LLM Evaluation Platforms in 2026: LangSmith, Braintrust, and Weave

At some point in every LLM product build, the team realizes they cannot tell if the last prompt change was actually better. They made it because something looked worse in a few examples. They shipped it because something else looked better in a few other examples. Now they are not sure what they have.

This is what LLM evaluation is for: replacing “looks about right to me” with a measurement that holds up as the application grows.

The three platforms I have used seriously in production are LangSmith, Braintrust, and Weights & Biases Weave. They solve the same fundamental problem in meaningfully different ways, and the right choice depends on what your team already uses and what kind of evaluator you want to write.

What an eval platform actually does

Every platform in this space does three things, with varying depth:

Tracing. Capture every call to an LLM — the prompt, the model, the parameters, the response, the latency, the cost — so you can debug failures and understand what your application actually sent and received. This is the most basic feature and the one that every platform does reasonably well.

Evaluation. Run a set of inputs through your application, score the outputs against expected behavior, and surface which cases pass and which fail. The hard part is the scorer: what makes an output “good”? Some platforms let you write that logic as a function. Others rely on LLM-as-judge, where a second model scores the output of the first. Both have failure modes.

Dataset management. Store and version the test cases you care about so you can run them consistently as your prompts and models change. This is the unglamorous part that determines whether your evals are actually useful six months from now.

LangSmith

LangSmith is LangChain’s observability and evaluation product. If you are building with LangChain or LangGraph, the tracing integration is close to zero-configuration — you set an environment variable and your chain runs show up in the UI.

The tracing UI is genuinely good. You can see every node in a chain execution, the intermediate steps, where latency accumulated, and the token counts. For debugging “why did the agent take the wrong path,” it is the fastest tool I have used.

The evaluation layer is where you feel the seams. LangSmith gives you the structure — datasets, runs, evaluators — but the evaluator logic is something you write yourself or delegate to an LLM-as-judge. The built-in evaluators cover basic things like trajectory correctness for agents and string similarity, but any domain-specific correctness logic is yours to implement.

For teams already using LangChain, LangSmith is the obvious default. For teams that are not, the tracing setup requires more work and the advantages narrow.

Braintrust

Braintrust is framework-agnostic and has, in my experience, the best ergonomics for writing custom eval functions. The workflow feels closer to writing unit tests: you define a function that takes an input and an output and returns a score, you register your dataset, and the platform runs everything and shows you the results in a clean table.

from braintrust import Eval

def correctness_scorer(output, expected):
    # Your logic here — exact match, fuzzy match, LLM judge, whatever
    return 1.0 if output.strip().lower() == expected.strip().lower() else 0.0

Eval(
    "my-project",
    data=lambda: [
        {"input": "What is 2+2?", "expected": "4"},
        # more test cases
    ],
    task=lambda input: my_llm_function(input),
    scores=[correctness_scorer],
)

The results UI is the clearest of the three: you see a score per case, per run, and per version of your prompt. Regression tracking — “did this change make things worse on cases that used to pass?” — is a first-class feature rather than something you build yourself.

Braintrust’s weakness is tracing. It is less complete than LangSmith’s chain-level view, and for debugging multi-step agents the visibility is thinner. It is strongest as an evaluation-first tool with tracing as a secondary concern.

Weights & Biases Weave

Weave is W&B’s LLM observability product. If your team uses W&B for ML experiment tracking — training runs, hyperparameter sweeps, model versioning — Weave adds LLM tracing and evaluation to the same workspace.

The pitch is unification: your model training experiments and your deployed LLM application quality metrics live in the same tool. For teams that do both traditional ML and LLM work, this is real value. You can correlate a fine-tuning experiment with its downstream application performance without switching contexts.

For teams that are purely in the LLM application space with no ML training work, the W&B tooling can feel like more surface area than you need. The eval SDK is solid, the tracing is good, but there is no obvious reason to choose it over Braintrust unless you already have a W&B account and want to keep everything together.

Platform comparison

Feature	LangSmith	Braintrust	W&B Weave
Tracing	Excellent (LangChain-native)	Good	Good
Custom evaluators	Requires setup	First-class	Solid
LLM-as-judge	Yes	Yes	Yes
Dataset management	Good	Excellent	Good
ML experiment integration	No	No	Excellent
Framework dependency	LangChain preferred	None	None
Free tier	Yes	Yes	Yes

Building a minimum viable eval pipeline

A platform is infrastructure. Before you connect to any of them, you need test cases. Here is the sequence that avoids buying the tooling before you have anything to put in it:

Week 1: log everything. Add logging to every LLM call in production. Store the input, the output, the model, the prompt version, and any user feedback signal you have. You are building a dataset of real behavior before you decide what to evaluate.

Week 2: tag your failures. Go through a week of logs and tag 20 to 50 cases that produced wrong, unhelpful, or risky output. These are your first eval cases. The specifics depend on your application — for a customer support bot, “wrong” might mean off-topic or factually incorrect. For a code completion tool, it might mean code that does not compile.

Week 3: write the scorer. Define what passing looks like for your tagged cases. This forces you to articulate what “good” means for your application, which most teams have not done explicitly. A scorer does not have to be complex — a simple keyword check or LLM judge call is enough to start.

Week 4: connect the platform. Now you have a dataset and a scorer. Connect to whichever platform matches your stack, run your eval against the current production prompt, and save that as your baseline. Every prompt change from here gets measured against it.

The most valuable thing you can do after that baseline is routine: run evals on every prompt change, expand the dataset as new failure modes appear, and treat a regression in any category the same way you would treat a failing test in CI.

The practical guide to testing LLM features more broadly — including how to write evals without a dedicated platform — is in LLM evals in practice.

A word on LLM-as-judge

All three platforms support using a second LLM to score the output of the first, which solves the hard problem of “how do I write a function that captures quality for open-ended text?” The catch is that the judge is itself a language model — it can be wrong, biased by the model it is using, or inconsistent across runs.

LLM-as-judge is useful for dimensions that are hard to specify programmatically: coherence, helpfulness, tone. It is unreliable for factual correctness, security properties, or anything that requires reasoning about external context. Build a portfolio of scorers rather than relying on a single LLM judge for everything, and calibrate your judges by checking their scores against human ratings on a sample.

The platforms let you ship a product you can reason about at scale. The eval cases and scorers are what make those platforms useful. Start building them before you start evaluating tools.