Avm

What This Skill Does

AVM (AI Virtual Memory) is a powerful, distributed memory architecture designed for the OpenClaw agent ecosystem. It functions as a shared memory layer that allows agents to store, retrieve, and share knowledge efficiently. At its core, AVM combines semantic embeddings and FTS5 (Full-Text Search) to ensure that recall is both contextually relevant and lightning-fast. It features an innovative TopicIndex for O(1) recall performance and utilizes a Gossip Protocol for decentralized agent discovery, ensuring that agents can collaborate without a central point of failure. Whether you need to manage short-term context, archival data, or collaborative knowledge across multiple agents, AVM provides the infrastructure to make your AI agents smarter and more connected.

Installation

You can install the AVM skill directly through the ClawHub CLI using the following command: clawhub install openclaw/skills/skills/bkmashiro/avm Once installed, ensure your environment is configured to allow the agent access to the required memory storage paths. You can verify installation by running avm --version in your terminal.

Use Cases

• Long-term Context Persistence: Store user preferences and historical project data so agents don't 'forget' previous interactions.
• Multi-Agent Collaboration: Use the Librarian or Gossip Protocol to allow a specialized 'Analyst' agent to consult a 'Research' agent about specific market topics.
• Knowledge Management: Organize large volumes of unstructured data into searchable memory spaces using tagged entries.
• Automated Memory Cleanup: Leverage lifecycle management features to automatically archive or delete outdated memory, keeping the agent's context window clean.

Example Prompts

• "Hey agent, please remember that my preferred coding style for Python is using type hints and following PEP 8 standards, store this in AVM."
• "Recall the market analysis we performed on NVDA last week and summarize the key RSI indicators."
• "Search for any agents in the network that have expertise in decentralized finance or crypto trading and see if they can provide insights on current trends."

Tips & Limitations

• Efficiency: Always provide relevant tags when storing information; this significantly boosts the accuracy of the TopicIndex.
• Safety: While the Gossip Protocol is excellent for decentralized discovery, ensure your agent permissions are correctly set if you are operating in a multi-tenant environment to avoid unauthorized memory access.
• Limitations: AVM performs best when memory entries are structured with clear topics. Highly ambiguous or non-contextual data may default to the slower FTS/embedding fallback mode instead of the optimized TopicIndex paths.