# comet-ml/opik

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17,787 stars · 1,357 forks · Python · apache-2.0

## Links

- GitHub: https://github.com/comet-ml/opik
- Homepage: https://www.comet.com/docs/opik/
- awesome-repositories: https://awesome-repositories.com/repository/comet-ml-opik.md

## Topics

`evaluation` `hacktoberfest` `hacktoberfest2025` `langchain` `llama-index` `llm` `llm-evaluation` `llm-observability` `llmops` `open-source` `openai` `playground` `prompt-engineering`

## Description

Opik is an observability and evaluation platform designed for generative AI applications and agentic workflows. It provides a centralized environment for tracing execution flows, managing prompt templates, and monitoring production performance, allowing teams to gain visibility into complex model interactions and tool usage without requiring manual application code changes.

The platform distinguishes itself through its integrated approach to the AI development lifecycle, combining distributed trace instrumentation with automated evaluation frameworks. It supports model-as-a-judge scoring, synthetic data generation, and the conversion of production traces into structured test cases, enabling developers to iteratively refine prompts and agent behavior. By offering a collaborative debugger and chat-based workspace management, it facilitates direct interaction with execution data to identify errors and implement code remediations.

Beyond core observability, the system includes tools for dataset versioning, custom metric definition, and cost analysis to track resource allocation across teams. It also features a model gateway to standardize logging and security across diverse model providers. The platform is built for flexible deployment, supporting containerized execution and orchestration via Kubernetes to ensure consistency across local and cloud environments.

## Tags

### Artificial Intelligence & ML

- [LLM Observability](https://awesome-repositories.com/f/artificial-intelligence-ml/llm-observability.md) — Provides end-to-end tracing, evaluation, and monitoring for generative AI applications and agentic workflows.
- [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 a centralized environment for tracing, benchmarking, and monitoring generative AI applications and agentic workflows. ([source](https://www.comet.com/docs/opik/v1/evaluation/manage_datasets/))
- [AI Evaluation Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-evaluation-analysis/ai-evaluation-frameworks.md) — Provides a comprehensive framework for automated testing, dataset management, and model-as-a-judge scoring.
- [AI Observability Tracing](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-observability-tracing.md) — Provides a collaborative interface for inspecting execution traces, labeling data, and debugging agentic workflows. ([source](https://www.comet.com/))
- [Automated Model Judges](https://awesome-repositories.com/f/artificial-intelligence-ml/automated-model-judges.md) — Uses secondary language models to automatically score and validate the quality, relevance, and accuracy of primary model outputs.
- [Prompt Management Systems](https://awesome-repositories.com/f/artificial-intelligence-ml/prompt-management-systems.md) — Centralizes the versioning, testing, and deployment of prompt templates.
- [AI Integration Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-integration-frameworks.md) — Provides automated instrumentation to capture execution traces from language models and agent orchestration tools. ([source](https://cdn.jsdelivr.net/gh/comet-ml/opik@main/README.md))
- [Automated Output Evaluation](https://awesome-repositories.com/f/artificial-intelligence-ml/automated-output-evaluation.md) — Runs systematic automated tests on AI outputs to assess quality without manual review. ([source](https://www.comet.com/docs/opik/quickstart/))
- [Trace-to-Dataset Converters](https://awesome-repositories.com/f/artificial-intelligence-ml/dataset-generation-suites/trace-to-dataset-converters.md) — Converts production observability traces into structured test cases for evaluation. ([source](https://www.comet.com/docs/opik/v1/evaluation/manage_datasets/))
- [Model Gateways](https://awesome-repositories.com/f/artificial-intelligence-ml/model-gateways.md) — Centralizes traffic through model gateways to standardize logging, security, and monitoring across diverse AI model providers. ([source](https://www.comet.com/docs/opik/integrations/overview/))
- [Prompt Management](https://awesome-repositories.com/f/artificial-intelligence-ml/prompt-management.md) — Stores and versions text prompts centrally to maintain consistency and enable dynamic updates across application components. ([source](https://www.comet.com/docs/opik/python-sdk-reference/))
- [Model Feedback Loops](https://awesome-repositories.com/f/artificial-intelligence-ml/model-feedback-loops.md) — Records user assessments and automated test results to iteratively refine prompts and agentic system behavior.
- [Agent Optimization](https://awesome-repositories.com/f/artificial-intelligence-ml/agent-optimization.md) — Analyzes trace data and test outcomes to suggest code improvements, automate prompt engineering, and manage regression testing. ([source](https://www.comet.com/))
- [Automated Code Remediation](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-coding-assistants/automated-code-remediation.md) — Analyzes execution traces to suggest and implement code fixes while automatically generating regression tests. ([source](https://www.comet.com/site/products/opik/))
- [Experiment Tracking](https://awesome-repositories.com/f/artificial-intelligence-ml/experiment-tracking.md) — Aggregates summary statistics and metrics across test runs to compare model performance. ([source](https://www.comet.com/docs/opik/v1/evaluation/evaluate_your_llm/))
- [Synthetic Data Generation](https://awesome-repositories.com/f/artificial-intelligence-ml/synthetic-data-generation.md) — Expands evaluation datasets by generating diverse, structurally similar samples to improve model robustness. ([source](https://www.comet.com/docs/opik/v1/evaluation/manage_datasets/))

