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LLM Agents
LLM Agents
LLM Agents
LLM Agents
LLM Agents

Build and Deploy Agents that Learn

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Agents Framework

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Letta agents have built-in memory (designed by the creators of MemGPT), reasoning, auto-persistence in Postgres, and support for 7k+ tools.

Letta ADE

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The Agent Development Environment (ADE) provides a visual interface for quickly iterating on agent prompts, tools, and model configurations.

Backed by research from
PLAY

Create and deploy AI agents that live forever and learn from experience. Brought to you by the creators of MemGPT, Letta is an open source platform for building stateful agents that actually last.

Iterate on your agents at the speed of thought

With the Agent Development Environment (ADE), you can visualize your agent’s memory, reasoning steps, and tool calls to understand their decision making, and make edits to their state in real time.

Try the ADE today

Built by the researchers behind MemGPT

Maintain memory across sessions for long running conversations. Ensure your agents can learn from interactions and improve over time, rather than derail.

Read the MemGPT paper

No more context window overflow

Overstuffed context windows confuse agents and degrade reasoning. Letta maximizes agent performance by compiling the most relevant information to pass to the LLM, while keeping token counts under a specified budget.

Learn about the Letta platform

Deploy and monitor agents using APIs

Use our REST API or Python and Node.js (TypeScript) SDKs to create agents running on a Letta server. Directly query or modify agent state, configurations, and message histories.

Browse our API Documentation

Scale your application to millions of agents

Deploy a Letta service on Kubernetes using Docker. Monitor and query real-time agent state persisted in Postgres. Execute agent tools in isolated sandboxes with configurable environment variables and secrets.

Deploy a Letta Server
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About the company

Born out of the Sky Computing Lab at UC Berkeley, Letta believes that programming agents starts with programming memory.

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We're hiring! Will you join us?

Build the next generation of agentic systems
Our values
Open AI and model freedom
Power to the developers
Nimble, talent-dense teams
Flat structure & high trust
We
in-person work
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Top tier medical coverage
Competitive salary & equity
Workstation stipend
Office located in the heart of SF
Additional benefits
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Latest news

Letta
Feb 13, 2025

RAG is not Agent Memory

Although RAG provides a way to connect LLMs and agents to more data than what can fit into context, traditional RAG is insufficient for building agent memory.

Letta
Feb 6, 2025

Stateful Agents: The Missing Link in LLM Intelligence

Introducing “stateful agents”: AI systems that maintain persistent memory and actually learn during deployment, not just during training.

Letta
Jan 15, 2025

Introducing the Agent Development Environment

Introducing the Letta Agent Development Environment (ADE): Agents as Context + Tools

Around the web

Press
Understanding memGPT: Enhancing Conversational AI through Memory Augmentation

Conversational AI has witnessed remarkable advancements with models like GPT, which excel in generating human-like text. However, a critical limitation of these models, including ChatGPT, lies in their inability to maintain coherent dialogue across extended conversations. Contextual retention and coherence degradation hinder the seamless flow of conversation, leading to disjointed interactions.

Press
MemGPT Giving AI Unlimited Prompt Size (Big Step Towards AGI?)

In this video, we look at MemGPT, a new way to give AI unlimited memory/context windows, breaking the limitation of highly restrictive context sizes. We first review the research paper, then I show you how to install MemGPT, and then we have special guests!

Press
MemGPT with Real-life Example: Bridging the Gap Between AI and OS

In this tutorial, we discuss and show how to run MemGPT - an LLM with the potential for infinite context understanding.

Press
MemGPT: Unlimited Memory without Token Constraints for Generation

MemGPT, standing for Memory-GPT, is a system devised to enhance the performance of Large Language Models (LLMs) by introducing a more advanced memory management scheme, helping to overcome the challenges posed by fixed context windows. Below are some of the key features of MemGPT: