Improves clarity and fixes punctuation in README and Multi-agent docu…

…mentation (Significant-Gravitas#40)

* Improves clarity and fixes punctuation in README and Multi-agent documentation

* fix broken colab link to agentchat_groupchat_research.ipynb (others are fine)

* fix typos, improves readability

README.md

@@ -26,15 +26,15 @@ This project is a spinoff from [FLAML](https://github.com/microsoft/FLAML).
 
 ## What is AutoGen
 
-AutoGen is a framework that enables development of LLM applications using multiple agents that can converse with each other to solve task. 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 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](https://github.com/microsoft/autogen/blob/main/website/static/img/autogen_agentchat.png)
 
-* AutoGen enables building next-gen LLM applications based on **multi-agent conversations** with minimal effort. It simplifies the orchestration, automation and optimization of a complex LLM workflow. It maximizes the performance of LLM models and overcome their weaknesses.
+* AutoGen enables building next-gen LLM applications based on **multi-agent conversations** 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** 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** from various domains and complexities. They demonstrate how AutoGen can easily support different conversation patterns.
-* AutoGen provides a drop-in replacement of `openai.Completion` or `openai.ChatCompletion` as an **enhanced inference API**. It allows easy performance tuning, utilities like API unification & caching, and advanced usage patterns, such as error handling, multi-config inference, context programming etc.
+* It provides a collection of working systems with different complexities. These systems span a **wide range of applications** from various domains and complexities. This demonstrates how AutoGen can easily support diverse conversation patterns.
+* AutoGen provides a drop-in replacement of `openai.Completion` or `openai.ChatCompletion` as an **enhanced inference API**. It allows easy performance tuning, utilities like API unification and caching, and advanced usage patterns, such as error handling, multi-config inference, context programming, etc.
 
 AutoGen is powered by collaborative [research studies](https://microsoft.github.io/autogen/docs/Research) from Microsoft, Penn State University, and University of Washington.
 
@@ -59,7 +59,7 @@ For LLM inference configurations, check the [FAQ](https://microsoft.github.io/au
 
 ## Quickstart
 
-* 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 human.
+* 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),
 ```python
 from autogen import AssistantAgent, UserProxyAgent, config_list_from_json
@@ -83,7 +83,7 @@ The figure below shows an example conversation flow with AutoGen.
 
 Please find more [code examples](https://microsoft.github.io/autogen/docs/Examples/AutoGen-AgentChat) for this feature.
 
-* Autogen also helps maximize the utility out of the expensive LLMs such as ChatGPT and GPT-4. It offers a drop-in replacement of `openai.Completion` or `openai.ChatCompletion` with powerful functionalities like tuning, caching, error handling, templating. For example, you can optimize generations by LLM with your own tuning data, success metrics and budgets.
+* Autogen also helps maximize the utility out of the expensive LLMs such as ChatGPT and GPT-4. It offers a drop-in replacement of `openai.Completion` or `openai.ChatCompletion` adding powerful functionalities like tuning, caching, error handling, and templating. For example, you can optimize generations by LLM with your own tuning data, success metrics and budgets.
 ```python
 # perform tuning
 config, analysis = autogen.Completion.tune(
@@ -126,7 +126,7 @@ a CLA and decorate the PR appropriately (e.g., status check, comment). Simply fo
 provided by the bot. You will only need to do this once across all repos using our CLA.
 
 This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
-For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
+For more information, see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
 contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
 
 # Legal Notices

notebook/agentchat_groupchat_research.ipynb

@@ -5,7 +5,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/groupchat_research.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+    "<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/agentchat_groupchat_research.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
    ]
   },
   {

website/docs/Use-Cases/agent_chat.md

@@ -10,8 +10,8 @@ This framework simplifies the orchestration, automation and optimization of a co
 AutoGen abstracts and implements conversable agents
 designed to solve tasks through inter-agent conversations. Specifically, the agents in AutoGen have the following notable features:
 
-- Conversable: Agent in AutoGen are conversable, which means that any agent can send
-and receive messages to and from the other agents to start or continue a conversation
+- Conversable: Agents in AutoGen are conversable, which means that any agent can send
+and receive messages from other agents to initiate or continue a conversation
 
 - Customizable: Agents in AutoGen can be customized to integrate LLMs, humans, tools, or a combination of them.
 
