Cost Optimizer

Analyze token waste in LLM agent traces and optimize cost through intelligent model routing.

Installation

pip install cost-optimizer

Quick Start

Analyze Traces

# Analyze a trace file for waste
costopt analyze trace.jsonl

# Specify model for cost calculation
costopt analyze trace.jsonl --model gpt-4o

# Save results
costopt analyze trace.jsonl -o results.json

Estimate Costs

# Estimate cost for 1M tokens on Claude 3.5 Sonnet
costopt estimate --model claude-3-5-sonnet-20241022 --tokens 1000000

# Compare costs across all models
costopt estimate --model claude-3-5-sonnet-20241022 --tokens 1000000 --compare

Optimize

# Get optimization recommendations
costopt optimize trace.jsonl

Programming API

from cost_optimizer import TokenAnalyzer, CostOptimizer, ModelRouter, PricingData

# Analyze traces
analyzer = TokenAnalyzer(default_model="claude-3-5-sonnet-20241022")
report = analyzer.analyze_trace("trace.jsonl")
print(f"Waste: {report.total_waste_tokens} tokens")

# Get optimization recommendations
optimizer = CostOptimizer()
optimizations = optimizer.optimize(report)
for opt in optimizations:
    print(f"{opt.strategy}: saves ${opt.estimated_savings_usd:.2f}")

# Route to cheaper models
router = ModelRouter()
model = router.route("simple formatting task")  # → claude-3-5-haiku-20241022
assessment = router.assess("design a distributed system architecture")
print(f"Complexity: {assessment.complexity_score}, Model: {assessment.recommended_model}")

# Calculate costs
cost = PricingData.calculate_cost(1000000, "claude-3-5-sonnet-20241022")

Supported Models

License

MIT

Ecosystem

Part of the FableForge ecosystem — 21 open-source projects built from 210K real agent traces: