Store a new memory in HCC.

Args: content: The memory text to store (required). user_id: Owner of the memory (e.g. "michael"). agent_id: Which agent this memory belongs to (e.g. "hanyan", "hermes", "openclaw-main"). Memories are scoped by agent_id — different agents don't see each other's memories unless explicitly queried across agents. type: Memory category, e.g. "general", "knowledge", "fact", "preference". summary: Optional short summary of the content. importance: Relevance score in [0, 1]. Defaults to 0.5. tags: Optional list of string tags. embedding: Deprecated, ignored. The server always computes its own embedding (ollama, server-side) so every memory lands in the same vector space — kept only so old callers that still pass one don't break.

Keyword-search memories (case-insensitive substring match on content/summary).

Args: query: Substring to search for (required). user_id: Restrict to a specific user. agent_id: Restrict to a specific agent's memories. type: Restrict to a specific memory type. limit: Max number of results (1-100). Defaults to 20.

Three-layer memory retrieval (conscious/preconscious/subconscious).

Better than plain keyword search for "what do I remember about X" — merges current-session context with database recall, ranked by relevance.

Args: query: What to recall. user_id: Restrict to a specific user. agent_id: Restrict to a specific agent's memories. limit: Max number of results. Defaults to 5.

Semantic-similarity search over stored memories (pgvector cosine distance).

Pass free-text query — the server embeds it (ollama, server-side) before searching. embedding remains available for passing a precomputed vector directly, but is no longer required.

Args: query: Free-text query, embedded server-side. Required unless embedding is given. embedding: Precomputed query embedding vector, advanced/optional. user_id: Restrict to a specific user. agent_id: Restrict to a specific agent's memories. type: Restrict to a specific memory type (e.g. "knowledge" for the Obsidian vault). limit: Max number of results (1-100). Defaults to 10.

Hybrid search: BM25 full-text + vector similarity, fused with Reciprocal Rank Fusion.

Best default choice for "find memories about X" — combines exact keyword matches (BM25) with semantic similarity, so it doesn't miss relevant memories that use different words than the query (e.g. "显卡" vs "GPU"). Passing just query runs both branches — the server embeds the query text itself (ollama, server-side) for the vector branch, no client-side embedding model needed. Provide at least one of query/embedding.

Args: query: Free-text query. Drives the BM25 branch (jieba-segmented server-side) and, unless embedding is given, is also embedded server-side for the vector branch. Optional if embedding is given. embedding: Precomputed query embedding for the vector branch, advanced/ optional — normally you just pass query and let the server embed it. user_id: Restrict to a specific user. agent_id: Restrict to a specific agent's memories. type: Restrict to a specific memory type. limit: Max number of results (1-100). Defaults to 10. rerank: Rerank the fused top results with the optional cross-encoder (Qwen3-Reranker-0.6B). Off by default — adds latency; silently falls back to RRF order if the reranker isn't enabled/available server-side (HCC_RERANK_ENABLED).

Return the most recently created memories.

Args: limit: Max number of results (1-100). Defaults to 20. user_id: Restrict to a specific user. agent_id: Restrict to a specific agent's memories.

Delete a memory by its id (permanent — use forget/apply via the REST API for reversible archiving instead if you just want it to fade, not vanish).

Args: memory_id: The id of the memory to delete (required).

Ask HCC's orchestrator whether a piece of content is worth remembering, before storing it.

Use this to avoid flooding long-term memory with trivial chatter — only call store_memory for content where should_store comes back true (or when you have an explicit reason to override, e.g. the user said "remember this").

Args: content: The text to evaluate. agent_id: Which agent is asking (for future per-agent tuning). user_id: Whose content this is.