# mem0

> Universal memory layer for AI Agents

## Facts
- **Category:** [Agent Infrastructure](https://agentsearchengine.app/category/agent-infrastructure)
- **Type:** Infrastructure
- **Pricing:** Open source
- **GitHub stars:** 62,353
- **Language:** Python
- **License:** Apache-2.0
- **Last repo activity:** 2 days ago (Aug 1, 2026)
- **Official site:** https://mem0.ai
- **Source code:** https://github.com/mem0ai/mem0
- **Listing on Agent Search Engine:** https://agentsearchengine.app/agents/mem0
- **Data verified:** July 2026

## Best for
Adding a persistent memory layer to agents across sessions and users.

## Avoid if
You only need simple conversation history in a single session.

## About mem0
All benchmarks run on the same production-representative model stack. Single-pass retrieval (one call, no agentic loops). What changed: - Single-pass ADD-only extraction -- one LLM call, no UPDATE/DELETE. Memories accumulate; nothing is overwritten. - Agent-generated facts are first-class -- when an agent confirms an action, that information is now stored with equal weight. - Entity linking -- entities are extracted, embedded, and linked across memories for retrieval boosting. - Multi-signal retrieval -- semantic, BM25 keyword, and entity matching scored in parallel and fused. - Temporal Reasoning -- time-aware… See the migration guide for upgrade instructions. The evaluation framework is open-sourced so anyone can reproduce the numbers.

_From the project's README._

## Install

```
npm install -g @mem0/cli      # or: pip install mem0-cli
```

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