Mem0
TypeScript · Apache-2.0
Mem0 is a long-term memory layer for AI agents, supporting cross-session memory management and personalized context retrieval.
memoryragpersonalizationagent
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Overview
Mem0 vs Letta: AI Agent Memory System Comparison
Compare Mem0 (lightweight memory layer) and Letta (full stateful Agent platform) across memory model, retrieval capability, and deployment complexity.
Projects Compared
Letta
Python · Apache-2.0
Letta (formerly MemGPT) is an open-source framework for building stateful AI agents with advanced reasoning and transparent long-term memory. It allows you to visually test, debug, and observe agents.
memoryagent-frameworkllmpython
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Feature Comparison
| Best for | Mem0 | Letta |
|---|---|---|
| Core positioning | Pluggable Agent memory layer, provides unified memory API, supports integration with LangChain, AutoGen, CrewAI and other frameworks | Complete Stateful Agent platform, built-in memory, tool calling, visual debugging, self-hosted runtime (Letta Server) |
| Memory model | Multi-layer memory based on vector database and LLM summaries, supports short-term/long-term/user preference three types of memory | Three-layer architecture based on "core memory + archival memory + Recall storage", Archival Memory supports millions of entries |
| Integration and deployment | Python SDK + REST API, 5-minute integration with existing Agent, optional hosted or self-hosted | Python/TypeScript SDK + Letta Cloud or self-hosted Docker, provides ADE (Agent Development Environment) |
| Best for | Existing Agent frameworks (LangChain/CrewAI) wanting to quickly add persistent memory without refactoring | Building stateful Agent from scratch, teams needing complete toolchain, debug UI, and long-term memory |
GitHub Stats
| Metric | Mem0 | Letta |
|---|---|---|
| Stars | 61.3k | 23.9k |
| Forks | 7.1k | 2.5k |
| Language | TypeScript | Python |
| License | Apache-2.0 | Apache-2.0 |
| Last commit | July 20, 2026 | July 3, 2026 |
Which one should you choose?
Choose based on your primary workflow, language ecosystem, and integration needs. Review each project's documentation and recent GitHub activity before adopting it in production.