AI Worker worker.md

pydantic-ai

AI Agent Framework, the Pydantic way

Agent framework 16,100 stars Python MIT Worker-compatible

Source#

Tags#

agent-frameworkgenaillmpydanticpython

Integration notes#

Framework-level abstraction; derive bounded worker contracts from concrete tasks and APIs in docs/examples.

worker.md example#

Starter worker.md contract mapped from this registry entry. Copy this file and adapt schemas, constraints, and statuses for your task.

---
id: pydantic-ai-repo-derived-worker
name: pydantic-ai Repo-Derived Worker
version: 1.0.0
source_registry_url: https://worker.md/registry/pydantic-ai/
source_repository: https://github.com/pydantic/pydantic-ai
repository_default_branch: main
repository_language: Python
repository_license: MIT
repository_updated_at: 2026-04-05
worker_mode: agent-orchestration-worker
derivation_method: github_repository_metadata_plus_raw_readme
derivation_confidence: 0.9
derived_on: 2026-04-05
tags:
  - agent-framework
  - genai
  - llm
  - pydantic
  - python
---

# pydantic-ai Repo-Derived Worker

## Repo-derived summary
- Registry summary: AI Agent Framework, the Pydantic way
- Repository description: AI Agent Framework, the Pydantic way
- Stars (snapshot): 16,100
- Primary language: Python
- Worker mode classification: agent-orchestration-worker

## Extracted from
- https://github.com/pydantic/pydantic-ai
- https://github.com/pydantic/pydantic-ai/blob/main/README.md
- https://docs.pydantic.dev
- https://docs.pydantic.dev/latest/
- https://ai.pydantic.dev/#tools-dependency-injection-example

## Evidence notes (from repository text)
- README summary paragraph: ### Pydantic AI is a Python agent framework designed to help you quickly, confidently, and painlessly build production grade applications and workflows with Generative AI.
- ### Pydantic AI is a Python agent framework designed to help you quickly, confidently, and painlessly build production grade applications and workflows with Generative AI.
- Yet despite virtually every Python agent framework and LLM library using Pydantic Validation, when we began to use LLMs in https://pydantic.dev/logfire, we couldn't find anything that gave us the same feeling.
- We built Pydantic AI with one simple aim: to bring that FastAPI feeling to GenAI app and agent development.
- Enables you to systematically test and https://ai.pydantic.dev/evals the performance and accuracy of the agentic systems you build, and monitor the performance over time in Pydantic Logfire.
- # Define a very simple agent including the model to use, you can also set the model when running the agent.

## Installation hints found in README
- No explicit package installation command detected in README text.

## worker.md contract (derived starter)
Purpose: Execute one orchestrated agent task as a bounded worker step.

### Input schema
```json
{
  "type": "object",
  "additionalProperties": false,
  "required": [
    "run_id",
    "task",
    "context"
  ],
  "properties": {
    "run_id": {
      "type": "string"
    },
    "task": {
      "type": "string"
    },
    "context": {
      "type": "object"
    }
  }
}
```

### Output schema
```json
{
  "type": "object",
  "additionalProperties": false,
  "required": [
    "run_id",
    "status",
    "result"
  ],
  "properties": {
    "run_id": {
      "type": "string"
    },
    "status": {
      "type": "string",
      "enum": [
        "ok",
        "retryable_error",
        "invalid_request",
        "invalid_output"
      ]
    },
    "result": {
      "type": "object"
    }
  }
}
```

### Constraints
- timeout_seconds: 30
- max_attempts: 2
- idempotency_key: run_id
- status_enum: [ok, retryable_error, invalid_request, invalid_output]
- notes: adapt to concrete APIs/classes documented in this repository before production use

## How this should be used
1. Treat this file as a repo-derived starter profile, not a claim of an official repository API contract.
2. Replace schemas with exact interfaces from code/docs you adopt.
3. Keep execution bounded and auditable using worker protocol constraints.

How to use#

  • Save this as a worker spec file (for example: pydantic-ai-my-task.worker.md).
  • Replace the input/output schemas and purpose with your real bounded task.
  • Enforce schema validation + timeout + retry policy in your runtime before production use.

Citation#

Reference URL: https://worker.md/registry/pydantic-ai/

Source URL: https://github.com/pydantic/pydantic-ai