider: openviking # or honcho, mem0, hindsight, holographic, retaindb, byterover, supermemory
```
## How It Works
@@ -382,6 +382,47 @@ hermes config set memory.provider byterover
---
### Supermemory
Semantic long-term memory with profile recall, semantic search, explicit memory tools, and session-end conversation ingest via the Supermemory graph API.
| | |
|---|---|
| **Best for** | Semantic recall with user profiling and session-level graph building |
| **Requires** | `pip install supermemory` + [API key](https://supermemory.ai) |
| **Data storage** | Supermemory Cloud |
| **Cost** | Supermemory pricing |
**Tools:** `supermemory_store` (save explicit memories), `supermemory_search` (semantic similarity search), `supermemory_forget` (forget by ID or best-match query), `supermemory_profile` (persistent profile + recent context)
**Setup:**
```bash
hermes memory setup # select "supermemory"
# Or manually:
hermes config set memory.provider supermemory
echo 'SUPERMEMORY_API_KEY=your-key-here' >> ~/.hermes/.env
```
**Config:** `$HERMES_HOME/supermemory.json`
| Key | Default | Description |
|-----|---------|-------------|
| `container_tag` | `hermes` | Container tag used for search and writes |
| `auto_recall` | `true` | Inject relevant memory context before turns |
| `auto_capture` | `true` | Store cleaned user-assistant turns after each response |
| `max_recall_results` | `10` | Max recalled items to format into context |
| `profile_frequency` | `50` | Include profile facts on first turn and every N turns |
| `capture_mode` | `all` | Skip tiny or trivial turns by default |
| `api_timeout` | `5.0` | Timeout for SDK and ingest requests |
**Key features:**
- Automatic context fencing — strips recalled memories from captured turns to prevent recursive memory pollution
- Session-end conversation ingest for richer graph-level knowledge building
- Profile facts injected on first turn and at configurable intervals
- Trivial message filtering (skips "ok", "thanks", etc.)
---
## Provider Comparison
| Provider | Storage | Cost | Tools | Dependencies | Unique Feature |
@@ -393,13 +434,14 @@ hermes config set memory.provider byterover
| **Holographic** | Local | Free | 2 | None | HRR algebra + trust scoring |
| **RetainDB** | Cloud | $20/mo | 5 | `requests` | Delta compression |
| **ByteRover** | Local/Cloud | Free/Paid | 3 | `brv` CLI | Pre-compression extraction |
| **Supermemory** | Cloud | Paid | 4 | `supermemory` | Context fencing + session graph ingest |
## Profile Isolation
Each provider's data is isolated per [profile](/docs/user-guide/profiles):
- **Local storage providers** (Holographic, ByteRover) use `$HERMES_HOME/` paths which differ per profile
- **Config file providers** (Honcho, Mem0, Hindsight) store config in `$HERMES_HOME/` so each profile has its own credentials
- **Config file providers** (Honcho, Mem0, Hindsight, Supermemory) store config in `$HERMES_HOME/` so each profile has its own credentials
- **Cloud providers** (RetainDB) auto-derive profile-scoped project names
- **Env var providers** (OpenViking) are configured via each profile's `.env` file

View File

@@ -209,7 +209,7 @@ memory:
## External Memory Providers
For deeper, persistent memory that goes beyond MEMORY.md and USER.md, Hermes ships with 7 external memory provider plugins — including Honcho, OpenViking, Mem0, Hindsight, Holographic, RetainDB, and ByteRover.
For deeper, persistent memory that goes beyond MEMORY.md and USER.md, Hermes ships with 8 external memory provider plugins — including Honcho, OpenViking, Mem0, Hindsight, Holographic, RetainDB, ByteRover, and Supermemory.
External providers run **alongside** built-in memory (never replacing it) and add capabilities like knowledge graphs, semantic search, automatic fact extraction, and cross-session user modeling.