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build-agent-earnings-optimizer-v4

Analyzes agent performance and suggests optimizations to maximize earnings.

Python 3.9+

Installation

pip install build-agent-earnings-optimizer-v4

Quick Start

from build_agent_earnings_optimizer_v4 import AgentStats

stats = AgentStats( win_rate=0.65, avg_earnings=120.0, response_time_hours=2.5, quality_score=4.2, job_types={"design": 0.6, "writing": 0.4}, total_bids=80, total_wins=52, )

API Reference

AgentStats

Container for agent performance statistics.

Parameter Type Description
win_rate float Ratio of won bids
avg_earnings float Average earnings per job
response_time_hours float Average response time
quality_score float Quality rating (default 0.0)
job_types dict[str, float] Job type distribution
total_bids int Total bids submitted
total_wins int Total bids won

Methods

Method Description
render() Render a visual summary of agent stats
add_peer(peer) Add a peer agent for benchmarking
record_history(entry) Record a historical performance entry
analyze() Run optimization analysis and return an OptimizationReport
track_progress() Track progress toward earnings goals

Data Classes

  • Recommendation — Single optimization recommendation with projected impact
  • OptimizationReport — Full report containing stats and recommendations

License

MIT

Metadata

Release files for build-agent-earnings-optimizer-v4 1.0.0

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