LangGraph LLM analytics installation

Install the PostHog SDK

Required

Setting up analytics starts with installing the PostHog SDK for your language. LLM analytics works best with our Python and Node SDKs.

pip install posthog
npm install @posthog/ai posthog-node

Install LangGraph

Required

Install LangGraph and LangChain. PostHog instruments your LLM calls through LangChain-compatible callback handlers that LangGraph supports.

pip install langgraph langchain-openai
npm install @langchain/langgraph @langchain/openai @langchain/core

Initialize PostHog

Required

Initialize PostHog with your project API key and host from your project settings, then create a LangChain CallbackHandler.

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

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

callback_handler = CallbackHandler(
    client=posthog,
    distinct_id="user_123", # optional
    trace_id="trace_456", # optional
    properties={"conversation_id": "abc123"}, # optional
    groups={"company": "company_id_in_your_db"}, # optional
    privacy_mode=False # optional
)
import { PostHog } from 'posthog-node';
import { LangChainCallbackHandler } from '@posthog/ai';

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

const callbackHandler = new LangChainCallbackHandler({
  client: phClient,
  distinctId: 'user_123', // optional
  traceId: 'trace_456', // optional
  properties: { conversationId: 'abc123' }, // optional
  groups: { company: 'company_id_in_your_db' }, // optional
  privacyMode: false, // optional
});

How this works

LangGraph is built on LangChain, so it supports LangChain-compatible callback handlers. PostHog's CallbackHandler captures $ai_generation events and trace hierarchy automatically without proxying your calls.

Run your graph

Required

Pass the callback_handler in the config when invoking your LangGraph graph. PostHog automatically captures generation events for each LLM call.

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?"}]},
    config={"callbacks": [callback_handler]}
)

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?" }] },
  { callbacks: [callbackHandler] }
);

console.log(result.messages[result.messages.length - 1].content);
phClient.shutdown();

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 the PostHog SDK

Install LangGraph

Initialize PostHog

Run your graph

Verify traces and generations

Next steps

Community questions

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