Copy as Markdown[Open in ChatGPT](https://chatgpt.com/?q=Read%20https%3A%2F%2Fcoralogix.com%2Fdocs%2Fintegrations%2Fai-observability%2Flangchain.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%2Flangchain.md%20and%20help%20me%20with%20my%20question%20about%20this%20Coralogix%20documentation%20page.) # LangChain Coralogix's AI Observability integrations make it easy to monitor any LangChain-powered application. With a dedicated LangChain integration, Coralogix consolidates spans emitted by OpenAI, Anthropic, AWS Bedrock, and other LangChain chat providers so teams can understand performance, drift, and tool usage without stitching logs across services. ## Overview[​](#overview "Direct link to Overview") This library offers customized [OpenTelemetry instrumentation](https://github.com/open-telemetry/opentelemetry-python-contrib/) for [LangChain](https://www.langchain.com/), optimized to support large language model (LLM) application development with streamlined integration, detailed production tracing, and effective debugging capabilities. ## Supported providers[​](#supported-providers "Direct link to Supported providers") The following providers are supported with full prompt/completion attributes: | Provider | Chat model class | System value | | ----------- | --------------------------------------------------- | ------------- | | OpenAI | `ChatOpenAI` | `openai` | | Anthropic | `ChatAnthropic` | `anthropic` | | AWS Bedrock | `ChatBedrock`, `ChatBedrockConverse`, `BedrockChat` | `aws.bedrock` | Other chat model classes are still instrumented with system value `langchain` and a span is always created (model name uses metadata or provider class name when not in standard keys). ## 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-langchain" ``` ## 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.langchain 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 LangChain. ### 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.langchain import LangChainInstrumentor LangChainInstrumentor().instrument() ``` ### Uninstrument[​](#uninstrument "Direct link to Uninstrument") To uninstrument clients, call the `uninstrument` method. ``` LangChainInstrumentor().uninstrument() ``` ### Full example[​](#full-example "Direct link to Full example") ``` from llm_tracekit.langchain import LangChainInstrumentor, setup_export_to_coralogix from langchain_openai import ChatOpenAI from langchain_core.messages import HumanMessage # 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 LangChainInstrumentor().instrument() # LangChain usage example llm = ChatOpenAI(model="gpt-4o-mini") messages = [HumanMessage(content="Write a short poem on open telemetry.")] response = llm.invoke(messages) # Pass user via config metadata response = llm.invoke( messages, config={"metadata": {"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 passed with the request | `{"type": "object", "properties": {"location": {"type": "string"}}}` | | `gen_ai.request.user` | string | A unique identifier representing the end user (from `config={"metadata": {"user": "..."}}`) | `user@company.com` | ### LangChain-specific attributes[​](#langchain-specific-attributes "Direct link to LangChain-specific attributes") | Attribute | Type | Description | Example | | ---------------------- | ------ | ----------------------------------------- | --------------------- | | `gen_ai.provider.name` | string | The provider name from LangChain metadata | `openai`, `anthropic` | ## 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.