ShenSeanChen

Launch DeepResearch Backend — AI skill for Claude Code

AI community

You Can Learn Deep Research AI Agent Design & Launch In 25 Min Kimi K2 0905, LangChain, OpenSource.

How to install Launch DeepResearch Backend

This entry records only its repository, not the path inside it, so there is no exact command to give. Open ShenSeanChen/launch-DeepResearch-Backend and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What Launch DeepResearch Backend does

You Can Learn Deep Research AI Agent Design & Launch In 25 Min Kimi K2 0905, LangChain, OpenSource.

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README

🔬 Deep Research Agent - Backend

A powerful FastAPI backend that provides intelligent deep research capabilities using multiple AI models (OpenAI, Anthropic, Kimi K2). Built with LangGraph for complex research workflows and designed for deployment on GCP Cloud Run. The backend is designed to work with the [Deep Research Frontend](https://github.com/ShenSeanChen/yt-deepresearch-frontend).

📹 Full YouTube Guide: [Youtube link](https://www.youtube.com/watch?v=dw9Vkig47S0&list=PLE9hy4A7ZTmpGq7GHf5tgGFWh2277AeDR&index=29)

🚀 X Post: [X link](https://x.com/ShenSeanChen/status/1969013359010873513)

💻 Launch Full Stack Product: [Github Repo](https://github.com/ShenSeanChen/launch-mvp-stripe-nextjs-supabase)

☕️ Buy me a coffee: [Cafe Latte](https://buy.stripe.com/5kA176bA895ggog4gh)

🤖️ Discord: [Invite link](https://discord.com/invite/TKKPzZheua)

✨ Features

Multi-Model Support: OpenAI GPT-4o, Anthropic Claude, and Kimi K2 0905
Streaming Research: Real-time progress updates with detailed step visibility
LangGraph Integration: Complex research workflows with clarification, briefing, execution, and reporting
Cloud Ready: Docker containerized for GCP Cloud Run deployment
Secure API Keys: Environment-based configuration with user-provided keys
Health Monitoring: Built-in health checks and metrics collection

🚀 Quick Start

Prerequisites

Python 3.11+
Docker (for containerization)
GCP Account (for deployment)

Local Development

**Clone the repository**

git clone https://github.com/ShenSeanChen/yt-DeepResearch-Backend.git
cd yt-DeepResearch-Backend

**Install dependencies**

# Create a virtual environment (you can name it .venv or venv)
python3 -m venv venv

# Activate the virtual environment
source venv/bin/activate   # On macOS/Linux
# .\venv\Scripts\activate  # On Windows (PowerShell)

# Install dependencies
pip install -r requirements.txt