Apache Burr (incubating)

Apache Burr (incubating) makes it easy to develop applications that make decisions (chatbots, agents, simulations, etc...) from simple python building blocks.

Apache Burr works well for any application that uses LLMs, and can integrate with any of your favorite frameworks. Burr includes a UI that can track/monitor/trace your system in real time, along with pluggable persisters (e.g. for memory) to save & load application state.

Link to documentation. Quick (<3min) video intro here. Longer video intro & walkthrough. Blog post here. Join discord for help/questions here.

Quick start

Install from pypi:

pip install "apache-burr[start]"

(see the docs if you're using poetry)

Then run the UI server:

burr

This will open up Burr's telemetry UI. It comes loaded with some default data so you can click around. It also has a demo chat application to help demonstrate what the UI captures, enabling you to see things changing in real-time. Hit the "Demos" side bar on the left and select chatbot. To chat it requires the OPENAI_API_KEY environment variable to be set, but you can still see how it works if you don't have an API key set.

Next, start coding / running examples:

git clone https://github.com/apache/burr && cd burr/examples/hello-world-counter
python application.py

You'll see the counter example running in the terminal, along with the trace being tracked in the UI. See if you can find it.

For more details see the getting started guide.

How does Apache Burr work?

With Apache Burr you express your application as a state machine (i.e. a graph/flowchart). You can (and should!) use it for anything in which you have to manage state, track complex decisions, add human feedback, or dictate an idempotent, self-persisting workflow.

The core API is simple -- the Burr hello-world looks like this (plug in your own LLM, or copy from the docs for gpt-X)

from burr.core import action, State, ApplicationBuilder

@action(reads=[], writes=["prompt", "chat_history"])
def human_input(state: State, prompt: str) -> State:
    # your code -- write what you want here, for example
    chat_item = {"role" : "user", "content" : prompt}
    return state.update(prompt=prompt).append(chat_history=chat_item)

@action(reads=["chat_history"], writes=["response", "chat_history"])
def ai_response(state: State) -> State:
    # query the LLM however you want (or don't use an LLM, up to you...)
    response = _query_llm(state["chat_history"]) # Burr doesn't care how you use LLMs!
    chat_item = {"role" : "system", "content" : response}
    return state.update(response=response).append(chat_history=chat_item)

app = (
    ApplicationBuilder()
    .with_actions(human_input, ai_response)
    .with_transitions(
        ("human_input", "ai_response"),
        ("ai_response", "human_input")
    ).with_state(chat_history=[])
    .with_entrypoint("human_input")
    .build()
)
*_, state = app.run(halt_after=["ai_response"], inputs={"prompt": "Who was Aaron Burr, sir?"})
print("answer:", app.state["response"])

Apache Burr includes:

1. A (dependency-free) low-abstraction python library that enables you to build and manage state machi