
Triton Inference Server for model repositories
Serve models from a versioned repository using backends appropriate to each model format.
Explore AI projects, understand what they do and find something you can build with.
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Serve models from a versioned repository using backends appropriate to each model format.

Serve language model workloads that reuse instructions or context across requests.

Collect research sources into notebooks and turn selected material into notes or audio.

Inspect published small-model checkpoints and the recipes used to build them.

Serve open-weight language models to multiple clients while managing attention-cache memory.

Set up separate chat workspaces around document collections and chosen model providers.
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.