# Letta vs LangGraph

## TL;DR

Letta (formerly MemGPT) is a stateful-agent platform: each agent keeps long-term memory in a git-backed file store and delegates to subagents, driven through a TypeScript Agent SDK, CLI or App Server. LangGraph is a Python graph runtime where you design state, memory and control flow yourself. Letta is a ready harness; LangGraph is a building kit.

## Facts side by side

|  | [Letta](https://multiagentguide.top/tools/letta.md) | [LangGraph](https://multiagentguide.top/tools/langgraph.md) |
| --- | --- | --- |
| Type | Framework | Framework |
| Languages / SDKs | TypeScript, Python | Python |
| License | Apache-2.0 | MIT |
| Pricing model | [Open core](https://docs.letta.com/pricing) | [Open core](https://www.langchain.com/pricing) |
| Orchestration pattern | Supervisor | Graph |
| GitHub stars | 24,981 (as of 2026-09-30) | 42,511 (as of 2026-09-30) |
| GitHub forks | 2,637 | 7,201 |
| Last push | 2026-09-10 | 2026-09-29 |
| Latest release | 0.16.8 | cli==0.4.32.dev0 |
| Repository | [letta-ai/letta](https://github.com/letta-ai/letta) | [langchain-ai/langgraph](https://github.com/langchain-ai/langgraph) |
| Website | [www.letta.com](https://www.letta.com) | [www.langchain.com](https://www.langchain.com/langgraph) |
| Documentation | [docs.letta.com](https://docs.letta.com/) | [docs.langchain.com](https://docs.langchain.com/oss/python/langgraph/overview) |
| Last verified | 2026-09-30 | 2026-09-30 |
| MCP support | [Yes (checked 2026-09-30)](https://docs.letta.com/agent-sdk/mcp) | [Yes (checked 2026-09-30)](https://docs.langchain.com/langsmith/server-mcp) |
| A2A support | Unknown (checked 2026-09-30) | [Yes (checked 2026-09-30)](https://docs.langchain.com/langsmith/server-a2a) |
| AG-UI support | Unknown (checked 2026-09-30) | [Partial (checked 2026-09-30)](https://docs.langchain.com/oss/python/langchain/frontend/integrations/copilotkit) |
| Install verified | Not yet | 2026-09-30 |

## Choose Letta if

- You want persistent agent memory managed for you, versioned as Markdown files in a git repository (MemFS).
- You build in TypeScript and want an SDK that creates an agent once and resumes sessions later.
- You want a ready agent harness with subagents and permission modes rather than a graph to design.

## Choose LangGraph if

- You are a Python team: Letta's Agent SDK is JavaScript/TypeScript only.
- You need custom control flow with conditional edges, parallel branches and step-level checkpoints.
- You need an A2A endpoint; no official A2A support was found for Letta.

## Migration notes

These tools sit at different layers, so migration is a redesign. Note first that the `letta-ai/letta` repository is now a landing page: the V1 Python server is retired and receives no fixes; current code lives in Letta Code and the Agent SDK. Guides that use the Python server packages or `letta/letta` Docker images target the retired server. Going from LangGraph to Letta, graph state and store contents become MemFS files, and nodes that called sub-agents become Letta subagents (each runs in its own subprocess and returns only its final message). Going from Letta to LangGraph, you must design a state schema, choose a checkpointer and a store for long-term memory, and rebuild tool permission prompts with `interrupt()`.

## FAQ

### Is Letta still the MemGPT Python server?

No. The repository's SECURITY.md states the V1 server is retired. The current product is the Letta agent harness (Letta Code) with a TypeScript Agent SDK.

### Can Letta be self-hosted?

Yes. The docs describe a local runtime and a self-hosted App Server that need no Letta account. Some features, such as dynamic workflows, are documented for Letta Cloud only.

### Which one has built-in long-term memory?

Letta: memory is a core feature (MemFS). LangGraph provides stores for cross-thread memory, but what to save and when is your code.

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Data as of 2026-09-30. Not affiliated with listed projects. HTML version: https://multiagentguide.top/compare/letta-vs-langgraph