https://github.com/ryaneggz/langgraph-sandbox
🦜🤖 LangGraph Sandbox for multi-agent AI workflows — with LinkedIn lead collector, Airtable sync, web search 🔍, VS Code ⚡, and Docker 🐳 support.
https://github.com/ryaneggz/langgraph-sandbox
agent-framework ai-agents automation chatbot deepagents langchain langgraph llm multi-agent ollama openai python
Last synced: 2 months ago
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🦜🤖 LangGraph Sandbox for multi-agent AI workflows — with LinkedIn lead collector, Airtable sync, web search 🔍, VS Code ⚡, and Docker 🐳 support.
- Host: GitHub
- URL: https://github.com/ryaneggz/langgraph-sandbox
- Owner: ryaneggz
- License: mit
- Created: 2025-08-23T02:00:45.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-08-28T06:05:14.000Z (10 months ago)
- Last Synced: 2025-08-28T12:49:12.178Z (10 months ago)
- Topics: agent-framework, ai-agents, automation, chatbot, deepagents, langchain, langgraph, llm, multi-agent, ollama, openai, python
- Language: Python
- Homepage:
- Size: 7.15 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Security: security/auth.py
Awesome Lists containing this project
README
# LangGraph Starter
A LangGraph-based starter template for building **collections of AI agents**. This template provides a foundation for creating multi-agent systems with capabilities like LinkedIn lead collection, web search, and Airtable data integration.
---
## ✨ Features
- Collection of configurable AI agents using **DeepAgents** and **LangGraph**
- Agent entrypoints defined in `langgraph.json` for easy management
- Example: **LinkedIn Lead Collector** agent
- **Airtable** integration for structured data storage
- Built-in **web search** tools
- **VS Code** debugging configuration included
- **Docker**-ready for containerized development & deployment
---
## 🚀 Quick Start
### Prerequisites
- **Python 3.11+**
- **Docker** (optional, for containerized deployment)
### Installation
1. **Clone the repository**
```bash
git clone langgraph-starter
cd langgraph-starter
```
2. **Install dependencies with uv (recommended)**
```bash
uv venv
source .venv/bin/activate
uv sync
```
3. **Set up environment variables**
```bash
cp .env.example .env
# Edit .env with your API keys and configuration
```
4. **Start the development server**
```bash
make dev
```
### MCP Server
This project includes an **MCP (Model Context Protocol) server** for enhanced AI integrations.
**Start the MCP server:**
```bash
python mcp
```
---
## 🖥️ Recommended LangGraph UIs
* **[LangGraph Studio](https://smith.langchain.com/studio)** — Official Studio for visual graph editing & monitoring
* **[Agent Chat UI](https://agentchat.vercel.app)** — Clean chat interface for testing your agents
---
## 🛠️ Development & Debugging
* **`.vscode/launch.json`** is pre-configured for:
* **Attach to LangGraph** — Debug a running server on port `5678`
* **Debug Script** — Run and debug individual scripts
**Steps:**
1. Run:
```bash
make debug
```
2. In VS Code, press **F5** and select **Attach to LangGraph**.
---
## 🗂️ Project Structure
```text
langgraph-starter/
├─ agents/
│ └─ linkedin_leads.py # LinkedIn lead collection agent (example)
├─ tools/
│ ├─ __init__.py
│ ├─ airtable.py # Airtable integration tools
│ └─ search.py # Web search tools
├─ .vscode/
│ └─ launch.json # VS Code debug config
├─ docker-compose.yml # Docker services
├─ langgraph.json # LangGraph configuration
├─ Makefile # Dev commands
├─ pyproject.toml # Python project configuration
└─ .env.example # Example environment variables
```
---
## 🧰 Commands
### Make targets
```bash
make dev # Start development server
make debug # Start with debugging enabled (port 5678)
make build # Build and push Docker image
```
### Using the LangGraph CLI directly
```bash
langgraph dev # Start dev server
langgraph dev --debug-port 5678 # Start with debugging
langgraph build # Build the application
```
---
## 🧠 How It Works
### Agent Architecture
* **Modular Design** — Each agent is a separate module under `agents/`
* **LangGraph Integration** — Agents and entrypoints are declared in `langgraph.json`
* **Tooling** — Shared tools under `tools/` (e.g., Airtable, search) can be reused
* **State & Orchestration** — LangGraph coordinates agent state and message flow
### Included Example Agents
1. **LinkedIn Lead Collector** — Searches LinkedIn for potential leads (example logic)
2. **Note Taker** — Normalizes and organizes collected data
3. **Airtable Integration** — Persists structured results to Airtable
### Adding a New Agent
1. Create a new file under `agents/` (e.g., `my_agent.py`)
2. Register it in `langgraph.json` (entrypoints, graph configuration)
3. Wire up any shared tools you need from `tools/`
---
## ⚙️ Configuration
Key files:
* `langgraph.json` — Graphs and entrypoints
* `.env` — Environment variables and API keys
* `pyproject.toml` — Python dependencies and project metadata
* `.vscode/launch.json` — VS Code debugging configuration
> Tip: Ensure any secrets (API keys, tokens) are only in `.env` and **excluded** from version control.
---
## 🐳 Docker (optional)
Build and run with Docker:
```bash
docker compose up --build
```
Mount local files (e.g., `/files`) via `docker-compose.yml` volumes for read/write access.
---
## 🤝 Contributing
1. Fork the repo
2. Create a feature branch:
```bash
git checkout -b feature/amazing-feature
```
3. Commit:
```bash
git commit -m "Add amazing feature"
```
4. Push:
```bash
git push origin feature/amazing-feature
```
5. Open a Pull Request
---
## 📄 License
This project is licensed under the terms specified in the **LICENSE** file.
```