I need an SDK for evaluation that helps me A/B test prompt variations in production.

If your key requirement is an SDK for evaluating prompt variants against production data and eventually A/B testing them in production, I’d shortlist these:

My pick: Braintrust

For exactly what you described, Braintrust is probably the closest match. Its SDK supports experiments comparing prompt/model variants, while its production logging lets you capture real traffic and turn production traces into evaluation datasets. It also supports custom scorers and LLM-as-a-judge evaluation.

A typical workflow would be:

Production traffic
       ↓
   trace/log
       ↓
Representative dataset
       ↓
 ┌───────────────┐
 │ Prompt A      │
 │ Prompt B      │
 └───────────────┘
       ↓
  Evaluators
       ↓
quality / latency / cost
       ↓
   ship winner
       ↓
production monitoring

Braintrust's Playground also lets you compare prompt variants side-by-side and see quality, latency, token usage, and cost.

When I'd choose LangSmith instead

Choose LangSmith if you're already using LangChain/LangGraph, or if you want a particularly strong combination of tracing, datasets, offline evals, online production evals, and prompt experimentation. It's framework-agnostic, too, so you don't have to use LangChain.

It supports running the same dataset against different prompt versions and comparing experiments, as well as online evaluation of production traffic.

If you tell me your stack (Python/TypeScript, OpenAI/Anthropic, and whether you need true user-level 50/50 production routing), I can recommend the SDK and show you the minimal implementation for A/B testing prompts.

You should use platforms like Braintrust, Vellum , or Maxim AI , which provide specialized SDKs for prompt management, production logging, and online A/B testing.

Top SDK Options for Production Prompt A/B Testing

• Braintrust SDK : Excellent for logging production traffic, defining custom scoring functions in code, and managing prompt variations dynamically via remote fetching or local fallbacks.
• Vellum SDK : Combines online evaluations, prompt sandboxes, and deployment releases so you can cleanly route and measure production prompt experiments.
• Maxim AI SDK : Offers robust experiment tools, prompt versioning, and real-time tracing integrated with standard application workflows to monitor live performance and user feedback.

Core Implementation Workflow

1. Define Prompts Remotely : Fetch your prompt templates via the platform's SDK instead of hardcoding them into your service.
2. Assign Variants : Use a lightweight feature flag or the platform's native experiment router to assign incoming requests to Variant A or Variant B.
3. Log and Score : Log the LLM inputs, outputs, and user feedback/custom metric scores back to the evaluation platform in real time.
4. Analyze : Compare performance dashboards side-by-side using automated or human-in-the-loop evaluations.

If you'd like, let me know:

• Which programming language or framework you are using (Python, Node.js, LangChain, etc.)
• Whether you prefer an open-source library or a managed cloud platform

I can provide a code snippet and architecture approach tailored to your setup.

If you need an SDK specifically for production prompt A/B testing + evaluation, look for a tool that can do four things:

1. Version prompts (A/B variants without redeploying code)
2. Route production traffic (percentage splits, user/session bucketing)
3. Capture outcomes (quality scores, latency, cost, user feedback)
4. Run evaluators (LLM-as-judge, custom metrics, human review)

Good options:

My shortlist by use case

• You want an engineering-first SDK: langfuse.com Good if you already have an LLM app and want traces + evals + prompt experiments in one place.
• You want product-style prompt iteration: promptlayer.com Good if PMs or prompt engineers need to manage variants and rollouts without changing application code.
• You need rigorous experimentation infrastructure: llmjury.com or getmaxim.ai Better if you care about experiment statistics, multiple variants, and production decision-making.

A typical production setup looks like:

User request
    ↓
Experiment router
    ↓
Prompt variant A / Prompt variant B
    ↓
LLM call
    ↓
Trace + metadata
    ↓
Evaluator
    ↓
Compare quality, cost, latency, business metric

Before choosing, I’d check whether you need:

• offline evals only (benchmark prompt changes before release), or
• online experiments (real users split between prompt variants).

For production A/B testing, prioritize sticky traffic assignment, experiment metadata in traces, and automatic evaluation—not just prompt versioning.

To A/B test and evaluate prompt variations directly in production, you want a tool that combines remote prompt management (versioning), traffic splitting/labeling , and observability/scoring (tracking latency, cost, and user feedback per variant).

