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

* main:
  control commands for machine mode
  Fix duplicate dump at end
  minor
  --machine mode flag for verbose CLI output
  fixed x,y detection in vision click
  screenshotting bug fix
  test
  Native api for screen capture
  replace tesseract with apple vision
  more macax tooling
  coach rigor +++
  thinning message highlighted
  warnings fix
  macax tools
  control commands
  Add --interactive-requirements flag for AI-enhanced requirements mode

.cargo/config.toml

@@ -1,5 +0,0 @@
-[target.aarch64-apple-darwin]
-rustflags = ["-C", "link-args=-Wl,-rpath,@executable_path"]
-
-[target.x86_64-apple-darwin]
-rustflags = ["-C", "link-args=-Wl,-rpath,@executable_path"]

README.md

@@ -136,12 +136,8 @@ G3 is designed for:
 # Build the project
 cargo build --release
 
-# Run from the build directory
+# Run G3
-./target/release/g3
+cargo run
-
-# Or copy both files to somewhere in your PATH (macOS only needs both files)
-cp target/release/g3 ~/.local/bin/
-cp target/release/libVisionBridge.dylib ~/.local/bin/  # macOS only
 
 # Execute a task
 g3 "implement a function to calculate fibonacci numbers"

crates/g3-core/src/lib.rs

@@ -325,10 +325,19 @@ impl ContextWindow {
 
     /// Update token usage from provider response
     pub fn update_usage_from_response(&mut self, usage: &g3_providers::Usage) {
-        // Add the tokens from this response to our running total
+        // Always use the provider's count as the authoritative value
-        // The usage.total_tokens represents tokens used in this single API call
+        // The provider knows best how many tokens were actually used
-        self.used_tokens += usage.total_tokens;
+        
-        self.cumulative_tokens += usage.total_tokens;
+        let old_used = self.used_tokens;
+        
+        // Use the provider's total as the current used tokens
+        self.used_tokens = usage.total_tokens;
+        self.cumulative_tokens += usage.total_tokens - old_used;
+        
+        info!(
+            "Updated token usage from provider - was: {}, now: {} (prompt={}, completion={}, total={})",
+            old_used, self.used_tokens, usage.prompt_tokens, usage.completion_tokens, usage.total_tokens
+        );
 
         debug!(
             "Added {} tokens from provider response (used: {}/{}, cumulative: {})",
@@ -445,8 +454,18 @@ Format this as a detailed but concise summary that can be used to resume the con
         if current_percentage >= 50 {
             let current_threshold = (current_percentage / 10) * 10; // Round down to nearest 10%
             if current_threshold > self.last_thinning_percentage && current_threshold <= 80 {
+                info!(
+                    "Context thinning triggered - usage: {}% ({}/{} tokens), threshold: {}%, last thinned at: {}%",
+                    current_percentage,
+                    self.used_tokens,
+                    self.total_tokens,
+                    current_threshold,
+                    self.last_thinning_percentage
+                );
                 return true;
             }
+        } else {
+            debug!("Context usage at {}% ({}/{} tokens) - no thinning needed", current_percentage, self.used_tokens, self.total_tokens);
         }
         
