AI Team Orchestrator - Multi-agent orchestration framework with 94% lower API costs
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Open-source framework that runs AI agents as a coordinated team. Features Director-led orchestration, workspace memory, automatic handoffs, and conditional quality gates. Includes 62K-word implementation guide documenting real failures and solutions.
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Hey PH. I'm open-sourcing AI Team Orchestrator - a multi-agent orchestration
framework I've been building.
## Why I'm sharing this now
The framework has reached a maturity point where it needs real-world usage
to evolve further. I believe the next phase requires a community of developers
who can stress-test it, provide feedback, and guide the roadmap with actual use cases.
This is ambitious, but I think we can build something significant together.
## What this is
A framework that runs multiple AI agents as a coordinated team.
Built on OpenAI Agents SDK. Everything is on GitHub.
The problem: Running multiple agents quickly becomes chaos - they don't share context, duplicate work, create infinite loops, and burn through API credits.
The OpenAI SDK gives you the primitives (agents, tools, handoffs) but not
the orchestration layer. That's what this provides.
## Core architecture
- Director agent that assembles teams based on project goals
- Executor with semantic priority calculation
- Workspace memory for context retention
- Quality gates that trigger conditionally
- Handoff system for agent-to-agent context passing
## What's included
1. Complete source code (Python/FastAPI backend, Next.js frontend)
2. Production database schema (Supabase)
3. 15 documented architecture patterns
4. 62,000-word implementation guide (free ebook)
The book documents real failures and solutions:
- Race conditions with concurrent agents
- Pessimistic locking at application level
- Why agents don't use tools when available
- Cost optimization through conditional quality gates
Documentation: https://books.danielepelleri.com
Questions? Feedback? Ideas for the roadmap? Let's discuss below.
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