AI_chatbot (LangGraph)

This folder contains a small chatbot based on LangGraph and a Streamlit frontend.

What's included

• langgraph_backend.py — backend composition and chatbot object
• streamlit_frontend.py — Streamlit UI that uses the backend
• Dockerfile, requirements.txt, .gitignore — for containerized deployment
• agentop/agentop-deploy.yaml — AgentOp deployment template (fill in values)
• scripts/deploy_agentop.sh — helper deploy script (template)

Quick local run

1. Create a virtualenv and install dependencies

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

2. Run the Streamlit frontend

streamlit run streamlit_frontend.py

Docker (build & run)

docker build -t yourname/langgraph-chatbot:latest .
docker run -p 8501:8501 yourname/langgraph-chatbot:latest

CI / Deployment

• A GitHub Actions workflow is added to the repository root to run a quick smoke check and build/push Docker images to GitHub Container Registry.
• The agentop/agentop-deploy.yaml file is a template you can adapt for AgentOp; it expects a container image reference and environment variables.

Next steps / notes

• Replace placeholder package names in requirements.txt ith exact package names if you use private packages (e.g., langgraph).

• Add your secrets (OpenAI key, GHCR credentials, AgentOp token) to GitHub Secrets when enabling CI or deploy workflows.

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