LLM Analytics Setup

Manual capture LLM analytics installation - Docs

1. 1

   Capture LLM events manually

   If you're using a different server-side SDK or prefer to use the API, you can manually capture the data by calling the capture method or using the capture API.

   API

   Capture via API

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   curl -X POST "https://us.i.posthog.com/i/v0/e/" \
           -H "Content-Type: application/json" \
           -d '{
               "api_key": "<ph_project_token>",
               "event": "$ai_generation",
               "properties": {
                   "distinct_id": "user_123",
                   "$ai_trace_id": "trace_id_here",
                   "$ai_model": "gpt-5-mini",
                   "$ai_provider": "openai",
                   "$ai_input": [{"role": "user", "content": "Tell me a fun fact about hedgehogs"}],
                   "$ai_input_tokens": 10,
                   "$ai_output_choices": [{"role": "assistant", "content": "Hedgehogs have around 5,000 to 7,000 spines on their backs!"}],
                   "$ai_output_tokens": 20,
                   "$ai_latency": 1.5,
                   "$ai_stream": true,
                   "$ai_time_to_first_token": 0.25
               }
           }'

   Node.js

   1. Install

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   npm install posthog-node

   2. Initialize PostHog

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

   3. Capture Event

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   // After your LLM call
   client.capture({
       distinctId: 'user_123',
       event: '$ai_generation',
       properties: {
           $ai_trace_id: 'trace_id_here',
           $ai_model: 'gpt-5-mini',
           $ai_provider: 'openai',
           $ai_input: [{ role: 'user', content: 'Tell me a fun fact about hedgehogs' }],
           $ai_input_tokens: 10,
           $ai_output_choices: [{ role: 'assistant', content: 'Hedgehogs have around 5,000 to 7,000 spines on their backs!' }],
           $ai_output_tokens: 20,
           $ai_latency: 1.5,
           // For streaming responses, also include:
           // $ai_stream: true,
           // $ai_time_to_first_token: 0.25
       }
   })
   client.shutdown()

   Python

   1. Install

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   pip install posthog

   2. Initialize PostHog

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

   3. Capture Event

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   # After your LLM call
   posthog.capture(
       distinct_id='user_123',
       event='$ai_generation',
       properties={
           '$ai_trace_id': 'trace_id_here',
           '$ai_model': 'gpt-5-mini',
           '$ai_provider': 'openai',
           '$ai_input': [{'role': 'user', 'content': 'Tell me a fun fact about hedgehogs'}],
           '$ai_input_tokens': 10,
           '$ai_output_choices': [{'role': 'assistant', 'content': 'Hedgehogs have around 5,000 to 7,000 spines on their backs!'}],
           '$ai_output_tokens': 20,
           '$ai_latency': 1.5,
           # For streaming responses, also include:
           # '$ai_stream': True,
           # '$ai_time_to_first_token': 0.25
       }
   )

   Go

   1. Install

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   go get github.com/posthog/posthog-go

   2. Initialize PostHog

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   import "github.com/posthog/posthog-go"
   client, _ := posthog.NewWithConfig("<ph_project_token>", posthog.Config{
       Endpoint: "https://us.i.posthog.com",
   })
   defer client.Close()

   3. Capture Event

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   // After your LLM call
   client.Enqueue(posthog.Capture{
       DistinctId: "user_123",
       Event:      "$ai_generation",
       Properties: map[string]interface{}{
           "$ai_trace_id":        "trace_id_here",
           "$ai_model":           "gpt-5-mini",
           "$ai_provider":        "openai",
           "$ai_input_tokens":    10,
           "$ai_output_tokens":   20,
           "$ai_latency":         1.5,
           // For streaming responses, also include:
           // "$ai_stream":              true,
           // "$ai_time_to_first_token": 0.25,
       },
   })

   Ruby

   1. Install

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   gem install posthog-ruby

   2. Initialize PostHog

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   require 'posthog-ruby'
   posthog = PostHog::Client.new({
       api_key: '<ph_project_token>',
       host: 'https://us.i.posthog.com'
   })

   3. Capture Event

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   # After your LLM call
   posthog.capture({
       distinct_id: 'user_123',
       event: '$ai_generation',
       properties: {
       '$ai_trace_id' => 'trace_id_here',
       '$ai_model' => 'gpt-5-mini',
       '$ai_provider' => 'openai',
       '$ai_input_tokens' => 10,
       '$ai_output_tokens' => 20,
       '$ai_latency' => 1.5
       # For streaming responses, also include:
       # '$ai_stream' => true,
       # '$ai_time_to_first_token' => 0.25
       }
   })

   PHP

   1. Install

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   composer require posthog/posthog-php

