Stateful AI agent platform from the MemGPT team, providing long-term memory, tools, and a managed runtime for production agents.
Stateful AI agent platform from the MemGPT team, providing long-term memory, tools, and a managed runtime for production agents.
Letta is the commercial platform built by the team behind the original MemGPT research and open-source project, which pioneered the idea of giving LLMs a virtual context-window manager so they can recall facts and conversations far beyond a single prompt. The Letta service offers a managed runtime for stateful agents: each agent has persistent memory blocks the model can read and rewrite, a tool registry, a per-agent ID and conversation log, and a REST and SDK API to interact with it. Developers can attach Python tools, connect external data sources, run agents in long-running background loops, and inspect every memory edit in the dashboard. The platform supports popular model providers (OpenAI, Anthropic, Google, OpenRouter, local Ollama) so memory and orchestration are decoupled from the underlying LLM. Letta has been integrating with the Model Context Protocol so MCP tools can be loaded directly into an agent, blending its own tool system with the broader MCP ecosystem. The Free tier is $0 with limited agents, Pro is roughly $20/mo for individuals, Team plans start near $100/mo, and Enterprise is custom. Common use cases include personal assistant agents that remember user preferences across sessions, customer support bots that retain account context, research agents that build a personal knowledge graph, and game NPCs with persistent backstory.
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Letta (formerly MemGPT) offers a distinctive memory-first approach where agents manage persistent state instead of relying only on prompt reconstruction. The self-editing memory model is powerful for long-running assistants, but it is a developer-oriented platform with operational and evaluation complexity.
Three-tier memory: core memory that stays in context and can be edited by the agent, recall memory for searchable conversation history, and archival memory for long-term vector-style storage.
Use Case:
A personal assistant agent that keeps your current project details in core memory, recent conversations in recall, and years of interaction history in archival memory.
The agent has explicit tools for memory operations such as appending or replacing core memory, inserting archival memory, searching archival memory, and searching conversation history.
Use Case:
An agent that proactively archives important details from a meeting conversation and later retrieves them when the user asks about action items.
Agents run as persistent server-backed entities with REST API endpoints. State is maintained between API calls without requiring the client to rebuild full context on every request.
Use Case:
Deploying a fleet of customer-specific agents where each agent remembers its customer's history and preferences across months of interactions.
Visual interface for creating agents, defining core memory blocks, attaching tools, configuring LLM providers, and testing agent interactions before deploying through the API.
Use Case:
A product manager defining a new support agent's personality, knowledge base, and tools through a visual interface before handing it to engineering for production integration.
Agents can participate in multi-agent workflows where each agent maintains independent state and memory while collaborating through tool and message patterns.
Use Case:
A research agent that gathers information and sends summarized findings to an analysis agent, which then passes conclusions to a report-writing agent.
Agents can be equipped with custom tools and connected to external data sources that populate archival memory. Tools are defined for the agent to call during conversations or workflows.
Use Case:
Equipping a sales agent with CRM lookup tools and product database access that it searches autonomously when customers ask about pricing or features.
$0
$20/mo
$100/mo
Custom
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Letta's current site highlights recent work including Context Constitution, Context Repositories for git-based memory in coding agents, Continual Learning improvements, Letta Code, Letta Auto, AgentFile portability, and expanded platform APIs for stateful agents.
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