CAM (Continuous Architectural Memory) solves an architectural problem:
CAM addresses a fundamental LLM limitation; statelessness. Each session starts fresh. Claude Hooks became the architectural driver to solve this.
The mechanism: Claude Hooks intercept operations (UserPromptSubmit, PostToolUse, SessionEnd, etc.) and feed data into CAM—code changes, tool use patterns, research, prompts, plans. CAM embeds everything, builds relationships, and injects relevant context before Claude processes requests. Allowing contextually aware planning, tool use, researching, diagnosing, debugging, implementations, etc.
The result: an infinitely updated, persisted codebase map + documentation source. CAM reads/writes/iterates/revises/queries/validates research and tool use across sessions, compounding context awareness over time.
This pattern generalizes beyond Claude. Any LLM integration with hooks can gain persistent memory this way.
Open source MVP. Uses Gemini embeddings, SciPy clustering, knowledge graphs.
CAM addresses a fundamental LLM limitation; statelessness. Each session starts fresh. Claude Hooks became the architectural driver to solve this.
The mechanism: Claude Hooks intercept operations (UserPromptSubmit, PostToolUse, SessionEnd, etc.) and feed data into CAM—code changes, tool use patterns, research, prompts, plans. CAM embeds everything, builds relationships, and injects relevant context before Claude processes requests. Allowing contextually aware planning, tool use, researching, diagnosing, debugging, implementations, etc.
The result: an infinitely updated, persisted codebase map + documentation source. CAM reads/writes/iterates/revises/queries/validates research and tool use across sessions, compounding context awareness over time.
This pattern generalizes beyond Claude. Any LLM integration with hooks can gain persistent memory this way.
Open source MVP. Uses Gemini embeddings, SciPy clustering, knowledge graphs.
https://github.com/blas0/Severance