Head to head

CrewAI vs Pydantic AI

Both are free, open-source Python agent frameworks. CrewAI supplies a role-based crew abstraction and governed managed runtime, but that runtime jumps from a capped free allowance to custom Enterprise pricing. Pydantic AI supplies typed outputs and durable Python composition without a managed runtime; its optional Logfire observability service is purchased separately.

Compare their current datasheets →Review your shortlist Jump to the verdict ↓

Pick Pydantic AI unless you specifically need several agents with distinct roles handing work between them, or you need governance you can buy rather than build. Both frameworks are free, open-source Python, and both shipped a release this week, so licence cost decides nothing. What decides is that CrewAI's genuinely differentiated asset (a governed managed runtime) is not purchasable below a custom Enterprise quote, while Pydantic AI's differentiator (a model response validated against a Pydantic output model before your code ever sees it) is in the free library, and the one thing Pydantic does sell has a published price.

That pricing asymmetry is the reverse of what the product shapes suggest. CrewAI is the framework with a managed cloud product, but its public page offers only a free Basic allowance and custom Enterprise pricing. Pydantic AI has no managed runtime; its optional Logfire observability service publishes self-serve paid tiers, but that is a separate product rather than the price of the Pydantic AI framework. Both frameworks remain free to self-host, so compare the framework guarantees first and treat observability or managed-runtime procurement as a separate decision.

On the framework itself, CrewAI owns the abstraction and Pydantic AI deliberately does not. CrewAI organises work into a crew (each agent gets a role, a goal and tools, and the framework manages the hand-offs) with an event-driven Flows layer for cases a plain crew cannot constrain. Pydantic AI documents four multi-agent patterns instead of one abstraction (agent delegation, programmatic agent hand-off, graph-based control flow and Deep Agents) and its own documentation puts the orchestration on you, describing "application code and/or a human in the loop responsible for deciding which agent to call next." That is ordinary Python you write and then maintain. What Pydantic AI gives back for it is durability on infrastructure you already run: six documented checkpointed-execution integrations, four co-maintained by the Pydantic and vendor teams (Temporal, DBOS, Prefect and Restate) plus Kitaru and Apache Airflow via external SDKs, which survive process restarts and long human-in-the-loop pauses. None of them are hosted by Pydantic.

The two projects are also moving at very different speeds, and that is a purchase criterion rather than trivia. Read from PyPI and the GitHub API on 2026-08-17: CrewAI reached 1.0.0 on 2025-10-20, locking its Crews and Flows APIs, and is now at 1.15.16, published 2026-08-14. Pydantic AI reached 1.0.0 earlier, on 2025-09-05, then broke backwards compatibility at 2.0.0 on 2026-06-23 (eight weeks ago) and has published 40 releases since that date, reaching 2.31.0 on 2026-08-15. CrewAI published 65 releases over the same eight weeks but under a major version it has not broken in ten months. Even Pydantic AI's documentation has moved: ai.pydantic.dev now returns HTTP 301 to pydantic.dev/docs/ai/, so every third-party comparison citing the old docs paths is citing a redirect. Thirty-one minor versions in eight weeks is a project adding capability fast; it is also a project whose surface your code is chasing.

On adoption, CrewAI leads by roughly three to one and it is not close. Checked against the GitHub API on 2026-08-17: crewAIInc/crewAI carries 57,202 stars and 8,166 forks against pydantic/pydantic-ai's 19,352 stars and 2,540 forks, a 2.96x margin, with CrewAI's repository created 2023-10-27 and Pydantic AI's first PyPI release on 2024-05-20. CrewAI's own site states, in prose on both its home page and its pricing page, that it is "Used by 65% of the Fortune 500," and names DocuSign, Experian, PepsiCo, IBM, AB InBev, Gelato, General Assembly and Konecta as customers. Its headline workflow-volume counter animates client-side and serves as zero in the HTML, so we do not quote a figure for it. Pydantic AI publishes no comparable adoption statistic at all, which is an absence of published evidence, not evidence that nobody runs it in production, and it is worth saying plainly that the Pydantic validation library underneath it is a dependency of much of the Python AI ecosystem.

How CrewAI and Pydantic AI compare

Scroll across to compare every side. Feature names stay in view.

