Langfuse

Enhance your AI application performance with comprehensive observability and real-time insights.

Control, optimize, and deploy prompts efficiently to enhance AI applications.

Streamline AI performance evaluation with structured workflows and automated assessments.

Empower teams with actionable insights and comprehensive metrics for effective AI application monitoring.

Trace and monitor LLM requests and agentic workflows end-to-end, including full request capture, cross-infrastructure correlation, and cost and token analytics for AI application reliability and spend control.

Products: Langfuse Observability, Langfuse Metrics

Automated and human-in-the-loop evaluation, dataset-based tests, online and regression evals, and synthetic-test generation to measure model quality and prevent regressions.

Products: Langfuse Evaluations

Centralized prompt versioning, testing, caching, linking to traces, and one-click deployment and rollback to manage prompt lifecycles and iterate on prompt-driven behaviors without redeploying code.

Products: Langfuse Prompt Management

Langfuse Observability

Langfuse Observability is the production tracing, metrics, and analytics component for AI applications. It captures LLM calls, tool invocations, and retrieval steps in hierarchical traces and provides cost, latency, and quality monitoring, dashboards, and alerts across multiple data modalities.

• Track Usage And Cost
  Gain clear visibility of application costs and performance metrics to optimize resource usage and enhance operational efficiency.
• Integrate Easily With Workflows
  Seamlessly integrate observability with your existing systems for real-time insights and improvements.
• Collect Detailed Observability Data
  Get comprehensive data on every aspect of your application’s performance for in-depth analysis and optimization.
• Set Alerts For Key Metrics
  Automatically notify your team of any deviations in core metrics to prevent issues before they impact users.
• Backfill Historical Data
  Enhance evaluation processes by applying rules to previous observations for continuity in insights.
• Trace Across Multiple Modalities
  Monitor and analyze performance across various inputs and outputs, enhancing debugging capabilities.

Langfuse Prompt Management

Langfuse Prompt Management centralizes prompts with versioning and deployment controls, decoupling prompts from code to enable instant updates, testing, and rollbacks without redeploying applications.

• Improve Analysis With Traces
  Connecting prompts to traces allows you to assess how different prompt versions perform in real use, thus optimizing the AI application's overall performance.
• Efficient Management of Prompt Evolution
  Organizing prompts through version control aids in managing updates, ensuring that different environments can operate with the appropriate prompt version.
• Instant Prompt Changes
  This feature facilitates rapid updates to prompts, ensuring that improvements can be deployed without delays, thus enhancing responsiveness to user needs.
• Interactive Testing Environment
  The playground allows experimentation with prompts using actual inputs, which facilitates iterative improvement and fine-tuning of prompts based on tangible results.
• Ensure Response Format Consistency
  This capability enhances reliability by ensuring that all responses from LLMs follow defined schemas, minimizing interpretation issues.
• Reduce Latency in Prompt Access
  Client-side caching of prompts enables faster access with no added latency, ensuring a seamless user experience during AI interactions.

Langfuse Evaluations

Langfuse Evaluations provides structured evaluation workflows to assess AI agent performance, including LLM-as-a-Judge, datasets, experiments, and human-in-the-loop workflows.

• Verify Structured Outputs
  Implement code evaluators for precise and repeatable checks on AI outputs, enhancing compliance and quality assurance.
• Automate Evaluative Judgments
  Utilize automated LLM evaluations to streamline assessment processes and ensure adherence to specific quality frameworks.
• Manage Evaluation Datasets
  Systematically manage datasets to improve the reliability of evaluations while tracking performance changes over time.
• Run Controlled Experiments
  Facilitate systematic testing of different configurations to optimize AI performance and outcomes.
• Curate Golden Data
  Enhance dataset quality through collaborative annotation processes that involve human feedback and validation.
• Utilize Backfilled Scores
  Kickstart evaluation processes with existing data while concurrently assessing new observations, ensuring comprehensive coverage.
• Set Up Score Alerts
  Receive notifications when evaluation thresholds are not met, helping maintain quality standards in real-time.

Langfuse Metrics

Langfuse Metrics provides dashboards, APIs, and data structures to quantify observability and evaluation results, with flexible exports and dimensional analysis.

• Automate Data Access
  Utilize a powerful API to retrieve and analyze metrics automatically, integrating with custom tools.
• Visualize Data Effortlessly
  Easily build visual representations of key performance metrics to track application health and performance.
• Stay Informed Proactively
  Ensure timely responses to metric deviations with customizable alerting mechanisms.
• Detailed Data Analysis
  Enable granular analysis of metrics by applying various dimensions to your data for deeper insights.
• Integrate with Analytics Tools
  Seamlessly send metrics data to Mixpanel for in-depth user analysis and engagement tracking.
• Enhanced Analytics Integration
  Facilitate comprehensive metric analysis through integration with PostHog.