Module 1 · AI
📖 3 min read · By ['[“HungHM15”]']

Assignment: Multi-Expert ReAct Research Agent#

Assignment Metadata#

Field

Description

Assignment Name

Multi-Expert ReAct Research Agent

Course

LangGraph and Agentic AI

Project Name

react-research-agent

Estimated Time

120 minutes

Framework

Python 3.10+, LangGraph, LangChain, OpenAI API

Learning Objectives#

By completing this assignment, you will be able to:

  • Understand the ReAct pattern (Reasoning + Acting) for agent design

  • Implement LLM-based expert tools instead of simple web search

  • Use LangGraph’s prebuilt ToolNode for tool execution

  • Apply iteration control to prevent infinite loops

  • Design coordinator prompts that enable structured reasoning

Problem Description#

You are building a research agent that can consult multiple domain experts to answer complex questions. Instead of using web search, the agent uses specialized LLM experts:

  • AI Research Expert: Specializes in ML papers, architectures, trends

  • Financial Analyst: Specializes in stocks, markets, valuations

The coordinator LLM uses ReAct pattern to decide which expert(s) to consult and synthesize their responses.

Technical Requirements#

Environment Setup#

  • Python 3.10 or higher

  • Required packages:

    • langgraph >= 0.2.0

    • langchain >= 0.1.0

    • langchain-openai >= 0.1.0

API Requirements#

  • OpenAI API key configured as environment variable

Tasks#

Task 1: Expert Tool Definition (25 points)#

  1. Create AI Research Expert tool that:

    • Uses a specialized system prompt for ML/AI topics

    • Invokes an LLM with research-focused instructions

    • Returns structured analysis with paper-style references

  2. Create Financial Analyst tool that:

    • Uses a specialized system prompt for market analysis

    • Provides data-driven insights with market context

    • Returns actionable financial information

Task 2: Coordinator Implementation (30 points)#

  1. Define ResearchState with:

    • messages: For conversation history

    • max_iterations: Limit for ReAct loops (default: 5)

    • current_iteration: Tracking current loop count

  2. Implement coordinator_node that:

    • Binds expert tools to the coordinator LLM

    • Includes ReAct reasoning in the prompt

    • Increments iteration counter on each call

  3. Create routing function (should_continue) that:

    • Checks for tool_calls in the last message

    • Enforces max_iterations limit

    • Routes to “tools” or “end” appropriately

Task 3: Graph Construction (25 points)#

  1. Use prebuilt ToolNode for tool execution (NOT custom implementation)

  2. Build the graph with:

    • Coordinator node as entry point

    • ToolNode for expert execution

    • Conditional edges for ReAct loop

    • Edge from tools back to coordinator

  3. Configure checkpointer for state persistence

Task 4: Testing Multi-Step Reasoning (20 points)#

  1. Test with complex queries requiring multiple experts:

    • “How are AI companies valued in the current market?”

    • “Compare NVIDIA vs AMD for AI workloads - technical and market position”

  2. Verify ReAct flow:

    • Iteration 1: First expert consultation

    • Iteration 2+: Additional consultations or synthesis

    • Final: Comprehensive answer combining perspectives

  3. Document the reasoning chain showing Think → Act → Observe steps

Submission Requirements#

Required Deliverables#

  • Source code in Python script or Jupyter notebook

  • README.md with setup instructions

  • Test output showing multi-step ReAct reasoning

  • Graph visualization

Submission Checklist#

  • Both expert tools implemented with specialized prompts

  • ToolNode from langgraph.prebuilt is used (not custom)

  • Iteration control prevents infinite loops

  • Coordinator successfully synthesizes multi-expert input

  • Code runs without errors

Evaluation Criteria#

Criteria

Points

Expert tool implementation

25

Coordinator with ReAct prompt

30

Graph construction with ToolNode

25

Multi-step reasoning tests

15

Code quality and documentation

5

Total

100

Hints#

  • Use llm.bind_tools([tool1, tool2]) to enable tool calling for the coordinator

  • The prebuilt ToolNode(tools) handles parsing, execution, and ToolMessage creation

  • Include few-shot examples in coordinator prompt to guide tool selection

  • Set temperature=0.3 for expert LLMs for consistent responses

References#