Shared Persistent Memory for AI Agents (Claude Code, Cursor, MCP) with Visualization and Time-Travel

Workflow value: 90/100
Status: active · Freshness: 70/100 · Confidence: 0.90 · Level: intermediate
Categories: Quality Control, Context & Memory, Debugging, Shipping, CLAUDE.md, Skills, MCP, Multi-Agent
Original source: r/ClaudeAI post/comment

What problem this solves

AI coding agents forget context between sessions, leading to repeated explanations, inability to build on past work, and lack of visibility into their reasoning process.

Summary

This workflow introduces 'kaeru', an open-source shared memory system for AI agents (including Claude Code, Cursor, Opencode, and MCP-speaking agents). It allows agents to persistently store, retrieve, and share knowledge across sessions and with other agents/humans. Key features include a 3D visualizer for agent knowledge, time-travel for reasoning history, importance levels for context loading, and a secret scanner for secure sharing.

Why it is useful

This workflow provides a critical solution to the common problem of AI agents forgetting context across sessions. It enables agents to build on their past work, collaborate with other agents and humans, and offers unprecedented visibility into their reasoning process through a unique 3D visualizer and time-travel features. Its open-source nature, multi-platform support, and explicit integration with Claude Code make it a highly valuable and adaptable tool for enhancing agent capabilities and developer productivity.

Workflow

1. Install 'kaeru' as a small background service on your infrastructure (Linux, macOS, Windows).
2. Configure your AI agent(s) (e.g., Claude Code) to connect to and use 'kaeru' as their primary memory system, potentially taking over built-in memory.
3. Allow agents to write what they learn, decisions they make, and code they generate into 'kaeru' during their work sessions.
4. When an agent starts a new session or task, it reads relevant context and past learnings from 'kaeru' based on importance levels and linked reasoning trails.
5. Optionally, use the 3D visualizer to explore the agent's accumulated knowledge, replay reasoning chains, or scrub the timeline to see memory growth.
6. Leverage features like time-travel to review past states of notes or strong connections to retrieve entire reasoning trails between ideas.

Tools / artifacts

• kaeru (open-source shared memory service)
• Claude Code
• Cursor
• Opencode
• MCP-speaking agents
• rig framework (for Rust agents)
• GitHub repository (for installation and source code)

Validation signals

• Explicitly states 'Agents actually use it. On Claude Code, kaeru can take over the built-in memory and point it at itself'.
• Provides a GitHub repository link with a specific release (v0.3.0).
• Includes an animated GIF demonstrating the 3D visualizer.
• Implies iterative development: 'It started as memory for a single agent across sessions, but it turned into something more useful'.
• Mentions 'secret-scanner so nothing sensitive leaks by accident' as a built-in safety feature.

Cautions

• Includes a built-in 'secret-scanner' to prevent sensitive information from leaking when shared, enhancing data security.

Limitations

• The tool is 'Still early and very much in testing', suggesting potential for bugs or incomplete features.
• Requires setting up and running a separate background service, which might be a barrier for less technical users.
• Low community validation due to its newness.

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