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name: context-compression description: Use when compressing agent context, implementing conversation summarization, reducing token usage in long sessions, or asking about "context compression", "conversation history", "token optimization", "context limits", "summarization strategies" version: 1.0.0

Context Compression Strategies

When agent sessions generate millions of tokens, compression becomes mandatory. Optimize for tokens-per-task (total tokens to complete a task), not tokens-per-request.

Compression Approaches

1. Anchored Iterative Summarization (Recommended)

• Maintain structured summaries with explicit sections
• On compression, summarize only newly-truncated content
• Merge with existing summary instead of regenerating
• Structure forces preservation of critical info

2. Opaque Compression

• Highest compression ratios (99%+)
• Sacrifices interpretability
• Cannot verify what was preserved

3. Regenerative Full Summary

• Generate detailed summary on each compression
• Readable but may lose details across cycles
• Full regeneration rather than merging

Structured Summary Format

Copy## Session Intent
[What the user is trying to accomplish]

## Files Modified
- auth.controller.ts: Fixed JWT token generation
- config/redis.ts: Updated connection pooling

## Decisions Made
- Using Redis connection pool instead of per-request
- Retry logic with exponential backoff

## Current State
- 14 tests passing, 2 failing
- Remaining: mock setup for session service tests

## Next Steps
1. Fix remaining test failures
2. Run full test suite
3. Update documentation

Compression Triggers

The Artifact Trail Problem

File tracking is the weakest dimension (2.2-2.5/5.0 in evaluations). Coding agents need:

• Which files were created
• Which files were modified and what changed
• Which files were read but not changed
• Function names, variable names, error messages

Solution: Separate artifact index or explicit file-state tracking.

Probe-Based Evaluation

Test compression quality with probes:

Compression Ratios

The 0.7% extra tokens buys 0.35 quality points—worth it when re-fetching costs matter.

Three-Phase Workflow (Large Codebases)

1. Research Phase: Explore and compress into structured analysis
2. Planning Phase: Convert to implementation spec (~2,000 words for 5M tokens)
3. Implementation Phase: Execute against the spec

Best Practices

1. Optimize for tokens-per-task, not tokens-per-request
2. Use structured summaries with explicit file sections
3. Trigger compression at 70-80% utilization
4. Implement incremental merging over regeneration
5. Test with probe-based evaluation
6. Track artifact trail separately if critical
7. Monitor re-fetching frequency as quality signal