g3/docs/coach-player-providers.md

Coach-Player Provider Configuration

G3 now supports specifying different LLM providers for the coach and player agents when running in autonomous mode. This allows you to optimize for different requirements:

• Player: The agent that implements code - might benefit from a faster, more cost-effective model
• Coach: The agent that reviews code - might benefit from a more powerful, analytical model

Configuration

In your config.toml file, under the [providers] section, you can specify:

[providers]
default_provider = "databricks"  # Used for normal operations
coach = "databricks"              # Provider for coach (code reviewer)
player = "anthropic"              # Provider for player (code implementer)

If coach or player are not specified, they will default to using the default_provider.

Example Use Cases

Cost Optimization

Use a cheaper, faster model for initial implementations (player) and a more powerful model for review (coach):

coach = "anthropic"  # Claude Sonnet for thorough review
player = "anthropic" # Claude Haiku for quick implementation

Speed vs Quality Trade-off

Use a local embedded model for fast iterations (player) and a cloud model for quality review (coach):

coach = "databricks"  # Cloud model for quality review
player = "embedded"   # Local model for fast implementation

Specialized Models

Use different models optimized for different tasks:

coach = "databricks"  # Model fine-tuned for code review
player = "openai"     # Model optimized for code generation

Requirements

• Both providers must be properly configured in your config file
• Each provider must have valid credentials
• The models specified for each provider must be accessible

How It Works

When running in autonomous mode (g3 --autonomous), the system will:

1. Use the player provider (or default) for the initial implementation
2. Switch to the coach provider (or default) for code review
3. Return to the player provider for implementing feedback
4. Continue this cycle for the specified number of turns

The providers are logged at startup so you can verify which models are being used:

🎮 Player provider: anthropic
👨‍🏫 Coach provider: databricks
ℹ️  Using different providers for player and coach

Benefits

• Cost Efficiency: Use expensive models only where they add the most value
• Speed Optimization: Use faster models for iterative development
• Specialization: Leverage models that excel at specific tasks
• Flexibility: Easy to experiment with different provider combinations