AI Agent Evaluation Framework Comparison: DeepEval, Ragas, Promptfoo, and More

April 5, 2026 · Editorial Team · 10 min read · evaluation observability ai-agents

Most teams that ship AI agents do it the same way: build something, try it manually, ship it, and wait for users to find the bugs. That works until an agent starts deleting data it wasn't supposed to delete, or starts hallucinating confidently wrong answers, or starts failing silently on edge cases you never thought to test.

Evaluation is the discipline that catches those problems before they reach users. In 2026 there are at least seven serious frameworks for evaluating LLM behavior and AI agent pipelines. They're not interchangeable. Each was built with a specific set of problems in mind, and picking the wrong one for your use case is a real cost in setup time and missing coverage.

This article breaks down DeepEval, Ragas, Promptfoo, OpenAI Evals, Inspect AI, Patronus AI, and Galileo, what each tool actually does well, what it doesn't, and which situations call for it.

Why evaluation is harder for agents than for models

Evaluating a static prompt is relatively simple. You run it against a test set, compare outputs, score with a judge. Agents are different because they're not deterministic and they act.

A single agent run might call five tools in sequence. The final answer could look correct even if intermediate steps were wrong. A tool might have been called with the right parameters but at the wrong time. Hallucinated reasoning in step two might not surface as a visible error until step six. And unlike a chatbot, a deployed agent can cause real effects: sending emails, writing to databases, making API calls.

This means a good agent evaluation framework needs to do more than score outputs. It needs to trace multi-step runs, check tool call correctness, surface reasoning failures, and ideally catch risky behavior before it executes. Not all of the tools below do all of these things.

DeepEval

DeepEval is the most general-purpose evaluation library on this list. It's Python-based, open source, and covers a wide surface area: faithfulness, answer relevancy, contextual precision, contextual recall, hallucination, toxicity, bias, and more. Each is implemented as a metric you can run against your outputs with a few lines of code.

from deepeval import evaluate
from deepeval.metrics import FaithfulnessMetric, AnswerRelevancyMetric
from deepeval.test_case import LLMTestCase

metric = FaithfulnessMetric(threshold=0.7)
test_case = LLMTestCase(
    input="What is the capital of France?",
    actual_output="Paris is the capital of France.",
    retrieval_context=["France is a country in Western Europe. Its capital is Paris."]
)
evaluate([test_case], [metric])

What I like: the coverage is genuinely broad, the API is clean, and the metrics are well-documented. The hallucination metric actually works, it uses an LLM judge under the hood but structures the evaluation carefully enough that the results are useful rather than noisy.

What's missing: DeepEval's agent-specific evaluation is still catching up to its RAG evaluation story. For tracing multi-step agent runs and evaluating tool call correctness across a sequence of actions, you'll need to wire that up yourself. It's a strong evaluation library, but it's not an agent observability platform.

DeepEval has a paid cloud offering (Confident AI) that adds a UI, dataset management, and regression testing over time. The open-source core is solid enough to use without it.

Pick DeepEval when: you want a Python-native evaluation library with broad metric coverage, particularly for RAG pipelines and LLM outputs.

Ragas

Ragas is purpose-built for RAG evaluation. If your agent retrieves documents before generating an answer, Ragas gives you the specific metrics you need to understand what's breaking.

The core metrics are: faithfulness (does the answer match the retrieved context?), answer relevancy (does the answer address the question?), context precision (is the retrieved context actually relevant?), and context recall (did you retrieve all the context needed to answer?). Together, these four metrics let you diagnose exactly where a RAG pipeline fails, whether it's a retrieval problem, a generation problem, or both.

from ragas import evaluate
from ragas.metrics import faithfulness, answer_relevancy, context_precision, context_recall
from datasets import Dataset

data = {
    "question": ["What year was Python released?"],
    "contexts": [["Python was first released in 1991 by Guido van Rossum."]],
    "answer": ["Python was released in 1991."],
    "ground_truth": ["Python was first released in 1991."]
}

dataset = Dataset.from_dict(data)
results = evaluate(dataset, metrics=[faithfulness, answer_relevancy, context_precision, context_recall])

Ragas is more opinionated and more focused than DeepEval. For pure RAG evaluation, that focus makes it the better tool. For anything outside RAG, general agent behavior, tool call evaluation, safety, you're looking at the wrong library.