         false
@@ -1444,13 +1463,6 @@ Template:
                 let available = model_limit
                     .saturating_sub(current_usage)
                     .saturating_sub(5000);
-                // Ensure we have at least 1 token available, otherwise we can't generate a summary
-                if available == 0 {
-                    return Err(anyhow::anyhow!(
-                        "Insufficient tokens available for summary generation. Current usage: {} tokens, model limit: {} tokens. Please start a new session.",
-                        current_usage, model_limit
-                    ));
-                }
                 Some(available.min(10_000))
             }
             "embedded" => {
@@ -1459,24 +1471,10 @@ Template:
                 let available = model_limit
                     .saturating_sub(current_usage)
                     .saturating_sub(1000);
-                // Ensure we have at least 1 token available
-                if available == 0 {
-                    return Err(anyhow::anyhow!(
-                        "Insufficient tokens available for summary generation. Current usage: {} tokens, model limit: {} tokens. Please start a new session.",
-                        current_usage, model_limit
-                    ));
-                }
                 Some(available.min(3000))
             }
             _ => {
                 let available = self.context_window.remaining_tokens().saturating_sub(2000);
-                // Ensure we have at least 1 token available
-                if available == 0 {
-                    return Err(anyhow::anyhow!(
-                        "Insufficient tokens available for summary generation. Current usage: {} tokens. Please start a new session.",
-                        self.context_window.used_tokens
-                    ));
-                }
                 Some(available.min(5000))
             }
         };
@@ -2368,13 +2366,6 @@ Template:
                     let available = model_limit
                         .saturating_sub(current_usage)
                         .saturating_sub(5000);
-                    // Ensure we have at least 1 token available, otherwise we can't generate a summary
-                    if available == 0 {
-                        return Err(anyhow::anyhow!(
-                            "Insufficient tokens available for summary generation. Current usage: {} tokens, model limit: {} tokens. Please start a new session.",
-                            current_usage, model_limit
-                        ));
-                    }
                     // Cap at a reasonable summary size (10k tokens max)
                     Some(available.min(10_000))
                 }
@@ -2386,26 +2377,12 @@ Template:
                     let available = model_limit
                         .saturating_sub(current_usage)
                         .saturating_sub(1000);
-                    // Ensure we have at least 1 token available
-                    if available == 0 {
-                        return Err(anyhow::anyhow!(
-                            "Insufficient tokens available for summary generation. Current usage: {} tokens, model limit: {} tokens. Please start a new session.",
-                            current_usage, model_limit
-                        ));
-                    }
                     // Cap at 3k for embedded models
                     Some(available.min(3000))
                 }
                 _ => {
                     // Default: conservative approach
                     let available = self.context_window.remaining_tokens().saturating_sub(2000);
-                    // Ensure we have at least 1 token available
-                    if available == 0 {
-                        return Err(anyhow::anyhow!(
-                            "Insufficient tokens available for summary generation. Current usage: {} tokens. Please start a new session.",
-                            self.context_window.used_tokens
-                        ));
-                    }
                     Some(available.min(5000))
                 }
             };
@@ -2717,7 +2694,12 @@ Template:
 
                             // Display tool execution result with proper indentation
                             if tool_call.tool != "final_output" {
-                                let output_lines: Vec<&str> = tool_result.lines().collect();
+                                // Skip displaying output for shell tool since it was already streamed
+                                let should_display_output = tool_call.tool != "shell";
+                                
+                                let output_lines: Vec<&str> = if should_display_output {
+                                    tool_result.lines().collect()
+                                } else { vec![] };
 
                                 // Check if UI wants full output (machine mode) or truncated (human mode)
                                 let wants_full = self.ui_writer.wants_full_output();
@@ -3229,13 +3211,16 @@ Template:
                         {
                             Ok(result) => {
                                 if result.success {
-                                    Ok(if result.stdout.is_empty() {
+                                    // Don't return stdout - it was already streamed to the UI
-                                        "✅ Command executed successfully".to_string()
+                                    // Returning it would cause duplicate output
+                                    Ok("✅ Command executed successfully".to_string())
                                 } else {
-                                        result.stdout.trim().to_string()
+                                    // For errors, return stderr since it wasn't streamed
+                                    Ok(if result.stderr.is_empty() {
+                                        "❌ Command failed".to_string()
+                                    } else {
+                                        format!("❌ Command failed: {}", result.stderr.trim())
                                     })
-                                } else {
-                                    Ok(format!("❌ Command failed: {}", result.stderr.trim()))
                                 }
                             }
                             Err(e) => Ok(format!("❌ Execution error: {}", e)),

crates/g3-providers/src/anthropic.rs

@@ -276,6 +276,7 @@ impl AnthropicProvider {
         let mut partial_tool_json = String::new(); // Accumulate partial JSON for tool calls
         let mut accumulated_usage: Option<Usage> = None;
         let mut byte_buffer = Vec::new(); // Buffer for incomplete UTF-8 sequences
+        let mut actual_completion_tokens: u32 = 0; // Track actual completion tokens
         
