comet-ml/opik

Features

• LLM Observability - Provides end-to-end tracing, evaluation, and monitoring for generative AI applications and agentic workflows.
• AI Observability and Evaluation - Provides a centralized environment for tracing, benchmarking, and monitoring generative AI applications and agentic workflows.
• AI Evaluation Frameworks - Provides a comprehensive framework for automated testing, dataset management, and model-as-a-judge scoring.
• Distributed Tracing Instrumentation - Captures nested execution flows and tool calls by wrapping application logic to provide visibility into complex agentic workflows.
• LLM Performance Monitoring - Tracks performance metrics, latency, and feedback scores for generative AI applications to assess production health.
• AI Observability Tracing - Provides a collaborative interface for inspecting execution traces, labeling data, and debugging agentic workflows.
• Automated Model Judges - Uses secondary language models to automatically score and validate the quality, relevance, and accuracy of primary model outputs.
• Prompt Management Systems - Centralizes the versioning, testing, and deployment of prompt templates.
• Debugger Interfaces - Offers a collaborative interface for inspecting execution traces and remediating errors in AI systems.
• Agent Execution Tracing - Records and visualizes the full lifecycle of agent reasoning, tool usage, and model interactions.
• LLM Evaluation - Runs automated tests against defined tasks using datasets and metrics to measure output quality and application behavior.
• AI Integration Frameworks - Provides automated instrumentation to capture execution traces from language models and agent orchestration tools.
• Automated Output Evaluation - Runs systematic automated tests on AI outputs to assess quality without manual review.
• Trace-to-Dataset Converters - Converts production observability traces into structured test cases for evaluation.
• Model Gateways - Centralizes traffic through model gateways to standardize logging, security, and monitoring across diverse AI model providers.
• Prompt Management - Stores and versions text prompts centrally to maintain consistency and enable dynamic updates across application components.
• Model Feedback Loops - Records user assessments and automated test results to iteratively refine prompts and agentic system behavior.
• AI Development Platforms - Evaluation and testing platform for LLM application lifecycles.
• AI Observability and Evaluation - Tracing and evaluation platform for LLM and RAG workflows.
• Application Development - Observability suite for testing and shipping LLM applications.
• Evaluation Frameworks - End-to-end development platform with integrated evaluation capabilities.
• General Machine Learning - Platform for evaluating and tracing LLM applications.
• Generative AI - Listed in the “Generative AI” section of the Free For Dev awesome list.
• LLM Development Frameworks - Platform for tracing, evaluating, and monitoring LLM applications.
• LLM Evaluation Tools - Platform for evaluating and testing LLM apps across the lifecycle.
• LLM Frameworks and Libraries - Observability and evaluation suite for LLM application lifecycles.
• Machine Learning Operations - ML platform for tracking, comparing and optimizing experiments.
• Model Evaluation and Benchmarking - Platform for evaluating, testing, and monitoring LLM applications.
• Observability and Evaluation - Open-source platform for evaluating and testing LLM workflows.
• Observability And Monitoring - Development platform providing integrated monitoring for model applications.
• Web Applications - End-to-end development platform for building LLM applications.
• Developer Playgrounds - Platform for evaluating, testing, and shipping LLM applications.
• Monitoring and Observability - Lifecycle platform for testing and evaluating LLM applications.
• Testing and Observability - Tool for tracing and evaluating agentic workflows.
• Dataset Versioning Platforms - Maintains snapshots of test cases and evaluation data to ensure reproducibility and auditability across experiment runs.
• Experimentation Sandboxes - Provides a sandbox for testing and versioning prompts and parameters before deployment.
• Agent Optimization - Analyzes trace data and test outcomes to suggest code improvements, automate prompt engineering, and manage regression testing.
• Automated Code Remediation - Analyzes execution traces to suggest and implement code fixes while automatically generating regression tests.
• Experiment Tracking - Aggregates summary statistics and metrics across test runs to compare model performance.
• Synthetic Data Generation - Expands evaluation datasets by generating diverse, structurally similar samples to improve model robustness.
• Dataset Snapshotting - Maintains immutable records of dataset states to ensure reproducibility, auditability, and the ability to roll back configurations.
• Execution Span Hierarchies - Organizes individual model calls and execution steps into parent-child relationships to visualize the internal logic of AI applications.
• Error Logging Utilities - Automatically logs detailed error information and context when agent or model executions fail.
• AI Usage Analytics - Tracks and audits model usage and configuration costs to optimize resource allocation.
• Chat-Based Administration Interfaces - Allows querying traces, scoring outputs, and running experiments directly through chat interfaces.
• Container Orchestration & Deployment - Supports containerized deployment and orchestration via Kubernetes for consistent local and cloud execution.
• Containerized Deployments - Provides standardized containerized packaging for consistent platform deployment across environments.
• Kubernetes Deployment - Installs the platform on Kubernetes clusters using Helm charts.
• Automated Trace Evaluation - Triggers automated scoring rules on historical traces to analyze past performance with updated criteria.
• Custom Metric Blueprints - Implements bespoke scoring logic using either simple output comparisons or advanced analysis of full execution spans.

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