A hierarchical memory architecture for AI agents that replaces monolithic files with searchable, categorized, and auto-archived data modules.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install openclaw-enhanced-memory
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 openclaw-enhanced-memory 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 Enhanced Memory?
Enhanced Memory is a sophisticated architectural upgrade for agents using Openclaw Skills, moving away from the limitations of a single, ever-growing MEMORY.md file. By implementing a directory-based storage system, it ensures that context remains organized and retrieval stays efficient even as the volume of data grows. This skill provides agents with a robust framework to store relationship data, project notes, and specific logs separately while maintaining a global index through specialized tags.
This system solves critical performance issues like token exhaustion and context pollution by segmenting active data from long-term archives. Using these Openclaw Skills components, developers can ensure their agents maintain high-speed responses by only loading the most relevant memory modules for any given task, rather than parsing a massive, unstructured text file.
Enhanced Memory Use Cases
- Managing complex long-term projects where context spans several months or years.
- Organizing personal relationship data and interaction history across multiple individuals.
- Scaling agent memory without hitting LLM token limits by archiving older entries.
- Implementing high-precision retrieval for specific data types like meal logs, workout routines, or meeting notes.
- Building multi-agent systems that require a standardized, searchable memory schema for shared context.
How Enhanced Memory Works
- The agent writes structured entries into specific subdirectories within the memory folder based on the topic or category.
- Relevant lines are tagged using the [category:value] format to enable high-precision indexing and multi-tag filtering.
- Retrieval scripts analyze user queries to determine the correct memory module to search, drastically reducing noise in the prompt context.
- A lifecycle manager automatically moves older files from active storage to a time-stamped archive folder to maintain system performance.
- The system executes multi-tag AND searches to find specific historical context across the entire repository based on technical metadata.
Enhanced Memory Setup
To integrate this memory architecture into your environment for Openclaw Skills, follow these steps:
- Initialize the required directory structure:
mkdir -p memory/{current,archived,RELATION,food,training,system,misc}
- Deploy the Python scripts (
memory_tag_search.py,memory_retrieval_strategy.py,memory_lifecycle_manager.py) to your scripts directory. - Update your AGENTS.md file to include the new retrieval strategy and tagging conventions.
- (Optional) Configure a cron job for automated monthly maintenance:
0 3 1 * * cd /path/to/workspace && python3 scripts/memory_lifecycle_manager.py
Enhanced Memory Data Schema & Taxonomy
The system organizes information into a tiered hierarchy to optimize retrieval for Openclaw Skills:
| Directory | Data Type | Retention Status |
|---|---|---|
memory/current/ |
Active daily logs and general notes | Active (0-6 Months) |
memory/archived/ |
Permanent time-stamped history | Archived (6+ Months) |
memory/RELATION/ |
Context files mapped to specific people | Permanent |
memory/food/ |
Specialized meal and nutrition logs | Module-Specific |
memory/training/ |
Exercise and workout records | Module-Specific |
All entries support metadata tagging using the [category:value] syntax, which the search engine uses to filter results by person, project, mood, or location.
Enhanced Memory Advanced Features
- Multi-tag AND search allows for pinpoint accuracy when retrieving historical records across different categories.
- Automated lifecycle management prevents memory bloat by migrating data based on configurable age thresholds (default 180 days).
- Smart query routing automatically classifies user intent to search the most relevant memory modules, saving tokens and improving accuracy.
- Zero-dependency Python implementation ensures seamless compatibility across diverse hosting environments and Openclaw Skills configurations.
- Customizable archive thresholds and query patterns allow power users to tune memory retention and retrieval behavior.
SKILL.md
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