Comparison · 2026
Langfuse alternatives, with an actual opinion
We build one of the six tools below, so discount this accordingly. But every listicle you just scrolled past ranked its own product first and called it research. This one at least tells you where the load-bearing difference is — and where Langfuse still wins.
The short version
Langfuse is a good observability product with evaluation attached. If what you need is evaluation of agents, its model fights you.
The specific reason
Its judges read one observation at a time and can't see the rest of the run. Agent failures live in the trajectory, not in a single span.
When to stay
You want prompt management, you value the largest community in the category, and your failures are single-call quality problems rather than multi-step ones.
The one that actually matters: a judge that can't see the run
Every tool here traces, scores and charts. Feature tables blur into each other. So here is the difference that changes what you can catch, straight from Langfuse's own documentation: their evaluators target a single trace or a single observation, and they do not load sibling or child observations. Conversation-level evaluation is not an evaluation target. The documented workaround is to write your own “logical root observation” that summarises the whole interaction, and judge that.
One span at a time
The judge sees refund_api returned a timeout. That span looks fine in isolation — the tool was called correctly.
It cannot see that the agent then told the customer their refund was processed.
The whole conversation
A conversation-level judge reads all six turns and fails the run: the agent claimed success after a tool error.
That's the bug that reaches the customer. It exists only in the trajectory.
Tracely runs judges at conversation, run or span level — it's a field on the evaluator, not an architecture you work around. If your agents are single-call classifiers this is irrelevant and Langfuse is fine. If they take six steps and call four tools, it's most of the failures you care about. We go deeper on the trade-offs in our guide to LLM evaluation.
The part that's opinion, stated as opinion
We think Langfuse's UI is the weakest part of the product. Dense, closer to a database console than a debugging tool, and slow to answer the only question you open it with: which run went wrong, and where? That's taste, not fact — go look and disagree.
We built Tracely's trace view around that one question. Evaluator scores are columns on the trace table, streaming in live as judges finish, so a failing run is something you spot in a list rather than something you go hunting for. You're not clicking through five levels to find out a judge failed; the column is red.
All six, side by side
| Tool | Licence | Pick it when |
|---|---|---|
| Langfuse | MIT core | You want one mature tool for tracing, prompts and evals, and the largest community in the category. |
| LangSmith | Closed | Your stack is LangChain end to end and you'd rather not think about it. |
| Braintrust | Closed | Evaluation quality is the centre of your workflow and hosted is fine. |
| Arize Phoenix | Open source | You live in Jupyter and want tracing that meets you there. |
| Helicone | Open source | You want cost and latency visibility today and nothing more. |
| Tracely | MIT, whole product | The same production failure keeps shipping twice and you're tired of maintaining the test set that was supposed to stop it. |
Langfuse
The default. Mature tracing, prompt management with versioning, evaluators, datasets and experiments — and the biggest community here.
Trade-off Evaluators read one observation in isolation — no conversation-level target. CI experiments need a dataset you author and make live model calls on every run. Prompt management is a large surface to carry if your prompts live in Git.
LangSmith
The tightest LangChain/LangGraph integration that exists, because they build both.
Trade-off Closed source, no self-host on lower tiers. One vendor owns your framework and your data.
Braintrust
A genuinely polished eval and experiment workflow, plus CI deployment blocking.
Trade-off Proprietary storage engine. Eval-centric rather than a general observability backend.
Arize Phoenix
OpenInference-native tracing with notebook-driven analysis.
Trade-off Phoenix is the OSS slice; the deeper platform lives in the commercial Arize product.
Helicone
One proxy line gets you logging, caching and cost tracking. Fastest setup on this page.
Trade-off Proxy-first means a request-level view — less natural for deep multi-step trajectories.
Tracely
Judges that grade a whole conversation, and production failures that become hermetic CI tests with no dataset to author.
Trade-off Youngest project here, smallest community. No prompt management — deliberately.
Both of us block bad merges. The difference is what it costs you
Langfuse ships CI/CD experiments and they work: create a dataset of test cases, write an experiment script, add evaluators, raise a RegressionError past your threshold, and their GitHub Action fails the job and comments on the PR. Anyone claiming Langfuse has no CI story is working from stale information.
Two costs come with it. The dataset is yours to author and maintain — the failures you thought of, not the ones you had. And the experiment runs your real code, so CI makes live model API calls: provider keys in GitHub secrets, a model bill on every push, and a suite that can go red because an API had a bad minute.
Tracely starts from a failure that already happened. A production trace a judge marked FAIL gets promoted in one click into a case: recorded input, every tool and model response bundled as fixtures, fail-to-pass contract attached. CI replays against those fixtures — no provider keys, no model spend, identical result every time. Nobody writes the dataset because production wrote it.
The honest limit: this only tests failures you've actually had. For a new capability that has never run in production, a hand-authored dataset is the only thing that works — and there, Langfuse does it well.
Trying another one is cheaper than reading about it
Langfuse, Phoenix and Tracely all speak OpenTelemetry. If your agent emits OTLP, a second backend is an endpoint and a key. Run two in parallel for a week and judge on your own traces rather than on anyone's comparison table — including this one.
See it on a failure you actually had
MIT-licensed, self-hostable, free tier hosted. Three lines to instrument. The first frozen case is one click, and the gate is two lines of YAML in the workflow you already have.