         while let Some(chunk_result) = stream.next().await {
             match chunk_result {
@@ -323,7 +324,12 @@ impl AnthropicProvider {
                                 let final_chunk = CompletionChunk {
                                     content: String::new(),
                                     finished: true,
-                                    usage: accumulated_usage.clone(),
+                                    usage: accumulated_usage.as_ref().map(|u| Usage {
+                                        prompt_tokens: u.prompt_tokens,
+                                        // Use actual completion tokens if we tracked them, otherwise use the estimate
+                                        completion_tokens: if actual_completion_tokens > 0 { actual_completion_tokens } else { u.completion_tokens },
+                                        total_tokens: u.prompt_tokens + if actual_completion_tokens > 0 { actual_completion_tokens } else { u.completion_tokens },
+                                    }),
                                     tool_calls: if current_tool_calls.is_empty() { None } else { Some(current_tool_calls.clone()) },
                                 };
                                 if tx.send(Ok(final_chunk)).await.is_err() {
@@ -337,6 +343,7 @@ impl AnthropicProvider {
                             match serde_json::from_str::<AnthropicStreamEvent>(data) {
                                 Ok(event) => {
                                     debug!("Parsed event type: {}, event: {:?}", event.event_type, event);
+                                    
                                     match event.event_type.as_str() {
                                         "message_start" => {
                                             // Extract usage data from message_start event
@@ -347,7 +354,10 @@ impl AnthropicProvider {
                                                         completion_tokens: usage.output_tokens,
                                                         total_tokens: usage.input_tokens + usage.output_tokens,
                                                     });
-                                                    debug!("Captured usage from message_start: {:?}", accumulated_usage);
+                                                    debug!("Captured initial usage from message_start - prompt: {}, completion: {} (estimated), total: {}",
+                                                        usage.input_tokens,
+                                                        usage.output_tokens,
+                                                        usage.input_tokens + usage.output_tokens);
                                                 }
                                             }
                                         }
@@ -396,6 +406,9 @@ impl AnthropicProvider {
                                         "content_block_delta" => {
                                             if let Some(delta) = event.delta {
                                                 if let Some(text) = delta.text {
+                                                    // Track actual completion tokens (rough estimate: 4 chars per token)
+                                                    actual_completion_tokens += (text.len() as f32 / 4.0).ceil() as u32;
+                                                    
                                                     debug!("Sending text chunk of length {}: '{}'", text.len(), text);
                                                     let chunk = CompletionChunk {
                                                         content: text,
@@ -416,6 +429,19 @@ impl AnthropicProvider {
                                                 }
                                             }
                                         }
+                                        "message_delta" => {
+                                            // Check if message_delta contains updated usage data
+                                            if let Some(delta) = event.delta {
+                                                if let Some(usage) = delta.usage {
+                                                    accumulated_usage = Some(Usage {
+                                                        prompt_tokens: usage.input_tokens,
+                                                        completion_tokens: usage.output_tokens,
+                                                        total_tokens: usage.input_tokens + usage.output_tokens,
+                                                    });
+                                                    debug!("Updated usage from message_delta - prompt: {}, completion: {}, total: {}", usage.input_tokens, usage.output_tokens, usage.input_tokens + usage.output_tokens);
+                                                }
+                                            }
+                                        }
                                         "content_block_stop" => {
                                             // Tool call block is complete - now parse the accumulated JSON
                                             if !current_tool_calls.is_empty() && !partial_tool_json.is_empty() {
@@ -450,11 +476,44 @@ impl AnthropicProvider {
                                             }
                                         }
