Copy as Markdown[Open in ChatGPT](https://chatgpt.com/?q=Read%20https%3A%2F%2Fcoralogix.com%2Fdocs%2Fintegrations%2Fai-observability%2Fbedrock.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%2Fbedrock.md%20and%20help%20me%20with%20my%20question%20about%20this%20Coralogix%20documentation%20page.) # Amazon bedrock Coralogix's AI Observability integrations enable organizations to gain deep insight into their AI applications, helping them monitor, analyze, and optimize performance across the stack. Through integrations with Amazon Bedrock, Coralogix delivers end-to-end visibility into AI workloads, supporting proactive issue detection and efficient performance tuning. ## Overview[​](#overview "Direct link to Overview") This library offers customized [OpenTelemetry instrumentation](https://github.com/open-telemetry/opentelemetry-python-contrib/) for [AWS Bedrock](https://aws.amazon.com/bedrock/), 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-bedrock" ``` ## 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.bedrock 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 AWS Bedrock. ### 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 all clients, call the `instrument` method. ``` from llm_tracekit.bedrock import BedrockInstrumentor BedrockInstrumentor().instrument() ``` ### Uninstrument[​](#uninstrument "Direct link to Uninstrument") To uninstrument clients, call the `uninstrument` method. ``` BedrockInstrumentor().uninstrument() ``` ### Full example[​](#full-example "Direct link to Full example") ``` import boto3 from llm_tracekit.bedrock import BedrockInstrumentor, setup_export_to_coralogix # 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 BedrockInstrumentor().instrument() # Bedrock usage example bedrock = boto3.client("bedrock-runtime") response = bedrock.converse( modelId="anthropic.claude-3-sonnet-20240229-v1:0", messages=[{"role": "user", "content": [{"text": "Write a short poem on open telemetry."}]}], system=[{"text": "You are a helpful assistant."}], requestMetadata={"user": "user@company.com"}, ) ``` ### 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. ## 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 `requestMetadata={"user": "..."}` for the `converse` API, or `sessionState={"sessionAttributes": {"userId": "..."}}` for the `invoke_agent` API) | `user@company.com` | ### Bedrock-specific attributes[​](#bedrock-specific-attributes "Direct link to Bedrock-specific attributes") | Attribute | Type | Description | Example | | ------------------------------- | ------ | --------------------------------------------------- | ------------ | | `gen_ai.bedrock.agent_alias.id` | string | The ID of the agent-alias in an `invoke_agent` call | `TSTALIASID` | ## 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.