Multi-Agent LangGraph News QA Assistant

Overview

This project is a multi-agent AI assistant built using LangGraph framework. It demonstrates how multiple agents can collaborate to extract topics from user queries, fetch relevant news, and generate concise summaries. The system is implemented in Python and leverages HuggingFace Transformers, LangGraph, and custom tools for a robust workflow.

The assistant currently runs as a console-based application, demonstrating multi-agent orchestration without requiring a UI.

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Key Features

Intent Detection

• Agent: intent_agent
• Extracts a clear topic keyword from the user’s query using a local LLM (FLAN-T5-small).
• Ensures structured input for downstream agents.

News Retrieval

• Agent: news_agent
• Fetches the top relevant news articles for the detected topic.
• Uses a custom tool get_news to retrieve articles dynamically.

Summarization

• Agent: summarizer_agent
• Summarizes each article clearly and concisely.
• Outputs structured summaries including title, brief summary, and URL.

Multi-Agent Orchestration

• Implements a StateGraph to connect agents:

  • Intent → News → Summarizer

• Agents communicate via a typed state dictionary (NewsState) to maintain structured data flow.

• Console outputs track workflow progress, providing feedback for each step.

Robustness

• Automatically detects GPU availability for faster model inference.
• Uses HuggingFace pipelines for efficient text-to-text generation.

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Technical Stack

• Python 3.x

• Transformers & HuggingFace Pipeline: FLAN-T5-small for local LLM inference

• LangGraph: Prebuilt REACT agents & StateGraph orchestration

• Custom Tools:

  • detect_topic_llm: Topic extraction
  • get_news: Fetch relevant news
  • summarize_articles: Article summarization

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How It Works

1️⃣ Model Initialization

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
from langchain_huggingface import ChatHuggingFace
from langchain_huggingface.llms import HuggingFacePipeline

device = 0 if torch.cuda.is_available() else -1
model_name = "google/flan-t5-small"
tokenizer = AutoTokenizer.from_pretrained(model_name)
base_model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
pipe = pipeline("text2text-generation", model=base_model, tokenizer=tokenizer, device=device)
llm_pipeline = HuggingFacePipeline(pipeline=pipe)
local_llm = ChatHuggingFace(llm=llm_pipeline)

2️⃣ Define State Structure

from typing import TypedDict, List

class NewsState(TypedDict):
    user_input: str
    topic: str
    articles: List[dict]
    summaries: List[dict]

3️⃣ Create REACT Agents

• Intent Agent: extracts topic keywords
• News Agent: retrieves news for the topic
• Summarizer Agent: generates concise summaries

intent_agent = create_react_agent(model=local_llm, tools=[detect_topic_llm], prompt="Extract topic keyword")
news_agent = create_react_agent(model=local_llm, tools=[get_news], prompt="Fetch relevant news")
summarizer_agent = create_react_agent(model=local_llm, tools=[summarize_articles], prompt="Summarize articles")

4️⃣ Build StateGraph Workflow

graph = StateGraph(NewsState)

graph.add_node(intent_node)
graph.add_node(news_node)
graph.add_node(summarizer_node)

graph.add_edge("intent_node", "news_node")
graph.add_edge("news_node", "summarizer_node")

graph.set_entry_point("intent_node")
graph.set_finish_point("summarizer_node")

5️⃣ Run Workflow

initial_state = NewsState(user_input="Get me the latest news about Netflix", topic="", articles=[], summaries=[])
compiled_graph = graph.compile()
final_state = compiled_graph.invoke(initial_state)

Multi-Agent LangGraph News QA Assistant

Project Repository / Source Code

The complete working code for this Multi-Agent LangGraph News QA Assistant can be accessed on GitHub:

GitHub Repository