Pick LangSmith if your app is LangChain/LangGraph-native and you want the deepest agent tracing without integration work. Pick Langfuse if you want open-source, self-hostable observability with strong OpenTelemetry support. Pick Braintrust if evaluation-driven release quality is your priority — eval scores live inside the trace view and gate deploys. Pick Helicone if you want a drop-in proxy for OpenAI-style traffic with the lowest integration overhead. The right answer is usually determined by your existing stack, your self-hosting requirements, and how central evals are to your release process.
Two years ago “LLM tracing” meant printing prompts to stdout and grepping through Cloud Run logs. In 2026, most serious AI teams run something purpose-built. The category has consolidated around a handful of platforms, and the choice matters — it shapes how you debug, how you evaluate, and how you gate deploys. This is a direct comparison of the four you’ll actually pick between.
We’re going to stay concrete: what each tool is genuinely good at, where its integration friction lives, and what picking it signals if you’re also thinking about your career surface as an AI engineer. There’s no “best” answer — there are four different bets, and different teams should make different ones.
The Four Platforms at a Glance
| Platform | Model | Best For | Weakest When |
|---|---|---|---|
| LangSmith | Commercial (LangChain Inc.) | LangChain/LangGraph apps, agent debugging | Non-LangChain stacks, self-hosting |
| Langfuse | Open-source (MIT), managed option | OSS-first teams, self-hosting, OTel-native | Turnkey enterprise support |
| Braintrust | Commercial | Eval-gated releases, dataset-first workflows | Simple tracing without heavy eval |
| Helicone | OSS core + managed | OpenAI-style proxy tracing, quickest setup | Deep agent graph inspection |
LangSmith
LangSmith
CommercialLangSmith is the observability platform built by the LangChain team, and it is unambiguously the deepest experience if you are building on LangChain or LangGraph. Traces render as full agent graphs rather than flat span lists. Prompt playground, annotation queues for human review, dataset creation from production traces, and native evaluation loops are all first-class. If you think in LangChain primitives, LangSmith turns your production runtime into a debugger.
Where it gets uncomfortable: if you’ve outgrown LangChain (which many teams now have), the tight coupling becomes friction. Self-hosting exists but is enterprise-tier. Pricing scales with traces, which can bite at scale on chatty agents. And migration off it, if you decide to go elsewhere, is genuinely painful — your instrumentation is entangled with your framework.
Langfuse
Langfuse
Open-sourceLangfuse is the open-source leader in the category. MIT-licensed, self-hostable, with native SDKs for Python and JavaScript and connectors for the major frameworks. Its OpenTelemetry support is the practical differentiator: you can instrument your app once with OTel spans and pipe them into Langfuse alongside your existing observability stack. That’s the pattern most infra-mature teams end up on.
Feature-wise, Langfuse covers the observability stack end-to-end — tracing with multi-turn conversation support, prompt versioning with a built-in playground, and flexible evaluation through LLM-as-judge, user feedback, or custom metrics. Session grouping across multi-turn agents is genuinely well-done.
Where it’s weaker: enterprise support is thinner than the fully-commercial options, and the eval workflow is more assembly-required. Teams that want a polished, turnkey experience sometimes prefer Braintrust or LangSmith. Teams that want control over their observability surface almost always land on Langfuse.
Braintrust
Braintrust
CommercialBraintrust’s pitch is that evaluation should be a first-class citizen of observability, not something bolted on afterward. In practice this means eval scores live inside the trace view: when you look at a production trace, you see the eval scores that ran against it, and you can trace regressions back to a specific commit or prompt change. Datasets, experiments, and CI/CD quality gates are integrated into the core surface.
For teams whose AI quality loop is dataset-driven — where you have a curated eval set, you want every change to run against it before merging, and you want production logs to feed the next eval iteration — Braintrust is the most opinionated fit. Release-gating on eval score is easier to set up here than on the alternatives.
Where it’s less compelling: if you’re early in your LLM app and don’t yet have datasets or evals, most of what Braintrust is good at goes unused. It shines once you have a real quality workflow to plug into.
Helicone
Helicone
OSS core + managedHelicone is the option for teams that want to move fast and don’t need the depth of the others. It works primarily as a proxy: change your OpenAI base URL, and Helicone captures every request and response. That’s the integration cost. From there you get request logging, cost tracking, latency metrics, cache hit rates, and rate limiting.
The tradeoff is depth. Helicone gives you a great view of “what did every LLM call look like” but it’s a thinner story for multi-step agent tracing, structured evaluation, or dataset workflows. For a solo dev shipping a RAG app, or a small team that mostly wants to see costs and errors, that’s often enough — and getting to enough in 15 minutes is meaningful.
