AI agent memory

Leon is a framework for building personal AI assistants that integrates large language models with local tool execution and persistent memory. It functions as an agentic workflow orchestrator and modular skill engine, enabling the creation of autonomous assistants capable of planning and executing multi-step tasks. The system features a retrieval-augmented generation memory architecture that indexes conversation history and user facts for context-aware grounding. It utilizes a modular skill system to interact with external binaries and APIs, supported by a loop that handles tool calling, sche

Letta is a framework for building, deploying, and managing autonomous AI agents that maintain persistent state across long-term interactions. It provides a comprehensive suite of primitives for defining agents with configurable personas, modular memory blocks, and tool-use capabilities, enabling them to retain user preferences and conversation history over extended sessions. The platform distinguishes itself through its advanced memory management and orchestration capabilities. It allows agents to autonomously update their own memory, perform retrieval-augmented generation, and coordinate com

Owl is a framework for agentic workflow automation and multi-agent orchestration. It functions as a system for coordinating autonomous large language model agents to decompose and execute complex tasks through shared communication and collaborative planning. The project distinguishes itself through a multi-modal toolset for processing images, audio, and video, alongside a synthetic data generator that produces domain-specific datasets using self-instruct and verifier loops. It further incorporates a retrieval-augmented generation pipeline framework that integrates long-term memory and real-ti

AutoGPT is an orchestration platform designed for building, managing, and deploying autonomous agents. It provides a visual canvas-based environment where users can assemble agents by connecting modular blocks that represent actions, data flows, and conditional logic. The platform supports the entire agent lifecycle, including task scheduling, execution monitoring, and configuration management, while offering a marketplace for discovering and sharing community-built workflows. The project includes a legacy framework for command-line agent execution and an extensible component system for devel

Agent Zero is an autonomous AI agent framework designed to execute complex, multi-step workflows by managing its own environment, persistent memory, and external tool interactions. It functions as a Python-based automation library that enables agents to write code, execute terminal commands, and perform system-level tasks independently. The system is built to handle large-scale operations through hierarchical agent delegation, allowing for the coordination of subordinate agents to maintain focus and context. The platform distinguishes itself through a focus on secure, isolated execution and s

Mastra is an orchestration framework designed for building, deploying, and managing autonomous AI agents and multi-agent systems. It provides a comprehensive suite of primitives for creating resilient AI applications, including durable workflow orchestration, event-driven agent loops, and semantic memory management. By integrating these core components, the platform enables developers to build complex, multi-step processes that can reason about goals and execute tasks without manual intervention. The framework distinguishes itself through its focus on observability and secure, isolated execut

This framework provides a development environment for building collaborative systems where autonomous agents interact to solve complex tasks through conversational workflows. It functions as a conversational workflow engine and event-driven runtime, coordinating multi-step processes by translating high-level goals into structured dialogue sequences between specialized agents. The system distinguishes itself through its message-passing orchestration, which manages state transitions and task delegation between independent participants. It supports dynamic conversation state management to provid

AIOS is an LLM agent operating system and orchestration kernel designed to manage memory, resource scheduling, and tool execution for multiple autonomous AI agents. It serves as a comprehensive framework for developing and deploying agents, featuring a dedicated resource manager that coordinates model backends, GPU memory, and isolated kernel instances. The system distinguishes itself through a semantic memory engine that uses vector search and autonomous clustering for long-term knowledge management, and a semantic file system that allows users to control computer files and system operations

OpenHands is an autonomous agent framework designed for software engineering workflows. It provides a modular platform for orchestrating AI agents that reason, plan, and execute tasks within isolated, containerized development environments. By integrating with standard version control and development tools, the system enables agents to autonomously navigate codebases, implement features, and resolve issues through iterative reasoning and tool execution. The platform distinguishes itself through a model-agnostic orchestrator that connects diverse language models to a unified tool registry. It

This project is a multi-channel AI agent and chatbot framework that allows a single AI intelligence to be deployed across various messaging platforms, web interfaces, and email accounts. It functions as a cross-model AI gateway, providing a unified interface to route requests between different large language model providers. The system is distinguished by its autonomous task planning and knowledge management capabilities. It can decompose complex goals into sequential execution steps using external tools and a headless browser, while simultaneously extracting information from conversations to

LangChain is an orchestration framework designed for building, managing, and deploying applications powered by large language models. It provides a unified integration layer that normalizes disparate model provider APIs into a consistent set of primitives, enabling developers to build complex, multi-step AI workflows that manage state, memory, and tool execution. The project distinguishes itself through a durable execution runtime that maintains persistent state across long-running processes by checkpointing progress to external storage. It models agent workflows as directed graphs, allowing

Hermes-webui is a self-hosted AI orchestrator and web interface for managing autonomous agents. It serves as a multi-provider gateway that connects cloud and local large language models, providing a central hub to execute scheduled background jobs, run shell commands, and manage agent memory on private hardware. The system distinguishes itself through a persistent memory manager that utilizes knowledge graphs and markdown files for long-term context across sessions. It features a model context protocol host for extending agent capabilities with standardized tools and supports the orchestratio

