AutoMem is an open source long-term memory service for AI assistants — a self-hosted Pinecone alternative that combines a graph database with vector search so agents recall the context and reasoning behind past decisions across sessions.

What is AutoMem?

AutoMem is an open source memory service that gives AI assistants durable, long-term memory across sessions. It pairs a graph database (FalkorDB) with a vector store (Qdrant), so instead of returning isolated semantic matches it recalls facts along with the typed relationships and reasoning that connect them.

What is AutoMem best for?

Developers who want their AI tools to remember decisions, context, and the “why” behind them across conversations — not just retrieve similar text. It suits people already living in MCP-compatible tools like Claude, Cursor, Codex, and ChatGPT who want a private memory layer they run themselves.

What can AutoMem do?

Store and recall memories over a REST API and the Model Context Protocol (MCP)
Run hybrid recall that blends vector similarity, keyword matching, metadata filtering, recency, and graph traversal — with no LLM call on a standard recall
Connect memories through 11 typed relationship edges (RELATES_TO, LEADS_TO, CONTRADICTS, and more) for multi-hop discovery
Run neuroscience-inspired consolidation cycles (decay, creative, cluster, forget) that strengthen important memories over time
Ship its full stack — FalkorDB, Qdrant, and FastEmbed embeddings — with no external embedding API keys required
Deploy via Docker Compose locally, on Railway, or as bare Python (3.10+)

Is AutoMem free?

Yes — AutoMem is fully open source under the MIT license, free to self-host with no paid tier or usage-based fees. You only pay for the infrastructure you run it on. There is no official managed cloud, so hosting is on you.

Where does AutoMem fall short?

It runs two datastores — FalkorDB for the graph and Qdrant for vectors — so it carries a heavier operational footprint than a single-service vector database.
On verbatim conversational recall benchmarks (the AMB Core-3 suite) it trails leaders like Hindsight; its measured strength is large-context scaling and token efficiency, not word-for-word chat playback.
It’s a young, single-maintainer project with a small community, so expect fewer integrations, guides, and battle-tested edge cases than an established vector platform.

What does AutoMem replace?

AutoMem is a self-hosted alternative to Pinecone for AI memory. Where Pinecone gives you a managed, pay-per-query vector index, AutoMem adds a graph layer and relationship-aware scoring on top of vector search and runs entirely on your own infrastructure — no usage-based pricing.

FAQ

Is AutoMem open source? Yes. AutoMem is released under the MIT license, one of the most permissive open source licenses, with no vendor lock-in.

Can I self-host AutoMem for free? Yes. Self-hosting is the intended way to run it and the software is free — you only pay for the server. It runs via Docker Compose, on Railway, or as bare Python.

Is AutoMem a good Pinecone alternative? For agent memory, yes — it adds graph relationships and hybrid recall that a pure vector DB like Pinecone doesn’t, and you avoid per-query pricing. For plain, large-scale vector search alone, a dedicated vector database may be simpler.

What do I need to run AutoMem? A host with Docker (for the FalkorDB + Qdrant stack) or Python 3.10+. Embeddings run locally through FastEmbed, so no external embedding API keys are needed.