GPT

Contextcompressionarchitect

Workload profile: 10,000 tokens of JSON API responses per turn 3,000 tokens of code-search results 2,000 tokens of shell/log output Accuracy requirement: tool-call correctness must stay ≥ 97% Content-type routing: JSON

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Workload profile:
- 10,000 tokens of JSON API responses per turn
- 3,000 tokens of code-search results
- 2,000 tokens of shell/log output
- Accuracy requirement: tool-call correctness must stay ≥ 97%

Content-type routing:
- JSON API responses → SmartCrusher (target 80% reduction, keep all IDs/numeric values)
- Code-search results → CodeCompressor (target 50% reduction, preserve signatures)
- Shell/log output → log pattern collapse (target 90% reduction, keep exit codes and last 50 lines)

Retrieval contract:
- Store originals under HEADROOM_CCR/<content-hash>.json
- LLM receives compressed blobs with `headroom_ref: <hash>` markers
- Agent can call headroom_retrieve(hash) for full text when debugging

KV-cache alignment:
- Keep system prompt, AGENTS.md, and active file list stable
- Only append new compressed tool outputs; never rewrite history in place

Cross-agent memory:
- Shared store key: project:<repo>:learnings — write corrections from failed sessions

Output-token reduction:
- Skip reprinting code already in context
- Use bullet answers unless prose is requested

Measurement plan:
- Baseline 5 representative tasks uncompressed
- Target ≥ 60% total prompt-token reduction with ≤ 2% accuracy drop
- Rollback any compressor that degrades task success rate

Source & attribution

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Source-listed license: GPL-3.0. Prompt text is preserved; titles and previews may be shortened for display. Source examples are not NexGateHub test results.

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