mirror of https://github.com/microsoft/autogen.git
148 lines
5.4 KiB
Plaintext
148 lines
5.4 KiB
Plaintext
import Tabs from "@theme/Tabs";
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import TabItem from "@theme/TabItem";
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# Getting Started
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AutoGen is an open-source programming framework for building AI agents and facilitating
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cooperation among multiple agents to solve tasks. AutoGen aims to provide an easy-to-use
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and flexible framework for accelerating development and research on agentic AI,
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like PyTorch for Deep Learning. It offers features such as agents that can converse
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with other agents, LLM and tool use support, autonomous and human-in-the-loop workflows,
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and multi-agent conversation patterns.
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### Main Features
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- AutoGen enables building next-gen LLM applications based on [multi-agent
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conversations](/docs/Use-Cases/agent_chat) with minimal effort. It simplifies
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the orchestration, automation, and optimization of a complex LLM workflow. It
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maximizes the performance of LLM models and overcomes their weaknesses.
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- It supports [diverse conversation
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patterns](/docs/Use-Cases/agent_chat#supporting-diverse-conversation-patterns)
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for complex workflows. With customizable and conversable agents, developers can
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use AutoGen to build a wide range of conversation patterns concerning
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conversation autonomy, the number of agents, and agent conversation topology.
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- It provides a collection of working systems with different complexities. These
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systems span a [wide range of
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applications](/docs/Use-Cases/agent_chat#diverse-applications-implemented-with-autogen)
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from various domains and complexities. This demonstrates how AutoGen can
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easily support diverse conversation patterns.
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AutoGen is powered by collaborative [research studies](/docs/Research) from
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Microsoft, Penn State University, and University of Washington.
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### Quickstart
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```sh
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pip install pyautogen
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```
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<Tabs>
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<TabItem value="nocode" label="No code execution" default>
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```python
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import os
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from autogen import AssistantAgent, UserProxyAgent
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llm_config = {"model": "gpt-4", "api_key": os.environ["OPENAI_API_KEY"]}
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assistant = AssistantAgent("assistant", llm_config=llm_config)
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user_proxy = UserProxyAgent("user_proxy", code_execution_config=False)
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# Start the chat
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user_proxy.initiate_chat(
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assistant,
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message="Tell me a joke about NVDA and TESLA stock prices.",
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)
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```
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</TabItem>
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<TabItem value="local" label="Local execution" default>
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:::warning
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When asked, be sure to check the generated code before continuing to ensure it is safe to run.
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:::
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```python
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import os
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import autogen
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from autogen import AssistantAgent, UserProxyAgent
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llm_config = {"model": "gpt-4", "api_key": os.environ["OPENAI_API_KEY"]}
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assistant = AssistantAgent("assistant", llm_config=llm_config)
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user_proxy = UserProxyAgent(
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"user_proxy", code_execution_config={"executor": autogen.coding.LocalCommandLineCodeExecutor(work_dir="coding")}
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)
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# Start the chat
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user_proxy.initiate_chat(
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assistant,
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message="Plot a chart of NVDA and TESLA stock price change YTD.",
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)
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```
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</TabItem>
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<TabItem value="docker" label="Docker execution" default>
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```python
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import os
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import autogen
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from autogen import AssistantAgent, UserProxyAgent
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llm_config = {"model": "gpt-4", "api_key": os.environ["OPENAI_API_KEY"]}
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with autogen.coding.DockerCommandLineCodeExecutor(work_dir="coding") as code_executor:
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assistant = AssistantAgent("assistant", llm_config=llm_config)
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user_proxy = UserProxyAgent(
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"user_proxy", code_execution_config={"executor": code_executor}
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)
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# Start the chat
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user_proxy.initiate_chat(
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assistant,
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message="Plot a chart of NVDA and TESLA stock price change YTD. Save the plot to a file called plot.png",
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)
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```
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Open `coding/plot.png` to see the generated plot.
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</TabItem>
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</Tabs>
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:::tip
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Learn more about configuring LLMs for agents [here](/docs/topics/llm_configuration).
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:::
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#### Multi-Agent Conversation Framework
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Autogen enables the next-gen LLM applications with a generic multi-agent conversation framework. It offers customizable and conversable agents which integrate LLMs, tools, and humans.
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By automating chat among multiple capable agents, one can easily make them collectively perform tasks autonomously or with human feedback, including tasks that require using tools via code. For [example](https://github.com/microsoft/autogen/blob/main/test/twoagent.py),
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The figure below shows an example conversation flow with AutoGen.
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### Where to Go Next?
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- Go through the [tutorial](/docs/tutorial/introduction) to learn more about the core concepts in AutoGen
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- Read the examples and guides in the [notebooks section](/docs/notebooks)
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- Understand the use cases for [multi-agent conversation](/docs/Use-Cases/agent_chat) and [enhanced LLM inference](/docs/Use-Cases/enhanced_inference)
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- Read the [API](/docs/reference/agentchat/conversable_agent/) docs
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- Learn about [research](/docs/Research) around AutoGen
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- Chat on [Discord](https://aka.ms/autogen-dc)
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- Follow on [Twitter](https://twitter.com/pyautogen)
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- See our [roadmaps](https://aka.ms/autogen-roadmap)
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If you like our project, please give it a [star](https://github.com/microsoft/autogen/stargazers) on GitHub. If you are interested in contributing, please read [Contributor's Guide](/docs/contributor-guide/contributing).
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<iframe
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src="https://ghbtns.com/github-btn.html?user=microsoft&repo=autogen&type=star&count=true&size=large"
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frameborder="0"
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scrolling="0"
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width="170"
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height="30"
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title="GitHub"
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></iframe>
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