The LLM Evaluation Landscape with Frameworks in 2026
Evaluating LLMs requires tools that assess multi-turn reasoning, production performance, and tool usage. 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. Specifically, we:
- Checked whether LLM eval tools support agent behavior tracking analysis and production monitoring.
- Grouped tools by functional evaluation focus.
- Compared evaluation capabilities, from single-turn tests to multi-turn, real-world assessments.
LLM evaluation landscape
Functional category | Tools | Primary purpose |
|---|---|---|
OpenAI Evals, DeepEval, MLflow (LLM Eval), RAGAS, TruLens, Deepchecks, Inspect AI | Evaluate LLM outputs using metrics for quality, accuracy, and coherence. | |
Promptfoo, Humanloop, Opik | Design, test, and optimize prompts for better model output. | |
LangChain Evals, LangSmith, LlamaIndex Eval | Evaluate LLMs within specific ecosystems like LangChain or LlamaIndex. | |
Arize Phoenix, Langfuse, Langtrace AI, Lunary | Continuous monitoring and analysis of model performance in production. |
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.
- Read LLM observability & evaluation platforms to learn more.
- 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).
It is 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 synthetic data generated with GPT-4 and a custom YAML configuration to register the evaluation within the framework:
DeepEval
It is a Python-first framework often described as “pytest for LLMs.” It stands out for its large set of research-backed metrics and its ability to test full pipelines or isolated components.
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
MLflow (LLM Eval)
It extends MLflow into LLM evaluation. Its key strength is experiment tracking and side-by-side comparison across runs and releases.
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 context.
- 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, which quantifies both retrieval and generation quality. 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. It is well suited for reasoning analysis and qualitative evaluation, not just accuracy.
Beyond accuracy testing, TruLens supports ethical and behavioral evaluation:
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.
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
Here is an example of a Q&A use case where the model answers a medical question about GVHD-related pain.
Source: Deepchecks6
Inspect AI
Inspect AI is an open-source LLM evaluation framework developed with a focus on research-grade assessments. It supports both model-level and agent-level evaluation, enabling users to assess not only single-step model outputs but also multi-step agent behavior, reasoning chains, and task execution over time.
The framework is straightforward to set up in isolated environments such as Docker containers or virtual machines, making it suitable for safely evaluating agentic workflows without exposing the host system. Inspect provides a clear task definition and execution model, allowing users to quickly define evaluation tasks, control sample sizes (e.g., for CI-style statistical standards), and integrate evaluations into automated pipelines.
Inspect also provides detailed step-by-step evaluation logs, including latency and token usage per step, along with a report on actions and tool calls. This level of granularity makes it easier to diagnose where and why a model or agent deviates from expected behavior.
Another good aspect of Inspect AI is that it is designed for offline evaluation, prioritizing correctness, transparency, and reproducibility over real-time telemetry features.
Prompt testing and optimization
Promptfoo
Promptfoo is an open-source toolkit for prompt engineering, 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.
Phoenix can be used 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.
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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