Building a Modular AI Memory System for Claude Code

Modular Memory Architecture

The author proposes a custom memory system for Claude Code that addresses the limitations of default implementations by decoupling storage, injection, and recall. Instead of relying on a single framework, the system integrates specific components from MemSearch, Hermes, and GBrain to create a more robust agentic workflow.

Implementation Strategy

• Storage: Use an automatic hook to trigger a summarization process after every turn. The system utilizes a lightweight model like Haiku to condense transcripts into a daily log, ensuring all information is captured without requiring the agent to decide what is worth saving.
• Injection: Implement a frozen snapshot mechanism inspired by Hermes. This approach captures identity, user profiles, and recent memories, capping the context at approximately 1,300 tokens to prevent bloat while ensuring essential data is cached and available at the start of every session.
• Recall: Deploy a multi-tier hybrid search system. The engine first checks the injected snapshot (Tier 0) before falling back to a local vector index for semantic and keyword-based retrieval.
• Verification: Integrate a re-ranking and citation layer based on the GBrain approach. This ensures the agent returns answers with explicit references to the source files, forcing the model to admit when information is missing rather than hallucinating.

Scaling for Teams

To scale this architecture for collaborative environments, the author recommends moving from isolated local memory files to a centralized database, such as Supabase, utilizing row-level security. This allows individual users to query a shared knowledge base while restricting access based on project or client-specific permissions, effectively creating a scoped "company brain."