Lesson 7: Architecture and Data Flow of claude-mem

⏱ Est. reading time: 3 min Updated on 5/7/2026

7.1 Automated Capture Workflow

claude-mem utilizes a Hook mechanism to implement fully automated background recording:

  1. SessionStart: Injects relevant historical context at the beginning of a session.
  2. PostToolUse: The core stage. Every time Claude Code executes a tool (e.g., Edit or run_command), the Hook passes the operation context to the background Worker.
  3. Distillation: The Worker calls the LLM to "distill" the raw interaction into a structured Observation record (including type, summary, etc.).
  4. Storage: The record is written to the local SQLite database and a vector embedding is stored in Chroma.
  5. Stop: Generates a summary report of the entire session when it concludes.

7.2 Data Flow During Retrieval

When the LLM needs to query history, the data flows as follows:

  • Initiate Query: The LLM calls the search MCP tool.
  • Hybrid Retrieval: The system simultaneously performs full-text matching in SQLite and semantic vector calculations in Chroma.
  • Return Results: Returns a compressed list of IDs.
  • Deepen Context: The LLM further calls timeline or get_observations to retrieve full details as needed.

7.3 Three-Layer Storage Abstraction

To balance performance and functionality, claude-mem builds a three-tier data structure:

  • Raw Layer: The original .jsonl session logs generated by Claude Code (located in ~/.claude/projects/*/sessions/).
  • Distillation Layer: The Observation records distilled by claude-mem, stored in SQLite and Chroma.
  • Usage Layer: Injected into the System Prompt via Hooks or read by the LLM via MCP tools.

7.4 Data Privacy and Compliance

Important Note: Although the data is stored locally, the "distillation" process requires calls to the remote LLM API. This means:

  • Context from every tool call is sent to Anthropic for summarization.
  • For sensitive projects, you should disable this plugin in .claude/settings.local.json to ensure data security.

Related Tools & Resources

Skill Marketplaces

M

Matt Pocock's AI Skills

A collection of small, adaptable, and composable AI agent skills designed to resolve common development issues, enhance agent-developer collaboration, and improve code quality.
A

Agent Skills

Production-grade engineering skills for AI coding agents, encoding workflows, quality gates, and best practices for software development.

Recommended Plugins

🏗️

Feature Dev

Comprehensive feature development workflow with a structured 7-phase approach. Agents: code-explorer, code-architect, code-reviewer.

Related Products

📦

mempalace

MemPalace is a local-first AI memory tool designed to store conversation history verbatim and retrieve it through semantic search, without summarizing or paraphrasing. It utilizes a structured index, categorizing information into "wings," "rooms," and "drawers" to enable scoped searches. The retrieval layer is pluggable (ChromaDB by default), ensuring all data remains on the user's machine unless opted out. It achieves an impressive 96.6% R@5 raw recall on the LongMemEval benchmark, notably without requiring LLMs or API calls, showcasing high performance in a local environment.
📦

LangGraph

LangGraph, from LangChain, Inc., is a powerful low-level orchestration framework engineered for building, managing, and deploying sophisticated, long-running, and stateful multi-agent applications. It represents application logic as cyclic Directed Acyclic Graphs (DAGs), giving developers precise control over LLM execution, including when to pause, loop, or transfer control. LangGraph ensures durable execution, seamless human-in-the-loop capabilities, and comprehensive short-term and long-term memory management. Deep integration with LangSmith provides robust debugging and production-ready deployment, making it the ideal choice for developing complex and reliable AI agents.