The LLM Evaluation Landscape: 16 Frameworks by Functionality
We spent 2 days reviewing popular LLM evaluation frameworks that provide structured metrics, logs, and traces to identify how and when a model deviates from expected behavior. During this process, we:
- Categorized frameworks by their functional evaluation focus, distinguishing among LLM evaluation, prompt evaluation, RAG evaluation, and monitoring.
- Examined each tool’s evaluation capabilities, from single-turn tests to multi-turn, real-world assessments.
- Analyzed which platforms also support evaluation of LLMs in production environments, including agent behavior tracking and tool-use monitoring capabilities.
LLM evaluation landscape
LLM evaluation capabilities
Explanation of evaluation capabilities:
- AI gateway (multi-model access): Platform’s capability to evaluate multiple foundation models through a unified API interface.
- Single-turn evals: Measures model performance on individual prompts for metrics such as accuracy, factuality, or coherence.
- Multi-turn evals: Supports evaluation of multi-step or conversational exchanges to test contextual reasoning and memory.
- Offline evals: Offline evaluations are used for checking LLM application results prior to releasing to production. Use offline evaluations for CI/CD checks of your LLM application.
- Custom LLM metrics: Allows to define domain-specific or task-specific evaluation metrics beyond preset scoring methods.
Agent behavior and tool monitoring capabilities
Evaluation tools can help with detection of mis-aligned agentic behavior, especially as you broaden what “evaluation” covers (not just prompt or answer, but agent behavior over time, tool use, side effects).
Anthropic suggests that evaluating how a model behaves, not just what it says, could become a crucial dimension of trust and safety in next-generation AI systems.1
If you are interested in production monitoring, system-level evaluation, you can jump LLM observability frameworks with evaluation capabilities section or for more detail you check: LLM observability & evaluation platforms like Langfuse and end-to-end GenAI development platforms with evaluation features like Datadog.
If you’re using RAG or task completion agents, we have a separate guide on agentic evaluation.
Core LLM evaluation frameworks
OpenAI Evals
OpenAI Evals is an open-source evaluation framework developed by OpenAI to systematically assess the performance of large language models (LLMs).
While OpenAI Evals was initially built to test OpenAI models (like GPT-5), it can also be adapted to evaluate other LLMs or local models by integrating them into the framework.
It is a a general-purpose evaluation infrastructure that allows users to measure model quality across a wide variety of tasks; from text generation and reasoning to structured output generation like code or SQL.
Here is an example evaluation pipeline built with OpenAI Evals, designed to assess a model’s ability to generate syntactically correct SQL queries. The eval uses a synthetic dataset generated with GPT-4 and a custom YAML configuration to register the evaluation within the framework:
DeepEval
DeepEval is an open-source LLM evaluation framework developed by Confident AI, designed to integrate seamlessly into Python testing workflows.
Often described as “Pytest for LLMs,” it provides a simple, unit-test-like interface for validating model outputs. Using a syntax similar to Pytest, developers can define tests for LLM responses, assertions for correctness, and metrics for quality within standard Python workflows.
The framework includes 30+ prebuilt, research-backed metrics, covering areas such as accuracy, relevance, factual consistency, coherence, and safety.
DeepEval supports both end-to-end and component-level evaluation. This means users can test entire application pipelines or isolate specific model components (e.g., retrievers, generators, or agent reasoning chains). It also integrates synthetic dataset generation, enabling teams to automatically produce diverse and evolving test cases using LLM-driven evolution techniques.
Beyond local evaluation, DeepEval connects to Confident AI, a cloud-based platform for continuous testing, regression analysis, red teaming, and production monitoring of LLM applications.
Here is an example of a trace evaluation, representing a single execution of an LLM application. Running evals on traces enables end-to-end assessment of model behavior, similar to single-turn evaluations conducted during development:
Source: ConfidentAI3
How it works: Confident AI enables LLM tracing through simple integrations or decorators in Python and TypeScript. It automatically builds an execution hierarchy of your LLM application by recording every function call.
Each span represents an individual operation (like an LLM call or tool use), and multiple spans together form a trace, representing a full execution flow. With tracing enabled, you can run evaluations at both the span and trace levels to analyze model performance and behavior end-to-end.
