AI Agent Evaluation Frameworks Compared: DeepEval, LangSmith, Braintrust, and Arize

DeepEval

DeepEval is an open-source Python framework that brings a pytest-style interface to LLM evaluation. You write evaluation tests as Python functions, define metrics, and run them in your existing test suite or CI/CD pipeline. DeepEval ships with fourteen built-in metrics including hallucination detection, answer relevancy, faithfulness, contextual precision, contextual recall, bias, and toxicity. Each metric uses an LLM judge under the hood (configurable to any OpenAI-compatible model) and returns a score between 0 and 1 with an explanation of why it scored the way it did.

The framework's core strength is its developer-friendly interface. If your team already uses pytest, adopting DeepEval requires almost no workflow change. You create test cases with input, actual output, expected output (optional), and retrieval context (for RAG), then assert that specific metrics pass a threshold. Failed assertions produce detailed explanations showing which claims were unsupported or which instructions were not followed, making debugging straightforward.

DeepEval also includes a hosted component called Confident AI that provides a web dashboard for viewing evaluation results, tracking metrics over time, and managing datasets collaboratively. The hosted component is optional; the core library runs entirely locally. For teams that want the library without vendor dependency, the pure open-source workflow writes results to local files or integrates with any data store through custom callbacks.

Best for: Teams with existing Python test suites who want to add LLM evaluation without adopting a new platform. Particularly strong for RAG evaluation because of its faithfulness and contextual relevancy metrics. Free to use, with costs limited to the LLM API calls your metrics require.

LangSmith

LangSmith is LangChain's managed evaluation and observability platform. It captures traces from LangChain applications automatically (and from non-LangChain applications through its SDK), stores them, and provides tools for running evaluations against historical traces or curated datasets. The evaluation workflow centers on experiments: you define a dataset, run your agent against it, apply evaluators (custom functions, LLM judges, or human annotators), and compare results across multiple runs.

The platform's strongest feature is its comparison view, which shows how performance changed between two versions of a prompt, model, or agent configuration side by side. You can filter by passing and failing cases, drill into individual traces to see exactly where behavior diverged, and annotate specific examples for team discussion. This makes it particularly effective for prompt engineering workflows where you iterate on instructions and need to verify that changes help without causing regressions.

LangSmith supports both automated evaluators (Python functions that score outputs) and human evaluation workflows where team members label outputs using custom rubrics. The annotation queue system lets you route samples to human reviewers based on criteria like low confidence scores or flagged content, enabling a hybrid workflow where automation handles the bulk and humans handle the difficult cases.

Pricing starts at a free tier for small volumes, with paid plans based on trace ingestion volume. The Developer plan is free up to 5,000 traces per month. The Plus plan at $39 per seat per month adds higher limits and team features. Enterprise pricing is custom and includes SSO, advanced access controls, and dedicated support.

Best for: Teams already using LangChain who want integrated tracing and evaluation, or teams that need collaborative evaluation workflows with human-in-the-loop annotation. The comparison views and annotation queues are difficult to replicate with open-source tools alone.

Braintrust

Braintrust positions itself as a platform for iterating on AI products, with evaluation as its core loop. The workflow is: run your agent against test cases, score the results, compare across experiments, identify regressions, and iterate. Braintrust provides SDKs for Python and TypeScript, a web interface for reviewing results, and tight integration with CI/CD systems through its CLI.

The platform's differentiator is its focus on the iteration speed. Everything is designed to minimize the time between making a change and understanding its impact. The web interface loads instantly, comparison views update in real time as experiments run, and the scoring pipeline is optimized for throughput so you can evaluate hundreds of test cases in minutes rather than hours. This matters because prompt engineering is an empirical discipline where faster feedback loops produce better results.

Braintrust includes built-in scoring functions for common metrics (similarity, factuality, formatting) and supports custom scorers written in Python or TypeScript. It also supports LLM-as-judge scoring with configurable prompts and models. The dataset management features let you maintain versioned test sets, add cases from production logs, and tag cases by category for granular analysis.

Pricing is usage-based. The free tier includes 1,000 evaluations per month. The Pro plan is $25 per user per month with 50,000 evaluations. Enterprise plans include custom limits, SSO, and private deployment options.

Best for: Teams that prioritize fast iteration cycles and need a clean, fast UI for comparing experiment results. Particularly good for teams doing heavy prompt engineering who make multiple changes per day and need rapid feedback on each one.

Arize Phoenix

Arize Phoenix is an open-source observability and evaluation tool that runs locally or in the cloud. It captures traces from any LLM application (through OpenTelemetry-compatible instrumentation), visualizes them, and lets you run evaluation scoring on historical traces. The key difference from LangSmith and Braintrust is that Phoenix is primarily an observability tool that added evaluation features, rather than an evaluation tool that added tracing.

