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docs/coach-player-providers.md
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# 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:
```toml
[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):
```toml
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):
```toml
coach = "databricks" # Cloud model for quality review
player = "embedded" # Local model for fast implementation
```
### Specialized Models
Use different models optimized for different tasks:
```toml
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