                                         "message_stop" => {
-                                            debug!("Received message stop event");
+                                            debug!("Received message_stop event: {:?}", event);
+                                            
+                                            // Check if message_stop contains final usage data
+                                            if let Some(message) = event.message {
+                                                if let Some(usage) = message.usage {
+                                                    // Update with final accurate usage data from message_stop
+                                                    // This should have the actual completion token count
+                                                    accumulated_usage = Some(Usage {
+                                                        prompt_tokens: usage.input_tokens,
+                                                        // Prefer the actual output_tokens from message_stop if available
+                                                        // Otherwise use our tracked count, and as last resort the initial estimate
+                                                        completion_tokens: if usage.output_tokens > 0 {
+                                                            usage.output_tokens
+                                                        } else if actual_completion_tokens > 0 {
+                                                            actual_completion_tokens
+                                                        } else { usage.output_tokens },
+                                                        total_tokens: usage.input_tokens + usage.output_tokens,
+                                                    });
+                                                    debug!("Updated with final usage from message_stop - prompt: {}, completion: {}, total: {}",
+                                                        usage.input_tokens,
+                                                        usage.output_tokens,
+                                                        usage.input_tokens + usage.output_tokens);
+                                                }
+                                            }
+                                            
                                             let final_chunk = CompletionChunk {
                                                 content: String::new(),
                                                 finished: true,
-                                                usage: accumulated_usage.clone(),
+                                                usage: accumulated_usage.as_ref().map(|u| Usage {
+                                                    prompt_tokens: u.prompt_tokens,
+                                                    // Use actual completion tokens if we tracked them and they're higher
+                                                    completion_tokens: if actual_completion_tokens > u.completion_tokens {
+                                                        actual_completion_tokens
+                                                    } else {
+                                                        u.completion_tokens
+                                                    },
+                                                    total_tokens: u.prompt_tokens + u32::max(actual_completion_tokens, u.completion_tokens),
+                                                }),
                                                 tool_calls: if current_tool_calls.is_empty() { None } else { Some(current_tool_calls.clone()) },
                                             };
                                             if tx.send(Ok(final_chunk)).await.is_err() {
@@ -496,10 +555,27 @@ impl AnthropicProvider {
         let final_chunk = CompletionChunk {
             content: String::new(),
             finished: true,
-            usage: accumulated_usage.clone(),
+            usage: accumulated_usage.as_ref().map(|u| Usage {
+                prompt_tokens: u.prompt_tokens,
+                completion_tokens: if actual_completion_tokens > u.completion_tokens {
+                    actual_completion_tokens
+                } else {
+                    u.completion_tokens
+                },
+                total_tokens: u.prompt_tokens + u32::max(actual_completion_tokens, u.completion_tokens),
+            }),
             tool_calls: if current_tool_calls.is_empty() { None } else { Some(current_tool_calls) },
         };
         let _ = tx.send(Ok(final_chunk)).await;
+        
+        // Log final usage for debugging
+        if let Some(ref usage) = accumulated_usage {
+            info!("Anthropic stream completed with final usage - prompt: {}, completion: {}, total: {}",
+                usage.prompt_tokens, usage.completion_tokens, usage.total_tokens);
+        } else {
+            warn!("Anthropic stream completed without usage data - token accounting will fall back to estimation");
+        }
+        
         accumulated_usage
     }
 }
@@ -737,6 +813,8 @@ struct AnthropicStreamMessage {
 struct AnthropicDelta {
     text: Option<String>,
     partial_json: Option<String>,
+    #[serde(default)]
+    usage: Option<AnthropicUsage>,
 }
 
