Letta
Build stateful agents with long-term memory
Letta is profiled here as a Coding Assistant tool for engineering teams. Read about features, pricing, and how it compares to related options in the tools directory.
Description
Letta is an open-source framework for stateful agents, created by the Berkeley researchers behind the MemGPT paper, Charles Packer and Sarah Wooders. It treats memory as a managed resource: an agent edits its own context, moves information between in-context and external memory, and so carries knowledge across sessions past the model's context window. A server-based architecture exposes agents through a REST API with persistence handled for the developer. Treating memory as a managed resource lets agents accumulate knowledge over time, which suits assistants that persist across many sessions. The development environment exposes an agent's memory and reasoning so teams can inspect what it retained.
Key Capabilities:
Self-editing memory that persists across sessions
Memory management beyond the model's context window
Agents exposed as services through a REST API
The Agent Development Environment for visual inspection of state
Model-agnostic operation across commercial and open LLMs
Apache 2.0 license with Python and TypeScript SDKs
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