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LangChain + Headroom Demo
Real-world demonstration of Headroom optimization on LangChain agents.
Quick Start
# Show compression in action (no API key needed)
PYTHONPATH=. python -m examples.langchain_demo.show_compression
# Verify 100% ERROR preservation
PYTHONPATH=. python -m examples.langchain_demo.verify_errors_kept
# Run full agent comparison (requires OPENAI_API_KEY)
export OPENAI_API_KEY='your-key-here'
PYTHONPATH=. python -m examples.langchain_demo.run_comparison
Results
Token Savings (with 100% ERROR preservation)
| Tool | Before | After | Saved |
|---|---|---|---|
| search_users (100 items) | 15,453 | 2,014 | 87% |
| search_logs (200 items) | 25,679 | 3,213 | 87% |
| get_metrics (100 items) | 11,517 | 8,425 | 27% |
| search_docs (50 items) | 6,912 | 2,127 | 69% |
| fetch_api_data (75 items) | 15,786 | 3,622 | 77% |
| TOTAL | 75,347 | 19,401 | 74% |
Critical Data Preservation
- 100% ERROR entries preserved (27/27 in test runs)
- 100% anomaly detection (CPU spikes, high error rates)
- First/last items always kept (context preservation)
Cost Impact (at gpt-4o $2.50/1M)
- Per request: $0.19 → $0.05
- At 1000 req/day: $4,196/month saved
What Headroom Does
SmartCrusher intelligently compresses tool outputs by:
- 100% ERROR preservation - NEVER drops error items (bug fix v1.1)
- Keeping first/last items - Context for pagination
- Keeping anomalies - High CPU, memory spikes (statistical detection)
- Relevance scoring - Items matching user's query
- Change points - Significant transitions in data
Files
mock_tools.py- Realistic tool output generatorsshow_compression.py- Standalone compression demoverify_errors_kept.py- Verify 100% ERROR preservationrun_comparison.py- Full agent before/after comparison
Eval Tests
Run the comprehensive eval suite:
PYTHONPATH=. pytest tests/test_integrations/test_langchain_evals.py -v
12 evals covering:
- Error preservation (100%)
- Anomaly detection
- Relevance matching
- Compression efficiency
- Schema preservation
- Edge cases