LLM Observability & Evaluation
LangChain LangSmith vs Braintrust: compare eval workflows (datasets from prod runs, side-by-side experiments, human review queues)
10 min read
Most teams evaluating LangSmith and Braintrust are trying to answer one question: which platform makes it easier to turn messy production behavior into reliable evals you can trust? That comes down to three workflows: how quickly you can build datasets from production runs, how cleanly you can compare model or prompt changes side-by-side, and how well you can pull humans into the loop for judgment and corrections.
Quick Answer: LangSmith is a trace‑first agent engineering platform that turns production runs into datasets, calibrates LLM-as-judge with human feedback, and runs side‑by‑side experiments directly against real traffic. Braintrust focuses more narrowly on evaluations and test suites. If your main pain is debugging agents and closing the loop from prod traces → datasets → evals → deployment, LangSmith is the more complete workflow; if you just need a test harness around model calls, Braintrust may be sufficient.
The Quick Overview
- What It Is: LangSmith is LangChain’s agent engineering platform for observing, evaluating, and deploying LLM applications and agents, with trace-based datasets, evaluators, and a durable runtime. Braintrust is an eval and testing platform focused on defining test cases and running model comparisons.
- Who It Is For: LangSmith is built for teams shipping agents into production—spanning product, ML, and infra—who need visibility into traces, tooling, and long‑running workflows. Braintrust is for teams who mainly want to run structured evals and regression tests on prompts and models.
- Core Problem Solved: LangSmith addresses the “you can’t fix what you can’t replay” problem: non-deterministic agents fail in subtle ways that only show up in traces. Braintrust addresses “we can’t reliably compare models and prompts” across a shared eval suite.
How It Works
LangSmith starts from traces, not just static test cases. Every agent or LLM call is captured as a structured run timeline: inputs, outputs, tool calls, intermediate steps, and errors. From there, you can:
- Convert production runs into reusable datasets.
- Attach automated evaluators (including LLM-as-judge) to score quality.
- Route low‑confidence or disagreed cases into human annotation queues.
- Run side-by-side experiments (prompts, models, code paths) on those datasets.
- Deploy agents on a durable runtime, then keep that loop going with online evals.
Braintrust starts from tests and experiments. You define tasks (inputs + expected behavior), plug in models or prompts, and run evaluations—some automated (LLM-as-judge, metrics), some human. It’s closer to a “test harness” for LLM calls than a full agent observability and deployment stack.
1. Datasets from Production Runs
LangSmith: Trace-first dataset creation
- Every run in production is a potential data point.
- You can:
- Filter traces by tags, tools, latency, scores, or error types.
- Bulk-select interesting runs and convert them into a dataset.
- Enrich them with metadata, ground truth labels, and evaluator outputs.
- Production-to-development becomes an explicit workflow: “turn this pattern of failures into a dataset, then iterate.”
This design matches how agents fail in reality—long, branching traces where the failure might be three tools deep, not just in the final message.
Braintrust: Test-first dataset definition
- You generally define test cases explicitly: prompts, inputs, reference outputs, acceptance logic.
- Production integration is possible, but it’s not built around full agent traces; it’s more about “requests and responses” plus metadata.
- You still need an external observability stack if you want to see every step of an agent, then selectively pull those into Braintrust tests.
2. Side-by-Side Experiments
LangSmith: Experiments tied to traces and datasets
- Once you have a dataset (production-derived or synthetic), you can:
- Swap prompts, models, or chain/graph versions.
- Run them side-by-side against the same inputs.
- Attach evaluators (LLM-as-judge, embedding similarity, regex checks, custom Python) to each variant.
- Results show:
- Per-example diffs (old vs new).
- Aggregate scoring.
- Links back to full traces so you can see not just which variant “won,” but why—what tools were called, how many steps, where latency or cost changed.
Braintrust: Side-by-side model and prompt testing
- Focuses more directly on:
- Comparing multiple models/prompts on the same test suite.
- Viewing scored outputs and differences.
- You can run “experiments” across models and scoring functions.
- However, it doesn’t natively show you a deep, multi-tool agent trace; you see the input/output behavior as defined in the test.
3. Human Review Queues
LangSmith: Annotation queues integrated with traces
- Any run or dataset row can be:
- Flagged for review.
- Routed into an annotation queue.
- Assigned to a subject-matter expert (SME) who doesn’t need to read code.
- SMEs can:
- Inspect the full trace (messages, tool calls, intermediate results).
- Label correctness, categorize failure types, and propose corrections.
- Supply “ideal answers” as ground truth.
- That feedback flows directly into:
- Evaluation datasets (for future regression tests).
- Calibration of LLM-as-judge evaluators via Align Evals (LLM-as-judge tuned with human corrections and few-shot examples).
- Product decisions (e.g., redesigning tools, tightening policies, updating prompts).
Braintrust: Human feedback embedded in evals
- Braintrust also supports human evaluation:
- Reviewers can rate outputs and add labels across test cases.
- The difference is scope:
- Human review centers on individual request/response pairs rather than multi-step traces.
- There’s less native support for “this tool call failed in step 7 of a 20-step agent run; label that root cause and loop it back into evals.”
