# Agent activity logging Supported in ADKPython v0.1.0Go v0.1.0Kotlin v0.1.0 Agent Development Kit (ADK) provides flexible and powerful logging capabilities to monitor agent behavior and debug issues effectively. ## Logging philosophy ADK's approach to logging is to provide detailed diagnostic information without being overly verbose by default. It is designed to be configured by the application developer, allowing you to tailor the log output to your specific needs, whether in a development or production environment. - **Standard Library Integration:** ADK uses the standard logging facilities of the host language (e.g., Python's `logging` module, Go's `log` package). - **Structured GenAI Logging:** ADK uses OpenTelemetry to log structured events for GenAI requests and responses, allowing for advanced monitoring and debugging in cloud environments. - **User-Configured:** While ADK provides defaults and integration with its CLI tools, it is ultimately the responsibility of the application developer to configure logging to suit their specific environment. ## Logging schema ADK emits logs using standard library facilities and structured GenAI events via OpenTelemetry. ### Structured GenAI logs Structured GenAI logs emitted via OpenTelemetry follow the [Semantic Conventions for GenAI](https://github.com/open-telemetry/semantic-conventions/blob/main/docs/gen-ai/gen-ai-events.md). By default prompt content is elided in logs for security. You can enable prompt logging using environment variables or programmatic configuration (see Setup section below). ### Log levels (Python) The following table describes what is logged at different levels in Python when using the standard logger: | Level | Description | Type of Information Logged | | ------------- | ---------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **`DEBUG`** | **Crucial for debugging.** The most verbose level for fine-grained diagnostic information. | - **Full LLM Prompts:** The complete request sent to the language model, including system instructions, history, and tools. - Detailed API responses from services. - Internal state transitions and variable values. | | **`INFO`** | General information about the agent's lifecycle. | - Agent initialization and startup. - Session creation and deletion events. - Execution of a tool, including its name and arguments. | | **`WARNING`** | Indicates a potential issue or deprecated feature use. The agent continues to function, but attention may be required. | - Use of deprecated methods or parameters. - Non-critical errors that the system recovered from. | | **`ERROR`** | A serious error that prevented an operation from completing. | - Failed API calls to external services (e.g., LLM, Session Service). - Unhandled exceptions during agent execution. - Configuration errors. | Note It is recommended to use `INFO` or `WARNING` in production environments. Only enable `DEBUG` when actively troubleshooting an issue, as `DEBUG` logs can be very verbose and may contain sensitive information. ## Logging setup ### Logging in ADK Web When running agents using the ADK's `adk web`, `adk api_server`, `adk deploy cloud_run` and `adk deploy gke` commands, you can control the log verbosity or destination. #### Logging level To start the web server with `DEBUG` level logging, run: ```bash adk web --log_level DEBUG path/to/your/agents_dir ``` The available log levels for the `--log_level` option are: `DEBUG`, `INFO` (default), `WARNING`, `ERROR`, `CRITICAL`. #### Capture prompt content By default a prompt content is elided in logs for security. You can enable prompt logging using the environment variable: ```bash export OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT=true ``` The available values for this variable are: `NO_CONTENT`, `EVENT_ONLY`, `SPAN_ONLY`, and `SPAN_AND_EVENT`. A boolean `true` or `1` means `EVENT_ONLY`, which records content on the emitted log events; any value outside these four falls back to `NO_CONTENT`. To record content on the inference span, `SPAN_ONLY` and `SPAN_AND_EVENT` also require `OTEL_SEMCONV_STABILITY_OPT_IN=gen_ai_latest_experimental`. Warning The `OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT` setting logs the full content of user prompts and agent responses. This is useful for debugging but may capture sensitive data or PII. In production, set this to false or ensure you have appropriate data handling policies in place. #### OTLP export To export logs to an OTLP-compatible backend, set the standard OTel environment variables: ```bash export OTEL_EXPORTER_OTLP_LOGS_ENDPOINT="http://your-collector:4318/v1/logs" adk web path/to/your/agents_dir ``` Note You can also set the general `OTEL_EXPORTER_OTLP_ENDPOINT` environment variable if you would like to send metrics and traces to the same endpoint in addition to logs. #### GCP export setup You can enable GCP export using the `--otel_to_cloud` flag: ```bash adk web --otel_to_cloud path/to/your/agents_dir ``` ### Activity