AutoGen Tutorial

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 # Misc
 .DS_Store

website/docs/Getting-Started.mdx

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+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# Getting Started
+
+AutoGen is a framework that enables development of LLM applications using
+multiple agents that can converse with each other to solve tasks. AutoGen agents
+are customizable, conversable, and seamlessly allow human participation. They
+can operate in various modes that employ combinations of LLMs, human inputs, and
+tools.
+
+![AutoGen Overview](/img/autogen_agentchat.png)
+
+### Main Features
+
+
+- AutoGen enables building next-gen LLM applications based on [multi-agent
+  conversations](/docs/Use-Cases/agent_chat) with minimal effort. It simplifies
+  the orchestration, automation, and optimization of a complex LLM workflow. It
+  maximizes the performance of LLM models and overcomes their weaknesses.
+- It supports [diverse conversation
+patterns](/docs/Use-Cases/agent_chat#supporting-diverse-conversation-patterns)
+for complex workflows. With customizable and conversable agents, developers can
+use AutoGen to build a wide range of conversation patterns concerning
+conversation autonomy, the number of agents, and agent conversation topology.
+- It provides a collection of working systems with different complexities. These
+  systems span a [wide range of
+  applications](/docs/Use-Cases/agent_chat#diverse-applications-implemented-with-autogen)
+  from various domains and complexities. This demonstrates how AutoGen can
+  easily support diverse conversation patterns.
+
+AutoGen is powered by collaborative [research studies](/docs/Research) from
+Microsoft, Penn State University, and University of Washington.
+
+### Quickstart
+
+```sh
+pip install pyautogen
+```
+
+<Tabs>
+    <TabItem value="local" label="Local execution" default>
+:::warning
+When asked, be sure to check the generated code before continuing to ensure it is safe to run.
+:::
+
+```python
+from autogen import AssistantAgent, UserProxyAgent
+from autogen.coding import LocalCommandLineCodeExecutor
+
+import os
+from pathlib import Path
+
+llm_config = {
+    "config_list": [{"model": "gpt-4", "api_key": os.environ["OPENAI_API_KEY"]}],
+}
+
+work_dir = Path("coding")
+work_dir.mkdir(exist_ok=True)
+
+assistant = AssistantAgent("assistant", llm_config=llm_config)
+
+code_executor = LocalCommandLineCodeExecutor(work_dir=work_dir)
+user_proxy = UserProxyAgent(
+    "user_proxy", code_execution_config={"executor": code_executor}
+)
+
+# Start the chat
+user_proxy.initiate_chat(
+    assistant,
+    message="Plot a chart of NVDA and TESLA stock price change YTD.",
+)
+```
+
+    </TabItem>
+    <TabItem value="docker" label="Docker execution" default>
+
+
+```python
+from autogen import AssistantAgent, UserProxyAgent
+from autogen.coding import DockerCommandLineCodeExecutor
+
+import os
+from pathlib import Path
+
+llm_config = {
+    "config_list": [{"model": "gpt-4", "api_key": os.environ["OPENAI_API_KEY"]}],
+}
+
+work_dir = Path("coding")
+work_dir.mkdir(exist_ok=True)
+
+with DockerCommandLineCodeExecutor(work_dir=work_dir) as code_executor:
+    assistant = AssistantAgent("assistant", llm_config=llm_config)
+    user_proxy = UserProxyAgent(
+        "user_proxy", code_execution_config={"executor": code_executor}
+    )
+
+    # Start the chat
+    user_proxy.initiate_chat(
+        assistant,
+        message="Plot a chart of NVDA and TESLA stock price change YTD. Save the plot to a file called plot.png",
+    )
+```
+
+Open `coding/plot.png` to see the generated plot.
+
+  </TabItem>
+</Tabs>
+
+:::tip
+Learn more about configuring LLMs for agents [here](/docs/topics/llm_configuration).
+:::
+
+
+
+#### Multi-Agent Conversation Framework
+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.
+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),
+
+The figure below shows an example conversation flow with AutoGen.
+
+![Agent Chat Example](/img/chat_example.png)
+
+
+### Where to Go Next?
+
+* Go through the [tutorial](/docs/tutorial/introduction) to learn more about the core concepts in AutoGen
+* Read the examples and guides in the [notebooks section](/docs/notebooks)
+* Understand the use cases for [multi-agent conversation](/docs/Use-Cases/agent_chat) and [enhanced LLM inference](/docs/Use-Cases/enhanced_inference)
+* Read the [API](/docs/reference/agentchat/conversable_agent/) docs
+* Learn about [research](/docs/Research) around AutoGen
+* Chat on [Discord](https://discord.gg/pAbnFJrkgZ)
+* Follow on [Twitter](https://twitter.com/pyautogen)
+
+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/Contribute).
+
+<iframe src="https://ghbtns.com/github-btn.html?user=microsoft&amp;repo=autogen&amp;type=star&amp;count=true&amp;size=large" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>