Letta

What is Letta?

Letta is an AI operating system for building stateful agents with persistent memory. It gives language models memory management so agents can retain experiences, learn over time, and carry state across model providers. Letta includes Letta Code for git-backed memory, skills, subagents, and cross-model deployment, plus a filesystem for document organization and SDKs for Python and TypeScript. It is built for developers who want to create AI agents that go beyond session-based chats.

Key Features

• Letta Code app: The Letta desktop app runs and manages personalized agents on a local machine, which matters for teams that want local execution without depending on a server.
• Context Repositories: Context Repositories use Git to version, branch, and revert agent memory, which matters when developers need to track knowledge changes like code.
• Sleep-time compute: Sleep-time compute lets agents reflect, consolidate memory, and improve prompts during idle periods, which matters for long-running agents that need to improve without user input.
• Remote Environments: Remote Environments separate where you chat with an agent from where it runs, which matters if you want to monitor a local agent from remote devices through chat.letta.com.
• Skills: Skills add modular client-side capabilities for tasks such as tool use and delegation, which matters for custom workflows that need functions agents can learn and refine.
• Subagents: Subagents create child agents inside a parent workflow, which matters for delegating work across more complex or distributed tasks.
• Conversations API: The Conversations API keeps shared agent memory across parallel interactions, which matters for collaborative or social agents that need state across branching conversations.
• Context Doctor: Context Doctor is a diagnostic tool in the Letta Code app that identifies and repairs corrupted agent memory, which matters for debugging persistent agent states and reducing data loss.

Use Cases

• Head of Product at 11x, an AI sales automation startup: Built the "Alice Deep Research Agent" on Letta in 72 hours to query an internal knowledge base in Pinecone, synthesize information across documents, and show customers which documents and reasoning steps informed each answer. Customer adoption jumped from 3 to 85 users overnight, and the opt-in feature saw higher reply rates and user adoption.

• Founding team at Bilt, a neighborhood commerce platform: Used Letta's memory-based architecture to move from rule-based scoring to a personalized recommendation system with millions of individual AI agents, one for each user or context. The system delivered personalized, neighborhood-specific recommendations to end users at scale.

• Recruiting automation team at Hunt Club, an executive search firm: Built an agent called "Hunter" to automate executive recruiting work such as candidate research, qualification, and matching. The team shifted a manual-heavy workflow to AI-assisted automation and kept quality standards for C-suite placements.

Strengths and Weaknesses

Strengths:

• Public review data was not available across G2, Capterra, Product Hunt, or Trustpilot at the time of indexing, so we could not verify user-reported strengths for Letta.
• The available sentiment data notes cross-platform discrepancies across G2, Capterra, Product Hunt, and Trustpilot, which means there is no consistent public rating to cite.

Weaknesses:

• Public review data was not available across G2, Capterra, Product Hunt, or Trustpilot at the time of indexing, so we could not verify user-reported weaknesses for Letta.
• The available sentiment data notes cross-platform discrepancies across G2, Capterra, Product Hunt, and Trustpilot, which limits direct comparison of public sentiment.

Pricing

• Free: $0/month. Free forever. Includes limited agents and requests with rotating free models. Limits listed in the research include 50 premium requests, 500 standard requests, 100 active agents, 2 agent templates, and 1 GB storage.
• Pro: $20/month. Includes everything in Free, plus usage quota for open-weights models and Letta Auto, pay-as-you-go for additional models or overage, and up to 20 stateful agents.
• Max Lite: $100/month. Includes everything in Pro, plus usage quota across all frontier model providers, 5X higher limits on Letta Auto, and up to 50 stateful agents.
• Max: $200/month. Includes everything in Max Lite, plus increased quota for frontier models, 20X higher limits on Letta Auto, and early access to new features.
• API Plan: $20/month base + usage-based. Includes unlimited agents, API key authentication, and server-side tools charged by CPU time. All usage is pay-as-you-go and there are no monthly quotas.
• Enterprise: Pricing not publicly disclosed. Includes increased quotas, role-based access control, SAML/OIDC SSO, and dedicated support.

Paid plans add quotas and pay-as-you-go overage. Enterprise pricing is available through sales contact.

Who Is It For?