 #[derive(Debug, Deserialize)]

test_token_accounting.py

@@ -0,0 +1,164 @@
+#!/usr/bin/env python3
+"""
+Test script to verify token accounting is working correctly with the Anthropic provider.
+This script will send multiple messages and verify that token counts accumulate properly.
+"""
+
+import subprocess
+import json
+import re
+import sys
+import time
+
+def run_g3_command(prompt, provider="anthropic"):
+    """Run a g3 command and capture the output."""
+    cmd = [
+        "cargo", "run", "--release", "--",
+        "--provider", provider,
+        prompt
+    ]
+    
+    env = {
+        "RUST_LOG": "g3_providers=debug,g3_core=info",
+        "RUST_BACKTRACE": "1"
+    }
+    
+    result = subprocess.run(
+        cmd,
+        capture_output=True,
+        text=True,
+        env={**subprocess.os.environ, **env}
+    )
+    
+    return result.stdout + result.stderr
+
+def extract_token_info(output):
+    """Extract token usage information from the output."""
+    token_info = {}
+    
+    # Look for token usage updates
+    usage_pattern = r"Updated token usage.*was: (\d+), now: (\d+).*prompt=(\d+), completion=(\d+), total=(\d+)"
+    matches = re.findall(usage_pattern, output)
+    if matches:
+        last_match = matches[-1]
+        token_info['was'] = int(last_match[0])
+        token_info['now'] = int(last_match[1])
+        token_info['prompt'] = int(last_match[2])
+        token_info['completion'] = int(last_match[3])
+        token_info['total'] = int(last_match[4])
+    
+    # Look for context percentage
+    context_pattern = r"Context usage at (\d+)%.*\((\d+)/(\d+) tokens\)"
+    matches = re.findall(context_pattern, output)
+    if matches:
+        last_match = matches[-1]
+        token_info['percentage'] = int(last_match[0])
+        token_info['used'] = int(last_match[1])
+        token_info['total_context'] = int(last_match[2])
+    
+    # Look for thinning triggers
+    thinning_pattern = r"Context thinning triggered.*usage: (\d+)%.*\((\d+)/(\d+) tokens\)"
+    matches = re.findall(thinning_pattern, output)
+    if matches:
+        token_info['thinning_triggered'] = True
+        token_info['thinning_percentage'] = int(matches[-1][0])
+    
+    # Look for final usage from Anthropic
+    final_usage_pattern = r"Anthropic stream completed with final usage.*prompt: (\d+), completion: (\d+), total: (\d+)"
+    matches = re.findall(final_usage_pattern, output)
+    if matches:
+        last_match = matches[-1]
+        token_info['final_prompt'] = int(last_match[0])
+        token_info['final_completion'] = int(last_match[1])
+        token_info['final_total'] = int(last_match[2])
+    
+    return token_info
+
+def main():
+    print("Testing Anthropic Provider Token Accounting")
+    print("="*50)
+    
+    # Build the project first
+    print("Building project...")
+    subprocess.run(["cargo", "build", "--release"], capture_output=True)
+    
+    # Test 1: Simple prompt
+    print("\nTest 1: Simple prompt")
+    print("-"*30)
+    output = run_g3_command("Say 'Hello, World!' and nothing else.")
+    tokens = extract_token_info(output)
+    
+    if tokens:
+        print(f"Token usage: {tokens.get('now', 'N/A')} tokens")
+        print(f"  Prompt tokens: {tokens.get('prompt', 'N/A')}")
+        print(f"  Completion tokens: {tokens.get('completion', 'N/A')}")
+        print(f"  Total from provider: {tokens.get('total', 'N/A')}")
+        
+        if 'final_total' in tokens:
+            print(f"  Final total from stream: {tokens['final_total']}")
+            if tokens.get('now') != tokens['final_total']:
+                print(f"  ⚠️  WARNING: Mismatch between tracked ({tokens.get('now')}) and final ({tokens['final_total']})")
+        
+        # Check if the completion tokens are reasonable (should be small for "Hello, World!")
+        if tokens.get('completion', 0) > 50:
+            print(f"  ⚠️  WARNING: Completion tokens seem high for a simple response: {tokens.get('completion')}")
+    else:
+        print("  ❌ No token information found in output")
+    
+    # Test 2: Longer response
+    print("\nTest 2: Longer response")
+    print("-"*30)
+    output = run_g3_command("Write a 3-paragraph essay about the importance of accurate token counting in LLM applications.")
+    tokens = extract_token_info(output)
+    
+    if tokens:
+        print(f"Token usage: {tokens.get('now', 'N/A')} tokens")
+        print(f"  Prompt tokens: {tokens.get('prompt', 'N/A')}")
+        print(f"  Completion tokens: {tokens.get('completion', 'N/A')}")
+        print(f"  Total from provider: {tokens.get('total', 'N/A')}")
+        
+        if 'final_total' in tokens:
+            print(f"  Final total from stream: {tokens['final_total']}")
+            if tokens.get('now') != tokens['final_total']:
+                print(f"  ⚠️  WARNING: Mismatch between tracked ({tokens.get('now')}) and final ({tokens['final_total']})")
+        
+        # Check if completion tokens are reasonable for a longer response
+        if tokens.get('completion', 0) < 100:
+            print(f"  ⚠️  WARNING: Completion tokens seem low for a 3-paragraph essay: {tokens.get('completion')}")
+    else:
+        print("  ❌ No token information found in output")
+    
+    # Test 3: Check for proper accumulation
+    print("\nTest 3: Token accumulation (multiple messages)")
+    print("-"*30)
+    
+    # First message
+    output1 = run_g3_command("Count from 1 to 5.")
+    tokens1 = extract_token_info(output1)
+    
+    # Second message (this would need to be in a conversation, but for now we test separately)
+    output2 = run_g3_command("Now count from 6 to 10.")
+    tokens2 = extract_token_info(output2)
+    
+    if tokens1 and tokens2:
+        print(f"First message: {tokens1.get('now', 'N/A')} tokens")
+        print(f"Second message: {tokens2.get('now', 'N/A')} tokens")
+        
+        # In a real conversation, tokens2['now'] should be greater than tokens1['now']
+        # But since these are separate invocations, we just check they're both reasonable
+        if tokens1.get('now', 0) > 0 and tokens2.get('now', 0) > 0:
+            print("  ✅ Both messages have token counts")
+        else:
+            print("  ❌ Missing token counts")
+    
+    print("\n" + "="*50)
+    print("Test Summary:")
+    print("Check the output above for any warnings or errors.")
+    print("Key things to verify:")
+    print("  1. Token counts are being captured from the provider")
+    print("  2. Completion tokens are reasonable for the response length")
+    print("  3. No mismatch between tracked and final token counts")
+    print("  4. Context thinning triggers at appropriate thresholds")
+
+if __name__ == "__main__":
+    main()

