> ## Documentation Index > Fetch the complete documentation index at: https://docs.monobot.ai/llms.txt > Use this file to discover all available pages before exploring further. # Debug Mode Debug Mode allows developers and operators to monitor the internal bot execution process in real time directly from the platform interface. While the assistant processes user requests, logs are streamed into the **Live Debug Feed**, showing step-by-step execution details including tool calls, actions, execution time, responses, warnings, and errors. Debug Mode ## Enabling Debug Mode Debug Mode can be enabled directly from the bot Flow editor. Navigate to: ```txt theme={null} Bot Configuration → Flow ``` Enable the **Debug mode** checkbox in the top-right corner of the Flow editor interface. Debug logs will start appearing in the Live Debug Feed only after Debug Mode is enabled. ## What is Debug Mode Debug Mode is a real-time debugging interface that helps monitor: * Active flows * Tool execution * Action execution * LLM requests and responses * Errors and warnings * Custom logs from Function actions * Execution timing and performance Logs are streamed directly from the backend and displayed live without refreshing the page. ## Why Use Debug Mode | Scenario | What Debug Mode Provides | | ---------------------------------- | ----------------------------------------------------------- | | Bot gives unexpected response | Shows active flows, tools, and passed arguments | | Action works slowly | Shows execution time for every action | | Need to inspect action result | Shows full JSON result returned by actions | | LLM behaves unexpectedly | Shows model name, API version, and full response | | Tool or action error | Shows `[ERROR]` and `[WARNING]` messages | | Developing custom Function actions | Shows custom logs created through `interaction["_LOGGING"]` | # Live Debug Feed The **Live Debug Feed** displays all interaction logs in chronological order. Each log entry contains: | Field | Description | | ---------- | ------------------------------------------- | | Timestamp | Time in `HH:MM:SS.mmm` format | | Level | `[INFO]`, `[WARNING]`, `[ERROR]`, `[DEBUG]` | | Event Type | `[CHAT]`, `Action`, `Tool`, or system event | | Message | Short description of the event | Clicking a log entry opens additional details including full JSON payloads, arguments, and execution results. ## Session Start Example ```txt theme={null} 16:37:02.024 [INFO] [CHAT] Session started 16:37:02.029 [INFO] Active flow: "Root Flow" ``` Shows the interaction start and currently active flow. ## User Request Example ```txt theme={null} 16:37:12.196 [INFO] [CHAT] Request "propan" Tools (1): "commodity_code_search" ``` Shows the incoming user request and available tools provided to the model. ## Tool Execution Example ```txt theme={null} 16:37:13.589 [INFO] [CHAT] Tool Running tool "commodity_code_search" Arguments: product_name: Liquefied propane... Tool description: Use this tool to search for... ``` Displays: * Tool name * Arguments passed by the LLM * Tool description * Tool execution start ## Action Execution Example ```txt theme={null} 16:37:13.946 [INFO] Action Action "reasoning_rag" Condition: "PASS" Execution time: 14.106 sec Result: {"answers": [...], "found_digit_code": null} ``` Each action execution contains: | Field | Description | | -------------- | ----------------------------------------------------- | | Condition | Whether execution condition passed (`PASS` or `SKIP`) | | Execution time | Time spent executing the action | | Result | Full JSON response returned by the action | *** ## Tool Completion Example ```txt theme={null} 16:37:28.294 [INFO] Tool execution finish: 14.705 sec ``` Shows total execution time for the entire tool execution chain. ## Bot Response Example ```txt theme={null} 16:37:29.183 [INFO] [CHAT] Response "Which best describes..." in 16.982 sec API version: Responses Model name: Openai "gpt-5.2 Chat" ``` Displays: * Final assistant response * Total generation time * API provider version * Model name # Custom Logs in Function Actions Function actions can send custom logs directly into Debug Mode using `interaction["_LOGGING"]`. ## Example ```python theme={null} def my_function(tool_params, interaction): interaction["_LOGGING"].info("Starting product lookup") interaction["_LOGGING"].warning("Product not found, using fallback") interaction["_LOGGING"].error("External API returned 500") interaction["_LOGGING"].debug("Raw response: " + str(raw)) ``` These logs appear together with system logs inside Live Debug Feed. ## Logging Levels | Level | Description | | ------- | ---------------------------------------- | | INFO | Standard execution information | | WARNING | Non-critical issue or fallback | | ERROR | Critical execution problem | | DEBUG | Detailed developer debugging information | # Full Debug Flow Example ```txt theme={null} 16:37:02.024 [INFO] [CHAT] Session started 16:37:02.029 [INFO] Active flow: "Root Flow" 16:37:12.196 [INFO] [CHAT] Request "propan" Tools (1): "commodity_code_search" 16:37:13.589 [INFO] [CHAT] Tool Running tool "commodity_code_search" 16:37:13.946 [INFO] Action Action "reasoning_rag" Condition: "PASS" Execution time: 14.106 sec 16:37:28.294 [INFO] Tool execution finish: 14.705 sec 16:37:29.183 [INFO] [CHAT] Response "Which best describes your propane shipment?" Model name: Openai "gpt-5.2 Chat" ``` # Technical Details * Logs are streamed through Redis pub/sub. * Events are stored in `events:{interaction_uuid}` queue with TTL. * Each log contains: * `id` * `timestamp` * `level` * `message` * optional `details` * optional nested `children` * Nested logs are displayed in UI as expandable trees. * Logs remain available during TTL lifetime after interaction completion. # Notes * Debug Mode is intended for development and troubleshooting. * Full responses and action results may contain sensitive information. * Excessive debug logging may affect readability during large interactions. * Custom Function logs are supported only inside Function-type actions.