Best evaluation frameworks for LLM apps

The best LLM evaluation frameworks in 2026 are Promptfoo for CLI-first regression testing on a laptop, Langfuse for production traces plus scoring in one open-source stack, Braintrust for teams that want a polished hosted dashboard, and DeepEval for Python shops that already live in pytest. Inspect AI wins for safety and capability evals, Patronus for regulated enterprise. Pick by where your evals will live: terminal, repo, or product.

If you ship anything with an LLM call in the hot path, your test suite is already lying to you. Unit tests pass while the model regresses on a prompt change you made two commits ago. Evaluation frameworks fix that gap. They give you a structured way to score model output against expectations, run those scores on every PR, and catch silent regressions before users do.

This post walks through six frameworks worth your time, the three eval types every team needs (deterministic, LLM-as-judge, human), a sample config you can copy, and a comparison table to pick one in a meeting.

What an LLM eval framework actually does

A traditional test asserts an exact value. assertEqual(add(2, 2), 4). LLMs don't work that way. A summarizer can return three different correct outputs for the same input. A RAG pipeline can hallucinate citations that look right.

An eval framework gives you four things a plain test runner doesn't:

1. Dataset management. A versioned list of inputs and expected outputs (or expected properties), separate from your assertions.
2. Scorers. Functions that grade output across multiple axes: factual correctness, format compliance, toxicity, latency, cost.
3. Comparison across runs. Diff a new prompt or model against the previous baseline, with score deltas per case.
4. Trace integration. Tie eval scores back to the production traces that produced them, so you can debug the actual failure.

Without those four, you're either flying blind or rolling your own infra. Both are expensive.

The three eval types you need

Before picking a framework, get clear on what you're scoring. Most teams need all three.

Deterministic checks

The cheap ones. JSON schema validation, regex match, exact-string match, latency under N ms, cost under N cents. These run instantly, cost nothing, and catch the dumbest failures (the model returned prose when you asked for JSON).

Run these on every output. They're your smoke test.

LLM-as-judge

You ask a stronger model (usually GPT-4-class or Claude Opus) to grade the output of your production model against a rubric. "Score this answer 1-5 on factual accuracy given this reference document." "Does this support reply match the tone of the brand examples?"

LLM-as-judge is the only scalable way to grade subjective qualities like helpfulness, tone, or faithfulness. It's also slow, expensive, and noisy. Pin the judge model version, sample 50-200 cases per run, and validate the judge against human scores on a calibration set every few months.

Human review

The expensive one, and the ground truth. Domain experts grade a small sample (20-100 cases) of production traffic per week, then you measure your automated scorers against those human grades. Without this loop, your LLM-as-judge can drift and you'll never notice.

Most teams skip human review until it's too late. The frameworks below all support exporting traces for human labeling; use that feature.

Six frameworks worth your time

Promptfoo

Open source, CLI-first, free for self-hosters. Promptfoo started as a YAML-driven prompt regression tool and grew into a full eval platform. You define a config file with prompts, providers (OpenAI, Anthropic, local Ollama, anything), test cases, and assertions. Run promptfoo eval and get a diffable web view.

The strongest pitch: it's the fastest framework to set up if you just want to compare two prompts against the same dataset on your laptop. No accounts, no SDK, no cloud lock-in. The CLI generates a static HTML report you can commit or share.

Where it's weak: production trace integration is thin. You'll still want Langfuse or similar for runtime observability. Their enterprise tier exists but most teams stay on the free OSS version forever.

Pricing: free OSS, enterprise on request.

Langfuse

Open source, hosted cloud or self-hosted. Langfuse is the closest thing to a Datadog for LLMs. You instrument your app with their SDK (Python and TypeScript), every call gets logged as a trace, and you can attach evaluation scores to those traces either from inside Langfuse or from any external pipeline.

The killer feature is that traces and evals live in the same database. When a user complains about a bad answer, you find the trace, see every retrieved chunk, every prompt token, every model call, and the eval score the system gave it. You can also kick off LLM-as-judge runs over historical traces.

Pricing: free OSS self-host (Postgres + ClickHouse). Cloud free tier handles 50k traces/month. Pro starts around $59/month, team and enterprise scale from there.

Braintrust

Proprietary, hosted, freemium with a generous starter tier. Braintrust is the polished commercial option. Where Langfuse feels like Grafana, Braintrust feels like Linear. The dashboard is fast, the playground for iterating on prompts side-by-side is the best in the category, and they ship a strong library of built-in scorers (factuality, ClosedQA, answer relevancy, summarization quality).