MLflow (LLM Eval)
The MLflow LLM Evaluation extension enhances the MLflow platform with capabilities for evaluating large language models (LLMs) as part of existing machine learning pipelines. It provides a modular approach to running evaluations, with built-in support for tasks such as question answering and retrieval-augmented generation (RAG).
Developed by Databricks, the mlflow.evaluate() API includes standard evaluation metrics and supports custom plugin integrations, enabling users to define and extend evaluation methods. For instance, Giskard’s integration with MLflow utilizes this plugin mechanism to add domain-specific metrics.
Key capabilities
- Dataset management: Build and maintain high-quality evaluation datasets; a single source of truth for testing prompts, responses, and expected outcomes.
- Human feedback: Incorporate human judgments into the evaluation process for subjective or nuanced assessments.
- LLM-as-a-Judge: Use models themselves to assess response quality based on predefined criteria.
- Systematic evaluation: Move from one-off tests to scalable, reproducible evaluations across experiments and releases.
- Production monitoring: Track application quality and drift once deployed, closing the loop between experimentation and production.
Here is an example of MLflow’s evaluation comparison view, which displays side-by-side results for multiple runs. In this case, the concise scorer metric improved by 33%, while concept coverage decreased by 11%..
Source: MLflow4
Ragas
RAGAS (Retrieval-Augmented Generation Assessment Suite) is an open-source evaluation framework specifically designed to measure the performance of Retrieval-Augmented Generation (RAG) and agentic LLM applications. It provides a lightweight experimentation environment; similar to using pandas for rapid data analysis.
RAGAS evaluates how effectively a system retrieves and integrates relevant context into its generated responses. It does this through a set of research-backed metrics, including:
- Faithfulness: how accurately the generated answer reflects retrieved evidence.
- Contextual relevancy: how relevant the retrieved documents are to the query.
- Answer relevancy: how relevant the generated answer is to the user’s question.
- Contextual recall and contextual precision: how completely and precisely relevant information is retrieved.
These metrics combine to produce an overall RAG score, quantifying retrieval and generation quality together. Beyond RAG, RAGAS now supports metrics for agentic workflows, tool use, SQL evaluation, and even multimodal tasks through extensions like Multimodal Faithfulness and Noise Sensitivity.
RAGAS also introduces new metrics over time, available in the RAGAS GitHub repository here.
Here is a Score distribution analysis by RAGAS:
Source: RAGAS5
TruLens
TruLens is an open-source library designed for the qualitative analysis of LLM outputs. It operates by injecting feedback functions that execute after each model call to evaluate the response. These feedback functions, powered by either another LLM or custom rule-based logic, assess the output for dimensions such as factuality, coherence, or relevance.
he framework also supports ground truth evaluations, where model outputs are compared to verified answers, and streaming evaluations for real-time applications. Here is a real-world example of evaluating an app using ground truth from TruLens.
Beyond accuracy testing, TruLens supports ethical and behavioral evaluation:
Through its built-in visualization tools and integrations with OpenAI, LangChain, and other ecosystems, developers can view, compare, and debug evaluation results directly from the TruLens dashboard or Colab notebooks.
Deepchecks (LLM)
Deepchecks (LLM) is an open-source evaluation framework originally built for ML model validation, now extended for large language models (LLMs) and RAG applications. It offers modules specifically tailored to evaluate LLM-powered retrieval pipelines.
Hoe it works: Deepchecks LLM Evaluation operates by analyzing uploaded interaction data and computing quantitative metrics across several performance dimensions; such as completeness, coherence, relevance, toxicity, and fluency. It aggregates these results into average property scores and an overall evaluation score, while visually highlighting weak or underperforming segments of the model’s output.
A distinctive feature of Deepchecks is its annotation system, which provides automated or manual scoring for each LLM interaction. The framework can generate estimated annotations to judge the quality of model outputs, allowing users to review, edit, or fine-tune these labels via a customizable configuration file.
Deepchecks (LLM) stands out for its focus on evaluation metrics and automation pipelines:
- Agent-as-a-Judge
- RAG evaluation
- LLM evaluation framework
- CI/CD pipelines
Supported use cases:
- Multi-agent workflows: Evaluate collaboration and coordination between multiple interacting agents.
