LLM Application Evaluation Frameworks

Comet LLM is an observability platform and evaluation framework designed for large language model applications and agentic workflows. It functions as a system for tracing, monitoring, and debugging execution flows while providing tools for prompt optimization and the enforcement of AI safety guardrails. The platform distinguishes itself through a combination of model-based scoring and heuristic metrics to quantify output quality and detect hallucinations. It includes a dedicated prompt and agent optimizer with an interactive playground for refining templates and tool configurations. For retrieval-augmented generation, it provides specific monitoring and evaluation tools to identify bottlenecks in document retrieval and synthesis. Broad capabilities cover production monitoring via token usage and feedback dashboards, detailed execution tracing through span recording, and automated performance evaluations integrated into continuous delivery pipelines. The system also implements safety profiles to constrain model outputs and ensure compliant behavior. The platform can be deployed via cloud-hosted workspaces or self-hosted on Kubernetes using Helm charts.

Arize Phoenix is an LLM observability platform and evaluation framework designed to capture execution traces and monitor large language model applications. It serves as a prompt management system for versioning and testing templates, and as a self-hosted AI operations infrastructure for managing telemetry and experiments. The platform differentiates itself through a specialized embedding visualization tool used to detect data drift and optimize vector search. It provides a comprehensive evaluation suite that utilizes judge-based evaluators and ground-truth datasets to score model outputs, and includes tools for RAG troubleshooting to inspect retrieval documents. Capabilities cover the entire development lifecycle, including automated output validation, systemic performance benchmarking, and prompt engineering optimization. The system also incorporates security and access controls, such as role-based access and sensitive data masking, alongside collaborative workspaces for sharing observability data. The platform can be deployed locally via a CLI or notebook, or scaled through Docker and Kubernetes.

Agenta is a Prompt Ops lifecycle manager and prompt management platform that decouples prompt engineering from application code. It serves as a centralized system for developing, versioning, and deploying prompt templates and model configurations across different environments. The platform functions as an AI agent orchestrator with a visual interface for building agent workflows and connecting models to external tools. It further acts as an evaluation framework and observability tool, utilizing OpenTelemetry to capture execution traces, monitor latency, and track token costs. The system covers a broad range of capabilities including judge-based evaluation for scoring model outputs, registry-based prompt management for version control, and environment-based deployment to promote configurations through development and production stages. It also provides tools for converting production traces into test datasets and managing role-based access control for multi-tenant organizations. The platform can be installed using Docker Compose with reverse proxy options for traffic management.

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.

OpenCompass is a comprehensive evaluation platform, benchmarking suite, and distributed model evaluator designed to measure the performance and accuracy of large language models. It provides a framework for benchmarking both open-source and API-based models against diverse datasets using standardized metrics and reproducible pipelines. The project features an automated judging framework that uses language models as judges to score and verify the quality of generated text. It includes a performance leaderboard system for comparing the relative capabilities of various models across industry-standard benchmarks. The platform covers a broad range of capabilities, including multimodal model assessment, mathematical reasoning verification, and model robustness assessment. It manages the full evaluation lifecycle through dataset acquisition, experiment management, and the application of various prompting paradigms. To handle large-scale assessments, the system utilizes distributed evaluation workloads and GPU hardware scaling to process billion-scale models across computing clusters.

Deepeval is a framework for testing and evaluating large language model applications. It provides a suite of tools for executing automated regression tests, validating model output quality against defined standards, and tracing the execution of complex agent workflows. By integrating these capabilities into development pipelines, the platform ensures consistent performance and reliability throughout the software lifecycle. The platform distinguishes itself through its focus on programmatic validation and observability. It utilizes secondary language models to score output quality and employs assertion-driven checks to verify performance thresholds. Beyond standard evaluation, it includes specialized utilities for generating synthetic test data to simulate edge cases and performing security red teaming to identify potential vulnerabilities before deployment. The system covers a broad range of operational needs, including the management of structured evaluation datasets and the instrumentation of multi-step agent interactions for debugging. It supports automated quality gates that can block deployments based on performance metrics, facilitating continuous integration and deployment workflows for intelligent systems.

