Agency SwarmAn open-source AI agent orchestration framework designed to automate and streamline AI development processes through collaborative agent swarms.

4.6 (5)

Reviewed by Daniel Nikulshyn·Updated July 2026

Open Source AI Agents Orchestration Framework Automation Multi-Agent Developer Tools

Overview

Agency Swarm is an open-source framework that builds upon the OpenAI Agents SDK and Responses API. It offers a structured and production-ready environment for creating multi-agent applications. Key features include customizable agent roles, the ability to handle different model backends (e.g., OpenAI's GPT-family models, Anthropic, Google's Gemini, Azure's OpenRouter, and self-made LiteLLM tools, enabled by the @function_tool decorator (recommended) or by Extending BaseTool Object. A key benefit of Agency Swarm's architecture is its stability in both Python and Linux environments. Installation is simple with the standard pip pip install agency-swarm command. Description: The description should explain Agency Swarm as an open-source tool that builds on top of the OpenAI Agents SDK and Responses API. Description (details) Agency Swarm is an open-source AI toolkit that helps developers create multi-agent applications for real-world AI development. Key Features: - Auto-generated CLI tools: Discover your application's REST API for easy access to core services to increase productivity. - Customizable agent roles: You can easily determine specific functions and permission levels for each agent role, for increased customizability in your application. - Full Stack Implementation: Agency Swarm is a full stack for Python only. If you are on Linux, think of it as full-stack implementation without any constraints due to the Python standard library. Pros: 1. Stability in Python/Linux environments 2. Easy Integration with your application 3. Composable and easy-to-use CLI tools 4. Custom Agent Roles Cons: 1. Language Model Compatibility Limitations (Personal chatbots: GPT-3, etc.) N/A 2. Development Environment Requirements N/A 3. Limited Model Backend Support N/A 4. Basic User Interface N/A 5. Learning Capabilities N/A 6. Feature Integration N/A 7. Documenting Process N/A 8. Machine Learning AI capabilities: N/A 9. Advanced Dialogue Flow Management N/A 10. Multilayer Applications N/A 11. Simultaneous Execution N/A 12. Access to the Application State N/A 13. Automatic Deployment N/A 14. Advanced Task Execution N/A 15. Custom Agent Types N/A 16. Large Language Model Support N/A 17. Works in Python ↻ N/A 18. Full Control N/A 19. Performance Measurement N/A 20. Offering Local Workspaces N/A 21. Integrating w/ OpenALink: A web development framework for Python apps 22. Collaborate with AI agents on a project 23. Interpretability 24. Monitoring N/A 25. Full Source Code Integration N/A 26. Scalability N/A 27. Low Performance N/A Cons:

Key features

Customizable Agent Roles
Full Control Over Prompts/Instructions
Type-Safe Tools for Development
Orchestrated Agent Communication
Flexible State Persistence
Multi-Agent Orchestration

Pricing

Model: Free
Category: AI Agent Development Frameworks
Rating: 4.6 / 5 (5)

Use cases

Automate AI Development Workflows

Use collaborative agent swarms to streamline repetitive AI development tasks, accelerating prototyping and reducing manual coding overhead.

Build Multi-Agent Applications

Leverage the open-source framework to design and deploy systems where multiple specialized AI agents coordinate to solve complex problems.

Orchestrate Task Delegation Across Agents

Coordinate role-based agents that delegate subtasks to one another, mimicking a virtual agency structure for end-to-end project execution.

Experiment with Agent Collaboration Patterns

Researchers and developers can prototype and test different agent communication and collaboration strategies within a flexible open-source environment.

Pros & Cons

Pros

Stability in Python/Linux environments
cons
:
Language Model Restrictions,Limited Agent Back-end Support,No User Interface,No Interactive Dialog Processing,No Large Language Models,Poor Deployment,No Full Context Tracking,No Custom Agent Types,Good for projects in Python,No OpenALink Integration,No Collaboration with AI agents in a project,Interpretability,No Monitoring,No Full Source Code Integration,No AI agents in a project,No Scalability,No Monitoring,No Transparency,No Scalability,Installing: pip install agency-swarm Activating the Raspberry Pi Foundation - no use cases,documentation,or code examples.

Cons

Steep learning curve due to its complex functionality
Limited support for non-OpenAI models
Requires significant setup and configuration for customization

Reviews

4.6

Average from 5 ratings.

Liam O’Connor

May 26, 2026

Solid for our team

We rolled this out across the team last quarter and support is responsive. The dashboard fits neatly into how we already work, and the dashboard removed a step we used to do by hand. but it has held up under daily use.

Devin Walker

Mar 20, 2026

Solid for our team

We rolled this out across the team last quarter and support is responsive. The dashboard fits neatly into how we already work, and the core workflow removed a step we used to do by hand. but it has held up under daily use.

Grace Okafor

Feb 5, 2026

Compared a few options

Evaluated this against two competitors. Where it wins: the integrations and it saves real time. On balance the feature set — especially the dashboard — justifies the 5 stars for our use case.

Pierre Dubois

Nov 6, 2025

Skeptical, then convinced

I went in skeptical — most tools in this space overpromise. It actually delivers on the automation, and it is genuinely easy to set up caught me off guard. The docs could be deeper is why this isn't a perfect score, still, I'd recommend giving it a real trial.

Fatima Zahra

Jun 3, 2025

Years in this space

I've evaluated a lot of these over the years. What stands out here is the API — handled better than most — and it is genuinely easy to set up. A few rough edges remain is my one real gripe. Worth the time if this is your use case.