Features & Benefits Breakdown
Below is a comparison framed as “what you can actually do” while evaluating agents and LLM apps.
| Core Feature | What It Does | Primary Benefit |
|---|---|---|
| Trace-based datasets (LangSmith) | Turn real production runs—full traces with tools and intermediate steps—into datasets. | Capture the exact failure modes your agents see in the wild and regress-test against them. |
| Eval experiments on traces (LangSmith) | Run side-by-side experiments on prompts, models, or agent graphs directly tied to those traces. | See not just which version scores better, but how it behaves across steps, tools, latency, and cost. |
| Annotation queues & Align Evals (LangSmith) | Route runs to SMEs, collect labels and corrections, and use them to calibrate LLM-as-judge. | Make automated scoring trustworthy at scale by anchoring it to real human judgments. |
| Test-suite based evals (Braintrust) | Define tasks and expectations, and run models/prompts against them with scoring. | Quickly spin up a shared test harness to compare models and prompts without building your own eval infra. |
| Human scoring on tests (Braintrust) | Let humans rate and label outputs for specific test cases. | Add human judgment to your evals without building annotation tools from scratch. |
| Framework-agnostic instrumentation (LangSmith) | Use SDKs (Python, TypeScript, Go, Java) or OpenTelemetry to trace any stack. | Avoid rewrites or lock‑in; you can bring any orchestration (OpenAI SDK, Anthropic, LangGraph, custom code). |
Ideal Use Cases
-
Best for teams running complex agents in production (LangSmith):
Because it starts from traces and gives you a closed loop: observe production → turn runs into datasets → calibrate evaluators with human feedback → run experiments → deploy on a runtime built for long-running, multi-agent work with human oversight. -
Best for teams primarily comparing models and prompts (Braintrust):
Because it gives you a dedicated environment to define test suites, run experiments, and tally scores for different models or prompt versions, especially where your app behavior is mostly “single-call LLM” rather than deeply tooled agents.
Limitations & Considerations
-
LangSmith: Requires you to think trace-first.
- If you only want a “quick accuracy score” on toy prompts, tracing and dataset setup may feel heavier than necessary.
- Workaround: Start by instrumenting just your core flows; you can still use LangSmith as “logs plus evals” and grow into the full agent workflow over time.
-
Braintrust: Limited visibility into full agent behavior.
- It’s strong as a test harness, weaker as a full observability and deployment platform. You’ll still need separate tooling for debugging multi-step agents, capturing traces, and running a stateful runtime.
- Workaround: Pair Braintrust with a tracing product—or, if you move into serious agents, consider shifting eval workflows into a trace-first platform like LangSmith.
Pricing & Plans
LangSmith and Braintrust both lean into usage-based pricing with tiers for different team sizes. From a buyer’s lens, the main difference is where costs accrue and how they connect to the rest of your stack.
LangSmith
- Usage-based on:
- Traces / events ingested.
- Storage & retention (e.g., base vs extended retention for runs and datasets).
- Seat-based for:
- Builders (engineering, data science).
- Reviewers/annotators (SMEs using annotation queues).
- Enterprise options:
- US/EU data residency.
- Hybrid and self-hosted deployments (keep data in your VPC).
- SSO/SAML, SCIM, RBAC/ABAC, audit logs, encryption.
- This matches teams who are serious about agents and want observability, evals, and deployment in one place rather than stitching together multiple tools.
Braintrust
- Typically usage-based on:
- Number of eval runs and tests.
- Potential add-ons for seats or advanced features.
- It fits teams who want a modular “eval service” they can bolt onto an existing production stack and are comfortable maintaining separate observability and runtime infra.
Because pricing evolves, the most accurate plan details will always be on each vendor’s site. But generally: LangSmith consolidates several layers (observability + evals + deployment) into one usage profile, while Braintrust narrows scope to evals and tests.
- LangSmith (Agent Engineering Platform): Best for teams needing unified traces, datasets from production, human review queues, calibrated LLM-as-judge, and a production runtime in one place.
- Braintrust (Eval/Test Harness): Best for teams needing a focused evaluation framework around model and prompt experiments, not a full agent observability and deployment stack.
Frequently Asked Questions
How do LangSmith and Braintrust differ for building datasets from production runs?
Short Answer: LangSmith makes production traces the primary source of truth and can turn them directly into datasets; Braintrust typically expects you to define tests and then optionally attach real traffic data.
Details:
In LangSmith, every run is a structured trace. You can filter by tags, latency, tools, or error types, then click “make this a dataset” to turn a slice of production behavior into a reusable eval set. Those datasets keep links back to the original traces, so when an eval fails you can replay the full timeline. Braintrust can ingest logs or requests as test cases, but it doesn’t treat multi-step agent traces as first-class objects in the same way. That matters when your failures come from complex, branching workflows rather than simple input/output mismatches.
How reliable is LLM-as-judge in LangSmith vs Braintrust, and how do humans fit in?
Short Answer: Both support LLM-as-judge; LangSmith puts more emphasis on calibrating and auditing it with human feedback via annotation queues and Align Evals.
Details:
LLM-as-judge is powerful but noisy. In LangSmith, you’re encouraged to treat evaluators like production code: you route samples into annotation queues, let SMEs flag disagreements, and use those corrections and few-shot examples to calibrate evaluators with Align Evals. Over time, that creates a feedback loop where evaluator scores actually track human judgment for your domain. Braintrust also uses LLM-as-judge and human ratings, but the calibration story is less tightly integrated with trace-level debugging and ongoing agent improvement. If your risk profile is high (e.g., financial workflows, policy-heavy decisions), the ability to audit and tune evaluators with human-in-the-loop is usually the deciding factor.
Summary
If you’re mostly running single-call LLM apps and want a dedicated test harness to compare models and prompts, Braintrust will cover a lot of ground. But if you’re running—or planning to run—serious agents with tools, long context, and branching logic, you need more than scores on test cases. You need to see exactly what happened, in what order, and why; you need to turn those traces into datasets; and you need to close the loop with evaluators, human review, and a runtime that can be rolled back when something regresses.
That’s the gap LangSmith is built to fill: a trace-first agent engineering platform where datasets come from production runs, evals are calibrated with human feedback, and side-by-side experiments are grounded in real agent behavior—not just synthetic prompts.