Langfuse

Langfuse is an open-source LLM engineering platform that provides observability and analytics for AI applications. This integration allows you to automatically sync LLM metrics from Langfuse into your Mixpanel dashboards, enabling you to understand how your AI features impact user behavior and business outcomes.

Use this integration to answer questions like:

“Are my most active users also the ones who are most engaged with my LLM content?”
“Does interacting with the LLM feature relate to higher retention rates?”
“How does the LLM feature impact my conversion rates?”
“Does the user feedback captured in Langfuse correlate with user behavior in Mixpanel?”

Setup

Prerequisites

An active Langfuse account with a project configured
Your Mixpanel Project Token (found in Project Settings)

Configure the integration

Log into your Langfuse account and navigate to your project settings
Find the Mixpanel integration section
Select your Mixpanel region:
- US: api.mixpanel.com
- EU: api-eu.mixpanel.com
- India: api-in.mixpanel.com
Enter your Mixpanel Project Token
Enable the integration

Data synchronization

Once enabled, Langfuse will:

Perform an initial sync of all historical data from your Langfuse project
Automatically sync new data every hour (with a 30-minute delay)

Your Mixpanel dashboards will stay up to date with your latest LLM metrics.

Data Schema

User Matching

Langfuse automatically maps user identifiers to ensure seamless data integration:

Langfuse Field	Mixpanel Field	Description
`user_id`	`distinct_id`	Primary user identifier
Trace/generation/score timestamp	`time`	Event timestamp (milliseconds since epoch)
`trace.metadata.$mixpanel_session_id`	`session_id`	Optional session identifier (add this to your Langfuse trace metadata for session tracking)

Events

The integration sends three event types to Mixpanel:

`[Langfuse] Trace`

Represents a complete LLM interaction (e.g., a user conversation or workflow).

Properties:

time: Milliseconds since epoch when the event occurred
distinct_id: User ID or anonymous identifier
$user_id: User ID sent to Mixpanel’s native user ID field
$insert_id: Unique identifier for deduplication
session_id: Optional session identifier (from $mixpanel_session_id in metadata, or falls back to Langfuse session_id)
langfuse_trace_name: The name of the trace
langfuse_url: The URL of the trace in Langfuse
langfuse_user_url: Deep link to the user profile in Langfuse
langfuse_id: The unique identifier of the trace
langfuse_cost_usd: The total cost associated with the trace
langfuse_count_observations: The number of observations (LLM calls) in the trace
langfuse_session_id: The session ID related to the event
langfuse_project_id: The project ID associated with the event
langfuse_user_id: User ID related to the event (defaults to langfuse_unknown_user if null)
langfuse_latency: The latency of the trace in milliseconds
langfuse_release: Release information associated with the trace
langfuse_version: The version of the trace
langfuse_tags: Tags associated with the trace
langfuse_environment: The environment associated with the trace (e.g., production, staging)
langfuse_event_version: The integration version of Langfuse

`[Langfuse] Generation`

Represents an individual LLM generation (e.g., a single API call to OpenAI, Anthropic, etc.).

Properties:

time: Milliseconds since epoch when the generation started
distinct_id: User ID or anonymous identifier
$user_id: User ID sent to Mixpanel’s native user ID field
$insert_id: Unique identifier for deduplication
session_id: Optional session identifier (from $mixpanel_session_id in metadata, or falls back to Langfuse session_id)
langfuse_generation_name: The name of the generation
langfuse_trace_name: Name of the trace related to the generation
langfuse_trace_id: The unique identifier of the trace related to the generation
langfuse_url: The URL of the generation in Langfuse
langfuse_user_url: Deep link to the user profile in Langfuse
langfuse_id: Unique identifier of the generation
langfuse_cost_usd: Computed total cost of the generation
langfuse_input_units: Number of tokens used in the input/prompt
langfuse_output_units: Number of tokens produced by the generation
langfuse_total_units: Total number of tokens consumed in the generation process
langfuse_session_id: The session ID associated with the trace of the generation
langfuse_project_id: The project ID where the generation occurred
langfuse_user_id: The user ID that started the trace linked to the generation (defaults to langfuse_unknown_user if unavailable)
langfuse_latency: The observed latency of the generation in milliseconds
langfuse_time_to_first_token: The time taken to generate the first token when streaming (milliseconds)
langfuse_release: Release information of the trace attached to the generation
langfuse_version: The version information about the generation
langfuse_model: The model used during this generation (e.g., gpt-4, claude-3-sonnet)
langfuse_level: The level associated with the generation
langfuse_tags: Tags attached to the trace of the generation
langfuse_environment: The environment associated with the generation
langfuse_event_version: The integration version with Langfuse

