Top 7 AI Agent Evaluation Frameworks (2026)

Evaluating an AI agent is not the same problem as evaluating a single LLM call. With a plain prompt, you have one input and one output, so you can score the answer and move on. An agent produces a trajectory: a chain of reasoning, tool calls, and intermediate steps that unfolds over many turns before it ever reaches a final answer. Two runs of the same task can take completely different paths and both be correct, or both be wrong for different reasons.

That changes what "good" means. Agent evals have to judge things a single-output metric can't see:

Trajectory quality — did the agent take a sensible path, or wander, loop, or backtrack?
Tool-call correctness — did it call the right tool, with the right arguments, at the right time?
Compounding failure — a bad result at step 3 can silently corrupt steps 4 through 10, so the final answer alone hides where things broke.
Non-determinism — the same input can produce different behavior on every run, so you're scoring distributions, not fixed answers.

There's also a second axis: when you run the eval. Offline evaluation runs before deployment, against a fixed set of test cases, so it's the gate in your CI pipeline and the thing you check before shipping a prompt change. Online evaluation scores real production traffic as it happens, catching regressions and edge cases your test set never anticipated. The tools below are offline-first by design, but most plug into online eval too: instead of a curated test set, you point the same evaluators at the traces your observability platform already collects. So the offline/online line is less about which tool you pick and more about where you wire it in.

Because of this, "best agent eval framework" doesn't reduce to one number. GitHub stars are a noisy signal here: the most-starred tools are often general observability platforms that picked up evals as a feature, while purpose-built agent-eval libraries are newer and smaller. So instead of ranking, this guide groups the leading open-source tools by the job they actually do, so you can pick the one that matches how you want to test.

First, do you even need one?

A framework is not a prerequisite for evaluating an agent. The core loop is simple: run your agent on a set of cases, score the result, and track whether scores move over time. You can write that harness yourself with a handful of test cases and an LLM-as-judge call, and for a small project that's often the fastest path. What a framework buys you is the time you'd otherwise spend building trajectory matchers, trace parsing, batching, and reporting from scratch. Reach for one when that plumbing becomes the bottleneck, not before.

Built for agent trajectories

These tools start from the agent-specific problem: scoring the path, not just the answer.

agentevals

langchain-ai/agentevals | Python & TypeScript | MIT

Readymade evaluators focused squarely on agent trajectories. It judges an execution either against an expected trajectory or with an LLM-as-judge, so you can assert that an agent took the right steps and called the right tools, not just that it landed on the right answer. It's small and unopinionated, which makes it easy to drop into an existing test suite. Reach for it when trajectory and tool-call matching is exactly the gap you need to fill, and pair it with its companion package, openevals, for everything else.

Strands Evals

strands-agents/evals | Python | Apache-2.0

AWS's evaluation framework, built around three concepts: a Case (input, expected output, expected tool trajectory), an Experiment (a bundle of cases plus evaluators), and an Evaluator (mostly LLM-as-judge). It goes beyond scoring with detectors that automate failure detection and root-cause analysis on execution traces, taking you from "test failed" to "here's what to fix" without manual trace reading. It reads OpenTelemetry traces, so it works on production runs as well as offline test cases. The newest and smallest entry here, but the most opinionated about the full agent-eval loop.

Full eval frameworks, agent-aware

Broader frameworks that cover the whole spectrum of LLM evaluation and have grown agent-specific features on top.

DeepEval

confident-ai/deepeval | Python & TypeScript | Apache-2.0

The closest thing to "Pytest for LLMs." DeepEval ships a deep catalog of metrics — G-Eval, task completion, answer relevancy, hallucination, and more — and runs them as unit tests you can wire into CI. Its 4.0 release added an agent-native workflow that applies those metrics directly to agent traces, and it plugs into OpenAI Agents, LangChain, CrewAI, and others. Pick it when you want a mature, batteries-included framework and a familiar test-driven workflow.

openevals

langchain-ai/openevals | Python & TypeScript | MIT

The general-purpose companion to agentevals: prebuilt evaluators for the common cases — LLM-as-judge, structured-output checks, extraction quality, and tool-call handling. It gives you a consistent harness and sensible defaults so you don't hand-roll scorers for every project. Start here for everyday LLM-app evals, then add agentevals when you specifically need trajectory scoring.

Eval plus security and red-teaming

promptfoo

promptfoo/promptfoo | CLI (npm & pip) | MIT

A CLI and library for evaluating and red-teaming LLM apps, driven by simple declarative configs that slot into CI/CD. Beyond standard evals, its red-team module scans for 50+ vulnerability types including prompt injection, jailbreaks, PII leaks, and tool misuse. It's used by hundreds of thousands of developers, and OpenAI acquired it in March 2026; it remains open source under the MIT license. Reach for it when correctness and security are the same checklist, especially if you want testing that runs from the command line without writing Python.

Composable eval library

Arize phoenix-evals

Arize-ai/phoenix | Python & TypeScript (alpha) | Elastic v2

Arize's arize-phoenix-evals is a standalone, pip-installable library of lightweight, composable building blocks for writing and running evals — hallucination, relevance, toxicity, and custom classifiers — with adapters for OpenAI, LiteLLM, and LangChain, plus built-in concurrency for fast batch scoring. It ships inside the broader Phoenix monorepo, but it's independent of the Phoenix observability UI, so you can use the evaluators on their own and feed results wherever you like. Choose it when you want eval primitives to compose into your own pipeline rather than a prescribed test framework.

Special mention

braintrustdata/autoevals (Python & TypeScript) — Braintrust's open-source scorer library, with model-graded checks for factuality, relevance, and safety. It's thin on its own, but a clean choice if you want drop-in scorers, and it pairs naturally with the Braintrust eval platform for logging and comparison.

How to choose

Before you pick from this list, audit how your agent was built. The framework you used to build the agent often decides the eval tool for you: many of them ship their own evaluation subpackage or have a first-party integration, so the cleanest option may already be installed. If you built on LangChain, openevals and agentevals slot in natively; if you built on Strands, Strands Evals reads its traces directly. Check for an in-house option before adding a dependency, then narrow by the job:

Gating agents in CI? DeepEval (test-driven) or promptfoo (declarative, command-line).
Checking the path and tool calls, not just the answer? agentevals or Strands Evals.
Security and red-teaming alongside correctness? promptfoo.
Composing your own scoring pipeline? Arize phoenix-evals or autoevals.
Running online evals on live traffic? Wire any of these into the traces your observability platform collects; Strands Evals and DeepEval's trace workflow are built for it.

Key Takeaways

Agent evals are trajectory-first. Scoring the final answer misses tool-call errors and compounding multi-step failure.
Don't judge by star count. The biggest repos are often observability platforms; the most useful purpose-built agent-eval tools are newer and smaller, so match the tool to the job instead.
Security is table stakes. Red-teaming for prompt injection and tool misuse now sits next to correctness, not in a separate bucket.
Offline and online are converging. The same evaluators run on a fixed test set before deploy and on live production traces after, usually through your observability platform, so the choice is mostly about where you wire them in.

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Tooling current as of June 2026. This space moves fast, so check each repo for the latest.