- Q&A: Assess answer accuracy, relevance, and factual consistency in question-answering systems.
- Summarization: Measure the completeness, conciseness, and faithfulness of generated summaries.
- Generation: Evaluate creative text generation for quality, coherence, and fluency.
- Classification: Test label accuracy, clarity, and distributional consistency in classification outputs.
- Feature extraction: Analyze how effectively models extract or structure information from input data.
- Chat: Evaluate multi-turn conversational quality, tone, and helpfulness in dialogue systems.
- Retrieval: Measure retrieval precision, recall, and contextual relevance in RAG pipelines.
- Custom interaction type: Define and evaluate bespoke LLM interaction types tailored to unique use cases.
Here is an example of a Q&A use case where the model answers a medical question about GVHD-related pain. The newer version (v2_improved_IR) gives a more complete and relevant response, which is rated as “good” by Deepchecks Evaluation:
Source: Deepchecks6
Prompt testing and optimization
Promptfoo
Promptfoo is s open-source toolkit for prompt testing and evaluation. It enables A/B testing of prompts and LLM outputs using simple YAML or command-line configurations and supports LLM-as-a-judge evaluations.
The toolkit is designed for lightweight experimentation, requiring no cloud setup or SDK dependencies, and is widely used by developers for rapid prompt iteration and automated robustness testing (such as prompt injection or toxicity checks). Best for integrating prompt evaluation into everyday development workflows.
Humanloop
Humanloop is a prompt evaluation and optimization platform centered on human-in-the-loop feedback. Enables teams to collect and analyze human judgments on LLM outputs, helping improve prompt quality, model alignment, and reliability.
Opik (by Comet)
Opik is an open-source LLM evaluation and monitoring platform developed by Comet. It provides tools to track, evaluate, and monitor LLM applications throughout their development and production lifecycle.
Opik logs complete traces and spans of prompt workflows, supports automated metrics (including complex ones like factual correctness via LLM-as-a-judge), and enables performance comparison across prompt or model versions.
Its distinction lies in combining prompt evaluation with experiment management and observability, bridging the gap between testing and production monitoring.
Framework-specific evaluation
LangChain Evals
LangChain Evals is a framework-specific evaluation tool for LangChain workflows. It provides a set of built-in evaluation templates and metrics tailored to assess the performance of LangChain applications, especially those involving complex chains of LLMs.
LangSmith
LangSmith is an evaluation and observability platform developed by the LangChain team. It provides tools for logging and analyzing LLM interactions, with specialized evaluation capabilities for tasks such as bias detection and safety testing.
It is a managed (hosted) service rather than a fully open-source tool, offering enterprise-level support for LangChain-based applications.
LlamaIndex Eval
LlamaIndex Eval is an evaluation toolkit integrated into the LlamaIndex (formerly GPT Index) framework, for assessing RAG pipelines built on LlamaIndex. It includes a Correctness Evaluator that compares generated answers against reference responses for a given query and can also use GPT-5 as a judge to evaluate answer quality in a reference-free manner.
Its functionality is similar to RAGAS, but it is natively embedded within the LlamaIndex workflow, allowing developers to evaluate retrieval and generation quality without introducing external dependencies.
LLM observability frameworks with evaluation capabilities
Arize Phoenix
Phoenix, developed by Arize AI (an ML observability company), is an open-source toolkit for analyzing and troubleshooting LLM behavior in production environments. Unlike traditional evaluation frameworks, Phoenix focuses on observability and exploratory analysis rather than predefined metrics.
Users can input logs of LLM interactions, including prompts, responses, and feedback, and Phoenix automatically clusters and visualizes these interactions to surface areas where the model may underperform.
For RAG systems, it can highlight patterns such as specific topics or query types that consistently produce poor results by analyzing response embeddings.
You can use Phoenix to monitor deployed RAG or LLM systems, and then turn to frameworks like RAGAS or Giskard for deeper metric-level evaluation of identified issues.