`[Langfuse] Score`

Represents user feedback, evaluations, or quality metrics.

Properties:

time: Milliseconds since epoch when the score event occurred
distinct_id: User ID or anonymous identifier
$user_id: User ID sent to Mixpanel’s native user ID field
$insert_id: Unique identifier for deduplication
session_id: Optional session identifier (from $mixpanel_session_id in metadata, or falls back to Langfuse session_id)
langfuse_score_name: The name associated with the score (e.g., “user_feedback”, “accuracy”)
langfuse_score_value: The numeric value of the score
langfuse_score_string_value: The string value of the score (for BOOLEAN and CATEGORICAL scores)
langfuse_score_data_type: The data type of the score (NUMERIC, BOOLEAN, or CATEGORICAL)
langfuse_score_comment: Comments attached to the score
langfuse_score_metadata: Additional metadata attached to the score
langfuse_trace_name: The name of the trace associated with the score
langfuse_trace_id: The unique identifier of the trace associated with the score
langfuse_user_url: Deep link to the user profile in Langfuse
langfuse_id: The unique identifier of the score
langfuse_session_id: The session ID related to the score’s trace
langfuse_project_id: The project ID linked with the score’s trace
langfuse_user_id: The user ID that triggered the trace tied to the score (defaults to langfuse_unknown_user if not available)
langfuse_release: The release information of the trace associated with the score
langfuse_tags: Tags related to the trace of the score
langfuse_environment: The environment associated with the score
langfuse_event_version: The integration version with Langfuse

Use Cases

Get Started with the Analytics for AI Dashboard Template

The fastest way to see value from this integration is to use Mixpanel’s Analytics for AI dashboard template. This pre-built dashboard provides instant insights into how your LLM features are performing and how they impact user behavior.

View the Analytics for AI Dashboard Template →

The template includes ready-to-use reports for:

LLM Feature Adoption: Track how many users are engaging with your AI features
Cost Analysis: Monitor your LLM spending by user and feature
Performance Metrics: Visualize latency, token usage, and generation times
User Feedback: Analyze scores and ratings from Langfuse
Retention Impact: Understand retention rates of AI feature users

Analyze LLM Feature Adoption

Create funnels to track:

Users who trigger [Langfuse] Trace events
Conversion to key actions in your product
Retention rates for AI feature users vs. non-users

Monitor LLM Costs by User Segment

Build insights to:

Group users by langfuse_cost_usd total spend
Segment by user properties (plan type, company size, etc.)
Identify high-cost users or sessions

Correlate User Feedback with Behavior

Analyze how [Langfuse] Score events relate to:

Session length and engagement
Feature usage patterns
Churn or upgrade likelihood

Track Model Performance Impact

Compare:

langfuse_latency across different langfuse_model values
Token usage efficiency (langfuse_total_units)
Cost differences between model versions

Troubleshooting

Events not appearing in Mixpanel?

Verify you selected the correct Mixpanel region in Langfuse
Confirm your Project Token is correct
Allow up to 90 minutes for the first sync to complete
Check that your Langfuse project has trace data

User matching issues?

Ensure the user_id in Langfuse matches the distinct_id in Mixpanel
For session tracking, add $mixpanel_session_id to your Langfuse trace metadata

Need additional help? Contact Langfuse support or submit a feature request on their ideas board.

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