Feature	CrewAI Visit CrewAI ↗	Pydantic AI★ Our pickVisit Pydantic AI ↗
Description	Open-source multi-agent framework with a governed enterprise runtime for building and operating agent workflows.	Typed Python agent framework: any model, any surface, with a harness for long-running coding and research agents.
Tags	APIAutonomousClaudeEnterpriseFree tierGPTMulti-agentNo-codeOpen modelsOpen sourceSelf-hosted	APIAutonomousClaudeFree tierGPTMulti-agentOpen modelsOpen sourceSelf-hosted
Pricing model	freemium	free
Model / LLM	Native SDKs for OpenAI, Anthropic, Google Gemini, Azure, AWS Bedrock and Snowflake Cortex. LiteLLM for Hugging Face, Mistral, Groq, Perplexity, Cerebras, SambaNova, Ollama and others.	Model-agnostic
Bring your own model	Yes	-
Interface	Library	Library
Deployment	Both	Self-host
Writes code	Partly	Yes
Debugs and fixes	Partly	Yes
Done-for-you service	Partly (Enterprise, costs extra)	No
Agent permissions	Yes	Partly (Pydantic AI Harness, other product line)
In your editor	Partly	Partly (Pydantic AI Harness (ACP, experimental), currently degraded)
In your pipeline	Partly	No
Self-hosted	Partly (Enterprise, available higher)	Yes
Open source	Partly (CrewAI Open Source, other product line)	Yes
API access	Partly (currently degraded)	Yes
A company can buy it	Yes	Partly (Pydantic Logfire, other product line)
Seat model	-	Partly (Pydantic Logfire (Team), other product line)
Pooled budget	Partly	Partly (Pydantic AI Gateway, other product line)
Admin controls	Yes	Partly (Pydantic AI Harness (Guardrails), other product line)
Audit log	Partly	Partly (Pydantic AI Gateway, other product line)
Single sign-on	Partly (Enterprise, available higher)	Partly (Pydantic Logfire (Enterprise), available higher)
Opt out of training	No	Partly (Pydantic AI Gateway, other product line)
Data residency	Partly (Enterprise, available higher)	Partly (Pydantic Logfire, other product line)
Getting out	Partly	Partly (Pydantic AI Gateway, other product line)
Certifications	Partly (Enterprise, higher tier)	Partly (Pydantic Logfire, other product line)
Limitations	Free Basic is capped at 50 workflow executions a month and the only path beyond that is a custom Enterprise sales conversation. There is no published self-serve paid tier. Building custom agents requires Python, scripting, environment variables and dependency management, despite the no-code visual editor Agent collaboration is limited to two coordination modes, Sequential and Hierarchical, with no built-in graph-style state control for finer orchestration Agents retain memory and context within a workflow but do not learn or improve from past executions	Durable execution is real since 2.0, but it comes via operating a separate orchestrator (Temporal, DBOS, Prefect or Restate) rather than a built-in, zero-infrastructure engine, and LangGraph still offers deeper native graph-branching control. The richest observability story nudges you toward Pydantic Logfire, and the deepest multi-provider routing and cost monitoring sit in Pydantic Gateway rather than the open-source core. Still fewer pre-built off-the-shelf integrations than LangChain, and getting full value assumes comfort with Pydantic and Python type hints.

Who each one is for

CrewAI

Developers who want to stand up a role-based, collaborating multi-agent system quickly in Python, with a stable MIT-licensed core and a hosted build and runtime for production.

From Free (month (50 workflow executions))

Pydantic AI

Python teams building type-safe agents who also want realtime voice, image generation and embeddings in one SDK, with optional durable execution via Temporal, DBOS, Prefect or Restate.

From Free

Verdict: CrewAI or Pydantic AI?

Pick Pydantic AI if the agent's output is consumed by something that will break on a malformed field, and you were going to run your own Python anyway. That is the whole product and the other framework does not have an equivalent: you declare a Pydantic output model, and a response that does not conform is caught before your code sees it rather than three services downstream. Two things make it the default rather than merely the safer choice. First, Pydantic's optional Logfire observability service has self-serve paid tiers, whereas the rung above CrewAI's free allowance is a sales conversation; Logfire remains a separate product rather than the price of Pydantic AI. Second, if you need an agent to survive a restart or a two-day human approval, Pydantic AI documents six checkpointed-execution integrations against orchestrators you may already operate (Temporal, DBOS, Prefect, Restate, Kitaru, Airflow) and CrewAI names no equivalent built-in durability feature, resting that story on the managed cloud layer you cannot price.

Pick CrewAI in three cases, and the first is the one Pydantic AI genuinely cannot answer. First, if the work really is a team of specialists handing tasks to each other: CrewAI's crew is that abstraction, with roles, goals and framework-managed hand-offs, and Pydantic AI's own documentation assigns that job to "application code and/or a human in the loop responsible for deciding which agent to call next" across four separate patterns. Choosing Pydantic AI for genuine multi-agent orchestration means writing and owning the orchestration. Second, if you have a procurement function and need governance you can buy instead of build: CrewAI's Enterprise tier publishes SSO, RBAC, workload identity, PII redaction, policy enforcement, VPC or on-premise deployment and a 45-day onboarding. Pydantic ships none of that and there is no vendor to buy it from: you build your own governance or you go without it, which for some buyers ends the conversation before the type system is discussed. Third, if the code you write this quarter has to still run in eighteen months without somebody paid to chase deprecations: CrewAI has held one major version since 2025-10-20, and Pydantic AI broke compatibility eight weeks ago and has shipped 31 minor versions since.

