Update README.md

This commit is contained in:
2026-06-04 19:19:15 +00:00
parent 92dd2fa20b
commit 529d9aa82f
+20 -36
View File
@@ -1,45 +1,29 @@
# Deep Agent
# Deep Agent from Scratch
This repository contains a minimal implementation of a **Deep Agent** inspired by the *Deep Agents from Scratch* course. The agent can:
This repository contains a minimal implementation of a deep agent that can:
1. Search the web (DuckDuckGo).
2. Create, read and combine virtual files.
3. Export the virtual files to the real filesystem.
4. Use LangChain + Ollama for LLM inference.
1. Search the web using DuckDuckGo.
2. Create virtual files in memory.
3. Export the virtual files to the real file system.
## Setup
The agent is built using the new LangChain 1.x and LangGraph 1.x APIs.
## Prerequisites
- Python 3.11 or newer
- Ollama running locally with a model such as `llama3.1`
- `pip install -r requirements.txt`
## Running the Agent
```bash
# Install dependencies
pip install -r requirements.txt
python agent.py
```
## Usage
The script will run the agent with a sample prompt, create a virtual file `report.txt`, and export it to the `exported_files` directory.
```bash
python deep_agent.py "Write a summary of the latest Python release and store it in summary.txt"
```
## File Structure
The agent will perform the task, create virtual files, and export them to `output_files/`.
## Architecture
- `DeepAgent` class implements the agent logic.
- `search` uses DuckDuckGo HTML API.
- `write_file`, `read_file`, `combine` manage a simple inmemory virtual filesystem.
- `export_files` writes the virtual files to disk.
- The agent loop uses a JSONbased action protocol.
## Requirements
- Python 3.10+
- LangChain >= 1.0.0
- LangGraph >= 1.0.0
- langchain_ollama
- langchain_text_splitters
- langchain_chroma
- requests
---
Feel free to extend the agent with more actions or integrate other LLM providers.
- `agent.py` Main implementation.
- `requirements.txt` Python dependencies.
- `README.md` This file.