Multi-Agent Conversational AI App Using LangGraph

1. Introduction

Background

This document outlines the engineering design and implementation plan for a multi-agent conversational AI system using LangGraph.

The system is intended to support three primary use cases:

1. Consultation & Q&A:?Answer factual inquiries about company services.
2. Ideation & Brainstorming:?Generate creative ideas based on user prompts.
3. Planning & Scheduling:?Provide structured planning assistance, such as travel itineraries or event schedules.

Objectives

• Modularity & Flexibility:?Use LangGraph’s multi-agent framework to dynamically route queries to the appropriate specialized agents.
• Scalability:?Support high-concurrency and parallel agent execution for fast response times.
• Adaptability:?Allow the addition of new domains (e.g., restaurant recommendations) with minimal changes.
• Reliability & Safety:?Implement response sanitation to ensure accuracy, safety, and compliance with company standards.

2. System Architecture Overview

The system is structured as a?multi-agent workflow, where each agent specializes in a specific function. LangGraph orchestrates the flow, ensuring efficient query processing.

Earlier I proposed the following solution, which is a single pipeline solution: Building an Enterprise-grade Conversational AI Platform

The?single-pipeline solution?follows a?linear workflow, where all queries pass through a?fixed sequence?(NLU → RAG → LLM → Sanitation), making it?simpler to implement?but?less flexible and scalable. In contrast, the?multi-agentic LangGraph solution?enables?dynamic routing?and?parallel processing, where specialized agents handle different query types (Consultation, Ideation, Planning) independently, ensuring?faster responses, richer context retrieval, and easier extensibility. While the single-pipeline approach is suitable for?basic Q&A bots, LangGraph's?modular, adaptable, and scalable?architecture is?ideal for enterprise AI assistants?handling?diverse, complex queries.

2.1 High-Level Architecture

2.2 Key Components & Responsibilities

Intent & Domain Analysis Agent

• Classifies the query into Consultation, Ideation, or Planning using an NLP model.
• Extracts metadata (e.g., topic, category, key entities) for further processing.

Routing & Orchestration Agent

• Directs queries to the appropriate specialized agent based on the extracted intent.
• Uses LangGraph to dynamically modify routing logic as needed.

Consultation & Q&A Agent

• Fetches knowledge base content for factual inquiries.
• Uses Retrieval-Augmented Generation (RAG) techniques for up-to-date responses.

Ideation & Brainstorming Agent

• Generates creative responses based on prompts.
• Uses a curated dataset of best practices and examples.

Planning & Scheduling Agent

• Retrieves relevant structured data for scheduling (e.g., weather forecasts, venue availability).
• Integrates with external APIs for real-time information (e.g., travel APIs, event databases).

Context Aggregation Agent

• Collects data from various sources and formats it for the Response Generation Agent.

Response Generation Agent

• Calls an LLM (GPT-4 or equivalent) with retrieved context to generate a final response.

Sanitation Module

• Ensures generated content is safe, accurate, and compliant.
• Uses filters for harmful content, bias mitigation, and factual verification.

Response Delivery

Formats and sends the final response back to the Communication App UI.

3. Implementation Using LangGraph

3.1 Install Dependencies

pip install langgraph langchain-openai        

3.2 Code Implementation

Initialize the Language Model

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4")        

Define Agents

from langgraph.graph import StateGraph
from langchain_core.messages import HumanMessage
from typing import Dict

def intent_analysis_agent(state: Dict):
    user_query = state["user_query"]
    if "strengths" in user_query:
        state["intent"] = "consultation"
    elif "ideas" in user_query:
        state["intent"] = "ideation"
    elif "plan" in user_query:
        state["intent"] = "planning"
    else:
        state["intent"] = "general"
    return state

def routing_agent(state: Dict):
    intent = state["intent"]
    state["next_agent"] = {"consultation": consultation_agent,
                            "ideation": ideation_agent,
                            "planning": planning_agent}.get(intent, general_fallback_agent)
    return state

def consultation_agent(state: Dict):
    context = "Company strengths: Fast, AI-driven solutions."
    state["response"] = llm.invoke([HumanMessage(content=f"Context: {context}\nUser: {state['user_query']}")]).content
    return state

def ideation_agent(state: Dict):
    context = "Examples of successful brainstorming ideas: Trivia, Charades."
    state["response"] = llm.invoke([HumanMessage(content=f"Context: {context}\nUser: {state['user_query']}")]).content
    return state

def planning_agent(state: Dict):
    context = "Kyoto itinerary: Visit Fushimi Inari, explore Kiyomizu-dera, have lunch at Nishiki Market."
    state["response"] = llm.invoke([HumanMessage(content=f"Context: {context}\nUser: {state['user_query']}")]).content
    return state

def sanitation_agent(state: Dict):
    if "not allowed" in state["response"]:
        state["response"] = "Sorry, I cannot provide that information."
    return state        

Configure LangGraph Workflow

workflow = StateGraph(Dict)
workflow.add_node("intent_analysis", intent_analysis_agent)
workflow.add_node("routing", routing_agent)
workflow.add_node("consultation", consultation_agent)
workflow.add_node("ideation", ideation_agent)
workflow.add_node("planning", planning_agent)
workflow.add_node("sanitation", sanitation_agent)
workflow.add_edge("intent_analysis", "routing")
workflow.add_conditional_edges("routing", lambda state: state["next_agent"], {consultation_agent: "consultation", ideation_agent: "ideation", planning_agent: "planning"})
workflow.add_edge("consultation", "sanitation")
workflow.add_edge("ideation", "sanitation")
workflow.add_edge("planning", "sanitation")
workflow.set_entry_point("intent_analysis")
workflow.set_finish_point("sanitation")
app = workflow.compile()        

4. Deployment Strategy

• Deploy agents as?containerized microservices?in Kubernetes.
• Monitor performance using?Prometheus & Grafana.
• Use?A/B testing?to improve response accuracy over time.

5. Conclusion

This?LangGraph-based multi-agent solution?ensures flexibility, scalability, and safety, making it an ideal conversational AI for high-demand enterprise applications.