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See it in action

Here’s a quick demo:

Davia supports LangGraph-style AI agents through the @app.graph decorator.

A graph represents an AI workflow — a sequence of steps connected through logic and memory (called state). It’s useful for building advanced agents like chatbots, assistants, or tool-using flows.

Davia connects directly to the graph you define and builds a UI so users can interact with it — with real-time inputs, state handling, and streaming output.

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1. What is a graph in AI?

A graph is an orchestration framework for building controllable AI agents. It enables complex workflows with customizable architectures, long-term memory, and human-in-the-loop capabilities to handle sophisticated tasks reliably.

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How Davia works with graphs

Davia seamlessly integrates with LangGraph-style AI agents through the @app.graph decorator. When you expose a graph to Davia:

  • It reads your graph’s structure and state
  • Builds an interactive UI that matches your workflow
  • Handles real-time inputs and streaming outputs
  • Manages state transitions automatically

If your agent is conversational, Davia can generate a chat-style interface to let users interact with it naturally.

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2. Full example: A minimal graph

Here’s a full example of a simple one-turn chatbot using LangGraph and Davia.

First, install Davia with LangGraph support (requires Python 3.11+):

pip install davia langgraph "langgraph-api==0.0.38"

Then create your agent:

from davia import Davia
from langgraph.graph import StateGraph, START, END, MessagesState


# Define a simple node
def start(state: MessagesState) -> MessagesState:
    return {"messages": [{"role": "ai", "content": "Hello, how can I help you today?"}]}


# Create and expose the graph
app = Davia()


@app.graph
def hello_graph():
    """
    A minimal LangGraph agent that returns a greeting.
    """
    graph = StateGraph(MessagesState)
    graph.add_node("start", start)
    graph.add_edge(START, "start")
    graph.add_edge("start", END)
    return graph


#  Launch the server
if __name__ == "__main__":
    app.run()

When you run this file, Davia opens the editor at:

🔗 https://davia.ai/dashboard

You’ll see a simple interface where:

  • Users can submit a message
  • The graph runs and returns a greeting
  • Responses are streamed back to the UI in real time

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3. How Davia integrates with LangGraph

When you expose a LangGraph agent:

  • Davia reads the state class (e.g. MessagesState) and builds an entire UI based on it,
  • It connects to the graph’s lifecycle: START → node(s) → END
  • It can stream the output live as each node in the graph runs
  • The entire UI is automatically adapted to your graph’s logic

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4. Best practices

  • ✅ Always return a Graph from your decorated function
  • ✅ Add a helpful docstring to describe your agent
  • ❌ Don’t return the graph directly at the top level — it must be wrapped in a function