CrewAI 1.15.28 or Pydantic AI 2.56.0: which should I use?

CrewAI vs Pydantic AI (2026)

Short answer: CrewAI is best for role-based multi-agent crews and flows with production debugging (`crewai eval`). Pydantic AI is best for typed Python agents with structured output: our hands-on offline test scored it 7.7/10 with tool calls working on the first run.

CrewAI vs Pydantic AI: verdict, side-by-side table and sources.

	CrewAI 1.15.28	Pydantic AI 2.56.0
What it is	Open-source multi-agent framework.	Open-source Python agent framework from the Pydantic team.
Pricing	Open source	Open source
Latest release we track	1.15.28 (14 Oct 2026)	2.56.0 (13 Oct 2026)
AgentPick Score	92/100	88/100 · Hands-on tested 7.7/10
Try it	Official source ↗	Official source ↗

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Our take

CrewAI is best for role-based multi-agent crews and flows with production debugging (`crewai eval`). Pydantic AI is best for typed Python agents with structured output: our hands-on offline test scored it 7.7/10 with tool calls working on the first run.

The sourced entries

CrewAI 1.15.28

Agent building AgentPick Score 92/100 · #2 in category Released 14 Oct 2026

Verdict: A useful debugging release for teams running crews in production: the new eval command targets the hardest question, where a run went wrong. Mostly fixes otherwise.

Test notes: The verdict is based on the linked public release notes only.

Pricing: Open source

Source: github.com · Last checked 15 Oct 2026

Pydantic AI 2.56.0

Agent building AgentPick Score 88/100 · #11 in category Hands-on tested · 7.7/10 (15 Oct 2026) Released 13 Oct 2026

Verdict: The cleanest typed-Python agent framework we've tried. In our offline hands-on test (7.7/10), tool calls and structured output worked on the first run with no API key, using the built-in TestModel.

Test notes: Hand-tested on 15 Oct 2026: installed pydantic-ai-slim==2.56.0 into a clean Python 3.13 venv, built an agent with one tool and a typed Pydantic output, and ran it with the built-in offline TestModel (no API key). The tool was called and a validated structured output came back in about 0.01 s. Set PYDANTIC_AI_NO_BANNER=1 to hide the observability/Logfire banner. Offline TestModel score; live-model runs use the same AgentPick Score and Hands-on tested badge when available.