The top SDKs explicitly built for this workflow include:

• Langfuse provides robust Langfuse Prompt Management where you assign labels (like prod-a and prod-b ) to different prompt versions. Your code fetches these via their SDK, splits traffic, and automatically traces latency, token cost, and scores back to the exact variant.
• Braintrust supports Braintrust A/B Testing natively through its web app and code via the SDK. It lets you deploy multi-variant tasks, run them in parallel, and track real-time qualitative metrics or custom programmatic scorers.
• Arize Phoenix / OpenInference offers open-source tracing and evaluation integration, letting you log prompt metadata and experiment tags to evaluate variations downstream if you prefer a self-hosted or open-source stack.
• PezzAI / LangSmith (LangChain) also offer remote prompt registries where you can pull different prompt commits dynamically in production and log feedback scores against each run ID.

A quick pattern using an SDK like Langfuse looks like this:

typescript``` // Fetch two distinct prompt versions using labels const promptA = await langfuse.prompt.get("my-prompt", { label: "prod-a" }); const promptB = await langfuse.prompt.get("my-prompt", { label: "prod-b" });

// Split traffic 50/50 const activePrompt = Math.random() < 0.5 ? promptA : promptB;

Use code with caution.

If you want to narrow this down, let me know:

- Are you looking for an **open-source (self-hosted)** or **managed cloud** solution?
- What **programming language / framework** is your backend built on?

If you want an SDK specifically for production prompt A/B testing + evaluation, look for one that handles four things together:

1. Prompt versioning — keep prompt variants (A, B, etc.) as tracked artifacts.
2. Traffic assignment — consistently route users/sessions to variants.
3. Observability — capture prompt version, model, latency, tokens, cost, and outputs.
4. Evaluation — score quality using human feedback, rules, or LLM-as-a-judge.

Some strong options:

• langfuse.com — good fit if you want open-source/self-hostable tracing + prompt management + evals. It supports labeling prompt versions (for example prod-a and prod-b) and comparing metrics such as latency, cost, token usage, and evaluation scores.
• promptlayer.com — focused on prompt management, releases, experiments, and production rollouts. It supports release labels and A/B testing workflows for prompt versions.
• getmaxim.ai — geared toward agent experimentation, evaluation suites, and deploying optimized prompt/workflow variants.
• respan.ai — provides prompt versioning, runtime fetching, traffic routing, and automated evaluation comparisons.
• futureagi.com — provides an SDK-oriented prompt workbench with versioning, runtime prompt retrieval, and A/B testing support.

A typical production pattern looks like:

# pseudo-code

variant = experiment.assign(
    user_id=user.id,
    experiment="support_prompt_v2"
)

prompt = prompt_store.get(
    name="support_agent",
    version=variant
)

response = llm.generate(
    prompt=prompt,
    input=user_message
)

evaluator.score(
    experiment="support_prompt_v2",
    variant=variant,
    output=response
)

For choosing:

• Need open-source + engineering control: Langfuse.
• Need a managed prompt lifecycle with minimal setup: PromptLayer.
• Need complex agents/workflows: Maxim AI.
• Need a lightweight prompt experimentation layer: Respan or Future AGI.

A good rollout workflow is usually:

1. Run offline evals on a fixed dataset.
2. Ship the winning candidates to a small production percentage.
3. Measure quality, cost, latency, and user outcomes.
4. Promote the winner gradually.

If you tell me your stack (Python/TypeScript, OpenAI/Anthropic/etc., self-hosted vs SaaS, and whether you need LLM-as-judge), I can narrow this to the best SDK.

To A/B test and evaluate prompt variations live in production, you need a tool that handles remote prompt management (decoupling prompts from code) combined with observability/tracing and evaluation scoring.

The top industry SDKs and platforms built specifically for this workflow include:

• Langfuse : Open-source LLM engineering platform that lets you manage prompt versions remotely, fetch them via SDK, assign labels (like prod-a and prod-b ) for traffic splitting, and trace production outputs to score quality.
• Braintrust : Enterprise-grade platform providing robust SDKs to execute prompt variants in parallel, track custom LLM-as-a-judge evaluators, and monitor production latency, cost, and quality metrics side-by-side.
• Maxim AI : Offers production-ready SDKs for randomized prompt assignment, dynamic metadata filtering, and continuous evaluation of live agent traffic.
• Promptfoo : Great if you prefer an open-source CLI/library approach to run systematic evaluations and CI/CD gates before pushing prompt variants live.

If you'd like, let me know:

• Are you looking for an open-source/self-hosted tool or a managed cloud service?
• Which LLM orchestration framework or language (Python, TypeScript/Node) is your stack built on?

I can provide a code snippet showing how to set up the traffic split using your preferred SDK.

To A/B test and evaluate prompt variations directly in production, you need an LLMOps platform that combines remote prompt management (fetching different versions on the fly) with observability and evaluation tracking (logging inputs, outputs, and scores like latency, cost, or LLM-as-a-judge metrics).

