LangMem vs Letta
Updated March 10, 2026
Overview
LangMem is LangChain's long-term memory solution for AI agents. It provides tools for extracting, storing, and retrieving memories from agent interactions, enabling agents to learn and improve over time. LangMem integrates natively with the LangGraph agent framework and supports multiple memory types including semantic, episodic, and procedural memory.
Letta (formerly MemGPT) is an open-source framework for building AI agents with long-term memory and self-editing capabilities. It implements a virtual context management system that allows LLMs to manage their own memory hierarchy, enabling persistent state, unbounded context, and adaptive memory across conversations.
Strengths and tradeoffs
What each tool does well, and the limitations to keep in mind.
Pros
- Developer-friendly platform
- Production-ready
- Good performance
- Active development
Cons
- Enterprise pricing varies
- Setup complexity
- Learning curve
Pros
- Advanced structured memory with entities, facts, and timelines
- Extreme interpretability with human-readable memory
- Strong balance between flexibility and production readiness
- USD 10M seed funding validates technology approach
Cons
- Requires architectural decisions around vector stores and RAG
- Not a plug-and-play solution—needs developer expertise
- Community feedback suggests learning curve for optimal use
Compare LangMem and Letta on your own traffic
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