When working with multiple AI tools, I kept encountering the same problem: each agent had its own context, and useful information rarely followed me between tools.
I use ChatGPT for writing and reflection, coding agents for software development, and local models for tasks I prefer to run on my own machine. Switching between them often meant repeating project details, preferences, and previous decisions.
To address this, I started building a shared, local memory repository using Markdown files.
The Approach
I wanted a solution that was simple, portable, and independent of any particular AI provider.
The repository stores persistent context in structured Markdown files, organized around different areas of my work and interests.
This allows AI agents to:
- Read relevant context before starting a task.
- Reuse information established in previous conversations.
- Update existing knowledge as projects and decisions evolve.
- Share context without depending on a specific provider's memory features.
What Makes It Challenging?
Creating the repository was relatively straightforward. Maintaining useful memory introduced more interesting questions.
What information should be preserved? How should agents decide when to update existing files? How can I prevent outdated or duplicated information from accumulating?
These questions became increasingly important as I started integrating the repository into my everyday workflows.
Implementation and Lessons Learned
I documented the approach, repository structure, integration with different AI tools, and the trade-offs I encountered in the full article.
Read the full article: Building a Local AI Memory Repository
I'm curious how other developers manage persistent context across multiple AI agents. Are you using built-in memory, MCP integrations, or your own file-based approach?
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