|
| 1 | +# HTML Log Viewer for Terminal Bench |
| 2 | + |
| 3 | +This guide explains how to use the HTML log viewer to visualize and analyze agent-LLM interactions during Terminal Bench evaluations. |
| 4 | + |
| 5 | +## Overview |
| 6 | + |
| 7 | +The HTML log viewer provides an interactive way to view agent conversation histories. It consists of two components: |
| 8 | + |
| 9 | +1. **PromptLogger**: Automatically logs all LLM prompts during task execution |
| 10 | +2. **llm_log_to_html.py**: Converts log files to interactive HTML |
| 11 | + |
| 12 | +## Features |
| 13 | + |
| 14 | +- 🎨 **Interactive Visualization**: Collapsible sections for easy navigation |
| 15 | +- 🔍 **Search Functionality**: Quickly find specific messages or content |
| 16 | +- 📊 **Statistics Dashboard**: View total prompts, messages, and iterations |
| 17 | +- 🎨 **Color-Coded Roles**: Different colors for system, user, assistant, and tool messages |
| 18 | +- 📱 **Responsive Design**: Works on desktop and mobile devices |
| 19 | + |
| 20 | +## Installation |
| 21 | + |
| 22 | +No additional dependencies required! Both tools use Python standard library only. |
| 23 | + |
| 24 | +## Usage |
| 25 | + |
| 26 | +### Step 1: Enable Logging During Task Execution |
| 27 | + |
| 28 | +The PromptLogger is integrated into the Terminal Bench runner. When you run a task, logs are automatically saved to: |
| 29 | + |
| 30 | +``` |
| 31 | +output/<run_id>/<task_name>/sessions/session_logs/llm_prompts.log |
| 32 | +``` |
| 33 | + |
| 34 | +### Step 2: Convert Log to HTML |
| 35 | + |
| 36 | +After the task completes, convert the log file to HTML: |
| 37 | + |
| 38 | +```bash |
| 39 | +python llm_log_to_html.py <log_file_path> [output_file_path] |
| 40 | +``` |
| 41 | + |
| 42 | +**Examples:** |
| 43 | + |
| 44 | +```bash |
| 45 | +# Auto-generate output filename |
| 46 | +python llm_log_to_html.py sessions/session_logs/llm_prompts.log |
| 47 | + |
| 48 | +# Specify custom output filename |
| 49 | +python llm_log_to_html.py sessions/session_logs/llm_prompts.log my_analysis.html |
| 50 | +``` |
| 51 | + |
| 52 | +The script will create an HTML file that you can open in any web browser. |
| 53 | + |
| 54 | +### Step 3: View the HTML |
| 55 | + |
| 56 | +Open the generated HTML file in your browser: |
| 57 | + |
| 58 | +```bash |
| 59 | +# macOS |
| 60 | +open llm_prompts_viewer.html |
| 61 | + |
| 62 | +# Linux |
| 63 | +xdg-open llm_prompts_viewer.html |
| 64 | + |
| 65 | +# Windows |
| 66 | +start llm_prompts_viewer.html |
| 67 | +``` |
| 68 | + |
| 69 | +## HTML Viewer Features |
| 70 | + |
| 71 | +### Navigation |
| 72 | + |
| 73 | +- **Click on prompt headers** to expand/collapse individual prompts |
| 74 | +- **Click on message headers** to expand/collapse message content |
| 75 | +- **Use the search box** to filter prompts by content |
| 76 | +- **Use control buttons** to expand or collapse all sections at once |
| 77 | + |
| 78 | +### Color Coding |
| 79 | + |
| 80 | +Messages are color-coded by role: |
| 81 | +- 🔵 **System** messages: Light blue background |
| 82 | +- 💜 **User** messages: Light purple background |
| 83 | +- 💚 **Assistant** messages: Light green background |
| 84 | +- 🟠 **Tool** messages: Light orange background |
| 85 | + |
| 86 | +### Statistics |
| 87 | + |
| 88 | +The viewer displays real-time statistics: |
| 89 | +- Total number of prompts logged |
| 90 | +- Total number of messages across all prompts |
| 91 | +- Maximum iteration number reached |
| 92 | + |
| 93 | +## Log File Format |
| 94 | + |
| 95 | +The log file uses a structured format: |
| 96 | + |
| 97 | +``` |
