Chapter 150 · LLM Analytics Setup
Subchapter 150.8
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Required
Setting up analytics starts with installing the PostHog SDK. CrewAI uses LiteLLM under the hood, and PostHog integrates with LiteLLM’s callback system.
pip install posthog2
Required
Install CrewAI. PostHog instruments your LLM calls through LiteLLM’s callback system that CrewAI uses natively.
pip install crewai litellm3
Required
Set your PostHog project token and host as environment variables, then configure LiteLLM to use PostHog as a callback handler. You can find your project token in your project settings (opens in a new tab).
import os
import litellm
from crewai import Agent, Task, Crew
# Set PostHog environment variables
os.environ["POSTHOG_API_KEY"] = "<ph_project_token>"
os.environ["POSTHOG_API_URL"] = "https://us.i.posthog.com"
# Enable PostHog callbacks in LiteLLM
litellm.success_callback = ["posthog"]
litellm.failure_callback = ["posthog"]How this works
CrewAI uses LiteLLM under the hood for LLM provider access. By configuring PostHog as a LiteLLM callback, all LLM calls made through CrewAI are automatically captured as $ai_generation events without proxying your calls.
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Required
Run your CrewAI agents as normal. PostHog automatically captures generation events for each LLM call.
researcher = Agent(
role="Researcher",
goal="Find interesting facts about hedgehogs",
backstory="You are an expert wildlife researcher.",
)
task = Task(
description="Research three fun facts about hedgehogs.",
expected_output="A list of three fun facts.",
agent=researcher,
)
crew = Crew(
agents=[researcher],
tasks=[task],
)
result = crew.kickoff()
print(result)You can expect captured $ai_generation 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) |
| […] (opens in a new tab) | See full list (opens in a new tab) of properties |
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.
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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 (opens in a new tab) | Learn the basics of how LLM calls become events in PostHog. |
| Generations (opens in a new tab) | Read about the $ai_generation event and its properties. |
| Traces (opens in a new tab) | Explore the trace hierarchy and how to use it to debug LLM calls. |
| Spans (opens in a new tab) | Review spans and their role in representing individual operations. |
| Anaylze LLM performance (opens in a new tab) | Learn how to create dashboards to analyze LLM performance. |
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