AutoMem MCP connects AI tools to a persistent memory service, allowing them to remember decisions, patterns, and context across sessions. It supports various MCP-compatible platforms for seamless integration.
From the registry: Graph-vector memory for AI assistants using FalkorDB and Qdrant

$ npx @verygoodplugins/mcp-automem setupPlease install the `mcp-automem` MCP server into my current AI client (that's you).
Required prerequisites (do these first if not already done):
- **Run setup** — Guided setup creates .env and prints config Run: `npx @verygoodplugins/mcp-automem setup`
- **AutoMem endpoint** — A running AutoMem backend (local or Railway) (https://github.com/verygoodplugins/automem)
Canonical MCP server config (stdio transport):
- command: `npx`
- args: ["-y","@verygoodplugins/mcp-automem"]
- required environment variables:
- `AUTOMEM_ENDPOINT`: AutoMem backend endpoint URL (example: `http://127.0.0.1:8001`)
- optional environment variables:
- `AUTOMEM_API_KEY`: API key (required for Railway, skip for local) (example: `<your-api-key>`)
Note: Also supports remote HTTP/SSE via sidecar service.
Add this MCP server to my current client's config in the correct format for you. If you need secrets or credentials I haven't provided, ASK me — do not invent values or leave raw placeholders. After adding it, tell me how to verify the server is connected.AUTOMEM_ENDPOINTrequiredThe URL of the AutoMem service endpoint.API_KEYrequiredAPI key for accessing the AutoMem service, required for Railway deployments.AI Agents Framework with Self Reflection and MCP support