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# Deep Agent
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# Deep Agent from Scratch
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This repository contains a minimal implementation of a **Deep Agent** inspired by the *Deep Agents from Scratch* course. The agent can:
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This repository contains a minimal implementation of a deep agent that can:
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1. Search the web (DuckDuckGo).
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1. Search the web using DuckDuckGo.
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2. Create, read and combine virtual files.
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2. Create virtual files in memory.
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3. Export the virtual files to the real filesystem.
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3. Export the virtual files to the real file system.
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4. Use LangChain + Ollama for LLM inference.
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## Setup
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The agent is built using the new LangChain 1.x and LangGraph 1.x APIs.
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## Prerequisites
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- Python 3.11 or newer
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- Ollama running locally with a model such as `llama3.1`
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- `pip install -r requirements.txt`
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## Running the Agent
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```bash
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```bash
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# Install dependencies
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python agent.py
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pip install -r requirements.txt
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```
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```
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## Usage
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The script will run the agent with a sample prompt, create a virtual file `report.txt`, and export it to the `exported_files` directory.
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```bash
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## File Structure
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python deep_agent.py "Write a summary of the latest Python release and store it in summary.txt"
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```
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The agent will perform the task, create virtual files, and export them to `output_files/`.
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- `agent.py` – Main implementation.
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- `requirements.txt` – Python dependencies.
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## Architecture
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- `README.md` – This file.
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- `DeepAgent` class implements the agent logic.
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- `search` uses DuckDuckGo HTML API.
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- `write_file`, `read_file`, `combine` manage a simple in‑memory virtual filesystem.
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- `export_files` writes the virtual files to disk.
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- The agent loop uses a JSON‑based action protocol.
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## Requirements
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- Python 3.10+
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- LangChain >= 1.0.0
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- LangGraph >= 1.0.0
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- langchain_ollama
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- langchain_text_splitters
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- langchain_chroma
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- requests
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---
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Feel free to extend the agent with more actions or integrate other LLM providers.
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