# Nexus: An Open-Source Multi-Agent Framework with 3 Orchestration Modes and Native A2A/MCP Support

Hi HN,

I've been building with LLM agents for a while and kept running into the same frustration: every framework forces you into one orchestration model. LangGraph gives you graphs. Swarm gives you handoffs. CrewAI gives you roles. But real-world systems need all three — sometimes in the same application.

So I built **Nexus** — a Python multi-agent framework with:

- **3 orchestration modes**: Static graph (DAG), dynamic router, and self-organizing adaptive (capability matching + ELO-like rating)
- **Protocol-native**: Built on Google A2A (agent-to-agent) and Anthropic MCP (model-context-protocol)
- **Production features**: Checkpoint/resume, token budget control with auto-degradation, retry → fallback → human escalation
- **Multi-provider**: OpenAI, Anthropic, Ollama (local)

The core API is minimal:

```python
async with Team(model="gpt-4o") as team:
    team.add_agent("researcher", instructions="...")
    team.add_agent("writer", instructions="...")
    result = await team.run("Write a blog post about AI agents")
```

The adaptive mode is what I'm most excited about — agents have 8-dimensional capability vectors, and the system does cosine similarity matching plus performance tracking to route tasks to the best agent. Over time it learns.

Architecture:
- 5 layers: Protocol → Runtime → Orchestration → Observability → DX
- 3 primitives: Agent, Task, Handoff
- ~3000 lines of Python, zero magic

Everything is MIT licensed.

**GitHub**: https://github.com/songtianye/nexus

I'd appreciate any feedback on the design, especially:
1. Is the 3-mode orchestration model useful or overengineered?
2. Does the A2A/MCP integration make sense for real inter-agent scenarios?
3. What's missing that would make you actually use this?

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