The LlamaIndex and LangChain integrations are solid. If you're already using either framework for your RAG pipeline, Ragas slots in naturally.

Pick Ragas when: your agent is primarily RAG-based and you want the most precise breakdown of where your retrieval or generation is failing.

Promptfoo

Promptfoo takes a different angle from DeepEval and Ragas. It's a CLI and testing framework designed for comparing prompt variants, model configurations, and LLM providers against each other. You define your test cases in a config file, run them against multiple configurations, and get a side-by-side comparison.

# promptfooconfig.yaml
providers:
  - anthropic:claude-3-7-sonnet-20250219
  - openai:gpt-5
prompts:
  - "Summarize this article in three sentences: {{article}}"
  - "Write a three-sentence summary of: {{article}}"
tests:
  - vars:
      article: "{{article_text}}"
    assert:
      - type: llm-rubric
        value: "The summary is accurate and doesn't add information not in the original"

Where Promptfoo really shines is adversarial testing. It has built-in support for red-teaming: generating adversarial inputs designed to elicit unsafe behavior, jailbreaks, prompt injections, and policy violations. For teams shipping agents that interact with users directly, running a promptfoo red-team evaluation before deployment is a genuinely useful safety check.

Promptfoo is also the most CI/CD-friendly tool on this list. The CLI interface, YAML configuration, and provider-agnostic design make it natural to run in a GitHub Actions workflow or a pre-deployment gate.

The weakness: Promptfoo is a testing and comparison framework, not a runtime observability platform. It tells you how your agent performs on your test set. It doesn't tell you how it's performing in production.

Pick Promptfoo when: you're comparing model providers or prompt variants, you want red-teaming / adversarial evaluation, or you need eval as part of a CI/CD pipeline.

OpenAI Evals

OpenAI Evals is an open-source framework from OpenAI for evaluating models and agent pipelines. It's primarily designed around the patterns OpenAI uses internally: defining evaluation tasks, running model completions, and scoring with a grader (which can be a model, a code function, or a human).

The framework has a large library of pre-built evaluation tasks, which is its main value proposition for teams doing model benchmarking. If you want to run standard benchmarks (reasoning, coding, instruction following) against a new model version, OpenAI Evals has a head start.

For custom agent evaluation, the framework is more work to adapt. It wasn't designed for multi-step agent traces or tool call evaluation. It works best as a way to evaluate individual LLM calls within a larger pipeline, not for evaluating the agent pipeline itself.

One practical consideration: the framework is OpenAI-designed and has natural friction when you're evaluating non-OpenAI models. Not insurmountable, but worth knowing.

Pick OpenAI Evals when: you want pre-built benchmark tasks for model evaluation, you're in the OpenAI ecosystem, or you want to contribute to the community library of evals.

Inspect AI

Inspect AI is Anthropic's evaluation framework, developed by the UK AI Safety Institute and released open source. It's designed specifically for evaluating AI models and agents against safety-relevant tasks, and its design reflects that priority.

Inspect structures evaluations as tasks: you define a dataset of scenarios, a solver (which runs your model or agent), and a scorer. The task abstraction is clean and makes it straightforward to build complex multi-step evaluations. The framework handles parallel execution, logging, and retry logic so you can run large evaluation sets efficiently.

What sets Inspect apart from the other tools here is its focus on agentic evaluation specifically. The tool use support, multi-turn conversation handling, and the scaffolding for complex reasoning tasks are all designed with agent pipelines in mind. It's the best framework on this list for evaluating whether an agent can actually complete a long-horizon task correctly.

Inspect is also genuinely useful for safety evaluations, testing whether an agent can be prompted into unsafe behavior, whether it refuses correctly, and whether safety properties hold across a range of inputs.

The learning curve is steeper than DeepEval or Ragas. The abstractions are powerful but require more setup. For teams doing serious safety or capability evaluation of autonomous agents, that investment pays off.

