A multi-layered memory architecture that combines semantic search with structured Markdown storage to maintain persistent AI agent context.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install openclaw-memory-format
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-memory-format 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 OpenClaw Memory Management Format?
The Memory Format is a foundational component of Openclaw Skills designed to solve the problem of context window limitations and data persistence in AI agents. By utilizing a three-layer storage system—Long-term, Daily, and Session memory—it allows agents to retain critical information across different sessions. This skill ensures that important decisions, technical notes, and project milestones are documented in a human-readable and machine-searchable format.
This framework uses a combination of manual curation and automated background processes. While session memory handles immediate tasks, the daily memory flush mechanism captures key events into dated logs, which are eventually distilled into a permanent MEMORY.md file. This structured approach to data persistence makes OpenClaw agents more reliable for complex, long-term software development projects.
OpenClaw Memory Management Format Use Cases
- Maintaining a persistent 'source of truth' for project requirements across multiple development days.
- Automatically summarizing long technical discussions into actionable checklists and decision logs.
- Performing semantic searches across historical conversation data to retrieve forgotten technical implementation details.
- managing large-scale refactors where architectural decisions must be referenced consistently over time.
How OpenClaw Memory Management Format Works
- The AI agent monitors the active context window based on predefined token thresholds like the softThresholdTokens and reserveTokensFloor.
- When context limits are reached, the memory flush process is triggered, extracting important information from the conversation.
- The extracted data is written to a daily memory file (memory/YYYY-MM-DD.md) using a standardized Markdown template.
- For immediate retrieval, the agent uses the memory_search tool, which performs a hybrid search (70% vector, 30% text) to find relevant past entries.
- Developers periodically review daily logs to move high-level architectural insights into the long-term MEMORY.md file for permanent system-prompt inclusion.
OpenClaw Memory Management Format Setup
To set up the memory structure for your project using Openclaw Skills, initialize the directory structure and required files in your workspace root:
# Create the long-term memory file
touch MEMORY.md
# Create the directory for automated daily logs
mkdir -p memory/
# (Optional) Pre-populate MEMORY.md with core project context
echo '# Project Memory' > MEMORY.md
OpenClaw Memory Management Format Data Schema & Taxonomy
The skill organizes information using a tiered metadata taxonomy and specific Markdown headings for optimal parsing.
| Component | Format | Storage Location |
|---|---|---|
| Long-term Memory | Manual Markdown | $WORKSPACE/MEMORY.md |
| Daily Memory | Structured Markdown | memory/YYYY-MM-DD.md |
| Session Memory | Volatile/In-memory | Auto-cleared on exit |
Daily memory files are organized into the following sections:
- Completed Today: List of finished tasks.
- Conversation Summary: Overview of user requests and involved systems.
- Key Decisions: P0-P3 priority modifications and architectural changes.
- Follow-up Items: Pending task checklists.
- Technical Notes: Specific configurations such as Docker network settings or search ratios.
OpenClaw Memory Management Format Advanced Features
- Hybrid Search Mode: Combines semantic vector similarity (0.35 minScore) with keyword-based text matching for precise retrieval.
- Intelligent Compaction: Distinguishes between a simple conversation summary (Internal) and a full Memory Flush (External file write).
- Automated Flush Triggers: Configurable force-flush triggers at 2MB of transcript data or specific token floors to prevent context loss.
- Multi-system Tracking: Explicitly logs interactions between Tool, Session, MCP, API, and Sandbox components within the memory architecture.
SKILL.md
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