Add Semantic Search to OpenClaw Memory: Vector-Powered Recall

OpenClaw stores everything as markdown files. Every decision, every conversation, every note your agent has ever made lives as plain text in a directory. This is great for portability and transparency -- you can read your agent's memory with any text editor, back it up with a file copy, and grep through it from the terminal.

But as your memory grows over weeks and months, grep stops working. You search for "caching" but the decision was phrased as "we picked Redis for the session store." You search for "deployment" but the relevant note said "pushing to production via Cloudflare Pages." Keyword search misses semantic matches, and loading entire memory files into the AI context wastes tokens on irrelevant content.

This guide shows you how to add vector-powered semantic search to your OpenClaw memory using memsearch. Search by meaning, not just words, and find any past memory in milliseconds.

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The Problem with Keyword Search

OpenClaw's memory system is simple and powerful: markdown files in a directory. But simple file-based storage creates a retrieval problem at scale:

Keyword search (grep) limitations:

• "What caching solution did we pick?" returns nothing because the note says "Redis for session store"
• "When did we decide on the frontend framework?" misses "agreed on React for the UI layer"
• Partial matches flood you with irrelevant results
• No ranking -- all matches are treated equally

Loading full context limitations:

• A month of active use generates megabytes of memory files
• Loading everything into the AI context window wastes tokens
• You pay for irrelevant content in every API call
• Context windows have limits, and you hit them fast

Semantic search solves both problems. You describe what you are looking for in natural language, and the system finds the relevant chunks by meaning, not by exact word matching.

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What You Will Build

A semantic search layer on top of your existing OpenClaw memory files:

1. Indexing: All markdown memory files are chunked, embedded, and stored in a vector database
2. Search: Natural language queries return the most relevant memory chunks ranked by semantic similarity
3. Live sync: A file watcher automatically re-indexes when memory files change
4. Hybrid search: Combines vector similarity with keyword matching (BM25) for best results

The key principle: your markdown files remain the source of truth. The vector index is just a derived cache that you can rebuild anytime. No files are ever modified.

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Skills You Need

memsearch is a standalone Python tool -- it does not require any OpenClaw skills from ClawHub.

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Step-by-Step Setup

Step 1: Install memsearch

pip install memsearch

For a fully local setup with no API keys required:

pip install "memsearch[local]"

Step 2: Run the Configuration Wizard

memsearch includes an interactive setup that walks you through choosing your embedding provider and database settings:

memsearch config init

The wizard asks you to choose:

• Embedding provider: OpenAI (best quality), Google, Voyage, Ollama, or local (no API key)
• Vector database: Milvus Lite (default, no setup needed) or external Milvus instance
• Chunk settings: Size and overlap for splitting documents

For most users, the defaults work well. If you want zero external dependencies, choose the local embedding provider.

Step 3: Index Your Memory Files

Point memsearch at your OpenClaw memory directory:

memsearch index ~/clawd/memory/

This command:

1. Scans all markdown files in the directory
2. Splits each file into semantic chunks
3. Generates embeddings for each chunk
4. Stores everything in the vector database
5. Computes SHA-256 hashes so re-indexing skips unchanged content

For a typical OpenClaw workspace with a few months of memories, initial indexing takes 30-60 seconds.

Step 4: Search Your Memories

Now search by meaning:

memsearch search "what caching solution did we pick?"

Output:

[1] memory/2026-02-15.md (chunk 3, score: 0.89)
    "Decided on Redis for the session store. PostgreSQL handles persistent
    data. Evaluated Memcached but Redis wins on feature set (pub/sub,
    sorted sets) and we already have it deployed..."

[2] memory/2026-02-10.md (chunk 7, score: 0.72)
    "Performance discussion: API response times are 200ms average.
    Adding a caching layer could bring this to under 50ms for repeated queries..."

[3] memory/dev-sessions/2026-02-14-api.md (chunk 2, score: 0.68)
    "Cache invalidation strategy: write-through for user data,
    TTL-based (5min) for search results..."

Notice: the query mentions "caching" but the top result says "Redis for session store." Semantic search understands they are about the same topic.

Step 5: Enable Live Sync

Start the file watcher so new memories are indexed automatically:

memsearch watch ~/clawd/memory/

This runs in the background and re-indexes any file that changes. Combined with the SHA-256 hashing, only new or modified content gets embedded, so there are no wasted API calls.

Step 6: Integrate with OpenClaw

Tell your OpenClaw agent to use memsearch for memory retrieval:

When I ask you to recall something from our past conversations or decisions:
1. Use memsearch to search your memory directory
2. Return the most relevant results with context
3. If the search returns nothing relevant, tell me and offer to do a broader search

Command: memsearch search "query" --top-k 5

Also use memsearch proactively when:
- I reference a past decision and you want to verify the details
- You need context from a previous discussion to inform a current task
- I ask "did we ever talk about X?"

