> ## Documentation Index > Fetch the complete documentation index at: https://docs.mixpanel.com/llms.txt > Use this file to discover all available pages before exploring further. # Langfuse [Langfuse](https://langfuse.com) 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 1. Log into your Langfuse account and navigate to your project settings 2. Find the Mixpanel integration section 3. Select your Mixpanel region: * **US**: api.mixpanel.com * **EU**: api-eu.mixpanel.com * **India**: api-in.mixpanel.com 4. Enter your Mixpanel Project Token 5. 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 →**](https://mixpanel.com/p/NaKPyubj6EuA4oV75taqrq) 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](https://langfuse.com/ideas). ## Learn More * [Langfuse Mixpanel Integration Documentation](https://langfuse.com/integrations/analytics/mixpanel) * [Langfuse Documentation](https://langfuse.com/docs) * [Langfuse GitHub](https://github.com/langfuse/langfuse) * [LLM Observability Best Practices](https://langfuse.com/docs/observability)