
Agency SwarmAn open-source AI agent orchestration framework designed to automate and streamline AI development processes through collaborative agent swarms.
Overview
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
Battle record
Across 1 battle in the Pantheon.
Last battle
Reviews
Average from 5 ratings.
Sign in to leave a review.
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.
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.
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.
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.
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.
Q&A
How do I deploy my agency to production?
Build a dedicated API backend (FastAPI is recommended) that manages authentication and persists thread state using callbacks. See the Deployment to Production guide for detailed steps.
Asked by Celia Ramirez · Nov 23, 2025
How can I serve an Agency as an API using FastAPI?
Embed your agency within a FastAPI endpoint: ```python from fastapi import FastAPI from uuid import uuid4 app = FastAPI() @app.post("/chat") async def chat(user_request: UserRequest): chat_id = user_request.chat_id or str(uuid4()) agency = Agency( agent, load_threads_callback=lambda: load_threads(chat_id), save_threads_callback=lambda thread_dict: save_threads(thread_dict, chat_id) ) response = await agency.get_response(user_request.message) return {"chat_id": chat_id, "response": response.final_output} ``` Or use the built‑in FastAPI integration: ```python agency.run_fastapi(host="0.0.0.0", port=8000) ```
Asked by Camille Laurent · Nov 17, 2025
Why is the CodeInterpreter tool automatically added?
When file types like .json, .docx, or .pptx are uploaded, the CodeInterpreter tool is auto‑added to process them. To change this behavior, update the agent’s instructions or create a custom file‑handling tool.
Asked by Tomáš Novák · Oct 23, 2025
How can I transfer data between tools and agents?
There are two ways to transfer data between tools and agents: use the agency context inside your tools, or create a tool that uploads files to storage and outputs a file ID. The file ID can then be used by other tools or agents.
Asked by Joanna Kowalski · Oct 20, 2025
How do I manage multiple users with Agency Swarm?
To support multiple users or chats, you need to load and save thread IDs in your database for each user. Ensure each chat/user has a unique thread ID and follow the Deployment to Production guide for more details.
Asked by Salome Beridze · Oct 6, 2025
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