Braintrust Alternative: Why Laminar Is the Pick for Agent Debugging

Braintrust is eval-first. It scores well at scorer-driven regression testing: write an LLM-as-judge or code scorer, sweep it across prompts and models, catch the diff before the PR merges. For teams whose bottleneck is CI regression, it works.

The trouble starts when CI regression is not the bottleneck. The bottleneck is a production agent that ran for ten minutes, called fifteen tools, spawned a sub-agent, and failed four tool calls deep. Braintrust shows you the trace, but the UX is built for scoring, not debugging. You wanted to know what the agent said to the user, what the user said back, and which tool call threw. That is a different product.

That is the gap Laminar fills. This article is the short answer for teams searching for a Braintrust alternative: what you get that Braintrust does not give you, why agent workloads break the eval-first model, and how to move without re-instrumenting.

If you are on Braintrust and your work is shifting from CI evals to production agent debugging, Laminar is the direct alternative. Apache 2.0, OpenTelemetry-native, OpenInference-compatible. Transcript view instead of an eval-centric trace. Signals for natural-language outcome tracking. SQL over traces. Agent rollout debugger. Self-host via Helm in one command, every feature on the OSS image, no seat fees, data-volume pricing.

If you want the full field of seven ranked options instead of a single answer, we published that too: Braintrust alternatives 2026.

Three recurring reasons. Only the first is about the product shape.

Braintrust's traces are built to feed scorers. You see a run, score it, feed the score into a regression dashboard. That is the right shape when your workflow is "did this change break X."

It is the wrong shape when your workflow is "it is 2 a.m. and this agent did something weird on prod." You want to read what the agent said and did, not click through spans hoping to find the failing one. Laminar's transcript view renders the run as a conversation by default. The span tree is one click away when you want it. On a 2,000-span trace this is a ten-second read instead of a ten-minute read.

Braintrust is a closed-source commercial SaaS. Self-hosting means an Enterprise "hybrid deployment" contract. If your team needs air-gapped, if your legal review requires OSS, or if you just want to run the thing on your own Helm chart without a sales cycle, Braintrust is out.

Laminar is Apache 2.0. The repo ships a production-ready Helm chart: clone, apply, run. All features ship on the OSS image, including Signals, the SQL editor, and the agent rollout debugger. No Enterprise gate.

Braintrust Starter is free with 1GB and 10k scores at 14-day retention. Pro is $249/month for 5GB and 50k scores at 30-day retention. Above Pro, it is Enterprise.

Two consequences. One, $249/month is the highest Pro-tier entry price in this category. Two, the GB + score-unit model means teams that emit many per-span outcomes hit the score threshold before the data threshold and have no middle tier between Pro and Enterprise.

Laminar's Pro is $150/month for 10GB with 90-day retention and unlimited seats. Billing is data volume only. No per-score unit counting.

Three primitives, in order of how often they change a team's mind.

Already covered. This is the first thing most Braintrust-to-Laminar migrators notice, because it is the first screen you see.

Braintrust has scorers that run on datasets. Signals are a different primitive. You describe an outcome in plain English: "agent asked the user for clarification and got a useful answer." Laminar extracts it, backfills it across your entire trace history, and fires on every new trace that matches.

The failure mode you care about today is not the one your scorers caught last month. Signals let you name the new failure and have it tagged retroactively, so dashboards, alerts, and search all update without re-running a scorer sweep or re-labeling data by hand.

Scorers and Signals are complementary: scorers are for CI regression against frozen datasets, Signals are for production outcome tracking across a live, growing trace store. A team can run both. But if production outcome tracking is the need, Signals are the primitive that fits.

Braintrust pushes ad-hoc analysis through its dataset API or a notebook. Laminar ships a SQL editor that queries traces, spans, events, and metadata directly. "How many runs called tool X more than five times and then errored" is one query. No dataset export, no API loop, no Python script.

Re-run an agent from any span in a captured trace. Change the prompt, swap the model, edit the tool call, and see what would have happened. Docs: platform/debugger.

Braintrust's playground is good for iterating on a single prompt against a dataset. Agent rollout is the same idea rooted in a real captured trace, with all the surrounding tool calls and state still wired up. When the bug is "this agent chose the wrong tool at span 1,400," a scorer sweep on a static dataset will not tell you why. Rollout does.

Criterion	Laminar	Braintrust
Primary workflow	Debug agents in production	CI eval regression
Trace UX default	Transcript view	Eval-centric trace
Natural-language outcome tracking	Signals (backfill + forward-fire)	Scorers on datasets
SQL over traces	Yes, in-product editor	No, API/notebook
Agent rollout debugger	Yes	Playground (single prompt)
License	Apache 2.0 (OSI)	Closed source
Self-host	Free, Helm chart, one command, all features	Enterprise-only hybrid
Pricing shape	Data volume only	Data + scores
Pro tier entry	$150/mo (10GB, 90-day retention)	$249/mo (5GB, 30-day retention)
OpenTelemetry	Native	OTel-based SDK, proprietary backend
Browser-agent session replay	Yes	No

Where Braintrust wins: if your primary workload is scorer-driven regression testing in CI, Braintrust's harness and regression dashboard are mature. Laminar is not trying to be that tool. The two products compose cleanly: Braintrust for CI, Laminar for production debugging.

