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========================= | ||
Developing / Contributing | ||
Contributing | ||
========================= | ||
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.. _examples: | ||
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# Agent patterns | ||
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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. | ||
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We have the following templates: | ||
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## Multimodal agent | ||
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[Code template](https://github.com/DAGWorks-Inc/burr/tree/main/examples/templates/multi_modal_agent) | ||
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![](./_agent_patterns/multi_modal_agent.png) | ||
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## Multi-agent collaboration | ||
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[Code template](https://github.com/DAGWorks-Inc/burr/tree/main/examples/templates/multi_agent_collaboration) | ||
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![](./_agent_patterns/multi_agent_collaboration.png) | ||
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## Supervisor agent | ||
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[Code template](https://github.com/DAGWorks-Inc/burr/tree/main/examples/templates/agent_supervisor) | ||
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![](./_agent_patterns/agent_supervisor.png) | ||
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## Hierarchical teams | ||
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[Code template](https://github.com/DAGWorks-Inc/burr/tree/main/examples/templates/hierarchical_agent_teams) | ||
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![](./_agent_patterns/hierarchical_agent_teams.png) |
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# Divide and Conquer | ||
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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. | ||
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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". | ||
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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). | ||
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![](./_divide-and-conquer.png) | ||
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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`. | ||
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## With Hamilton | ||
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<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> | ||
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## With LangChain Expression Language (LCEL) | ||
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<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> |
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========== | ||
🤖 Agents | ||
========== | ||
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Burr allows you to create agents that can interact with each other via State. | ||
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.. toctree:: | ||
:maxdepth: 2 | ||
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divide-and-conquer | ||
agent-patterns |
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