Kicking off: Context Memory System
Excited to start building Context Memory System - a Git-inspired, version-controlled memory layer for long-running AI agents.
The Problem
AI agents lose track of what matters in long conversations. They either run out of context space or silently forget important details. Most memory systems handle this as a black box: they summarize or drop information with no visibility into what was kept, what was lost, or why.
Our Approach
We're treating AI memory like version control:
- Commit: every memory update is tracked with a reason (kept, summarized, or discarded)
- Checkout: jump back to any earlier memory state
- Branch: explore alternate directions without losing the original
- Merge: combine branches, with conflicts flagged instead of silently overwritten
- Self-healing: automatically detect contradicting facts and ask for clarification
Progress so far
- Mapped out the full architecture (FastAPI backend and SQLite for commit storage)
- Designed the commit schema (id, parent_id, timestamp, topic, action, reason, confidence)
- Planning the classifier logic that decides what gets kept, summarized, or discarded
Next steps
- Build the core commit engine
- Implement branch and merge logic
- Start on the UI for browsing memory history
More updates soon as the core engine comes together.
AI LLM AgenticAI ContextMemory
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