William Valentin
83 lines
2.2 KiB
Markdown
83 lines
2.2 KiB
Markdown
# Phase 3 PR #1 Checklist: Adaptive Memory Injection and Compaction Weighting
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Created: 2026-02-12
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Owner: Flynn core
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Status: ready to implement
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## Goal
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Improve memory usefulness and compaction quality by injecting relevant memory context adaptively and preserving high-value turns during compaction.
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## Scope
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In scope:
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- adaptive memory relevance scoring
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- configurable memory injection strategy
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- compaction weighting for important turns (tool outcomes, corrections, preferences)
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- tests and perf guardrails
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Out of scope:
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- new vector backend work
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- UI controls for weighting
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## Files
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New:
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- `src/memory/adaptive.ts`
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- `src/memory/adaptive.test.ts`
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- `src/context/weighting.ts`
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- `src/context/weighting.test.ts`
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Modified:
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- `src/backends/native/orchestrator.ts`
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- `src/context/compaction.ts`
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- `src/memory/store.ts`
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- `src/config/schema.ts`
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## Implementation Steps
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1. Add adaptive memory scorer:
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- keyword overlap with recent turns
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- recency weighting
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- token budget clipping
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2. Add config flags:
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- `memory.injection_strategy` (`all|recent|adaptive`)
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- `memory.max_injection_tokens`
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- `compaction.importance_threshold`
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3. Integrate adaptive injector in orchestrator memory injection path.
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4. Add compaction message weighting and selection algorithm.
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5. Ensure fallback to existing behavior on errors/timeouts.
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6. Add tests for relevance selection and weighted compaction ordering.
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## Validation Commands
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```bash
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pnpm typecheck
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pnpm test:run src/memory/adaptive.test.ts
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pnpm test:run src/context/weighting.test.ts
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pnpm test:run src/context/compaction.test.ts
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pnpm test:run src/backends/native/orchestrator.test.ts
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pnpm test:run
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pnpm lint
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pnpm build
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```
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## Acceptance Criteria
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- Adaptive mode injects fewer but more relevant memory snippets.
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- Compaction preserves high-importance turns over low-value chatter.
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- Token budgets are respected consistently.
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- Existing behavior preserved when adaptive features disabled.
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- No measurable latency regression beyond agreed budget.
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## Risks
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- Relevance scoring false positives/negatives.
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- Mitigation: conservative scoring + extensive fixtures.
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- Added latency in hot path.
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- Mitigation: bounded scoring time and fallback mode.
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## Commit Message
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`feat(memory): add adaptive injection and weighted compaction`
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