Evidently is an AI observability platform and evaluation framework designed to quantify the performance of machine learning models and large language models. It functions as a monitoring tool for detecting data drift and quality degradation in tabular datasets, while providing a specialized analyzer for the faithfulness and correctness of retrieval augmented generation systems. The project distinguishes itself through an evaluation framework that utilizes judge models and custom rubrics to score language model outputs. It includes tools for iterative prompt optimization and the generation of synthetic test datasets, including adversarial inputs for risk and brand safety testing. The platform covers a broad range of capabilities including real-time telemetry tracing for AI workflows, automated quality assurance via CI/CD integration, and performance trend tracking. It provides visual dashboards for reporting and a threshold-based alerting system to notify users when quality metrics cross predefined limits. Users can deploy a local workspace to manage projects and reports or use a no-code interface to configure evaluation workflows.

OpenCompass is an open-source framework for standardized benchmarking of large language models. It provides a configurable evaluation pipeline that supports both objective and subjective assessment, using a dual-engine architecture to handle closed-form answer comparison and open-ended response rating. The framework is designed as a modular platform where datasets, models, and metrics are composed through declarative YAML configuration files. The framework distinguishes itself through its extensible model integration layer, which supports custom models, HuggingFace models, and third-party API services through a common subclassing interface. It includes an automated judge system that delegates subjective scoring to a separate LLM evaluator, enabling quality assessment of open-ended outputs. A single-command benchmark suite runner allows executing predefined evaluation sets against any integrated model. The evaluation surface covers multiple capability dimensions, including examination, knowledge, reasoning, understanding, language, and safety. Specific assessment areas include agentic tool use, code generation, mathematical ability, instruction following, and language proficiency. Each dataset declares its own scoring function and post-processing steps, allowing per-task custom metrics. The framework supports evaluating base models, chat models, and API-deployed models through its configurable harness.

RagaAI-Catalyst is a suite of software implementation tools providing an SDK, dashboard, and platform for monitoring, debugging, red-teaming, and evaluating agentic AI workflows. It serves as an observability framework for tracing the execution paths of large language models and multi-agent systems. The project distinguishes itself through a security suite for automated red-teaming and vulnerability scanning to detect biases, alongside a centralized prompt registry that decouples templates from application code. It further provides an evaluation platform that combines synthetic data generation with custom metric frameworks to quantify model accuracy and reliability. The system covers broad operational domains including agent behavioral observability, prompt lifecycle management, and the application of output guardrails to block undesirable content. Its monitoring capabilities include trace-based execution graphing, timeline-based event sequencing, and diagnostic tools for analyzing multi-agent interaction flows. The core functionality is delivered via a Python library for recording tool calls and decision-making processes.

TensorZero is an inference gateway and experimentation framework designed to manage the lifecycle of large language models in production environments. It functions as a central proxy that routes requests across multiple artificial intelligence providers while providing the infrastructure necessary to monitor performance, track costs, and ensure service reliability. The platform distinguishes itself by integrating a comprehensive evaluation engine and an observability pipeline directly into the request flow. It enables developers to conduct controlled experiments and A/B tests to compare different model variants and prompt strategies. By capturing real-time inference data, the system facilitates automated feedback loops that allow for the continuous refinement of model configurations and prompt settings based on production outcomes. Beyond its core routing and experimentation capabilities, the project provides tools for automated quality assurance. It supports both heuristic-based checks and judge-based scoring to validate that generated content meets predefined accuracy and safety standards before reaching end users. These features collectively support the ongoing optimization of autonomous agents and the maintenance of consistent performance across complex machine learning workflows.

