Exploring `cursor-memory-bank`: A Developer's Journey with AI-Driven Project Management
Introduction
As a developer diving into AI-assisted project management, I explored the vanzan01/cursor-memory-bank project to streamline my workflow in Cursor. This blog post shares my experience, critiques, and plans for adapting this framework, based on my project notebook notes, with a brief overview of its features.
What is cursor-memory-bank?
The cursor-memory-bank is a modular framework designed to enhance AI-driven development in Cursor. It uses custom modes (VAN, PLAN, CREATIVE, IMPLEMENT) to guide AI through structured workflows, maintaining persistent memory via documentation files like tasks.md and activeContext.md. It leverages Mermaid diagrams for visual process mapping and supports task complexity scaling, making it ideal for projects needing consistent AI context across sessions.
My Journey with cursor-memory-bank
Initial Impressions
I was drawn to cursor-memory-bank for its promise of dynamic project management, a step beyond static Product Requirement Documents (PRDs). I forked it as a Git submodule to avoid dependency issues and began experimenting. However, my initial use revealed flaws: the AI had too much freedom, hallucinating unwanted files and duplicating content. It needed stricter rules to align with my goal of a controlled AI workflow, inspired by my PRD meta-script idea.
Deep Dive and Challenges
Using Gemini 2.5 Pro, I hit a snag when the AI suggested code fixes but failed to commit changes due to undocumented task creation workflows. A thorough review of the project’s docs showed it was geared toward bootstrapping, not ongoing management, and seemed optimized for older models like Claude Sonnet 3.7. The lack of essential task management rules and outdated workflows made it feel incomplete for my needs. Still, I loved the diagram-driven approach, which added clarity to AI interactions.
Cursor’s Limitations
Integrating cursor-memory-bank with Cursor was tricky. Cursor lacks a /new_task feature, cluttering context, and doesn’t support cheap models for simple tasks. My attempts to feed file paths for rules were inefficient, and a manual rules loader hack failed. After consulting my AI agent, I settled on using an internal variable to manage rules, a promising workaround for Cursor’s constraints.
Plans for Adaptation
Despite its flaws, I see potential in cursor-memory-bank. My plan is to use it as a base, combining it with roo-code-memory-bank for a modular rule system. This setup will support my current Cursor workflow and ease future migration to Roo Code. I’ll extend task management by adding explicit creation rules to tasks.md, inspired by task-master, and tighten AI constraints to prevent hallucination. Tools like envmcp and composio will streamline MCP integration, addressing token usage concerns.
Key Takeaways
- Pros: Modular structure, persistent memory, and visual diagrams make
cursor-memory-banka solid starting point for AI-driven projects. - Cons: Loose AI constraints, incomplete task workflows, and outdated model assumptions limit its out-of-the-box utility.
- Next Steps: Merge with
roo-code-memory-bank, enhance task creation, and leverage MCP tools for efficiency.
Conclusion
My journey with vanzan01/cursor-memory-bank highlighted its potential and pitfalls. While it didn’t fully meet my needs, its framework is a foundation I can build upon. By combining it with other tools and tightening rules, I’m optimistic about creating a robust AI-assisted workflow. Stay tuned for updates as I refine this setup!
Update of 2025-05-15
After Gemini 2.5 May update this project became a total overkill just, producing worse results than the clear setup or a simple memory bank idea. It was ahead of its time, but now it is just a mess.
Important takeaway:
- Diagrams are great to make the workflow clear. Claude Sonnet 3.5 - 3.7 is great in creating them, while Gemini 2.5 needs some extra tweaks to make them rendering.