> ## Documentation Index
> Fetch the complete documentation index at: https://docs.openlit.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Monitor LangGraph using OpenTelemetry
OpenLIT uses OpenTelemetry Auto-Instrumentation to help you monitor AI applications built using LangGraph. This includes tracking agent workflows, tool usage, graph execution, and how users interact with the application.
Auto-instrumentation means you don't have to set up monitoring manually for different LLMs, frameworks, or databases. By simply adding OpenLIT in your application, all the necessary monitoring configurations are automatically set up.
The integration is compatible with
* LangGraph Python SDK client >= 0.2.0
* OpenLIT Typescript SDK >= 1.0.0
## Get started
Open your command line or terminal and run:
```shell theme={null}
pip install openlit
```
```shell theme={null}
npm install openlit
```
Perfect for existing applications - no code modifications needed:
```bash theme={null}
# Configure via CLI arguments
openlit-instrument \
--service-name my-ai-app \
--environment production \
--otlp-endpoint YOUR_OTEL_ENDPOINT \
python your_app.py
```
```bash theme={null}
# Configure via environment variables
export OTEL_SERVICE_NAME=my-ai-app
export OTEL_DEPLOYMENT_ENVIRONMENT=production
export OTEL_EXPORTER_OTLP_ENDPOINT=YOUR_OTEL_ENDPOINT
# Run with zero code changes
openlit-instrument python your_app.py
```
**Perfect for**: Legacy applications, production systems where code changes need approval, quick testing, or when you want to add observability without touching existing code.
```python theme={null}
import openlit
openlit.init(otlp_endpoint="YOUR_OTEL_ENDPOINT")
```
Add the following two lines to your application code:
```python theme={null}
import openlit
openlit.init()
```
Then, configure the your OTLP endpoint using environment variable:
```shell theme={null}
export OTEL_EXPORTER_OTLP_ENDPOINT=YOUR_OTEL_ENDPOINT
```
```typescript theme={null}
import openlit from "openlit"
openlit.init({ otlpEndpoint: "YOUR_OTEL_ENDPOINT" })
```
Add the following two lines to your application code:
```typescript theme={null}
import openlit from "openlit"
openlit.init()
```
Then, configure the your OTLP endpoint using environment variable:
```shell theme={null}
export OTEL_EXPORTER_OTLP_ENDPOINT=YOUR_OTEL_ENDPOINT
```
**Replace:** `YOUR_OTEL_ENDPOINT` with the URL of your OpenTelemetry backend, such as `http://127.0.0.1:4318` if you are using OpenLIT and a local OTel Collector.
To send metrics and traces to other Observability tools, refer to the [supported destinations](/latest/sdk/destinations/overview).
For more advanced configurations and application use cases, visit the [OpenLIT Python repository](https://github.com/openlit/openlit/tree/main/sdk/python) or [OpenLIT Typescript repository](https://github.com/openlit/openlit/tree/main/sdk/typescript).
***
Production-ready AI monitoring setup in 2 simple steps with zero code changes
Configure the OpenLIT SDK according to you requirements.
Send telemetry to Datadog, Grafana, New Relic, and other observability stacks
Kubernetes
}
href="/latest/operator/overview"
>
Automatically inject instrumentation into existing workloads without modifying pod specs, container images, or application code.