Multi-Agent Orchestration

Multi-agent software development framework where AI agents collaborate as programmers, designers, and testers to build software. Apache 2.0 licensed.

First and best multi-agent framework for building scalable agent systems. Apache 2.0 licensed with extensive tooling for agent communication and task automation.

Batteries-included LangChain agent harness for building and running structured multi-agent workflows with reusable runtime patterns.

Local-first desktop workspace for coordinating AI agent teams across conversations, files, terminal, browser, Git, memory, plugins, and verification.

Bleeding-edge enterprise multi-agent orchestration.

TypeScript-first agent framework with built-in RAG, workflows, tool integrations, observability and observational memory.

Workflow engine for AI where agent work is captured as reviewable .nika.yaml DAG files, statically checked before execution (schema, permits, cost floor) with tamper-evident traces after. Local-first (Ollama, llama.cpp, vLLM), MCP client and server. AGPL-3.0 licensed.

Open-source long-horizon SuperAgent harness that researches, codes, and creates. Handles tasks from minutes to hours with sandboxes, memories, tools, skills, subagents, and message gateway.

Production-ready lightweight framework for multi-agent workflows. The evolution of Swarm with enhanced orchestration capabilities and enterprise-grade features.

Turns project work into isolated, autonomous implementation runs. Monitors work boards, spawns agents to handle tasks, and provides proof of work including CI status, PR reviews, and walkthrough videos. Engineering preview for managing work instead of supervising coding agents. Apache 2.0 licensed.

AI agent company and orchestration framework with 55K+ stars. MIT licensed.

Alibaba's production-ready multi-agent framework with 23K+ stars. Features built-in MCP and A2A support, message hub for flexible orchestration, and AgentScope Runtime for production deployment.

Microsoft's official framework combining AutoGen's agent abstractions with Semantic Kernel's enterprise features. Supports Python and .NET with graph-based workflows.

A ready-to-run AI agent team workspace whose agents remember, collaborate, and use tools from day one. Apache 2.0 licensed.

Reliable multi-agent orchestration framework built on top of the OpenAI Assistants API with organizational structure modeling.

Autonomous multi-agent framework for building and deploying AI-powered applications. Features Discord/Telegram/Farcaster connectors, RAG support, and a modern web dashboard.

AI Agent Networks for Open Collaboration. Platform for building collaborative multi-agent systems with shared knowledge and distributed task execution. Apache 2.0 licensed.

Production-grade multi-agent orchestration framework with 10K+ stars. Apache 2.0 licensed.

Flexible multi-agent orchestration framework with intelligent intent classification and context management. Supports Python and TypeScript with pre-built agents for Bedrock, Lex, and custom integrations. Apache 2.0 licensed.

Hierarchical multi-agent system for deep research tasks with automated task decomposition and execution across complex domains.

Agentic orchestrator for parallel coding agents. Plans tasks, spawns agents, and autonomously handles CI fixes, merge conflicts, and code reviews. MIT licensed.

MIT-licensed TypeScript-native orchestration framework that plans multi-agent task DAGs at runtime, with approval gates, tracing, evaluation, checkpoints, and resumable execution in your own environment.

Production-ready multi-agent framework in Python and TypeScript. Features workflow orchestration, ACP/MCP protocol support, and deep watsonx integration. Part of Linux Foundation AI & Data program.

Deployable starter kit for building virtual towns where AI characters live, chat and socialize. Inspired by Stanford's Generative Agents research with persistent agent memory and social interactions. MIT licensed.