test_token_accounting.sh

@@ -0,0 +1,46 @@
+#!/bin/bash
+
+# Test script to verify token accounting with Anthropic provider
+
+echo "Testing token accounting with Anthropic provider..."
+echo "This test will send a few messages and check if token counts are properly tracked."
+echo ""
+
+# Set up environment for testing
+export RUST_LOG=g3_providers=debug,g3_core=info
+export RUST_BACKTRACE=1
+
+# Build the project first
+echo "Building project..."
+cargo build --release 2>&1 | grep -E "(Compiling|Finished)" || true
+
+echo ""
+echo "Running test with Anthropic provider..."
+echo "Watch for these log messages:"
+echo "  - 'Captured initial usage from message_start'"
+echo "  - 'Updated usage from message_delta' (if available)"
+echo "  - 'Updated with final usage from message_stop' (if available)"
+echo "  - 'Anthropic stream completed with final usage'"
+echo "  - 'Updated token usage from provider'"
+echo "  - 'Context thinning triggered' (when reaching thresholds)"
+echo ""
+
+# Create a simple test that will generate some tokens
+cat << 'EOF' > /tmp/test_prompt.txt
+Please write a short paragraph about the importance of accurate token counting in LLM applications. Then list 3 reasons why token accounting might fail.
+EOF
+
+# Run the test
+echo "Sending test prompt..."
+cargo run --release -- --provider anthropic "$(cat /tmp/test_prompt.txt)" 2>&1 | tee /tmp/token_test.log
+
+echo ""
+echo "Analyzing results..."
+echo ""
+
+# Check for token accounting messages
+echo "Token accounting messages found:"
+grep -E "(usage from|token usage|Context thinning|Context usage)" /tmp/token_test.log | head -20
+
+echo ""
+echo "Test complete. Check /tmp/token_test.log for full output."