Claude Agent SDK 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.

Install the PostHog SDK

Required

Setting up analytics starts with installing the PostHog Python SDK.

pip install posthog

2
Install the Claude Agent SDK
Required
Install the Claude Agent SDK. PostHog instruments your agent queries by wrapping the query() function. The PostHog SDK does not proxy your calls.
pip install claude-agent-sdk
Proxy note
These SDKs do not proxy your calls. They only fire off an async call to PostHog in the background to send the data. You can also use AI observability with other SDKs or our API, but you will need to capture the data in the right format. See the schema in the manual capture section for more details.

Initialize PostHog and run a query

Required

Initialize PostHog with your project token and host from your project settings. Then use the PostHog query() wrapper as a drop-in replacement for claude_agent_sdk.query(). This automatically captures $ai_generation, $ai_span, and $ai_trace events, including a span for each tool the agent uses, such as Bash below.

import asyncio
from posthog import Posthog
from posthog.ai.claude_agent_sdk import query
from claude_agent_sdk import ClaudeAgentOptions

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

async def main():
    options = ClaudeAgentOptions(
        max_turns=5,
        allowed_tools=["Read", "Glob", "Grep"],
        cwd="/path/to/your/project",
    )

    async for message in query(
        prompt="Read the README and summarize this project",
        options=options,
        posthog_client=posthog,
        posthog_distinct_id="user_123", # optional
        posthog_trace_id="trace_123", # optional
        posthog_properties={"conversation_id": "abc123", "$ai_session_id": "conversation-abc"}, # optional
        posthog_groups={"company": "company_id_in_your_db"}, # optional
        posthog_privacy_mode=False, # optional
    ):
        print(message)

asyncio.run(main())
posthog.shutdown()

Notes:

The wrapper yields all original messages unchanged, fully transparent to your code.

Pass $ai_session_id in posthog_properties to group every generation, span, and trace from a call into one PostHog session.

If you want to capture LLM events anonymously, do not pass a distinct ID. See our docs on anonymous vs identified events to learn more.

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)
[...]	See full list of properties

Reusable configuration with instrument()

Optional

If you make multiple query() calls with the same PostHog configuration, use instrument() to configure once and reuse across queries.

import asyncio
from posthog import Posthog
from posthog.ai.claude_agent_sdk import instrument
from claude_agent_sdk import ClaudeAgentOptions

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

ph = instrument(
    client=posthog,
    distinct_id="user_123",
    properties={"app": "my-agent"},
)

options = ClaudeAgentOptions(max_turns=10)

async def main():
    # All queries share the same PostHog config
    async for msg in ph.query(prompt="Question 1", options=options):
        ...
    async for msg in ph.query(prompt="Question 2", options=options):
        ...

asyncio.run(main())

You can override any PostHog parameter per-query:

async for msg in ph.query(
    prompt="...",
    options=options,
    posthog_distinct_id="different_user",
    posthog_properties={"extra": "data"},
):
    ...

Tool usage and multi-turn conversations

Optional

The recommended example above already runs a tool-using query, and max_turns lets Claude take more than one internal turn before it replies. Inspect the message stream to see each turn and tool call as they happen.

from claude_agent_sdk import AssistantMessage, TextBlock, ToolUseBlock

async for message in query(
    prompt="Read the README and summarize this project",
    options=options,
    posthog_client=posthog,
    posthog_distinct_id="user_123",
):
    if isinstance(message, AssistantMessage):
        for block in message.content:
            if isinstance(block, TextBlock):
                print(block.text)
            elif isinstance(block, ToolUseBlock):
                print(f"Tool: {block.name}")

Across the whole query, PostHog captures:

$ai_generation events for each LLM turn (with token counts, cost, and cache metrics)
$ai_span events for each tool use (Read, Glob, Grep, Bash, etc.)
An $ai_trace event grouping the entire conversation with total cost and latency

Multi-turn conversations with history

Optional

For stateful, multi-turn conversations where each follow-up has full context from previous turns, use PostHogClaudeSDKClient. This wraps the Claude Agent SDK's ClaudeSDKClient and instruments each turn automatically. All turns share a single trace.

from posthog import Posthog
from posthog.ai.claude_agent_sdk import PostHogClaudeSDKClient
from claude_agent_sdk import ClaudeAgentOptions, AssistantMessage
from claude_agent_sdk.types import TextBlock

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

options = ClaudeAgentOptions(max_turns=5)

async with PostHogClaudeSDKClient(
    options,
    posthog_client=posthog,
    posthog_distinct_id="user_123",
    posthog_properties={"app": "my-agent"},
) as client:
    # Turn 1
    await client.query("What is the capital of France?")
    async for msg in client.receive_response():
        if isinstance(msg, AssistantMessage):
            for block in msg.content:
                if isinstance(block, TextBlock):
                    print(block.text)

    # Turn 2 — has full conversation history
    await client.query("What language do they speak there?")
    async for msg in client.receive_response():
        if isinstance(msg, AssistantMessage):
            for block in msg.content:
                if isinstance(block, TextBlock):
                    print(block.text)

Each receive_response() cycle emits $ai_generation events for that turn. When the client disconnects, exiting the async with block, it emits a single $ai_trace event covering the entire session with aggregate latency.

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.

Claude Agent SDK AI Observability installation

Contents

Install the PostHog SDK

Install the Claude Agent SDK

Initialize PostHog and run a query

Reusable configuration with instrument()

Tool usage and multi-turn conversations

Multi-turn conversations with history

Verify traces and generations

Next steps

Community questions

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