FastTweet

AI-assisted tweet drafting with research, generation, and reflection loops built with LangGraph, Groq, Tavily Search, and Streamlit.

What It Does

• Searches for recent context on a topic with Tavily.
• Generates a draft tweet with a Groq-hosted Llama model.
• Runs a reflection loop to critique and improve the draft.
• Shows the final tweet, research snippets, and generation process in a Streamlit UI.

Setup

1. Install dependencies:

   pip install -r requirements.txt

2. Create a local environment file:

   Copy-Item .env.example .env

3. Add your API keys to .env:

   GROQ_API_KEY=your_groq_api_key_here
   TAVILY_API_KEY=your_tavily_api_key_here

4. Run the app:

   streamlit run fastbot.py

Security Note

Do not commit .env files or API keys. Use .env.example for placeholders only.