memory_search finds relevant notes from your memory files, even when the wording differs from the original text. It chunks memory into small pieces and searches them with embeddings, keywords, or both.

Quick start

Apus uses OpenAI embeddings by default. To use another provider, set it explicitly:

{
  memory: {
    search: {
      provider: "openai", // or "gemini", "voyage", "mistral", "bedrock", "local", "ollama", "lmstudio", "github-copilot", "openai-compatible"
    },
  },
}

provider can also reference a custom models.providers.<id> entry (for example ollama-5080), as long as that entry sets api to "ollama" or another provider id with a memory embedding adapter.

For local embeddings with no API key, install and configure the official llama.cpp provider, then set provider: "local":

apus plugins install @openclaw/llama-cpp-provider

Choose llama.cpp once in interactive setup. Apus installs a verified llama-server, downloads the embedding GGUF, and writes its managed service configuration.

EmbeddingGemma uses its trained task prefixes automatically for queries and indexed documents, including through Ollama, LM Studio, and OpenAI-compatible providers. After upgrading, an existing unprefixed EmbeddingGemma index rebuilds once on the next search or sync. Apus generates fresh embeddings rather than reusing unprefixed cache entries. Keyword search remains available if the rebuild cannot finish immediately; no manual memory index --force is needed. Remove any proxy workaround that adds these prefixes so they are not applied twice.

Some OpenAI-compatible embedding endpoints require asymmetric input_type labels, such as "query" for searches and "document"/"passage" for indexed chunks. Set these with queryInputType and documentInputType; see Memory configuration reference.

Supported providers

How search works

Apus 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 --> D["Recency and importance"]
    D --> R["MMR diversity"]
    R --> O["Top results"]

• Vector search matches similar meaning ("gateway host" matches "the machine running Apus").
• BM25 keyword search matches exact terms (IDs, error strings, config keys). It accepts NFC and NFD Unicode spellings without rewriting notes or rebuilding existing indexes, including notes that mix those forms across words.
• Filename search indexes paths separately from note bodies. Exact full paths, basenames, and filename stems rank ahead of partial path matches, while snippets and body keyword scores still come from note content.

If only one path is available, the other runs alone.

The builtin engine then applies deterministic ranking:

hybrid relevance × recency decay × importance multiplier

Importance is scored once when an entry is written by a memory workflow that already has a model in the loop. Missing importance is neutral, so existing indexes keep their previous relevance signal. Dated daily notes decay with a 30-day half-life; curated files such as MEMORY.md and USER.md are evergreen. This follows the relevance, recency, and importance result in Generative Agents (arXiv:2304.03442) without adding a query-time model call.

MMR then reorders the scored hybrid candidate set to reduce redundant snippets. It does not change scores, threshold eligibility, or make another provider call.

Search preserves keyword matches when every ranked result falls below the configured minimum score. Hybrid search can also fill remaining result slots with keyword-only matches. These rules also apply in project sessions; semantic-only matches still need to meet the configured minimum score.

Deterministic trigger recall

On eligible interactive turns, the builtin engine also compares the inbound message with short trigger phrases stored on indexed entries. Strong matches can add up to three compact entries to hidden context before the reply. The prefilter uses the existing keyword and vector retrieval paths and does not run a recall model.

Automatic injection is deliberately narrower than memory_search: only promoted, trusted entries qualify. Until indexed provenance is available, that means entries from root MEMORY.md and USER.md only. Daily notes, imported transcripts, and session transcripts remain available through explicit memory tools or Active Memory escalation, but are never injected automatically.

FTS-only mode. Set provider: "none" to intentionally disable embeddings and search with keywords only. Leaving provider unset or set to "auto" falls back to keyword-only ranking when embedding setup or a request fails, as does provider: "local" (the GGUF/llama.cpp provider). Creation-time fallback still indexes text for keyword search, including manual and background indexing before the first search. memory_search includes the redacted embedding-bootstrap reason in debug.embeddingBootstrap even when there are no matches.

A failed local embedding request preserves keyword access to a matching index and records the degraded provider in memory status and Gateway logs. A real model or index-configuration mismatch still pauses search instead of serving mismatched data.

Explicit provider unavailable. If you name any other provider explicitly (for example openai, ollama, gemini) and it becomes unavailable at request time (bad auth, network failure), memory_search reports memory as unavailable instead of silently degrading to FTS-only results. This keeps a broken configured provider visible. Set provider: "none" for deliberate FTS-only recall, or fix the provider/auth configuration to restore semantic ranking.

Improving search quality

Two deterministic ranking passes are enabled by default for hybrid search.

Recency decay

Old notes gradually lose ranking weight so recent information surfaces first. With the default 30-day half-life, a note from last month scores at 50% of its original weight. MEMORY.md, USER.md, and undated files under memory/ remain evergreen. Dated YYYY-MM-DD.md and YYYY-MM-DD-<slug>.md files decay at any depth, including session-memory notes and nested dreaming reports.

Session transcript hits use the source activity timestamp captured during indexing. Retained transcript archives use their indexed file modification time. Individual message timestamps remain provenance metadata and do not determine the source's recency weight.

MMR (diversity)

Reduces redundant results. If five notes all mention the same router config, MMR favors a similarly relevant result with different content instead of repeating near-identical snippets. The fixed relevance-biased setting uses lambda 0.7 with Jaccard overlap over snippet tokens. Its local work is O(k²): ordinary defaults request 200 candidates per retrieval leg, for at most 400 unique non-exact candidates before overlap; broader project and identifier searches remain separately capped.

Multimodal memory

With gemini-embedding-2, you can index images and audio alongside Markdown. This only applies to files under memory.search.extraPaths; default memory roots (MEMORY.md, memory/*.md) stay Markdown-only. Search queries remain text, but they match against visual and audio content. See Memory configuration reference for setup.

Session memory search

For exact full-text recall from session transcripts, use sessions_search and then open a result with sessions_history. Session-memory search remains the semantic, experimental complement.

Optionally index session transcripts so memory_search can recall earlier conversations. This is opt-in: set experimental.sessionMemory: true and add "sessions" to sources (default sources is ["memory"]).

Use corpus: "memory" to search only memory notes. Results containing no session transcripts do not load session history or perform session-visibility lookups.

Session hits obey tools.sessions.visibility, which defaults to "all". memory_search still searches the selected agent's indexed corpus; use sessions_search for transcript search across agents on the Gateway. Visible transcripts can include other users' conversations. Cross-agent session access is on by default and governed by tools.agentToAgent; set enabled: false to block ordinary cross-agent access (requester-owned native subagent and ACP child sessions stay reachable under tree or all) or use allow to restrict agent pairs. A per-peer session.dmScope separates DM context but does not restrict transcript access through session tools. Choose explicit "agent" for same-agent recall, "tree" for current plus spawned scope (with an agent-wide exception for main), or "self" for strict current-session recall. Sandbox spawned-only clamps and incognito exclusions still apply.

Troubleshooting

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

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

Local embeddings time out? ollama, lmstudio, and local use longer provider-owned batch deadlines. Run apus memory status --deep to inspect the managed server endpoints before rebuilding the index.

OpenAI-compatible embedding requests honor the caller's deadline, including the longer indexing budget, without an earlier HTTP header or body timeout. Deep status probes make one attempt using the provider's query budget: normally 60 seconds for remote providers or 5 minutes for local, unless the provider supplies its own query budget. Managed server readiness keeps its separate budget. A stalled probe reports memory embedding probe timed out after Ns.

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

Related

• Memory overview
• Memory architecture
• Active memory
• Builtin memory engine
• Memory configuration reference
• Memory LanceDB