Agent Frameworks

Multi-agent coordination framework

A framework for coordinating multiple AI agents working together on complex development tasks. Defines agent roles, communication patterns, task delegation strategies, and inter-agent workflows to break down large projects into manageable, parallel workstreams handled by specialized agents. Ideal for teams experimenting with multi-agent architectures where different AI models handle distinct aspects of software development.

Microsoft's zero-code-change RL trainer for AI agents

Agent Lightning is Microsoft Research's open-source framework that makes AI agents trainable through reinforcement learning with virtually zero code changes. Supports RL, Automatic Prompt Optimization, and Supervised Fine-tuning across any agent framework including LangChain, OpenAI Agents SDK, AutoGen, and CrewAI. 14K+ GitHub stars, ranked among Microsoft's top 50 most-starred projects.

Transparent AI agent framework with 100+ skills and real-time visibility

Agent Zero is an open-source general-purpose AI agent framework with 16,700+ GitHub stars that uses the computer itself as a tool. Unlike structured orchestration frameworks, it provides full transparency where every thought, action, and tool call is visible and editable in real time, supporting 100+ extensible skills.

Browser-based autonomous AI agent platform

AgentGPT is an open-source browser-based platform with 36K+ GitHub stars for creating and deploying autonomous AI agents without any setup or installation. Give an agent a name and goal in plain language, and it autonomously decomposes the objective into subtasks, executes them, and iterates toward completion. Built with Next.js and supports multiple LLM providers. Features include web search, code execution, and task chaining. No technical expertise required to create functional AI agents.

Production-ready multi-agent platform by Alibaba

AgentScope is an open-source multi-agent platform with 22K+ GitHub stars developed by Alibaba. Designed for production-ready multi-agent applications with built-in distributed execution, fault tolerance, and agent-to-agent messaging. Features memory management with compression, a drag-and-drop workstation for visual agent building, multi-modal support, and flexible pipelines for sequential, parallel, and conditional agent orchestration. Supports all major LLM providers.

Non-agent approach to automated software engineering via localize-and-repair

Agentless takes a deliberate non-agent approach to LLM-powered software engineering. Instead of autonomous agents making tool calls, it uses a structured localize-then-repair pipeline: first narrowing down which files and functions are relevant, then generating targeted patches. Achieved competitive SWE-Bench results at $0.34 average cost per issue. Adopted by OpenAI for o3 evaluations. 3,000+ GitHub stars, MIT licensed. A counterpoint to the agent-heavy trend in AI coding tools.

AI agent that builds other AI agents

Archon is an open-source AI meta-agent created by Cole Medin that autonomously builds, refines, and optimizes other AI agents. Now evolving into Archon OS, it serves as a knowledge and task management backbone for AI coding assistants. The system uses an agentic coding workflow with framework knowledge bases for Pydantic AI, LangGraph, and other agent frameworks, enabling developers to describe what they need and let Archon generate the agent code, test it, and iterate until it works.

Agentic IM chatbot platform with multi-platform LLM integration

AstrBot is an open-source agentic chatbot infrastructure that connects multiple instant messaging platforms including Telegram, Discord, Slack, WeChat, QQ, Feishu, and DingTalk to AI language models. It supports multi-provider LLM integration, MCP protocol, knowledge bases, persona management, multimodal input, and a plugin ecosystem with over 1,000 community extensions. Features include a web management UI, sandbox code execution, and auto-context compression for efficient conversations.

Build AI agents like LEGO — modular and predictable

Atomic Agents is a lightweight Python framework by BrainBlend AI that applies Atomic Design principles to AI agent development. Each component — agents, tools, context providers — is a single-purpose, reusable building block with Pydantic-enforced input/output schemas for type safety. Built on Instructor for structured LLM outputs, it prioritizes predictability and developer control over the autonomous-but-unpredictable behavior of larger frameworks like LangChain or CrewAI.

Open-source autonomous AI agent platform

AutoGPT is an open-source autonomous AI agent platform with 183K+ GitHub stars that breaks goals into subtasks and executes them independently. Features a visual Agent Builder for creating workflows without coding, persistent cloud-based agents running on triggers, a marketplace of pre-built agents, and a plugin system. Agents can browse the web, write code, manage files, and call tools autonomously while maintaining memory across sessions.

AI agents running automated research

Autonomous research framework by Andrej Karpathy that lets AI agents plan and execute machine learning experiments end-to-end. Agents design experiments, run training loops, analyze results, and iterate on hypotheses with minimal human oversight. Runs efficiently on single-GPU setups and applies agentic patterns beyond software development — bringing autonomous agent workflows to the research process itself.

