Langfuse vs Braintrust vs LangSmith 2026

TL;DR

Choose Langfuse when you want open-source, self-hostable LLM observability that stays framework-agnostic. Choose LangSmith when you are already building with LangChain or LangGraph and want the smoothest tracing and evaluation path. Choose Braintrust when evaluations are the center of your AI engineering process and you want experimentation, review, and scoring to be first-class.

Quick Comparison

Why this matters in 2026

In 2026, the hardest AI bugs are usually not “the API returned 500.” They are:

• the prompt drifted and quality dropped silently
• the tool call sequence was technically valid but logically wrong
• the cost doubled after a model switch
• the retriever changed enough to hurt answer quality even though latency improved

That is why tracing alone is no longer enough. Teams need a loop that connects traces, datasets, evaluation, prompt revisions, and release confidence. Langfuse, Braintrust, and LangSmith all cover that loop, but they emphasize different parts of it.

What actually changes the decision

• Choose based on ecosystem gravity. LangSmith is best when LangChain or LangGraph is already central.
• Choose based on control of data and deployment. Langfuse is the easiest recommendation when self-hosting and data residency matter.
• Choose based on whether evaluation is the main event. Braintrust is strongest when AI quality gates are your primary operational concern.
• Bundle size is secondary here because these SDKs mostly live on the server, but dependency surface still hints at complexity.

Package-by-package breakdown

Langfuse

Package: langfuse | Weekly downloads: ~1.1M | Latest: 3.38.20 | Bundlephobia gzip: ~17 KB

Langfuse is the most generally useful choice in this set because it balances observability depth with ecosystem independence.

import Langfuse from "langfuse";

const langfuse = new Langfuse({
  publicKey: process.env.LANGFUSE_PUBLIC_KEY!,
  secretKey: process.env.LANGFUSE_SECRET_KEY!,
  baseUrl: process.env.LANGFUSE_BASE_URL,
});

const trace = langfuse.trace({ name: "support-answer", userId: "user_123" });
const span = trace.span({ name: "retrieve-context" });
span.end({ output: { hits: 4 } });

Why teams pick it:

• Self-hosting and open-source are real differentiators, not marketing side notes.
• It works across raw SDKs, AI SDK, OpenAI clients, and custom orchestration code.
• It covers prompt management, traces, and evaluation without forcing you into one framework family.

Watch-outs:

• You still need to define your own quality process.
• If your stack is deeply LangGraph-shaped, LangSmith often exposes more native structure with less effort.

Braintrust

Package: braintrust | Weekly downloads: ~792K | Latest: 3.9.0 | Bundlephobia gzip: ~138 KB

Braintrust is the most evaluation-first tool here. The pitch is not just “record what happened.” It is “prove that your AI system got better before you ship it.”

import { Eval } from "braintrust";

await Eval("support-answer-quality", {
  data: supportDataset,
  task: async (input) => generateAnswer(input),
  scores: [helpfulnessScore, citationScore],
});

Why teams pick it:

• It treats evals as a daily engineering workflow instead of a side dashboard.
• It is a strong fit for teams running prompt experiments, review queues, and regression suites.
• It encourages a more disciplined release process for AI changes.

Watch-outs:

• If you mainly want lightweight observability and traces, Braintrust can feel heavier than necessary.
• Teams without a culture of datasets and evaluation criteria may underuse its best features.

LangSmith

Package: langsmith | Weekly downloads: ~4.6M | Latest: 0.5.24 | Bundlephobia gzip: ~38 KB

LangSmith is the default answer for many LangChain and LangGraph teams because it understands their runtime model better than generic tracing layers do.

import { traceable } from "langsmith/traceable";

export const answerQuestion = traceable(async (input: string) => {
  return agent.invoke({ messages: [{ role: "user", content: input }] });
}, {
  name: "answer-question",
});

Why teams pick it:

• It is the most natural companion to LangChain and LangGraph.
• Graph runs, agent traces, datasets, and experiment history are all close together.
• If you are already paying the LangChain abstraction cost, LangSmith usually maximizes the upside.

Watch-outs:

• Its biggest advantages are ecosystem-specific.
• Teams on raw SDKs or custom orchestration may prefer Langfuse’s neutrality.

Which one should you choose?

• Choose Langfuse when you want the best overall mix of observability, prompt management, and self-hosting flexibility.
• Choose Braintrust when evaluation is the center of your AI delivery process and you want scoring and experiments to drive releases.
• Choose LangSmith when your application is already built on LangChain or LangGraph and you want the most native operational layer.

Final recommendation

For most teams starting from scratch, Langfuse is the safest recommendation because it stays useful even if your orchestration stack changes later. Braintrust is the best fit for organizations that already know they need rigorous eval workflows. LangSmith is the strongest choice when you are committed to LangChain or LangGraph and want the shortest path from framework code to actionable traces.

Data note: npm package versions and weekly download figures were checked against the npm registry on 2026-04-24. Bundle figures come from Bundlephobia.

Related reading

Langfuse vs LangSmith vs Helicone · Mastra vs LangChain.js vs GenKit · OpenAI Agents SDK vs Mastra vs GenKit