mirror of https://github.com/microsoft/autogen.git
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Qingyun Wu
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<br>
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</p>
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:fire: The automated multi-agent chat framework in [autogen](https://microsoft.github.io/FLAML/docs/Use-Cases/Autogen) is in preview from v2.0.0.
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:fire: The automated multi-agent chat framework in [autogen](https://microsoft.github.io/autogen/) is in preview from v2.0.0.
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:fire: FLAML is highlighted in OpenAI's [cookbook](https://github.com/openai/openai-cookbook#related-resources-from-around-the-web).
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:fire: [autogen](https://microsoft.github.io/FLAML/docs/Use-Cases/Autogen) is released with support for ChatGPT and GPT-4, based on [Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference](https://arxiv.org/abs/2303.04673).
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:fire: [autogen](https://microsoft.github.io/autogen/) is released with support for ChatGPT and GPT-4, based on [Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference](https://arxiv.org/abs/2303.04673).
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:fire: FLAML supports Code-First AutoML & Tuning – Private Preview in [Microsoft Fabric Data Science](https://learn.microsoft.com/en-us/fabric/data-science/).
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@ -44,7 +44,7 @@ FLAML requires **Python version >= 3.8**. It can be installed from pip:
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pip install flaml
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```
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Minimal dependencies are installed without extra options. You can install extra options based on the feature you need. For example, use the following to install the dependencies needed by the [`autogen`](https://microsoft.github.io/FLAML/docs/Use-Cases/Autogen) package.
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Minimal dependencies are installed without extra options. You can install extra options based on the feature you need. For example, use the following to install the dependencies needed by the [`autogen`](https://microsoft.github.io/autogen/) package.
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```bash
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pip install "flaml[autogen]"
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```
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@ -54,7 +54,7 @@ Each of the [`notebook examples`](https://github.com/microsoft/FLAML/tree/main/n
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## Quickstart
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* (New) The [autogen](https://microsoft.github.io/FLAML/docs/Use-Cases/Autogen) package enables the next-gen GPT-X applications with a generic multi-agent conversation framework.
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* (New) The [autogen](https://microsoft.github.io/autogen/) package enables the next-gen GPT-X applications with a generic multi-agent conversation framework.
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It offers customizable and conversable agents which integrate LLMs, tools and human.
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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,
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```python
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"source": [
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"# Auto Generated Agent Chat: Using MathChat to Solve Math Problems\n",
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"\n",
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"`flaml.autogen` offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framwork allows tool use and human participance through multi-agent conversation. Please find documentation about this feature [here](https://microsoft.github.io/FLAML/docs/Use-Cases/Autogen#agents).\n",
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"`flaml.autogen` offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framwork allows tool use and human participance through multi-agent conversation. Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).\n",
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"\n",
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"MathChat is an experimental convesational framework for math problem solving. In this notebook, we demonstrate how to use MathChat to solve math problems. MathChat uses the `AssistantAgent` and `MathUserProxyAgent`, which is similar to the usage of `AssistantAgent` and `UserProxyAgent` in other notebooks (e.g., [Automated Task Solving with Code Generation, Execution & Debugging](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_agentchat_auto_feedback_from_code_execution.ipynb)). Essentially, `MathUserProxyAgent` implements a different auto reply mechanism corresponding to the MathChat prompts. You can find more details in the paper [An Empirical Study on Challenging Math Problem Solving with GPT-4](https://arxiv.org/abs/2306.01337) or the [blogpost](https://microsoft.github.io/FLAML/blog/2023/06/28/MathChat).\n",
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"\n",
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"# Auto Generated Agent Chat: Using RetrieveChat for Retrieve Augmented Code Generation and Question Answering\n",
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"\n",
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"`flaml.autogen` offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framwork allows tool use and human participance through multi-agent conversation.\n",
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"Please find documentation about this feature [here](https://microsoft.github.io/FLAML/docs/Use-Cases/Autogen#agents).\n",
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"Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).\n",
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"\n",
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"RetrieveChat is a convesational system for retrieve augmented code generation and question answering. In this notebook, we demonstrate how to utilize RetrieveChat to generate code and answer questions based on customized documentations that are not present in the LLM's training dataset. RetrieveChat uses the `RetrieveAssistantAgent` and `RetrieveUserProxyAgent`, which is similar to the usage of `AssistantAgent` and `UserProxyAgent` in other notebooks (e.g., [Automated Task Solving with Code Generation, Execution & Debugging](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_agentchat_auto_feedback_from_code_execution.ipynb)). Essentially, `RetrieveAssistantAgent` and `RetrieveUserProxyAgent` implement a different auto-reply mechanism corresponding to the RetrieveChat prompts.\n",
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"\n",
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"# Auto Generated Agent Chat: Task Solving with Code Generation, Execution & Debugging\n",
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"\n",
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"FLAML offers conversable LLM agents, which can be used to solve various tasks with human or automatic feedback, including tasks that require using tools via code.\n",
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"Please find documentation about this feature [here](https://microsoft.github.io/FLAML/docs/Use-Cases/Autogen#agents).\n",
