How To Use Letta Memgpt
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What it is
Letta (formerly MemGPT) is an open-source framework for stateful agents — LLMs that manage their own long-term memory across conversations.
Install / sign up
MemGPT started as a Berkeley research project that gave LLMs an operating-system-style memory hierarchy: a small in-context working set, a larger archival store, and tools to page between them. It rebranded as Letta in 2024 and now ships a server, a Python/TypeScript SDK, and the Agent Development Environment (ADE) — a visual debugger for stateful agents.
First session
Letta runs a persistent server that owns agent state: core memory blocks (persona, human), archival memory (vector store), and message history. Agents are addressable by ID and survive restarts. You talk to them over REST or WebSocket; they call tools, update their own memory blocks, and keep learning across sessions.
Everyday workflows
Open the ADE at http://localhost:8283, create an agent, and start chatting. Watch the memory panel on the right — when you mention your name, you’ll see the agent update its “human” block in real time.
Gotchas and tips
Archival memory uses a vector store (pgvector by default) — point it at a durable Postgres in production, not the in-container SQLite, or you’ll lose memories on restart. Letta supports any OpenAI-compatible endpoint, so local models via Ollama or vLLM work fine for privacy-sensitive deployments.
Who it’s for
Core memory blocks are small (a few KB) on purpose — they’re always in context. Push larger facts into archival and let the agent retrieve them. The agent’s self-editing of memory is powerful but occasionally overwrites useful info; version your memory blocks via the API if that matters.