Letta
Build stateful LLM agents that remember, learn, and improve over time by self-managing a tiered memory (in-context blocks plus archival/recall stores) via tool calls.
Description
Letta (formerly MemGPT) is the open-source platform created by the authors of the MemGPT paper for building stateful agents with advanced memory. Letta agents process messages through a tool-calling loop in which the model can read and edit its own memory: in-context memory blocks live inside the prompt, archival memory is a vector store queried on demand, and recall memory logs full conversational history. All agent state — memory blocks, messages, reasoning, and tool calls — is persisted in a database so nothing is lost even when content is evicted from the context window. Letta exposes its agents as a stateful REST API.
Solution
Tool-calling loop on top of MemGPT-style virtual context. Memory blocks are always-visible XML-like sections prepended to the prompt; the agent can call memory tools to edit them, archive content, or search recall/archival memory. All state — memory, messages, reasoning, tool calls — is written to a database after each step, so the agent persists across server restarts and is addressable through the Letta REST API.
Primary use cases
- long-running personal/companion agents that retain user context across sessions
- research agents that accumulate facts in archival memory
- stateful customer-support agents exposed as a REST service
- MemGPT-style virtual-context experiments and continual learning
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