LORE.md: An Open Standard for Extracting Structured Knowledge from AI Conversations

LORE.md is an open standard for extracting structured knowledge from AI conversations, specifically designed to address the problem of valuable insights being lost in chat logs. The standard defines a structured format that captures the durable knowledge from any AI conversation.

What LORE.md Captures

The format is designed to extract several key elements from conversations:

• Decisions with full rationale: Not just what was chosen, but the underlying assumptions that would need to change to revisit the decision
• Insights: Key realizations surfaced during conversations
• Patterns: Recurring themes or behaviors identified
• Open questions: Unresolved issues or topics for further exploration
• Next steps: Action items or follow-up tasks

All captured knowledge links together across sessions, allowing users to connect current conversations with previous discussions on the same topics.

Implementation Details

The project includes several practical components:

• System prompt: Works with any LLM - paste a conversation transcript and get structured knowledge back
• Bulk pipeline: For processing Claude data exports in volume
• Open source: MIT licensed and available on GitHub

The tool addresses the specific problem of being unable to search conversation history, connect insights across different sessions, or provide AI assistants with a comprehensive map of previously established knowledge.

This type of tool is useful for developers and researchers who regularly use AI assistants for problem-solving and want to maintain a searchable, structured knowledge base from their interactions.