Create, deploy, and manage your agents at scale with Letta Cloud. Build production applications backed by agent microservices with REST APIs.
Letta adds memory to your LLM services to give them advanced reasoning capabilities and transparent long-term memory (powered by MemGPT).
Sleep-time compute is a new way to scale AI capabilities: letting models "think" during downtime. Instead of sitting idle between tasks, AI agents can now use their "sleep" time to process information and form new connections by rewriting their memory state.
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.
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
Letta v0.6.4 adds Python 3.13 support and an official TypeScript SDK.
Understanding the AI agents stack landscape.
DeepLearning.AI has released a new course on agent memory in collaboration with Letta.
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.
Letta v0.5 adds dynamic model (LLM) listings across multiple providers.
Letta v0.4.1 adds support for Composio, LangChain, and CrewAI tools.
We are excited to publicly announce Letta.
The MemGPT open source project is now part of Letta.
