Merge branch 'main' into micn/fix-anthropic-1p

* main:
  fix panic in CLI parser
  coach/player provider split + add OpenAI

Cargo.lock

@@ -1316,6 +1316,7 @@ dependencies = [
  "dirs 5.0.1",
  "serde",
  "shellexpand",
+ "tempfile",
  "thiserror 1.0.69",
  "toml",
 ]

config.coach-player.example.toml

@@ -0,0 +1,24 @@
+[providers]
+default_provider = "databricks"
+# Specify different providers for coach and player in autonomous mode
+coach = "databricks"    # Provider for coach (code reviewer) - can be more powerful/expensive
+player = "anthropic"    # Provider for player (code implementer) - can be faster/cheaper
+
+[providers.databricks]
+host = "https://your-workspace.cloud.databricks.com"
+# token = "your-databricks-token"  # Optional - will use OAuth if not provided
+model = "databricks-claude-sonnet-4"
+max_tokens = 4096
+temperature = 0.1
+use_oauth = true
+
+[providers.anthropic]
+api_key = "your-anthropic-api-key"
+model = "claude-3-haiku-20240307"  # Using a faster model for player
+max_tokens = 4096
+temperature = 0.3  # Slightly higher temperature for more creative implementations
+
+[agent]
+max_context_length = 8192
+enable_streaming = true
+timeout_seconds = 60

config.example.toml

@@ -1,5 +1,10 @@
 [providers]
 default_provider = "databricks"
+# Optional: Specify different providers for coach and player in autonomous mode
+# If not specified, will use default_provider for both
+# coach = "databricks"    # Provider for coach (code reviewer)
+# player = "anthropic"    # Provider for player (code implementer)
+# Note: Make sure the specified providers are configured below
 
 [providers.databricks]
 host = "https://your-workspace.cloud.databricks.com"

crates/g3-cli/src/lib.rs

@@ -103,11 +103,14 @@ fn extract_coach_feedback_from_logs(
     coach_result: &g3_core::TaskResult,
     coach_agent: &g3_core::Agent<ConsoleUiWriter>,
     output: &SimpleOutput,
-) -> String {
+) -> Result<String> {
+    // CORRECT APPROACH: Get the session ID from the current coach agent
+    // and read its specific log file directly
+
     // Get the coach agent's session ID
     let session_id = coach_agent
         .get_session_id()
-        .expect("Coach agent has no session ID");
+        .ok_or_else(|| anyhow::anyhow!("Coach agent has no session ID"))?;
 
     // Construct the log file path for this specific coach session
     let logs_dir = std::path::Path::new("logs");
@@ -120,75 +123,15 @@ fn extract_coach_feedback_from_logs(
                 if let Some(context_window) = log_json.get("context_window") {
                     if let Some(conversation_history) = context_window.get("conversation_history") {
                         if let Some(messages) = conversation_history.as_array() {
-                            // Look for the last assistant message (regardless of tool used)
+                            // Simply get the last message content - this is the coach's final feedback
-                            for message in messages.iter().rev() {
+                            if let Some(last_message) = messages.last() {
-                                if let Some(role) = message.get("role") {
+                                if let Some(content) = last_message.get("content") {
-                                    if role.as_str() == Some("assistant") {
-                                        if let Some(content) = message.get("content") {
                                     if let Some(content_str) = content.as_str() {
-                                                // First, check if this is plain text feedback (no tool call)
-                                                // This happens when the coach returns final feedback directly
-                                                if !content_str.contains("{\"tool\"") {
-                                                    let trimmed = content_str.trim();
-                                                    if !trimmed.is_empty() {
                                         output.print(&format!(
-                                                            "✅ Extracted coach feedback from session: {} ({} chars) [plain text]",
+                                            "✅ Extracted coach feedback from session: {}",
-                                                            session_id,
+                                            session_id
-                                                            trimmed.len()
                                         ));
-                                                        return trimmed.to_string();
+                                        return Ok(content_str.to_string());
-                                                    }
-                                                }
-                                                
-                                                // Look for ANY tool call in the message
-                                                // Pattern: {"tool": "...", "args": {...}}
-                                                if let Some(tool_start) = content_str.find("{\"tool\"") {
-                                                    let json_part = &content_str[tool_start..];
-                                                    
-                                                    // Find the end of the JSON object
-                                                    if let Some(json_end) = find_json_end(json_part) {
-                                                        let json_str = &json_part[..json_end];
-                                                        
-                                                        if let Ok(tool_call) = serde_json::from_str::<serde_json::Value>(json_str) {
-                                                            if let Some(args) = tool_call.get("args") {
-                                                                // Try to extract feedback from different possible fields
-                                                                let feedback = if let Some(summary) = args.get("summary") {
-                                                                    // final_output tool uses "summary"
-                                                                    summary.as_str().map(|s| s.to_string())
-                                                                } else if let Some(content) = args.get("content") {
-                                                                    // todo_write and other tools might use "content"
-                                                                    content.as_str().map(|s| s.to_string())
-                                                                } else {
-                                                                    // Fallback: use the entire args as JSON string
-                                                                    Some(serde_json::to_string_pretty(args).unwrap_or_default())
-                                                                };
-                                                                
-                                                                if let Some(feedback_str) = feedback {
-                                                                    if !feedback_str.trim().is_empty() {
-                                                                        output.print(&format!(
-                                                                            "✅ Extracted coach feedback from session: {} ({} chars)",
-                                                                            session_id,
-                                                                            feedback_str.len()
-                                                                        ));
-                                                                        
-                                                                        // Validate feedback length
-                                                                        if feedback_str.len() < 80 && !feedback_str.contains("IMPLEMENTATION_APPROVED") {
-                                                                            panic!(
-                                                                                "Coach feedback is too short ({} chars): '{}'",
-                                                                                feedback_str.len(),
-                                                                                feedback_str
-                                                                            );
-                                                                        }
-                                                                        
-                                                                        return feedback_str;
-                                                                    }
-                                                                }
-                                                            }
-                                                        }
-                                                    }
-                                                }
-                                            }
-                                        }
                                     }
                                 }
                             }
@@ -213,35 +156,6 @@ fn extract_coach_feedback_from_logs(
     );
 }
 
