Mastering OpenClaw's Memory Search and Recall System

OpenClaw’s memory_search and memory_get tools form the backbone of reliable, long-term context management. Unlike standard LLM context windows that vanish on reset, OpenClaw’s memory system persists across reboots, sessions, and even model changes.

This article explains how the system works, when to use it, and how to get the most out of semantic recall.

Why Memory Search is Built In

Most AI assistants treat memory as "in-context" — everything you’ve said in the chat. That model collapses on session restart.

OpenClaw takes a different approach: files are the memory.

The workspace isn’t just scratch space — it’s your agent’s permanent repository of knowledge. memory_search is the retrieval engine that makes that knowledge useful.

The Two-Part Recall System

OpenClaw’s memory tools are split into two distinct phases:

1. memory_search: Find Relevant Context

When you ask a question about prior work, dates, people, or decisions, OpenClaw doesn’t guess — it searches.

The memory_search tool scans:

• MEMORY.md — your long-term knowledge base
• memory/YYYY-MM-DD.md — daily logs (current + archive)
• Optional: session transcripts (if enabled)

It uses semantic search (vector embeddings) to find high-relevance snippets, not just keyword matches.

Always call memory_search before answering questions that rely on memory. This is mandatory in OpenClaw.

2. memory_get: Extract the Snippet

Once memory_search returns results, use memory_get to read the exact portion of the file you need.

memory_get(path: "/Users/jorden/.openclaw/workspace/MEMORY.md", from: 420, lines: 15)

This keeps context tight and focused — you’re not loading the entire memory file into the prompt.

How Search Actually Works

OpenClaw supports multiple semantic search backends:

• SQLite-vec — On-device vector store (default, fast, private)
• QMD — Experimental hybrid search (BM25 + vectors + reranking)
• Remote APIs — OpenAI, Gemini, Voyage (for scalability)
• Local GGUF embeddings — via node-llama-cpp and CPU/GPU inference

All embeddings are generated locally by default. Remote providers are only used if explicitly configured.

Search is Automatic — But You Can Trigger It

In the main agent session, memory search is often triggered automatically when you ask certain questions.

But you can (and should) use it proactively:

memory_search(query: "What was the date/time bug impact?")

This returns ranked snippets with:

• path: File location
• lines: Line numbers
• score: Relevance (0-1)
• text: The content

Best Practices for Reliable Recall

1. Always Write It Down

"Mental notes don’t survive session restarts. Files do."

If you want to remember something — write it to a file. Use:

• memory/YYYY-MM-DD.md for raw events
• MEMORY.md for distilled insights
• Dedicated project files for deep topics

Then memory_search will find it.

2. Use Descriptive Language

When writing, use full sentences and context. Instead of:

"Fix date bug — high priority"

Write:

"Priority 1 bug: AI books appointments 1 day off. Customers miss appointments. Direct revenue impact due to dropped conversions. Fix status unknown."

Rich text = better search results.

3. Curate MEMORY.md Regularly

Don’t treat MEMORY.md as a dumping ground. Every few days, review recent logs and update it with what’s worth keeping.

Remove outdated or irrelevant info. Keep it tight.

4. Know When Not to Use It

Do not load MEMORY.md in group chats or public channels. It contains private context.

Use memory_search only when:

• In a main, private session
• The user asks about personal context
• You’re doing operational work for the owner

Privacy first.

Under the Hood: The Recall Workflow

Here’s what happens when you ask a memory-based question:

1. Agent receives prompt: "What are the current Assistable.ai bugs?"
2. Automatically calls: memory_search(query: "Assistable.ai bugs")
3. Gets back top 3 snippets from MEMORY.md and daily logs
4. Uses memory_get to read exact lines
5. Answers using the real data, not guesswork

No hallucinations. No memory lapses. Just reliable recall.

Common Pitfalls and Fixes

| Issue | Cause | Fix | |-------|-------|-----| | memory_search returns no results | No vector store initialized | Run openclaw memory init or check auth | | Search is slow | Using remote API with latency | Switch to SQLite-vec or local embeddings | | Misses relevant content | Poor document structure | Break MEMORY.md into smaller, topic-specific files | | Hits API limits | Overusing remote embeddings | Configure local backend or cache results |

Final Thoughts

The power of OpenClaw isn’t in any single tool — it’s in the system.

memory_search and memory_get turn ephemeral context into durable knowledge. When combined with disciplined file management, they enable agents that grow smarter over time.

Your memory is only as good as your recall system.

With OpenClaw, yours is built to last.