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# Mem0 Memory Provider
Server-side LLM fact extraction with semantic search and hybrid multi-signal retrieval via the Mem0 Platform v3 API.
## 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
Behavioral settings live in `$HERMES_HOME/mem0.json` (set them via `hermes memory setup`). Only the secret `MEM0_API_KEY` belongs in `~/.hermes/.env`.
| Key | Default | Description |
|-----|---------|-------------|
| `mode` | `platform` | `platform` (Mem0 Cloud) or `oss` (self-managed, in-process) |
| `host` | — | Self-hosted Mem0 server URL (the Docker dashboard). When set, connects over HTTP with `X-API-Key`. Don't combine with `mode: oss` |
| `user_id` | `hermes-user` | User identifier on Mem0 |
| `agent_id` | `hermes` | Agent identifier |
| `rerank` | `false` | Rerank search results for relevance (platform mode only) |
The plugin has three connection modes:
- **Platform** — Mem0's hosted cloud (`api.mem0.ai`). Set `MEM0_API_KEY`. (default)
- **Self-hosted dashboard** — a Mem0 server you run yourself via Docker. Set `host`. See below.
- **OSS** — run Mem0 in-process with your own LLM + vector store. Set `mode: oss`. See below.
## Self-Hosted Dashboard (Server) Mode
Connect the plugin to a standalone Mem0 server you run yourself — the Docker-shipped Mem0 dashboard/server with its own REST API. Unlike OSS mode (which runs `mem0ai` in-process with your own vector store), here the plugin just talks HTTP to your server.
1. Run the Mem0 server (FastAPI + pgvector) from its Docker image and note its URL and `ADMIN_API_KEY`.
2. Point the plugin at it — via the setup wizard:
```bash
hermes memory setup # select "mem0" → "Self-hosted server"
# Or non-interactive:
hermes memory setup mem0 --mode selfhosted --host http://localhost:8888 --api-key your-admin-api-key
```
or via env vars:
```bash
echo "MEM0_HOST=http://localhost:8888" >> ~/.hermes/.env
echo "MEM0_API_KEY=your-admin-api-key" >> ~/.hermes/.env
```
or in `$HERMES_HOME/mem0.json`:
```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](#self-hosted-dashboard-server-mode) above.
### Interactive Setup
```bash
hermes memory setup
# Select "mem0" → "Open Source (self-hosted)"
# Follow prompts for LLM, embedder, and vector store
```
### Agent-Driven Setup (Flags)
```bash
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
```bash
hermes memory setup mem0 --mode oss --oss-llm-key sk-...
```
Or edit `$HERMES_HOME/mem0.json` directly:
```json
{
"mode": "oss",
"oss": {
"llm": {"provider": "openai", "config": {"model": "gpt-5-mini"}},
"embedder": {"provider": "openai", "config": {"model": "text-embedding-3-small"}},
"vector_store": {"provider": "qdrant", "config": {"path": "~/.hermes/mem0_qdrant"}}
}
}
```
### OSS to Platform
```bash
hermes memory setup mem0 --mode platform --api-key sk-...
```
### Dry Run (preview without writing)
```bash
hermes memory setup mem0 --mode oss --oss-llm-key sk-... --dry-run
```
## Tools
| Tool | Description |
|------|-------------|
| `mem0_search` | Semantic search by meaning |
| `mem0_add` | Store a fact verbatim (no LLM extraction) |
| `mem0_update` | Update a memory's text by ID |
| `mem0_delete` | Delete a memory by ID |
## Troubleshooting
### "Mem0 temporarily unavailable"
Circuit breaker tripped after 5 consecutive failures. Resets after 2 minutes.
- **Platform mode**: Check API key and internet connectivity.
- **OSS mode**: Check that your vector store (qdrant/pgvector) is running.
### OSS: Qdrant connection refused
```bash
# If using local Qdrant, check the storage path is writable:
ls -la ~/.hermes/mem0_qdrant
# If using Qdrant server, check it's reachable:
curl http://localhost:6333/healthz
```
### OSS: PGVector connection refused
```bash
# Verify PostgreSQL is running and accepting connections:
pg_isready -h localhost -p 5432
```
### OSS: Ollama not reachable
```bash
# Check Ollama is running:
curl http://localhost:11434/api/tags
```
### Memories not appearing
- `mem0_add` stores verbatim (no extraction). Use `sync_turn` for LLM extraction.
- Search uses semantic matching — try broader queries.
- Check `user_id` matches between sessions (`$HERMES_HOME/mem0.json`).