Python and TypeScript framework for production multi-agent systems

BeeAI Framework is an Apache-2.0 toolkit for building production-ready AI agents and multi-agent systems in Python and TypeScript. Its docs cover agents, tools, RAG, memory, workflows, backend providers, serving, and A2A/MCP integration surfaces, making it a vendor-neutral option for teams comparing LangGraph, CrewAI, Mastra, and related agent runtimes.

Open-source agentic browser that runs local AI agents in your browsing workflow.

BrowserOS is a privacy-first, open-source agentic browser for running AI assistants locally inside real browsing sessions instead of handing every task to a remote cloud browser.

State machine framework for AI agent tracking

Burr is an open-source Python framework for building applications as state machines with built-in observability and persistence. Designed for AI agents, chatbots, and RAG pipelines where tracking state transitions, debugging decisions, and replaying execution is critical. Features automatic state persistence, a visual UI for inspecting execution flows, time-travel debugging, and integration with LangChain, OpenAI, and any Python code. Apache 2.0 licensed by DAGWorks.

Multi-agent framework for finding scaling laws of agents

CAMEL-AI is an open-source multi-agent framework with 16,500+ GitHub stars, built by a research community of over 100 researchers focused on finding the scaling laws of agents. It supports role-playing agent societies, synthetic data generation pipelines, large-scale social simulations with up to 1M agents via the OASIS platform, and real-world task automation through the OWL project accepted at NeurIPS 2025. CAMEL works with OpenAI, Anthropic, Gemini, Mistral, and local models.

Multi-agent software company simulation for automated development

ChatDev simulates an entire virtual software company through multi-agent collaboration where LLM-powered roles including CEO, CTO, programmer, tester, and designer work together to produce complete software. With 32,000+ GitHub stars and a NeurIPS 2025 accepted paper, it offers a novel approach to automated software development through role-based agent orchestration.

Official agent SDK by Anthropic

Anthropic's Python SDK for building agentic AI applications powered by Claude models. Provides primitives for creating agents with tool use, multi-step reasoning, guardrails, handoffs between specialized agents, and structured output. Supports building complex agent workflows with tracing and observability. Designed for developers building production AI agents that interact with external systems, databases, and APIs using Claude as the reasoning backbone.

AI-powered task management for agentic coding workflows

Claude Task Master is an AI-powered task management system designed for agentic development workflows in IDEs like Cursor, Windsurf, Lovable, and Roo. It breaks complex projects into structured task trees with dependencies, priorities, and complexity scores so AI coding agents can execute work methodically. The MCP server integration enables direct task operations from any compatible client, while tagged task lists support multi-context management across branches and environments.

Build and deploy AI agents on Cloudflare's edge network

Cloudflare Agents is an open-source SDK for building and deploying AI agents that run on Cloudflare's global edge network. It provides durable state, scheduled tasks, WebSocket communication, and browser rendering capabilities within Workers. Agents persist across requests using Durable Objects and can orchestrate multi-step workflows with built-in MCP server support. Over 7,000 GitHub stars.

Knowledge graph memory engine for AI agents

Cognee is an open-source knowledge engine that builds persistent memory for AI agents by combining vector search with graph databases. It ingests data from 38+ source formats, structures information into a knowledge graph with embeddings, and enables semantic and relational queries through its ECL pipeline. Its cognitive science-inspired architecture provides superior cross-document entity identification compared to traditional RAG approaches.

Workflow orchestration engine

Netflix-originated workflow orchestration platform with JSON and code-based workflow definitions, human-in-the-loop support, and AI agent orchestration capabilities. With 18k+ GitHub stars, Conductor handles complex distributed workflows at massive scale, offering built-in retry logic, event-driven triggers, and visual workflow monitoring for microservice coordination.

Full-stack framework for building AI copilots with generative UI

CopilotKit is an open-source full-stack framework for building AI-native applications with generative user interfaces. It provides React and Angular SDKs that enable agents to dynamically generate and render UI components, synchronize state between frontend and backend in real time, and implement human-in-the-loop workflows. Supports integration with LangChain, LangGraph, CrewAI and protocols including AG-UI, MCP, and A2A for standardized agent interaction.

Full-lifecycle AI agent optimization and monitoring

CozeLoop is an open-source AI agent optimization platform from ByteDance's Coze ecosystem providing full-lifecycle management from development to production monitoring. It enables developers to debug agent prompts, evaluate agent performance across test cases, optimize reasoning processes, and monitor deployed agents in real-time. Built on Go and React with SDKs for Go, Python, and Node.js, CozeLoop is designed for enterprise-grade AI agent development and operation.

The 5MB open-source agent daemon that hides nothing

CrabTalk is a lightweight five-megabyte daemon that streams every AI agent event to your client in real time including text deltas, tool calls, and thinking steps. It provides complete transparency into agent operations with one-curl installation and bring-your-own-model support. Designed as the observable alternative to opaque agent runtimes where you cannot see what the AI is actually doing.

