LangChain & LangGraph Observability with OpenTelemetry

SigNoz has released a new guide for instrumenting LangChain and LangGraph AI agent workflows with OpenTelemetry, enabling real-time observability of agent reasoning steps, tool invocations, and model responses. The integration allows developers to trace every user request from initial prompt through final answer, correlate traces with logs, and set alerts for latency or errors to improve AI application reliability.

What is LangChain and LangGraph Observability? LangChain and LangGraph observability gives you real-time visibility into your AI agent workflows by collecting traces, spans, and logs using OpenTelemetry. This guide shows you how to instrument your Python-based LangChain https://www.langchain.com/ or LangGraph application and send telemetry to SigNoz, so you can monitor agent reasoning steps, tool invocations, chain executions, and model responses end-to-end. With full LangChain and LangGraph observability in SigNoz, you can trace every user request from the initial prompt through each reasoning step, tool execution, and final answer. Correlate traces with logs, set alerts for latency or errors, and continuously improve the reliability of your AI applications. To get started, check out our example LangChain trip planner agent with OpenTelemetry-based observability/monitoring via OpenInference . View the LangChain trip planner agent repository https://github.com/SigNoz/langchain-monitoring-demo . You can also check out our LangChain SigNoz MCP agent repository https://github.com/SigNoz/signoz-mcp-demo . Prerequisites - A Python application using Python 3.8+ - LangChain/LangGraph integrated into your app - Basic understanding of AI Agents and tool calling workflow - SigNoz setup choose one : SigNoz Cloud account https://signoz.io/teams/ with an active ingestion key- Self-hosted SigNoz instance pip installed for managing Python packages- Internet access to send telemetry data to SigNoz Cloud Optional but recommended A Python virtual environment to isolate dependencies Instrument LangChain and LangGraph with OpenTelemetry To capture detailed telemetry from LangChain/LangGraph without modifying your core application logic, we use OpenInference https://arize.com/docs/ax/observe/tracing/configure-tracing-options/instrument-with-openinference-helpers , a community-driven standard that provides pre-built instrumentation for popular AI frameworks like LangChain, built on top of OpenTelemetry. This allows you to trace your LangChain application with minimal configuration. Check out the openinference-instrumentation-langchain package on PyPI https://pypi.org/project/openinference-instrumentation-langchain/ for detailed setup instructions. No-code auto-instrumentation is recommended for quick setup with minimal code changes. It's ideal when you want to get observability up and running without modifying your application code and are leveraging standard instrumentor libraries. Step 1: Install the necessary packages in your Python environment. pip install \ opentelemetry-distro \ opentelemetry-exporter-otlp \ opentelemetry-instrumentation-httpx \ opentelemetry-instrumentation-system-metrics \ langgraph \ langchain \ openinference-instrumentation-langchain Step 2: Add Automatic Instrumentation opentelemetry-bootstrap --action=install Step 3: Configure logging level To ensure logs are properly captured and exported, configure the root logger to emit logs at the INFO level or higher: python import logging logging.getLogger .setLevel logging.INFO This sets the minimum log level for the root logger to INFO, which ensures that logger.info calls and higher severity logs WARNING, ERROR, CRITICAL are captured by the OpenTelemetry logging auto-instrumentation and sent to SigNoz. Step 4: Run an example php from langchain.agents import create agent def add numbers a: int, b: int - int: """Add two numbers together and return the result.""" return a + b agent = create agent model="openai:gpt-5-mini", tools= add numbers , system prompt="You are a helpful math tutor who can do calculations using the provided tools.", Run the agent agent.invoke {"messages": {"role": "user", "content": "what is 42 + 58?"} }, 📌 Note: Ensure that the OPENAI API KEY environment variable is properly defined with your API key before running the code. Step 5: Run your application with auto-instrumentation OTEL RESOURCE ATTRIBUTES="service.name=<service name " \ OTEL EXPORTER OTLP ENDPOINT="https://ingest.