Continual Learning for AI Coding Agents
Your agent forgets everything between sessions. Continual learning fixes that.
The Loop
Quick Start
Install the hook (one step):
Auto-initializes on first session. No config needed.
Two-Tier Memory
Global (~/.copilot/learnings.db) — follows you across all projects:
- Tool patterns (which tools fail, which work)
- Cross-project conventions
- General coding preferences
Local (.copilot-memory/learnings.db) — stays with this repo:
- Project-specific conventions
- Common mistakes for this codebase
- Team preferences
How Learnings Get Stored
Automatic (via hooks)
The hook observes tool outcomes and detects failure patterns:
Agent-native (via store_memory / SQL)
The agent can write learnings directly:
Categories: pattern, mistake, preference, tool_insight
Manual (memory files)
For human-readable, version-controlled knowledge:
Compaction
Learnings decay over time:
- Entries older than 60 days with low hit count are pruned
- High-value learnings (frequently referenced) persist indefinitely
- Tool logs are pruned after 7 days
This prevents unbounded growth while preserving what matters.
Best Practices
- One step to install — if it takes more than
cp -r, it won't get adopted - Scope correctly — global for tool patterns, local for project conventions
- Be specific —
"Use semantic_configuration_name="beats"use the right parameter" - Let it compound — small improvements per session create exponential gains over weeks
