Open-source framework for building collaborative multi-agent systems using OpenAI's Assistants API with a focus on real-world agency workflows.
Create a swarm of specialized AI agents that communicate and delegate tasks to each other — like building your own AI company.
Agency Swarm is an open-source framework for creating collaborative multi-agent systems built on top of OpenAI's Assistants API. It provides a structured approach to designing agent teams (called 'agencies') where specialized agents communicate through defined channels to accomplish complex tasks.
The framework's key design principle is that agents should be organized like a real company — with a CEO agent that interfaces with users, manager agents that coordinate work, and specialist agents that execute specific tasks. Communication flows through defined channels rather than allowing all agents to talk to each other, which reduces chaos and improves reliability in multi-agent systems.
Each agent in Agency Swarm is defined by its role, instructions, tools, and communication permissions. Tools are created as Python classes with Pydantic validation, ensuring type safety and clear interfaces. The framework includes a tool creation assistant that can generate tool implementations from natural language descriptions.
Agency Swarm leverages OpenAI's Assistants API features — including threads, file search, code interpreter, and function calling — while adding multi-agent coordination on top. This means agents get built-in capabilities like file processing, code execution, and retrieval without additional infrastructure.
The framework includes a Gradio-based UI for testing agencies interactively. Users can watch agent communications in real-time, seeing how the CEO agent delegates to specialists and how results flow back up the chain. This visibility is invaluable for debugging and refining agent team designs.
Agency Swarm has found strong adoption in building internal automation systems — agencies that handle customer onboarding, content creation, data analysis, and business process automation. Its structured communication model prevents the 'agent chaos' that occurs in fully connected multi-agent systems.
The open-source nature and active community have produced a growing library of pre-built agents and tools covering web browsing, document processing, API integration, and domain-specific capabilities. The framework's opinionated design makes it faster to build reliable multi-agent systems compared to more flexible but complex alternatives.
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Agents communicate through explicitly defined channels rather than free-form messaging, reducing chaos and improving reliability.
Use Case:
Building a content agency where the editor can request changes from writers but writers cannot bypass the editor to publish directly.
Agents organized hierarchically (CEO, managers, specialists) mimicking real company structures for natural task delegation.
Use Case:
Creating a research agency with a CEO agent that receives requests, a research manager that plans, and specialist agents for web search and analysis.
Tools defined as Python classes with Pydantic validation for type safety, automatic schema generation, and clear interfaces.
Use Case:
Creating a CRM tool with validated input fields that ensures agents provide complete, correctly-typed data.
Built on top of Assistants API, providing built-in file search, code interpreter, and persistent threads without additional infrastructure.
Use Case:
Giving agents the ability to process uploaded documents and run data analysis code without setting up separate RAG or code execution services.
Gradio-based interface for testing agencies with real-time visibility into agent communications and delegation chains.
Use Case:
Debugging a multi-agent workflow by watching the CEO agent delegate tasks and reviewing each specialist's responses.
AI-powered tool generator that creates Python tool implementations from natural language descriptions.
Use Case:
Rapidly creating a custom API integration tool by describing what it should do rather than coding it from scratch.
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Content creation workflows
Multi-step research and analysis
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We believe in transparent reviews. Here's what Agency Swarm doesn't handle well:
Agency Swarm is built on OpenAI's Assistants API, so it primarily works with OpenAI models. Some community forks add support for other providers, but it's not officially supported.
Agency Swarm uses OpenAI's Assistants API with structured channels; CrewAI is model-agnostic with role-based design. Agency Swarm offers more control over communication flow; CrewAI is more flexible with model choice.
Yes, many teams use Agency Swarm in production. The structured communication model and Assistants API foundation provide reliability. Add monitoring and error handling for production deployments.
Tools are Python classes inheriting from BaseTool with Pydantic fields. The tool creation assistant can also generate tool code from natural language descriptions.
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See how Agency Swarm compares to CrewAI and other alternatives
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CrewAI is an open-source Python framework for orchestrating autonomous AI agents that collaborate as a team to accomplish complex tasks. You define agents with specific roles, goals, and tools, then organize them into crews with defined workflows. Agents can delegate work to each other, share context, and execute multi-step processes like market research, content creation, or data analysis. CrewAI supports sequential and parallel task execution, integrates with popular LLMs, and provides memory systems for agent learning. It's one of the most popular multi-agent frameworks with a large community and extensive documentation.
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