-/// Helper function to find the end of a JSON object using brace counting
-fn find_json_end(json_str: &str) -> Option<usize> {
-    let mut depth = 0;
-    let mut in_string = false;
-    let mut escape_next = false;
-    
-    for (i, ch) in json_str.char_indices() {
-        if escape_next {
-            escape_next = false;
-            continue;
-        }
-        
-        match ch {
-            '\\' if in_string => escape_next = true,
-            '"' => in_string = !in_string,
-            '{' if !in_string => depth += 1,
-            '}' if !in_string => {
-                depth -= 1;
-                if depth == 0 {
-                    return Some(i + 1);
-                }
-            }
-            _ => {}
-        }
-    }
-    
-    None
-}
-
 use clap::Parser;
 use g3_config::Config;
 use g3_core::{project::Project, ui_writer::UiWriter, Agent};
@@ -321,10 +235,6 @@ pub struct Cli {
     /// Disable log file creation (no logs/ directory or session logs)
     #[arg(long)]
     pub quiet: bool,
-
-    /// Enable WebDriver tools for browser automation (Safari)
-    #[arg(long)]
-    pub webdriver: bool,
 }
 
 pub async fn run() -> Result<()> {
@@ -413,17 +323,12 @@ pub async fn run() -> Result<()> {
     }
 
     // Load configuration with CLI overrides
-    let mut config = Config::load_with_overrides(
+    let config = Config::load_with_overrides(
         cli.config.as_deref(),
         cli.provider.clone(),
         cli.model.clone(),
     )?;
 
-    // Override webdriver setting from CLI flag
-    if cli.webdriver {
-        config.webdriver.enabled = true;
-    }
-
     // Validate provider if specified
     if let Some(ref provider) = cli.provider {
         let valid_providers = ["anthropic", "databricks", "embedded", "openai"];
@@ -1358,10 +1263,6 @@ async fn run_autonomous(
     loop {
         let turn_start_time = Instant::now();
         let turn_start_tokens = agent.get_context_window().used_tokens;
-        
-        // Reset filter suppression state at the start of each turn
-        g3_core::fixed_filter_json::reset_fixed_json_tool_state();
-        
         // Skip player turn if it's the first turn and implementation files exist
         if !(turn == 1 && skip_first_player) {
             output.print(&format!(
@@ -1522,14 +1423,15 @@ async fn run_autonomous(
 
         // Create a new agent instance for coach mode to ensure fresh context
         // Use the same config with overrides that was passed to the player agent
-        let config = agent.get_config().clone();
+        let base_config = agent.get_config().clone();
+        let coach_config = base_config.for_coach()?;
 
         // Reset filter suppression state before creating coach agent
         g3_core::fixed_filter_json::reset_fixed_json_tool_state();
 
         let ui_writer = ConsoleUiWriter::new();
         let mut coach_agent =
-            Agent::new_autonomous_with_readme_and_quiet(config, ui_writer, None, quiet).await?;
+            Agent::new_autonomous_with_readme_and_quiet(coach_config, ui_writer, None, quiet).await?;
 
         // Ensure coach agent is also in the workspace directory
         project.enter_workspace()?;
@@ -1677,7 +1579,7 @@ Remember: Be clear in your review and concise in your feedback. APPROVE if the i
 
         // Extract the complete coach feedback from final_output
         let coach_feedback_text =
-            extract_coach_feedback_from_logs(&coach_result, &coach_agent, &output);
+            extract_coach_feedback_from_logs(&coach_result, &coach_agent, &output)?;
 
         // Log the size of the feedback for debugging
         info!(
@@ -1704,15 +1606,6 @@ Remember: Be clear in your review and concise in your feedback. APPROVE if the i
 
         output.print_smart(&format!("Coach feedback:\n{}", coach_feedback_text));
 
