agency-agents
Agency Agents is a curated collection of specialized AI agent personas with prompts, tools, and best practices for assembling multi-agent teams.
Frameworks for multi-agent collaboration
Agency Agents is a curated collection of specialized AI agent personas with prompts, tools, and best practices for assembling multi-agent teams.
100+ AI Agent and RAG apps you can actually run — clone, customize, and ship. A great reference for quickly building LLM-powered applications.
Generate short videos with one click using AI LLM. Multi-step automation workflow from script generation to video composition.
TradingAgents is a multi-agent trading framework built with LangGraph that mirrors real-world trading firm dynamics with specialized LLM-powered agents for fundamental analysis, sentiment analysis, risk management, and more.
OpenHands is an open-source AI software engineering agent platform that can automatically execute development tasks, modify code, and support collaborative iteration.
The Multi-Agent Framework for building the first AI Software Company, enabling natural language programming with multi-role collaboration for automated requirement analysis, design, coding, and testing.
MetaGPT is a multi-agent framework that combines collaborative intelligence with software-engineering SOPs so agents cooperate like a real software company to ship code and docs.
Multi-agent framework that encodes a software company's org chart into LLM workflows to auto-produce code.
Open-source framework that orchestrates multi-agents as a software company.
Multi-agent swarm intelligence engine that extracts seed information from the real world, constructs a high-fidelity parallel digital world with thousands of agents, and predicts future trajectories through social evolution simulations.
The leading agent orchestration platform for Claude. Deploy intelligent multi-agent swarms, coordinate autonomous workflows, and build conversational AI systems. Features enterprise-grade architecture, distributed swarm intelligence, RAG integration, and native Claude Code/Codex integration.
Microsoft AutoGen is a multi-agent conversation framework that lets you create multiple agents to collaborate through dialogue and solve complex tasks.
Microsoft Research's open-source multi-agent programming framework.
CrewAI is a multi-agent framework for orchestrating role-playing, autonomous AI agents that collaborate like a team to tackle complex tasks.
Role-based multi-agent Python framework for task orchestration.
A multi-agent collaboration framework where AI agents form crews to accomplish complex tasks together. Role definition, task assignment, tool sharing, and process orchestration.
A multi-agent collaboration framework where AI agents form crews to accomplish complex tasks together. Role definition, task assignment, tool sharing, and process orchestration.
CowAgent (formerly chatgpt-on-wechat) is a powerful AI assistant framework built on LLMs with autonomous planning, tool use, long-term memory, multi-agent collaboration, and multi-channel integration for WeChat, Feishu, DingTalk, and more.
An accessible multi-agent sentiment analysis assistant that breaks filter bubbles, reveals true public opinion, and predicts trends — built from scratch without external frameworks.
Intelligent automation and multi-agent orchestration for Claude Code. Supports automated workflows, task coordination, and intelligent agent system building.
Teams-first multi-agent orchestration for Claude Code. Designed for team collaboration with support for multi-agent coordination, task distribution, and result integration to enhance team AI development efficiency.
Stanford NLP's programming model for LLM pipelines.
ChatDev 2.0 enables full-lifecycle software development through LLM-powered multi-agent collaboration, simulating role-based teamwork in a virtual software company.
ChatDev simulates a software company with multi-agent collaboration.
(24 / 151)
A systematic guide to seven tool-call fault tolerance patterns: timeout hierarchy, exponential backoff with jitter, circuit breakers, fallback provider chains, recoverable error classification, structured validation, and idempotency keys -- keeping agents stable in unstable real-world environments.
Most agent workflows fail at the orchestration layer, not the model. A practical comparison of DAG, state machine, and visual builder approaches with production-ready code for error handling, human approval gates, and conditional branching.
An in-depth comparison of mainstream AI agent frameworks including LangChain, LangGraph, CrewAI, and AutoGen to help you choose the best development stack.
A step-by-step tutorial for installing and running AutoGPT locally, including environment setup, Docker deployment, and common troubleshooting.
A practical breakdown of browser-use strengths and limits in web task automation, with strategies for stable execution and failure recovery.
A hands-on guide to building a complete AI agent from scratch, covering environment setup, core components, and tool integration.