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Mem0 is a focused memory layer for AI agents with open-source and hosted options.
Open-source and hosted memory layer that gives AI agents persistent, self-updating recall across sessions, users, and tools.
Open-source and hosted memory layer that gives AI agents persistent, self-updating recall across sessions, users, and tools.
Mem0 is a memory layer purpose-built for LLM applications, letting agents remember user preferences, past conversations, and long-lived facts without re-stuffing everything into every prompt. Under the hood it maintains a hybrid store — vector embeddings for semantic recall, a graph store for entity and relationship facts, and a keyed key-value store for account-level attributes — then reconciles new observations against existing memories on write, so contradictory or outdated facts are updated rather than appended blindly. Developers call add, search, update, and delete on a user or agent scope, and Mem0 returns just the memories relevant to the current turn, dramatically cutting context bloat compared to naive RAG-over-chat-history. Mem0 supports OpenAI, Anthropic, Google, Ollama, Groq, Together, and Bedrock for the extraction pass, and Pinecone, Qdrant, Weaviate, pgvector, Chroma, or Redis for storage. There is a Python and TypeScript SDK, a hosted API with a generous free tier, and a growing MCP server ecosystem — several implementations expose Mem0 as an MCP tool so Claude Desktop, Cursor, and other clients can persist and retrieve user memories across sessions. Pricing on the Mem0 Cloud starts free (1M tokens/month, 10K memories), Pro at $19/month for higher limits, and Enterprise for on-prem, HIPAA, and custom SLAs. The open-source library is Apache 2.0 licensed and can run entirely on-device with local models.
Mem0 is a strong option for developers adding persistent memory to AI agents, especially when they want SDKs, hosted infrastructure, an open-source path, and MCP-compatible workflows verified against current documentation.
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$19/month
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View Pricing Options →Mem0 works with these platforms and services:
We believe in transparent reviews. Here's what Mem0 doesn't handle well:
AI Infrastructure
Managed vector database for RAG and AI search with serverless scaling, hybrid retrieval, and an inference layer.
Vector Database
An open-source and managed vector database for semantic search, retrieval, recommendation, and agent memory.
AI Database
Weaviate review of vector and hybrid search, managed and open-source deployment, current cloud pricing, advantages, tradeoffs, and practical RAG use cases.
Vector Database
Open-source AI application database with vector, full-text, and metadata search — designed to be embeddable, easy to run locally, and now offered as Chroma Cloud with usage-based serverless pricing from $5/month.
AI Memory
pgvector is an open-source PostgreSQL extension for storing embeddings and running vector similarity search with SQL. It is best for teams already using PostgreSQL that want semantic search, RAG retrieval, or AI memory without operating a separate vector database, while accepting PostgreSQL scaling and tuning tradeoffs.
Pricing and features change often. If anything on this Mem0 page is wrong or missing, tell us and include a link to the source. We review every correction.