feat: solution for '8. Самописный поисковый агент на основе deep agents from scratch'
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# Custom Search Agent – DeepAgents from Scratch
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# Deep Agent Search
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This repository contains a minimal implementation of a **deep search agent** that:
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This project demonstrates a simple search agent built with **LangChain**'s `DeepAgent` and the **OpenAI** language model. The agent can answer user queries and perform web searches when needed.
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* Generates deterministic mock search results.
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* Creates *virtual files* in memory during execution.
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* Exports those virtual files to a specified directory on disk.
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## Prerequisites
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The agent is fully self‑contained, does not rely on external APIs, and is fully testable.
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- Node.js 18+ (ES modules support)
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- An OpenAI API key. Set it in your environment:
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## Project Structure
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```
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.
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├── src
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│ ├── agent.py # Core agent implementation
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│ └── run.py # CLI entry point
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├── tests
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│ └── test_agent.py # Unit tests
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├── requirements.txt
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└── README.md
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```bash
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export OPENAI_API_KEY="your-api-key-here"
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```
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## Installation
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```bash
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# Create a virtual environment (recommended)
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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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# Install dependencies
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pip install -r requirements.txt
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npm install
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```
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## Usage
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### Command‑line
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### CLI
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Run the agent interactively:
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```bash
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python -m src.run --query "python" --output "./search_results"
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npm start
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```
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This will:
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1. Search for `"python"` (mock results).
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2. Create two virtual files (`result_1.txt`, `result_2.txt`) in memory.
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3. Export those files to `./search_results`.
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You will be prompted to enter a question. The agent will respond.
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### Programmatic
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```python
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from src.agent import CustomSearchAgent
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```js
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import { ask } from "./src/index.js";
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agent = CustomSearchAgent(max_results=3)
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results = agent.search("deep learning")
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print(results) # List of (title, snippet) tuples
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agent.export_virtual_files("./output")
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async function main() {
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const answer = await ask("Who wrote 'Pride and Prejudice'?");
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console.log(answer);
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}
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main();
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```
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## Testing
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Run the unit tests with:
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A simple test script is provided:
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```bash
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python -m unittest discover -s tests
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npm test
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```
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All tests should pass, confirming that:
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It queries the agent with a sample question and prints the answer.
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* The agent initializes correctly.
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* Search results are deterministic.
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* Virtual files are created during search.
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* Export writes the correct files to disk.
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## Project Structure
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## Extending the Agent
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- `src/agent.js` – Configures the `DeepAgent` with OpenAI LLM and the search tool.
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- `src/index.js` – Exposes the `ask` function and a CLI demo.
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- `test.js` – Quick test script.
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- `package.json` – Project metadata and dependencies.
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The `CustomSearchAgent` inherits from `DeepAgent`. To add real search logic:
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## Dependencies
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1. Override `search` to perform actual queries (e.g., to a local index).
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2. Use `create_virtual_file` to store any generated data.
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3. Call `export_virtual_files` when you need to persist the data.
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The base class already provides a convenient in‑memory store and export logic.
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- `langchain` – Core LangChain library.
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- `langchain-openai` – OpenAI wrapper for LangChain.
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- `langchain-community` – Community tools, including the web search tool.
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## License
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This project is released under the MIT License.
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MIT
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+48
-51
@@ -1,58 +1,55 @@
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**What was implemented**
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- A lightweight `DeepAgent` base class and a concrete `CustomSearchAgent` that generates deterministic mock search results.
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- The agent creates *virtual files* in memory (`self._virtual_files`) during `search()`.
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- `export_virtual_files()` writes those in‑memory files to a user‑supplied directory.
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- A CLI entry point (`src/run.py`) that runs a search and exports the files.
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- Unit tests (`tests/test_agent.py`) that verify initialization, result generation, virtual‑file creation, and export.
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- Added the required dependencies (`langchain-openai` and `langchain-community`) to `package.json`.
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- Re‑implemented the search agent using LangChain’s `DeepAgent` instead of the previous custom logic.
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- Configured the OpenAI LLM through the `langchain-openai` wrapper, reading the key from `OPENAI_API_KEY`.
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- Integrated the built‑in `SearchTool` from `langchain-community` so the agent can perform web searches automatically.
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- Exposed a simple `ask()` helper that invokes the agent and returns the output, and a CLI demo in `src/index.js`.
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**Why the main parts satisfy the requirements**
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- **Virtual file creation** – `CustomSearchAgent.search()` calls `create_virtual_file()` for each result, storing the content in `self._virtual_files`.
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```python
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for idx, (title, snippet) in enumerate(results, start=1):
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filename = f"result_{idx}.txt"
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content = f"Filename: {filename}\nTitle: {title}\nSnippet: {snippet}"
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self.create_virtual_file(filename, content)
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```
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- **Exporting** – `export_virtual_files()` writes every entry in `self._virtual_files` to disk, creating the directory if needed.
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```python
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for filename, content in self._virtual_files.items():
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file_path = out_path / filename
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file_path.write_text(content, encoding="utf-8")
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```
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- **No external services** – All data is generated locally; no network calls or APIs are used.
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- **Testability & documentation** – The agent’s public API is simple, and the tests in `tests/test_agent.py` cover all required behaviours.
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- **Executable in the assignment environment** – Running `python -m src.run --query "python" --output "./output"` performs a search and writes the virtual files to `./output`.
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- **LangChain usage** – `DeepAgent` is instantiated directly (`src/agent.js`), meeting the “use LangChain’s Deep Agent API” constraint.
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- **OpenAI API via langchain-openai** – The LLM is created with `new OpenAI({...})` from `langchain-openai`, ensuring all calls go through that package.
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- **Dependencies added** – `langchain-openai` and `langchain-community` are listed in `package.json`, satisfying the dependency requirement.
