
Phoenix for inspecting LLM traces and evaluations
Use OpenTelemetry-based traces to inspect model, retrieval and agent activity.
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Use OpenTelemetry-based traces to inspect model, retrieval and agent activity.

Collect traces around model calls, prompts and application steps.

Evaluate checkpoints and inference backends using standardized or custom tasks.

Run documented language-model benchmark tasks through a shared evaluation interface.

Use a registry and evaluation runner to define test cases for model responses.

Measure retrieval and answer behavior with metrics designed for RAG workflows.
Each page introduces the project, explains its capabilities and highlights practical takeaways. Follow the repository link for the latest documentation and setup instructions.
Sign in, choose Add Repo and paste the main URL of a public AI repository, not a file or issue link. Franklin AI uses the project’s README to build and publish a page and links your contribution to your profile. Each account can add up to 20 repositories a day.
Private repositories cannot be added. If the README is too short for an automatic page, or the project is already being reviewed, the repository is saved in your submissions with the reason for an admin to complete, and you are credited when it is published.
Its existing page stays as it is. Adding it again links that page to your profile instead of creating a second one.
Use the Category filter. Each card shows its categories, such as AI Agents or AI Development, and the repository’s owner and name.