Ideal for:

• AI Platform Engineer at a mid-market sales automation startup: Letta fits teams building research or sales agents that need persistent memory and transparent reasoning. It works well in stacks that already use vector databases such as Pinecone and want to move from prototype to production in days.
• Backend Developer at a growth-stage recommendation company: Letta suits developers replacing basic scoring or recommendation logic with personalized agents that keep memory over time. It is aimed at teams scaling these experiences across large user bases.
• AI Product Builder or data scientist in enterprise automation: Letta is a match for teams prototyping memory-augmented agents for logistics, business operations, or analytics workflows. It is relevant when internal AI plumbing slows delivery and the team wants a model-agnostic setup tied to LLMs and existing tools.

Not ideal for:

• Non-technical business users or no-code builders: Letta requires coding and does not center on drag-and-drop setup, so tools like Zapier Agents or Flowise are a better fit.
• Teams that only need simple chatbots or one-shot queries: Letta is built around memory and reasoning transparency, so Dialogflow, Botpress, or standard LLM APIs may be a simpler choice.

Use Letta if your team is developer-led, works with LLMs and vector stores, and needs production agents with persistent memory. Skip it if you want no-code setup or only need stateless chat flows.

Alternatives and Comparisons

• Mem0: Letta does more for teams that want self-editing memory and a full agent runtime, and the research cites about 83.2 percent on LongMemEval. Mem0 does better for simple API integration and community size, with 51,800+ GitHub stars versus 13,000+ for Letta. Choose Letta if you are building self-improving agents and want a fuller runtime, choose Mem0 if you want drop-in API compatibility and broader community support. Switching difficulty is listed as medium.

• Zep (with Graphiti): Letta does better for users who want a complete runtime focused on self-improving agents. Zep does better on temporal reasoning, and the research cites 71.2 percent on LongMemEval for that strength. Choose Letta if you need a full agent framework, choose Zep if graph-based temporal data handling is the main requirement.

• LangMem: Letta does better for teams that want self-editing agents, a full runtime, and a REST API for LangGraph-related workflows. LangMem does better for native LangGraph integration, zero infrastructure, and a free open-source tier. Choose Letta if benchmarked runtime capabilities matter more, choose LangMem if you are already centered on LangGraph and want less infrastructure to manage.

Getting Started

Setup:

• Signup: Letta supports email-only signup, has a free trial, does not require a credit card, and includes SSO at signup.
• Time to first result: Public information suggests first results in minutes, with an empty dashboard and minimal interaction to get started. Sample templates are available.

Learning curve:

• Letta is quicker to set up for people comfortable with JavaScript, Node.js, basic API handling, and agent concepts. It is steeper for no-coders because core setup centers on creating an identity and building agents through the SDK or API.
• Beginner: 1 to 2 hours for a first agent via tutorials. Experienced: same-day for autonomous workflows.

Where to get help:

• Official help includes a tutorial for customer-specific agents through the API: https://docs.letta.com/tutorials/customer-specific-agents-api/
• Discord is the main support channel. Public information says it has over 10k members, a dedicated support channel, and regular office hours with maintainers, but we found no independent user reports that confirm response speed.
• The forum appears underused. Community health looks stagnant, maintainers seem to answer mainly in Discord office hours, and many forum topics have 0 replies. Third-party guides, courses, and user-made tutorials appear limited.

Watch out for:

• The product starts from an empty dashboard, so first-time users may need to decide on identities and agent setup without much in-app guidance.
• Letta has a programmatic focus, and non-coders may get stuck on SDK installation or identity uniqueness.

Integration Ecosystem

Users describe Letta's integration ecosystem as limited and developer-focused. Public reports point to an API-first approach with a Python SDK, and users generally see it as a tool for custom builds rather than a service with many ready-made app connections. Discussion volume is low, but there are no major reports of broken integrations, and users often frame the setup as DIY through APIs instead of no-code links.

• API workflows: Users say Letta works best when connected through its APIs, especially for teams building their own agent flows in code.
• Python SDK: Users discuss the Python SDK as the main path for connecting Letta to other systems and custom tools.
• Webhooks and message queues: Users note these can be supported indirectly through agent tools, though not as native plug-and-play integrations.