logging with plugins ADK provides built-in plugins to capture agent activity (user messages, model requests/responses, tool calls, and session state) without modifying your agent logic. #### Console logging with `LoggingPlugin` To print structured activity logs to the console during execution, attach `LoggingPlugin` to your `App`: ```python from google.adk.apps import App from google.adk.plugins import LoggingPlugin app = App( name="my_app", root_agent=root_agent, plugins=[LoggingPlugin()], ) ``` ```kotlin // Use the LoggingPlugin for structured activity logging to the console val runner = InMemoryRunner( App(appName = agent.name, rootAgent = agent, plugins = listOf(LoggingPlugin())), ) ``` #### Full debug capture to a file with `DebugLoggingPlugin` Supported in ADKPython v1.23.0Kotlin v0.6.0 To record complete interaction data as human-readable YAML appended to `adk_debug.yaml` rather than truncated console output, use `DebugLoggingPlugin`: ```python from google.adk.apps import App from google.adk.plugins import DebugLoggingPlugin app = App( name="my_app", root_agent=root_agent, plugins=[ DebugLoggingPlugin( output_path="adk_debug.yaml", include_session_state=True, include_system_instruction=True, ), ], ) ``` ```kotlin // includeSystemInstruction = false logs has_system_instruction, not the instruction text val debugPlugin = DebugLoggingPlugin(includeSystemInstruction = false) val debugRunner = InMemoryRunner( App(appName = agent.name, rootAgent = agent, plugins = listOf(debugPlugin)), ) ``` Warning The output file holds raw prompts, tool arguments, and session state. Although credentials and `temp:`-scoped state keys are automatically redacted in Python, treat the output file as sensitive. ### Programmatic setup While plugins help inspect individual agent runs during local development, programmatic setup configures the underlying logging framework and OpenTelemetry exporters for system-level diagnostics and production observability: - **Python:** Uses the standard `logging` module and OpenTelemetry for structured GenAI logs. - **Kotlin:** Uses standard JVM logging facilities (defaulting to Flogger) and OpenTelemetry for structured GenAI logs. - **Go:** Uses the `google.golang.org/adk/v2/telemetry` package for OpenTelemetry configuration and the standard `log` package for general events (written to `stderr` by default). #### Logging level To enable detailed logging, including `DEBUG` level messages, add the following to the top of your script: ```python import logging logging.basicConfig( level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' ) ``` ADK uses standard JVM logging facilities (defaulting to Flogger). Configure your JVM logger backend (e.g., `java.util.logging` or SLF4J) to adjust log verbosity. General events (such as server startup or HTTP requests) are logged using the standard Go `log` package and written to `stderr` by default. #### Capture prompt content You can enable full prompt logging programmatically by setting an environment variable: ```python import os os.environ["OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT"] = "true" ``` To scope content capture to a single run instead of the whole process, set `RunConfig.telemetry` rather than the environment variable: ```python from google.adk.agents.run_config import RunConfig from google.adk.telemetry import ContentCapturingMode, TelemetryConfig run_config = RunConfig( telemetry=TelemetryConfig( capture_message_content=ContentCapturingMode.SPAN_AND_EVENT, ), ) ``` You can enable full prompt logging by configuring the global `TelemetryConfig`: ```kotlin // Enable full prompt and response logging TelemetryConfig.captureMessageContent = true ``` You can enable full prompt logging programmatically when initializing telemetry: ```go package main import ( "context" "google.golang.org/adk/v2/telemetry" ) func main() { ctx := context.Background() tp, err := telemetry.New(ctx, telemetry.WithGenAICaptureMessageContent(true), ) if err != nil { // handle error } defer tp.Shutdown(ctx) tp.SetGlobalOtelProviders() } ``` #### OTLP export To export logs to an OpenTelemetry Collector (or an OTLP-compatible backend) programmatically: ```python from google.adk.telemetry.setup import maybe_set_otel_providers import os os.environ["OTEL_EXPORTER_OTLP_LOGS_ENDPOINT"] = "http://your-collector:4318/v1/logs" os.environ["OTEL_SERVICE_NAME"] = "your-adk-agent" os.environ["OTEL_RESOURCE_ATTRIBUTES"] = "key1=value1,key2=value2" maybe_set_otel_providers() ``` ADK automatically uses the `GlobalOpenTelemetry` instance on the JVM. Configure your OpenTelemetry SDK exporter before starting the agent: ```kotlin // 1. Configure OpenTelemetry (Traces) // ADK Kotlin uses GlobalOpenTelemetry to resolve its tracer on the JVM. val spanExporter = OtlpGrpcSpanExporter.builder().setEndpoint("http://localhost:4317").build() val resource = Resource.getDefault() .merge( Resource.create( Attributes.of(AttributeKey.stringKey("service.name"), "my-kotlin-agent"), ), ) val