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How the Technology Works

Vector Embeddings

Each chunk of text is converted into a high-dimensional vector (typically 768 or 1536 dimensions) that captures its semantic meaning. Similar concepts produce similar vectors, regardless of the specific words used.

For example:

• "Redis for session caching" and "in-memory cache with Redis" produce nearly identical vectors
• "Redis for session caching" and "Italian restaurant on 5th street" produce very different vectors

Hybrid Search (Dense + BM25)

memsearch does not rely on vectors alone. It combines two search methods:

• Dense vector search: Finds semantically similar content (great for "what did we decide about performance?")
• BM25 full-text search: Finds exact keyword matches (great for "find mentions of Redis")
• Reciprocal Rank Fusion (RRF): Merges both result sets into a single ranked list

This hybrid approach catches both meaning-based and exact-match queries, giving you the best of both worlds.

Smart Deduplication

Every chunk is identified by a SHA-256 hash of its content. When you re-index (or the file watcher triggers), memsearch compares hashes:

• Unchanged chunks are skipped (zero API calls)
• New chunks are embedded and added
• Deleted chunks are removed from the index

This means you can run memsearch index as a cron job without worrying about costs.

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Embedding Provider Comparison

For most users, OpenAI text-embedding-3-small offers the best balance of quality and cost. For privacy-sensitive setups, the local provider keeps everything on your machine.

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Practical Use Cases

Finding Past Decisions

memsearch search "what deployment strategy did we choose?"
memsearch search "which database for the user service?"
memsearch search "why did we reject the GraphQL approach?"

Locating Configuration Details

memsearch search "SSH credentials for the staging server"
memsearch search "API key for the weather service"
memsearch search "environment variables for production"

Retrieving Meeting Context

memsearch search "what did we discuss about the Q2 roadmap?"
memsearch search "action items from the design review"
memsearch search "feedback on the landing page mockup"

Building on Past Work

memsearch search "prompts that worked well for content generation"
memsearch search "lessons learned from the last deployment"
memsearch search "user feedback on the beta release"

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Tips for Better Search Results

1. Write descriptive memory entries. The quality of search results depends on the quality of your memories. "Did stuff" is unsearchable. "Migrated the auth service from JWT to session cookies because of XSS concerns" is gold.

2. Use natural language queries. "What was the thing about performance?" works better than "performance optimization caching Redis." Write your query the way you would ask a colleague.

3. Adjust chunk size for your content. If your memories are mostly short daily notes, smaller chunks (200-300 tokens) work well. For longer dev session logs, larger chunks (500-800 tokens) preserve more context.

4. Re-index periodically. While the file watcher handles real-time updates, running memsearch index weekly ensures nothing is missed.

5. Combine with the second brain. If you use the second brain workflow, your captured notes become searchable too. Text a thought to your bot, and search for it later by meaning.

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How ClawRapid Makes This Easier

memsearch requires Python and some command-line setup. With ClawRapid:

• Python environment pre-configured on your OpenClaw server
• Memory directory already populated from your bot conversations
• Easy installation of memsearch via the terminal
• Cron jobs available for scheduled re-indexing

Deploy with ClawRapid and add semantic search to your existing memories in minutes.

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FAQ

Does memsearch modify my memory files? No. Your markdown files are read-only from memsearch's perspective. The vector index is a separate derived cache. You can delete the entire index and rebuild it anytime with memsearch index.

How much does embedding cost? For a typical OpenClaw workspace with 3 months of daily memories (roughly 500KB of text), initial indexing costs about $0.01 with OpenAI text-embedding-3-small. Incremental updates (daily re-indexing) cost fractions of a cent because only new content is embedded.

Can I use it without any API keys? Yes. Install with pip install "memsearch[local]" and set the embedding provider to local. Quality is slightly lower than cloud providers but it is completely free and private.

Does it work with non-English memories? Yes. Modern embedding models handle multilingual content well. A memory written in French can be found with an English query. The quality depends on the embedding model -- OpenAI and Google models have excellent multilingual support.

How is this different from the knowledge base workflow? The knowledge base ingests external content (articles, videos, tweets). Semantic memory search indexes your own agent's memory files (conversations, decisions, notes). They complement each other: one searches what you have read, the other searches what you have done.

Can multiple agents share a search index? Yes. Point memsearch at a shared directory that multiple OpenClaw instances write to. All agents can search the same index. Be mindful of concurrent writes if using the file watcher.

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What to Build Next

Semantic search is a building block for more powerful memory workflows:

• Knowledge base with RAG for the same vector search on external content
• Second brain for the capture layer that feeds your searchable memory
• Automated memory maintenance that uses search to identify and merge duplicate memories

Explore all available workflows in our complete use cases guide.