Semantic Kernel is an artificial intelligence orchestration framework designed to integrate large language models with existing codebases. It functions as an agentic workflow engine, providing a standardized interface that connects generative models to traditional application logic, data sources, and external tools to automate complex, multi-step business tasks. The platform distinguishes itself through a modular plugin architecture and a planner-based reasoning engine that decomposes high-level goals into executable sequences of functions. By utilizing a connector-based abstraction layer, it

The agent-framework is an LLM agent orchestration framework and multi-agent workflow engine designed for building autonomous AI agents. It provides a tool integration layer for binding external functions, APIs, and sandboxed code as executable tools for language models. The framework distinguishes itself through a graph-based system for designing sequential and parallel task flows, featuring state management and checkpointing for long-running processes. It implements comprehensive conversational state management and an observability suite that uses telemetry to trace execution flows and monit

Eliza is a modular framework designed for building and deploying autonomous agents that operate across diverse digital environments. It functions as an orchestrator for intelligent software, enabling agents to manage tasks, maintain persistent memory, and execute automated processes through a centralized runtime. The framework distinguishes itself through a plugin-based architecture that facilitates cross-platform social automation and blockchain transaction capabilities. By utilizing state-machine logic for decision-making and vector-based memory for context retention, the system allows agen

Agentscope is a comprehensive toolkit for developing and orchestrating autonomous multi-agent systems. It provides a unified framework for building agents that can reason, execute tools, and manage memory, enabling the creation of complex, collaborative workflows where multiple specialized agents interact to solve multi-step objectives. The platform distinguishes itself through a robust orchestration engine that supports both sequential and concurrent agent pipelines. It utilizes a centralized event bus for real-time telemetry, allowing developers to track agent reasoning, tool usage, and sys

Langroid is a multi-agent orchestration framework and tool integration suite designed for building complex AI applications. It serves as a multi-modal integration layer that connects diverse local and remote language models with an agentic retrieval-augmented generation system. The project distinguishes itself through a collaborative message-exchange paradigm, allowing specialized agents to delegate tasks hierarchically and coordinate via structured communication. It features an advanced state management system for conversational AI, including the ability to rewind and prune conversation hist

LangGraph is a framework for building stateful, multi-step agentic workflows by modeling application logic as a directed graph. It provides a runtime environment where complex tasks are orchestrated through interconnected nodes and edges, allowing developers to manage state transitions, persistent memory, and control flow across long-running automated processes. The platform distinguishes itself through its native support for human-in-the-loop automation, enabling developers to define breakpoints that pause execution for manual review, modification, or approval. It also features checkpoint-ba

BrowserOS is an AI agent browser orchestrator and automation framework designed to manage browser state and execute complex web workflows. It functions as a local AI browser assistant and a Model Context Protocol controller, enabling the control of browser tabs, windows, and navigation through programmable AI agents and standardized context protocols. The system distinguishes itself through a graph-based visual workflow builder for creating repeatable automation sequences and the use of markdown-based files to define agent personalities and task recipes. It supports multi-provider orchestrati

Eino is an AI agent development kit and LLM application framework designed for building autonomous agents and orchestrating complex language model workflows. It serves as a multi-agent orchestration engine and workflow orchestrator, providing a graph-based execution model to route data between models, tools, and retrievers. The framework distinguishes itself through a robust set of multi-agent coordination patterns, including supervisor-led management, sequential flows, and autonomous reasoning loops like ReAct. It features advanced agent execution controls such as active turn preemption, che

This project is a comprehensive framework for building and managing autonomous agent systems. It provides a unified architecture for orchestrating multi-agent societies, where specialized agents collaborate through roleplay to decompose and solve complex tasks. The system integrates language models with external environments, enabling agents to perform real-world actions through a standardized tool-calling abstraction layer. The framework distinguishes itself through its focus on iterative reasoning and data reliability. It employs automated feedback loops to refine agent outputs and self-eva

CrewAI is a multi-agent orchestration framework designed for building autonomous systems that execute complex, multi-step workflows. It provides a development platform where specialized agents are defined with specific roles, goals, and tool sets to perform tasks collaboratively. By leveraging a declarative workflow engine, the system manages task dependencies, state transitions, and execution logic, allowing for the creation of structured, stateful sequences of operations. The framework distinguishes itself through its hierarchical management capabilities, which utilize manager agents to coo

This project is a framework for developing multimodal AI agents that function as programmable participants in real-time communication rooms. It enables the construction of agents that can see, hear, and speak by integrating speech-to-text, large language models, and text-to-speech pipelines to facilitate low-latency, natural conversations. The system is distinguished by its advanced orchestration of real-time media and conversational flow, including support for full-duplex speech, preemptive response generation, and sophisticated interruption management. It further differentiates itself throu

