# LangGraph LLM analytics installation - Docs

1.  1

    ## Install dependencies

    Required

    **Full working examples**

    See the complete [Node.js](https://github.com/PostHog/posthog-js/tree/main/examples/example-ai-langgraph) and [Python](https://github.com/PostHog/posthog-python/tree/master/examples/example-ai-langgraph) examples on GitHub. If you're using the PostHog SDK wrapper instead of OpenTelemetry, see the [Node.js wrapper](https://github.com/PostHog/posthog-js/tree/e08ff1be/examples/example-ai-langgraph) and [Python wrapper](https://github.com/PostHog/posthog-python/tree/7223c52/examples/example-ai-langgraph) examples.

    Install the OpenTelemetry SDK, the LangChain instrumentation, and LangGraph with OpenAI.

    PostHog AI

    ### Python

    ```bash
    pip install langgraph langchain-core langchain-openai opentelemetry-sdk posthog[otel] opentelemetry-instrumentation-langchain
    ```

    ### Node

    ```bash
    npm install @langchain/langgraph @langchain/openai @langchain/core zod @posthog/ai @opentelemetry/sdk-node @opentelemetry/resources @traceloop/instrumentation-langchain
    ```

2.  2

    ## Set up OpenTelemetry tracing

    Required

    Configure OpenTelemetry to auto-instrument LangChain calls and export traces to PostHog. LangGraph is built on LangChain, so the same instrumentation captures all LLM calls. PostHog converts `gen_ai.*` spans into `$ai_generation` events automatically.

    PostHog AI

    ### Python

    ```python
    from opentelemetry import trace
    from opentelemetry.sdk.trace import TracerProvider
    from opentelemetry.sdk.resources import Resource, SERVICE_NAME
    from posthog.ai.otel import PostHogSpanProcessor
    from opentelemetry.instrumentation.langchain import LangchainInstrumentor
    resource = Resource(attributes={
        SERVICE_NAME: "my-app",
        "posthog.distinct_id": "user_123", # optional: identifies the user in PostHog
        "foo": "bar", # custom properties are passed through
    })
    provider = TracerProvider(resource=resource)
    provider.add_span_processor(
        PostHogSpanProcessor(
            api_key="<ph_project_token>",
            host="https://us.i.posthog.com",
        )
    )
    trace.set_tracer_provider(provider)
    LangchainInstrumentor().instrument()
    ```

    ### Node

    ```typescript
    import { NodeSDK } from '@opentelemetry/sdk-node'
    import { resourceFromAttributes } from '@opentelemetry/resources'
    import { PostHogSpanProcessor } from '@posthog/ai/otel'
    import { LangChainInstrumentation } from '@traceloop/instrumentation-langchain'
    const sdk = new NodeSDK({
      resource: resourceFromAttributes({
        'service.name': 'my-app',
        'posthog.distinct_id': 'user_123', // optional: identifies the user in PostHog
        foo: 'bar', // custom properties are passed through
      }),
      spanProcessors: [
        new PostHogSpanProcessor({
          apiKey: '<ph_project_token>',
          host: 'https://us.i.posthog.com',
        }),
      ],
      instrumentations: [new LangChainInstrumentation()],
    })
    sdk.start()
    ```

3.  3

    ## Run your graph

    Required

    Use LangGraph as normal. The OpenTelemetry instrumentation automatically captures `$ai_generation` events for each LLM call — no callback handlers needed.

    PostHog AI

    ### Python

    ```python
    from langgraph.prebuilt import create_react_agent
    from langchain_openai import ChatOpenAI
    from langchain_core.tools import tool
    @tool
    def get_weather(city: str) -> str:
        """Get the weather for a given city."""
        return f"It's always sunny in {city}!"
    model = ChatOpenAI(api_key="your_openai_api_key")
    agent = create_react_agent(model, tools=[get_weather])
    result = agent.invoke(
        {"messages": [{"role": "user", "content": "What's the weather in Paris?"}]}
    )
    print(result["messages"][-1].content)
    ```

    ### Node

    ```typescript
    import { createReactAgent } from '@langchain/langgraph/prebuilt';
    import { ChatOpenAI } from '@langchain/openai';
    import { tool } from '@langchain/core/tools';
    import { z } from 'zod';
    const getWeather = tool(
      (input) => `It's always sunny in ${input.city}!`,
      {
        name: 'get_weather',
        description: 'Get the weather for a given city',
        schema: z.object({
          city: z.string().describe('The city to get the weather for'),
        }),
      }
    );
    const model = new ChatOpenAI({ apiKey: 'your_openai_api_key' });
    const agent = createReactAgent({ llm: model, tools: [getWeather] });
    const result = await agent.invoke(
      { messages: [{ role: 'user', content: "What's the weather in Paris?" }] }
    );
    console.log(result.messages[result.messages.length - 1].content);
    ```

    > **Note:** If you want to capture LLM events anonymously, omit the `posthog.distinct_id` resource attribute. See our docs on [anonymous vs identified events](/docs/data/anonymous-vs-identified-events.md) to learn more.

    PostHog automatically captures `$ai_generation` events and creates a trace hierarchy based on how LangGraph components are nested. You can expect captured events to have the following properties:

    | Property | Description |
    | --- | --- |
    | $ai_model | The specific model, like gpt-5-mini or claude-4-sonnet |
    | $ai_latency | The latency of the LLM call in seconds |
    | $ai_time_to_first_token | Time to first token in seconds (streaming only) |
    | $ai_tools | Tools and functions available to the LLM |
    | $ai_input | List of messages sent to the LLM |
    | $ai_input_tokens | The number of tokens in the input (often found in response.usage) |
    | $ai_output_choices | List of response choices from the LLM |
    | $ai_output_tokens | The number of tokens in the output (often found in response.usage) |
    | $ai_total_cost_usd | The total cost in USD (input + output) |
    | [[...]](/docs/llm-analytics/generations.md#event-properties) | See [full list](/docs/llm-analytics/generations.md#event-properties) of properties |

4.  ## Verify traces and generations

    Recommended

    *Confirm LLM events are being sent to PostHog*

    Let's make sure LLM events are being captured and sent to PostHog. Under **LLM analytics**, you should see rows of data appear in the **Traces** and **Generations** tabs.

    ![LLM generations in PostHog](https://res.cloudinary.com/dmukukwp6/image/upload/SCR_20250807_syne_ecd0801880.png)![LLM generations in PostHog](https://res.cloudinary.com/dmukukwp6/image/upload/SCR_20250807_syjm_5baab36590.png)

    [Check for LLM events in PostHog](https://app.posthog.com/llm-analytics/generations)

5.  4

    ## Next steps

    Recommended

    Now that you're capturing AI conversations, continue with the resources below to learn what else LLM Analytics enables within the PostHog platform.

    | Resource | Description |
    | --- | --- |
    | [Basics](/docs/llm-analytics/basics.md) | Learn the basics of how LLM calls become events in PostHog. |
    | [Generations](/docs/llm-analytics/generations.md) | Read about the $ai_generation event and its properties. |
    | [Traces](/docs/llm-analytics/traces.md) | Explore the trace hierarchy and how to use it to debug LLM calls. |
    | [Spans](/docs/llm-analytics/spans.md) | Review spans and their role in representing individual operations. |
    | [Anaylze LLM performance](/docs/llm-analytics/dashboard.md) | Learn how to create dashboards to analyze LLM performance. |

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