### System Administration & Monitoring

- [Distributed Tracing Instrumentation](https://awesome-repositories.com/f/system-administration-monitoring/distributed-tracing-instrumentation.md) — Captures nested execution flows and tool calls by wrapping application logic to provide visibility into complex agentic workflows.
- [LLM Performance Monitoring](https://awesome-repositories.com/f/system-administration-monitoring/monitoring-and-observability/observability-platforms/metric-performance-monitors/llm-performance-monitoring.md) — Tracks performance metrics, latency, and feedback scores for generative AI applications to assess production health. ([source](https://www.comet.com/docs/opik/v1/production/production_monitoring/))
- [Agent Execution Tracing](https://awesome-repositories.com/f/system-administration-monitoring/agent-execution-tracing.md) — Records and visualizes the full lifecycle of agent reasoning, tool usage, and model interactions. ([source](https://www.comet.com/))
- [Experimentation Sandboxes](https://awesome-repositories.com/f/system-administration-monitoring/agent-observability/experimentation-sandboxes.md) — Provides a sandbox for testing and versioning prompts and parameters before deployment. ([source](https://www.comet.com/site/products/opik/))
- [Error Logging Utilities](https://awesome-repositories.com/f/system-administration-monitoring/error-logging-utilities.md) — Automatically logs detailed error information and context when agent or model executions fail. ([source](https://www.comet.com/docs/opik/integrations/adk))
- [Automated Trace Evaluation](https://awesome-repositories.com/f/system-administration-monitoring/automated-trace-evaluation.md) — Triggers automated scoring rules on historical traces to analyze past performance with updated criteria. ([source](https://www.comet.com/docs/opik/v1/production/rules/))
- [Custom Metric Blueprints](https://awesome-repositories.com/f/system-administration-monitoring/service-metrics-monitoring/custom-metric-blueprints.md) — Implements bespoke scoring logic using either simple output comparisons or advanced analysis of full execution spans. ([source](https://www.comet.com/docs/opik/v1/evaluation/evaluate_your_llm/))

### Development Tools & Productivity

- [Debugger Interfaces](https://awesome-repositories.com/f/development-tools-productivity/debugging-profiling-testing/debugging-diagnostics/debugging-inspection-tools/debugger-interfaces.md) — Offers a collaborative interface for inspecting execution traces and remediating errors in AI systems.
- [Chat-Based Administration Interfaces](https://awesome-repositories.com/f/development-tools-productivity/chat-based-administration-interfaces.md) — Allows querying traces, scoring outputs, and running experiments directly through chat interfaces. ([source](https://www.comet.com/docs/opik/))

### Testing & Quality Assurance

- [LLM Evaluation](https://awesome-repositories.com/f/testing-quality-assurance/model-testing/llm-evaluation.md) — Runs automated tests against defined tasks using datasets and metrics to measure output quality and application behavior. ([source](https://www.comet.com/docs/opik/v1/evaluation/evaluate_your_llm/))

### Data & Databases

- [Dataset Versioning Platforms](https://awesome-repositories.com/f/data-databases/data-versioning/dataset-versioning-platforms.md) — Maintains snapshots of test cases and evaluation data to ensure reproducibility and auditability across experiment runs.
- [Dataset Snapshotting](https://awesome-repositories.com/f/data-databases/data-engineering-infrastructure/data-persistence-storage/data-persistence-strategies/dataset-snapshotting.md) — Maintains immutable records of dataset states to ensure reproducibility, auditability, and the ability to roll back configurations. ([source](https://www.comet.com/docs/opik/v1/evaluation/manage_datasets/))
- [Execution Span Hierarchies](https://awesome-repositories.com/f/data-databases/data-visualization/hierarchical-performance-visualizers/execution-span-hierarchies.md) — Organizes individual model calls and execution steps into parent-child relationships to visualize the internal logic of AI applications.

### Business & Productivity Software

- [AI Usage Analytics](https://awesome-repositories.com/f/business-productivity-software/spend-tracking-tools/ai-usage-analytics.md) — Tracks and audits model usage and configuration costs to optimize resource allocation. ([source](https://www.comet.com/site/products/opik/))

### DevOps & Infrastructure

- [Container Orchestration & Deployment](https://awesome-repositories.com/f/devops-infrastructure/container-orchestration-deployment.md) — Supports containerized deployment and orchestration via Kubernetes for consistent local and cloud execution.
- [Containerized Deployments](https://awesome-repositories.com/f/devops-infrastructure/containerized-deployments.md) — Provides standardized containerized packaging for consistent platform deployment across environments. ([source](https://www.comet.com/docs/opik/self-host/local_deployment))
- [Kubernetes Deployment](https://awesome-repositories.com/f/devops-infrastructure/kubernetes-deployments/kubernetes-deployment.md) — Installs the platform on Kubernetes clusters using Helm charts. ([source](https://www.comet.com/docs/opik/self-host/kubernetes/))