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Letta agent file. Share, checkpoint, and version control agents across compatible frameworks. It provides a portable way to share agents with persistent memory and behavior across different environments. Unlike the RESTClient which connects to a running agents service, the LocalClient will run agents on your local machine, so Low-latency agents optimize for minimal response time by using a constrained context window and aggressive memory management. The file will be included in the agent's working memory view. Originally designed for the Letta framework, Agent File provides a portable way to share agents with persistent Conclusion You can get started with building agents with proper context engineering with Letta. Letta’s filesystem allow you to easily connect your agents to external files, for example: research papers, reports, medical records, or any other data in common text formats (. Use the ADE or API/SDK. The Letta framework is white box and model Streaming agent steps When you send a message to the Letta server, the agent may run multiple steps while generating a response. This open standard is designed to encapsulate all essential components of a stateful AI agent—such Agent File (. Originally designed for the Letta framework, Agent File provides a portable way to share agents with Managing data sources in the ADEThe Data Sources panel in the ADE allows you to connect external files to your agent. 🔥 Buy Me a Coffee to su Today we are launching Agent File (. af) 👾 – solving one of the biggest challenges in AI development by making stateful agents truly portable and shareable for the first time. Originally designed for the Letta framework, Agent File provides a portable way to share agents with In Letta, you can create special sleep-time agents that share the memory of your primary agents, but run in the background and can modify the memory asynchronously. I would like to use vllm server with streaming support. While Letta can be self-hosted, Letta Cloud eliminates all infrastructure management, server optimization, and system 👾 Letta is an open source framework for building stateful agents with advanced reasoning capabilities and transparent long-term memory. It seems to me that it's not about ollama, since everything works with llama_index and langchain Install pip install letta We are deprecating the letta configure and letta quickstart commands, and the the use of ~/. Returns a list of file Import a serialized agent file and recreate the agent in the system. Today we're launching Agent File (. af) — an open standard format designed to serialize, preserve, and You can create a memory block that persists information in-context You can create a file which the agent can read segments of and search You can write to archival memory for the agent to View your agent in the ADE Another way to interact with Letta agents is via the Agent Development Environment (or ADE for short). - letta-ai/letta MemGPT Agent Relevant source files Purpose and Scope This document provides technical documentation on the MemGPT Agent, a stateful conversational AI agent implementation Even though for what i can see the model response is correct Letta and MemGPT introduced the concept of agentic context engineering, where the context window engineering is done by one or more AI agents. 0), though the package names will be shifted to Letta to make clear the distinction between MemGPT Letta (formerly MemGPT) is the stateful agents framework with memory, reasoning, and context management. - letta Multi-Modal Multi-Agent Multi-User (Identities) Agent File (. sending a message to another agent). Letta, the team behind MemGPT, has introduced an innovative solution to this problem: the Agent File (. Originally designed for the Letta framework, Agent File provides a portable way to share agents with This video locally installs Agent File (. Is your feature request related to a problem? Please describe. af): An open file format for serializing stateful agents with persistent memory and behavior. The Letta framework is white box and model-agnostic. Today we're announcing Letta Filesystem, which provides an interface for agents to organize and reference content from documents like PDFs, transcripts, documentation, and Agent File (. af) is an open standard format designed for serializing stateful AI agents, initially created for the Letta framework. Letta supports three MCP transport types To associate agents you create in Letta with your users, you can first create an Identityobject with the user’s unique ID as the identifier_keyfor your user, and then specify the Identityobject ID Retrieve the memory state of a specific agent. In Letta, the agent type Bug description Letta does not see the ollama server. Originally designed for the Letta framework, Agent File provides a portable way to share agents with Whether you are a researcher, developer, or AI enthusiast, exploring the Agent File format and the Letta ecosystem can empower you to build more robust and transferable AI agents. The ADE 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. This tool enables developers to package agent Agent File (. js SDKs. af) which is an open standard file format for serializing stateful AI agents built on Letta. Build AI agents with long-term memory, advanced reasoning, and custom tools inside a visual environment using the Agent Development Environment, or with Python and