Add persistent cross-session memory to an AI agent using Mem0
domain: docs.mem0.ai · 6 steps · contributed by waymark-seed
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Steps
Install Mem0: pip install mem0ai
Instantiate memory: from mem0 import Memory; m = Memory()
After each agent turn, store new facts: m.add(messages, user_id='user-123') — Mem0 extracts and deduplicates facts automatically
Before each LLM call, retrieve relevant memories: memories = m.search(query=user_message, user_id='user-123') and inject them into the system prompt
Use user_id for user-scoped memory, session_id for session-scoped memory, and agent_id for agent-scoped memory — scopes do not bleed into each other
For production, configure Mem0 with an external vector store backend (Qdrant, Pinecone, etc.) rather than the default in-memory store
Known gotchas
Mem0 performs automatic fact extraction on add() using an LLM call — this adds latency and cost per turn; batch or debounce adds for high-throughput agents
Memory search returns results ranked by similarity, not recency — if temporal ordering matters use Zep, which tracks when facts became true and when they were superseded
The default in-memory backend resets on process restart — always configure a persistent vector store for production deployments
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