examples/README.md - burr - Git at Google

 # 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:

 ```python
 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](other-examples/), but those do not yet adhere to the same format/are as well documented.

 # Index

 - [simple-chatbot-intro](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](https://blog.dagworks.io/p/burr-develop-stateful-ai-applications).
 - [conversational-rag](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](hello-world-counter/) - This is an example of a simple state machine, used in the docs.
 - [llm-adventure-game](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](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](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](https://github.com/langchain-ai/langgraph/blob/main/examples/multi_agent/multi-agent-collaboration.ipynb).
 - [multi-modal-chatbot](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](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](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](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.
	# 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:

	```python
	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](other-examples/), but those do not yet adhere to the same format/are as well documented.

	# Index

	- [simple-chatbot-intro](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](https://blog.dagworks.io/p/burr-develop-stateful-ai-applications).
	- [conversational-rag](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](hello-world-counter/) - This is an example of a simple state machine, used in the docs.
	- [llm-adventure-game](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](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](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](https://github.com/langchain-ai/langgraph/blob/main/examples/multi_agent/multi-agent-collaboration.ipynb).
	- [multi-modal-chatbot](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](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](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](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.