Building a Chatbot

from crewai import Agent, LLM

llm = LLM(model="openrouter/anthropic/claude-sonnet-4")

chat_agent = Agent(
    role="Helpful Assistant",
    goal="Assist users by answering their questions clearly and helpfully",
    backstory=(
        "You are a friendly and knowledgeable assistant. You provide clear, "
        "concise, and helpful responses to user questions."
    ),
    llm=llm,
    respect_context_window=True,
    verbose=False,
)

from crewai import Agent, LLM

llm = LLM(model="openrouter/anthropic/claude-sonnet-4")

suggest_agent = Agent(
    role="Follow-up Question Generator",
    goal="Generate 3 relevant follow-up questions based on a conversation",
    backstory=(
        "You analyze conversations and suggest exactly 3 brief follow-up "
        "questions the user might want to ask next. Return only the 3 questions, "
        "one per line, without numbering or bullet points."
    ),
    llm=llm,
    respect_context_window=True,
    verbose=False,
)

from crewai.flow.flow import Flow, listen, start
from pydantic import BaseModel

from my_chatbot.agents.chat_agent import chat_agent
from my_chatbot.agents.suggest_agent import suggest_agent


class ChatState(BaseModel):
    query: str = ""
    messages: list[dict] = []
    suggested_questions: list[str] = []


class ChatFlow(Flow[ChatState]):
    @start()
    def chat(self):
        """Add the user query to messages, call the agent, and return the response."""
        user_msg = {"role": "user", "content": self.state.query}
        history = self.state.messages + [user_msg]

        result = chat_agent.kickoff(history)

        assistant_msg = {"role": "assistant", "content": result.raw}
        self.state.messages = self.state.messages + [user_msg, assistant_msg]
        return result.raw

    @listen(chat)
    def suggest(self, chat_response: str):
        """Generate 3 follow-up questions based on the conversation."""
        recent = self.state.messages[-6:]
        conversation = "\n".join(f"{m['role']}: {m['content']}" for m in recent)

        result = suggest_agent.kickoff(
            f"Based on this conversation, suggest exactly 3 brief follow-up questions "
            f"the user might want to ask next. Return only the 3 questions, one per line, "
            f"without numbering or bullet points.\n\n{conversation}"
        )

        questions = [q.strip() for q in result.raw.strip().split("\n") if q.strip()][:3]
        self.state.suggested_questions = questions

        return {
            "messages": self.state.messages,
            "suggested_questions": self.state.suggested_questions,
        }

[deployment]
framework = "crewai"
entrypoint = "my_chatbot.flows.chat_flow:ChatFlow"
python = "3.11"

[chat]
input_key = "query"
output_key = "messages"