Oumi is a comprehensive large language model development platform designed for synthesizing data, fine-tuning models, and running performance evaluations. It serves as a unified environment for the entire model lifecycle, encompassing a training and fine-tuning suite, an evaluation framework, and tools for synthetic data generation and model distillation. The platform is distinguished by its iterative, failure-driven synthesis approach, which analyzes model weaknesses during evaluation to generate targeted training data. It utilizes an LLM-based judge framework to programmatically score response quality and factual accuracy, and supports on-policy model distillation to transfer knowledge from teacher models to student models. The system covers a broad range of capabilities including automated dataset preparation, parameter-efficient fine-tuning via LoRA, and cloud-agnostic job orchestration across multiple GPU providers. It also provides tools for model artifact export and local or cloud-based inference serving through an OpenAI-compatible API. Administrative features include multi-tenant workspace isolation, role-based access control, and the use of JSON-based workflow recipes to standardize and repeat development steps.

Ragas is an evaluation framework designed to measure the performance of retrieval-augmented generation pipelines and autonomous agent workflows. It provides a comprehensive suite of tools for benchmarking system outputs, utilizing language models as automated judges to score performance against defined rubrics and reference data. By standardizing inputs, retrieved contexts, and generated responses into a unified schema, the project enables consistent analysis across complex AI applications. The framework distinguishes itself through its ability to generate synthetic test datasets from existing documents, allowing developers to simulate diverse user queries and scenarios for rigorous testing. It supports component-wise metric decomposition, which isolates the performance of individual retrieval and generation modules to identify specific bottlenecks. Additionally, the project incorporates graph-based knowledge extraction to structure document collections, enabling multi-hop query generation and relationship-based testing that goes beyond simple string matching. Beyond its core evaluation capabilities, the project offers extensive support for workflow automation, observability, and configuration management. It includes asynchronous execution harnesses for high-throughput testing, integration primitives for various language model providers and orchestration frameworks, and advanced monitoring tools for tracking metrics and execution traces. Users can further customize evaluation logic through prompt-driven metric definitions and automated optimization strategies.

Ragas is an evaluation framework and performance benchmark designed to quantify the quality of retrieval augmented generation pipelines. It functions as an application optimizer to identify bottlenecks in language model workflows using automated metrics and model-based scoring. The framework includes a system for generating synthetic datasets that mimic production scenarios and edge cases to create realistic test cases. It enables reference-free assessment, allowing the evaluation of response quality by analyzing grounding in the provided context without requiring gold-standard labels. The system covers several analytical areas, including retrieval quality assessment, model accuracy measurement, and the optimization of application performance through the analysis of live usage data.

This project is a collection of utilities designed for machine learning experiment tracking, data versioning, and the observability of large language model applications. It provides a client for recording hyperparameters and metrics during training to visualize performance trends and compare different model versions. The tool includes a model evaluation framework that uses custom scorers and automated judges to assess the quality of generated text outputs. It also provides observability tools to monitor and debug the execution flow and runtime behavior of language model applications. The system manages the broader machine learning lifecycle, covering the process of training, fine-tuning, and deploying models. This includes tracking dataset changes across iterations to maintain data lineage and providing the infrastructure to host experiment tracking platforms on cloud or private environments.

This project is a development platform for managing the lifecycle of generative artificial intelligence models. It provides a unified environment for accessing, fine-tuning, and deploying large language models, serving as an orchestrator that handles the integration of diverse models into custom applications. The platform distinguishes itself by offering a managed infrastructure for hosting and scaling models, which removes the requirement for manual server maintenance or configuration. It includes integrated tools for supervised fine-tuning and vector embedding optimization, allowing for the refinement of model performance to meet specialized domain requirements. The framework incorporates comprehensive capabilities for monitoring and governance, including automated quality evaluation services that use programmatic rubrics to assess output accuracy. It also enforces responsible artificial intelligence standards through policy-driven content filtering, ensuring that generated responses remain aligned with established safety and ethical guidelines. The repository provides a collection of Jupyter Notebooks that serve as documentation and implementation guides for these development and deployment workflows.

