# langfuse/langfuse

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22,077 stars · 2,193 forks · TypeScript · other

## Links

- GitHub: https://github.com/langfuse/langfuse
- Homepage: https://langfuse.com/docs
- awesome-repositories: https://awesome-repositories.com/repository/langfuse-langfuse.md

## Topics

`analytics` `autogen` `evaluation` `langchain` `large-language-models` `llama-index` `llm` `llm-evaluation` `llm-observability` `llmops` `monitoring` `observability` `open-source` `openai` `playground` `prompt-engineering` `prompt-management` `self-hosted` `ycombinator`

## Description

Langfuse is an open-source observability and evaluation platform designed for language model applications. It provides a centralized system for tracking execution traces, monitoring performance metrics, and managing prompt templates. By capturing hierarchical units of work and telemetry data, the platform enables developers to debug complex application lifecycles and analyze token usage, latency, and model interactions in production environments.

The platform distinguishes itself through an integrated evaluation framework that allows for systematic benchmarking and automated scoring of model outputs. Users can perform comparative experimentation by running multiple prompt or model versions side-by-side, and convert production traces into versioned test datasets to validate performance against ground truth. A dedicated prompt management system further decouples logic from application code, offering a playground for refinement and dynamic fetching of versioned templates.

Beyond core observability, the project supports a comprehensive suite of administrative and operational tools, including organizational access controls, identity provider integration, and automated workflow triggers. It is built for flexible deployment, supporting containerized orchestration in private, cloud, or Kubernetes-based environments to ensure data control and high-availability scaling.

The platform is designed for self-hosting and provides infrastructure-as-code templates to facilitate consistent environment setup. It integrates with standard observability ecosystems through open telemetry support and offers programmatic interfaces for headless management and automated deployment workflows.

## Tags

### Artificial Intelligence & ML

- [LLM Observability](https://awesome-repositories.com/f/artificial-intelligence-ml/llm-observability.md) — Monitors and debugs language model applications by tracking prompts, completions, latency, and token usage.
- [AI Observability and Evaluation](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/training-monitoring-and-profiling/ai-observability/ai-observability-and-evaluation.md) — Provides systematic experiments and automated scoring against datasets to validate model performance and output quality.
- [Prompt Registries](https://awesome-repositories.com/f/artificial-intelligence-ml/prompt-registries.md) — Decouples prompt logic from application code by serving versioned templates through a managed interface for dynamic retrieval.
- [Prompt Management Workflows](https://awesome-repositories.com/f/artificial-intelligence-ml/prompt-engineering/prompt-management-workflows.md) — Centrally manages, versions, and tests prompt templates to ensure consistent model behavior across environments.
- [Trace-to-Dataset Converters](https://awesome-repositories.com/f/artificial-intelligence-ml/dataset-generation-suites/trace-to-dataset-converters.md) — Converts production traces into test items to build evaluation sets based on real-world application performance. ([source](https://langfuse.com/docs/evaluation/dataset-runs/datasets))
- [Experiment Tracking](https://awesome-repositories.com/f/artificial-intelligence-ml/experiment-tracking.md) — Enables the execution of versioned experiments to evaluate historical data snapshots and track performance changes over time. ([source](https://langfuse.com/docs/evaluation/dataset-runs/datasets))
- [AI Evaluation Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-evaluation-analysis/ai-evaluation-frameworks.md) — Provides a system for running systematic experiments, benchmarking model outputs, and automating quality scoring.
- [Prompt Templates](https://awesome-repositories.com/f/artificial-intelligence-ml/prompt-templates.md) — Retrieves production-labeled prompt templates from a central store to separate prompt logic from application code. ([source](https://langfuse.com/docs/prompt-management/get-started))
- [Human-in-the-Loop Systems](https://awesome-repositories.com/f/artificial-intelligence-ml/human-in-the-loop-systems.md) — Runs automated and human-in-the-loop assessments using custom judges to validate model performance. ([source](https://langfuse.com/docs/evaluation/overview))
- [Experimentation Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/experimentation-frameworks.md) — Executes multiple prompt or model versions side-by-side to measure the impact of configuration changes. ([source](https://langfuse.com/docs/playground))

