GOOGLE_GENAI_USE_VERTEXAI=FALSE
GOOGLE_API_KEY=PASTE_YOUR_ACTUAL_API_KEY_HERE

pip install fastapi uvicorn google-adk google-generativeai

import streamlit as st
import requests

st.set_page_config(page_title="Agent Chat", layout="centered")

if "messages" not in st.session_state:
    st.session_state.messages = []

st.title("Multi-Agent Parallel Researcher")

for msg in st.session_state.messages:
    with st.chat_message(msg["role"]):
        st.markdown(msg["content"])

user_query = st.chat_input("Ask to search for real-time data or anything...")

if user_query:
    st.chat_message("user").markdown(user_query)
    st.session_state.messages.append({"role": "user", "content": user_query})

    try:
        response = requests.post("http://localhost:8000/ask", json={"query": user_query})
        response.raise_for_status()
        all_replies = response.json().get("responses", ["No response."])
        for reply in all_replies:
            st.chat_message("assistant").markdown(reply)
            st.session_state.messages.append({"role": "assistant", "content": reply})
    except Exception as e:
        error_msg = f"Error: {str(e)}"
        st.chat_message("assistant").markdown(error_msg)
        st.session_state.messages.append({"role": "assistant", "content": error_msg})

from fastapi import FastAPI
from pydantic import BaseModel
from google.adk.agents import Agent, LlmAgent, SequentialAgent, ParallelAgent
from google.adk.runners import Runner
from google.adk.sessions import InMemorySessionService
from google.adk.memory import InMemoryMemoryService
from google.adk.tools import google_search
import uvicorn

class TopicOutput(BaseModel):
    subtopic_1: str
    subtopic_2: str
    subtopic_3: str

# Định nghĩa các agent
topic_setter = LlmAgent(
    name="TopicSetterAgent",
    model="gemini-2.5-flash",
    instruction="""You are a research planner...""",
    output_schema=TopicOutput,
    output_key="topic_output"
)
researcher_agent_1 = LlmAgent(
    name="SubResearcherOne",
    model="gemini-2.5-flash",
    instruction="""Research subtopic: "{topic_output.subtopic_1}"...""",
    tools=[google_search]
)
# Tương tự researcher_agent_2 và researcher_agent_3

parallel_research_agent = ParallelAgent(
    name="ParallelWebResearchAgent",
    sub_agents=[researcher_agent_1, researcher_agent_2, researcher_agent_3]
)

merger_agent = LlmAgent(
    name="ResearchSynthesizer",
    model="gemini-2.5-flash",
    instruction="""Merge findings..."""
)

research_pipeline = SequentialAgent(
    name="GeneralResearchPipeline",
    sub_agents=[topic_setter, parallel_research_agent, merger_agent]
)

# Khởi tạo server FastAPI, session, memory, runner...

uvicorn agent:app --host 0.0.0.0 --port 8000

python3 -m streamlit run app.py

{
  "subtopic_1": "Definition and architecture of LLM Agents",
  "subtopic_2": "Applications and use cases of LLM Agents",
  "subtopic_3": "Challenges and future directions for LLM Agents"
}