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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.