tracerProvider = SdkTracerProvider.builder() .addSpanProcessor(BatchSpanProcessor.builder(spanExporter).build()) .setResource(resource) .build() OpenTelemetrySdk.builder().setTracerProvider(tracerProvider).buildAndRegisterGlobal() // 2. Optional: Configure ADK Telemetry behavior // Enable capturing full message content in traces (use with caution in production) TelemetryConfig.captureMessageContent = true // 3. Initialize Agent and Runner with LoggingPlugin for console output val agent = LlmAgent(name = "my_agent", model = Gemini(name = "gemini-flash-latest")) val runner = InMemoryRunner( App(appName = "my_agent", rootAgent = agent, plugins = listOf(LoggingPlugin())), ) // The runner will now automatically emit traces via GlobalOpenTelemetry // and log activity to the console via the LoggingPlugin. runner.run( userId = "user123", sessionId = "session456", newMessage = Content.fromText(Role.USER, "Hello!"), ) ``` To export logs to an OTLP-compatible backend, configure the standard OpenTelemetry environment variables (e.g., `OTEL_EXPORTER_OTLP_ENDPOINT` or `OTEL_EXPORTER_OTLP_LOGS_ENDPOINT`). The ADK telemetry package will automatically use these settings when initialized. #### GCP export setup To export logs to Google Cloud Logging programmatically, use the OpenTelemetry Google Cloud exporter. Here is an example in Python: ```python from google.adk.telemetry.google_cloud import get_gcp_exporters from google.adk.telemetry.setup import maybe_set_otel_providers import os gcp_exporters = get_gcp_exporters( enable_cloud_logging = True, ) os.environ["OTEL_SERVICE_NAME"] = "your-adk-agent" os.environ["OTEL_RESOURCE_ATTRIBUTES"] = "key1=value1,key2=value2" maybe_set_otel_providers([gcp_exporters]) ``` ADK Kotlin does not provide a built-in GCP exporter wrapper. Because it uses the standard `GlobalOpenTelemetry` instance on the JVM, you can export to Google Cloud by configuring your `OpenTelemetrySdk` with a standard OTLP exporter targeting the Google Cloud Telemetry endpoint before starting your agent. To export logs to Google Cloud Logging, use the `WithOtelToCloud` option: ```go package main import ( "context" "google.golang.org/adk/v2/telemetry" ) func main() { ctx := context.Background() tp, err := telemetry.New(ctx, telemetry.WithOtelToCloud(true), ) if err != nil { // handle error } defer tp.Shutdown(ctx) tp.SetGlobalOtelProviders() } ``` If using the Go launcher, you can also enable GCP export via the CLI flag: ```bash go run main.go web -otel_to_cloud ``` ## Understanding log output ### Sample Python log entry ```text 2025-07-08 11:22:33,456 - DEBUG - google_adk.google.adk.models.google_llm - LLM Request: contents { ... } ``` | Log Segment | Format Specifier | Meaning | | ----------------------------------------- | ---------------- | ---------------------------------------------- | | `2025-07-08 11:22:33,456` | `%(asctime)s` | Timestamp | | `DEBUG` | `%(levelname)s` | Severity level | | `google_adk.google.adk.models.google_llm` | `%(name)s` | Logger name (the module that produced the log) | | `LLM Request: contents { ... }` | `%(message)s` | The actual log message | By reading the logger name, you can immediately pinpoint the source of the log and understand its context within the agent's architecture. ADK loggers are named `google_adk.` followed by the module's fully-qualified name, so every ADK logger is a child of the `google_adk` logger. Configure them as a group with `logging.getLogger("google_adk")`. ### Debugging example After enabling `DEBUG` logging (see [Logging level](#logging-level) above), run your agent and look for messages from the `google_adk.google.adk.models.google_llm` logger. The output shows the full LLM request and response: ```text 2025-07-10 15:26:13,778 - DEBUG - google_adk.google.adk.models.google_llm - LLM Request: ----------------------------------------------------------- System Instruction: You roll dice and answer questions about the outcome of the dice rolls. ... ----------------------------------------------------------- Contents: {"parts":[{"text":"Roll a 6 sided dice"}],"role":"user"} {"parts":[{"function_call":{"args":{"sides":6},"name":"roll_die"}}],"role":"model"} {"parts":[{"function_response":{"name":"roll_die","response":{"result":2}}}],"role":"user"} ----------------------------------------------------------- Functions: roll_die: {'sides': {'type': }} check_prime: {'nums': {'items': {'type': }, 'type': }} ----------------------------------------------------------- 2025-07-10 15:26:14,309 - INFO - google_adk.google.adk.models.google_llm - LLM Response: ----------------------------------------------------------- Text: I have rolled a 6 sided die, and the result is 2. ... ``` From this output you can verify: - Is the system instruction correct? - Is the conversation history (`user` and `model` turns) accurate? - Are the correct tools being provided to the model? - Are the tools correctly called by the model? - How long it takes for the model to respond?