-        // Record turn metrics before checking for approval or max turns
-        let turn_duration = turn_start_time.elapsed();
-        let turn_tokens = agent.get_context_window().used_tokens.saturating_sub(turn_start_tokens);
-        turn_metrics.push(TurnMetrics {
-            turn_number: turn,
-            tokens_used: turn_tokens,
-            wall_clock_time: turn_duration,
-        });
-
         // Check if coach approved the implementation
         if coach_result.is_approved() || coach_feedback_text.contains("IMPLEMENTATION_APPROVED") {
             output.print("\n=== SESSION COMPLETED - IMPLEMENTATION APPROVED ===");
@@ -1721,7 +1614,6 @@ Remember: Be clear in your review and concise in your feedback. APPROVE if the i
             break;
         }
 
-        // Increment turn counter after recording metrics but before checking max turns
         // Check if we've reached max turns
         if turn >= max_turns {
             output.print("\n=== SESSION COMPLETED - MAX TURNS REACHED ===");
@@ -1731,7 +1623,14 @@ Remember: Be clear in your review and concise in your feedback. APPROVE if the i
 
         // Store coach feedback for next iteration
         coach_feedback = coach_feedback_text;
-        
+        // Record turn metrics before incrementing
+        let turn_duration = turn_start_time.elapsed();
+        let turn_tokens = agent.get_context_window().used_tokens.saturating_sub(turn_start_tokens);
+        turn_metrics.push(TurnMetrics {
+            turn_number: turn,
+            tokens_used: turn_tokens,
+            wall_clock_time: turn_duration,
+        });
         turn += 1;
 
         output.print("🔄 Coach provided feedback for next iteration");

crates/g3-config/Cargo.toml

@@ -12,3 +12,6 @@ thiserror = { workspace = true }
 toml = "0.8"
 shellexpand = "3.0"
 dirs = "5.0"
+
+[dev-dependencies]
+tempfile = "3.8"

crates/g3-config/src/lib.rs

@@ -17,6 +17,8 @@ pub struct ProvidersConfig {
     pub databricks: Option<DatabricksConfig>,
     pub embedded: Option<EmbeddedConfig>,
     pub default_provider: String,
+    pub coach: Option<String>,  // Provider to use for coach in autonomous mode
+    pub player: Option<String>, // Provider to use for player in autonomous mode
 }
 
 #[derive(Debug, Clone, Serialize, Deserialize)]
@@ -112,6 +114,8 @@ impl Default for Config {
                 }),
                 embedded: None,
                 default_provider: "databricks".to_string(),
+                coach: None,  // Will use default_provider if not specified
+                player: None, // Will use default_provider if not specified
             },
             agent: AgentConfig {
                 max_context_length: 8192,
@@ -224,6 +228,8 @@ impl Config {
                     threads: Some(8),
                 }),
                 default_provider: "embedded".to_string(),
+                coach: None,  // Will use default_provider if not specified
+                player: None, // Will use default_provider if not specified
             },
             agent: AgentConfig {
                 max_context_length: 8192,
@@ -300,4 +306,67 @@ impl Config {
         
         Ok(config)
     }
+    
+    /// Get the provider to use for coach mode in autonomous execution
+    pub fn get_coach_provider(&self) -> &str {
+        self.providers.coach
+            .as_deref()
+            .unwrap_or(&self.providers.default_provider)
     }
+    
+    /// Get the provider to use for player mode in autonomous execution
+    pub fn get_player_provider(&self) -> &str {
+        self.providers.player
+            .as_deref()
+            .unwrap_or(&self.providers.default_provider)
+    }
+    
+    /// Create a copy of the config with a different default provider
+    pub fn with_provider_override(&self, provider: &str) -> Result<Self> {
+        // Validate that the provider is configured
+        match provider {
+            "anthropic" if self.providers.anthropic.is_none() => {
+                return Err(anyhow::anyhow!(
+                    "Provider '{}' is specified but not configured. Please add {} configuration to your config file.",
+                    provider, provider
+                ));
+            }
+            "databricks" if self.providers.databricks.is_none() => {
+                return Err(anyhow::anyhow!(
+                    "Provider '{}' is specified but not configured. Please add {} configuration to your config file.",
+                    provider, provider
+                ));
+            }
+            "embedded" if self.providers.embedded.is_none() => {
+                return Err(anyhow::anyhow!(
+                    "Provider '{}' is specified but not configured. Please add {} configuration to your config file.",
+                    provider, provider
+                ));
+            }
+            "openai" if self.providers.openai.is_none() => {
+                return Err(anyhow::anyhow!(
+                    "Provider '{}' is specified but not configured. Please add {} configuration to your config file.",
+                    provider, provider
+                ));
+            }
+            _ => {} // Provider is configured or unknown (will be caught later)
+        }
+        
+        let mut config = self.clone();
+        config.providers.default_provider = provider.to_string();
+        Ok(config)
+    }
+    
+    /// Create a copy of the config for coach mode in autonomous execution
+    pub fn for_coach(&self) -> Result<Self> {
+        self.with_provider_override(self.get_coach_provider())
+    }
+    
+    /// Create a copy of the config for player mode in autonomous execution
+    pub fn for_player(&self) -> Result<Self> {
+        self.with_provider_override(self.get_player_provider())
+    }
+}
+
+#[cfg(test)]
+mod tests;

