Evals docs in Logfire (points to Pydantic AI) #1374
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summerscope
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Sep 10, 2025
dmontagu
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Sep 10, 2025
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| !!! note "Code-First Evaluation" | ||
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| Evals are created and run using the [Pydantic Evals](https://ai.pydantic.dev/evals/) a sub-package of Pydantic AI. Logfire serves as a read-only observability layer where you can view and compare results. |
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| Evals are created and run using the [Pydantic Evals](https://ai.pydantic.dev/evals/) a sub-package of Pydantic AI. Logfire serves as a read-only observability layer where you can view and compare results. | |
| Evals are created and run using the [Pydantic Evals](https://ai.pydantic.dev/evals/) package, which is developed in tandem with Pydantic AI. Logfire serves as an observability layer where you can view and compare results. |
dmontagu
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Sep 10, 2025
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| # Evals (beta) | |||
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| View and analyze your evaluation results in Pydantic Logfire's web interface. Evals provides observability into how your AI systems perform across different test cases and experiments over time. | |||
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| View and analyze your evaluation results in Pydantic Logfire's web interface. Evals provides observability into how your AI systems perform across different test cases and experiments over time. | |
| View and analyze your evaluation results in Pydantic Logfire's web interface. Evals provide observability into how your AI systems perform across different test cases and experiments over time. |
dmontagu
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Sep 10, 2025
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| ## What are Evals? | ||
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| Evals help you systematically test and evaluate AI systems by running them against predefined test cases. Each evaluation experiment appears in Logfire automatically when you run the Pydantic Evals package with Logfire integration enabled. |
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| Evals help you systematically test and evaluate AI systems by running them against predefined test cases. Each evaluation experiment appears in Logfire automatically when you run the Pydantic Evals package with Logfire integration enabled. | |
| Evals help you systematically test and evaluate AI systems by running them against predefined test cases. Each evaluation experiment appears in Logfire automatically when you run the `pydantic_evals.Dataset.evaluate` method with Logfire integration enabled. |
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This isn't precious - just trying to get something into Logfire docs to start with, feel free to slash / edit as you feel fit. Mostly just wanted to make sure people go to the Pydantic AI docs to read about Evals properly.