Kilocode is an autonomous engineering platform designed to orchestrate AI agents for complex software development tasks. It functions as a comprehensive system for automating coding, testing, and repository management by integrating directly with your codebase and terminal. The platform provides a unified gateway for model orchestration, allowing for the management of agentic workflows, event-driven automation, and persistent session state across distributed development environments. The platform distinguishes itself through its federated task management and policy-based access control, which

Deepagents is an LLM agent orchestration platform and stateful application server designed for deploying and managing AI agents built with computational graphs. It provides a containerized runtime environment that handles agent execution, state persistence, and the versioning of AI assistants. The platform distinguishes itself through deep integration with the Model Context Protocol, allowing agents to function as servers that expose tools and capabilities to external clients. It features a sophisticated observability suite for capturing execution traces, performing LLM-based evaluations agai

LangChain is a framework for building applications that chain large language models with external data sources and third-party tools. It serves as an orchestrator for autonomous agents that use language models to plan and execute multi-step tasks, while providing a toolkit for linking interoperable AI components into sequences to prototype complex model behaviors. The project provides a model agnostic integration layer, allowing users to switch between different language model providers using a standardized interface. It also includes tools for observability and evaluation to track the perfor

LlamaIndex is a comprehensive development framework designed to connect private or external data sources to large language models. It functions as a data-centric toolkit that enables the construction of retrieval-augmented generation systems, allowing developers to build applications that provide context-aware answers based on specific organizational information. The project distinguishes itself through a robust agentic orchestration engine that supports the creation of autonomous agents capable of multi-step reasoning, memory management, and complex tool execution. Beyond simple retrieval, i

This is a framework for building autonomous agents that use large language models to plan, execute, and refine their own tasks. It functions as an autonomous task orchestrator and agent framework, utilizing a function registry to manage the code-based tools and plugins the agents use to achieve complex goals. The system is distinguished by its ability to perform autonomous code generation, where the agent analyzes requirements to write new reusable functions on the fly. It employs a recursive loop-based planning model to continuously update its goal list and refine its performance based on ex

This is an LLM agent framework and symbolic learning system designed for building self-evolving autonomous agents. It functions as a computational graph orchestrator that organizes agent interactions and tool sequences as a trainable graph of nodes. The framework focuses on data-centric agent optimization, allowing agent pipelines and prompts to be upgraded through data-driven training rather than manual engineering. It utilizes a symbolic learning process that applies language-based loss and textual reflections to refine the operational logic and symbolic components of an agent. The system

The Open Agent Platform is an orchestration environment for building, deploying, and managing autonomous AI agents. It provides a framework for constructing both single-task performers and complex multi-agent systems, utilizing a central supervisor pattern to coordinate collaborative workflows and task delegation. The platform distinguishes itself through a graph-based execution model that defines the sequence of logic and tool calls, paired with a visual configuration interface that allows for the creation of agent workflows without manual coding. It incorporates enterprise-grade security by

This project is a development framework for building autonomous agents that utilize language models to reason through multi-step tasks. It functions as an orchestrator that manages iterative loops of thought, action, and observation, allowing systems to process information and reach solutions without manual intervention. The framework distinguishes itself through a modular tool abstraction that connects language models to external data sources and code execution environments. By injecting tool-binding metadata into the prompt context, the system enables models to dynamically invoke custom fun

LibreChat is an artificial intelligence orchestration platform that provides a unified interface for interacting with multiple language models. It functions as a centralized workspace where users can switch between different intelligence engines, manage complex conversational workflows, and maintain persistent memory across sessions through a vector-database-backed storage system. The platform distinguishes itself through an extensible agent framework that supports autonomous task execution and the integration of external tools. It features a secure, containerized environment for executing co

This project provides a command-line interface for managing autonomous agent workflows, task orchestration, and system-level automation. It includes a comprehensive framework for defining agent skills, managing persistent memory, and delegating tasks to specialized subagents. Users can configure complex planning modes, execute shell commands with safety constraints, and integrate external tools through standardized protocols. The platform supports non-interactive execution via a headless mode and provides an event-driven hook framework for custom lifecycle automation. It features centralized

autoMate is an AI agent tool server and multi-model AI gateway that exposes local system tools and data to chat clients via a standard protocol. It functions as a computer use agent capable of controlling desktop interfaces and browsers to automate local system tasks through natural language. The project serves as a SaaS integration platform, bridging AI agents to third-party services such as GitHub, Notion, Slack, and Jira. It also operates as a personal knowledge management system that stores markdown notes and files using a hybrid keyword and full-text search memory system. The software f

Fabric is a command-line orchestrator designed to automate complex data processing and content generation tasks by chaining artificial intelligence models with modular prompt templates. It functions as a terminal-based tool that utilizes standard input and output streams, allowing users to pipe data directly into predefined reasoning strategies. By providing a model-agnostic abstraction layer, the system decouples execution logic from specific artificial intelligence vendors, normalizing requests and responses across different service providers. The platform distinguishes itself through its p