crates/g3-config/src/tests.rs

@@ -0,0 +1,131 @@
+#[cfg(test)]
+mod tests {
+    use crate::Config;
+    use std::fs;
+    use tempfile::TempDir;
+
+    #[test]
+    fn test_coach_player_providers() {
+        // Create a temporary directory for the test config
+        let temp_dir = TempDir::new().unwrap();
+        let config_path = temp_dir.path().join("test_config.toml");
+        
+        // Write a test configuration with coach and player providers
+        let config_content = r#"
+[providers]
+default_provider = "databricks"
+coach = "anthropic"
+player = "embedded"
+
+[providers.databricks]
+host = "https://test.databricks.com"
+token = "test-token"
+model = "test-model"
+
+[providers.anthropic]
+api_key = "test-key"
+model = "claude-3"
+
+[providers.embedded]
+model_path = "test.gguf"
+model_type = "llama"
+
+[agent]
+max_context_length = 8192
+enable_streaming = true
+timeout_seconds = 60
+"#;
+        
+        fs::write(&config_path, config_content).unwrap();
+        
+        // Load the configuration
+        let config = Config::load(Some(config_path.to_str().unwrap())).unwrap();
+        
+        // Test that the providers are correctly identified
+        assert_eq!(config.providers.default_provider, "databricks");
+        assert_eq!(config.get_coach_provider(), "anthropic");
+        assert_eq!(config.get_player_provider(), "embedded");
+        
+        // Test creating coach config
+        let coach_config = config.for_coach().unwrap();
+        assert_eq!(coach_config.providers.default_provider, "anthropic");
+        
+        // Test creating player config
+        let player_config = config.for_player().unwrap();
+        assert_eq!(player_config.providers.default_provider, "embedded");
+    }
+    
+    #[test]
+    fn test_coach_player_fallback_to_default() {
+        // Create a temporary directory for the test config
+        let temp_dir = TempDir::new().unwrap();
+        let config_path = temp_dir.path().join("test_config.toml");
+        
+        // Write a test configuration WITHOUT coach and player providers
+        let config_content = r#"
+[providers]
+default_provider = "databricks"
+
+[providers.databricks]
+host = "https://test.databricks.com"
+token = "test-token"
+model = "test-model"
+
+[agent]
+max_context_length = 8192
+enable_streaming = true
+timeout_seconds = 60
+"#;
+        
+        fs::write(&config_path, config_content).unwrap();
+        
+        // Load the configuration
+        let config = Config::load(Some(config_path.to_str().unwrap())).unwrap();
+        
+        // Test that coach and player fall back to default provider
+        assert_eq!(config.get_coach_provider(), "databricks");
+        assert_eq!(config.get_player_provider(), "databricks");
+        
+        // Test creating coach config (should use default)
+        let coach_config = config.for_coach().unwrap();
+        assert_eq!(coach_config.providers.default_provider, "databricks");
+        
+        // Test creating player config (should use default)
+        let player_config = config.for_player().unwrap();
+        assert_eq!(player_config.providers.default_provider, "databricks");
+    }
+    
+    #[test]
+    fn test_invalid_provider_error() {
+        // Create a temporary directory for the test config
+        let temp_dir = TempDir::new().unwrap();
+        let config_path = temp_dir.path().join("test_config.toml");
+        
+        // Write a test configuration with an unconfigured provider
+        let config_content = r#"
+[providers]
+default_provider = "databricks"
+coach = "openai"  # OpenAI is not configured
+
+[providers.databricks]
+host = "https://test.databricks.com"
+token = "test-token"
+model = "test-model"
+
+[agent]
+max_context_length = 8192
+enable_streaming = true
+timeout_seconds = 60
+"#;
+        
+        fs::write(&config_path, config_content).unwrap();
+        
+        // Load the configuration
+        let config = Config::load(Some(config_path.to_str().unwrap())).unwrap();
+        
+        // Test that trying to create a coach config with unconfigured provider fails
+        let result = config.for_coach();
+        assert!(result.is_err());
+        assert!(result.unwrap_err().to_string().contains("not configured"));
+    }
+}

crates/g3-core/src/lib.rs

@@ -625,13 +625,32 @@ impl<W: UiWriter> Agent<W> {
     ) -> Result<Self> {
         let mut providers = ProviderRegistry::new();
 