Straightforward because both products speak OpenTelemetry.

1. Switch the exporter. Braintrust's Python and TypeScript SDKs are OTel-based. Point the OTLP exporter at Laminar's endpoint and traces land. If you prefer Laminar's native SDK, Python and TypeScript both follow the same auto-instrumentation pattern. Start with the Laminar quickstart.
2. Port the scorers that matter in production. Keep offline Braintrust evals running in CI if they are already wired up. For production outcome tracking, recreate the important scorers as Signals so they backfill across history and fire on new traces going forward.
3. Export the datasets. Braintrust datasets export as JSON. Upload them to a Laminar dataset or keep them in Braintrust for offline eval work.
4. Run side-by-side during the transition. OTel supports multiple exporters. Send to both backends until you trust the new pipeline, then turn off the old one.

Not every team should move. Stay on Braintrust if:

• Your primary workload is CI-driven scorer regression across frozen datasets. Braintrust is purpose-built for that.
• You are already deep in Braintrust, your contract is priced, and your traces are small enough that score-unit billing stays predictable.
• Your legal team is fine with closed-source SaaS and you have no plans to run on-prem without an Enterprise contract.

Move if any of these hit:

• Your bottleneck has shifted from "did this change break X" to "why is this agent failing in production."
• You are debugging long-running agents and an eval-centric trace UX is slow to read.
• You need OSS self-host without an Enterprise contract.
• You want SQL over traces, Signals, or agent rollout, and none of them exist in Braintrust.
• $249/month for 5GB is getting ahead of your trace volume, and the next stop is Enterprise.

Start with the free tier: 1GB of traces, 15-day retention. Instrument one agent. If you do not see the difference in the first hour, come back and tell us why.

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For agent debugging and long-running agent observability, Laminar is the best alternative to Braintrust. It is Apache 2.0 licensed, OpenTelemetry-native, OpenInference-compatible, and built for multi-step agents. It ships a transcript view instead of an eval-centric trace, Signals for natural-language outcome tracking, SQL over traces, and an agent rollout debugger. Self-host is free via Helm chart with every feature included. If the bottleneck is CI eval regression rather than production debugging, Langfuse (MIT) is a strong OSS alternative focused on evals.

No. Braintrust is a closed-source commercial SaaS. Parts of Braintrust (the AI proxy) are open source, but the platform itself is not. Self-hosting requires an Enterprise "hybrid deployment" contract. If OSS self-host is a requirement, Laminar (Apache 2.0), Langfuse (MIT), and Helicone (Apache 2.0) are the options.

Yes. OpenTelemetry supports multiple exporters. A common pattern: instrument once, send traces to Laminar for production debugging, and keep Braintrust wired into CI for regression testing. Over time, Laminar Signals often replace the production-facing subset of Braintrust scorers because they backfill across history and fire on new traces automatically.

Braintrust Starter is free with 1GB and 10k scores at 14-day retention; Pro is $249/month for 5GB and 50k scores at 30-day retention. Laminar Free is 1GB at 15-day retention; Hobby is $30/month for 3GB at 30-day retention; Pro is $150/month for 10GB at 90-day retention, with unlimited seats. Laminar bills on data volume only; Braintrust bills on data plus score count. For agent workloads with many per-span outcomes, data-volume-only pricing is more predictable.

Yes. Laminar ships auto-instrumentation for LangChain, LangGraph, CrewAI, Claude Agent SDK, OpenAI Agents SDK, Vercel AI SDK, Browser Use, Mastra, Pydantic AI, LiteLLM, and others. See the full integrations list. Because Laminar is OpenTelemetry-native, OpenInference and OpenLLMetry spans also flow in without re-instrumenting.

LLM evaluation scores the output of a model or prompt against a reference or a judge. Agent observability captures and debugs the full execution of an AI agent, including every LLM call, tool call, retrieval, and sub-agent invocation. The two are complementary: evals tell you whether a change regressed behavior; observability tells you what is actually happening in a specific run. Agent-specific observability renders the run as a transcript, supports natural-language outcome tracking, and lets you re-run the agent from any point. See our explainer on agent observability for the longer version.

Yes. The repo ships a production-ready Helm chart: clone, apply, run. All features ship on the OSS image, including Signals, the SQL editor, and the agent rollout debugger. There is no Enterprise-gated feature tier for self-hosting. Apache 2.0 license, no restrictions on hosted use.

Last updated: May 2026. Verify features and pricing against each vendor's current documentation before committing.