A lightweight, dependency-free semantic search utility for indexing and querying local Openclaw memory files.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install local-memory-search
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install local-memory-search using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Local Memory Search?
Local Memory Search is a specialized utility designed to bring semantic search capabilities to your AI agent's local environment without relying on external vector databases or expensive API services. By processing MEMORY.md and various markdown files within the memory directory, this tool enables high-speed retrieval of relevant context, ensuring your Openclaw Skills remain grounded in historical data and previous interactions.
This skill is particularly valuable for developers who prioritize privacy and performance, as it operates entirely within the local filesystem. It bridges the gap between raw text storage and intelligent context retrieval by utilizing a TF-IDF (Term Frequency-Inverse Document Frequency) approach to score and rank document snippets based on relevance to a specific user query, providing a robust search layer for any local AI setup.
Local Memory Search Use Cases
- Quickly retrieving specific context from large Openclaw Skills memory archives.
- Debugging agent history by searching for specific interactions or historical decisions.
- Implementing long-term memory retrieval in resource-constrained or offline-first environments.
- Automating the extraction of relevant snippets for complex multi-agent prompt engineering.
How Local Memory Search Works
- The tool scans the MEMORY.md file and all markdown files located in the memory/ directory to identify relevant text data.
- It builds an inverted index that maps terms to their specific file locations and line numbers for rapid access.
- Upon receiving a query, the system applies a TF-IDF scoring algorithm to evaluate the importance of terms across the document set.
- The skill ranks the results based on their semantic similarity to the search query.
- It returns the top snippets alongside their corresponding file paths and line numbers for precise context injection.
Local Memory Search Setup
Ensure you have Python 3.8 or higher installed on your system. This skill utilizes the Python standard library exclusively, so no additional pip installations are required.
To build the initial index of your memory files:
python3 search.py --build
To perform a search query against your Openclaw Skills data:
python3 search.py "your search query"
Local Memory Search Data Schema & Taxonomy
The skill organizes and accesses data through a straightforward filesystem-based approach as detailed below:
| Component | Description |
|---|---|
| Source Files | Processes MEMORY.md and all .md files located in the /memory subdirectory. |
| Inverted Index | A local data structure mapping keywords to file offsets and line indices. |
| Scoring Model | TF-IDF (Term Frequency-Inverse Document Frequency) weights for relevance calculation. |
| Metadata | Includes file paths, line numbers, and ranked snippets for user output. |
Local Memory Search Advanced Features
- Zero-dependency architecture ensuring maximum portability across different environments.
- Native support for both root-level and nested memory directory structures.
- Optimized for high-speed local execution without the latency of cloud-based vector searches.
- Transparent metadata reporting that allows Openclaw Skills to reference exact line numbers in source documentation.
SKILL.md
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