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By the end of this guide, you’ll have an agent configured in the dashboard, tested in chat, and ready to call through the public API.

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Prerequisites

  • A Fetch Hive workspace
  • A workspace API key if you want to run the public cURL example - see API Keys

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Step 1: Create an agent

Open Agents in the sidebar, then click Add Agent. Enter a Name. If your workspace uses categories, you can also choose a Category. Click Save to open the agent editor.

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Step 2: Configure the root agent

In the editor, click the root agent node to open Agent Settings. Add a short Instruction Prompt that tells the agent what to do. You can also confirm the Model, but you can keep the default settings for this first pass. For example: 
You are a research assistant. Answer clearly and keep responses brief unless the user asks for more detail.

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Step 3: Test the agent in chat

Click Chat in the editor header. Send a short test message in the chat panel and confirm that the agent responds the way you expect. This chat panel is for dashboard testing. The UI explicitly says these messages are not saved, so treat it as a safe place to iterate on behavior before you wire the agent into your app.

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Step 4: Copy the public invoke snippet

Open Code Snippet in the editor header, or use More in the sidebar and click Get Code. Select your Agent and API Key. The dialog shows the public cURL example for POST /v1/agent/invoke. The dialog provides a cURL snippet here. The Python and TypeScript tabs are still marked Coming Soon. 
curl 'https://api.fetchhive.com/v1/agent/invoke' \
  -H 'Authorization: Bearer YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  -H 'Accept: application/json' \
  --data-raw '{
    "agent": "YOUR_AGENT_ID",
    "message": "Summarize the latest AI infrastructure trends",
    "streaming": true
  }' \
  --compressed
Replace YOUR_API_KEY and YOUR_AGENT_ID with your real values. The in-app Chat panel uses a private dashboard testing flow. The snippet above is the public API flow you use outside the dashboard.

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Next steps