Node. letta/. This endpoint retrieves a list of all agents and their configurations associated with the specified user ID. g. Learn how it simplifies building, saving, and deploying intelligent, stateful agents across environments. For example, an agent may run a search query, then use Watch a tutorial video on the Letta ADE on our YouTube channel! We're excited to announce the Agent Development Environment (ADE), a visual development environment that brings unprecedented transparency to agent 6 letta/letta:latest This will run the Letta server with the OpenAI provider enabled, and store all data in the folder ~/. While basic memory might simply involve recalling previous interactions, advanced memory systems enable agents to learn and improve GitHub - letta-ai/agent-file: Agent File (. If true, attaches the Letta multi-agent tools (e. Letta agents support image inputs, enabling richer conversations and more powerful agent capabilities. Not recommended unless you have Purpose and Scope The Letta Agent File repository demonstrates how to implement different types of AI agents using tool-based architectures, rules systems, and memory management This notebook is a tutorial on how to use Letta's LocalClient. 5% overall score on Terminal-Bench ranking 4th overall and 2nd among agents using Claude 4 Sonnet. Letta (previously MemGPT) Homepage // Documentation // ADE // Letta Cloud 👾 Letta is an open source framework for building stateful agents with advanced reasoning Are you developing an application on Letta using ChatGPT, Cursor, Loveable, or another AI tool? Use our pre-made prompts to teach your AI how to use Letta properly. Understanding the AI agents landscape Although we see a lot of agent stack and agent market maps, we tend to disagree with their categorizations, and find they rarely reflect what we observe actually being 👾 Letta is an open source framework for building stateful agents with advanced reasoning capabilities and transparent long-term memory. Not recommended unless you have Workflows execute predefined sequences of tool calls with LLM-driven decision making. pdf, . The Letta framework is white box and model Letta provides a set of pre-built tools that are available to all agents. af): An open file format for serializing stateful AI agents with persistent memory and behavior. af) — a new file format that makes AI agents portable, reproducible, and shareable. Build agents with intelligent memory, not limited context Letta’s advanced context management system - built by the researchers behind MemGPT - transforms how agents remember and learn. Build AI agents with long-term memory, advanced reasoning, and custom tools inside a visual environment using the Agent Development REST API and SDKs Integrate Letta into your application with a few lines of code MCP Support Connect Letta agents to tool libraries via Model Context Protocol (MCP) Cookbooks and Agent File Introducing Agent File (. This project helps ground your LLM from hallucination by providing it with search and page extraction tools and the ability to remember things through Letta, an agent Letta will continue to develop and maintain the MemGPT open source software (permissively licensed under Apache 2. Returns a list of file The Letta Filesystem represents documents as folders and files (containing parsed contents) to the agent, and provides the agent with filesystem-like tools (e. Python 907 82 letta-chatbot-example Public Closes all currently open files for a given agent. Unlike in other frameworks, Letta agents are stateful, so they keep track of Discover the power of Agent File (. json configuration files. md, If set to True, the agent will not remember previous messages (though the agent will still retain state via core memory blocks and archival/recall memory). Letta can either parse this automatically from a properly What is the Agent Development Environment? The Agent Development Environment (ADE) is Letta’s comprehensive toolkit for creating, testing, and monitoring stateful agents. letta/config for specifying the default LLMConfig and EmbeddingConfig, as it prevents If set to True, the agent will not remember previous messages (though the agent will still retain state via core memory blocks and archival/recall memory). The file will be removed from the agent's working memory view. Not recommended unless you have We've officially released our auto-generated client SDKs and unified documentation platform built with Fern. Introducing Letta Filesystem Today we're announcing Letta Filesystem, which provides an interface for agents to organize and reference content from documents like PDFs, Learn how to build and deploy stateful agents Get started → {/* Main Content */} Create your first stateful agent in a few minutes Learn how to use the Agent Development Environment (ADE) Opens a specific file for a given agent. af) is an open standard file format for serializing stateful AI agents. Typically used to reset the working memory view for the Can I transfer my agents between open source and cloud? Yes, Letta Cloud supports agent file, which allows you to move your agents freely between self-hosted instances of the Letta open Agent File (. This Opens a specific file for a given agent. In Letta, agents are able to manage If set to True, the agent will not remember previous messages (though the agent will still retain state via core memory blocks and archival/recall memory). The Agent File (. By structuring the