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# LangGraph research brief generator # LangGraph Comparison Agent
## Overview ## Overview
This repository contains a LangGraph agent that generates a connected research brief on a single topic. The agent: This repository contains a **LangGraph** agent that, given three entities (e.g. vector databases, frameworks, or products), builds a comparative review. The agent:
1. Uses an LLM to generate concise section headings for the brief. 1. **Plans criteria** LLM generates 35 comparison criteria based on the entities.
2. For each heading, performs a Tavily web search to fetch relevant content. 2. **Researches each entitycriterion pair** uses **Tavily** web search to fetch a short note for each pair.
3. Aggregates the sections into a single Markdown brief with a summary. 3. **Builds a Markdown table** rows are criteria, columns are entities.
4. **Produces a verdict** LLM gives a concise recommendation.
The agent is implemented in `src/main.py` and can be run from the command line. The final output is a Markdown table followed by a verdict paragraph.
## Requirements ## Requirements
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pip install -r requirements.txt pip install -r requirements.txt
``` ```
The following environment variables must be set: Environment variables:
- `OPENAI_API_KEY` key for the OpenAI model used by LangChain. - `OPENAI_API_KEY` key for the OpenAI model used by LangChain.
- `TAVILY_API_KEY` key for the Tavily API. - `TAVILY_API_KEY` key for the Tavily API.
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## Usage ## Usage
```bash ```bash
# Default demo generates a brief on "Artificial Intelligence" # Default demo compares Chroma, FAISS, Qdrant
python -m src.main python -m src.main
# Custom topic # Custom entities
python -m src.main "Quantum Computing" python -m src.main "Chroma,FAISS,Qdrant"
``` ```
The script prints the generated brief in Markdown format. The script prints the comparison table and verdict in Markdown format.