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tree: daef31addabf7ce01e1f92d279019d21b35c38a2 [path history] [tgz]
  1. adaptive-crag/
  2. conversational-rag/
  3. custom-serde/
  4. deep-researcher/
  5. deployment/
  6. email-assistant/
  7. hamilton-integration/
  8. haystack-integration/
  9. hello-world-counter/
  10. image-telephone/
  11. instructor-gemini-flash/
  12. integrations/
  13. llm-adventure-game/
  14. ml-training/
  15. multi-agent-collaboration/
  16. multi-modal-chatbot/
  17. openai-compatible-agent/
  18. opentelemetry/
  19. other-examples/
  20. parallelism/
  21. pytest/
  22. rag-lancedb-ingestion/
  23. ray/
  24. recursive/
  25. simple-chatbot-intro/
  26. simulation/
  27. streaming-fastapi/
  28. streaming-overview/
  29. talks/
  30. templates/
  31. test-case-creation/
  32. tool-calling/
  33. tracing-and-spans/
  34. typed-state/
  35. web-server/
  36. youtube-to-social-media-post/
  37. __init__.py
  38. README.md
  39. validate_examples.py
examples/README.md

Examples

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.

Index

  • 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.