LangChain-powered agent harness with planning and subagents

Deep Agents is a production-ready agent framework built on LangChain and LangGraph for complex agentic workflows. It features a planning system for task decomposition, a filesystem backend for persistent operations, sandboxed shell execution, and isolated subagents with independent context windows. Automatic context summarization keeps agents coherent across long sessions, while smart defaults simplify prompt engineering for multi-step autonomous tasks.

ByteDance's open-source agentic workflow platform

DeerFlow is ByteDance's open-source agentic workflow platform that combines LLM-powered agents with deep research capabilities. It features multi-agent orchestration, web research automation, report generation, and podcast creation from research outputs. Built with LangGraph, it supports configurable LLM backends and provides a modern React-based UI for managing complex AI-driven research and content workflows.

MCP gateway and integration catalog for AI agents

Executor is an MIT-licensed integration layer and MCP gateway for AI agents. It gives Claude Code, Cursor, Codex, and other MCP-speaking clients one endpoint for connected OpenAPI specs, GraphQL APIs, MCP servers, Google Discovery sources, and custom JavaScript tools, with local, cloud, and self-hosted deployment options for teams centralizing tool access.

Visual workflow builder by ByteDance

FlowGram is an open-source visual workflow and flowchart editor framework developed by ByteDance for building node-based workflow applications. It provides a React-based canvas with drag-and-drop node creation, configurable edge routing, zoom and pan controls, and a plugin architecture for extending functionality. FlowGram supports both fixed layout and free-form canvas modes, making it suitable for building AI agent pipelines, data processing flows, and automation builders.

Natural language scripting for LLM-system interaction

GPTScript is an Apache 2.0 licensed framework with 3,300+ GitHub stars that enables natural language scripting where LLMs interact with local systems, APIs, and tools through simple prompt definitions. It supports multiple model providers including OpenAI-compatible APIs and local models, providing a lightweight approach to building AI agents that can execute CLI commands, call APIs, and process files.

Agent Development Kit by Google

Google's open-source framework for building AI agents with Gemini models. Supports multi-agent orchestration, tool use, and deployment to Vertex AI or Cloud Run. Provides a structured approach to agent development with built-in evaluation, testing, and monitoring capabilities, making it the official path for teams building agent systems within the Google Cloud ecosystem.

Modular AI agent framework with off-prompt data

Griptape is an open-source Python framework for building AI agents and workflows with a focus on modularity and enterprise-grade off-prompt data handling. It separates predictable pipeline logic from unpredictable LLM interactions, providing structures for sequential and parallel task execution with built-in memory management and tool integration.

NLP and RAG pipeline framework by deepset

Haystack is an open-source AI orchestration framework by deepset for building production-ready LLM applications with explicit control over retrieval, routing, memory, and generation pipelines. Its component-based architecture lets developers chain specialized pieces into branching, looping pipelines for semantic search, RAG, QA, and autonomous agents. Integrates with OpenAI, Anthropic, Mistral, Cohere, Hugging Face, Azure, AWS Bedrock, and major vector stores.

Agent memory system that learns, not just remembers

Hindsight is an agent memory system that enables AI agents to learn from experience rather than just store conversations. It organizes memories into three biomimetic categories: World knowledge for facts, Experiences for agent events, and Mental Models for learned understanding. The system provides retain, recall, and reflect operations backed by a temporal knowledge graph with parallel retrieval strategies including semantic, keyword, graph traversal, and temporal search.

Open-source natural-language browser automation framework for AI agents

An AGPL-3.0 TypeScript framework that drives a real browser from natural-language tasks — the self-hostable OSS framework from Hyperbrowser, distinct from the vendor's paid cloud browser product.

BEAM/Elixir-native framework for durable multi-agent systems

Jido is an Elixir-native AI agent framework that leverages the BEAM virtual machine's concurrency and fault-tolerance for building durable, distributed multi-agent systems. It provides primitives for agent lifecycle management, skill composition, and message-based coordination. Designed for teams running Elixir in production who need agent capabilities. Apache-2.0 with 1,600+ GitHub stars.

Open-source post-building layer for agents — tracing, evals, and online monitoring

Judgeval is the open-source post-building layer for AI agents from Judgment Labs, providing OpenTelemetry-based tracing, hosted and custom evaluation scorers, and online behavior monitoring for LLM-powered applications. Instrument any function with a single decorator, score live production traffic against faithfulness and instruction-adherence checks, and feed real-world failures back into reinforcement learning or supervised fine-tuning loops.

