Product Researcher Agent

Product Researcher Agent searches the web for information about user-supplied products and returns structured analysis, comparisons, and recommendations.

🚀 Quickstart with LangGraph server

Set API keys for the LLM of choice (Anthropic is set by default in src/agent/graph.py) and Tavily API:

cp .env.example .env

Clone the repository and launch the assistant using the LangGraph server:

curl -LsSf https://astral.sh/uv/install.sh | sh
git clone https://github.com/j-cunanan/product-researcher.git
cd product-researcher
uvx --refresh --from "langgraph-cli[inmem]" --with-editable . --python 3.11 langgraph dev

How it works

Product Researcher Agent follows a multi-step research and analysis workflow:

• Research Phase: The system performs comprehensive product research:
  • Executes concurrent web searches via Tavily API for:
    1. Product specifications and features
    2. User reviews and feedback
    3. Expert opinions and professional reviews
  • Retrieves up to max_search_results results per search type
• Analysis Phase: After research is complete, the system:
  • Generates a structured comparison table of top products
  • Provides detailed analysis of top options
  • Creates final recommendations and buying advice
• Output Phase: The system delivers three main components:
  • Comparison table with key features, pros, and cons
  • Detailed analysis of top options with market overview
  • Final recommendations including top pick, premium, and budget options

Configuration

The configuration for Product Researcher Agent is defined in the src/agent/configuration.py file:

• max_search_queries: int = 3 # Max search queries per product
• max_search_results: int = 3 # Max search results per query
• comparison_table: bool = True # Whether to include comparison table
• detailed_analysis: bool = True # Whether to include detailed analysis
• final_recommendation: bool = True # Whether to include final recommendation

Inputs

The user inputs are:

* query: str - Product search query
* category: str - Product category (e.g., electronics, outdoors)
* price_range: Optional[str] - Desired price range (defaults to "Any")

Output Format

The system provides structured output in three main sections:

1. Comparison Table: A markdown-formatted table comparing 3-5 top products, including:

   • Model name
   • Price
   • Rating
   • Key features
   • Pros and cons

2. Detailed Analysis:

   • Market overview and trends
   • Analysis of top options
   • Key decision factors
   • Price-performance analysis

3. Final Recommendations:

   • Top recommendation with justification
   • Premium option
   • Budget option
   • Specialized recommendations
   • Usage/buying tips

Evaluation

Prior to engaging in any optimization, it is important to establish a baseline performance. This repository includes:

1. A dataset consisting of a list of companies and the expected structured information to be extracted for each company.
2. An evaluation script that can be used to evaluate the agent on this dataset.

Set up

Make sure you have the LangSmith CLI installed:

pip install langsmith

And set your API key:

export LANGSMITH_API_KEY=<your_langsmith_api_key>
export ANTHROPIC_API_KEY=<your_anthropic_api_key>

Evaluation metric

A score between 0 and 1 is assigned to each extraction result by an LLM model that acts as a judge.

The model assigns the score based on how closely the extracted information matches the expected information.

Get the dataset

Create a new dataset in LangSmith using the code in the eval folder:

python eval/create_dataset.py

Run the evaluation

To run the evaluation, you can use the run_eval.py script in the eval folder. This will create a new experiment in LangSmith for the dataset you created in the previous step.

python eval/run_eval.py --experiment-prefix "My custom prefix" --agent-url http://localhost:2024