microsoft · qingyun-wu · Oct 13, 2023 · Oct 13, 2023 · Oct 13, 2023 · Oct 13, 2023
diff --git a/notebook/agentchat_MathChat.ipynb b/notebook/agentchat_MathChat.ipynb
@@ -15,7 +15,7 @@
    "source": [
     "# Auto Generated Agent Chat: Using MathChat to Solve Math Problems\n",
     "\n",
-    "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",
+    "AutoGen offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framework allows tool use and human participation through multi-agent conversation. Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).\n",
     "\n",
     "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/autogen/blob/main/notebook/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/autogen/blog/2023/06/28/MathChat).\n",
     "\n",

diff --git a/notebook/agentchat_RetrieveChat.ipynb b/notebook/agentchat_RetrieveChat.ipynb
@@ -16,7 +16,7 @@
     "<a id=\"toc\"></a>\n",
     "# Auto Generated Agent Chat: Using RetrieveChat for Retrieve Augmented Code Generation and Question Answering\n",
     "\n",
-    "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",
+    "AutoGen offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framework allows tool use and human participation through multi-agent conversation.\n",
     "Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).\n",
     "\n",
     "RetrieveChat is a conversational 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/autogen/blob/main/notebook/agentchat_auto_feedback_from_code_execution.ipynb)). Essentially, `RetrieveAssistantAgent` and  `RetrieveUserProxyAgent` implement a different auto-reply mechanism corresponding to the RetrieveChat prompts.\n",

diff --git a/notebook/agentchat_chess.ipynb b/notebook/agentchat_chess.ipynb
@@ -15,7 +15,7 @@
    "source": [
     "# Auto Generated Agent Chat: Chess Game Playing While Chitchatting by GPT-4 Agents\n",
     "\n",
-    "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",
+    "AutoGen offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framework allows tool use and human participation through multi-agent conversation.\n",
     "Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).\n",
     "\n",
     "This notebook is modified based on https://github.com/ekzhu/FLAML/blob/evaluation/evaluation/chess/play_chess.ipynb\n",

diff --git a/notebook/agentchat_function_call.ipynb b/notebook/agentchat_function_call.ipynb
@@ -17,7 +17,7 @@
    "source": [
     "# Auto Generated Agent Chat: Task Solving with Provided Tools as Functions\n",
     "\n",
-    "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",
+    "AutoGen offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framework allows tool use and human participation through multi-agent conversation. Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).\n",
     "\n",
     "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",
     "\n",

diff --git a/notebook/agentchat_groupchat_research.ipynb b/notebook/agentchat_groupchat_research.ipynb
@@ -15,7 +15,7 @@
    "source": [
     "# Auto Generated Agent Chat: Performs Research with Multi-Agent Group Chat\n",
     "\n",
-    "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",
+    "AutoGen offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framework allows tool use and human participation through multi-agent conversation.\n",
     "Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).\n",
     "\n",
     "## Requirements\n",

diff --git a/notebook/agentchat_groupchat_vis.ipynb b/notebook/agentchat_groupchat_vis.ipynb
@@ -15,7 +15,7 @@
    "source": [
     "# Auto Generated Agent Chat: Group Chat with Coder and Visualization Critic\n",
     "\n",
-    "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",
+    "AutoGen offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framework allows tool use and human participation through multi-agent conversation.\n",
     "Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).\n",
     "\n",
     "## Requirements\n",

diff --git a/notebook/agentchat_human_feedback.ipynb b/notebook/agentchat_human_feedback.ipynb
@@ -19,7 +19,7 @@
    "source": [
     "# Auto Generated Agent Chat: Task Solving with Code Generation, Execution, Debugging & Human Feedback\n",
     "\n",
-    "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",
+    "AutoGen offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framework allows tool use and human participation through multi-agent conversation.\n",
     "Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).\n",
     "\n",
     "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",

diff --git a/notebook/agentchat_planning.ipynb b/notebook/agentchat_planning.ipynb
@@ -19,7 +19,7 @@
    "source": [
     "# Auto Generated Agent Chat: Collaborative Task Solving with Coding and Planning Agent\n",
     "\n",
-    "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",
+    "AutoGen offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framework allows tool use and human participation through multi-agent conversation.\n",
     "Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).\n",
     "\n",
     "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",

diff --git a/notebook/agentchat_stream.ipynb b/notebook/agentchat_stream.ipynb
@@ -19,7 +19,7 @@
    "source": [
     "# Interactive LLM Agent Dealing with Data Stream\n",
     "\n",
-    "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",
+    "AutoGen offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framework allows tool use and human participation through multi-agent conversation.\n",
     "Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).\n",
     "\n",
     "In this notebook, we demonstrate how to use customized agents to continuously acquires news from the web and ask for investment suggestions.\n",

diff --git a/notebook/agentchat_teaching.ipynb b/notebook/agentchat_teaching.ipynb
@@ -15,7 +15,7 @@
    "source": [
     "# Auto Generated Agent Chat: Teaching\n",
     "\n",
-    "AutoGen offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framwork makes it easy to build many advanced applications of LLMs.\n",
+    "AutoGen offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framework makes it easy to build many advanced applications of LLMs.\n",
     "Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).\n",
     "\n",
     "This notebook demonstrates how AutoGen enables a user to teach AI new skills via natural agent interactions, without requiring knowledge of programming language. It is modified based on https://github.com/microsoft/FLAML/blob/evaluation/notebook/research_paper/teaching.ipynb and https://github.com/microsoft/FLAML/blob/evaluation/notebook/research_paper/teaching_recipe_reuse.ipynb.\n",

diff --git a/notebook/agentchat_two_users.ipynb b/notebook/agentchat_two_users.ipynb
@@ -19,7 +19,7 @@
    "source": [
     "# Auto Generated Agent Chat: Collaborative Task Solving with Multiple Agents and Human Users\n",
     "\n",
-    "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",
+    "AutoGen offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framework allows tool use and human participation through multi-agent conversation. Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).\n",
     "\n",
     "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",
     "\n",

diff --git a/notebook/agentchat_web_info.ipynb b/notebook/agentchat_web_info.ipynb
@@ -19,7 +19,7 @@
    "source": [
     "# Auto Generated Agent Chat: Solving Tasks Requiring Web Info\n",
     "\n",
-    "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",
+    "AutoGen offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framework allows tool use and human participation through multi-agent conversation.\n",
     "Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).\n",
     "\n",
     "In this notebook, we demonstrate how to use `AssistantAgent` and `UserProxyAgent` to perform tasks which require acquiring info from the web:\n",

diff --git a/website/docs/Examples/AutoGen-AgentChat.md b/website/docs/Examples/AutoGen-AgentChat.md
@@ -1,6 +1,6 @@
 # AutoGen - Automated Multi Agent Chat
 
-AutoGen offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framework allows tool use and human participance via multi-agent conversation.
+AutoGen offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framework allows tool use and human participation via multi-agent conversation.
 Please find documentation about this feature [here](/docs/Use-Cases/agent_chat).
 
 Links to notebook examples: