hermes-memory-providers
Agent BuildingInstall and configure Mnemosyne as a Hermes Agent memory provider — local SQLite with vector search, episodic consolidation, and temporal knowledge graphs.
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Mnemosyne — Hermes Memory Provider
Mnemosyne is a local-first memory layer for AI agents. When deployed as a Hermes memory provider, it replaces the built-in MEMORY.md/USER.md system with SQLite-backed vector + FTS5 hybrid search, episodic consolidation, temporal knowledge graphs, and optional bidirectional sync.
100% local. Zero cloud. Sub-millisecond recall.
What It Gives You
- System prompt injection —
# Mnemosyne Memorycontext block in every prompt - Pre-turn prefetch — relevant memories injected before each LLM call
- Post-turn sync — conversation turns auto-stored to episodic memory
- 20 tools auto-injected into the model's tool surface (remember, recall, sleep, triples, scratchpad, graph, sync, diagnostics, etc.)
- 3 lifecycle hooks —
pre_llm_call,on_session_start,post_tool_call - CLI commands —
hermes mnemosyne {stats|sleep|inspect|export|import|clear|version}
All without touching Hermes core — deployed purely through the plugin directory.
Quick Check
hermes memory status # See active provider and installed plugins
Install
Step 1 — Install the package
pip install mnemosyne-hermes
Debian/Trixie users (bare pip blocked): use a venv first:
python3 -m venv ~/.hermes/hermes-agent/venv
source ~/.hermes/hermes-agent/venv/bin/activate
pip install mnemosyne-hermes
mnemosyne-hermes wraps the core mnemosyne-memory library with the plugin
manifest and entry points Hermes needs. It does not pull embeddings or LLM
deps — pair it with one of:
| Extra | When | RAM |
|---|---|---|
| (core only) | Raspberry Pi, remote embedding API | ~50 MB |
mnemosyne-memory[embeddings] | Local vector search (fastembed ONNX) | ~800 MB |
mnemosyne-memory[all] | Local embeddings + local LLM consolidation | ~1.5 GB |
Step 2 — Link the plugin
mnemosyne-hermes install
This creates the symlink ~/.hermes/plugins/mnemosyne/ → <installed package>
so Hermes discovers it on startup.
Docker / read-only venv — use persistent wrapper mode so the plugin survives image rebuilds:
mnemosyne-hermes install --mode wrapper --python /path/to/venv/bin/python --hermes-home /opt/data
mnemosyne-hermes status --hermes-home /opt/data
hermes gateway restart
Step 3 — Activate
hermes config set memory.provider mnemosyne
hermes memory setup
Step 4 — (Optional) Disable built-in memory
Mnemosyne is additive by default — the built-in MEMORY.md/USER.md keeps
running alongside it. To make Mnemosyne the sole memory system, edit
~/.hermes/config.yaml:
memory:
memory_enabled: false
user_profile_enabled: false
Do NOT run hermes tools disable memory — that also kills all 20
Mnemosyne-registered tools.
Step 5 — Verify
hermes memory status # Should show "Provider: mnemosyne"
hermes mnemosyne stats # Working + episodic memory counts
Test in a conversation:
hermes chat -q "Remember that I love apples. What do I love?"
You should see mnemosyne_remember and mnemosyne_recall calls succeed.
If
hermes mnemosyne statsgives "invalid choice: 'mnemosyne'", the plugin CLI registration didn't load. Usehermes hermes-mnemosyne statsas a fallback, or re-run Step 2 to relink.
MCP vs. Provider Plugin
Mnemosyne ships an MCP server (mnemosyne mcp, stdio + SSE transports) that
exposes 35 tools — usable with any MCP-compatible client (Claude Desktop,
etc.):
mnemosyne mcp # stdio transport
mnemosyne mcp --transport sse --port 8080 # SSE transport
For Hermes, prefer the provider plugin over MCP. The provider plugin
gives deeper integration that MCP cannot: the pre_llm_call context
injection hook, on_session_start initialization, post_tool_call memory
capture, and the hermes mnemosyne CLI subcommands. MCP is a generic
fallback for non-Hermes agents.
Switching Back
hermes memory off # Disable external provider, revert to built-in
hermes memory setup # Or use the interactive picker
Or manually:
hermes config set memory.provider memory
Then restart Hermes.
CLI Commands
hermes mnemosyne stats # Current session stats
hermes mnemosyne stats --global # Stats across all sessions
hermes mnemosyne inspect "query" # Search memories
hermes mnemosyne sleep # Run consolidation (working → episodic)
hermes mnemosyne export --output backup.json
hermes mnemosyne import --input backup.json
hermes mnemosyne clear # Clear scratchpad
hermes mnemosyne version # Show version
Data Location
~/.hermes/mnemosyne/
└── data/
├── mnemosyne.db # Main SQLite database (WAL mode)
├── triples.db # Standalone TripleStore
└── banks/<name>/mnemosyne.db # Named memory banks (per-bank isolation)
Persists across sessions via ~/.hermes/ (including on ephemeral VMs like Fly.io).
Optional: Host LLM Routing
Mnemosyne's consolidation (sleep) and fact extraction can use a local GGUF or
a remote OpenAI-compatible API. Hermes users with OAuth-backed providers
(e.g. openai-codex) can route those LLM calls through Hermes' authenticated
auxiliary client instead — no extra credentials needed:
export MNEMOSYNE_HOST_LLM_ENABLED=true
See docs/hermes-llm-integration.md for the full behavior model and config.
Troubleshooting
| Symptom | Cause / Fix |
|---|---|
hermes memory status shows built-in only | Provider not loaded; restart Hermes after install. |
Plugin listed but unavailable | Missing Python deps in Hermes' venv; pip install mnemosyne-hermes in that venv. |
hermes mnemosyne stats → "invalid choice" | Plugin CLI registration didn't load; use hermes hermes-mnemosyne stats or relink (Step 2). |
| Memory not recalled across sessions | Provider loaded but session didn't restart; new sessions pick up the provider. |
mnemosyne_hermes import error in Docker | Use wrapper mode: mnemosyne-hermes install --mode wrapper --python <venv>/bin/python. |
References
- Hermes Integration Guide — canonical setup reference
- Hermes Auxiliary LLM Integration — host LLM routing
- Mnemosyne vs Hindsight Comparison — architecture and feature comparison
hermes-agentskill — general Hermes setup, config, and plugin system