mixpanel · ryancurtis1 · Nov 3, 2025 · Nov 1, 2025 · Nov 1, 2025 · Nov 1, 2025
@@ -161,6 +161,8 @@
     "Kameleoon",
     "Kapa",
     "Kapture",
+    "Langfuse",
+    "langfuse",
     "Leanplum",
     "Lookback",
     "Mailchimp",

@@ -493,6 +493,11 @@ next step.**
     title="Google Sheets"
     href="/docs/tracking-methods/integrations/google-sheets"
   />
+  <Cards.Card
+    icon
+    title="Langfuse"
+    href="/docs/tracking-methods/integrations/langfuse"
+  />
   <Cards.Card
     icon
     title="LaunchDarkly"

@@ -24,6 +24,7 @@ you're looking for [reach out to us](https://mixpanel.com/get-support).
   <Cards.Card icon title="Google Cloud Storage" href="/docs/tracking-methods/integrations/google-cloud-storage" />
   <Cards.Card icon title="Google Pubsub" href="/docs/tracking-methods/integrations/google-pubsub" />
   <Cards.Card icon title="Google Sheets" href="/docs/tracking-methods/integrations/google-sheets" />
+  <Cards.Card icon title="Langfuse" href="/docs/tracking-methods/integrations/langfuse" />
   <Cards.Card icon title="LaunchDarkly" href="/docs/tracking-methods/integrations/launchdarkly" />
   <Cards.Card icon title="mParticle" href="/docs/tracking-methods/integrations/mparticle" />
   <Cards.Card icon title="Next.js" href="/docs/tracking-methods/integrations/nextjs" />

@@ -13,6 +13,7 @@ export default {
   "google-pubsub": "Google Pubsub",
   "google-sheets": "Google Sheets",
   "google-tag-manager": "Google Tag Manager",
+  "langfuse": "Langfuse",
   "launchdarkly": "LaunchDarkly",
   "mparticle": "mParticle",
   "nextjs": "Next.js",

@@ -0,0 +1,215 @@
+# 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)