+
+ """).lstrip()
+
+ for file in files:
+ file.parent.mkdir(parents=True, exist_ok=True)
+ if file.name == "quick-start.mdx":
+ file.write_text(quick_start_content)
+ else:
+ file.touch()
+
+ mkdocs_output_dir = Path(tmpdir) / "mkdocs_output"
+ mkdocs_output_dir.mkdir()
+
+ process_and_copy_files(src_dir, mkdocs_output_dir, files)
+
+ actual = list(filter(lambda x: x.is_file(), mkdocs_output_dir.rglob("*")))
+ expected = [
+ mkdocs_output_dir / "home" / "agent.png",
+ mkdocs_output_dir / "home" / "quick-start.md",
+ mkdocs_output_dir / "user-guide" / "advanced-concepts" / "groupchat" / "chat.txt",
+ mkdocs_output_dir / "user-guide" / "advanced-concepts" / "groupchat" / "groupchat.md",
+ ]
+ assert len(actual) == len(expected)
+ assert sorted(actual) == sorted(actual)
+
+ # Assert the content of the transformed markdown file
+ expected_quick_start_content = dedent("""
+ !!! tip
+ It is important to never hard-code secrets into your code, therefore we read the OpenAI API key from an environment variable.
+
+ !!! warning
+ It is important to never hard-code secrets into your code, therefore we read the OpenAI API key from an environment variable.
+
+ !!! note
+ It is important to never hard-code secrets into your code, therefore we read the OpenAI API key from an environment variable.
+
+
+
+
+### Key Features
+
+
+
+
+ 
+
+
+
+
+
+
+### Explore content
+
+
+
+
+ 
+
+
+ Multi-Agent Conversation Framework
+AG2 provides multi-agent conversation framework as a high-level abstraction. With this framework, one can conveniently build LLM workflows.
+
+
+
+
+ 
+
+
+ Easily Build Diverse Applications
+AG2 offers a collection of working systems spanning a wide range of applications from various domains and complexities.
+
+
+
+ 
+
+
+ Enhanced LLM Inference & Optimization
+AG2 supports enhanced LLM inference APIs, which can be used to improve inference performance and reduce cost.
+
+
+
+### Popular resources
+
+
+
+
+
+
+
+
+
+ Quick Start
+Start building your AG2 application.
+
+
+
+
+
+
+ Concepts
+Work through the key concepts of AG2 including ConversableAgent, GroupChat, Swarm, and tools.
+
+
+
+
+
+
+ Advanced Concepts
+Advance to RAG, Code Execution, and more complex GroupChats and Swarms.
+
+
+
+
+
+
+ Use Cases
+Try out use case workflows including Customer Service, Travel Planning, and Game Design.
+
+
+
+
+
+
+ Notebook Examples
+A collection of interactive notebooks across all AG2 topics.
+
+
+
+
+
+
+ API Reference
+Delve into the AG2 API reference.
+
+
+
+
+
+ How to Contribute
+Get involved with AG2 by adding what you need and making the framework even better!
+
+
diff --git a/website/mkdocs/docs/docs/home/quick-start.md b/website/mkdocs/docs/docs/home/quick-start.md
new file mode 100644
index 0000000000..9f0708035b
--- /dev/null
+++ b/website/mkdocs/docs/docs/home/quick-start.md
@@ -0,0 +1,179 @@
+---
+title: "Quick Start"
+---
+
+AG2 (formerly AutoGen) is an open-source programming framework for building AI agents and facilitating their cooperation to solve tasks. AG2 supports tool use, autonomous and human-in-the-loop workflows, and multi-agent conversation patterns.
+
+### Let's go
+
+```sh
+pip install ag2
+```
+!!! note
+ If you have been using `autogen` or `pyautogen`, all you need to do is upgrade it using:
+ ```bash
+ pip install -U autogen
+ ```
+ or
+ ```bash
+ pip install -U pyautogen
+ ```
+ as `pyautogen`, `autogen`, and `ag2` are aliases for the same PyPI package.
+
+=== "Chat with an agent"
+
+ ```python
+ # 1. Import our agent class
+ from autogen import ConversableAgent
+
+ # 2. Define our LLM configuration for OpenAI's GPT-4o mini
+ # uses the OPENAI_API_KEY environment variable
+ llm_config = {"api_type": "openai", "model": "gpt-4o-mini"}
+
+ # 3. Create our LLM agent
+ my_agent = ConversableAgent(
+ name="helpful_agent",
+ llm_config=llm_config,
+ system_message="You are a poetic AI assistant, respond in rhyme.",
+ )
+
+ # 4. Run the agent with a prompt
+ chat_result = my_agent.run("In one sentence, what's the big deal about AI?")
+
+ # 5. Print the chat
+ print(chat_result.chat_history)
+ ```
+
+=== "Two agent chat"
+
+ ```python
+ # Chat between two comedian agents
+
+ # 1. Import our agent class
+ from autogen import ConversableAgent
+
+ # 2. Define our LLM configuration for OpenAI's GPT-4o mini,
+ # uses the OPENAI_API_KEY environment variable
+ llm_config = {"api_type": "openai", "model": "gpt-4o-mini"}
+
+ # 3. Create our agents who will tell each other jokes,
+ # with Jack ending the chat when Emma says FINISH
+ jack = ConversableAgent(
+ "Jack",
+ llm_config=llm_config,
+ system_message=(
+ "Your name is Jack and you are a comedian "
+ "in a two-person comedy show."
+ ),
+ is_termination_msg=lambda x: True if "FINISH" in x["content"] else False
+ )
+ emma = ConversableAgent(
+ "Emma",
+ llm_config=llm_config,
+ system_message=(
+ "Your name is Emma and you are a comedian "
+ "in a two-person comedy show. Say the word FINISH "
+ "ONLY AFTER you've heard 2 of Jack's jokes."
+ ),
+ )
+
+ # 4. Run the chat
+ chat_result = jack.initiate_chat(
+ emma,
+ message="Emma, tell me a joke about goldfish and peanut butter.",
+ )
+
+ # 5. Print the chat
+ print(chat_result.chat_history)
+ ```
+
+=== "Group chat"
+
+ ```python
+ # Group chat amongst agents to create a 4th grade lesson plan
+ # Flow determined by Group Chat Manager automatically, and
+ # should be Teacher > Planner > Reviewer > Teacher (repeats if necessary)
+
+ # 1. Import our agent and group chat classes
+ from autogen import ConversableAgent, GroupChat, GroupChatManager
+
+ # Define our LLM configuration for OpenAI's GPT-4o mini
+ # uses the OPENAI_API_KEY environment variable
+ llm_config = {"api_type": "openai", "model": "gpt-4o-mini"}
+
+ # Planner agent setup
+ planner_message = "Create lesson plans for 4th grade. Use format:
+
+
+
+
+
+