   2. Initialize PostHog

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   <?php
   require_once __DIR__ . '/vendor/autoload.php';
   use PostHog\PostHog;
   PostHog::init('<ph_project_token>', [
       'host' => 'https://us.i.posthog.com'
   ]);

   3. Capture Event

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   // After your LLM call
   PostHog::capture([
       'distinctId' => 'user_123',
       'event' => '$ai_generation',
       'properties' => [
           '$ai_trace_id' => 'trace_id_here',
           '$ai_model' => 'gpt-5-mini',
           '$ai_provider' => 'openai',
           '$ai_input_tokens' => 10,
           '$ai_output_tokens' => 20,
           '$ai_latency' => 1.5
           // For streaming responses, also include:
           // '$ai_stream' => true,
           // '$ai_time_to_first_token' => 0.25
       ]
   ]);

   Event Properties

   Each event type has specific properties. See the tabs below for detailed property documentation for each event type.

   Generation

   A generation is a single call to an LLM.

   Event name: $ai_generation

   Core properties

   Property	Description
   $ai_trace_id	The trace ID (a UUID to group AI events) like conversation_idMust contain only letters, numbers, and special characters: -, _, ~, ., @, (, ), !, ', :, |Example: d9222e05-8708-41b8-98ea-d4a21849e761
   $ai_session_id	(Optional) Groups related traces together. Use this to organize traces by whatever grouping makes sense for your application (user sessions, workflows, conversations, or other logical boundaries).Example: session-abc-123, conv-user-456
   $ai_span_id	(Optional) Unique identifier for this generation
   $ai_span_name	(Optional) Name given to this generationExample: summarize_text
   $ai_parent_id	(Optional) Parent span ID for tree view grouping
   $ai_model	The model usedExample: gpt-5-mini
   $ai_provider	The LLM providerExample: openai, anthropic, gemini
   $ai_input_tokens	The number of tokens in the input (often found in response.usage)
   $ai_output_tokens	The number of tokens in the output (often found in response.usage)
   $ai_latency	(Optional) The latency of the LLM call in seconds
   $ai_time_to_first_token	(Optional) Time to first token in seconds. Only applicable for streaming responses.
   $ai_http_status	(Optional) The HTTP status code of the response
   $ai_base_url	(Optional) The base URL of the LLM providerExample: https://api.openai.com/v1
   $ai_request_url	(Optional) The full URL of the request made to the LLM APIExample: https://api.openai.com/v1/chat/completions
   $ai_is_error	(Optional) Boolean to indicate if the request was an error
   $ai_error	(Optional) The error message or object

   Cost properties

   Cost properties are optional as we can automatically calculate them from model and token counts. If you want, you can provide your own cost properties or custom pricing instead.

   Pre-calculated costs

   Property	Description
   $ai_input_cost_usd	(Optional) The cost in USD of the input tokens
   $ai_output_cost_usd	(Optional) The cost in USD of the output tokens
   $ai_request_cost_usd	(Optional) The cost in USD for the requests
   $ai_web_search_cost_usd	(Optional) The cost in USD for the web searches
   $ai_total_cost_usd	(Optional) The total cost in USD (sum of all cost components)

   Custom pricing

   Property	Description
   $ai_input_token_price	(Optional) Price per input token (used to calculate $ai_input_cost_usd)
   $ai_output_token_price	(Optional) Price per output token (used to calculate $ai_output_cost_usd)
   $ai_cache_read_token_price	(Optional) Price per cached token read
   $ai_cache_write_token_price	(Optional) Price per cached token write
   $ai_request_price	(Optional) Price per request
   $ai_request_count	(Optional) Number of requests (defaults to 1 if $ai_request_price is set)
   $ai_web_search_price	(Optional) Price per web search
   $ai_web_search_count	(Optional) Number of web searches performed

   Cache properties

   Property	Description
   $ai_cache_read_input_tokens	(Optional) Number of tokens read from cache
   $ai_cache_creation_input_tokens	(Optional) Number of tokens written to cache (Anthropic-specific)
   $ai_cache_reporting_exclusive	(Optional) Whether cache tokens are excluded from $ai_input_tokens. When true, cache tokens are separate from input tokens. When false, input tokens already include cache tokens. Defaults to true for Anthropic provider or Claude models, false otherwise.

   Model parameters

   Property	Description
   $ai_temperature	(Optional) Temperature parameter used in the LLM request
   $ai_stream	(Optional) Whether the response was streamed
   $ai_max_tokens	(Optional) Maximum tokens setting for the LLM response

   Trace

   A trace is a group that contains multiple spans, generations, and embeddings. Traces can be manually sent as events or appear as pseudo-events automatically created from child events.