### System Administration & Monitoring

- [Automated Trace Evaluation](https://awesome-repositories.com/f/system-administration-monitoring/automated-trace-evaluation.md) — Executes automated scoring and custom logic against captured traces to validate output quality against defined performance benchmarks.
- [Observability Instrumentation](https://awesome-repositories.com/f/system-administration-monitoring/observability-instrumentation.md) — Captures hierarchical units of work and execution flows from model operations to visualize performance and cost across distributed components. ([source](https://langfuse.com/integrations/model-providers/openai-py))
- [Distributed Tracing Instrumentation](https://awesome-repositories.com/f/system-administration-monitoring/distributed-tracing-instrumentation.md) — Captures nested execution flows by linking parent-child relationships across distributed components to visualize complex application lifecycles.
- [Monitoring and Observability](https://awesome-repositories.com/f/system-administration-monitoring/monitoring-and-observability.md) — Streams internal performance metrics and trace data to external observability tools while optimizing transmission through batching and sampling. ([source](https://langfuse.com/docs/integrations/vercel-ai-sdk))
- [Execution Metadata](https://awesome-repositories.com/f/system-administration-monitoring/monitoring-and-observability/observability-platforms/distributed-tracing-execution-analysis/execution-metadata/execution-metadata.md) — Enriches execution logs with custom attributes and user identifiers to enable granular filtering and performance analysis across sessions.
- [Trace Metadata](https://awesome-repositories.com/f/system-administration-monitoring/monitoring-and-observability/execution-tracing-analysis/trace-metadata.md) — Attaches custom metadata, user identifiers, and session tags to execution traces to provide context for debugging and analysis. ([source](https://langfuse.com/docs/integrations/llama-index/get-started))
- [Distributed Tracing](https://awesome-repositories.com/f/system-administration-monitoring/monitoring-and-observability/observability-platforms/distributed-tracing-execution-analysis/distributed-tracing.md) — Ingests execution traces and telemetry data to monitor distributed AI application pipelines and agent behavior.
- [OpenTelemetry Exporters](https://awesome-repositories.com/f/system-administration-monitoring/opentelemetry-exporters.md) — Integrates with standard observability ecosystems to collect and export telemetry data from any language or runtime. ([source](https://langfuse.com/docs/sdk))
- [Telemetry Ingestion](https://awesome-repositories.com/f/system-administration-monitoring/telemetry-ingestion.md) — Buffers high-volume telemetry data in background queues to maintain host application responsiveness during intensive monitoring tasks.

### Development Tools & Productivity

- [Prompt Playgrounds](https://awesome-repositories.com/f/development-tools-productivity/human-in-the-loop-interfaces/interactive-prompts/prompt-playgrounds.md) — Provides an interactive environment to refine prompts and model parameters before deploying them to production applications. ([source](https://cdn.jsdelivr.net/gh/langfuse/langfuse@main/README.md))
- [Workflow Automation Triggers](https://awesome-repositories.com/f/development-tools-productivity/workflow-automation-triggers.md) — Triggers actions based on performance events and provides interfaces for external agents to query data and execute tasks. ([source](https://langfuse.com/roadmap))

### DevOps & Infrastructure

- [Self-Hosted Infrastructure](https://awesome-repositories.com/f/devops-infrastructure/self-hosted-infrastructure.md) — Enables self-hosting and scaling of observability platforms within private or cloud environments for full data control.
- [Private Infrastructure Hosting](https://awesome-repositories.com/f/devops-infrastructure/private-infrastructure-hosting.md) — Supports hosting within isolated network environments to maintain data control and security. ([source](https://langfuse.com/docs/deployment/self-host))
- [Kubernetes Orchestration](https://awesome-repositories.com/f/devops-infrastructure/kubernetes-orchestration.md) — Orchestrates application containers and dependencies within cluster environments using standardized packaging. ([source](https://langfuse.com/self-hosting/aws))
- [Cloud Infrastructure Deployment](https://awesome-repositories.com/f/devops-infrastructure/cloud-infrastructure-deployment.md) — Automates the provisioning of compute and storage resources on major cloud platforms. ([source](https://langfuse.com/self-hosting/aws))
- [Workload Scheduling and Scaling](https://awesome-repositories.com/f/devops-infrastructure/container-orchestration/workload-scheduling-scaling.md) — Supports high-availability deployments using container orchestration to manage large-scale observability data and traffic. ([source](https://langfuse.com/docs/deployment/self-host))

### Security & Cryptography

- [API Request Authentication](https://awesome-repositories.com/f/security-cryptography/identity-access-management/authentication-strategies/machine-and-protocol-identity/api-machine-authentication/api-request-authentication.md) — Validates client identity using keys to secure access to platform resources. ([source](https://langfuse.com/docs/api))
- [Identity Provider Integrations](https://awesome-repositories.com/f/security-cryptography/identity-provider-integrations.md) — Connects to external identity providers to manage user access and enforce single sign-on protocols across the organization. ([source](https://langfuse.com/self-hosting/kubernetes-helm))
- [Data Encryption](https://awesome-repositories.com/f/security-cryptography/data-encryption.md) — Secures stored information using encryption at rest to protect configuration and application data from unauthorized access. ([source](https://langfuse.com/self-hosting/local))
- [Identity Provisioning](https://awesome-repositories.com/f/security-cryptography/identity-provisioning.md) — Synchronizes user accounts and permissions from external identity systems to ensure consistent access management. ([source](https://langfuse.com/self-hosting/local))

### Business & Productivity Software

- [Organization Management](https://awesome-repositories.com/f/business-productivity-software/organization-management.md) — Allows for the creation and administration of organizational structures and user access permissions through external interface calls. ([source](https://langfuse.com/self-hosting/local))

### Software Engineering & Architecture

- [Data Schema Validation](https://awesome-repositories.com/f/software-engineering-architecture/data-schema-validation.md) — Checks dataset items against defined structures to ensure input and output consistency across team contributions. ([source](https://langfuse.com/docs/evaluation/dataset-runs/datasets))

### Testing & Quality Assurance

- [Test Data Management](https://awesome-repositories.com/f/testing-quality-assurance/general-testing-utilities/test-utilities-assertions/test-data-management.md) — Organizes collections of inputs and expected outputs into versioned groups to facilitate consistent application testing and evaluation. ([source](https://langfuse.com/docs/evaluation/dataset-runs/datasets))