autogen/python/packages/autogen-core/samples

• Examples
• • Core examples
  • Tool use examples
  • Pattern examples
  • Demos
  • Bring Your Own Agent
  • Running the examples
  • • Prerequisites
    • Using Azure OpenAI API
    • Using OpenAI API
    • Running

Examples

This directory contains examples and demos of how to use AGNext.

• common: Contains common implementations and utilities used by the examples.
• core: Contains examples that illustrate the core concepts of AGNext.
• tool-use: Contains examples that illustrate tool use in AGNext.
• patterns: Contains examples that illustrate how multi-agent patterns can be implemented in AGNext.
• demos: Contains interactive demos that showcase applications that can be built using AGNext.

See Running the examples for instructions on how to run the examples.

Core examples

We provide examples to illustrate the core concepts of AGNext: agents, runtime, and message passing.

• 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.
• inner_outer_direct.py: A simple example of how to create an agent that calls an inner agent using direct communication.
• two_agents_pub_sub.py: An example of how to create two agents that communicate using broadcast communication (i.e., pub/sub).

Tool use examples

We provide examples to illustrate how to use tools in AGNext:

• coding_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.
• coding_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.
• custom_tool_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.
• coding_direct_with_intercept.py: an example showing human-in-the-loop for approving or denying tool execution.

Pattern examples

We provide examples to illustrate how multi-agent patterns can be implemented in AGNext:

• 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.
• coder_reviewer.py: An example of how to create a coder-reviewer reflection pattern.
• group_chat.py: An example of how to create a round-robin group chat among three agents.
• mixture_of_agents.py: An example of how to create a mixture of agents.
• multi_agent_debate.py: An example of how to create a sparse multi-agent debate pattern.

Demos

We provide interactive demos that showcase applications that can be built using AGNext:

• assistant.py: a demonstration of how to use the OpenAI Assistant API to create
  a ChatGPT agent.
• chat_room.py: An example of how to create a chat room of custom agents without
  a centralized orchestrator.
• illustrator_critics.py: a demo that uses an illustrator, critics and descriptor agent
  to implement the reflection pattern for image generation.
• software_consultancy.py: a demonstration of multi-agent interaction using
  the group chat pattern.
• chest_game.py: an example with two chess player agents that executes its own tools to demonstrate tool use and reflection on tool use.
• slow_human_in_loop.py: an example showing human-in-the-loop which waits for human input before making the tool call.

Bring Your Own Agent

We provide examples on how to integrate other agents with the platform:

• llamaindex_agent.py: An example that shows how to consume a LlamaIndex agent.
• langgraph_agent.py: An example that shows how to consume a LangGraph agent.

Running the examples

Prerequisites

First, you need a shell with AGNext and required dependencies installed.

Using Azure OpenAI API

For Azure OpenAI API, you need to set the following environment variables:

export OPENAI_API_TYPE=azure
export AZURE_OPENAI_API_ENDPOINT=your_azure_openai_endpoint
export AZURE_OPENAI_API_VERSION=your_azure_openai_api_version

By default, we use Azure Active Directory (AAD) for authentication.
You need to run az login first to authenticate with Azure.
You can also
use API key authentication by setting the following environment variables:

export AZURE_OPENAI_API_KEY=your_azure_openai_api_key

This requires azure-identity installation:

pip install azure-identity

Using OpenAI API

For OpenAI API, you need to set the following environment variables.

export OPENAI_API_TYPE=openai
export OPENAI_API_KEY=your_openai_api_key

Running

To run an example, just run the corresponding Python script. For example:

hatch shell
python core/one_agent_direct.py

Or simply:

hatch run python core/one_agent_direct.py