openclaw-memory-stack

What This Skill Does

OpenClaw Memory Stack is a high-performance cognitive layer for your AI agent that effectively solves the "forgetfulness" problem common in LLM interactions. By implementing a 5-engine search architecture, the skill ensures that your agent maintains total recall of past conversations, decisions, and documents without exhausting your token budget. It achieves a 90% reduction in token consumption by utilizing a sophisticated 3-tier retrieval system that scales the level of detail based on the current context requirements.

The system operates via an offline-first design, processing data through a hybrid of keyword-based search (FTS5), semantic indexing (BM25), structured Markdown memory, and a Lossless DAG summary. By applying Reciprocal Rank Fusion (RRF) and Maximum Marginal Relevance (MMR) diversity, the Memory Stack ensures that the information surfaced is both relevant and distinct. Crucially, the "Rescue Store" acts as a persistent SQLite database that intercepts key facts and deadlines before they are pruned during the automatic compaction process, ensuring that critical data survives long-term context shifts.

Installation

To integrate this memory architecture into your agent, run the following command in your terminal:

clawhub install openclaw/skills/skills/apptah/openclaw-memory-stack

Once installed, the skill initializes automatically in the background. There are no configuration files to manage, and it integrates seamlessly with existing agent workflows.

Use Cases

• Project Management: Track long-term deadlines and evolving project decisions across months of conversation history.
• Complex Research: Keep track of disparate sources and synthesize them into a single knowledge graph without re-reading thousands of tokens.
• Development Workflow: Maintain a searchable database of coding decisions, library preferences, and past debugging logs for faster troubleshooting.
• Knowledge Retrieval: Quickly pull specific factual details from previous sessions without triggering full context re-evaluation.

Example Prompts

1. "What was the final decision we made regarding the database migration strategy last month?"
2. "Summarize all the constraints and milestones we've discussed for the current project roadmap."
3. "Who are the stakeholders identified for the marketing phase, and what tools did we decide to use for their onboarding?"

Tips & Limitations

• Token Control: Leverage the 3-tier output system. Use L0 for quick factual checks to save money, and save L2 requests for deep, full-text analysis when the stakes are high.
• Performance: Because it runs locally and offline, the memory stack is highly private. However, remember that the initial indexing of massive archives might take a brief moment of system overhead.
• Data Integrity: The Rescue Store is your primary safety net. If you have specific, mission-critical details, mention them explicitly during a conversation to ensure they are tagged for the knowledge graph.
• Storage: Since this tool uses SQLite, ensure your local environment has sufficient disk space for the growing knowledge graph if you handle multi-gigabyte document sets.