---
language:
- en
tags:
- agents
- orchestration
- tool-calling
- controller
- planning
- routing
license: apache-2.0
pipeline_tag: text-generation
---

# 🎛️ Multi-Agent Orchestrator

`multiagent-orchestrator` is a small **planning & coordination** model built on **Llama3.2:1b** that acts as a _conductor_ for your AI **agents** and **tools**.

It is **not** a general chatbot. Instead, it reads a **task state** and an **agent/tool registry** and returns the **next action** as strict JSON:

```json
{
  "action": "call_agent" | "call_tool" | "ask_user" | "finish",
  "target": "agent_or_tool_name_or_null",
  "arguments": { "any": "json" },
  "final_answer": "string or null",
  "reason": "short natural language rationale"
}
```

You run your own loop that:

1. Calls this model to get the next action
2. Executes the chosen agent/tool
3. Updates task state
4. Repeats until action == "finish"

### Example (pseudo-usage)

```python
action = orchestrator(agents=agent_registry, state=task_state)

if action["action"] == "call_agent":
    result = call_agent(action["target"], action["arguments"])
elif action["action"] == "call_tool":
    result = call_tool(action["target"], action["arguments"])
...

task_state = update_state(task_state, action, result)

```

## Intended use: 

As a controller in multi-agent / tool-using systems (researcher + coder agents, RAG pipelines, etc.), where you want a central brain choosing what happens next, not generating the final content itself.