Users most often ask for broader vector database support beyond the defaults, including Pinecone and Weaviate. Requests also mention messaging apps such as Slack and Discord, plus CRM connections like Salesforce and HubSpot.

Developer Experience

Letta exposes a Python SDK, REST API, and CLI for building stateful AI agents with memory management, tool use, and multi-agent orchestration. Developers can deploy agents locally, self-hosted, or through Letta Cloud. Public sources describe the Python SDK as the main interface, and developers report 15 to 45 minutes to get a basic setup running, while documentation gets mixed feedback because it covers setup and core concepts but has gaps in advanced examples and some outdated snippets.

What developers like:

• The Python SDK is seen as approachable and intuitive for creating and modifying agents.
• Developers often call out Letta's stateful agent model as a useful change from stateless workflows.
• Local-first deployment options are a plus for teams that want to run agents outside a managed cloud setup.

Common frustrations:

• Memory management can be confusing, especially when developers move beyond basic agent behavior.
• Tool and function signatures can feel brittle, which can slow iteration when wiring tools into agents.
• Error messages are described as vague, and docs gaps can add extra debugging time.

Security and Privacy

• Data training: The vendor states that user data is used for training.

Product Momentum

• Release pace: Users perceive Letta as shipping frequently, with multiple product announcements in early 2026. Public release notes, blog posts, and a roadmap point to ongoing development.
• Recent releases: Letta launched the Letta Code app on April 6, 2026, and public reaction was positive around local personalized agents. It also released Context Constitution principles on April 2, 2026, and Context Repositories on February 12, 2026.
• Growth: Public signals point to growth, and ecosystem expansion includes integrations with Composio, LangChain, CrewAI, plus support for multiple LLM providers.
• Search interest: Google Trends does not show a clear direction. Reported change was +0.0%, with a latest interest score of 0/100 and a peak of 0/100.
• Risks: Planned deprecations are framed as part of a shift toward frontier capabilities. Dependency risk appears lower because Letta is designed to be model-agnostic, and abandonment risk looks low based on active long-term infrastructure commitments.

FAQ

What does Letta do?

Letta is a platform for building stateful AI agents that remember, learn, and improve over time. Public materials mention use cases such as deep research agents and personal agents for emails and calendars.

What does letta mean?

Letta describes itself as an AI platform building an "AI Operating System" for stateful agents. Its agents are designed to keep memory about themselves, users, and the world over time.

What is a letta?

A Letta is a stateful AI agent built on the Letta platform. It is designed for long-term memory, ongoing improvement, and personalized behavior across interactions.

What is Letta Code?

Letta Code is Letta's desktop app for running and interacting with personalized agents locally on a user's machine. Public information says it supports developers who want agents that learn from experience without depending on a server.

Is Letta free?

Yes. Letta has a Free plan at $0/month, and the pricing notes say it is a free forever tier with limited agents and requests using free models.

Does Letta offer paid plans?

Yes. Research notes say paid plans add higher quotas and pay-as-you-go overage. One public positioning source also references a $20 Pro plan.

Do you need a credit card to sign up for Letta?

No. The getting started data says signup is available and a credit card is not required.

How do you get started with Letta?

The setup starts from an empty dashboard. The core first steps are creating an identity and creating agents through the SDK or API, and time to first result is listed as minutes.

Who is Letta for?

Letta is aimed at developer-led teams building production agents with persistent memory and transparent reasoning. The research summary points to AI and SaaS companies working on sales, recommendations, or enterprise analytics.

Does Letta support local or self-hosted use?

Yes, based on public information. Letta Code runs agents locally on a desktop, and one positioning source mentions a self-hosted Docker tier.

Does Letta have many native integrations?

Public research suggests integration coverage is limited out of the box. The ecosystem summary says users tend to view Letta as a developer-focused framework that favors extensibility through code over plugin breadth.

Can Letta work with vector databases or knowledge bases?

Yes. The research data mentions Pinecone as an example integration for knowledge bases, and the ideal-for summary says Letta fits teams integrating with vector stores.

How is Letta positioned compared with other agent tools?

Public sources position Letta around stateful, self-improving agents and a full agent runtime. The research also describes it as developer-focused, with memory and scaling as central themes rather than a large set of ready-made integrations.