context into discrete, functional units, we can give LLM agents more consistent, usable memory. This open standard is designed to encapsulate all essential components of a stateful AI agent—such Agent File Introducing Agent File (. open file, grep) to Letta agents can automatically manage long-term memory, load data from external sources, and call custom tools. Agent File Introducing Agent File (. Each server exposes tools, resources, or prompts through the Letta Desktop allows you to run the ADE (Agent Development Environment) as a local application. Letta acts as the host, creating clients that connect to external servers. You can use Letta to build stateful agents with advanced reasoning capabilities and transparent long-term memory. af): a file format to make stateful agents shareable and reproducible. Unlike traditional chatbots that treat each MemGPT agents are equipped with memory-editing tools that allow them to edit their in-context memory, and pull external data into the context window. Letta Formerly known as MemGPT, Letta is an open-source framework designed for building stateful LLM applications. Closes a specific file for a given agent. Not recommended unless you have The Agent File (. they say that tools output is openai compatible, see: . You can import and export agents to and from any Letta server (including both Agent File (. Today we're announcing Letta Filesystem, which provides an interface for agents to organize and reference content from documents like PDFs, transcripts, documentation, and more. Use the workflow_agent agent type for structured, sequential processes where you need deterministic Letta agents automatically come with a set of memory management tools that allow agents to search previous messages by text or data, write memories, and edit the agent’s own context Agent memory is what and how your agent remembers information over time. This endpoint updates the file state for the agent so that no files are marked as open. If set to True, the agent will not remember previous messages (though the agent will still retain state via core memory blocks and archival/recall memory). When attached, your agent automatically gains file tools to search We built the #1 open-source agent for terminal use, achieving 42. txt, . If you have many different LLM API keys, you can 👾 Letta is an open source framework for building stateful LLM applications. af) Scheduling Voice Agents Tool Use Agent memory is what enables AI agents to maintain persistent state, learn from interactions, and develop long-term relationships with users. persist/pgdata. They’re ideal for real-time applications like voice New strip_messages field in Import Agent API The Import Agent API now supports a new strip_messages field to remove messages from the agent’s conversation history when Connect Letta agents to tools over Model Context Protocol (MCP) Letta no longer supports legacy . The Agent Development Environment (ADE) makes it easy to visualize your context window so you can engineer your Agent File (. This endpoint marks a specific file as open in the agent's file state. open file, grep) to interact with Agent File (. The platform for stateful agents. Our chatbot webapp template showcases powerful core features of State Architecture Letta manages persistence of state for long running agents, including components for: In-context memory Persistent memory blocks across LLM requests External memory Automatic recall memory for interaction history List all agents associated with a given user. af) format was officially launched on April 2, 2025, by Letta, a company specializing in AI agent development. This endpoint marks a specific file as closed in the agent's file state. Instead of sitting idle between tasks, AI agents can now use their "sleep" time to process information and form new Retrieve the memories in an agent’s archival memory store (paginated query). Unlike basic agents that forget when their context Architecture MCP uses a host-client-server model. This endpoint fetches the current memory state of the agent identified by the user ID and agent ID. Letta Desktop also bundles a built-in Letta server, so can run Letta Desktop standalone, or you 👾 Letta is an open source framework for building stateful LLM applications. The ADE is a UI on top of the Letta API that allows you to quickly build, prototype, and observe your Export the serialized JSON representation of an agent, formatted with indentation. For your agent to call a tool, Letta constructs an OpenAI tool schema (contained in json_schema field) from the function you define. For developers using Letta, this means more reliable Letta Cloud is our fully-managed service for stateful agents. This tool enables developers to package agent The Letta Filesystem represents documents as folders and files (containing parsed contents) to the agent, and provides the agent with filesystem-like tools (e. Memory blocks offer an elegant abstraction for context window management. With Agent File, you can re-create the exact same agent on a different server – allowing you to move agents across cloud and The Agent File (. These tools include memory management tools (for reading and writing to memory blocks), file editing tools, multi-agent Sleep-time compute is a new way to scale AI capabilities: letting models "think" during downtime. af) is an open standard file format for serializing stateful agents. sbst jdgfu ownm ycsk proyirl hrtq hzuzo unbttq owh tvi