This contains a series of examples. Each example is meant to demonostrate a feature/use-case of the Burr library.

Each example contains:

1. A README.md file that explains the example (what its teaching/how it works)
2. A application.py file that contains the code for the example. This will have a function application that creates the example, and a mainline that demonstrates it.
3. A requirements.txt file that contains the dependencies for the example
4. A notebook.ipynb file that contains the example in a Jupyter notebook
5. A statemachine.png file that contains the graphical representation of the state machine
6. A __init__.py file that allows the example to be imported as a module

You can run any example with the following commands:

pip install -r examples/<example>/requirements.txt # use your favorite package manager/venv tool
python examples/<example>/application.py

Note we have a few more in other-examples, but those do not yet adhere to the same format/are as well documented.

• simple-chatbot-intro - This is a simple chatbot that shows how to use Burr to create a simple chatbot. This is a good starting point for understanding how to use Burr -- the notebook follows the original blog post.
• conversational-rag - This shows multiple examples on how to use Burr to create a conversational RAG chatbot. This shows how to use state/prior knowledge to augment your LLM call with Burr.
• hello-world-counter - This is an example of a simple state machine, used in the docs.
• llm-adventure-game - This is an example of a simple text-based adventure game using LLMs -- it shows how to progress through hidden states while reusing components.
• ml-training - This is an example of a simple ML training pipeline. It shows how to use Burr to track the training of a model. This is not complete.
• multi-agent-collaboration - This example shows how to use Burr to create a multi-agent collaboration. This is a clone of the following LangGraph example.
• multi-modal-chatbot - This example shows how to use Burr to create a multi-modal chatbot. This demonstrates how to use a model to delegate to other models conditionally.
• streaming-overview - This example shows how we can use the streaming API to respond to return quicker results to the user and build a seamless experience
• tracing-and-spans - This example shows how to use Burr to create a simple chatbot with additional visibility. This is a good starting point for understanding how to use Burr's tracing functionality.
• web-server - This example shows how to use Burr in a web server. This is a good starting point for understanding how to use Burr for interaction.