Langfuse
Langfuse is primarily focused on monitoring both LLM (Large Language Model) and RAG (Retrieval-Augmented Generation) systems. It helps teams track and analyze how models are performing in real-time production environments.
While it can evaluate model performance through various metrics, its core strength lies in providing observability into how LLM and RAG pipelines behave during operation. This includes tracking performance across LLM outputs, retrieval quality, and model drift, ensuring that models continue to meet quality standards as they interact with dynamic datasets or change over time.
Langfuse’s role complements traditional metric-based evaluation frameworks by focusing on ongoing monitoring and issue detection in active, deployed systems, making it particularly useful for teams with complex production workflows.
Langtrace AI
Langtrace AI specializes in evaluating LLM applications by capturing detailed traces and performance metrics. It offers tools for monitoring key aspects such as token usage, latency, accuracy, and cost, providing a comprehensive view of model behavior and performance.
Lunary
Lunary specializes in providing deep observability into LLM interactions, enabling developers to monitor and evaluate model behavior in real-time production environments.
LLM evaluation metrics
LLM evaluation metrics have evolved from traditional statistical scorers to model-based and now LLM-as-a-judge approaches, here is a brief explanation for each:
- Statistical scorers (reference-based): Metrics like accuracy, precision, recall, F1, BLEU, and ROUGE measure overlap with a reference answer. They work well for structured tasks (e.g., classification, summarization) but struggle with open-ended outputs.
- Model-based scorers (reference-free): Metrics such as Supert, BLANC, SummaC, or QAFactEval evaluate text quality, factuality, or logical consistency without exact references.
- LLM-based scorers (LLM-as-a-judge): Evaluations use another model (e.g., GPT-5) to assess response quality in context.
For more see: Agentic evals: How we evaluate LLM applications?
Why LLM evals are hard
Evaluating LLMs is anything but simple. Beyond the fact that quality criteria vary by use case, the evaluation process itself is fundamentally different from traditional software testing or predictive ML evaluation.
One key difficulty is non-determinism: LLMs generate probabilistic outputs, so the same input can produce different responses each time, making consistency and reproducibility harder to measure.
Image source: AI world7
While the probabilistic nature of LLMs allows for creative and diverse responses, it also makes testing harder; you must determine whether a range of outputs still meets expectations rather than checking for a single correct answer.
No single ground truth: LLMs often tackle open-ended tasks like writing, summarization, or conversation. In these cases, many valid answers can exist. Evaluating such systems requires measuring semantic similarity, tone, style, or factual accuracy, not just matching reference text.
Diverse input space: LLM applications face a vast variety of inputs, for example, a customer support bot may handle questions about returns, billing, or account security. Effective evaluation needs scenario-based test sets that capture this diversity.
Even well-designed offline tests can fail in real-world deployment, where users introduce unexpected prompts and edge cases. This highlights the need for continuous, in-production evaluation and observability to ensure consistent model quality over time.
Unique risks in LLM evaluation
Working with probabilistic, instruction-following systems introduces new and complex risks that traditional AI evaluation rarely covers:
- Hallucinations: The model may generate false or misleading facts — for instance, inventing products, citing non-existent sources, or providing incorrect medical or legal advice.
- Jailbreaks: Adversarial users can exploit prompts to bypass safety constraints, coaxing the model into producing harmful, biased, or disallowed content.
- Data leaks: An LLM might unintentionally reveal sensitive or proprietary information from its training data or connected systems.
To mitigate these, teams need robust evaluation workflows that go beyond accuracy metrics:
- Stress-test models with adversarial and edge-case inputs to uncover vulnerabilities.
- Run red teaming and safety evaluations to test the model’s resilience to malicious prompts.
- Continuously monitor live interactions to detect emerging issues like drift, privacy leaks, or unsafe outputs in production.
LLM evaluation methods
LLM evaluation methods help measure how well a language model performs across tasks like reasoning, summarization, and dialogue. Statistical metrics (e.g., BLEU, ROUGE) to LLM-as-a-judge approaches, where another model assesses quality, safety, and factual accuracy. There are also agentic and behavioral testing evaluation methods monitoring how models act over time and use tools.
For a deeper overview of key approaches and their challenges, check our full article on LLM evaluation methods.
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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