<region .signoz.cloud:443" \ OTEL EXPORTER OTLP HEADERS="signoz-ingestion-key=<your-ingestion-key " \ OTEL EXPORTER OTLP PROTOCOL=grpc \ OTEL TRACES EXPORTER=otlp \ OTEL METRICS EXPORTER=otlp \ OTEL LOGS EXPORTER=otlp \ OTEL PYTHON LOG CORRELATION=true \ OTEL PYTHON LOGGING AUTO INSTRUMENTATION ENABLED=true \ opentelemetry-instrument <your run command is the name of your service <service name <region : Your SigNoz Cloud region https://signoz.io/docs/ingestion/signoz-cloud/overview/ endpoint <your-ingestion-key : Your SigNoz ingestion key https://signoz.io/docs/ingestion/signoz-cloud/keys/ - Replace <your run command with the actual command you would use to run your application. For example: python main.py Using self-hosted SigNoz? Most steps are identical. To adapt this guide, update the endpoint and remove the ingestion key header as shown in Cloud → Self-Hosted https://signoz.io/docs/ingestion/cloud-vs-self-hosted/ cloud-to-self-hosted . Code-based instrumentation gives you fine-grained control over your telemetry configuration. Use this approach when you need to customize resource attributes, sampling strategies, or integrate with existing observability infrastructure. Step 1: Install OpenInference and OpenTelemetry related packages pip install openinference-instrumentation-langchain \ opentelemetry-exporter-otlp \ opentelemetry-sdk \ langgraph \ langchain Step 2: Import the necessary modules in your Python application python from opentelemetry.sdk.trace import TracerProvider from opentelemetry.sdk.trace.export import BatchSpanProcessor from opentelemetry.exporter.otlp.proto.http.trace exporter import OTLPSpanExporter from opentelemetry.sdk.resources import Resource from openinference.instrumentation.langchain import LangChainInstrumentor Step 3: Set up the OpenTelemetry Tracer Provider to send traces directly to SigNoz Cloud resource = Resource.create {"service.name": "<service name "} provider = TracerProvider resource=resource span exporter = OTLPSpanExporter endpoint="https://ingest.<region .signoz.cloud:443/v1/traces", headers={"signoz-ingestion-key": "<your-ingestion-key "}, provider.add span processor BatchSpanProcessor span exporter is the name of your service <service name - Set the to match your SigNoz Cloud <region region - Replace with your SigNoz <your-ingestion-key ingestion key Using self-hosted SigNoz? Most steps are identical. To adapt this guide, update the endpoint and remove the ingestion key header as shown in Cloud → Self-Hosted https://signoz.io/docs/ingestion/cloud-vs-self-hosted/ cloud-to-self-hosted . Step 4: Instrument LangChain using OpenInference Use the LangChainInstrumentor from OpenInference to automatically trace LangChain operations with your OpenTelemetry setup: LangChainInstrumentor .instrument 📌 Important: Place this code at the start of your application logic — before any LangChain/LangGraph functions are called or used — to ensure telemetry is correctly captured. Step 5: Run an example php from langchain.agents import create agent def add numbers a: int, b: int - int: """Add two numbers together and return the result.""" return a + b agent = create agent model="openai:gpt-5-mini", tools= add numbers , system prompt="You are a helpful math tutor who can do calculations using the provided tools.", Run the agent agent.invoke {"messages": {"role": "user", "content": "what is 42 + 58?"} }, 📌 Note: Ensure that the OPENAI API KEY environment variable is properly defined with your API key before running the code. Once configured, LangChain and LangGraph automatically emit traces, spans, and attributes for every agent request. LangChain and LangGraph traces are available in SigNoz under the Traces tab: When you click on a trace ID in SigNoz, you'll see a detailed view of the trace, including all associated spans, along with their events and attributes: Use the SigNoz MCP to automatically recognize important LangChain and LangGraph trace attributes and create a custom dashboard using natural language https://signoz.io/docs/ai/use-cases/dashboard-creation-natural-language/ , with no manual configuration needed. LangChain and LangGraph Observability in JavaScript You can instrument your LangChain/LangGraph applications in JavaScript using the OpenInference LangChain Instrumentor https://www.npmjs.com/package/@arizeai/openinference-instrumentation-langchain package. For detailed guidance on instrumenting JavaScript applications with OpenTelemetry and connecting them to SigNoz, see the SigNoz OpenTelemetry JavaScript instrumentation docs https://signoz.io/docs/instrumentation/opentelemetry-javascript/ .