Building a Learning System for AI Agents That Remembers Across Repos
Building a Learning System for AI Agents That Remembers Across Repos
Every developer has experienced it - you correct your AI agent for the third time on the same mistake. It uses the wrong import path. It forgets your test conventions. It ignores your preferred error handling pattern. Each session starts fresh, and the agent learns nothing from repetition.
The Immune System Analogy
Biological immune systems do not just fight infections - they remember them. When the same pathogen appears again, the response is faster and more targeted. AI agents need the same kind of adaptive memory.
The idea is simple: when an agent encounters a correction, it should store that pattern. When it sees a similar situation across any repository, it should recall the correction before making the same error. This is not about fine-tuning a model. It is about building a memory layer on top of the agent that tracks patterns across projects.
How Cross-Repo Memory Works
A practical implementation stores corrections as pattern-match pairs:
- Trigger - the situation that caused the mistake (e.g., importing from a monorepo package)
- Correction - what the developer said to fix it (e.g., use workspace aliases instead of relative paths)
- Context - which repos and file types this applies to
When the agent starts a new session in any repo, it loads relevant patterns from its memory store. If it is about to write an import statement in a monorepo, it checks whether it has been corrected on this before.
Why This Matters for Desktop Agents
Desktop agents like Fazm operate across your entire machine - multiple repos, multiple projects, multiple contexts. Without cross-repo memory, each project is an isolated experience. With it, the agent gets smarter the more you use it, accumulating knowledge about your preferences and patterns regardless of which codebase you are working in.
The key insight is that most corrections are not project-specific. They reflect how you think about code. An agent that learns this becomes genuinely useful over time instead of perpetually feeling like day one.
Fazm is an open source macOS AI agent. Open source on GitHub.