2026 Complete Guide to OpenClaw memorySearch: Supercharge Your AI Assistant

🎯 Key Takeaways (TL;DR)

• OpenClaw memorySearch is a powerful semantic search feature that enables your AI assistant to recall relevant information from indexed Markdown files automatically
• The system uses hybrid search combining vector embeddings with keyword matching for more accurate results
• Configuration options range from beginner-friendly auto-detection to advanced provider-specific setups with fine-tuned parameters
• Proper setup can dramatically improve your AI assistant context awareness and response quality

Table of Contents

1. What is OpenClaw memorySearch?
2. How memorySearch Works
3. Recommended Complete Configuration
4. Simplified Configuration for Beginners
5. Provider Options and Auto-Detection
6. Advanced Features: Hybrid Search
7. Best Practices and Tips
8. FAQ
9. Summary & Recommendations

What is OpenClaw memorySearch?

OpenClaw memorySearch is a semantic recall system built into the OpenClaw AI assistant framework. It allows your AI assistant to search through indexed Markdown files using natural language queries, retrieving the most relevant information from your personal knowledge base.

Unlike traditional keyword-based search, memorySearch uses vector embeddings to understand the semantic meaning behind your queries. This means searching for "my investment goals" will also find content about "financial objectives" or "money targets" — even if those exact words do not appear in your notes.

The system exposes two agent-facing tools:

• memory_search: Semantic recall over indexed snippets
• memory_read: Retrieve full content from specific memory entries

This creates a powerful long-term memory system for AI agents, addressing key pain points in long-running workflows where context preservation is critical.

How memorySearch Works

OpenClaw memory system represents a thoughtful evolution of RAG (Retrieval-Augmented Generation) architecture. Here is the breakdown:

File-First Storage

All memories are stored as Markdown files in your workspace, ensuring:

• Transparency: You can read, edit, and version-control your memories
• Portability: Easy to back up, sync, or share
• Simplicity: No proprietary database required

Automatic Indexing

When enabled, memorySearch automatically watches your specified directories and indexes new or modified files. The indexing process:

1. Reads Markdown files from your workspace
2. Generates vector embeddings using your chosen provider
3. Stores embeddings in a local cache for fast retrieval
4. Continues syncing as files change (with debounce to avoid excessive API calls)

Semantic Query Processing

When you ask your AI assistant to recall something:

1. Your query is converted to a vector embedding
2. The system searches the indexed memories using similarity matching
3. Results are ranked and returned with relevance scores
4. The AI uses this context to provide more informed responses

Recommended Complete Configuration

For users who want full control over their memorySearch setup, here is the recommended complete configuration:

agents: {
  defaults: {
    memorySearch: {
      enabled: true,
      provider: "gemini",
      model: "gemini-embedding-001",
      remote: {
        apiKey: "YOUR_REAL_GEMINI_API_KEY"
      },
      sync: {
        watch: true,
        watchDebounceMs: 1500
      },
      query: {
        maxResults: 8,
        hybrid: {
          enabled: true,
          vectorWeight: 0.7,
          textWeight: 0.3,
          candidateMultiplier: 4,
          mmr: {
            enabled: true,
            lambda: 0.7
          },
          temporalDecay: {
            enabled: true,
            halfLifeDays: 30
          }
        }
      },
      cache: {
        enabled: true,
        maxEntries: 50000
      }
    }
  }
}

Configuration Explained

💡 Pro Tip The candidateMultiplier setting is crucial — setting it to 4 (the default) significantly improves result quality by considering more candidates before ranking.

Simplified Configuration for Beginners

If you are new to memorySearch, start with this minimal configuration:

agents: {
  defaults: {
    memorySearch: {
      enabled: true,
      sync: {
        watch: true
      }
    }
  }
}

OpenClaw will automatically detect and use the best available provider based on what is configured in your environment.

Provider Options and Auto-Detection

OpenClaw supports multiple embedding providers, selected in this order:

1. local — If you have a local model running
2. openai — If OpenAI API key is configured
3. gemini — If Google Gemini API key is configured
4. voyage — If Voyage AI API key is configured
5. mistral — If Mistral API key is configured

API Key Configuration

You can set API keys directly in the config or use environment variables:

⚠️ Important Never commit API keys to version control. Use environment variables or secrets management for production setups.

Advanced Features: Hybrid Search

One of memorySearch most most powerful features is hybrid search, which combines:

• Vector Search: Semantic similarity using embeddings
• Keyword Search: Traditional text matching

This combination provides the best of both worlds — understanding context while still catching exact matches.

Max Marginal Relevance (MMR)

The MMR feature ensures your search results are diverse, not just similar to each other. This is particularly useful when searching for broad topics where you want varied perspectives.

Temporal Decay

The temporal decay feature makes newer memories slightly more relevant than older ones, with a configurable half-life (default: 30 days). This helps your assistant prioritize recent information while still accessing older memories when relevant.

Best Practices and Tips

1. Organize Your Memory Files

Structure your Markdown files logically:

memory/
├── projects/
│   ├── work-projects.md
│   └── personal-projects.md
├── knowledge/
│   ├── meetings.md
│   └── decisions.md
└── reference/
    ├── passwords.md
    └── contacts.md

2. Use Descriptive Frontmatter

Add metadata to your memory files:

---
title: Project Alpha Notes
tags: [project, important]
date: 2026-01-15
---

# Project Alpha

Content here...

3. Monitor Your API Usage

Check your provider dashboard regularly to track embedding API calls and costs.

4. Enable Caching

The cache significantly speeds up repeated queries. Keep it enabled unless you have a specific reason not to.

5. Adjust Weights Based on Use Case

• Research-heavy tasks: Higher vector weight (0.8)
• Exact-match needs: Higher text weight (0.5+)
• General use: Balanced (0.7/0.3 as recommended)

FAQ

Q: Do I need an API key for memorySearch?

A: Yes, unless you have a local embedding model. You can use providers like OpenAI, Gemini, Voyage, or Mistral. The simplified configuration will auto-detect available providers.

Q: How does memorySearch differ from traditional search?

A: Traditional search looks for exact keyword matches. memorySearch uses semantic understanding, so related concepts are found even without exact word matches. It is like asking a human who read your notes rather than a simple text search.

Q: Can I use memorySearch with custom containers?

A: Yes! When custom container tags are enabled, memorySearch supports a containerTag parameter for routing searches to specific containers.

Q: What is the difference between memory_search and memory_read?

A: memory_search finds relevant snippets across all indexed memories using semantic search. memory_read retrieves the full content of specific memory entries by ID or query.

Q: How do I troubleshoot rate limiting?

A: If you are using Gemini free tier or another rate-limited provider:

• Switch to OpenAI for more reliable service
• Reduce maxResults to lower embedding calls
• Temporarily disable vector search and use text-only mode

Summary & Recommendations

OpenClaw memorySearch transforms your AI assistant from a stateless tool into a context-aware collaborator that learns from your accumulated knowledge. Key takeaways:

1. Start simple — Use the beginner configuration and let OpenClaw auto-detect your provider
2. Scale up gradually — Add advanced features like hybrid search once you understand the basics
3. Monitor costs — Keep an eye on API usage, especially with high-volume indexing
4. Organize well — The quality of your memories directly impacts search relevance
5. Enable caching — Dramatically improves repeated query performance

With proper configuration, memorySearch becomes an indispensable part of your AI workflow, enabling your assistant to recall relevant context instantly and provide more personalized, informed responses.