# LangChain & LangGraph Observability with OpenTelemetry

> Source: <https://signoz.io/docs/langchain-observability>
> Published: 2026-05-26 00:00:00+00:00

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/).