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- banditdb
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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:
- Create a campaign — define a campaign ID, the arms (choices), and context feature dimension.
- Get a prediction — pass a context vector, receive the recommended arm and an interaction ID.
- 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.
Designing Context Vectors
The context vector is the most important design decision. Each float encodes something about the current situation. Normalize values to roughly 0–1 range.
Examples:
- Notification timing:
[hour_of_day/24, day_of_week/7, messages_today/10, last_response_delay_mins/60] - Tool selection:
[query_length/500, has_code_mention, has_url, specificity_score] - Prompt strategy:
[task_complexity, domain_familiarity, output_length_needed, structured_output]
Use Cases for OpenClaw Agents
- Smart notifications — learn when/how to reach the user (arms: morning/afternoon/evening, channel variants)
- Tool routing — which tool to use for a query type (arms: web_search/memory/file_lookup/ask)
- Model selection — which model for which task (arms: opus/sonnet/haiku)
- Response style — learn user preferences (arms: brief/detailed/bullet_points)
- Heartbeat frequency — when to check in vs stay quiet
Key Details
- Algorithms: LinUCB (default, supports causal analysis) or Thompson Sampling
- Cold start: meaningful lift typically after 300–1500 interactions depending on noise
- Parquet export available for offline causal analysis (LinUCB only)
- WAL ensures crash recovery — no data loss on restart
- ~10K predictions/s on a single node