Easy, extendable and fast LLM agent deployment template
—
Many tutorials show how to implement an LLM agent. However, resources on deploying these agents behind an API or a user-friendly UI are limited. This post addresses this gap with a step-by-step guide to implementing and deploying a minimal yet functional LLM agent. This provides a starting point for your LLM agent proof of concept, whether for personal use or to share with others.
Our implementation has several parts:
- Agent Implementation: Using LangGraph as the agent framework and Fireworks AI as the LLM service.
- User Interface: Exposing the agent through a UI built with FastAPI and NiceGUI.
- Containerization: Packaging the application into a Docker image.
- Deployment: Deploying the Docker image to Google Cloud Run.
Full code and demo app linked at the end of the post.
Building the Agent
The agent requires two core components:
- LLM Serving Service: We’ll use Fireworks AI.