Pydantic AI alternatives: graphs, SDKs and crews
Updated By Agent Radar5 min read
Short answer
Pydantic AI alternatives compared by what you build: LangGraph, OpenAI Agents SDK, Google ADK, Agno, CrewAI and DSPy, with license and lock-in notes.
Top pick: LangGraph. MIT-licensed graph runtime for agents that need explicit state, branches and human checkpoints.
Quick comparison
| Pick | License | Pricing | Source |
|---|---|---|---|
| 1.LangGraph | MIT | Free library; hosted LangSmith services are billed separately. | Official repository |
| 2.OpenAI Agents SDK | MIT | Free SDK; model usage is billed by the provider. | Official repository |
| 3.Google ADK | Apache-2.0 | Free library; Gemini and Vertex AI usage is billed separately. | Official repository |
| 4.Agno | Apache-2.0 | Free framework; model usage is billed by the provider. | Official repository |
| 5.CrewAI | MIT | Free framework; CrewAI also sells a hosted platform. | Official repository |
| 6.DSPy | MIT | Free library; model usage is billed by the provider. | Official repository |
Most people looking for a Pydantic AI alternative want one of three things: more explicit control flow than a single agent loop, a framework closer to their model vendor, or something that tunes prompts instead of hand-writing them. Each points to a different tool.
Short answer
If you need branches, state and human checkpoints you can see, use LangGraph. If you are on OpenAI models and want the smallest API, use the OpenAI Agents SDK. If you deploy to Google Cloud, use Google ADK. If you want to run agents as a service with an API and a UI, look at Agno. If you think in roles, CrewAI. If you have an eval set and want prompts optimized against it, DSPy.
Pick by what you are building
| You want | Start with | Watch out for |
|---|---|---|
| Explicit graphs with checkpoints | LangGraph | More boilerplate |
| A small SDK on OpenAI models | OpenAI Agents SDK | Vendor gravity |
| Multi-agent apps on Google Cloud | Google ADK | Best on Gemini |
| Agents served as an API | Agno | Bigger surface |
| Role-based teams | CrewAI | Hidden control flow |
| Prompts tuned to a metric | DSPy | Needs an eval set |
The order is editorial, not a benchmark score.
What you give up by leaving Pydantic AI
Pydantic AI is MIT-licensed and built around typed, validated outputs, dependency injection and model-agnostic providers. It integrates with Temporal, DBOS and Prefect for durable runs, and version 2.67, released on November 10, 2026, moved native-tool options into provider_settings and made more steps durable. If typed outputs and durable runs are why you picked it, most alternatives below are a sideways move at best.
The alternatives
LangGraph
LangGraph models an agent as a graph of nodes and edges with shared state, persistence and interrupts for human approval. It suits workflows where you need to see and test every branch. You write more code than with a Pydantic AI agent. See Pydantic AI vs LangGraph and LangChain vs LangGraph.
OpenAI Agents SDK
The OpenAI Agents SDK keeps the API small: agents, tools, handoffs between agents, guardrails and tracing. It is MIT-licensed and can call other providers, but it is smoothest on OpenAI models. Our OpenAI Agents alternatives guide covers the reverse question.
Google ADK
Google's Agent Development Kit is Apache-2.0 and built for multi-agent hierarchies, with an evaluation tool and a path to Vertex AI Agent Engine. It is the natural pick if you already run on Google Cloud. See Pydantic AI vs Google ADK.
Agno
Agno is an SDK plus the AgentOS runtime, which runs agents as a durable service behind a REST API and an MCP server, with runs and sessions managed in a UI. It covers more ground than Pydantic AI, which also means more to learn. Our Agno vs Pydantic AI comparison goes into the details.
CrewAI
CrewAI lets you describe a team of roles with tasks, and its flows add deterministic steps around them. It is quick for prototypes, but the role prompts can hide control flow you may later want to see. See Pydantic AI vs CrewAI.
DSPy
DSPy takes a different approach: you write typed signatures and let optimizers such as GEPA tune the prompts against a metric. Version 3.5, also released on November 10, 2026, made LiteLLM optional and XML the default adapter, so check its upgrade guide first. It pays off only when you have an eval set.