JavaScript framework for building and visualizing multi-agent workflows on a Kanban board

KaibanJS is an MIT-licensed JavaScript framework for defining AI agents, tasks, tools, and teams, then orchestrating their work through a Kanban-inspired runtime and visual board. It can run inside Node.js, React, or Next.js projects, supports custom UIs and headless workflows, and provides real-time task-state visibility for multi-agent applications.

Control plane for autonomous AI agents

Keycard is the control plane for autonomous agents, providing identity verification, policy enforcement, and scoped access management. Resolves agent identity, enforces security policies, and issues time-limited resource-specific access tokens. Provides full visibility into every agent action with drift detection, automatic remediation, and integrations with Datadog, Linear, GitHub, and other services for agent-driven incident response and security operations.

MCP integration platform for agent tool use at scale

Klavis AI is an Apache-2.0 MCP integration platform for teams connecting AI agents to external SaaS tools and APIs. The public repo and official docs position it as infrastructure for reliable tool access at scale, so it fits teams that want reusable MCP connectors without treating every integration as a one-off script or custom OAuth maintenance project.

Kotlin-native AI agent framework by JetBrains with MCP support

Koog is JetBrains official Kotlin-native framework for building predictable, fault-tolerant AI agents. It provides structured agent workflows with MCP protocol support, type-safe tool definitions, and deterministic execution patterns designed for JVM production environments. As the first production-grade Kotlin agent framework, it fills the gap in a JVM ecosystem dominated by Python-based alternatives and integrates naturally with existing Kotlin and Java backend infrastructure.

RAG-based document QA with multi-user support and agent reasoning

Kotaemon is an open-source RAG-powered document question-answering interface backed by Cinnamon AI. It supports multi-user workspaces with access controls, advanced retrieval pipelines including hybrid search and knowledge graph extraction, and agentic reasoning for complex multi-step queries. The web UI handles PDFs, Office documents, and images with citations pointing to exact source passages, making it suitable for both individual research and team knowledge management.

Visual framework for building multi-agent AI apps

LangFlow is an open-source visual framework for building multi-agent AI apps with drag-and-drop. Built on LangChain, it lets developers compose chains, agents, and RAG pipelines by connecting modular components visually. Features real-time interaction, Python customization, one-click deployment, and export to LangChain code. Supports all major LLM providers, vector stores, and tools. With 146K+ GitHub stars, it bridges visual prototyping and production deployment.

Multi-agent programming framework inspired by the Actor model

Langroid is a lightweight Python framework from CMU and UW-Madison researchers for building LLM applications using a multi-agent programming paradigm inspired by the Actor Framework. Agents are first-class citizens that encapsulate LLM state, vector stores, and tools, then collaborate via message passing through hierarchical task delegation. With 3,900+ GitHub stars, Langroid works with any LLM provider and does not depend on LangChain or other frameworks.

Sentry-style observability for AI agent conversations

Latitude is an agent observability platform for teams that need to inspect LLM traces, conversations, issues, and evaluation feedback in one workflow. Its public repo and docs position it as a Sentry-style monitor for AI agents, with semantic search, issue detection, annotations, MCP-assisted fixes, and cloud or self-hosted deployment paths for production debugging.

Twelve-lesson curriculum for building AI coding agents from scratch

Learn Claude Code is a comprehensive educational framework with 12 structured lessons teaching AI coding agent construction from first principles. With over 46,000 GitHub stars and 7,000 forks, it provides complete runnable Python implementations progressing from a 50-line bash agent to a 550-line skills agent with modular extensibility and subagent orchestration.

Memory-first coding agent that learns over time

Letta Code is a memory-first CLI coding agent that persists across sessions and improves over time. Unlike stateless tools like Claude Code or Codex, it works with long-lived agents remembering your codebase, preferences, and past interactions. Model-agnostic supporting Claude, GPT, Gemini, and more. Features git-backed context repositories, skill learning from experience, sleep-time reflection, and subagent orchestration. #1 model-agnostic OSS harness on Terminal-Bench.

Open-source real-time voice, video, and AI agent infrastructure

LiveKit is an open-source WebRTC infrastructure platform for building real-time voice, video, and data applications. It powers ChatGPT Advanced Voice Mode and serves as the foundation for AI agent voice interactions. LiveKit provides server-side SDKs for Go, Python, Node.js, and Rust alongside client SDKs for every major platform. The Agents framework lets developers build voice-enabled AI assistants with speech-to-text, LLM processing, and text-to-speech pipelines in Python or Node.js.

Enterprise-grade RAG and MCP knowledge base with one-click deployment

MaxKB is an enterprise-grade RAG platform with 21,000+ GitHub stars from the 1Panel team. It provides one-click deployment of knowledge bases with built-in LLM integration, MCP support, and a streamlined approach to document ingestion and retrieval that prioritizes operational simplicity over configuration complexity.