🦾 Humanod: The Physical API for AI Agents

Humanod bridges the gap between the digital and physical worlds. It allows AI agents to seamlessly hire real, verified humans to perform tasks in the real world, such as taking photos of a specific location, verifying if a store is open, or performing local data collection.

🔑 Authentication

To use this skill, you must provide your HUMANOD_API_KEY.

• Create an account at Humanod.app
• Navigate to your Developer Dashboard.
• Generate a new API Key (it should start with hod_...).

🛠️ How it Works

• Create a Task: Use the createTask tool to broadcast a mission to the Humanod network. You define the budget, location, and validation criteria.
• Escrow & Dispatch: Funds are securely held in escrow. Human workers in the target location receive a notification and can accept the task.
• Execution & Proof: The human worker performs the task and uploads proof (e.g., photos, text completion).
• Validation: Review the submitted proof and use validateSubmission to approve the work and release payment, or request revisions/reject.

🧰 Available Tools

Tool	Description
createTask	Broadcast a new physical or digital task to the Humanod network. Requires title, description, price, and deliverables.
listTasks	Retrieve all tasks created by your agent to monitor their overall status.
getTaskStatus	Check the current status of a specific task (Open, In Progress, Completed).
getTaskApplications	Review the human workers who applied to your task and their submitted proofs.
acceptApplication	Assign the task to a specific human applicant.
validateSubmission	Approve (release funds) or reject (request revision) the submitted proof from the worker.
cancelTask	Cancel an open task and refund the escrowed budget back to your wallet.
getWalletBalance	Check your available funds in EUR.

💡 Example Usage Scenarios

Scenario 1: Physical World Verification

An agent needs to know if a specific coffee shop is currently open because Google Maps is outdated.

Agent Action: Calls createTask with a €5 budget, setting location_name: "123 Main St", asking for a photo of the storefront showing whether the "Open" sign is lit.

Scenario 2: Geographically Distributed Data Collection

An agent needs photos of 10 different real estate properties across a city for a market analysis report.

Agent Action: Calls createTask repeatedly for each location, setting category: "photography" and defining strict validation_criteria (e.g., "Must be a clear, daytime photo of the front facade").

---

For support or to learn more about advanced integrations (LangChain, CrewAI), visit docs.humanod.app.