Building an Email Assistant Application with Burr

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Tailwind CSS

A utility-first CSS framework for rapidly building custom user interfaces.
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You can use any frontend framework you want — react-based tooling, however, has a natural advantage as it models everything as a function of state, which can map 1:1 with the concept in Burr. In the demo app we use react, react-query, and tailwind, but we’ll be skipping over this largely (it is not central to the purpose of the post).

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Streamlit

Turn python scripts into beautiful ML tools
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Note that there are many tools that make this easier/simpler to prototype, including chainlit, streamlit, etc… The backend API we built is amenable to interacting with them as well.

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Langfuse

Open source tracing and analytics for LLM applications
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Using the Burr UI to monitor is not the only way. You can integrate your own by leveraging lifecycle hooks, enabling you to log data in a custom format to, say, datadog, langsmith, or langfuse.

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FastAPI

FastAPI is an Open Source, modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints.
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In this tutorial, I will demonstrate how to use Burr, an open source framework (disclosure: I helped create it), using simple OpenAI client calls to GPT4, and FastAPI to create a custom email assistant agent. We’ll describe the challenge one faces and then how you can solve for them. For the application frontend we provide a reference implementation but won’t dive into details for it.

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