Feature-by-Feature Comparison
| Feature | LangSmith | Langfuse | Braintrust | Helicone |
|---|---|---|---|---|
| Self-hosting | Enterprise only | Yes (MIT) | Limited | Yes (OSS core) |
| OpenTelemetry | Partial | Native | Yes | Partial |
| Agent graph view | Best-in-class | Good | Good | Basic |
| Eval integration | Good | Good | Best-in-class | Basic |
| Dataset workflows | Yes | Yes | Best-in-class | Limited |
| Prompt playground | Yes | Yes | Yes | Basic |
| Integration effort | Zero if LangChain | Medium | Medium | Lowest (proxy) |
| Framework independence | Weakest | Strong | Strong | Strong |
| Cost tracking | Yes | Yes | Yes | Strong focus |
How to Actually Decide
Rather than a chart of features, three questions do most of the work:
- Is your app LangChain/LangGraph-native? If yes, LangSmith is the default; the agent graph tracing alone will save you enough debugging hours to justify it. If no — if you’re on raw OpenAI/Anthropic SDKs, Vercel AI SDK, LlamaIndex, or your own orchestration — you’re choosing between the other three.
- Do you have compliance or self-hosting requirements? If your app handles regulated data (health, finance, EU workloads with strict residency), Langfuse is often the only option that lets you host observability in your own VPC without paying enterprise pricing.
- How central is evaluation to your release process? If you gate deploys on eval scores, run regression suites against curated datasets, and want that surface tightly integrated with production logs — Braintrust is the most opinionated fit. If your evals are lightweight or exploratory, this differentiator matters less.
For the largest bucket — teams building non-LangChain apps without heavy compliance or eval-gating requirements — Langfuse is a strong default. It’s framework-neutral, self-hostable, well-instrumented, and doesn’t lock you in. You can always move to Braintrust or LangSmith later if your workflow shifts.
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Teams building on all four of these platforms are hiring — especially for engineers who can talk fluently about tracing, evaluation, and production LLM debugging. Browse open AI engineering roles at companies vetted for engineering culture.
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If you’re an AI engineer thinking about the career surface, one thing to internalize: the specific platform matters less than the muscle. Hiring managers screening for “can this person actually run an LLM app in production” are looking for evidence that you’ve done four things:
- Instrumented a real LLM system with traces. Any of these four platforms count. Being able to describe the shape of your traces — what a span looks like, what metadata you attach, how you correlate across services — is what signals depth.
- Built an offline evaluation loop. Curated a dataset, defined metrics (correctness, safety, cost, latency), run structured evals, tracked score deltas across model or prompt changes. Fluency here is genuinely rare.
- Debugged a production incident using your instrumentation. The story that lands in interviews is: “user reported X, I traced the request, found the failing tool call, wrote a regression eval, deployed the fix.” That’s the loop.
- Gated a deploy on an eval score. Even a lightweight version — a CI job that fails if regression eval scores drop — puts you in a small minority of engineers who’ve actually operationalized quality gates.
Companies building modern AI systems — including Anthropic, OpenAI, and the wave of well-funded AI infrastructure startups — screen aggressively for these signals. Naming a specific platform is less impressive than being able to describe the four-step muscle above using whatever platform you’ve worked with.
Migration Notes
If you’re already on one of these and thinking about moving, three practical notes:
- Instrument with OpenTelemetry if you can. Langfuse and Braintrust both consume OTel, so instrumenting once and exporting to two backends is a cheap way to run a bake-off. LangSmith has its own SDK but can coexist during migration.
- Migrate the eval story first, tracing second. Traces are relatively easy to rebuild. Curated eval datasets, review annotations, and scoring rubrics carry the accumulated quality knowledge of your team. Export first, cut over second.
- Preserve the alerting surface. Whichever platform you leave, don’t let “a big prompt regression is happening in prod” go undetected during the switch. Duplicate alerts to both backends until you’ve validated the new one.
The Honest Take
All four of these platforms are legitimately good. There’s no wrong answer — there are only bad matches to your specific stack, workflow, and hiring incentives. LangSmith is the right choice if you’re LangChain-native. Langfuse is the right choice for OSS-first, framework-agnostic, self-hostable observability. Braintrust is the right choice when evaluation drives release. Helicone is the right choice when you want to be instrumented in 15 minutes and evolve later.
What you don’t want is to pick based on brand recognition, or because your last team used one of them. Read the three questions above, make a bet, and revisit the choice at 6 and 12 months. Observability is a system that grows with your app — it’s fine to switch if the fit stops working. What’s not fine is running production LLM apps with no observability at all in 2026. That is now the low bar — and clearing it, on any of these four, is table stakes for teams that ship reliably.
LLM Tracing Tools FAQ
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The best AI engineering teams screen for exactly the muscle this article describes: tracing, evaluation, and production LLM debugging. Browse open AI/ML roles at culture-first companies.
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