LangSmith tests the cases you chose. Prefactor catches the ones you didn't.
LangSmith is at its best inside the LangChain ecosystem; Prefactor judges the live run on any framework and holds a risky action for review before it reaches a user.
What is the difference between LangSmith and Prefactor?
LangSmith evaluates outputs against curated datasets during development, strongest inside LangChain. Prefactor scores each production run against the agent's goal, whatever framework built it, and flags drift after a change. Test with LangSmith while you build, then add Prefactor once real traffic arrives.
LangSmith or Prefactor, in one table
| Decision factor | LangSmith | Prefactor |
|---|---|---|
| Where it fits | Tracing and testing during development | Knowing whether the agent did its job live |
| What it evaluates | Outputs against a curated dataset | The outcome of a production run, per agent |
| Framework scope | Strongest inside LangChain | Evaluates agents from any framework |
| On drift | Regression against a test set | Drift against a live baseline, per agent |
| On a risky action | Flags it in a report | Holds or escalates it before it reaches a user |
| Use them together? | Test and iterate with LangSmith | Judge live outcomes with Prefactor |
What each one is for
- LLM tracing: detailed traces of each call with inputs, outputs, latency, token usage, and chain execution laid out step by step.
- Dataset evaluation: run evals against curated datasets, compare prompt versions, and detect quality regressions.
- Dataset management: curate test sets and collect production examples to build evaluation pipelines around real inputs.
- Prompt playground: iterate on prompts interactively and compare outputs side by side.
- Production monitoring: track latency, error rates, and usage across LLM applications.
- LangChain integration: automatic tracing and deep coverage for applications built on LangChain.
Best for development teams on LangChain who need to evaluate, debug, and iterate on LLM quality.
- Evaluates each run for outcome quality, cost, and whether the agent stayed in its approved scope.
- A quality score per agent tracked across versions, so a regression shows up as a trend rather than a surprise.
- Drift detection when behaviour shifts after a model update or a prompt edit, before a user hits it.
- Holds or escalates a risky action for review before it reaches a user, not after.
- One record across frameworks: LangChain, CrewAI, and custom agents judged from the same place, with an audit trail per decision.
Best for teams running agents in production who need to know each one is doing its job, and prove it.
Side by side, by lifecycle stage
| Capability | LangSmith | Prefactor |
|---|---|---|
| Tracing and development evaluation | ||
| LLM call tracing | ✓ | — |
| Dataset-driven evaluation | ✓ | Against live runs, not datasets |
| Prompt playground and comparison | ✓ | — |
| Regression detection against a test set | ✓ | — |
| Production monitoring | ✓ | ✓ |
| Framework-agnostic | Strongest in LangChain | ✓ |
| Evaluating agents in production | ||
| Quality score per run | — | ✓ |
| Drift detection against a live baseline | — | ✓ |
| Cost attributed per agent and version | Per trace | ✓ |
| Hold or escalate a risky action | — | ✓ |
| Across your stack | ||
| One queryable record per agent | — | ✓ |
| Audit trail for a decision | Partial | ✓ |
Where LangSmith stops: whether the agent did its job
We sell the layer this section describes. Read it with that in mind.
LangSmith measures quality the way development wants: run a curated dataset, compare versions, catch a regression before you ship. It does not say how the agent is doing on live traffic, at acceptable quality and cost.
A dataset tells you how the agent did on the cases you thought to include. Live traffic includes the ones you did not.
A passing test set says nothing about today's traffic. Prefactor evaluates each production run against the agent's goal and tracks it per agent across versions.
Regressions surface against a test set, when you rerun it. Prefactor flags drift against a live baseline after a prompt or model change, before a user hits it.
A report flags the action after it ran. Prefactor holds or escalates it for review before it reaches a user, on any framework rather than only LangChain.
Which one fits
Stay with LangSmith alone if
- You are building on LangChain and iterating during development.
- Dataset evals and prompt comparison cover what you need today.
- Your agents are not yet taking actions for real users.
Add Prefactor when
- Agents are doing real work for real users, on any framework.
- You need a verdict on live runs, not only against a test set.
- A regression after a prompt or model change has to surface before a user hits it.
- Someone asks you to prove an agent behaved.
Does Prefactor replace LangSmith?
Is LangSmith only for LangChain?
LangSmith already does evaluation. How is Prefactor different?
Does Prefactor work with LangSmith traces?
Do I still need production evaluation if I run dataset evals?
Take your evals into production
Book a demo and we will evaluate a live agent on a fleet like yours: quality per run, drift after a change, and cost per agent and version.
Unified performance platform for agents, authentication, and risk management