BanditDB Skill

BanditDB is a self-hosted decision database. It learns which choice works best for which context through contextual multi-armed bandits — no ML pipeline required.

Setup

Install BanditDB from GitHub releases or run the Docker image (see references/api.md for details). Default port: 8080. Verify by requesting GET /campaigns.

Core Workflow

Three-step loop — create once, then predict and reward repeatedly:

1. Create a campaign — define a campaign ID, the arms (choices), and context feature dimension.
2. Get a prediction — pass a context vector, receive the recommended arm and an interaction ID.
3. Record a reward — report the outcome (0.0–1.0) for the interaction ID.

For full API details, request/response examples, and MCP tool registration, see references/api.md.