010a43d203
Allows coach and player LLM providers to be separately specified. Also adds OpenAI provider
76 lines
2.6 KiB
Markdown
76 lines
2.6 KiB
Markdown
# Coach-Player Provider Configuration
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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:
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- **Player**: The agent that implements code - might benefit from a faster, more cost-effective model
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- **Coach**: The agent that reviews code - might benefit from a more powerful, analytical model
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## Configuration
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In your `config.toml` file, under the `[providers]` section, you can specify:
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```toml
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[providers]
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default_provider = "databricks" # Used for normal operations
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coach = "databricks" # Provider for coach (code reviewer)
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player = "anthropic" # Provider for player (code implementer)
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```
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If `coach` or `player` are not specified, they will default to using the `default_provider`.
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## Example Use Cases
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### Cost Optimization
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Use a cheaper, faster model for initial implementations (player) and a more powerful model for review (coach):
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```toml
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coach = "anthropic" # Claude Sonnet for thorough review
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player = "anthropic" # Claude Haiku for quick implementation
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```
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### Speed vs Quality Trade-off
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Use a local embedded model for fast iterations (player) and a cloud model for quality review (coach):
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```toml
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coach = "databricks" # Cloud model for quality review
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player = "embedded" # Local model for fast implementation
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```
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### Specialized Models
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Use different models optimized for different tasks:
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```toml
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coach = "databricks" # Model fine-tuned for code review
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player = "openai" # Model optimized for code generation
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```
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## Requirements
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- Both providers must be properly configured in your config file
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- Each provider must have valid credentials
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- The models specified for each provider must be accessible
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## How It Works
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When running in autonomous mode (`g3 --autonomous`), the system will:
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1. Use the `player` provider (or default) for the initial implementation
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2. Switch to the `coach` provider (or default) for code review
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3. Return to the `player` provider for implementing feedback
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4. Continue this cycle for the specified number of turns
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The providers are logged at startup so you can verify which models are being used:
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```
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🎮 Player provider: anthropic
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👨🏫 Coach provider: databricks
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ℹ️ Using different providers for player and coach
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```
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## Benefits
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- **Cost Efficiency**: Use expensive models only where they add the most value
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- **Speed Optimization**: Use faster models for iterative development
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- **Specialization**: Leverage models that excel at specific tasks
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- **Flexibility**: Easy to experiment with different provider combinations
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