Deep Agents from Scratch – LangChain Search Agent

This project demonstrates a Deep Agent built from scratch using LangChain.
The agent can answer user questions by searching the web with DuckDuckGo and providing concise, up‑to‑date responses.

Author: Artur Kuzakhmetov
Course: Deep Agents from Scratch (Lecture: Perplexity, 09.04.2026)
Deadline: 31.08.2026

Features

Custom Search Tool – queries DuckDuckGo’s instant answer API.
Conversation Memory – keeps context across turns.
REACT Agent – follows the “Reason → Act → Think” pattern.
CLI – simple command‑line interface for interactive use.
Unit Tests – basic tests for the search tool.

Setup

Clone the repository

git clone https://git.brojs.ru/kuzakhmetovartur/8.-samopisnyy-poiskovyy-agent-na-osnove-.git
cd 8.-samopisnyy-poiskovyy-agent-na-osnove-

Create a virtual environment

python -m venv .venv
source .venv/bin/activate   # On Windows: .venv\Scripts\activate

Install dependencies
```
pip install -r requirements.txt
```
Set up OpenAI API key

Create a .env file in the project root:
```
OPENAI_API_KEY=sk-...
```
Replace sk-... with your actual key.

Usage

Run the agent:

python -m src.index

You will see:

Deep Agents from Scratch - LangChain Search Agent
Type 'exit' or 'quit' to stop.

Enter your question:

Type a question, e.g.:

What is the capital of France?

The agent will search the web and return an answer.

Running Tests

python -m unittest discover -s tests

Project Structure

├── src
│   └── index.py          # Main agent implementation
├── tests
│   └── test_search_tool.py  # Unit tests for the search tool
├── requirements.txt      # Project dependencies
└── README.md             # Documentation

Contributing

Feel free to fork the repository, create a feature branch, and submit a pull request.
Please ensure tests pass before merging.

License

MIT License.

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