michaelschecht Mem0 — Persistent Memory for AI Applications
Mem0 provides a managed memory layer that stores, searches, and retrieves user-specific context across AI agent sessions.
Prerequisites
- Python 3.10+ or Node.js 18+
MEM0_API_KEYenvironment variable (from https://app.mem0.ai)
Installation
# Python
pip install mem0ai
# TypeScript / Node.js
npm install mem0ai
Core Setup
from mem0 import MemoryClient
client = MemoryClient() # uses MEM0_API_KEY from env
import { MemoryClient } from "mem0ai";
const client = new MemoryClient({ apiKey: process.env.MEM0_API_KEY });
Key Operations
| Method | Purpose |
|---|---|
add(messages, user_id=...) |
Store a new memory |
search(query, user_id=...) |
Retrieve relevant memories |
get_all(user_id=...) |
List all memories for a user |
update(memory_id, data) |
Update a specific memory |
delete(memory_id) |
Remove a memory |
Core Pattern: Retrieve → Generate → Store
def chat(user_message: str, user_id: str) -> str:
# 1. Retrieve relevant memories
memories = client.search(user_message, user_id=user_id)
context = "\n".join(m["memory"] for m in memories)
# 2. Generate response with memory context
response = llm.generate(
system=f"Relevant memories:\n{context}",
user=user_message
)
# 3. Store the interaction
client.add([
{"role": "user", "content": user_message},
{"role": "assistant", "content": response}
], user_id=user_id)
return response
Important Behaviors
- Search is async: Allow 2–3 seconds after
add()before results appear insearch() - Filter syntax:
filters={"user_id": "alice"}(values are case-sensitive) - SDK v3 defaults:
top_k=20,threshold=0.1,rerank=False - v2 compatibility: Pass
api_version="v1"if you need legacy behavior
Framework Integrations
Mem0 has drop-in integrations for:
- LangChain — use as a retriever or memory store
- CrewAI — persistent agent memory across crew runs
- OpenAI Agents SDK — tool-based memory access
- LlamaIndex — index memories alongside document retrieval
- AutoGen — cross-agent shared memory
- LangGraph — stateful node memory
- Pipecat — voice/conversation pipeline memory
Use Cases
- Chatbots that remember user preferences and past conversations
- Personal assistants with long-term user profiles
- Multi-session coding agents with project context
- Customer support agents with CRM-style memory
- Educational tutors that adapt to learner history
Related Skills
mem0-cli— Terminal-based memory management commandsmcp-builder— Build an MCP server wrapping Mem0 API