symbolic-memory

What This Skill Does

The symbolic-memory skill provides a robust architecture for stateless symbolic memory, allowing LLM agents to maintain long-term coherence without relying on local, error-prone persistent storage. Instead, it utilizes a PostgreSQL-backed substrate that separates factual data from canonical semantics. By using a JIT (Just-In-Time) retrieval mechanism, the agent activates specific, budgeted facts only when they are contextually relevant to the ongoing interaction. This approach ensures that the model remains focused while having access to a versioned, shared knowledge base that is consistent across multiple agents.

Installation

You can install this skill directly via the ClawHub CLI using the following command: clawhub install openclaw/skills/skills/th3hypn0tist/symbolic-memory

Use Cases

• Multi-Agent Collaboration: Ensure all agents operating on a shared project access the same versioned facts and canonical definitions.
• Complex Domain Knowledge: Store domain-specific jargon and entity relationships in a database to keep the LLM context window clean.
• Stateless Knowledge Management: Ideal for high-throughput environments where agents must remain stateless but require access to a durable, external knowledge graph.
• Long-term Project Coherence: Maintain a consistent project state, such as architectural decisions or stakeholder requirements, across months of development sessions.

Example Prompts

1. "Check the symbolic memory for the 'Alpha-Project' canonical entity and explain its current state relative to my request."
2. "Activate facts related to 'user-preferences' from the symbolic memory and apply them to the current drafting task."
3. "Summarize the historical evolution of the 'system-architecture' symbol based on the latest versioned facts in our PostgreSQL substrate."

Tips & Limitations

• Separation of Concerns: Always distinguish between the raw facts in the database and the processed meaning computed by your agent. Do not store inferred interpretations as immutable facts.
• Budgeting: Since this tool fetches facts JIT, be mindful of your token budget. Only activate the symbols strictly necessary for the immediate prompt to maintain performance.
• Dependency: While the skill can operate independently, it thrives when paired with a properly indexed PostgreSQL instance. Ensure your schema is compatible with the JIT Symbolic Memory design pattern referenced in the documentation.