LangGraph vs Pydantic AI

LangGraph and Pydantic AI can both build serious agent systems, yet they approach the problem from different directions. LangGraph is a graph-based orchestration framework for stateful, controllable workflows. Pydantic AI is a Python agent framework from the Pydantic team. It emphasizes typed agents and structured outputs, adds dependency injection and validation, and aims for a developer experience aligned with modern Python application code.

The official LangGraph overview explains LangGraph's role in orchestrating stateful agent workflows. The official Pydantic AI documentation explains Pydantic AI's typed agent framework. The practical choice depends on whether the hard part of the system is workflow control or typed agent construction.

The short answer

Choose LangGraph when the workflow needs explicit state, branching, loops, persistence, human review, or multi-agent orchestration. Choose Pydantic AI when the core need is building typed Python agents with strong schema validation, structured results, dependency injection, and maintainable application code.

These are not mutually exclusive in every architecture, but they occupy different mental models. LangGraph asks, "How does state move through this workflow?" Pydantic AI asks, "How do we define this agent cleanly with types, dependencies, tools, and validated outputs?"

What each framework is

LangGraph models agent workflows as graphs. Nodes perform work, edges route execution, and state moves through the graph. Developers get explicit control over the shape of the workflow, which matters once the process turns complex. A research workflow may loop until sources are sufficient. A claims workflow branches on missing documents. A legal review workflow pauses for human approval. A customer support workflow might retry a failed tool call, escalate to a person, or resume after an external event. The framework is strongest when the agent system behaves like a stateful workflow rather than a single model call.

Pydantic AI takes a different starting point: typed application development. It uses concepts familiar to Python teams that already rely on Pydantic, such as schemas, validation, type hints, and structured data. The docs cover agents, tools, model interactions, dependencies, and typed outputs.

The payoff is developer discipline. When an agent must return a structured object, call typed tools, or operate with explicit dependencies, Pydantic AI makes the code easier to validate and reason about. That value rises when agent output feeds another system rather than only a human reader. The point goes beyond output parsing; the goal is agent code that reads like well-structured Python application code.

How they differ

The difference surfaces during design. A diagram full of branches, loops, and checkpoints points toward LangGraph, which speaks directly to that shape. A system whose central worry is a validated return object and typed tool dependencies points toward Pydantic AI instead.

The two mental models look different even at a glance:

When to choose each

Reach for LangGraph in workflows where the execution path matters. Good examples include:

• Multi-step research agents.
• Human-in-the-loop review systems.
• Multi-agent coordination.
• Business workflows with routing and retries.
• Processes that need durable state and observability around transitions.

LangGraph earns its place when the team needs to say, "Here are the states, here are the allowed transitions, and here is what happens when a node fails."

Reach for Pydantic AI when correctness at the agent interface matters. Good examples include:

• Agents that must return structured, validated data.
• Python services where type hints and Pydantic models are already standard.
• Tool-heavy agents that benefit from explicit dependencies.
• Workflows where model output feeds deterministic downstream code.
• Teams that want agent definitions to feel like normal Python application code.

Pydantic AI fits teams that value typed contracts around model behavior. Those contracts do not make the model deterministic, but they do make the application boundary clearer.

Combining the two is possible when the architecture justifies it. A LangGraph node could call code that uses a typed agent pattern, or a Pydantic AI agent could sit inside a larger workflow system. The decision should be deliberate, because every added framework layer brings more dependencies, more abstractions, and a wider debugging surface. For most teams the cleaner answer is to pick the framework that matches the dominant complexity. If the workflow is the hard part, start with LangGraph. If typed agent interfaces are the hard part, start with Pydantic AI.

Common mistakes and adoption

Three errors recur. The first is reaching for LangGraph when the real problem is structured output validation. A graph does not automatically make outputs safe or typed; schemas, validation, and tests remain the team's job. The second is reaching for Pydantic AI when the real problem is workflow state. A typed agent can be well designed and still sit inside a messy orchestration loop when the process itself is never modeled. The third is confusing validation with truth. A Pydantic model validates shape, not factual correctness, so retrieval, source checks, evals, and human review still matter.

A useful starting move is to draw two sketches. The workflow sketch shows states, branches, loops, and human checkpoints. The data contract sketch shows inputs, outputs, tools, and validation rules. Whichever sketch carries more complexity points to the framework worth evaluating first.

When the workflow sketch is the source of risk, build a small LangGraph graph that includes at least one branch, one failure path, and one state persistence concern. When the data contract sketch is the source of risk, build a small Pydantic AI agent that returns the exact structured object downstream code needs and fails safely on ambiguous input.

At XY Space, orchestration tooling is usually chosen by the operational shape of the system. LangGraph wins when a client needs a visible workflow engine for agent behavior. Pydantic AI wins when a Python system needs typed agent boundaries and structured outputs that downstream code can trust after validation and review.

Sources

• LangGraph overview
• Pydantic AI documentation