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# Deep Agent based on LangGraph & LangChain
# Deep Agent
This repository contains a minimal implementation of a **Deep Agent** that can:
This repository contains a minimal implementation of a **Deep Agent** inspired by the *Deep Agents from Scratch* course. The agent can:
1. Search the web using the Tavily search API.
2. Create virtual files in an inmemory file system.
3. Export all virtual files to the real filesystem.
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.
The agent is built on top of the *LangGraph* framework and uses the *LangChain* tools API.
## Installation
## Setup
```bash
# Create a virtual environment (recommended)
python -m venv .venv
source .venv/bin/activate
# Install dependencies
pip install -r requirements.txt
```
> **Note**: The Tavily API key is required for the web search tool. Set it via the
> environment variable `TAVILY_API_KEY`.
## Usage
```python
from agent import create_agent_executor, vfs
# Create an executor
executor = create_agent_executor()
# Run the agent with a simple prompt
result = executor.invoke({"input": "Find the latest news about Python and create a file called news.txt with the summary."})
print(result)
# After the agent finishes, export the virtual files
vfs.export_to_disk("output")
```
The `output` directory will contain `news.txt` with the content produced by the agent.
## Testing
The project includes a simple test that verifies the existence of the two
required functions:
```bash
python -m unittest discover -s tests
python deep_agent.py "Write a summary of the latest Python release and store it in summary.txt"
```
## License
The agent will perform the task, create virtual files, and export them to `output_files/`.
MIT
## 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.