Letta v0.6.4 release
We just released Letta 0.6.4, which adds for 🐍 Python 3.13 support and a TypeScript SDK (alpha build)!
The TS SDK is in early alpha, and can be downloaded on npm via @letta-ai/letta:
npm i @letta-ai/letta@0.1.0-alpha.5
The Letta TS client makes it easy to create agents, message them, and render their responses:
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For the full release notes, see our GitHub releases:
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 v0.5.2 adds tool rules, which allows you to constrain the behavior of your Letta agents similar to graphs.
Letta v0.5.1 adds support for auto-loading entire external tool libraries into your Letta server.
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.
