It is a platform designed to create, deploy, and manage AI agents at scale, enabling the development of production applications backed by agent microservices with REST APIs.
It is a platform designed to create, deploy, and manage AI agents at scale, enabling the development of production applications backed by agent microservices with REST APIs. Letta Cloud enhances large language models (LLMs) by adding memory, advanced reasoning capabilities, and transparent long-term memory, powered by MemGPT. Letta agents are equipped with built-in memory, reasoning, auto-persistence in Postgres, and support for over 7,000 tools, making them highly versatile and efficient.
The Agent Development Environment (ADE) provides a visual interface for rapidly iterating on agent prompts, tools, and model configurations. This environment allows users to visualize an agent’s memory, reasoning steps, and tool calls, enabling real-time edits to their state and decision-making processes. Letta ensures that agents maintain memory across sessions, facilitating long-running conversations and continuous learning from interactions, which improves their performance over time.
Letta addresses the issue of overstuffed context windows, which can confuse agents and degrade reasoning. It maximizes agent performance by compiling the most relevant information to pass to the LLM while keeping token counts within a specified budget. Users can create and deploy agents using REST APIs or Python and Node.js (TypeScript) SDKs, allowing direct querying or modification of agent state, configurations, and message histories. Additionally, Letta services can be deployed on Kubernetes using Docker, with real-time agent state monitored and persisted in Postgres. Agent tools are executed in isolated sandboxes with configurable environment variables and secrets for enhanced security.
Born out of the Sky Computing Lab at UC Berkeley, Letta emphasizes the importance of programming memory as the foundation for building stateful agents. Unlike traditional retrieval-augmented generation (RAG), which is insufficient for building agent memory, Letta introduces stateful agents—AI systems that maintain persistent memory and learn during deployment, not just during training. This approach overcomes the limitations of fixed context windows, enabling agents to handle extended conversations and complex tasks more effectively.
Letta’s platform is built by the researchers behind MemGPT, offering a solution to context window overflow and enabling the deployment and monitoring of agents at scale. It supports the creation of AI agents that live forever, learn from experience, and can be scaled to millions of agents. The platform is open source, fostering innovation and collaboration in the development of stateful agents.
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