Building Production-Ready AI Agents with LangGraph.pdf

Building Production-Ready AI Agents with LangGraph.pdf

• 1.
  Building Production-Ready AIAgents with LangGraph By - Tamanna NextGen_Outlier 1
• 2.
  Overview Introduction What is LangGraph? WhyLangGraph for Production? Real-Life Use Case: Travel Planning Assistant LangGraph Components Implementation Workflow Diagram Sample Output Production-Readiness Tips for Success Conclusion NextGen_Outlier 2
• 3.
  Introduction: What isan AI Agent? AI agents perform tasks autonomously using AI models. Examples: Chatbots, virtual assistants, automated workflows. Challenge: Building agents that are reliable, scalable, and production-ready. Solution: LangGraph, a framework for stateful, cyclic AI workflows. NextGen_Outlier 3
• 4.
  What is LangGraph? Extensionof LangChain for stateful, multi-actor AI agents. Key Features: Stateful workflows: Saves state after each step. Human-in-the-loop: Allows human intervention. Cyclic graphs: Supports loops and conditional logic. Integrates with LangChain and LangSmith for debugging. Scalable for production environments. Analogy: Like a GPS for AI agents, adapting to dynamic conditions. NextGen_Outlier 4
• 5.
  Why LangGraph forProduction? Handles noisy inputs and edge cases. Scales to handle thousands of requests. Provides error recovery and state persistence. Offers observability with LangSmith. Enables modular workflows with nodes and edges. NextGen_Outlier 5
• 6.
  Real-Life Use Case:Travel Planning Assistant Goal: Plan a trip (e.g., "New York to Paris next weekend"). Tasks: Extract details from user input. Fetch flight and hotel options via APIs. Compile and display itinerary. Optionally send itinerary via email. Why LangGraph? Supports multi-step workflows, conditional logic, and human-in-the-loop. NextGen_Outlier 6
• 7.
  LangGraph Components Nodes: Individualtasks (e.g., fetch flights, send email). Edges: Connections between tasks (direct or conditional). State: Shared data structure tracking progress. Graph: Ties nodes and edges into a workflow. Analogy: Nodes as workers, edges as conveyor belts, state as a clipboard. NextGen_Outlier 7
• 8.
  Implementation: Defining theState from typing_extensions import TypedDict class AgentState(TypedDict): user_request: str flight_options: list hotel_options: list itinerary: str send_email: bool NextGen_Outlier 8
• 9.
  Implementation: Key Nodes defprocess_request(state: AgentState) -> Dict: prompt = PromptTemplate( input_variables=["user_request"], template="Extract destination, origin, and dates from: {user_request}" ) response = llm.invoke(prompt.format(user_request=state["user_request"])) return {"user_request": response.content} def fetch_flights(state: AgentState) -> Dict: flight_data = [{"airline": "Air France", "price": "$500", "time": "10:00 AM"}] return {"flight_options": flight_data} NextGen_Outlier 9
• 10.
  Implementation: Building theWorkflow from langgraph.graph import StateGraph, START, END workflow = StateGraph(AgentState) workflow.add_node("process_request", process_request) workflow.add_edge(START, "process_request") workflow.add_edge("process_request", "fetch_flights") workflow.add_edge("fetch_flights", "fetch_hotels") workflow.add_edge("fetch_hotels", "compile_itinerary") workflow.add_edge("compile_itinerary", "ask_email") workflow.add_conditional_edges( "ask_email", lambda state: state.get("send_email", "end"), {"send_email": "send_email", "end": END} ) agent = workflow.compile() NextGen_Outlier 10
• 11.
  Workflow Diagram graph TD A[START]--> B[Process Request] B --> C[Fetch Flights] C --> D[Fetch Hotels] D --> E[Compile Itinerary] E --> F{Ask Email} F -->|send_email| G[Send Email] F -->|end| H[END] G --> H NextGen_Outlier 11
• 12.
  Sample Output Final Itinerary: Flights: [{'airline':'Air France', 'price': '$500', 'time': '10:00 AM'}] Hotels: [{'hotel': 'Paris Inn', 'price': '$150/night'}] Sending email with itinerary: ... NextGen_Outlier 12
• 13.
  Why It’s Production-Ready ErrorHandling: Resumes after failures using state management. Human-in-the-Loop: User approves email step. Modular: Easy to update nodes (e.g., swap APIs). Scalable: Deploy with LangGraph Platform. Observable: Use LangSmith for debugging. NextGen_Outlier 13
• 14.
  Tips for Success Startsimple, then add complexity. Use conditional edges for dynamic workflows. Test edge cases and noisy inputs. Monitor with LangSmith for insights. Include human-in-the-loop for sensitive tasks. NextGen_Outlier 14
• 15.
  Conclusion LangGraph enables robust,scalable AI agents. Travel Planning Assistant demonstrates practical application. Modular nodes, edges, and state management ensure flexibility. Ready to build? Explore LangGraph on GitHub! NextGen_Outlier 15
• 16.
  Thank You!! NextGen_Outlier 16