LeoOS

💡 Inspiration

Modern software development is often bogged down by context switching, boilerplate code, and communication overhead. We wanted to see how far we could push OpenAI's new Swarm framework. What if you could talk to an entire software engineering team—Product Manager, Architect, Developer, and QA—as a single OS? That’s how LeoOS was born.

⚙️ How we built it

LeoOS is powered entirely by OpenAI Swarm and the GPT-4o model. We designed a multi-agent routing system where:

• The PM Agent refines user text requirements into tasks.
• The Architect Agent designs the system structure.
• The Coder Agent writes the actual implementation.
• The QA Agent executes tests and passes feedback back to the Coder.

We used FastAPI for the backend orchestration and a sleek Next.js dashboard to visualize the agents collaborating and passing tokens in real time.

🚀 Challenges we ran into

The biggest challenge was handling "infinite loops" where agents kept passing tasks back and forth without resolving the root conflict. We solved this by implementing a state-machine supervisor that tracks agent handoffs and forces human-in-the-loop intervention if an issue persists for more than 3 cycles.

🎓 Accomplishments that we're proud of

We successfully built a complete, end-to-end multi-agent workflow that can generate, test, and deploy a simple Todo web application within 2 minutes from a single prompt, with zero human code intervention.

🧠 What we learned

We learned the intricacies of agent orchestration and how strict schema definitions (via Pydantic) drastically reduce hallucination rates when agents communicate with each other.

🔮 What's next for LeoOS

We plan to integrate the OpenAI Realtime API so users can verbally brainstorm features with the entire agent team in real-time, and add persistent memory vector stores for long-term project context.

Built With

• fastapi
• gpt-4o
• langchain
• next.js
• openai-swarm
• python
• typescript
• vector