Kubernetes for AI agents — a meta-orchestrator that coordinates specialist agents with structured handoffs, SOPs, and quality gates.
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CrewAI, LangGraph, and AutoGen solve multi-agent prototyping fast. But once you have 5-10 agents, the real problem emerges: how do they hand work to each other reliably? How do you audit a 7-agent pipeline? How do you know which agent produced what, with what confidence?
Existing tools give you agents. They don't give you an operating system to coordinate them.
TeamOlimpo is that OS.
git clone https://github.com/teamolimpo/TeamOlimpo.git
cd TeamOlimpo
uv sync
opencode .Then ask Poros (the orchestrator):
"Research async Python patterns and write a technical summary."
Poros will:
- Route to Proteo (research) → produces structured handoff
- Route handoff to Hermione (technical writing) → produces document
- Return the result
Every step writes an auditable handoff file. No output is delivered without one.
No other multi-agent system has this. Every time an agent finishes work, it writes a structured handoff before returning control. Status, priority, deviation reports, quality scores — all in one file.
Handoff { st: done | fail | hold, devi: { cause, corrective_action }, quality_score: 1-5, refs: [...] }
No handoff = task incomplete. Enforced at the agent level.
👉 Full handoff protocol spec →
Standard operating procedures define how work gets done, not just what:
cb870dc6— Handoff protocol900191a0— Agent creation standards5f609a1a— Routing and delegation
SOPs are versioned and referenced, never duplicated.
All tools are MCP servers — not REST wrappers, not SDK abstractions. Frameworks like LangGraph and CrewAI are adding MCP support. TeamOlimpo started with it.
- Efesto (Python dev):
ruff check → ruff format → mypy → pytestbefore delivery - Hermione (writer): confidence markers, gap declarations
- Proteo (researcher): minimum 2-3 independent sources per key claim
| Agent | Role |
|---|---|
| Poros | Orchestrator — routes, tracks, synthesizes |
| Atena | Agent designer — builds the agents that build |
| Proteo | Multi-source domain researcher |
| Pythagoras | Academic researcher (peer-reviewed sources) |
| Efesto | Python developer — production-ready code |
| Hermione | Technical writer — structured documentation |
| Euterpe | Italian essay writer |
| Clio | Vault archivist — PDF, cataloging, verification |
| Dike | Risk analyst (Emerson DeltaV) |
| Metis | Strategic thinking partner |
| Eunomia | Contextual analyst (email vault) |
| Feature | LangGraph | CrewAI | AutoGen | TeamOlimpo |
|---|---|---|---|---|
| Category | State machine | Role-based | Conversation | Meta-orchestrator |
| Handoff protocol | ❌ | ❌ | ❌ | ✅ Mandatory + auditable |
| SOP-driven | ❌ | ❌ | ❌ | ✅ Versioned SOPs |
| MCP-native | 🛠 Adding | 🛠 Adding | 🛠 Adding | ✅ Day one |
| Quality gates | ❌ | ❌ | ❌ | ✅ Per-agent |
TeamOlimpo is not a framework for building agents — it coordinates them after you have them.
TeamOlimpo/
├── .opencode/agents/ # Agent prompt files
├── Team/
│ ├── Members/ # Agent identity profiles
│ └── SOPs/ # Standard operating procedures
├── tools/ # MCP servers (Python)
│ ├── synapsis/ # Search, task, handoff, session
│ ├── executor/ # Shell execution with Token Juice
│ └── email_processor/ # Email vault processing
└── Library/ # Private data (gitignored)
Alpha — 777+ handoffs executed, 11 agents operational, core protocol solid.
- Poros orchestration with IntentGate routing (17 categories)
- Structured handoff protocol (11 types, 4 statuses, deviation reporting)
- Synapsis tool suite (search, task, session, handoff, admin)
- All 11 agent profiles with SOP enforcement
- Quality gates for code and documentation
- AutoGen-to-TeamOlimpo migration tooling
- Visual handoff graph (auto-generated Mermaid)
- Pluggable agent SDK adapters
- Runtime quality analytics
- PyPI package
- No handoff, no merge — every PR must include handoff specs
- SOPs before code — new workflows start with an SOP proposal
- Quality gates — code must pass ruff, mypy, pytest
Start with Team/SOPs/ — read the handoff protocol (cb870dc6) and the agent methodology (900191a0).
⭐ Star on GitHub — it helps the project grow
📖 Read on Substack — full story behind the system
MIT License — LICENSE