Today we’re launching the Letta Code app, a new way to interact with deeply personalized agents that learn over time and work locally on your machine.
Letta builds agents that learn. Agents with persistent memory, real computer access, and the infrastructure to improve from their own lived experience and work. Letta Code is the runtime that brings these together: git-backed memory, skills, subagents, and deployment that works across every model provider.
Using remote environments, you can message an agent working on your laptop from your phone.
The Conversations API allows you to build agents that can maintain shared memory across parallel experiences with users
Introducing Letta Code, a memory-first coding agent. Letta Code is the #1 model-agnostic open source agent on the leading AI coding benchmark Terminal-Bench.
The Letta API now supports programmatic tool calling for any LLM model, enabling agents to generate their own workflows.
Introducing Letta Evals: an open-source evaluation framework for systematically testing stateful agents.
Introducing Letta's new agent architecture, optimized for frontier reasoning models.
Letta agents can now take full advantage of Sonnet 4.5’s advanced memory tool capabilities to dynamically manage their own memory blocks.
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.
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.
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.
We're 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.
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.
Introducing “stateful agents”: AI systems that maintain persistent memory and actually learn during deployment, not just during training.
Introducing the Letta Agent Development Environment (ADE): Agents as Context + Tools
Understanding the AI agents stack landscape.
DeepLearning.AI has released a new course on agent memory in collaboration with Letta.
We are excited to publicly announce Letta.
The MemGPT open source project is now part of Letta.
