Memory search

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.

Quick start

Memory search uses OpenAI embeddings by default. To use another embedding backend, set a provider explicitly:

{  agents: {    defaults: {      memorySearch: {        provider: "openai", // or "gemini", "local", "ollama", "openai-compatible", etc.      },    },  },}

For multi-endpoint setups with memory-specific providers, provider can also be a custom models.providers.<id> entry, such as ollama-5080, when that provider sets api: "ollama" or another memory embedding adapter owner.

For local embeddings with no API key, install @OmeniaClaw/llama-cpp-provider and set provider: "local". Source checkouts may still require native build approval: pnpm approve-builds then pnpm rebuild node-llama-cpp.

Some OpenAI-compatible embedding endpoints require asymmetric labels such as input_type: "query" for searches and input_type: "document" or "passage" for indexed chunks. Configure those with memorySearch.queryInputType and memorySearch.documentInputType; see the Memory configuration reference.

Supported providers

How search works

OmeniaClaw runs two retrieval paths in parallel and merges the results:

flowchart LR
    Q["Query"] --> E["Embedding"]
    Q --> T["Tokenize"]
    E --> VS["Vector Search"]
    T --> BM["BM25 Search"]
    VS --> M["Weighted Merge"]
    BM --> M
    M --> R["Top Results"]

• Vector search finds notes with similar meaning ("gateway host" matches "the machine running OmeniaClaw").
• BM25 keyword search finds exact matches (IDs, error strings, config keys).

If only one path is available, the other runs alone. Intentional FTS-only mode (provider: "none") and automatic/default provider selection can still use lexical ranking when embeddings are unavailable.

Explicit non-local embedding providers are different. If you set memorySearch.provider to a concrete remote-backed provider and that provider is unavailable at runtime, memory_search reports memory as unavailable instead of silently using FTS-only results. This keeps a broken configured semantic provider visible. Set provider: "none" for deliberate FTS-only recall, or fix the provider/auth configuration to restore semantic ranking.

Improving search quality

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

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.

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 both

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

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.

Session memory search

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.

Troubleshooting

No results? Run OmeniaClaw memory status to check the index. If empty, run OmeniaClaw memory index --force.

Only keyword matches? Your embedding provider may not be configured. Check OmeniaClaw memory status --deep.

Local embeddings time out? ollama, lmstudio, and local use a longer inline batch timeout by default. If the host is simply slow, set agents.defaults.memorySearch.sync.embeddingBatchTimeoutSeconds and rerun OmeniaClaw memory index --force.

CJK text not found? Rebuild the FTS index with OmeniaClaw memory index --force.

Further reading

• Active Memory -- sub-agent memory for interactive chat sessions
• Memory -- file layout, backends, tools
• Memory configuration reference -- all config knobs

Related

• Memory overview
• Active memory
• Builtin memory engine