8.-samopisnyy-poiskovyy-agent-na-osnove-/README.md

Deep Search Agent – LangChain Implementation

This repository contains a minimal implementation of a search agent built with LangChain, following the “Deep Agents from Scratch” template.
The agent can answer arbitrary questions by performing a web search and reasoning over the results.

Features

• Uses OpenAI GPT‑4o‑mini as the language model.
• Performs web searches via SerpAPI (Google/SerpAPI).
• Maintains conversation context with a memory buffer.
• Implements the Zero‑Shot React agent pattern.
• Simple command‑line interface for interactive use.

Prerequisites

• Python 3.10+
• An OpenAI API key.
• A SerpAPI key (free tier available).

Setup

# Clone the repository
git clone https://git.brojs.ru/kuzakhmetovartur/8.-samopisnyy-poiskovyy-agent-na-osnove-<repo>.git
cd <repo>

# Create a virtual environment (optional but recommended)
python -m venv .venv
source .venv/bin/activate   # On Windows: .venv\\Scripts\\activate

# Install dependencies
pip install -r requirements.txt

Create a .env file in the project root with your credentials:

OPENAI_API_KEY=sk-...
SERPAPI_KEY=your-serpapi-key

Usage

Run the agent interactively:

python -m src.agent

You will be prompted to enter a question. The agent will search the web and return a concise answer.

Example

Enter your question: What is the capital of France?
Processing...

=== Answer ===
The capital of France is Paris.

Testing

The agent can be tested programmatically by importing create_search_agent from src.agent and calling agent.run("your question").

License

MIT License