How to switch without losing a week
Take one agent you already run in Pydantic AI, with its tools and a handful of real inputs. Rebuild it in the alternative with the same model, then compare output validity, tool-call count and cost per run. If you rely on durable execution, check that the alternative supports your engine before you port anything else.
Sources checked November 11, 2026: release notes, official repositories and GitHub license metadata. We did not benchmark any of these frameworks. For neighbouring comparisons, see Pydantic AI vs LangChain, LangChain alternatives, Google ADK alternatives DSPy vs LangGraph, LangGraph vs CrewAI, LangGraph vs Google ADK, Agno vs LangGraph, Agno vs CrewAI and Google ADK vs Agno. More options are in frameworks.
The picks
MIT-licensed graph runtime for agents that need explicit state, branches and human checkpoints.
Pros
- State and control flow are explicit nodes and edges you can test.
- Built-in persistence and interrupts for human review.
Cons
- More boilerplate than a single typed agent call.
- Typed outputs are less central than in Pydantic AI.
Pricing Free library; hosted LangSmith services are billed separately.
OpenAI's MIT-licensed Python SDK with handoffs, guardrails and tracing.
Pros
- Small API: agents, tools, handoffs and guardrails.
- Tracing works out of the box with OpenAI's dashboard.
Cons
- Smoothest on OpenAI models.
- Fewer durable-execution integrations than Pydantic AI.
Pricing Free SDK; model usage is billed by the provider.
Google's Apache-2.0 Agent Development Kit for multi-agent apps that deploy to Google Cloud.
Pros
- Multi-agent hierarchies and an evaluation tool are built in.
- Clear path to Vertex AI Agent Engine.
Cons
- Best documented on Gemini and Google Cloud.
- Heavier than Pydantic AI for one typed agent.
Pricing Free library; Gemini and Vertex AI usage is billed separately.
Apache-2.0 SDK and AgentOS runtime for running agents as a durable service.
Pros
- Serves agents through a REST API and an MCP server.
- Can run agents built with other SDKs, not only its own.
Cons
- Larger surface to learn than Pydantic AI.
- Less emphasis on validated, typed outputs.
Pricing Free framework; model usage is billed by the provider.
MIT-licensed framework for role-based agent crews and event-driven flows.
Pros
- Fast to sketch a team of roles with tasks.
- Flows add deterministic steps around crews.
Cons
- Role prompts hide control flow you may want to see.
- Harder to type-check than Pydantic AI agents.
Pricing Free framework; CrewAI also sells a hosted platform.
MIT-licensed framework that compiles and optimizes prompts from typed signatures.
Pros
- Optimizers such as GEPA tune prompts against your metric.
- Since 3.5, LiteLLM is optional and the install is much smaller.
Cons
- Needs an eval set to pay off.
- 3.5 changed default prompts, so optimized programs need re-evaluation.
Pricing Free library; model usage is billed by the provider.
Questions
What is the best open-source Pydantic AI alternative?
For explicit, stateful workflows, LangGraph. For a small SDK with handoffs, the OpenAI Agents SDK. For multi-agent apps on Google Cloud, Google ADK. All three are open source; LangGraph and the OpenAI Agents SDK are MIT, ADK is Apache-2.0.
Pydantic AI vs LangGraph: which should I pick?
Pick Pydantic AI when you want typed, validated agent outputs with little code. Pick LangGraph when control flow, branching and human checkpoints matter more than output types. Our [Pydantic AI vs LangGraph](/blog/pydantic-ai-vs-langgraph) guide goes deeper.
Which Python agent frameworks are not tied to one model vendor?
LangGraph, Agno, CrewAI and DSPy all document several providers, as does Pydantic AI. The OpenAI Agents SDK and Google ADK support other models but are smoothest on their own vendor's.
Do I need to leave Pydantic AI for durable execution?
Usually not. Pydantic AI integrates with Temporal, DBOS and Prefect, and 2.67 records guardrail verdicts and more tool steps as durable operations.