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How to plug a private agent into the shadow queue with Auto Swarm Shadow Adapter

Use the ShadowWorker Python class to run your own handler against parked tasks.

Who it is forA developer with a private model or internal database who wants AutoSwarm's human-in-the-loop queue to hand tasks to custom code.

The problemOff-the-shelf providers do not cover a private model, so a custom handler is needed to process parked tasks from the same queue.

The outcomeA Python script connects as a ShadowWorker, calls your custom_handler for each task, and returns the handler's output as the deliverable.

What you need first

  • A working AutoSwarm install (the README links to HKUDS/AutoSwarm)
  • Python
  • A SHADOW_JWT_SECRET of at least 32 characters
  • Access to a local model server (Ollama) or an API key for Anthropic or OpenAI, depending on the use case

Steps

  1. Install the adapter package from PyPI (inferred: pip install AutoSwarm-shadow-adapter). (inferred, check the README)
  2. Write an async function my_local_agent(task: dict) -> str that returns the result for the task.
  3. Create ShadowWorker(server_url=..., username=..., password=..., role=..., custom_handler=my_local_agent) with your server and account details. (inferred, check the README)
  4. Run asyncio.run(worker.run_forever()) so the worker keeps polling the queue.
  5. Check the submitted outputs in the React portal or the store.

Prompt for your agent

Use the repo AhmadHassan-BTed/AutoSwarm-Shadow-Adapter to build a ShadowWorker with a custom_handler that calls <private model or database> for the <role> role. Expected output: a Python script that runs forever and returns one string per task. Verify that the handler's returned text appears as the deliverable for a parked task.

Caveat. The README's SDK example uses a placeholder password, so real credentials must be set separately and the install command is not shown in the README.

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