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"Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).\n",
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"\n",
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"In this notebook, we demonstrate how to use `AssistantAgent` and `UserProxyAgent` to write code and execute the code. Here `AssistantAgent` is an LLM-based agent that can write Python code (in a Python coding block) for a user to execute for a given task. `UserProxyAgent` is an agent which serves as a proxy for the human user to execute the code written by `AssistantAgent`, or automatically execute the code. Depending on the setting of `human_input_mode` and `max_consecutive_auto_reply`, the `UserProxyAgent` either solicits feedback from the human user or returns auto-feedback based on the result of code execution (success or failure and corresponding outputs) to `AssistantAgent`. `AssistantAgent` will debug the code and suggest new code if the result contains error. The two agents keep communicating to each other until the task is done.\n",
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"\n",
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"# Auto Generated Agent Chat: Chess Game Playing While Chitchatting by GPT-4 Agents\n",
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"\n",
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"`flaml.autogen` offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framwork allows tool use and human participance through multi-agent conversation.\n",
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"Please find documentation about this feature [here](https://microsoft.github.io/FLAML/docs/Use-Cases/Autogen#agents).\n",
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"Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).\n",
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"\n",
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"This notebook is modified based on https://github.com/ekzhu/FLAML/blob/evaluation/evaluation/chess/play_chess.ipynb\n",
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"\n",
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"source": [
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"# Auto Generated Agent Chat: Task Solving with Provided Tools as Functions\n",
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"\n",
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"`flaml.autogen` offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framwork allows tool use and human participance through multi-agent conversation. Please find documentation about this feature [here](https://microsoft.github.io/FLAML/docs/Use-Cases/Autogen#agents).\n",
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"`flaml.autogen` offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framwork allows tool use and human participance through multi-agent conversation. Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).\n",
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"\n",
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"In this notebook, we demonstrate how to use `AssistantAgent` and `UserProxyAgent` to make function calls with the new feature of OpenAI models (in model version 0613). A specified prompt and function configs need to be passed to `AssistantAgent` to initialize the agent. The corresponding functions need to be passed to `UserProxyAgent`, which will be responsible for executing any function calls made by `AssistantAgent`. Besides this requirement of matching descriptions with functions, we recommend checking the system message in the `AssistantAgent` to make sure the instructions align with the function call descriptions.\n",
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"\n",
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"# Auto Generated Agent Chat: Group Chat\n",
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"\n",
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"`flaml.autogen` offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framwork allows tool use and human participance through multi-agent conversation.\n",
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"Please find documentation about this feature [here](https://microsoft.github.io/FLAML/docs/Use-Cases/Autogen#agents).\n",
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"Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).\n",
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"\n",
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"This notebook is modified based on https://github.com/microsoft/FLAML/blob/4ea686af5c3e8ff24d9076a7a626c8b28ab5b1d7/notebook/autogen_multiagent_roleplay_chat.ipynb\n",
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"\n",
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"# Auto Generated Agent Chat: Performs Research with Multi-Agent Group Chat\n",
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"\n",
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"`flaml.autogen` offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framwork allows tool use and human participance through multi-agent conversation.\n",
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"Please find documentation about this feature [here](https://microsoft.github.io/FLAML/docs/Use-Cases/Autogen#agents).\n",
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"Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).\n",
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"\n",
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"## Requirements\n",
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"\n",
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"# Auto Generated Agent Chat: Task Solving with Code Generation, Execution, Debugging & Human Feedback\n",
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"\n",
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"`flaml.autogen` offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framwork allows tool use and human participance through multi-agent conversation.\n",
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"Please find documentation about this feature [here](https://microsoft.github.io/FLAML/docs/Use-Cases/Autogen#agents).\n",
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"Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).\n",
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"\n",
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"In this notebook, we demonstrate how to use `AssistantAgent` and `UserProxyAgent` to solve a challenging math problem with human feedback. Here `AssistantAgent` is an LLM-based agent that can write Python code (in a Python coding block) for a user to execute for a given task. `UserProxyAgent` is an agent which serves as a proxy for a user to execute the code written by `AssistantAgent`. By setting `human_input_mode` properly, the `UserProxyAgent` can also prompt the user for feedback to `AssistantAgent`. For example, when `human_input_mode` is set to \"ALWAYS\", the `UserProxyAgent` will always prompt the user for feedback. When user feedback is provided, the `UserProxyAgent` will directly pass the feedback to `AssistantAgent`. When no user feedback is provided, the `UserProxyAgent` will execute the code written by `AssistantAgent` and return the execution results (success or failure and corresponding outputs) to `AssistantAgent`.\n",
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"\n",
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"# Auto Generated Agent Chat: Collaborative Task Solving with Coding and Planning Agent\n",