Pick Inspect AI when: you're doing safety evaluation, you need to evaluate multi-step agentic task completion, or you're working with research-grade evaluation standards.

Patronus AI

Patronus AI is an enterprise-focused evaluation platform. It covers hallucination detection, faithfulness scoring, PII detection, and policy compliance out of a managed API. You send your LLM inputs and outputs, and Patronus scores them across whatever dimensions you configure.

The selling point is that you don't have to build or maintain evaluation infrastructure. Patronus handles the judge models, the scoring logic, and the API. You call their API and get scores back. For teams that want evaluation coverage without the engineering overhead of setting up an open-source evaluation framework, that's a real value.

The Lynx hallucination detection model that Patronus developed is worth calling out specifically. It's a specialized small model trained for detecting hallucinations in RAG outputs, and it's more accurate than using a general-purpose LLM as a judge for that specific task.

The tradeoff is cost and control. Patronus pricing scales with usage, and you're sending your model inputs and outputs to a third-party API. For teams with sensitive data or tight budgets, that's a real consideration.

Pick Patronus AI when: you want managed evaluation infrastructure, hallucination detection is a priority, or you need policy compliance checks without building your own classifier.

Galileo

Galileo is an ML observability platform that has expanded into LLM and agent evaluation. Its focus is production monitoring: you instrument your agent pipeline, send trace data to Galileo, and get dashboards showing quality metrics over time, error distributions, and alerting when metrics degrade.

The key difference from the other tools on this list: Galileo is primarily a runtime observability platform, not a pre-deployment testing framework. You use it to understand how your agent is performing on real production traffic, not to validate it before deployment.

The evaluation capabilities within Galileo (hallucination detection, PII detection, chunk attribution) run on your production traces in near-real-time. This means you can catch quality degradation as it happens rather than waiting for user reports.

Galileo integrates well with frameworks like LangChain and LlamaIndex, which is where most teams using it are building. The setup involves adding Galileo's logger to your agent pipeline, which is straightforward for framework-based projects.

The platform is enterprise-focused and enterprise-priced. It's not the right tool for a side project or early-stage product. For a team with a deployed agent handling meaningful production traffic, the production monitoring angle is genuinely useful.

Pick Galileo when: you need production monitoring for a deployed agent, you want real-time quality metrics on production traffic, or your team uses LangChain/LlamaIndex and wants integrated observability.

How to choose

The tools here solve different problems. Treating them as interchangeable is the mistake most teams make.

Here's the clearest decision path I can offer:

Building a RAG agent? Start with Ragas for pipeline evaluation. Add DeepEval for broader output quality checks.

Need to compare prompts or models? Promptfoo. The provider-agnostic comparison and CI/CD integration are the best available.

Shipping a safety-critical or autonomous agent? Evaluate with Inspect AI before deployment. Add Patronus's Lynx for production hallucination detection.

Production agent already deployed? Galileo for runtime monitoring. DeepEval for regression testing when you update the pipeline.

Need red-teaming for adversarial safety? Promptfoo has the best built-in support for this.

One thing I'd add: you almost certainly need more than one of these. Promptfoo to test before deployment, something like Galileo or Langfuse for production monitoring, and Ragas or DeepEval for unit-level pipeline evaluation. The tools are complementary, not competing.

For production AI agent pipelines, Langfuse is also worth looking at alongside these, it focuses on tracing and observability rather than scoring, but it pairs well with any of the evaluation frameworks here.

What the market is missing

The gap I see consistently: evaluation for tool-use correctness across multi-step agent traces. Most of these frameworks evaluate inputs and outputs at the call level. They don't tell you whether the agent's decision to call a specific tool at step three was correct given the state it was in at step two.

Inspect AI is closest to addressing this. The rest of the market is still catching up. For teams building serious autonomous agents, this is a gap you'll have to fill with custom evaluation logic until the frameworks mature.

Evaluation is not a one-time activity. Models change, prompts drift, and user behavior shifts. Whichever tool you pick, treat it as ongoing infrastructure rather than a pre-launch checkbox.