State Rendering

This video demonstrates the implementation section applied to our coagents starter project.

What is this?#

All CrewAI Flow agents are stateful. This means that as your agent progresses through nodes, a state object is passed between them perserving the overall state of a session. CopilotKit allows you to render this state in your application with custom UI components, which we call Agentic Generative UI.

When should I use this?#

Rendering the state of your agent in the UI is useful when you want to provide the user with feedback about the overall state of a session. A great example of this is a situation where a user and an agent are working together to solve a problem. The agent can store a draft in its state which is then rendered in the UI.

Implementation#

Run and Connect your CrewAI Flow to CopilotKit#

First, you'll need to make sure you have a running CrewAI Flow. If you haven't already done this, you can follow the getting started guide

This guide uses the CoAgents starter repo as its starting point.

Define your agent state#

If you're not familiar with CrewAI, your flows are stateful. As you progress through function, a state object is updated between them. CopilotKit allows you to easily render this state in your application.

For the sake of this guide, let's say our state looks like this in our agent.

agent.py

# ...
from copilotkit.crewai import CopilotKitState # extends MessagesState
# ...

# This is the state of the agent.
# It inherits from the CopilotKitState properties from CopilotKit.
class AgentState(CopilotKitState):
    searches: list[dict]

Simulate state updates#

Next, let's write some logic into our agent that will simulate state updates occurring.

agent.py

from crewai.flow.flow import start
from litellm import completion
from copilotkit.crewai import copilotkit_stream, CopilotKitState, copilotkit_emit_state
from typing import TypedDict

class Searches(TypedDict):
    query: str
    done: bool

class AgentState(CopilotKitState):
    searches: list[Searches] = [] 

@start
async def chat(self):
    self.state.searches = [
        {"query": "Initial research", "done": False},
        {"query": "Retrieving sources", "done": False},
        {"query": "Forming an answer", "done": False},
    ]
    await copilotkit_emit_state(self.state)

    # Simulate state updates 
    for search in self.state.searches:
        await asyncio.sleep(1)
        search["done"] = True
        await copilotkit_emit_state(self.state)

    # Run the model to generate a response
    response = await copilotkit_stream(
        completion(
            model="openai/gpt-5.4",
            messages=[
                {"role": "system", "content": "You are a helpful assistant."},
                *self.state.get("messages", [])
            ],
            stream=True
        )
    )

Render state of the agent in the chat#

Now we can utilize useAgent with a render function to render the state of our agent in the chat.

app/page.tsx

// ...
// ...

// Define the state of the agent, should match the state of the agent in your Flow.
type AgentState = {
  searches: {
    query: string;
    done: boolean;
  }[];
};

function YourMainContent() {
  // ...

  // styles omitted for brevity
  useAgent({
    agentId: "sample_agent",
    render: ({ state }) => (
      <div>
        {state.searches?.map((search, index) => (
          <div key={index}>
            {search.done ? "✅" : "❌"} {search.query}{search.done ? "" : "..."}
          </div>
        ))}
      </div>
    ),
  });

  // ...

  return <div>...</div>;
}

Render state outside of the chat#

You can also render the state of your agent outside of the chat. This is useful when you want to render the state of your agent anywhere other than the chat.