You orchestrate with one and check the work with the other, so they are not alternatives: Prefactor judges each production run against the crew's job, on any framework.[1][2]
CrewAI coordinates crews of role-based agents on a shared task; Prefactor evaluates each crew's production runs and shows which member drove a regression. Build the crew with CrewAI, then add Prefactor once it does real work and each outcome needs checking.
| Decision factor | CrewAI | Prefactor |
|---|---|---|
| Where it fits | Orchestrating the crew | Knowing the crew works in production |
| Lifecycle stage | Development time | Production time |
| Framework scope | Builds CrewAI crews | Evaluates agents and crews from any framework |
| How it attaches | You write the crew in it | Native SDK, core SDK, or OpenTelemetry ingest, no rebuild |
| What you get | A working crew, faster | A quality score, drift detection, and cost per agent |
| Use them together? | Orchestrate with CrewAI | Evaluate with Prefactor |
Best for teams building multi-agent workflows where coordination between agents is central to solving the problem.
Best for teams running crews in production who need to know each one is doing its job, and prove it.
| Capability | CrewAI | Prefactor |
|---|---|---|
| Building crews | ||
| Multi-agent framework (roles, tasks, delegation) | ✓ | — |
| Hierarchical decision-making between agents | ✓ | — |
| Shared memory across a crew | ✓ | — |
| Evaluating crews in production | ||
| Quality score per run | — | ✓ |
| Cost attributed per agent and version | Not the focus | ✓ |
| Drift detection against a baseline | — | ✓ |
| Hold or escalate a risky action | Human handoff you code | ✓ |
| 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.
CrewAI answers how you orchestrate a crew: which agent holds which role, how tasks are delegated, how results are aggregated. It does not say whether the crew did its job, at acceptable quality and cost.
A run where the crew delegated cleanly but returned a wrong answer looks, in the logs, much like one that got it right: same roles, same steps, similar token counts. Prefactor judges each run on its outcome.
Prefactor tracks quality per agent across versions, so you can see which member of the crew a regression came from.
When crew behaviour shifts after a model update or a prompt edit, Prefactor flags it.
Every run leaves a record you can give to a customer or an auditor. Prefactor reads the traces CrewAI already emits, 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 crew 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.