Copy as Markdown[Open in ChatGPT](https://chatgpt.com/?q=Read%20https%3A%2F%2Fcoralogix.com%2Fdocs%2Fintegrations%2Fai-observability%2Fstrands.md%20and%20help%20me%20with%20my%20question%20about%20this%20Coralogix%20documentation%20page.)[Open in Claude](https://claude.ai/new?q=Read%20https%3A%2F%2Fcoralogix.com%2Fdocs%2Fintegrations%2Fai-observability%2Fstrands.md%20and%20help%20me%20with%20my%20question%20about%20this%20Coralogix%20documentation%20page.) # Strands agents Monitor applications built with the [Strands Agents SDK](https://github.com/strands-agents/sdk-python) using Coralogix AI Observability. The Strands integration enriches the built-in OpenTelemetry tracing provided by the SDK with GenAI semantic convention attributes, so you can track prompt content, completions, tool calls, and user identity across every agent run. ## Overview[​](#overview "Direct link to Overview") Strands Agents SDK already includes built-in OpenTelemetry tracing. This instrumentation enriches those existing spans with additional GenAI semantic convention attributes, optimized to support large language model (LLM) application development with streamlined integration, detailed production tracing, and effective debugging capabilities. ## Requirements[​](#requirements "Direct link to Requirements") * Python 3.10–3.13. * Coralogix [API keys](https://coralogix.com/docs/docs/user-guides/account-management/api-keys/api-keys/.md). ## Installation[​](#installation "Direct link to Installation") Run the following command. ``` pip install "llm-tracekit-strands" ``` ## Authentication[​](#authentication "Direct link to Authentication") Authentication data is passed during OTel Span Exporter definition: 1. Choose the ingress.:443 endpoint that corresponds to your Coralogix [domain](https://coralogix.com/docs/docs/user-guides/account-management/account-settings/coralogix-domain/.md) using the domain selector at the top of the page. 2. Use your [customized API key](https://coralogix.com/docs/docs/user-guides/account-management/api-keys/api-keys/.md) in the authorization request header. 3. Provide the [application and subsystem names](https://coralogix.com/docs/docs/user-guides/account-management/account-settings/application-and-subsystem-names/.md). ``` from llm_tracekit.strands import setup_export_to_coralogix setup_export_to_coralogix( coralogix_token=, coralogix_endpoint="ingress.eu2.coralogix.com:443", service_name="ai-service", application_name="ai-application", subsystem_name="ai-subsystem", capture_content=True, ) ``` Note All of the authentication parameters can also be provided through environment variables (`CX_TOKEN`, `CX_ENDPOINT`, etc.). ## Usage[​](#usage "Direct link to Usage") This section describes how to set up instrumentation for Strands Agents. ### Set up tracing[​](#set-up-tracing "Direct link to Set up tracing") **Automatic** Use the `setup_export_to_coralogix` function to set up tracing and export traces to Coralogix. See the code snippet in the [Authentication](#authentication) section. **Manual** Alternatively, you can set up tracing manually. ``` from opentelemetry import trace from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter from opentelemetry.sdk.resources import SERVICE_NAME, Resource from opentelemetry.sdk.trace import TracerProvider from opentelemetry.sdk.trace.export import SimpleSpanProcessor tracer_provider = TracerProvider( resource=Resource.create({SERVICE_NAME: "ai-service"}), ) exporter = OTLPSpanExporter() span_processor = SimpleSpanProcessor(exporter) tracer_provider.add_span_processor(span_processor) trace.set_tracer_provider(tracer_provider) ``` ### Instrument[​](#instrument "Direct link to Instrument") To instrument Strands, call the `instrument` method. ``` from llm_tracekit.strands import StrandsInstrumentor StrandsInstrumentor().instrument() ``` ### Uninstrument[​](#uninstrument "Direct link to Uninstrument") To uninstrument clients, call the `uninstrument` method. ``` StrandsInstrumentor().uninstrument() ``` ### Full example[​](#full-example "Direct link to Full example") ``` from llm_tracekit.strands import StrandsInstrumentor, setup_export_to_coralogix from strands import Agent # Optional: Configure sending spans to Coralogix # Reads Coralogix connection details from the following environment variables: # - CX_TOKEN # - CX_ENDPOINT