feat: solution for '8. Самописный поисковый агент на основе deep agents from scratch'
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# Deep Agent from Scratch
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# Deep Agents from Scratch – LangChain Search Agent
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This repository demonstrates a **Deep Agent** implementation using the **LangChain** library.
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The agent follows the “Deep Agents from Scratch” template and can answer arbitrary questions by leveraging an LLM (OpenAI GPT‑3.5‑Turbo by default). It also showcases how to integrate a simple tool (`Echo`) and use a Planner/Executor pattern for a more realistic agent workflow.
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This project demonstrates a **Deep Agent** built from scratch using **LangChain**.
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The agent can answer user questions by searching the web with DuckDuckGo and
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providing concise, up‑to‑date responses.
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> **Author**: Artur Kuzakhmetov
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> **Course**: Deep Agents from Scratch (Lecture: Perplexity, 09.04.2026)
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> **Deadline**: 31.08.2026
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---
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## Features
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- Implements the **Planner** and **Executor** pattern from the Deep Agents from Scratch template.
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- Uses LangChain’s `OpenAI`, `Tool`, `PromptTemplate`, and `ConversationBufferMemory`.
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- Configurable LLM model, temperature, and token limits.
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- Simple command‑line interface for quick testing.
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- Environment‑variable based configuration for API keys and model selection.
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- Demonstrates tool integration (Echo tool) and the full agent template.
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- **Custom Search Tool** – queries DuckDuckGo’s instant answer API.
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- **Conversation Memory** – keeps context across turns.
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- **REACT Agent** – follows the “Reason → Act → Think” pattern.
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- **CLI** – simple command‑line interface for interactive use.
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- **Unit Tests** – basic tests for the search tool.
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## Prerequisites
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- Node.js 18+ (or any LTS version)
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- An OpenAI API key
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---
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## Setup
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```bash
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# Clone the repository
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git clone https://git.brojs.ru/kuzakhmetovartur/8.-samopisnyy-poiskovyy-agent-na-osnove-
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cd 8.-samopisnyy-poiskovyy-agent-na-osnove-
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1. **Clone the repository**
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# Install dependencies
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npm install
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```
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```bash
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git clone https://git.brojs.ru/kuzakhmetovartur/8.-samopisnyy-poiskovyy-agent-na-osnove-.git
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cd 8.-samopisnyy-poiskovyy-agent-na-osnove-
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```
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Create a `.env` file in the project root:
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2. **Create a virtual environment**
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```dotenv
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OPENAI_API_KEY=your_openai_api_key_here
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OPENAI_MODEL=gpt-3.5-turbo # optional, defaults to gpt-3.5-turbo
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```
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```bash
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python -m venv .venv
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source .venv/bin/activate # On Windows: .venv\Scripts\activate
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```
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> **Tip:** Keep your `.env` file out of version control. Add it to `.gitignore` if you plan to push the repo.
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3. **Install dependencies**
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```bash
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pip install -r requirements.txt
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```
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4. **Set up OpenAI API key**
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Create a `.env` file in the project root:
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```dotenv
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OPENAI_API_KEY=sk-...
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```
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Replace `sk-...` with your actual key.
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---
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## Usage
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Run the agent with a question:
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Run the agent:
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```bash
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npm start -- "What is the tallest mountain in the world?"
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python -m src.index
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```
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Or simply:
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You will see:
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```
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Deep Agents from Scratch - LangChain Search Agent
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Type 'exit' or 'quit' to stop.
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Enter your question:
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```
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Type a question, e.g.:
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```
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What is the capital of France?
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```
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The agent will search the web and return an answer.
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---
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## Running Tests
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```bash
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node src/index.js "Your question here"
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python -m unittest discover -s tests
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```
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The agent will output the answer to the console.
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---
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## Project Structure
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```
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├── package.json # Project metadata and dependencies
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├── src/
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│ ├── deepAgent.js # Core DeepAgent implementation (Planner/Executor)
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│ └── index.js # CLI entry point
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├── src
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│ └── index.py # Main agent implementation
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├── tests
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│ └── test_search_tool.py # Unit tests for the search tool
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├── requirements.txt # Project dependencies
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└── README.md # Documentation
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```
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## Extending the Agent
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---
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- **Add more sophisticated prompts**: Edit the `Planner` prompt in `deepAgent.js`.
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- **Integrate additional tools**: Use LangChain’s `Tool` and add them to the `tools` array.
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- **Switch LLM providers**: Replace `OpenAI` with another LangChain LLM implementation (e.g., `AzureOpenAI`, `Anthropic`).
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## Contributing
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Feel free to fork the repository, create a feature branch, and submit a pull request.
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Please ensure tests pass before merging.
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---
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## License
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MIT © 2026
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---
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MIT License.
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