Methodology
Same task. Same constraints. Different context path.
The Performance Benchmark runs a declared baseline and treatment against identical workloads, records context and cost deltas, and validates that quality remains above the threshold.
Measure
What did the agent actually consume? Provider usage, cache hits, tool calls, context volume, latency, cost.
Compare
Same task, same source revision, same constraints. Baseline and treatment side by side.
Verify
Quality threshold held? Receipt valid? Methodology known and reproducible?
Results
Observed performance under controlled conditions.
Baseline
Stock agent, no optimization
$1.00
five-turn coding task · reference cost
Treatment
LeanCTX context path active
$0.17
same task · same quality threshold
Reduction
Context + cost overhead
−79–86%
across controlled workloads
Real OpenAI API calls · Provider caching recognized · Five-turn multi-file coding tasks · Quality floor enforced
Canonical Receipt
Every run produces verifiable evidence.
Run Your Own
Benchmark your agent in minutes.
# Install LeanCTX
pip install lean-ctx
# Run a controlled benchmark
lean-ctx evidence realworld \
--baseline "stock" \
--treatment "optimized" \
--workload "./tasks/code-review.yaml"
# Generate verifiable report
lean-ctx evidence report --format htmlWorkload-specific result. Not a universal savings promise.
Context Performance Infrastructure