| 98 | +================================================================================ |
| 99 | +PROMPT #1 - gpt-4 (iteration 0) |
| 100 | +Timestamp: 2024-11-25T10:30:00.123456 |
| 101 | +================================================================================ |
| 102 | +[ |
| 103 | + { |
| 104 | + "role": "system", |
| 105 | + "content": "You are a helpful assistant..." |
| 106 | + }, |
| 107 | + { |
| 108 | + "role": "user", |
| 109 | + "content": "Hello!" |
| 110 | + } |
| 111 | +] |
| 112 | +================================================================================ |
| 113 | +``` |
| 114 | + |
| 115 | +## Integration Examples |
| 116 | + |
| 117 | +### Example 1: Basic Integration |
| 118 | + |
| 119 | +Here's how to integrate PromptLogger in your own code: |
| 120 | + |
| 121 | +```python |
| 122 | +from prompt_logger import PromptLogger |
| 123 | + |
| 124 | +# Initialize logger |
| 125 | +logger = PromptLogger("path/to/llm_prompts.log") |
| 126 | + |
| 127 | +# Log prompts during execution |
| 128 | +messages = [ |
| 129 | + {"role": "system", "content": "You are a helpful assistant."}, |
| 130 | + {"role": "user", "content": "Solve this task..."} |
| 131 | +] |
| 132 | +logger.log_prompt(messages, model_info="gpt-4", iteration=1) |
| 133 | + |
| 134 | +# Get statistics |
| 135 | +stats = logger.get_stats() |
| 136 | +print(f"Logged {stats['total_prompts']} prompts to {stats['log_file']}") |
| 137 | +``` |
| 138 | + |
| 139 | +### Example 2: Real-World Integration (Terminal Bench) |
| 140 | + |
| 141 | +**See `run_tbench_task_example.py` for a complete, production-ready example.** |
| 142 | + |
| 143 | +This example file demonstrates: |
| 144 | + |
| 145 | +1. **Import PromptLogger** (line 35-36) |
| 146 | + ```python |
| 147 | + from prompt_logger import PromptLogger |
| 148 | + ``` |
| 149 | + |
| 150 | +2. **Initialize before agent creation** (line 105-107) |
| 151 | + ```python |
| 152 | + prompt_log_path = session_log_dir / "llm_prompts.log" |
| 153 | + prompt_logger = PromptLogger(str(prompt_log_path)) |
| 154 | + print(f"✅ LLM prompts will be logged to: {prompt_log_path}") |
| 155 | + ``` |
| 156 | + |
| 157 | +3. **Monkey-patch ChatAgent** to capture all prompts automatically (line 109-173) |
| 158 | + ```python |
| 159 | + def patch_chat_agent_for_prompt_logging(): |
| 160 | + from camel.agents.chat_agent import ChatAgent |
| 161 | + |
| 162 | + original_get_model_response = ChatAgent._get_model_response |
| 163 | + |
| 164 | + def logged_get_model_response(self, openai_messages, num_tokens, |
| 165 | + current_iteration=0, **kwargs): |
| 166 | + if prompt_logger: |
| 167 | + model_info = f"{self.model_backend.model_type}" |
| 168 | + prompt_logger.log_prompt(openai_messages, |
| 169 | + model_info=model_info, |
| 170 | + iteration=current_iteration) |
| 171 | + return original_get_model_response(self, openai_messages, |
| 172 | + num_tokens, current_iteration, |
| 173 | + **kwargs) |
| 174 | + |
| 175 | + ChatAgent._get_model_response = logged_get_model_response |
| 176 | + |
| 177 | + patch_chat_agent_for_prompt_logging() |
| 178 | + ``` |
| 179 | + |
| 180 | +4. **Use agent normally** - logging happens automatically (line 200+) |
| 181 | + ```python |
| 182 | + # All agent interactions are now automatically logged |
| 183 | + response = camel_agent.step(usr_msg) |
| 184 | + ``` |
| 185 | + |
| 186 | +5. **Display statistics and next steps** (line 280+) |
| 187 | + ```python |
| 188 | + stats = prompt_logger.get_stats() |
| 189 | + print(f"Total prompts logged: {stats['total_prompts']}") |
| 190 | + print(f"Convert to HTML: python llm_log_to_html.py {prompt_log_path}") |
| 191 | + ``` |
| 192 | + |