Deepagents is an LLM agent orchestration platform and stateful application server designed for deploying and managing AI agents built with computational graphs. It provides a containerized runtime environment that handles agent execution, state persistence, and the versioning of AI assistants. The platform distinguishes itself through deep integration with the Model Context Protocol, allowing agents to function as servers that expose tools and capabilities to external clients. It features a sophisticated observability suite for capturing execution traces, performing LLM-based evaluations against datasets, and conducting side-by-side model output comparisons. The system covers a broad range of operational capabilities, including cron-based task scheduling, multi-tenant workspace isolation, and human-in-the-loop review workflows. It also manages long-term memory through semantic search and provides automated scaling of compute resources across cloud environments. A command-line interface is provided for local agent validation, graph packaging, and rapid testing via a local development server.

mcp-context-forge is a Model Context Protocol federation gateway that unifies diverse AI tool servers and APIs into a single consistent interface for discovery and execution. It acts as a centralized proxy that aggregates multiple servers and APIs, allowing AI agents to access and invoke a unified set of tools, prompts, and resources. The project distinguishes itself through a multi-protocol translation bridge that converts communication between standard I/O, SSE, gRPC, and REST to enable interoperability between disparate tool servers. It includes a comprehensive LLM evaluation framework for assessing model output quality, safety, and grounding, alongside an AI tool governance platform that enforces role-based access control and content guardrails. The system provides a broad surface of capabilities including AI agent observability via OpenTelemetry, enterprise identity integration through OIDC and SAML, and secure code execution within sandboxed environments. It also features extensive content management utilities for processing documents, spreadsheets, and code, as well as traffic management tools such as circuit breakers and rate limiting. The project can be deployed using Helm charts for Kubernetes or via Docker Compose, with support for air-gapped installations.

LlamaIndex is a comprehensive development framework designed to connect private or external data sources to large language models. It functions as a data-centric toolkit that enables the construction of retrieval-augmented generation systems, allowing developers to build applications that provide context-aware answers based on specific organizational information. The project distinguishes itself through a robust agentic orchestration engine that supports the creation of autonomous agents capable of multi-step reasoning, memory management, and complex tool execution. Beyond simple retrieval, it provides a flexible, event-driven architecture for composing modular pipelines, enabling developers to chain data ingestion, transformation, and retrieval steps into sophisticated, multi-agent systems that can coordinate tasks and hand off control between individual agents. The platform covers the entire lifecycle of language model applications, including advanced document processing for parsing and structuring complex file formats, and a diagnostic layer for observability that tracks execution traces and performance metrics. It also includes a suite of evaluation tools for measuring retrieval effectiveness and response quality, alongside mechanisms for query routing and custom post-processing to ensure high-precision information delivery.

DSPy is a declarative programming framework designed for building complex language model applications. It treats model interactions as modular, composable programs, allowing developers to define task logic through typed class schemas rather than relying on manually written prompts. By organizing workflows into hierarchical, reusable Python objects, the framework enables the construction of sophisticated AI systems that manage state and execution flow independently. The framework distinguishes itself through an automated optimization engine that iteratively refines prompt instructions and few-shot demonstrations. By evaluating candidate programs against defined metrics and feedback loops, it systematically improves performance without requiring manual prompt engineering. This process is supported by a programmatic evaluation harness that measures output quality using custom metrics and model-based judges, ensuring consistent behavior across multi-stage pipelines. Beyond core orchestration, the system provides a robust interface for structured data extraction and tool integration. It includes mechanisms for wrapping Python functions as tools, executing iterative reasoning loops, and adapting model outputs into validated data structures. These capabilities are complemented by comprehensive state management and persistence utilities, which allow for the versioning and tracking of program configurations throughout the development lifecycle.