   Event name: $ai_trace

   Core properties

   Property	Description
   $ai_trace_id	The trace ID (a UUID to group related AI events together)Must contain only letters, numbers, and special characters: -, _, ~, ., @, (, ), !, ', :, |Example: d9222e05-8708-41b8-98ea-d4a21849e761
   $ai_session_id	(Optional) Groups related traces together. Use this to organize traces by whatever grouping makes sense for your application (user sessions, workflows, conversations, or other logical boundaries).Example: session-abc-123, conv-user-456
   $ai_latency	(Optional) The latency of the trace in seconds
   $ai_span_name	(Optional) The name of the traceExample: chat_completion, rag_pipeline
   $ai_is_error	(Optional) Boolean to indicate if the trace encountered an error
   $ai_error	(Optional) The error message or object if the trace failed

   Pseudo-trace Events

   When you send generation ($ai_generation), span ($ai_span), or embedding ($ai_embedding) events with a $ai_trace_id, PostHog automatically creates a pseudo-trace event that appears in the dashboard as a parent grouping. These pseudo-traces:

   • Are not actual events in your data

   • Automatically aggregate metrics from child events (latency, tokens, costs)

   • Provide a hierarchical view of your AI operations

   • Do not require sending an explicit $ai_trace event

   This means you can either:

   1. Send explicit $ai_trace events to control the trace metadata

   2. Let PostHog automatically create pseudo-traces from your generation/span events

   Span

   A span is a single action within your application, such as a function call or vector database search.

   Event name: $ai_span

   Core properties

   Property	Description
   $ai_trace_id	The trace ID (a UUID to group related AI events together)Must contain only letters, numbers, and the following characters: -, _, ~, ., @, (, ), !, ', :, |Example: d9222e05-8708-41b8-98ea-d4a21849e761
   $ai_session_id	(Optional) Groups related traces together. Use this to organize traces by whatever grouping makes sense for your application (user sessions, workflows, conversations, or other logical boundaries).Example: session-abc-123, conv-user-456
   $ai_span_id	(Optional) Unique identifier for this spanExample: bdf42359-9364-4db7-8958-c001f28c9255
   $ai_span_name	(Optional) The name of the spanExample: vector_search, data_retrieval, tool_call
   $ai_parent_id	(Optional) Parent ID for tree view grouping (trace_id or another span_id)Example: 537b7988-0186-494f-a313-77a5a8f7db26
   $ai_latency	(Optional) The latency of the span in secondsExample: 0.361
   $ai_is_error	(Optional) Boolean to indicate if the span encountered an error

   Embedding

   An embedding is a single call to an embedding model to convert text into a vector representation.

   Event name: $ai_embedding

   Core properties

   Property	Description
   $ai_trace_id	The trace ID (a UUID to group related AI events together). Must contain only letters, numbers, and special characters: -, _, ~, ., @, (, ), !, ', :, |Example: d9222e05-8708-41b8-98ea-d4a21849e761
   $ai_session_id	(Optional) Groups related traces together. Use this to organize traces by whatever grouping makes sense for your application (user sessions, workflows, conversations, or other logical boundaries).Example: session-abc-123, conv-user-456
   $ai_span_id	(Optional) Unique identifier for this embedding operation
   $ai_span_name	(Optional) Name given to this embedding operationExample: embed_user_query, index_document
   $ai_parent_id	(Optional) Parent span ID for tree-view grouping
   $ai_model	The embedding model usedExample: text-embedding-3-small, text-embedding-ada-002
   $ai_provider	The LLM providerExample: openai, cohere, voyage
   $ai_input	The text to embedExample: "Tell me a fun fact about hedgehogs" or array of strings for batch embeddings
   $ai_input_tokens	The number of tokens in the input
   $ai_latency	(Optional) The latency of the embedding call in seconds
   $ai_http_status	(Optional) The HTTP status code of the response
   $ai_base_url	(Optional) The base URL of the LLM providerExample: https://api.openai.com/v1
   $ai_request_url	(Optional) The full URL of the request made to the embedding APIExample: https://api.openai.com/v1/embeddings
   $ai_is_error	(Optional) Boolean to indicate if the request was an error
   $ai_error	(Optional) The error message or object if the embedding failed

   Cost properties

   Cost properties are optional as we can automatically calculate them from model and token counts. If you want, you can provide your own cost property instead.

   Property	Description
   $ai_input_cost_usd	(Optional) Cost in USD for input tokens
   $ai_output_cost_usd	(Optional) Cost in USD for output tokens (usually 0 for embeddings)
   $ai_total_cost_usd	(Optional) Total cost in USD

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