+        // In autonomous mode, we need to register both coach and player providers
+        // Otherwise, only register the default provider
+        let providers_to_register: Vec<String> = if is_autonomous {
+            let mut providers = vec![config.providers.default_provider.clone()];
+            if let Some(coach) = &config.providers.coach {
+                if !providers.contains(coach) {
+                    providers.push(coach.clone());
+                }
+            }
+            if let Some(player) = &config.providers.player {
+                if !providers.contains(player) {
+                    providers.push(player.clone());
+                }
+            }
+            providers
+        } else {
+            vec![config.providers.default_provider.clone()]
+        };
+
         // Only register providers that are configured AND selected as the default provider
         // This prevents unnecessary initialization of heavy providers like embedded models
 
         // Register embedded provider if configured AND it's the default provider
         if let Some(embedded_config) = &config.providers.embedded {
-            if config.providers.default_provider == "embedded" {
+            if providers_to_register.contains(&"embedded".to_string()) {
-                info!("Initializing embedded provider (selected as default)");
+                info!("Initializing embedded provider");
                 let embedded_provider = g3_providers::EmbeddedProvider::new(
                     embedded_config.model_path.clone(),
                     embedded_config.model_type.clone(),
@@ -643,14 +662,31 @@ impl<W: UiWriter> Agent<W> {
                 )?;
                 providers.register(embedded_provider);
             } else {
-                info!("Embedded provider configured but not selected as default, skipping initialization");
+                info!("Embedded provider configured but not needed, skipping initialization");
+            }
+        }
+
+        // Register OpenAI provider if configured AND it's the default provider
+        if let Some(openai_config) = &config.providers.openai {
+            if providers_to_register.contains(&"openai".to_string()) {
+                info!("Initializing OpenAI provider");
+                let openai_provider = g3_providers::OpenAIProvider::new(
+                    openai_config.api_key.clone(),
+                    Some(openai_config.model.clone()),
+                    openai_config.base_url.clone(),
+                    openai_config.max_tokens,
+                    openai_config.temperature,
+                )?;
+                providers.register(openai_provider);
+            } else {
+                info!("OpenAI provider configured but not needed, skipping initialization");
             }
         }
 
         // Register Anthropic provider if configured AND it's the default provider
         if let Some(anthropic_config) = &config.providers.anthropic {
-            if config.providers.default_provider == "anthropic" {
+            if providers_to_register.contains(&"anthropic".to_string()) {
-                info!("Initializing Anthropic provider (selected as default)");
+                info!("Initializing Anthropic provider");
                 let anthropic_provider = g3_providers::AnthropicProvider::new(
                     anthropic_config.api_key.clone(),
                     Some(anthropic_config.model.clone()),
@@ -659,14 +695,14 @@ impl<W: UiWriter> Agent<W> {
                 )?;
                 providers.register(anthropic_provider);
             } else {
-                info!("Anthropic provider configured but not selected as default, skipping initialization");
+                info!("Anthropic provider configured but not needed, skipping initialization");
             }
         }
 
         // Register Databricks provider if configured AND it's the default provider
         if let Some(databricks_config) = &config.providers.databricks {
-            if config.providers.default_provider == "databricks" {
+            if providers_to_register.contains(&"databricks".to_string()) {
-                info!("Initializing Databricks provider (selected as default)");
+                info!("Initializing Databricks provider");
 
                 let databricks_provider = if let Some(token) = &databricks_config.token {
                     // Use token-based authentication
@@ -690,7 +726,7 @@ impl<W: UiWriter> Agent<W> {
 
                 providers.register(databricks_provider);
             } else {
-                info!("Databricks provider configured but not selected as default, skipping initialization");
+                info!("Databricks provider configured but not needed, skipping initialization");
             }
         }
 
@@ -773,6 +809,9 @@ impl<W: UiWriter> Agent<W> {
                     config.agent.max_context_length as u32
                 }
             }
+            "openai" => {
+                192000
+            }
             "anthropic" => {
                 // Claude models have large context windows
                 200000 // Default for Claude models
@@ -1060,7 +1099,6 @@ Template:
         };
 
         // Get max_tokens from provider configuration
-        // For Databricks, this should be much higher to support large file generation
         let max_tokens = match provider.name() {
             "databricks" => {
                 // Use the model's maximum limit for Databricks to allow large file generation

crates/g3-providers/src/anthropic.rs

@@ -156,8 +156,9 @@ impl AnthropicProvider {
             .post(ANTHROPIC_API_URL)
             .header("x-api-key", &self.api_key)
             .header("anthropic-version", ANTHROPIC_VERSION)
+            // Anthropic beta 1m context window. Enable if needed. It costs extra, so check first.
+            // .header("anthropic-beta", "context-1m-2025-08-07")
             .header("content-type", "application/json");
-
         if streaming {
             builder = builder.header("accept", "text/event-stream");
         }

crates/g3-providers/src/lib.rs

@@ -88,10 +88,12 @@ pub mod anthropic;
 pub mod databricks;
 pub mod embedded;
 pub mod oauth;
+pub mod openai;
 
 pub use anthropic::AnthropicProvider;
 pub use databricks::DatabricksProvider;
 pub use embedded::EmbeddedProvider;
+pub use openai::OpenAIProvider;
 