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"\n",
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"`flaml.autogen` offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framwork allows tool use and human participance through multi-agent conversation.\n",
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"Please find documentation about this feature [here](https://microsoft.github.io/FLAML/docs/Use-Cases/Autogen#agents).\n",
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"Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).\n",
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"\n",
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"In this notebook, we demonstrate how to use multiple agents to work together and accomplish a task which requires finding info from the web and coding. `AssistantAgent` is an LLM-based agent that can write and debug Python code (in a Python coding block) for a user to execute for a given task. `UserProxyAgent` is an agent which serves as a proxy for a user to execute the code written by `AssistantAgent`. We further create a planning agent for the assistant agent to consult. The planning agent is a variation of the LLM-based `AssistantAgent` with a different system message.\n",
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"\n",
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"# Interactive LLM Agent Dealing with Data Stream\n",
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"\n",
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"`flaml.autogen` offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framwork allows tool use and human participance through multi-agent conversation.\n",
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"Please find documentation about this feature [here](https://microsoft.github.io/FLAML/docs/Use-Cases/Autogen#agents).\n",
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"Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).\n",
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"\n",
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"In this notebook, we demonstrate how to use customized agents to continuously acquires news from the web and ask for investment suggestions.\n",
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"\n",
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"source": [
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"# Auto Generated Agent Chat: Collaborative Task Solving with Multiple Agents and Human Users\n",
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"\n",
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"`flaml.autogen` offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framwork allows tool use and human participance through multi-agent conversation. Please find documentation about this feature [here](https://microsoft.github.io/FLAML/docs/Use-Cases/Autogen#agents).\n",
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"`flaml.autogen` offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framwork allows tool use and human participance through multi-agent conversation. Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).\n",
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"\n",
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"In this notebook, we demonstrate an application involving multiple agents and human users to work together and accomplish a task. `AssistantAgent` is an LLM-based agent that can write Python code (in a Python coding block) for a user to execute for a given task. `UserProxyAgent` is an agent which serves as a proxy for a user to execute the code written by `AssistantAgent`. We create multiple `UserProxyAgent` instances which can represent different human users.\n",
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"\n",
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"# Auto Generated Agent Chat: Solving Tasks Requiring Web Info\n",
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"\n",
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"`flaml.autogen` offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framwork allows tool use and human participance through multi-agent conversation.\n",
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"Please find documentation about this feature [here](https://microsoft.github.io/FLAML/docs/Use-Cases/Autogen#agents).\n",
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"Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).\n",
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"\n",
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"In this notebook, we demonstrate how to use `AssistantAgent` and `UserProxyAgent` to perform tasks which require acquiring info from the web:\n",
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"* discuss a paper based on its URL.\n",
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### Bring AutoML to One's Fingertips
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FLAML offers an off-the-shelf AutoML solution that enables users to quickly discover high-quality models or configurations for common ML/AI tasks. By automatically selecting models and hyperparameters for training or inference, FLAML saves users time and effort. FLAML has significantly reduced development time for developers and data scientists alike, while also providing a convenient way to integrate new algorithms into the pipeline, enabling easy extensions and large-scale parallel tuning. These features make FLAML a valuable tool in R&D efforts for many enterprise users.
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FLAML is capable of handling a variety of common ML tasks, such as [classification](https://microsoft.github.io/FLAML/docs/Examples/AutoML-Classification), [regression](https://microsoft.github.io/FLAML/docs/Examples/AutoML-Regression), [time series forecasting](https://microsoft.github.io/FLAML/docs/Examples/AutoML-Time%20series%20forecast), [NLP tasks](https://microsoft.github.io/FLAML/docs/Examples/AutoML-Rank), and [generative tasks](https://microsoft.github.io/FLAML/docs/Use-Cases/Autogen), providing a comprehensive solution for various applications.
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FLAML is capable of handling a variety of common ML tasks, such as [classification](https://microsoft.github.io/FLAML/docs/Examples/AutoML-Classification), [regression](https://microsoft.github.io/FLAML/docs/Examples/AutoML-Regression), [time series forecasting](https://microsoft.github.io/FLAML/docs/Examples/AutoML-Time%20series%20forecast), [NLP tasks](https://microsoft.github.io/FLAML/docs/Examples/AutoML-Rank), and [generative tasks](https://microsoft.github.io/autogen/), providing a comprehensive solution for various applications.
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### Speed and Efficiency: The FLAML Advantage
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What sets FLAML apart from other AutoML libraries is its exceptional efficiency, thanks to the economical and efficient hyperparameter optimization and model selection methods developed in our [research](https://microsoft.github.io/FLAML/docs/Research). FLAML is also capable of handling large search spaces with heterogeneous evaluation costs, complex constraints, guidance, and early stopping. The [zero-shot AutoML](https://microsoft.github.io/FLAML/docs/Use-Cases/Zero-Shot-AutoML) option further reduces the cost of AutoML, making FLAML an even more attractive solution for a wide range of applications with low resources.
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