This means Phoenix excels at production monitoring workflows: capture all production traces, sample and score them using automated evaluators, set alerts when quality drops, and investigate specific failures through the trace viewer. The evaluation is continuous rather than batch-oriented, which catches quality degradation faster than periodic offline eval runs. Phoenix's trace viewer shows the full execution tree with timing, token counts, and evaluation scores at each node, making it straightforward to identify exactly which step in a complex agent workflow caused a failure.

Phoenix's evaluation capabilities include built-in LLM-as-judge evaluators for hallucination, relevance, and toxicity, plus a framework for custom evaluators. You can run evaluations inline (scoring each response as it arrives) or in batch (scoring a set of historical traces). The platform stores evaluation results alongside traces, so you can query for all traces where the hallucination score exceeded a threshold and examine what went wrong.

As open-source software, Phoenix is free to self-host. Arize also offers a managed cloud version with additional features like team collaboration, longer data retention, and enterprise security. Managed pricing starts at approximately $50 per month for small teams.

Best for: Teams that want production monitoring and evaluation in one tool, especially teams that already use OpenTelemetry for application observability. The open-source option makes it accessible for experimentation without commitment.

RAGAS

RAGAS (Retrieval Augmented Generation Assessment) is an open-source framework specifically designed for evaluating RAG pipelines. While the other tools in this guide handle general agent evaluation, RAGAS focuses on the specific problem of measuring whether a RAG system retrieves the right context and generates accurate answers from that context.

RAGAS defines four core metrics: faithfulness (is the answer supported by the retrieved context), answer relevancy (does the answer address the question), context precision (are the retrieved documents relevant), and context recall (did the retrieval find all the necessary information). These metrics decompose RAG quality into its component parts, letting you identify whether failures originate in the retrieval step (context metrics) or the generation step (faithfulness, relevancy).

The framework integrates with LangChain, LlamaIndex, and any custom RAG pipeline through a simple evaluation interface. You provide questions, retrieved contexts, generated answers, and optionally ground truth answers, and RAGAS returns scores for each metric. It uses LLM-as-judge evaluation internally, so you need an API key for a judge model (GPT-4 or equivalent).

Best for: Teams building RAG agents who need specialized metrics for retrieval and generation quality. Use RAGAS for focused RAG evaluation alongside a general-purpose tool for broader agent evaluation.

OpenAI Evals

OpenAI Evals is an open-source framework from OpenAI for evaluating language model outputs. It provides a registry of evaluation tasks, a runner that executes evaluations, and a set of built-in grading methods including exact match, fuzzy match, model-graded scoring, and human evaluation interfaces. The framework is model-agnostic despite its name; you can evaluate any model's outputs through its API.

The framework's design philosophy favors simplicity and composability. Evaluations are defined as YAML files that specify the dataset, the completion function (which model or system to evaluate), and the grading function. This declarative approach makes it easy to share evaluation definitions across teams and to version-control your eval suite alongside your code. The registry concept lets you build a library of reusable evaluation components that different projects can reference.

OpenAI Evals is lower-level than the managed platforms, providing building blocks rather than a complete solution. You will not get dashboards, comparison views, or collaboration features out of the box. What you get is a solid foundation for building custom evaluation pipelines that do exactly what you need without platform constraints. Many teams use OpenAI Evals as the core runner inside a larger evaluation system that adds storage, visualization, and CI/CD integration.

Best for: Teams that want maximum flexibility and are willing to build infrastructure around a core evaluation engine. Good for organizations that evaluate multiple models and need a standardized framework that works across all of them.

Choosing the Right Framework

The decision depends on three factors: your team's engineering capacity, your evaluation maturity, and whether you need production evaluation or only development-time testing.

If you are just starting with agent evaluation and want the fastest path to useful results, start with DeepEval or RAGAS (for RAG-specific needs). Both are free, install with pip, and produce actionable scores within minutes of setup. You can add a managed platform later when you outgrow local tooling.

If you need collaborative evaluation with multiple team members reviewing results, comparing experiments, and annotating edge cases, LangSmith or Braintrust provide purpose-built interfaces for those workflows. The monthly cost is justified if it saves engineering time that would otherwise go into building custom dashboards and annotation tools.

If your primary concern is production quality monitoring rather than development-time evaluation, Arize Phoenix offers the best integration of observability and evaluation in a single tool. It captures production traces continuously and scores them, providing an early warning system for quality degradation that batch eval runs would miss.

Most mature teams end up using multiple tools: a local evaluation framework (DeepEval or custom) in CI/CD for development-time regression testing, a managed platform (LangSmith or Braintrust) for collaborative experiment management, and an observability tool (Phoenix, Langfuse, or Helicone) for production monitoring. The tools complement rather than replace each other because they serve different stages of the agent lifecycle.