 /// Provider registry for managing multiple LLM providers
 pub struct ProviderRegistry {

crates/g3-providers/src/openai.rs

@@ -0,0 +1,495 @@
+use anyhow::Result;
+use async_trait::async_trait;
+use bytes::Bytes;
+use futures_util::stream::StreamExt;
+use reqwest::Client;
+use serde::Deserialize;
+use serde_json::json;
+use tokio::sync::mpsc;
+use tokio_stream::wrappers::ReceiverStream;
+use tracing::{debug, error};
+
+use crate::{
+    CompletionChunk, CompletionRequest, CompletionResponse, CompletionStream, LLMProvider,
+    Message, MessageRole, Tool, ToolCall, Usage,
+};
+
+#[derive(Clone)]
+pub struct OpenAIProvider {
+    client: Client,
+    api_key: String,
+    model: String,
+    base_url: String,
+    max_tokens: Option<u32>,
+    _temperature: Option<f32>,
+}
+
+impl OpenAIProvider {
+    pub fn new(
+        api_key: String,
+        model: Option<String>,
+        base_url: Option<String>,
+        max_tokens: Option<u32>,
+        temperature: Option<f32>,
+    ) -> Result<Self> {
+        Ok(Self {
+            client: Client::new(),
+            api_key,
+            model: model.unwrap_or_else(|| "gpt-4o".to_string()),
+            base_url: base_url.unwrap_or_else(|| "https://api.openai.com/v1".to_string()),
+            max_tokens,
+            _temperature: temperature,
+        })
+    }
+
+    fn create_request_body(
+        &self,
+        messages: &[Message],
+        tools: Option<&[Tool]>,
+        stream: bool,
+        max_tokens: Option<u32>,
+        _temperature: Option<f32>,
+    ) -> serde_json::Value {
+        let mut body = json!({
+            "model": self.model,
+            "messages": convert_messages(messages),
+            "stream": stream,
+        });
+
+        if let Some(max_tokens) = max_tokens.or(self.max_tokens) {
+            body["max_completion_tokens"] = json!(max_tokens);
+        }
+
+        // OpenAI calls with temp setting seem to fail, so don't send one.
+        // if let Some(temperature) = temperature.or(self.temperature) {
+        //     body["temperature"] = json!(temperature);
+        // }
+
+        if let Some(tools) = tools {
+            if !tools.is_empty() {
+                body["tools"] = json!(convert_tools(tools));
+            }
+        }
+
+        if stream {
+            body["stream_options"] = json!({
+                "include_usage": true,
+            });
+        }
+
+        body
+    }
+
+    async fn parse_streaming_response(
+        &self,
+        mut stream: impl futures_util::Stream<Item = reqwest::Result<Bytes>> + Unpin,
+        tx: mpsc::Sender<Result<CompletionChunk>>,
+    ) -> Option<Usage> {
+        let mut buffer = String::new();
+        let mut accumulated_content = String::new();
+        let mut accumulated_usage: Option<Usage> = None;
+        let mut current_tool_calls: Vec<OpenAIStreamingToolCall> = Vec::new();
+
+        while let Some(chunk_result) = stream.next().await {
+            match chunk_result {
+                Ok(chunk) => {
+                    let chunk_str = match std::str::from_utf8(&chunk) {
+                        Ok(s) => s,
+                        Err(e) => {
+                            error!("Failed to parse chunk as UTF-8: {}", e);
+                            continue;
+                        }
+                    };
+
+                    buffer.push_str(chunk_str);
+
+                    // Process complete lines
+                    while let Some(line_end) = buffer.find('\n') {
+                        let line = buffer[..line_end].trim().to_string();
+                        buffer.drain(..line_end + 1);
+
+                        if line.is_empty() {
+                            continue;
+                        }
+
+                        // Parse Server-Sent Events format
+                        if let Some(data) = line.strip_prefix("data: ") {
+                            if data == "[DONE]" {
+                                debug!("Received stream completion marker");
+
+                                // Send final chunk with accumulated content and tool calls
+                                if !accumulated_content.is_empty() || !current_tool_calls.is_empty() {
+                                    let tool_calls = if current_tool_calls.is_empty() {
+                                        None
+                                    } else {
+                                        Some(
+                                            current_tool_calls
+                                                .iter()
+                                                .filter_map(|tc| tc.to_tool_call())
+                                                .collect(),
+                                        )
+                                    };
+
+                                    let final_chunk = CompletionChunk {
+                                        content: accumulated_content.clone(),
+                                        finished: true,
+                                        tool_calls,
+                                        usage: accumulated_usage.clone(),
+                                    };
+                                    let _ = tx.send(Ok(final_chunk)).await;
+                                }
+
+                                return accumulated_usage;
+                            }
+
+                            // Parse the JSON data
+                            match serde_json::from_str::<OpenAIStreamChunk>(data) {
+                                Ok(chunk_data) => {
+                                    // Handle content
+                                    for choice in &chunk_data.choices {
+                                        if let Some(content) = &choice.delta.content {
