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99 lines
4.6 KiB
Markdown
# Examples
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This directory contains examples and demos of how to use AGNext.
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- `common`: Contains common implementations and utilities used by the examples.
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- `core`: Contains examples that illustrate the core concepts of AGNext.
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- `tool-use`: Contains examples that illustrate tool use in AGNext.
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- `patterns`: Contains examples that illustrate how multi-agent patterns can be implemented in AGNext.
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- `demos`: Contains interactive demos that showcase applications that can be built using AGNext.
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See [Running the examples](#running-the-examples) for instructions on how to run the examples.
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## Core examples
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We provide examples to illustrate the core concepts of AGNext: agents, runtime, and message passing.
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- [`one_agent_direct.py`](core/one_agent_direct.py): A simple example of how to create a single agent powered by ChatCompletion model client. Communicate with the agent using direct communication.
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- [`inner_outer_direct.py`](core/inner_outer_direct.py): A simple example of how to create an agent that calls an inner agent using direct communication.
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- [`two_agents_pub_sub.py`](core/two_agents_pub_sub.py): An example of how to create two agents that communicate using broadcast communication (i.e., pub/sub).
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## Tool use examples
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We provide examples to illustrate how to use tools in AGNext:
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- [`coding_one_agent_direct.py`](tool-use/coding_one_agent_direct.py): a code execution example with one agent that calls and executes tools to demonstrate tool use and reflection on tool use. This example uses direct communication.
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- [`coding_two_agent_pub_sub.py`](tool-use/coding_two_agent_pub_sub.py): a code execution example with two agents, one for calling tool and one for executing the tool, to demonstrate tool use and reflection on tool use. This example uses broadcast communication.
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- [`custom_function_tool_one_agent_direct.py`](tool-use/custom_function_tool_one_agent_direct.py): a custom function tool example with one agent that calls and executes tools to demonstrate tool use and reflection on tool use. This example uses direct communication.
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## Pattern examples
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We provide examples to illustrate how multi-agent patterns can be implemented in AGNext:
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- [`coder_executor.py`](patterns/coder_executor.py): An example of how to create a coder-executor reflection pattern. This example creates a plot of stock prices using the Yahoo Finance API.
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- [`coder_reviewer.py`](patterns/coder_reviewer.py): An example of how to create a coder-reviewer reflection pattern.
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- [`group_chat.py`](patterns/group_chat.py): An example of how to create a round-robin group chat among three agents.
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- [`mixture_of_agents.py`](patterns/mixture_of_agents.py): An example of how to create a [mixture of agents](https://github.com/togethercomputer/moa).
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- [`multi_agent_debate.py`](patterns/multi_agent_debate.py): An example of how to create a [sparse multi-agent debate](https://arxiv.org/abs/2406.11776) pattern.
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## Demos
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We provide interactive demos that showcase applications that can be built using AGNext:
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- [`assistant.py`](demos/assistant.py): a demonstration of how to use the OpenAI Assistant API to create
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a ChatGPT agent.
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- [`chat_room.py`](demos/chat_room.py): An example of how to create a chat room of custom agents without
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a centralized orchestrator.
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- [`illustrator_critics.py`](demos/illustrator_critics.py): a demo that uses an illustrator, critics and descriptor agent
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to implement the reflection pattern for image generation.
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- [`software_consultancy.py`](demos/software_consultancy.py): a demonstration of multi-agent interaction using
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the group chat pattern.
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- [`chest_game.py`](demos/chess_game.py): an example with two chess player agents that executes its own tools to demonstrate tool use and reflection on tool use.
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## Running the examples
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### Prerequisites
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First, you need a shell with AGNext and the examples dependencies installed.
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To do this, in the example directory, run:
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```bash
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hatch shell
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```
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Then, you need to set the `OPENAI_API_KEY` environment variable to your OpenAI API key.
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```bash
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export OPENAI_API_KEY=your_openai_api_key
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```
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For Azure OpenAI API, you need to set the following environment variables:
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```bash
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export AZURE_OPENAI_API_KEY=your_azure_openai_api_key
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export AZURE_OPENAI_ENDPOINT=your_azure_openai_endpoint
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```
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By default, OpenAI API is used.
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To use Azure OpenAI API, set the `OPENAI_API_TYPE`
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environment variable to `azure`.
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```bash
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export OPENAI_API_TYPE=azure
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```
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### Running
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To run an example, just run the corresponding Python script. For example:
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```bash
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hatch shell
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python core/one_agent_direct.py
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```
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Or simply:
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```bash
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hatch run python core/one_agent_direct.py
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```
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