You build with one and measure with the other, so they are not alternatives: Prefactor reads the runs your LangChain agents already emit.[1][2]
LangChain is the framework you build agents with; Prefactor evaluates those agents once they run in production, on any framework. Build with LangChain, then add Prefactor when agents do real work and you need to know each run did its job.
| Decision factor | LangChain | Prefactor |
|---|---|---|
| Where it fits | Building the agent | Knowing the agent works in production |
| Lifecycle stage | Development time | Production time |
| Framework scope | Builds LangChain and LangGraph agents | Evaluates agents from any framework |
| How it attaches | You write the agent in it | Native SDK, core SDK, or OpenTelemetry ingest, no rebuild |
| What you get | A working agent, faster | A quality score, drift detection, and cost per agent |
| Use them together? | Build with LangChain | Evaluate with Prefactor |
Best for teams building agents and moving quickly from idea to working prototype.
Best for teams running agents in production who need to know each one is doing its job, and prove it.
| Capability | LangChain | Prefactor |
|---|---|---|
| Building agents | ||
| Agent framework (chains, tools, memory) | ✓ | — |
| Development-time tracing (LangSmith) | ✓ | — |
| Multi-provider model support | ✓ | For evaluation |
| Evaluating agents in production | ||
| Quality score per run | — | ✓ |
| Cost attributed per agent and version | Per trace, build it yourself | ✓ |
| Drift detection against a baseline | — | ✓ |
| Hold or escalate a risky action | — | ✓ |
| Across your stack | ||
| Evaluates agents built on other frameworks | — | ✓ |
| Works with custom agents | — | ✓ |
| One queryable record per agent | — | ✓ |
| Audit trail for a decision | — | ✓ |
We sell the layer this section describes. Read it with that in mind.
LangChain answers how you build an agent; LangSmith answers what it did on a given run. Neither says whether the agent is doing its job, at acceptable quality and cost, with evidence you can show.
A trace of a wrong answer and a trace of a correct one look the same: same steps, same latency, same token counts. Prefactor evaluates each run against the agent's job.
Prefactor tracks quality per agent across versions and flags when behaviour drifts after a change.
Every run leaves a record you can give to a customer or an auditor.
Prefactor reads the traces you already emit, from LangChain or any OpenTelemetry source, so you keep what you built.
Reviewed against public product and documentation pages on March 19, 2026. If a vendor has changed a feature, product name, or positioning since then, send a correction and we will update it. Numbered source links in the page body point to the ordered sources below.
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.
Prefactor helps teams observe, evaluate, and improve their AI agents in production, across every framework and provider.