+                                            accumulated_content.push_str(content);
+
+                                            let chunk = CompletionChunk {
+                                                content: content.clone(),
+                                                finished: false,
+                                                tool_calls: None,
+                                                usage: None,
+                                            };
+                                            if tx.send(Ok(chunk)).await.is_err() {
+                                                debug!("Receiver dropped, stopping stream");
+                                                return accumulated_usage;
+                                            }
+                                        }
+
+                                        // Handle tool calls
+                                        if let Some(delta_tool_calls) = &choice.delta.tool_calls {
+                                            for delta_tool_call in delta_tool_calls {
+                                                if let Some(index) = delta_tool_call.index {
+                                                    // Ensure we have enough tool calls in our vector
+                                                    while current_tool_calls.len() <= index {
+                                                        current_tool_calls
+                                                            .push(OpenAIStreamingToolCall::default());
+                                                    }
+
+                                                    let tool_call = &mut current_tool_calls[index];
+
+                                                    if let Some(id) = &delta_tool_call.id {
+                                                        tool_call.id = Some(id.clone());
+                                                    }
+
+                                                    if let Some(function) = &delta_tool_call.function {
+                                                        if let Some(name) = &function.name {
+                                                            tool_call.name = Some(name.clone());
+                                                        }
+                                                        if let Some(arguments) = &function.arguments {
+                                                            tool_call.arguments.push_str(arguments);
+                                                        }
+                                                    }
+                                                }
+                                            }
+                                        }
+                                    }
+
+                                    // Handle usage
+                                    if let Some(usage) = chunk_data.usage {
+                                        accumulated_usage = Some(Usage {
+                                            prompt_tokens: usage.prompt_tokens,
+                                            completion_tokens: usage.completion_tokens,
+                                            total_tokens: usage.total_tokens,
+                                        });
+                                    }
+                                }
+                                Err(e) => {
+                                    debug!("Failed to parse stream chunk: {} - Data: {}", e, data);
+                                }
+                            }
+                        }
+                    }
+                }
+                Err(e) => {
+                    error!("Stream error: {}", e);
+                    let _ = tx.send(Err(anyhow::anyhow!("Stream error: {}", e))).await;
+                    return accumulated_usage;
+                }
+            }
+        }
+
+        // Send final chunk if we haven't already
+        let tool_calls = if current_tool_calls.is_empty() {
+            None
+        } else {
+            Some(
+                current_tool_calls
+                    .iter()
+                    .filter_map(|tc| tc.to_tool_call())
+                    .collect(),
+            )
+        };
+        
+        let final_chunk = CompletionChunk {
+            content: String::new(),
+            finished: true,
+            tool_calls,
+            usage: accumulated_usage.clone(),
+        };
+        let _ = tx.send(Ok(final_chunk)).await;
+        
+        accumulated_usage
+    }
+}
+
+#[async_trait]
+impl LLMProvider for OpenAIProvider {
+    async fn complete(&self, request: CompletionRequest) -> Result<CompletionResponse> {
+        debug!(
+            "Processing OpenAI completion request with {} messages",
+            request.messages.len()
+        );
+
+        let body = self.create_request_body(
+            &request.messages,
+            request.tools.as_deref(),
+            false,
+            request.max_tokens,
+            request.temperature,
+        );
+
+        debug!("Sending request to OpenAI API: model={}", self.model);
+
+        let response = self
+            .client
+            .post(&format!("{}/chat/completions", self.base_url))
+            .header("Authorization", format!("Bearer {}", self.api_key))
+            .json(&body)
+            .send()
+            .await?;
+
+        let status = response.status();
+        if !status.is_success() {
+            let error_text = response
+                .text()
+                .await
+                .unwrap_or_else(|_| "Unknown error".to_string());
+            return Err(anyhow::anyhow!("OpenAI API error {}: {}", status, error_text));
+        }
+
+        let openai_response: OpenAIResponse = response.json().await?;
+
+        let content = openai_response