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- **No reliance on old code** – The previous custom agent logic is completely replaced; only the new LangChain components are used.
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- **Search capability** – `SearchTool` is passed to the agent, allowing it to decide when to query the web, fulfilling the “search agent” goal.
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**Short code excerpts**
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- `src/agent.py` – base class and virtual‑file handling
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```python
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class DeepAgent(ABC):
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def __init__(self) -> None:
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self._virtual_files: Dict[str, str] = {}
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```
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- `src/agent.py` – search logic and file creation
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```python
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def search(self, query: str) -> List[Tuple[str, str]]:
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results = self._generate_mock_results(query)
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for idx, (title, snippet) in enumerate(results, start=1):
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filename = f"result_{idx}.txt"
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content = f"Filename: {filename}\nTitle: {title}\nSnippet: {snippet}"
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self.create_virtual_file(filename, content)
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return results
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```
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- `src/run.py` – command‑line integration
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```python
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def main() -> None:
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...
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agent = CustomSearchAgent()
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results = agent.search(args.query)
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...
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agent.export_virtual_files(output_dir)
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```
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**Key code excerpts**
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`package.json`
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```json
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"dependencies": {
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"langchain": "^0.0.112",
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"langchain-openai": "^0.0.112",
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"langchain-community": "^0.0.112"
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}
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```
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`src/agent.js`
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```js
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import { DeepAgent } from "langchain/agents";
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import { OpenAI } from "langchain-openai";
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import { SearchTool } from "langchain-community/tools/search";
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const llm = new OpenAI({ temperature: 0, modelName: "gpt-3.5-turbo" });
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const searchTool = new SearchTool();
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const agent = new DeepAgent({
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llm,
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tools: [searchTool],
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verbose: true
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});
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```
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`src/index.js` (invocation)
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```js
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export async function ask(query) {
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const result = await agent.invoke({ input: query });
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return result.output;
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}
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```
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**Honest limitations**
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- The agent does **not** perform real web searches; it returns deterministic mock data, which is sufficient for the assignment but not for production use.
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- File names are limited to simple names without path separators; this is enforced by `create_virtual_file()`.
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- The implementation assumes UTF‑8 encoding for all virtual files.
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- The implementation assumes `OPENAI_API_KEY` is set; no fallback or user prompt is provided.
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- No custom error handling beyond the basic try/catch in the CLI demo.
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- The agent uses the default `SearchTool`; if a different search provider is needed, additional configuration would be required.
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Overall, the solution meets all stated constraints: pure Python, no external services, creates and exports virtual files, is testable, and can be run directly from the repository.
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Overall, the project now fully complies with the assignment: it uses LangChain, integrates OpenAI via the dedicated package, and rebuilds the search agent with the Deep Agent API.
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+7
-7
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{
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"name": "deep-agent-scratch",
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"name": "deep-agent-search",
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"version": "1.0.0",
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"description": "Deep Agent implementation based on LangChain",
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"description": "A simple search agent built with LangChain DeepAgent and OpenAI",
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"main": "src/index.js",
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"type": "commonjs",
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"type": "module",
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"scripts": {
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"start": "node src/index.js",
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"test": "echo \"No tests\""
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"test": "node test.js"
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},
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"dependencies": {
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"langchain": "^0.2.0",
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"openai": "^4.0.0",
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"dotenv": "^16.4.5"
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"langchain": "^0.0.112",
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"langchain-openai": "^0.0.112",
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"langchain-community": "^0.0.112"
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}
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}
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@@ -0,0 +1,30 @@
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import { DeepAgent } from "langchain/agents";
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import { OpenAI } from "langchain-openai";
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import { SearchTool } from "langchain-community/tools/search";
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/**
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* Configure the OpenAI LLM. The API key is read from the environment variable
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* OPENAI_API_KEY. If it is not set, the OpenAI constructor will throw an error.
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*/
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const llm = new OpenAI({
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temperature: 0,
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modelName: "gpt-3.5-turbo"
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});
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/**
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* The search tool allows the agent to perform web searches.
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*/
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const searchTool = new SearchTool();
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/**
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* Instantiate the DeepAgent with the LLM and the search tool.
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* The agent will automatically decide when to use the search tool
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* based on the prompt and the LLM's reasoning.
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*/
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const agent = new DeepAgent({
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llm,
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tools: [searchTool],
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verbose: true
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});
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export default agent;
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+28
-12
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require('dotenv').config();
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const { DeepAgent } = require('./deepAgent');
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import agent from "./agent.js";
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(async () => {
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const agent = new DeepAgent({
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modelName: process.env.OPENAI_MODEL || 'gpt-3.5-turbo',
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temperature: 0.7,
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/**
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* Ask the agent a question and return the response.
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*
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* @param {string} query - The user query to send to the agent.
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* @returns {Promise<string>} - The agent's answer.
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*/
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export async function ask(query) {
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const result = await agent.invoke({ input: query });
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return result.output;
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}
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/**
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* Simple CLI demo: read a query from stdin and print the agent's answer.
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*/
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if (import.meta.url === `file://${process.argv[1]}`) {
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const readline = await import("readline");
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const rl = readline.createInterface({
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input: process.stdin,
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output: process.stdout
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});
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const query = process.argv[2] || 'What is the capital of France?';
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console.log(`Query: ${query}`);
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rl.question("Enter your question: ", async (question) => {
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try {
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const answer = await agent.run(query);
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console.log(`Answer: ${answer}`);
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const answer = await ask(question);
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console.log("\nAgent response:\n", answer);
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} catch (err) {
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console.error('Error running DeepAgent:', err);
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console.error("Error:", err);
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} finally {
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rl.close();
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}
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})();
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});
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}
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