Troubleshooting

Below are suggestions on how to fix some common errors you might encounter while using Pydantic AI. If the issue you're experiencing is not listed below or addressed in the documentation, please feel free to ask in the Pydantic Slack or create an issue on GitHub.

Jupyter Notebook Errors

RuntimeError: This event loop is already running

Modern Jupyter/IPython (7.0+): This environment supports top-level await natively. You can use [Agent.run()][pydantic_ai.agent.Agent.run] directly in notebook cells without additional setup:

from pydantic_ai import Agent

agent = Agent('openai:gpt-5.2')
result = await agent.run('Who let the dogs out?')

Legacy environments or specific integrations: If you encounter event loop conflicts, use nest-asyncio:

import nest_asyncio

from pydantic_ai import Agent

nest_asyncio.apply()

agent = Agent('openai:gpt-5.2')
result = agent.run_sync('Who let the dogs out?')

Note: This also applies to Google Colab and Marimo environments.

RuntimeError: Event loop is closed

Synchronous methods like [Agent.run_sync()][pydantic_ai.agent.AbstractAgent.run_sync] reuse the thread's current event loop, and install a fresh one if other code closed it. If this error is raised from inside httpx or httpcore during a model request, the agent was already used before its event loop was closed: the provider's HTTP connection pool still holds connections bound to the dead loop. Recreate the agent together with its model and provider (or pass a fresh http_client to the provider); reusing an existing Model instance keeps the dead connection pool. Avoid closing an event loop that other code is still using.

API Key Configuration

[UserError][pydantic_ai.exceptions.UserError]: Set the [PROVIDER]_API_KEY environment variable or pass it via the provider's api_key=... argument

If you're running into issues with setting the API key for your model, visit the Models page to learn more about how to set an environment variable and/or pass in an api_key argument.

To try Pydantic AI without an API key, use the built-in 'test' model: [Agent('test')][pydantic_ai.agent.Agent].

Monitoring HTTPX Requests

You can use custom httpx clients in your models in order to access specific requests, responses, and headers at runtime.

It's particularly helpful to use logfire's HTTPX integration to monitor the above.