memoryai

What This Skill Does

MemoryAI is a sophisticated persistent storage engine designed specifically for OpenClaw agents, effectively solving the 'amnesia' problem inherent in stateless LLM sessions. By functioning as a long-term synthetic hippocampus, it enables your agent to retain context across days, weeks, and months of interaction. The system categorizes information into Hot, Warm, and Cold storage tiers, mimicking human cognitive consolidation patterns. This allows your agent to stay sharp on active project requirements, retrieve historical design decisions, and archive legacy project details without cluttering the working memory window. With native support for session handoff and intelligent compaction, MemoryAI ensures that every significant interaction is distilled into useful knowledge.

Installation

To integrate this skill into your environment, use the OpenClaw CLI: clawhub install openclaw/skills/skills/ch270035/memoryai. Once installed, you must configure your credentials. Navigate to your base directory and update the config.json file with your api_key obtained from https://memoryai.dev, or set the corresponding HM_API_KEY and HM_ENDPOINT environment variables. Verification is performed by running python {baseDir}/scripts/client.py stats.

Use Cases

• Software Development: Maintain continuity in complex codebases by storing framework preferences, API architectural constraints, and folder structures.
• Project Management: Track ongoing goals, meeting decisions, and task progress across multiple distinct sessions over long durations.
• Personal Research: Archive findings and key takeaways from deep-dive brainstorming sessions to avoid repeating exploratory work.
• Roleplay & Persona Management: Maintain consistent character backstories and previous relationship dynamics in long-running creative writing sessions.

Example Prompts

1. "Store in my memory that we prefer using Zod for validation in all our Next.js API routes, tagging this as a developer preference."
2. "Search through my deep history to recall the architectural decision we made about authentication back in October."
3. "Summarize the last three weeks of work on this project and compact the key decisions into a single memory entry."

Tips & Limitations

• Compaction is Key: Use the compact command regularly. Treating memory like a brain requires periods of consolidation to prevent the retrieval pool from becoming disorganized.
• Tagging Discipline: Apply specific tags during the store process. This significantly improves the accuracy of recall operations when searching through high-volume data.
• Token Management: While MemoryAI manages external state, ensure you are aware of how much context the agent feeds into the LLM during retrieval to stay within model limits.
• Privacy: Since MemoryAI uses an external API for persistent storage, ensure sensitive data is filtered if your organizational policies restrict off-device storage.