---
name: Memory Recall
slug: memory-recall-4
category: AI Engineering
description: Memory Recall searches and recalls relevant memories from past sessions so an agent can use historical context when answering. Use it when a question could benefit from past decisions, debugging notes, previous conversations, or project knowledge.
github: "https://github.com/zilliztech/memsearch/tree/main/plugins/openclaw/skills/memory-recall"
language: Python
stars: 2482
forks: 227
install: "npx degit https://github.com/zilliztech/memsearch/tree/main/plugins/openclaw/skills/memory-recall ~/.claude/skills/memory-recall"
installs_to: ~/.claude/skills/memory-recall
source_path: plugins/openclaw/skills/memory-recall/SKILL.md
collection_size: 13
category_size: 2451
collection_url: "https://dirskills.com/collections/zilliztech/memsearch"
added: 2026-08-18T06:57:45.058Z
last_synced: 2026-08-18T06:57:45.058Z
canonical_url: "https://dirskills.com/skills/memory-recall-4"
---

# Memory Recall

Memory Recall searches and recalls relevant memories from past sessions so an agent can use historical context when answering. Use it when a question could benefit from past decisions, debugging notes, previous conversations, or project knowledge.

**Install:**

```bash
npx degit https://github.com/zilliztech/memsearch/tree/main/plugins/openclaw/skills/memory-recall ~/.claude/skills/memory-recall
```

## README

You have three memory tools for progressive recall. Start with search, go deeper only when needed.

## Tools (use progressively)

### 1. memory_search — Start here
Semantic search across all past conversation memories.
- Returns: chunk summaries with dates, topics, chunk_hash identifiers
- Use for: "What did we discuss about X?", "Have I asked about Y before?"

### 2. memory_get — When search results aren't detailed enough
Expands a specific chunk_hash to show the full markdown section with surrounding context.
- Input: chunk_hash from memory_search results
- Returns: full section text, may include transcript anchors (<!-- session:UUID transcript:PATH -->)
- Use for: "Show me the details", "I need more context on that result"

### 3. memory_transcript — When you need the exact original conversation
Parses the original session transcript to retrieve the raw dialogue.
- Input: transcript_path from the anchor comment in memory_get results
- Returns: formatted conversation with [User]/[Assistant] labels and tool calls
- Use for: "What exactly did I say?", "Show me the original conversation"
- If the anchor format is unfamiliar (e.g. `rollout:`, `turn:`, `db:` instead of `transcript:`), try reading the referenced file directly to explore its structure and locate the relevant conversation by the session or turn identifiers in the anchor.

## Decision guide

| User intent | Tools to use |
|---|---|
| Quick recall ("did we discuss X?") | memory_search only |
| Need details ("what was the solution?") | memory_search → memory_get |
| Need original dialogue ("show me the exact conversation") | memory_search → memory_get → memory_transcript |

## Tips
- memory_search returns chunk_hash — pass it to memory_get for expansion
- memory_get may reveal `<!-- session:UUID transcript:PATH -->` anchors — pass the path to memory_transcript
- If memory_search returns no results, try rephrasing with different keywords
- Results are sorted by relevance (hybrid BM25 + vector search)

## When unsure what to search

The SessionStart injection already shows you a heading-level preview of recent memory files — skim it first to spot concrete topics (dates, session numbers, task names). If that preview doesn't surface an obvious query, try broad keywords like `overview`, `recent work`, or a topic guess — hybrid BM25 + vector retrieval will surface chunks that share any of the terms, and you can iterate from there.