Master Letta with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Define your first Letta use case and success metric. Connect a foundation model and configure credentials. Attach retrieval/tools and set guardrails for execution. Run evaluation datasets to benchmark quality and latency. Deploy with monitoring, alerts, and iterative improvement loops.
💡 Quick Start: Follow these 1 steps in order to get up and running with Letta quickly.
Explore the key features that make Letta powerful for ai memory & search workflows.
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.
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.
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.
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.
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.
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.
Equipping a sales agent with CRM lookup tools and product database access that it searches autonomously when customers ask about pricing or features.
Letta is used to build stateful AI agents that remember information across sessions, manage long-running context, and interact with tools through an API. It is designed for developers building persistent assistants, coding agents, support agents, and agentic applications.
Letta is the platform that evolved from the MemGPT research project and agent design pattern. The company describes Letta as born from MemGPT at UC Berkeley and focused on production stateful agents.
Letta has a Free plan at $0/month with limited agents, limited Letta Auto usage, and support for bring-your-own API keys. Pro is $20/month and includes Letta Auto quota and up to 20 stateful agents. API usage starts at $20/month plus metered usage.
Traditional RAG usually retrieves relevant chunks from a vector store and inserts them into a prompt according to a retrieval rule. Letta adds an agent architecture where the agent can manage memory, choose when to retrieve, update stored context, and persist state across interactions.
Yes. Letta's documentation and pricing materials describe BYOK support, so users can bring their own API keys and route usage through provider accounts instead of relying only on bundled model usage.
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Tutorial updated March 2026