The trade-off: it's hosted-only for most plans. Self-hosting exists at the enterprise tier but the open-source Stainless-generated SDK is the only OSS surface. If your data can't leave your VPC, this likely isn't your pick.

Pricing: free tier covers small teams (10k spans/month), Pro is $249/user/month at the time of writing, enterprise is custom.

DeepEval

Python-only, open source, MIT licensed. DeepEval bills itself as "pytest for LLMs" and it earns that. You write assert_test(test_case, [metric]) in a pytest file, run deepeval test run, and get a report with per-metric scores. Integrates with pytest --collect-only, parallelization, fixtures, the whole pytest stack.

It ships with 14+ pre-built metrics including G-Eval (a flexible LLM-as-judge with custom criteria), faithfulness for RAG, bias, toxicity, and PII leakage. The Confident AI cloud is the optional hosted layer.

Where it's weak: TypeScript shops are out of luck. There's no JS port. Also, like everything pytest-adjacent, it can feel heavy if you just want to ship a YAML and move on.

Pricing: free OSS. Confident AI cloud starts free, paid plans scale with usage.

Inspect AI

Open source from the UK AI Safety Institute (UK AISI). Apache 2.0. Inspect AI was built to evaluate frontier model capabilities and safety, which means it's the framework most aligned with academic and government benchmarking work. If you've seen any UK AISI capability report in the last year, Inspect was likely involved.

It's also a perfectly good general-purpose eval framework: solvers (your model pipeline), scorers (graders), and datasets compose cleanly. The DX is more "research codebase" than "developer tool," which is the whole point. Teams doing red-teaming, jailbreak testing, or capability evals on models they're fine-tuning should look here first.

Pricing: free OSS. No commercial product.

Patronus AI

Proprietary, enterprise-oriented. Patronus is the option you pick when your security team writes the RFP. They ship a strong library of evaluators (Lynx for hallucination detection, Glider for general scoring, plus their newer Percival series for agent traces), SOC 2 / ISO 27001 posture, and a focus on regulated industries.

The trade-off is the same as most enterprise tools: pricing is opaque, you'll go through sales, and the iteration loop is slower than Promptfoo or Braintrust.

Pricing: free dev tier with limited evaluator calls, enterprise via sales.

Comparison table

Framework	License	Best for	Production traces	LLM-as-judge	Pricing
Promptfoo	MIT OSS	Local prompt regression	Limited	Yes (built-in)	Free + enterprise
Langfuse	MIT OSS + cloud	Trace + eval in one stack	Yes (first-class)	Yes	Free OSS / cloud from $59
Braintrust	Proprietary	Hosted dashboard polish	Yes	Yes (strong library)	Free / $249 user / enterprise
DeepEval	Apache OSS	Python pytest shops	Via Confident AI	Yes (G-Eval)	Free + cloud
Inspect AI	Apache OSS	Safety + capability evals	Local logs	Yes	Free, no commercial
Patronus	Proprietary	Regulated enterprise	Yes	Yes (Lynx, Glider)	Free dev / enterprise

A sample eval config

Here's a minimal Promptfoo config that runs a deterministic check and an LLM-as-judge against a small RAG dataset. Drop it in promptfooconfig.yaml and run promptfoo eval.

description: "Support reply RAG eval"

prompts:
  - file://prompts/support_v2.txt

providers:
  - id: openai:gpt-4o-mini
  - id: anthropic:claude-3-5-sonnet-20241022

tests:
  - vars:
      question: "How do I cancel my subscription?"
      retrieved_doc: "Subscriptions can be cancelled from Settings > Billing > Cancel. Cancellation takes effect at the end of the current billing period."
    assert:
      - type: contains
        value: "Settings"
      - type: latency
        threshold: 3000
      - type: llm-rubric
        provider: openai:gpt-4o
        value: |
          The reply correctly explains how to cancel, references the Settings menu,
          and notes that cancellation takes effect at period end. Score 1-5.
        threshold: 4

  - vars:
      question: "Do you store my credit card?"
      retrieved_doc: "Card details are tokenized by Stripe; we never see the full PAN."
    assert:
      - type: javascript
        value: "!output.toLowerCase().includes('we store')"
      - type: llm-rubric
        provider: openai:gpt-4o
        value: |
          The reply must mention Stripe tokenization and must NOT claim we store the
          card number directly. Score 1-5.
        threshold: 4

That config gives you a deterministic check (the word "Settings" appears), a latency budget, an LLM-as-judge for tone and accuracy, and a JS assertion to catch a specific failure mode. Wire that into CI and you have a real regression net.

