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---
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. |