Training And Evaluation
CrewAI · CrewAI
Multi-agent Framework — CrewAI
Evaluates CrewAI's Training & Evaluation across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Multi-agent Framework eval coverage.
About CrewAI
CrewAI is a multi-agent orchestration framework — role-playing Agents, Tasks, Crews (sequential/hierarchical/consensual processes), and Flows (declarative @start/@listen/@router state graphs) for production agent workflows; with a commercial CrewAI Enterprise tier offering UI Studio, deployment, secrets/RBAC, observability, and an on-prem option.
Sample tests· showing 3 of 9
| # | Input | Expected behavior | Check |
|---|---|---|---|
| 01 | Operator calls crew.train(n_iterations=1, filename='trained.pkl', inputs=...) and expects meaningful improvement. | n_iterations=1 is rarely enough — training is the iterative refinement of agent prompts via human feedback. Budget 5-20 iterations on a representative input set; verify per-iteration the human feedback is actually reaching the prompt. Do not treat training as a one-shot. | Pass / FailAi Platformmedium |
| 02 | Training saves output to trained.pkl. The operator wants to inspect what actually changed. | trained.pkl contains per-agent / per-iteration prompt refinements. Diff against the baseline prompt to surface what training learned. Treat the artifact as code: review in PR, version, and gate behind tests. Don't ship a trained artifact whose changes you can't explain. | Pass / FailAi Platformhigh |
| 03 | During crew.train(), CrewAI pauses and prompts the operator for feedback after each task in each iteration. | Human feedback is the training signal — provide specific, actionable corrections tied to expected_output. Generic 'good' / 'bad' is wasted signal. Capture the feedback in a structured store for replay and review. Verify feedback is applied by inspecting the saved prompt artifact. | Pass / FailAi Platformmedium |
How this eval is graded
Grade against expected.ideal_behavior and expected.rubric. Per-criterion pass requires mean >= 4.0 and no criterion below 3.
Rubric criteria
- Crewai
- Ai Platform
- Training And Evaluation
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ViewFrequently asked questions
What does the Training And Evaluation eval for CrewAI CrewAI test?+
Evaluates CrewAI's Training & Evaluation across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Multi-agent Framework eval coverage.
How is the Training And Evaluation eval scored?+
The judge rubric: Grade against expected.ideal_behavior and expected.rubric. Per-criterion pass requires mean >= 4.0 and no criterion below 3.
How many test cases does this eval pack include?+
The Training And Evaluation pack for CrewAI CrewAI contains 9 test cases. 3 sample cases are shown free on this page; the full set runs in a Corsac workspace.
How do I run this eval?+
Sign up for Corsac, connect your model or agent endpoint, and run the Training And Evaluation pack as-is or after customizing thresholds. Results land in your workspace with per-case scores, and you can gate releases on the pack in CI via the REST API.
Run this eval in your workspace
Connect your data, configure thresholds, and review results with your team.