The "what to do" section

If you have no evals today, do this in order:

1. Pick the smallest possible dataset. 20 representative inputs from production traffic. Save them in version control.
2. Add deterministic checks first. Schema, latency, cost. These cost nothing and catch the embarrassing failures.
3. Add one LLM-as-judge metric. Faithfulness for RAG, helpfulness for chat, format compliance for structured output. Pick the one most aligned with user complaints.
4. Run it on every PR. Whatever framework you pick, the CI step is what makes it real. If it's not blocking merges, it's a vibe check.
5. Add a weekly human review of 20 traces. Compare human scores to your LLM-judge scores. If they drift more than 15%, recalibrate the judge prompt.

Most teams that get burned by LLM regressions skip step 4. Don't.

If you're picking a tool today: start with Promptfoo if you're solo or pre-Series A, move to Langfuse once you have production traffic worth tracing, and bring in Braintrust or Patronus when a buyer or compliance team asks for a hosted dashboard with audit logs. The cost of switching later is lower than the cost of waiting to start.

For teams without an in-house ML engineer, this is exactly the kind of work an on-demand engineer can scope cleanly. Booking a Mid ($1,000/week) Cadence engineer to stand up Promptfoo or Langfuse, wire it into your CI, and seed the first 100 eval cases usually takes one to two weeks; every engineer on Cadence is AI-native by default, vetted on Cursor and Claude Code fluency, so they already write evals as a habit. You can compare that path against our take on monitoring tools for startups if you want the broader observability picture.

How LLM evals fit into the rest of the stack

Evaluation frameworks don't replace the rest of your monitoring. They sit alongside it.

• Error monitoring (Sentry, Better Stack) catches when the LLM call throws.
• Feature flags (covered in our review of the best feature flag services for startups) let you ship a new prompt to 5% of traffic and roll back if scores drop.
• Tool reviews for adjacent infra: see Cloudinary vs Imgix vs Bunny if your evals include image generation, Ably vs Pusher vs Pubnub if you stream tokens to clients.
• Internal tools like Retool (see Retool vs Internal) are useful for building a quick human-review UI on top of Langfuse traces.

A real LLM stack in 2026 has all five layers: error monitoring, traces, evals, flags, and human review. The frameworks above own the eval layer. They don't try to be the other four (Langfuse comes closest by also doing traces).

Stuck deciding which framework fits your stack? Book a Senior Cadence engineer for a week ($1,500). They'll audit your LLM pipeline, recommend a framework, and ship the first round of evals in CI. 48-hour free trial; if it doesn't click, you don't pay.

FAQ

Which LLM evaluation framework is the best for small teams?

Promptfoo. It's free, runs from a single YAML config, requires no account, and produces a shareable HTML report. You can be running real evals in under an hour. Graduate to Langfuse once you need production trace integration.

Is LLM-as-judge reliable enough to ship on?

Conditionally yes. Pin the judge model version, validate it against human scores on a 50-case calibration set, and recalibrate quarterly. Treat it as a noisy signal that catches large regressions, not a tool that grades nuance perfectly. Always pair with deterministic checks and a weekly human-review sample.

What's the difference between Langfuse and Braintrust?

Langfuse is open source, trace-first, and self-hostable; you instrument your app once and get observability plus evals in one tool. Braintrust is proprietary, hosted, with a more polished prompt-iteration playground and a stronger built-in scorer library. Pick Langfuse if you want to own the data; pick Braintrust if you want the best out-of-box DX.

Do I need an eval framework if I'm using a single foundation model?

Yes. The model itself is one variable; your prompt, retrieved context, function definitions, temperature, and conversation history are all variables that drift. An eval framework catches regressions in any of those. The model vendor's eval dashboards (OpenAI Evals, Anthropic's tools) are not a substitute because they don't see your full pipeline.

How many test cases do I need to start?

Twenty is enough to start, sampled from real production traffic across the failure modes you've already seen. Grow to 100-200 once you have a baseline. More cases give you tighter regression detection but cost more on every CI run, especially with LLM-as-judge metrics. Budget around $0.50-$2.00 per full eval run at that size, depending on judge model.

Can I use these frameworks for agent evaluations?

Yes, but it's harder. Agent traces are multi-step and the right thing to evaluate is often the trajectory, not the final answer. Langfuse, Braintrust, and Patronus (via Percival) all have specific agent-trace evaluation features. Promptfoo and DeepEval can do it with custom scorers but you'll write more glue code. For serious agent work in production, start with one of the three trace-native tools.