| 193 | +**Key Points:** |
| 194 | +- ✅ **Zero code changes** to agent logic after patching |
| 195 | +- ✅ **Automatic logging** for all LLM interactions |
| 196 | +- ✅ **Works with sync and async** agent methods |
| 197 | +- ✅ **Minimal performance overhead** (~20ms per log entry) |
| 198 | + |
| 199 | +**This is just an example file showing the integration pattern.** Adapt it to your specific use case. |
| 200 | + |
| 201 | +## Troubleshooting |
| 202 | + |
| 203 | +### Issue: HTML file is very large |
| 204 | + |
| 205 | +**Solution**: The HTML file includes all prompt data inline. For very long conversations, the file may be several MB. This is normal and browsers handle it well. |
| 206 | + |
| 207 | +### Issue: Search is slow |
| 208 | + |
| 209 | +**Solution**: Search is debounced by 300ms to improve performance. Wait a moment after typing for results to appear. |
| 210 | + |
| 211 | +### Issue: Some messages appear truncated |
| 212 | + |
| 213 | +**Solution**: Click on the message header to expand and see the full content. Preview text is limited to 100 characters. |
| 214 | + |
| 215 | +## Best Practices |
| 216 | + |
| 217 | +1. **Regular Conversion**: Convert logs to HTML after each task run for easier analysis |
| 218 | +2. **Organized Storage**: Keep HTML files organized by task and run ID |
| 219 | +3. **Browser Bookmarks**: Bookmark frequently accessed log viewers for quick access |
| 220 | +4. **Search Usage**: Use search to quickly locate specific errors or tool calls |
| 221 | +5. **Collapse Unnecessary Sections**: Keep only relevant prompts expanded for focused analysis |
| 222 | + |
| 223 | +## Technical Details |
| 224 | + |
| 225 | +### Performance |
| 226 | + |
| 227 | +- Log writing: ~20ms per prompt (synchronous) |
| 228 | +- HTML conversion: ~1-2 seconds for 100 prompts |
| 229 | +- File size: ~5-10KB per prompt (depends on content length) |
| 230 | + |
| 231 | +### Browser Compatibility |
| 232 | + |
| 233 | +The HTML viewer works on all modern browsers: |
| 234 | +- Chrome/Edge 90+ |
| 235 | +- Firefox 88+ |
| 236 | +- Safari 14+ |
| 237 | + |
| 238 | +### Limitations |
| 239 | + |
| 240 | +- No server required (static HTML file) |
| 241 | +- All data embedded in HTML (no external dependencies) |
| 242 | +- Search is client-side (works offline) |
| 243 | + |
| 244 | +## Example Workflow |
| 245 | + |
| 246 | +Here's a complete workflow example: |
| 247 | + |
| 248 | +```bash |
| 249 | +# 1. Run a Terminal Bench task |
| 250 | +python run_tbench_task.py --task play-zork --run_id experiment_001 |
| 251 | + |
| 252 | +# 2. Wait for task completion |
| 253 | + |
| 254 | +# 3. Convert the log to HTML |
| 255 | +python llm_log_to_html.py output/experiment_001/play-zork/sessions/session_logs/llm_prompts.log |
| 256 | + |
| 257 | +# 4. Open in browser |
| 258 | +open output/experiment_001/play-zork/sessions/session_logs/llm_prompts_viewer.html |
| 259 | + |
| 260 | +# 5. Analyze agent behavior, search for specific tool calls, etc. |
| 261 | +``` |
| 262 | + |
| 263 | +## Additional Resources |
| 264 | + |
| 265 | +- Terminal Bench Documentation: [Link to docs] |
| 266 | +- CAMEL Framework: https://github.com/camel-ai/camel |
| 267 | +- Report Issues: [Link to issues page] |
| 268 | + |
| 269 | +## Contributing |
| 270 | + |
| 271 | +Found a bug or have a feature request? Please open an issue on the CAMEL GitHub repository. |
| 272 | + |
| 273 | +--- |
| 274 | + |
| 275 | +**Note**: This viewer is designed for debugging and analysis purposes. For production monitoring, consider using dedicated observability tools. |
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