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:

Calls this model to get the next action
Executes the chosen agent/tool
Updates task state
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.

erukude/multiagent-orchestrator

🎛️ Multi-Agent Orchestrator

Example (pseudo-usage)

Intended use:

erukude/
multiagent-orchestrator