Build a Hermes Knowledge Base That Self-Improves — AI Summary

Summary

Current AI agents like Hermes suffer from 'amnesia,' limited only to conversational history. By integrating an Obsidian-based LLM Wiki, you can create a persistent, self-referential knowledge base that links your files, meetings, and research, effectively giving your AI a long-term memory that grows and improves independently.

Key Topics

• AI Amnesia: This creates a major hurdle for agents aiming to be long-term assistants. By relying on conversational memory alone, the AI lacks the depth required to understand the user's evolving context. Overcoming this requires externalizing that memory.
• Obsidian RAG (LLM Wiki): This approach anchors the AI's knowledge in a local, human-readable file structure. It allows the agent to reference specific files and folders, effectively giving it a dedicated library rather than just a fleeting memory.
• Self-Improving Knowledge Base: Instead of static storage, this architecture uses the LLM to identify contradictions or link new insights to existing pages. It ensures that your collection of notes remains accurate and increasingly valuable over time.

Key Takeaways

• Setup a local Obsidian vault and connect it to your primary AI agent.
• Create an automated ingestion workflow for your meeting notes.