Tools Reference

Once installed, openclaw-amem exposes four tools to OpenClaw agents.

---

memory_add

Write a new memory to the store.

memory_add(text="Your memory content here.")

Parameters

Parameter	Type	Required	Description
text	string	✅	The content to store as a memory.

What happens internally

1. Exact hash dedup check — skips if identical content already exists
2. High-similarity dedup check (cosine ≥ 0.95) — updates existing note instead of creating duplicate
3. LLM note construction — extracts keywords, tags, context summary, category
4. Link generation — finds up to 6 candidates, LLM verifies bidirectional links
5. Memory evolution — updates attributes on up to 3 linked notes
6. Saves to Qdrant with embedding

---

memory_search

Search long-term memories using hybrid retrieval.

memory_search(query="your search query", limit=5)

Parameters

Parameter	Type	Default	Description
query	string	—	Natural language search query.
limit	number	topK config value	Maximum results to return.

Returns

An array of memory objects ranked by relevance:

[
  {
    "id": "uuid",
    "content": "Original memory text",
    "keywords": ["keyword1", "keyword2"],
    "tags": ["tag1", "tag2"],
    "context": "One-sentence summary",
    "category": "Technical",
    "retrieval_count": 3,
    "score": 0.842
  }
]

Retrieval pipeline

1. Embeds query locally (ONNX, 384-dim)
2. BM25 ranking with Jieba tokenization for CJK
3. Dense vector cosine similarity search
4. RRF fusion (k=60)
5. Heat boost: score × (1 + 0.05 × ln(1+count) / (age_days+1))
6. 2-hop BFS graph expansion with cos-sim ≥ 0.25 gate

---

memory_list

Return the total count of active memories for the current agent.

memory_list()

Returns

{ "count": 42 }

---

memory_consolidate

Manually trigger same-day semantic deduplication and link cascading.

memory_consolidate()

This is also run automatically at 02:30 AM daily. Use this tool to trigger it on-demand (e.g. after a bulk import).

What it does

1. Groups all active notes by category
2. Finds pairs with cosine similarity ≥ 0.75 within each group
3. Merges duplicates into a unified note via LLM
4. Soft-deletes (is_active: false) merged source notes
5. Cascades all link references from deleted notes to the merged note