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Documentation Index

Fetch the complete documentation index at: https://docs.openbunny.ai/llms.txt

Use this file to discover all available pages before exploring further.

OpenBunny uses QMD (Quantum Memory Database) for hybrid search across tasks and messages. This combines three search strategies for high-quality results.

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How it works

  1. BM25 keyword search — fast lexical matching based on term frequency
  2. Semantic search — vector similarity using embeddings to find conceptually related results
  3. LLM reranking — a language model reranks the combined results for relevance
The three strategies run in parallel, and results are merged and deduplicated before final ranking.

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Search API

Search tasks via the REST API: 
curl "http://localhost:3100/tasks/search?q=fix+login+bug"

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Duplicate detection

The search system powers duplicate detection. When the agent creates a new task, it first calls find_duplicates which uses vector similarity to check for existing tasks with similar content. 
curl -X POST http://localhost:3100/tasks/find-duplicates \
  -H "Content-Type: application/json" \
  -d '{"text": "Fix the login timeout issue"}'

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Indexing

Tasks and messages are automatically indexed when created or updated. The search index is initialized at service startup and maintained incrementally.