Stated as the two mistakes. Choosing CrewAI for the managed runtime, on a team with no procurement budget, means using 50 executions a month and then discovering the next step is a custom quote. You end up with the open-source crew you could have had for nothing and without the cloud layer that decided the purchase. Choosing Pydantic AI for something that is genuinely a crew of specialists means re-implementing hand-offs, role assignment and delegation in your own Python, less well than a framework built for it, on your own maintenance budget.

Visit CrewAI ↗Visit Pydantic AI ↗

The digest

Decided? Watch the bench move. New agents, fresh verdicts, moves in the rankings, one short email, every Tuesday.

Frequently asked questions

Which is cheaper, CrewAI or Pydantic AI?: Both frameworks are free and open source. CrewAI's managed cloud publishes a free Basic allowance and custom Enterprise pricing, with no self-serve tier between them. Pydantic AI has no managed runtime; its optional Logfire observability service has a separate self-serve price list.
Which has more GitHub traction?: CrewAI, by 2.96x. Checked against the GitHub API on 2026-08-17, crewAIInc/crewAI carries 57,202 stars and 8,166 forks and pydantic/pydantic-ai carries 19,352 stars and 2,540 forks. CrewAI has a head start (its repository was created 2023-10-27, against Pydantic AI's first PyPI release on 2024-05-20) and a broader general-purpose multi-agent remit, while Pydantic AI is a narrower, validation-focused library. Star counts measure attention rather than production use, and neither figure should decide a build.
Does Pydantic AI have a crew or team abstraction like CrewAI?: No, and this is the clearest reason to choose CrewAI. CrewAI organises agents into a crew (each with a role, a goal and tools) and the framework manages the hand-offs between them. Pydantic AI documents four multi-agent patterns instead: agent delegation, programmatic agent hand-off, graph-based control flow, and Deep Agents. Its own documentation describes "application code and/or a human in the loop responsible for deciding which agent to call next," so multi-agent composition is ordinary Python control flow you write and maintain rather than a framework abstraction you configure.
Which framework is more stable to build on?: CrewAI, on the evidence of its own release history. Read from PyPI on 2026-08-17: CrewAI reached 1.0.0 on 2025-10-20, locking its Crews and Flows APIs, and has stayed on that major version for ten months, currently at 1.15.16 (published 2026-08-14). Pydantic AI reached 1.0.0 earlier, on 2025-09-05, then shipped a backwards-incompatible 2.0.0 on 2026-06-23 and has published 40 releases in the eight weeks since, reaching 2.31.0 on 2026-08-15. Its documentation moved in the same period: ai.pydantic.dev now returns HTTP 301 to pydantic.dev/docs/ai/. Fast movement is capability being added, but it is surface your code has to keep up with.
Can either agent survive a crash or a long human approval step?: Pydantic AI documents this explicitly and CrewAI does not. Pydantic AI's durable-execution documentation names six integrations that checkpoint an agent's progress across failures and long human-in-the-loop pauses: Temporal, DBOS, Prefect and Restate, co-maintained by the Pydantic and vendor teams, plus Kitaru and Apache Airflow through external SDKs. Pydantic hosts none of them, so you run the orchestrator. CrewAI names no equivalent built-in durability feature; its long-running-reliability story rests on the managed AMP cloud layer, which is only available on the free 50-execution tier or a custom Enterprise contract.
How much of the Fortune 500 uses CrewAI?: CrewAI states in prose on both its home page and its pricing page, read 2026-08-17, that it is "Used by 65% of the Fortune 500," and names DocuSign, Experian, PepsiCo, IBM, AB InBev, Gelato, General Assembly and Konecta among its customers. That is a vendor claim we can point at but not audit. CrewAI's headline workflow-volume counter animates client-side and serves as zero in the page HTML, so we do not quote a number for it. Pydantic AI publishes no comparable adoption statistic, which is an absence of published evidence rather than evidence of an absence.

Still deciding? Get a quote from Pydantic AI

Tell us what you need. We make the introduction to Pydantic AI.

Advertise here

Reach buyers mid-decision. Reach builders choosing their next agent. Promote your brand with a display placement or bring your listing into focus with Featured.

Explore owner options →Advertise on this page →

The digestFree

Which agents actually ship.

What we re-checked, what got added, and one number from the index. Tuesdays.

One-click unsubscribe

At a glance