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memory_search finds relevant notes from your memory files, even when the wording differs from the original text. It works by indexing memory into small chunks and searching them using embeddings, keywords, or both.

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Quick start

If you have a GitHub Copilot subscription, OpenAI, Gemini, Voyage, or Mistral API key configured, memory search works automatically. To set a provider explicitly: 
{
  agents: {
    defaults: {
      memorySearch: {
        provider: "openai", // or "gemini", "local", "ollama", etc.
      },
    },
  },
}
For local embeddings with no API key, use provider: "local" (requires node-llama-cpp).

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Supported providers

ProviderIDNeeds API keyNotes
BedrockbedrockNoAuto-detected when the AWS credential chain resolves
GeminigeminiYesSupports image/audio indexing
GitHub Copilotgithub-copilotNoAuto-detected, uses Copilot subscription
LocallocalNoGGUF model, ~0.6 GB download
MistralmistralYesAuto-detected
OllamaollamaNoLocal, must set explicitly
OpenAIopenaiYesAuto-detected, fast
VoyagevoyageYesAuto-detected

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How search works

FluffBuzz runs two retrieval paths in parallel and merges the results:
  • Vector search finds notes with similar meaning (“gateway host” matches “the machine running FluffBuzz”).
  • BM25 keyword search finds exact matches (IDs, error strings, config keys).
If only one path is available (no embeddings or no FTS), the other runs alone. When embeddings are unavailable, FluffBuzz still uses lexical ranking over FTS results instead of falling back to raw exact-match ordering only. That degraded mode boosts chunks with stronger query-term coverage and relevant file paths, which keeps recall useful even without sqlite-vec or an embedding provider.

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Improving search quality

Two optional features help when you have a large note history:

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Temporal decay

Old notes gradually lose ranking weight so recent information surfaces first. With the default half-life of 30 days, a note from last month scores at 50% of its original weight. Evergreen files like MEMORY.md are never decayed.
Enable temporal decay if your agent has months of daily notes and stale information keeps outranking recent context.

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MMR (diversity)

Reduces redundant results. If five notes all mention the same router config, MMR ensures the top results cover different topics instead of repeating.
Enable MMR if memory_search keeps returning near-duplicate snippets from different daily notes.

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Enable both

{
  agents: {
    defaults: {
      memorySearch: {
        query: {
          hybrid: {
            mmr: { enabled: true },
            temporalDecay: { enabled: true },
          },
        },
      },
    },
  },
}

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Multimodal memory

With Gemini Embedding 2, you can index images and audio files alongside Markdown. Search queries remain text, but they match against visual and audio content. See the Memory configuration reference for setup. You can optionally index session transcripts so memory_search can recall earlier conversations. This is opt-in via memorySearch.experimental.sessionMemory. See the configuration reference for details.

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Troubleshooting

No results? Run fluffbuzz memory status to check the index. If empty, run fluffbuzz memory index --force. Only keyword matches? Your embedding provider may not be configured. Check fluffbuzz memory status --deep. CJK text not found? Rebuild the FTS index with fluffbuzz memory index --force.

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Further reading