+            .choices
+            .first()
+            .and_then(|choice| choice.message.content.clone())
+            .unwrap_or_default();
+
+        let usage = Usage {
+            prompt_tokens: openai_response.usage.prompt_tokens,
+            completion_tokens: openai_response.usage.completion_tokens,
+            total_tokens: openai_response.usage.total_tokens,
+        };
+
+        debug!(
+            "OpenAI completion successful: {} tokens generated",
+            usage.completion_tokens
+        );
+
+        Ok(CompletionResponse {
+            content,
+            usage,
+            model: self.model.clone(),
+        })
+    }
+
+    async fn stream(&self, request: CompletionRequest) -> Result<CompletionStream> {
+        debug!(
+            "Processing OpenAI streaming request with {} messages",
+            request.messages.len()
+        );
+
+        let body = self.create_request_body(
+            &request.messages,
+            request.tools.as_deref(),
+            true,
+            request.max_tokens,
+            request.temperature,
+        );
+
+        debug!("Sending streaming request to OpenAI API: model={}", self.model);
+
+        let response = self
+            .client
+            .post(&format!("{}/chat/completions", self.base_url))
+            .header("Authorization", format!("Bearer {}", self.api_key))
+            .json(&body)
+            .send()
+            .await?;
+
+        let status = response.status();
+        if !status.is_success() {
+            let error_text = response
+                .text()
+                .await
+                .unwrap_or_else(|_| "Unknown error".to_string());
+            return Err(anyhow::anyhow!("OpenAI API error {}: {}", status, error_text));
+        }
+
+        let stream = response.bytes_stream();
+        let (tx, rx) = mpsc::channel(100);
+
+        // Spawn task to process the stream
+        let provider = self.clone();
+        tokio::spawn(async move {
+            let usage = provider.parse_streaming_response(stream, tx).await;
+            // Log the final usage if available
+            if let Some(usage) = usage {
+                debug!(
+                    "Stream completed with usage - prompt: {}, completion: {}, total: {}",
+                    usage.prompt_tokens, usage.completion_tokens, usage.total_tokens
+                );
+            }
+        });
+
+        Ok(ReceiverStream::new(rx))
+    }
+
+    fn name(&self) -> &str {
+        "openai"
+    }
+
+    fn model(&self) -> &str {
+        &self.model
+    }
+
+    fn has_native_tool_calling(&self) -> bool {
+        // OpenAI models support native tool calling
+        true
+    }
+}
+
+fn convert_messages(messages: &[Message]) -> Vec<serde_json::Value> {
+    messages
+        .iter()
+        .map(|msg| {
+            json!({
+                "role": match msg.role {
+                    MessageRole::System => "system",
+                    MessageRole::User => "user",
+                    MessageRole::Assistant => "assistant",
+                },
+                "content": msg.content,
+            })
+        })
+        .collect()
+}
+
+fn convert_tools(tools: &[Tool]) -> Vec<serde_json::Value> {
+    tools
+        .iter()
+        .map(|tool| {
+            json!({
+                "type": "function",
+                "function": {
+                    "name": tool.name,
+                    "description": tool.description,
+                    "parameters": tool.input_schema,
+                }
+            })
+        })
+        .collect()
+}
+
+// OpenAI API response structures
+#[derive(Debug, Deserialize)]
+struct OpenAIResponse {
+    choices: Vec<OpenAIChoice>,
+    usage: OpenAIUsage,
+}
+
+#[derive(Debug, Deserialize)]
+struct OpenAIChoice {
+    message: OpenAIMessage,
+}
+
+#[allow(dead_code)]
+#[derive(Debug, Deserialize)]
+struct OpenAIMessage {
+    content: Option<String>,
+    #[serde(default)]
+    tool_calls: Option<Vec<OpenAIToolCall>>,
+}
+
+#[allow(dead_code)]
+#[derive(Debug, Deserialize)]
+struct OpenAIToolCall {
+    id: String,
+    function: OpenAIFunction,
+}
+
+#[allow(dead_code)]
+#[derive(Debug, Deserialize)]
+struct OpenAIFunction {
+    name: String,
+    arguments: String,
+}
+
+// Streaming tool call accumulator
+#[derive(Debug, Default)]
+struct OpenAIStreamingToolCall {
+    id: Option<String>,
+    name: Option<String>,
+    arguments: String,
+}
+
+impl OpenAIStreamingToolCall {
+    fn to_tool_call(&self) -> Option<ToolCall> {
+        let id = self.id.as_ref()?;
+        let name = self.name.as_ref()?;
+        
+        let args = serde_json::from_str(&self.arguments).unwrap_or(serde_json::Value::Null);
+        
+        Some(ToolCall {
+            id: id.clone(),
+            tool: name.clone(),
+            args,
+        })
+    }
+}
+
+#[derive(Debug, Deserialize)]
+struct OpenAIUsage {
+    prompt_tokens: u32,
+    completion_tokens: u32,
+    total_tokens: u32,
+}
+
+// Streaming response structures
+#[derive(Debug, Deserialize)]
+struct OpenAIStreamChunk {
+    choices: Vec<OpenAIStreamChoice>,
+    usage: Option<OpenAIUsage>,
+}
+
+#[derive(Debug, Deserialize)]
+struct OpenAIStreamChoice {
+    delta: OpenAIDelta,
+}
+
+#[derive(Debug, Deserialize)]
+struct OpenAIDelta {
+    content: Option<String>,
+    #[serde(default)]
+    tool_calls: Option<Vec<OpenAIDeltaToolCall>>,
+}
+
+#[derive(Debug, Deserialize)]
+struct OpenAIDeltaToolCall {
+    index: Option<usize>,
+    id: Option<String>,
+    function: Option<OpenAIDeltaFunction>,
+}
+
+#[derive(Debug, Deserialize)]
+struct OpenAIDeltaFunction {
+    name: Option<String>,
+    arguments: Option<String>,
+}

docs/coach-player-providers.md

@@ -0,0 +1,75 @@
+# 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