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Merge pull request #348 from DAGWorks-Inc/docs/simplify-concept-pages
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`docs` Created a `Cookbook` section
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zilto authored Aug 30, 2024
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3 changes: 2 additions & 1 deletion docs/conf.py
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extensions = [
"sphinx.ext.autodoc",
"sphinx.ext.autosummary",
"myst_parser",
"myst_nb",
"sphinx_sitemap",
"sphinx_toolbox.collapse",
]
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},
}

nb_execution_mode = "off"

exclude_patterns = ["README-internal.md"]

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2 changes: 1 addition & 1 deletion docs/contributing/index.rst
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=========================
Developing / Contributing
Contributing
=========================

.. _examples:
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60 changes: 0 additions & 60 deletions docs/examples/agents.rst

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31 changes: 31 additions & 0 deletions docs/examples/agents/agent-patterns.md
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# Agent patterns

We have the bones of a few applications that can be used to create agents. These are not fully fleshed out, but they
can be used as a starting point for creating your own agents.

We have the following templates:

## Multimodal agent

[Code template](https://github.com/DAGWorks-Inc/burr/tree/main/examples/templates/multi_modal_agent)

![](./_agent_patterns/multi_modal_agent.png)

## Multi-agent collaboration

[Code template](https://github.com/DAGWorks-Inc/burr/tree/main/examples/templates/multi_agent_collaboration)

![](./_agent_patterns/multi_agent_collaboration.png)

## Supervisor agent

[Code template](https://github.com/DAGWorks-Inc/burr/tree/main/examples/templates/agent_supervisor)

![](./_agent_patterns/agent_supervisor.png)


## Hierarchical teams

[Code template](https://github.com/DAGWorks-Inc/burr/tree/main/examples/templates/hierarchical_agent_teams)

![](./_agent_patterns/hierarchical_agent_teams.png)
26 changes: 26 additions & 0 deletions docs/examples/agents/divide-and-conquer.md
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# Divide and Conquer

A single agent can usually operate effectively using a handful of tools within a single domain, but even using powerful models like `gpt-4`, it can be less effective at using many tools.

One way to approach complicated tasks is through a "divide-and-conquer" approach: create a "specialized agent" for each task or domain and route tasks to the correct "expert". This means that each agent can become a sequence of LLM calls that chooses how to use a specific "tool".

The examples we link to below are inspired by the paper [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation by Wu, et. al.](https://arxiv.org/abs/2308.08155).
They can be found in [this part of our repository](https://github.com/DAGWorks-Inc/burr/tree/main/examples/multi-agent-collaboration).

![](./_divide-and-conquer.png)

We provide two implementations of this idea: with [Hamilton](https://github.com/DAGWorks-Inc/burr/tree/main/examples/multi-agent-collaboration/hamilton) and with [LangChain LCEL](https://github.com/DAGWorks-Inc/burr/tree/main/examples/multi-agent-collaboration/lcel). From Burr's standpoint, both look similar and you're free to use your preferred framework within an `action`.

## With Hamilton

<a target="_blank" href="https://colab.research.google.com/github/dagworks-inc/burr/blob/main/examples/multi-agent-collaboration/hamilton/notebook.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>



## With LangChain Expression Language (LCEL)

<a target="_blank" href="https://colab.research.google.com/github/dagworks-inc/burr/blob/main/examples/multi-agent-collaboration/lcel/notebook.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
11 changes: 11 additions & 0 deletions docs/examples/agents/index.rst
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==========
🤖 Agents
==========

Burr allows you to create agents that can interact with each other via State.

.. toctree::
:maxdepth: 2

divide-and-conquer
agent-patterns
26 changes: 0 additions & 26 deletions docs/examples/chatbot.rst

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