Introduction
In today’s fast-paced AI landscape, the focus is shifting from isolated models to multi-agent systems that work together to reason, plan, and act autonomously. Modern frameworks now enable developers to create production-ready agents that can handle complex workflows by integrating diverse components such as local language models, retrieval systems, and communication protocols. This guide offers an in-depth look at how to build next-generation AI agents using a robust framework that synergizes Google’s Agent Development Kit with standardized protocols for tool integration and real-time data retrieval.
Understanding the Framework
The foundation of this approach is a modular, open-source framework designed for building and orchestrating AI agents. By leveraging a model-agnostic architecture, this framework supports multiple large language models and allows developers to seamlessly integrate tools for external data access and real-time interactions. Some core aspects of the system include:
- Simplified Development: The framework brings together deterministic workflows and dynamic routing, reducing complexity and making AI development resemble traditional software engineering.
- Interoperability: With support for various LLMs and toolkits, the platform ensures that agents can interact with external APIs, databases, and even custom tools using standardized protocols.
- Production-Readiness: Built-in evaluation, debugging, and state management features ensure that agents perform reliably even in enterprise environments.
- Real-Time Interactions: Native streaming support paves the way for natural, human-like text, audio, and video interactions.
Core Components and Their Role
The power of these next-generation AI agents comes from the integration of several critical components:
- Agent Development Kit: Provides the essential tools and developer experience to build, test, and deploy multi-agent systems. Its structured design supports complex task orchestration and modular workflows.
- MCP (Model Context Protocol): Standardizes communication between AI models and external services, ensuring agents can access and use various data sources seamlessly.
- RAG (Retrieval-Augmented Generation): Enhances the responses of language models by combining real-time data retrieval with generative capabilities. This integration ensures that the agents provide accurate and contextually relevant answers.
- Ollama: Facilitates the use of local language models to ensure cost efficiency, privacy, and the ability to operate offline when necessary.
Step-by-Step Guide: Building a Multi-Agent Chatbot
Below is a high-level walkthrough for creating a multi-agent chatbot that leverages the integrated stack for real-time query handling and comprehensive response generation.
Step 1: Setting Up Your Environment
Begin by setting up your Python environment and installing the necessary libraries. This process includes creating a virtual environment and defining environment variables for local model interactions.
# Create and activate a virtual environment
python -m venv venv
source venv/bin/activate
# Install dependencies
pip install google-adk mcp-youtube-search litellm fastapi streamlit
# Set environment variables for local model API access
export OLLAMA_API_BASE="http://localhost:11434"
Step 2: Configuring External Tools
Deploy external services using Docker. For instance, run a protocol server and a database container to support retrieval-augmented generation (RAG) pipelines.
# Example docker-compose.yml
version: '3.8'
services:
mcp-toolbox:
image: google/mcp-toolbox:latest
ports:
- "5000:5000"
environment:
- MCP_TRANSPORT=SSE
- MCP_URL=http://localhost:5000/mcp/sse
postgres:
image: postgres:latest
environment:
- POSTGRES_DB=rag_db
- POSTGRES_USER=user
- POSTGRES_PASSWORD=password
ports:
- "5432:5432"
Step 3: Implementing Knowledge Retrieval
Create a retrieval pipeline by indexing documents and setting up a vector store. This step is essential for using RAG to supplement agent responses with contextual information.
from langchain.vectorstores import Chroma
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
def initialize_vectorstore(documents):
splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
chunks = splitter.split_documents(documents)
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
vectorstore = Chroma.from_documents(chunks, embeddings)
return vectorstore
def retrieve_docs(query, vectorstore):
return vectorstore.similarity_search(query, k=3)
Step 4: Defining Your Agent
Integrate the local language model, retrieval system, and protocol tools into an AI agent. The agent is designed to assess queries and leverage multiple components—such as YouTube search and document retrieval—to build comprehensive, informative responses.
from google.adk.agents import LlmAgent
from google.adk.models.lite_llm import LiteLlm
from mcp_youtube_search import YouTubeSearch
youtube_tool = YouTubeSearch()
vectorstore = initialize_vectorstore(documents=[...]) # Insert your documents here
chatbot = LlmAgent(
model=LiteLlm(model="openai/gemma-3:12b"),
name="chatbot",
description="A multi-agent chatbot combining external data tools and local LLM capabilities.",
instruction="""
You are a helpful assistant. For complex queries, use the YouTube search tool and document retrieval to structure your response in three parts:
1. Video Resources,
2. Knowledge Base,
3. Combined Analysis.
""",
tools=[youtube_tool, retrieve_docs],
)
def process_query(query):
response = chatbot.run(query)
return response
Step 5: Deploying the Agent
Expose your agent through a REST API using FastAPI so that it can handle incoming queries programmatically. A simple web UI built with Streamlit allows you to interact and test the agent in real time.
# FastAPI server (server.py)
from fastapi import FastAPI
from agent import process_query
app = FastAPI()
@app.post("/run")
async def run_agent(query: str):
return {"response": process_query(query)}
# Streamlit UI (app.py)
import streamlit as st
import requests
st.title("Multi-Agent Chatbot")
query = st.text_input("Enter your query:")
if st.button("Submit"):
response = requests.post("http://localhost:8000/run", json={"query": query}).json()
st.write(response["response"])
Conclusion
By integrating a robust agent development framework with standardized protocols and retrieval systems, developers can build AI agents that are not only capable of autonomous reasoning, but also of dynamic interaction with real-world data sources. The modular design and comprehensive tooling make it feasible to develop and deploy systems that are adaptable to varied environments—ensuring scalability, performance, and ultimately, a superior user experience. As AI evolves, such integrated approaches will continue to drive innovation, enabling more precise and intelligent solutions in customer support, research, and beyond.