setup_export_to_coralogix( service_name="ai-service", application_name="ai-application", subsystem_name="ai-subsystem", capture_content=True, ) # Activate instrumentation StrandsInstrumentor().instrument() # Strands usage example agent = Agent(system_prompt="You are a helpful assistant.") response = agent("Write a short poem on open telemetry.") ``` ### Enable message content capture[​](#enable-message-content-capture "Direct link to Enable message content capture") By default, message content — prompt contents, completions, function arguments, and return values — is not captured. To capture message content as span attributes: * Pass `capture_content=True` when calling `setup_export_to_coralogix`. * Set the environment variable `OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT` to `true`. Most Coralogix AI evaluations require message contents to function properly, so enabling message capture is strongly recommended. ### Pass user identity[​](#pass-user-identity "Direct link to Pass user identity") To capture the end-user identifier, pass the `user` parameter in your model's `params` configuration: ``` from strands.models.openai import OpenAIModel model = OpenAIModel( model_id="gpt-4o", params={"user": "user@example.com"}, ) agent = Agent(model=model) ``` ## Semantic conventions[​](#semantic-conventions "Direct link to Semantic conventions") | Attribute | Type | Description | Example | | ------------------------------------------------------------------------------------ | ------ | ----------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------- | | `gen_ai.prompt..role` | string | Role of message author for user message `` | `system`, `user`, `assistant`, `tool` | | `gen_ai.prompt..content` | string | Contents of user message `` | `What's the weather in Paris?` | | `gen_ai.prompt..tool_calls..id` | string | ID of tool call in user message `` | `call_O8NOz8VlxosSASEsOY7LDUcP` | | `gen_ai.prompt..tool_calls..type` | string | Type of tool call in user message `` | `function` | | `gen_ai.prompt..tool_calls..function.name` | string | The name of the function used in tool call within user message `` | `get_current_weather` | | `gen_ai.prompt..tool_calls..function.arguments` | string | Arguments passed to the function used in tool call within user message `` | `{"location": "Seattle, WA"}` | | `gen_ai.prompt..tool_call_id` | string | Tool call ID in user message `` | `call_mszuSIzqtI65i1wAUOE8w5H4` | | `gen_ai.completion..role` | string | Role of message author for choice `` in model response | `assistant` | | `gen_ai.completion..finish_reason` | string | Finish reason for choice `` in model response | `stop`, `tool_calls`, `error` | | `gen_ai.completion..content` | string | Contents of choice `` in model response | `The weather in Paris is rainy and overcast, with temperatures around 57°F` | | `gen_ai.completion..tool_calls..id` | string | ID of tool call in choice `` | `call_O8NOz8VlxosSASEsOY7LDUcP` | | `gen_ai.completion..tool_calls..type` | string | Type of tool call in choice `` | `function` | | `gen_ai.completion..tool_calls..function.name` | string | The name of the function used in tool call within choice `` | `get_current_weather` | | `gen_ai.completion..tool_calls..function.arguments` | string | Arguments passed to the function used in tool call within choice `` | `{"location": "Seattle, WA"}` | | `gen_ai.request.tools..type` | string | Type of tool definition advertised to the model | `function` | | `gen_ai.request.tools..function.name` | string | Name of the tool/function exposed to the model | `get_current_weather` | | `gen_ai.request.tools..function.description` | string | Description of the tool/function | `Get the current weather in a given location` | | `gen_ai.request.tools..function.parameters` | string | JSON schema describing the tool/function parameters | `{"type": "object", "properties": {"location": {"type": "string"}}, "required": ["location"]}` | | `gen_ai.request.user` | string | A unique identifier representing the end user (from model `params={"user": "..."}`) | `user@company.com` | ## Next steps[​](#next-steps "Direct link to Next steps") Once your integration is set up, explore the [AI Center Overview](https://coralogix.com/docs/docs/user-guides/ai/monitor/.md) to monitor performance, costs, quality issues, and security across all your AI applications — and to set up [Guardrails](https://coralogix.com/docs/docs/user-guides/ai/guardrails/.md) for real-time policy enforcement.