Building twins. We deployed identical Ecommerce assistants using both Pydantic AI and LangGraph to see which framework dominates in 2026. The results fundamentally challenge how we think about orchestration.

When selecting a framework for an enterprise multi-agent system, engineering teams face a crucial decision: do we script our agents like traditional software variables using functional schemas? Or do we orchestrate them dynamically through a continuously looping graph?

To answer this definitively, we challenged our engineering team to build the exact same Ecommerce Customer Service Assistant across two frameworks using the blindingly fast gemini-1.5-flash-latest model.

The Objective

Both assistants needed to securely manage an authenticated user_id context, track orders, lookup real-time warehouse inventory, and natively draft and execute a live shopping cart checkout.

The Pydantic AI Architecture: "Intent-First Scripting"

Pydantic AI feels remarkably close to writing standard Python. Because it forces the Large Language Model to conform structurally to strict Pydantic definitions, data handling is exceptionally clean.

We simply bundled our customer's context into a dataclass:

@dataclass
class CustomerDeps:
    user_id: str
    cart: list[str]

And then mapped our complex domain capabilities using the standard @agent.tool decorator:

@ecommerce_agent.tool
def book_order(ctx: RunContext[CustomerDeps], items: list[str], shipping_speed: str) -> dict:
    """Draft a new order booking containing the specified items."""
    # Logic executes seamlessly with static type guarantees

The Verdict: Pydantic AI shines when speed and maintainable typing are your top priorities. It took exactly half the lines of code to implement compared to Langchain, mostly because it hides the underlying message-loop plumbing perfectly.

The LangGraph Architecture: "Stateful Orchestration"

Where Pydantic AI obscures the loop, LangGraph exposes it completely. This paradigm requires defining an explicit, cyclic StateGraph which manages a dedicated memory block.

First, we define the memory block containing standard historical trajectories:

class AgentState(TypedDict):
    messages: Annotated[list[BaseMessage], add_messages]
    user_id: str

Next, instead of injecting tools into the LLM implicitly, we manually bind a ToolNode and draw conditional traversal edges dictating when the LLM is permitted to speak versus when the environment enforces tool execution:

builder.add_conditional_edges("chatbot", should_continue, {"tools": "tools", END: END})
builder.add_edge("tools", "chatbot")
ecommerce_graph = builder.compile()

The Verdict: LangGraph is heavy. The overhead for simple chatbots is palpable. However, the moment your execution paths require human-in-the-loop approvals (e.g., verifying a massive purchase confirmation before tool execution), LangGraph's observable boundary limits become a superpower.

Which Should You Choose?

If your goal is to quickly strap data-fetching and type-safe tool execution onto an API wrapper, Pydantic AI wins the day comprehensively.

But if you are building an orchestration engine—a multi-agent swarm where bots pass statefully to one another (like transferring a customer from a standard helper drone directly into a Finance-specific return-authorization agent)—the raw structural control of LangGraph is the only way forward.