continual-learning

Continual Learning for AI Coding Agents

Your agent forgets everything between sessions. Continual learning fixes that.

The Loop

Experience → Capture → Reflect → Persist → Apply
     ↑                                       │
     └───────────────────────────────────────┘

Quick Start

Install the hook (one step):

cp -r hooks/continual-learning .github/hooks/

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:

Session 1: bash tool fails 4 times → learning stored: "bash frequently fails"
Session 2: hook surfaces that learning at start → agent adjusts approach

Agent-native (via store_memory / SQL)

The agent can write learnings directly:

INSERT INTO learnings (scope, category, content, source)
VALUES ('local', 'convention', 

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

1. One step to install — if it takes more than cp -r, it won't get adopted
2. Scope correctly — global for tool patterns, local for project conventions
3. Be specific — "Use semantic_configuration_name=" beats "use the right parameter"
4. Let it compound — small improvements per session create exponential gains over weeks