LangGraph AI Observability installation

Let AI instrument your LLM calls for you

Skip the manual setup — run this in your project and the wizard installs the SDK and wires up AI Observability for you.

Learn more

Install dependencies

Required

Full working examples

See the complete Node.js and Python examples on GitHub.

Install the PostHog SDK and LangGraph with OpenAI.

pip install posthog langgraph langchain-core langchain-openai
npm install posthog-node @posthog/ai @langchain/langgraph @langchain/openai @langchain/core zod

Configure PostHog

Required

Create a PostHog client once, then build a callback handler for each request or conversation. distinct_id ties each call to a user, and $ai_session_id groups calls in one conversation.

from posthog import Posthog
from posthog.ai.langchain import CallbackHandler

posthog = Posthog("<ph_project_token>", host="https://us.i.posthog.com")

def create_handler(user_id: str, session_id: str) -> CallbackHandler:
    return CallbackHandler(
        client=posthog,
        distinct_id=user_id,
        properties={"$ai_session_id": session_id},
    )
import { PostHog } from 'posthog-node'
import { LangChainCallbackHandler } from '@posthog/ai/langchain'

const posthog = new PostHog('<ph_project_token>', { host: 'https://us.i.posthog.com' })

function createHandler(userId: string, sessionId: string): LangChainCallbackHandler {
  return new LangChainCallbackHandler({
    client: posthog,
    distinctId: userId,
    properties: { $ai_session_id: sessionId },
  })
}

Note: If you want to capture LLM events anonymously, omit distinct_id/distinctId when constructing the handler. See our docs on anonymous vs identified events to learn more.

Run your graph

Required

Attach the handler through config when you invoke the graph, inside the function that handles a turn. Because create_react_agent runs the turn as a single root run, PostHog nests each tool call as an $ai_span under the trace automatically, with real latency.

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])

def ask(user_input: str, user_id: str, conversation_id: str) -> str:
    handler = create_handler(user_id=user_id, session_id=conversation_id)
    result = agent.invoke(
        {"messages": [{"role": "user", "content": user_input}]},
        config={"callbacks": [handler]},
    )
    return result["messages"][-1].content

print(ask("What's the weather in Paris?", "user_123", "conversation-abc"))
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({ model, tools: [getWeather] })

async function ask(userInput: string, userId: string, conversationId: string): Promise<string> {
  const handler = createHandler(userId, conversationId)
  const result = await agent.invoke(
    { messages: [{ role: 'user', content: userInput }] },
    { callbacks: [handler] }
  )
  return result.messages[result.messages.length - 1].content
}

console.log(await ask("What's the weather in Paris?", 'user_123', 'conversation-abc'))

PostHog automatically captures an $ai_generation event along with these 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

The handler also builds a trace hierarchy automatically based on how you structure your graph. Pass the same $ai_session_id to every handler you construct for a conversation, to group its calls into one session. Pass trace_id/traceId too, to control the top-level trace ID instead of letting PostHog generate one.

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 AI Observability, 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 AI Observability 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 AI Observability installation

Contents

Install dependencies

Configure PostHog

Run your graph

Verify traces and generations

Next steps

Community questions

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