Letta v0.5.1 release
🛠️ Pre-load Composio, CrewAI, & LangChain tools into the Letta server
You can now auto-load tools from external libraries with the environment variable export LETTA_LOAD_DEFAULT_EXTERNAL_TOOLS=true. Then, if you run letta server, tools which do not require authorization.
export LETTA_LOAD_DEFAULT_EXTERNAL_TOOLS=true
# recommended: use with composio
export COMPOSIO_API_KEY=...
pip install `letta[external-tools,server]`
letta server
💭 Addition of put_inner_thoughts_in_kwargs field in LLMConfig
Some models (e.g. gpt-4o-mini) need to have inner thoughts as keyword arguments in the tool call (as opposed to having inner thoughts be in the content field). If you see your model missing inner thoughts generation, you should set put_inner_thoughts_in_kwargs=True in the LLMConfig.
🔐 Deprecation of Admin
Letta no longer requires authentication to use the Letta server and ADE. Although we still have a notion of user_id which can be passed in the BEARER_TOKEN, we expect a separate service to manage users and authentication. You will no longer need to create a user before creating an agent (agents will be assigned a default user_id).
Read the full v0.5.1 changelog on GitHub.
Traditional LLMs operate in a stateless paradigm—each interaction exists in isolation, with no knowledge carried forward from previous conversations. Agent memory solves this problem.
As AI agents become more sophisticated, understanding how to design and manage their context windows (via context engineering) has become crucial for developers.
Although RAG provides a way to connect LLMs and agents to more data than what can fit into context, traditional RAG is insufficient for building agent memory.
Today we're announcing Letta Filesystem, which provides an interface for agents to organize and reference content from documents like PDFs, transcripts, documentation, and more.
We've releasing new client SDKs (support for TypeScript and Python) and upgraded developer documentation
Introducing Agent File (.af): An open file format for serializing stateful agents with persistent memory and behavior.
Introducing the Letta Agent Development Environment (ADE): Agents as Context + Tools
Letta v0.6.4 adds Python 3.13 support and an official TypeScript SDK.
Letta v0.5.2 adds tool rules, which allows you to constrain the behavior of your Letta agents similar to graphs.
We're excited to announce the Letta Leaderboard, a comprehensive benchmark suite that evaluates how effectively LLMs manage agentic memory.
Memory blocks offer an elegant abstraction for context window management. By structuring the context into discrete, functional units, we can give LLM agents more consistent, usable memory.
