hermes-agent/plugins/memory/mem0

Mem0 Memory Provider

Server-side LLM fact extraction with semantic search and hybrid multi-signal retrieval via the Mem0 Platform v3 API.

Requirements

• The mem0ai SDK, prepared through PM by hermes memory setup when you select Mem0. Restart Hermes after preparation; do not install into its selected environment with pip.
• Mem0 API key from app.mem0.ai

Setup

hermes memory setup    # select "mem0"

Or manually:

hermes config set memory.provider mem0
echo "MEM0_API_KEY=your-key" >> ~/.hermes/.env

Config

Behavioral settings live in $HERMES_HOME/mem0.json (set them via hermes memory setup). Only the secret MEM0_API_KEY belongs in ~/.hermes/.env.

Key	Default	Description
mode	platform	platform (Mem0 Cloud) or oss (self-managed, in-process)
host	—	Self-hosted Mem0 server URL (the Docker dashboard). When set, connects over HTTP with X-API-Key. Don't combine with mode: oss
user_id	hermes-user	User identifier on Mem0
agent_id	hermes	Agent identifier
rerank	false	Rerank search results for relevance (platform mode only)
sync_max_chars	450	Per-message character cap applied before each turn is sent for fact extraction (cut at the last sentence boundary). Default fits 512-token embedders; raise it (e.g. 6000) for 8k-token embedders such as text-embedding-3-small, jina-embeddings-v3, bge-m3

The plugin has three connection modes:

• Platform — Mem0's hosted cloud (api.mem0.ai). Set MEM0_API_KEY. (default)
• Self-hosted dashboard — a Mem0 server you run yourself via Docker. Set host. See below.
• OSS — run Mem0 in-process with your own LLM + vector store. Set mode: oss. See below.

Self-Hosted Dashboard (Server) Mode

Connect the plugin to a standalone Mem0 server you run yourself — the Docker-shipped Mem0 dashboard/server with its own REST API. Unlike OSS mode (which runs mem0ai in-process with your own vector store), here the plugin just talks HTTP to your server.

1. Run the Mem0 server (FastAPI + pgvector) from its Docker image and note its URL and ADMIN_API_KEY.
2. Point the plugin at it — via the setup wizard:

   hermes memory setup    # select "mem0" → "Self-hosted server"
   # Or non-interactive:
   hermes memory setup mem0 --mode selfhosted --host http://localhost:8888 --api-key your-admin-api-key
   

   or via env vars:

   echo "MEM0_HOST=http://localhost:8888" >> ~/.hermes/.env
   echo "MEM0_API_KEY=your-admin-api-key" >> ~/.hermes/.env
   

   or in $HERMES_HOME/mem0.json:

   {
     "host": "http://localhost:8888",
     "api_key": "your-admin-api-key"
   }
   

3. Start a fresh Hermes session and call mem0_search — it connects to your server.

The plugin authenticates with X-API-Key and uses the server's /search and /memories routes. api_key is optional — omit it only for servers running with AUTH_DISABLED.

Setting host routes to the self-hosted server automatically. Don't set mode: oss — OSS takes precedence and ignores host.

OSS (Self-Hosted) Mode

Run Mem0 locally with your own LLM, embedder, and vector store. This is the in-process SDK mode. To instead connect to a Mem0 server you run via Docker, see Self-Hosted Dashboard (Server) Mode above.

Interactive Setup

hermes memory setup
# Select "mem0" → "Open Source (self-hosted)"
# Follow prompts for LLM, embedder, and vector store

Agent-Driven Setup (Flags)

hermes memory setup mem0 --mode oss \
  --oss-llm openai --oss-llm-key sk-... \
  --oss-vector qdrant

Supported Providers

Component	Providers
LLM	openai, ollama
Embedder	openai, ollama
Vector Store	qdrant (local/server), pgvector

Flags Reference

Flag	Description
--mode	platform or oss
--oss-llm	LLM provider (default: openai)
--oss-llm-key	LLM API key
--oss-embedder	Embedder provider (default: openai)
--oss-vector	Vector store (default: qdrant)
--oss-vector-path	Qdrant local path
--user-id	User identifier

Switching Modes

Platform to OSS

hermes memory setup mem0 --mode oss --oss-llm-key sk-...

Or edit $HERMES_HOME/mem0.json directly:

{
  "mode": "oss",
  "oss": {
    "llm": {"provider": "openai", "config": {"model": "gpt-5-mini", "is_reasoning_model": true}},
    "embedder": {"provider": "openai", "config": {"model": "text-embedding-3-small"}},
    "vector_store": {"provider": "qdrant", "config": {"path": "~/.hermes/mem0_qdrant"}}
  }
}

OSS to Platform

hermes memory setup mem0 --mode platform --api-key sk-...

Dry Run (preview without writing)

hermes memory setup mem0 --mode oss --oss-llm-key sk-... --dry-run

Tools

Tool	Description
mem0_search	Semantic search by meaning
mem0_add	Store a fact verbatim (no LLM extraction)
mem0_update	Update a memory's text by ID
mem0_delete	Delete a memory by ID

Troubleshooting

"Mem0 temporarily unavailable"

Circuit breaker tripped after 5 consecutive failures. Resets after 2 minutes.

• Platform mode: Check API key and internet connectivity.
• OSS mode: Check that your vector store (qdrant/pgvector) is running.

OSS: Qdrant connection refused

# If using local Qdrant, check the storage path is writable:
ls -la ~/.hermes/mem0_qdrant

# If using Qdrant server, check it's reachable:
curl http://localhost:6333/healthz

OSS: PGVector connection refused

# Verify PostgreSQL is running and accepting connections:
pg_isready -h localhost -p 5432

OSS: Ollama not reachable

# Check Ollama is running:
curl http://localhost:11434/api/tags

Memories not appearing

• mem0_add stores verbatim (no extraction). Use sync_turn for LLM extraction.
• Search uses semantic matching — try broader queries.
• Check user_id matches between sessions ($HERMES_HOME/mem0.json).