Supercharge your CrewAI agents with Generative UI powered by C1 by Thesys. Instead of limiting agents to text responses, you can render interactive UIs, create dynamic workflows, and plug in real-time dashboards - all generated on the fly.
This guide assumes you have basic knowledge of CrewAI. You’ll also need a Thesys API key from the C1 Console.
1

Create a new CrewAI agent

You can follow the CrewAI Quickstart to create a new CrewAI agent.
crewai create crew crewai-genui
cd genui-agent
2

Create a new LLM class for Thesys

Extend the BaseLLM class to create a new LLM class for Thesys.
src/crewai-genui/thesys_llm.py
from crewai import BaseLLM, Task
import os
import requests
from typing import List, Optional, Union, Dict, Any

class ThesysLLM(BaseLLM):
    def __init__(self, model: str="c1/anthropic/claude-sonnet-4/v-20250815", temperature: Optional[float] = None):
        super().__init__(model=model, temperature=temperature)
        self.endpoint = "https://api.thesys.dev/v1/embed"
        self.api_key = os.getenv("THESYS_API_KEY")

    def call(
        self,
        messages: Union[str, List[Dict[str, str]]],
        tools: Optional[List[dict]] = None,
        **kwargs: Any
    ) -> str:
        """Call the LLM with the given messages."""
        # Convert string to message format if needed
        if isinstance(messages, str):
            messages = [{"role": "user", "content": messages}]

        # Prepare request
        payload = {
            "model": self.model,
            "messages": messages,
            "temperature": self.temperature,
        }

        # Add tools if provided and supported
        if tools and self.supports_function_calling():
            payload["tools"] = tools

        # Make API call
        response = requests.post(
            self.endpoint + "/chat/completions",
            headers={
                "Authorization": f"Bearer {self.api_key}",
                "Content-Type": "application/json"
            },
            json=payload,
            timeout=300
        )
        response.raise_for_status()

        result = response.json()
        return result["choices"][0]["message"]["content"]
3

Update the reporting agent to use Thesys

In the crew.py file, the researcher and reporting_analyst agents would be defined. The job of of the reporting_analyst agent is to analyze the research and provide a summary of the findings. This is the perfect place to use Thesys as the LLM and generate a properly formatted report complete with charts and tables rather than just a plain text response.So lets go ahead and update the reporting_analyst agent to use Thesys.
src/crewai-genui/crew.py
@CrewBase
class CrewaiGenUI:
    def __init__(self):
        super().__init__()
        self.thesys = ThesysLLM()

    @agent
    def reporting_analyst(self) -> Agent:
        return Agent(
            config=self.agents_config['reporting_analyst'], # type: ignore[index]
            verbose=True,
            llm=self.thesys
    )
4

Rendering the response

Now that we have the reporting_analyst agent using Thesys, we need to render the response. There are multiple ways to go about it but for this guide we will use the Streamlit SDK to render the response.First we need to install the dependencies.
pip install streamlit streamlit-thesys
And then we need to update the main.py file to serve a streamlit app.
src/crewai-genui/main.py
import streamlit as st
import streamlit_thesys as thesys

def streamlit_app():
    # Streamlit app
    st.title("CrewAI Research & Reporting")
    topic = st.text_input("Enter topic:", value="AI LLMs")

    if st.button("Run Analysis"):
        inputs = {
            'topic': topic,
            'current_year': str(datetime.now().year)
        }

        with st.spinner("Running analysis..."):
            try:
                result = CrewaiGenui().crew().kickoff(inputs=inputs)
                thesys.render_response(result.raw if hasattr(result, 'raw') else str(result))
            except Exception as e:
                st.error(f"Error: {e}")

if __name__ == "__main__":
    streamlit_app()
5

Run the app

export THESYS_API_KEY=<your-api-key>
streamlit run main.py
This will start the Streamlit app at http://localhost:8501 and you should be able to run your crewai agent using the UI.

View the code

Find more examples and complete code on our GitHub repository.