Simple, extendable and quick LLM agent deployment template
Many tutorials present tips on how to implement an LLM agent. Nevertheless, sources on deploying these brokers behind an API or a user-friendly UI are restricted. This publish addresses this hole with a step-by-step information to implementing and deploying a minimal but practical LLM agent. This supplies a place to begin in your LLM agent proof of idea, whether or not for private use or to share with others.
Our implementation has a number of elements:
- Agent Implementation: Utilizing LangGraph because the agent framework and Fireworks AI because the LLM service.
- Person Interface: Exposing the agent by means of a UI constructed with FastAPI and NiceGUI.
- Containerization: Packaging the appliance right into a Docker picture.
- Deployment: Deploying the Docker picture to Google Cloud Run.
Full code and demo app linked on the finish of the publish.
The agent requires two core elements:
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