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

Assignment: Tool Calling & Tavily Search Integration#

Assignment Metadata#

Field

Description

Assignment Name

Tool Calling & Tavily Search Integration

Course

LangGraph and Agentic AI

Project Name

tavily-research-agent

Estimated Time

90 minutes

Framework

Python 3.10+, LangGraph, LangChain, Tavily API, OpenAI API

Learning Objectives#

By completing this assignment, you will be able to:

  • Understand Tool/Function Calling mechanics in LLMs

  • Integrate Tavily Search API for real-time web information

  • Build agents with multiple tools working in parallel

  • Implement error handling and retry logic for tool execution

  • Apply caching and rate limiting for production readiness

Problem Description#

Building on the Multi-Expert Research Agent from Assignment 02, you will add a Web Search tool using Tavily API. This enables the agent to:

  1. Access real-time information beyond LLM knowledge cutoff

  2. Combine expert analysis with current web data

  3. Execute multiple tools in parallel when needed

Technical Requirements#

Environment Setup#

  • Python 3.10 or higher

  • Required packages:

    • langgraph >= 0.2.0

    • langchain >= 0.1.0

    • langchain-community >= 0.1.0

    • tavily-python >= 0.3.0

API Requirements#

  • OpenAI API key (OPENAI_API_KEY)

  • Tavily API key (TAVILY_API_KEY) - Get free at tavily.com

Tasks#

Task 1: Tavily Search Tool Setup (25 points)#

  1. Configure TavilySearchResults tool with:

    • max_results: 5

    • search_depth: “advanced”

    • include_answer: True (for AI-generated summary)

  2. Create a wrapper tool with proper description:

    • Clear description of when to use (current events, real-time data)

    • Specify input format expectations

    • Handle search errors gracefully

Task 2: Multi-Tool Agent Integration (35 points)#

  1. Extend existing Research Agent to include:

    • AI Research Expert (from Assignment 02)

    • Financial Analyst (from Assignment 02)

    • Web Search Tool (new Tavily integration)

  2. Update coordinator prompt to:

    • Advise when to use web search vs expert LLMs

    • Enable parallel tool calling for independent queries

    • Guide synthesis of web results with expert analysis

  3. Ensure parallel execution capability:

    • Coordinator can call multiple tools in single response

    • ToolNode executes all tool_calls and returns all ToolMessages

Task 3: Error Handling & Optimization (25 points)#

  1. Implement retry logic for Tavily API:

    • Use tenacity for exponential backoff

    • Handle rate limits and timeouts gracefully

  2. Add basic caching for search results:

    • Cache identical queries for 1 hour

    • Use hash-based cache keys

  3. Implement rate limiting:

    • Max 10 searches per minute

    • Queue overflow handling

Task 4: Testing & Validation (15 points)#

  1. Test queries requiring web search:

    • “What are the latest AI announcements this week?”

    • “Current NVIDIA stock performance and recent news”

  2. Test combined expert + web search:

    • “Analyze the impact of recent AI regulations on tech companies”

  3. Verify parallel tool execution:

    • Show both expert and search tools called simultaneously

    • Document execution time comparison (parallel vs sequential)

Submission Requirements#

Required Deliverables#

  • Source code in Python script or Jupyter notebook

  • README.md with setup instructions and API key configuration

  • Test output showing web search integration

  • Comparison of response quality with/without web search

Submission Checklist#

  • Tavily tool configured with proper parameters

  • All three tools work together (2 experts + web search)

  • Parallel tool execution demonstrated

  • Error handling implemented for API failures

  • Code runs without errors

Evaluation Criteria#

Criteria

Points

Tavily Search tool setup

25

Multi-tool integration

35

Error handling & optimization

25

Testing & validation

10

Code quality and documentation

5

Total

100

Hints#

  • Use TavilySearchResults from langchain_community.tools.tavily_search

  • For parallel calls, the coordinator returns multiple tool_calls in one AIMessage

  • Store Tavily API key in environment variable, never hardcode

  • Use @retry(stop=stop_after_attempt(3)) from tenacity for retries

References#