Integrating CrewAI with Thesys

Give your CrewAI agents interactive UI components, dynamic workflows, and live dashboards powered by Thesys C1

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.