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burr
Build applications that make decisions (chatbots, agents, simulations, etc...). Monitor, persist, and execute on your own infrastructure.
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hamilton
Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage and metadata. Runs and scales everywhere python does.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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Burr is a lightweight python library you use to build applications as state machines. You construct your application out of a series of actions (these can be either decorated functions or objects), which declare inputs from state, as well as inputs from the user. These specify custom logic (delegating to any framework), as well as instructions on how to update state. State is immutable, which allows you to inspect it at any given point. Burr handles orchestration, monitoring and persistence.
Note that this uses simple OpenAI calls — you can replace this with Langchain, LlamaIndex, Hamilton (or something else) if you prefer more abstraction, and delegate to whatever LLM you like to use. And, you should probably use something a little more concrete (E.G. instructor) to guarantee output shape.
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).
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.
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).
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.