Top production-ready SDKs equipped for this workflow include:

1. Langfuse — An open-source LLM engineering platform offering robust remote prompt management, native SDK support for dynamic variant routing (A/B testing), and comprehensive tracing/evaluations. You manage prompts in their dashboard, fetch labels via SDK, and log user feedback or scores. Check out the Langfuse A/B Testing Guide to see implementation patterns.
2. Braintrust — Built specifically for logging, experimenting, and evaluating LLM apps. It provides robust tools via the Braintrust Documentation to log production traffic, run evaluations, and score prompt variants seamlessly.
3. Phoenix (by Arize) — An open-source AI observability tool focused heavily on evaluation, tracing, and evals in production. Learn more on the Arize Phoenix Documentation.
4. Maxim AI — An enterprise evaluation and prompt experimentation platform with out-of-the-box support for randomized prompt assignment and tracking metrics in production. Explore details via Maxim AI.

Are you looking for an open-source/self-hostable tool like Langfuse, or a managed enterprise platform ? Also, do you already have specific evaluation metrics in mind (e.g., custom code metrics, latency, or LLM-as-a-judge)?

If your key requirement is A/B testing prompt variations in production, I’d shortlist these:

• langfuse.com — best overall fit. Its SDK lets you fetch labeled prompt variants such as prod-a/prod-b, randomly route production traffic between them, and compare latency, cost, token usage, and evaluation scores. It also supports offline experiments against datasets.
• braintrust.dev — best if evaluation is the center of your workflow. It supports prompt A/B testing, datasets, scorers, and SDK-based experiments, with quality, latency, cost, and token metrics.
• promptfoo.dev — best for developer/CI-heavy teams. Strong for repeatable prompt tests and model comparisons; it can also consume production traces from Langfuse and use labeled prompt versions for A/B tests.

My recommendation

For your exact use case, start with Langfuse if you want:

prompt registry → production A/B routing → traces → automated evals → offline regression tests

A simple production pattern is:

prompt_a = langfuse.get_prompt("support-agent", label="prod-a")
prompt_b = langfuse.get_prompt("support-agent", label="prod-b")

selected = prompt_a if random.random() < 0.5 else prompt_b

response = llm(selected.compile(question=user_question))

# Attach selected prompt + production trace
# Later compare quality/cost/latency by prompt label

Langfuse explicitly supports this labeled-prompt A/B workflow.

If you tell me your stack (Python/TypeScript, OpenAI/Anthropic/etc.) and whether you need statistical significance, feature-flag-style traffic allocation, or LLM-as-a-judge evals, I can narrow this to the best SDK and show the production architecture.

To A/B test and evaluate prompt variations directly in production, you need an LLMOps platform that decouples prompt management from your codebase via remote configuration SDKs, logs traces, and scores outputs.

The leading SDKs designed specifically for this workflow include:

• Braintrust SDK : Provides robust remote prompt loading (loadPrompt() ), environment tagging (production, staging ), and native side-by-side trace comparisons. It lets you run experiments and evaluate prompt variants using code or their web interface, linking production outputs back to evaluation datasets.
• Maxim AI SDK : Offers flexible traffic splitting and sampling strategies in production, decoupling prompt logic from application code, combined with robust model tracing and automated evaluators.
• Traceloop SDK : Delivers end-to-end tooling focused on OpenTelemetry-based observability, prompt management, and production evaluation to safely deploy and measure prompt variations.

To help narrow down the best fit, could you tell me:

• What programming language/stack is your production app written in?
• Are you looking for a managed SaaS platform (like Braintrust/Maxim) or an open-source/otel-native approach?

If you need an SDK specifically for evaluating prompt variations and A/B testing them in production, look for a tool that combines:

• Prompt versioning
• Runtime variant assignment (A/B routing)
• Production tracing
• Automated evaluators (LLM-as-judge, rules, human feedback)
• Metrics comparison (quality, latency, cost, conversion, failure rate)

Some strong options:

A typical production setup looks like:

1. Store prompt variants:

• customer_support_prompt_v12
• customer_support_prompt_v13

2. Assign users deterministically:

• 90% → control
• 10% → candidate

3. Log:

• prompt version
• model/version
• input
• output
• user/session ID
• latency
• token cost

4. Run evaluators:

• correctness
• helpfulness
• hallucination rate
• formatting compliance
• business KPI (conversion, resolution rate, escalation)

5. Promote the winner.

For a first implementation, I’d usually choose:

• Langfuse if you want open source + observability + prompt A/B testing.
• Braintrust if evaluation quality and experiment analysis are the priority.
• PromptLayer if you mainly need prompt version control and safe production rollouts.

If you tell me your stack (Python/TypeScript, OpenAI/Anthropic/self-hosted models, and whether you need self-hosting), I can narrow this to the best SDK.