LangGraph LLM analytics installation

Install dependencies

Required

Full working examples

See the complete Node.js and Python examples on GitHub. If you're using the PostHog SDK wrapper instead of OpenTelemetry, see the Node.js wrapper and Python wrapper examples.

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

pip install langgraph langchain-openai opentelemetry-sdk opentelemetry-exporter-otlp-proto-http opentelemetry-instrumentation-langchain
npm install @langchain/langgraph @langchain/openai @langchain/core @posthog/ai @opentelemetry/sdk-node @opentelemetry/resources @traceloop/instrumentation-langchain

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.

from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import SimpleSpanProcessor
from opentelemetry.sdk.resources import Resource, SERVICE_NAME
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
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
})

exporter = OTLPSpanExporter(
    endpoint="https://us.i.posthog.com/i/v0/ai/otel",
    headers={"Authorization": "Bearer <ph_project_token>"},
)

provider = TracerProvider(resource=resource)
provider.add_span_processor(SimpleSpanProcessor(exporter))
trace.set_tracer_provider(provider)

LangchainInstrumentor().instrument()
import { NodeSDK, tracing } from '@opentelemetry/sdk-node'
import { resourceFromAttributes } from '@opentelemetry/resources'
import { PostHogTraceExporter } 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 tracing.SimpleSpanProcessor(
      new PostHogTraceExporter({
        apiKey: '<ph_project_token>',
        host: 'https://us.i.posthog.com',
      })
    ),
  ],
  instrumentations: [new LangChainInstrumentation()],
})
sdk.start()

Run your graph

Required

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

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)
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 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)
[...]	See full list of properties

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.

Check for LLM events in PostHog

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	Learn the basics of how LLM calls become events in PostHog.
Generations	Read about the `$ai_generation` event and its properties.
Traces	Explore the trace hierarchy and how to use it to debug LLM calls.
Spans	Review spans and their role in representing individual operations.
Anaylze LLM performance	Learn how to create dashboards to analyze LLM performance.

LangGraph LLM analytics installation

Contents

Install dependencies

Set up OpenTelemetry tracing

Run your graph

Verify traces and generations

Next steps

Community questions

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