@@ -21,9 +21,9 @@ The figure below shows the built-in agents in AutoGen.
 We have designed a generic `ConversableAgent` class for Agents that are capable of conversing with each other through the exchange of messages to jointly finish a task. An agent can communicate with other agents and perform actions. Different agents can differ in what actions they perform after receiving messages. Two representative subclasses are `AssistantAgent` and `UserProxyAgent`.
 
 
-- The `AssistantAgent` is designed to act as an AI assistant, using LLMs by default but not requiring human input or code execution. It could write Python code (in a Python coding block) for a user to execute when a message (typically a description of a task that needs to be solved) is received. Under the hood, the Python code is written by LLM (e.g., GPT-4). It can also receive the execution results and suggest code with bug fix. Its behavior can be altered by passing a new system message. The LLM [inference](#enhanced-inference) configuration can be configured via `llm_config`.
+- The `AssistantAgent` is designed to act as an AI assistant, using LLMs by default but not requiring human input or code execution. It could write Python code (in a Python coding block) for a user to execute when a message (typically a description of a task that needs to be solved) is received. Under the hood, the Python code is written by LLM (e.g., GPT-4). It can also receive the execution results and suggest corrections or bug fixes. Its behavior can be altered by passing a new system message. The LLM [inference](#enhanced-inference) configuration can be configured via `llm_config`.
 
-- The `UserProxyAgent` is conceptually a proxy agent for humans, soliciting human input as the agent's reply at each interaction turn by default and also having the capability to execute code and call functions. The `UserProxyAgent` triggers code execution automatically when it detects an executable code block in the received message and no human user input is provided. Code execution can be disabled by setting `code_execution_config` to False. LLM-based response is disabled by default. It can be enabled by setting `llm_config` to a dict corresponding to the [inference](/docs/Use-Cases/enhanced_inference) configuration. When `llm_config` is set to a dict, `UserProxyAgent` can generate replies using an LLM when code execution is not performed.
+- The `UserProxyAgent` is conceptually a proxy agent for humans, soliciting human input as the agent's reply at each interaction turn by default and also having the capability to execute code and call functions. The `UserProxyAgent` triggers code execution automatically when it detects an executable code block in the received message and no human user input is provided. Code execution can be disabled by setting the `code_execution_config` parameter to False. LLM-based response is disabled by default. It can be enabled by setting `llm_config` to a dict corresponding to the [inference](/docs/Use-Cases/enhanced_inference) configuration. When `llm_config` is set as a dictionary, `UserProxyAgent` can generate replies using an LLM when code execution is not performed.
 
 The auto-reply capability of `ConversableAgent` allows for more autonomous multi-agent communication while retaining the possibility of human intervention.
 One can also easily extend it by registering reply functions with the `register_reply()` method.
@@ -44,7 +44,7 @@ user_proxy = UserProxyAgent(name="user_proxy")
 
 ### A Basic Two-Agent Conversation Example
 
-Once the participating agents are constructed properly, one can start a multi-agent conversation session by an initialization step as shown in following code:
+Once the participating agents are constructed properly, one can start a multi-agent conversation session by an initialization step as shown in the following code:
 ```python
 # the assistant receives a message from the user, which contains the task description
 user_proxy.initiate_chat(
@@ -62,14 +62,14 @@ After the initialization step, the conversation could proceed automatically. Fin
 
 ### Supporting Diverse Conversation Patterns
 
-#### Conversations with different autonomisity, and human involvement patterns
+#### Conversations with different levels of autonomy, and human-involvement patterns
 On the one hand, one can achieve fully autonomous conversations after an initialization step. On the other hand, AutoGen can be used to implement human-in-the-loop problem-solving by configuring human involvement levels and patterns (e.g., setting the `human_input_mode` to `ALWAYS`), as human involvement is expected and/or desired in many applications.
 
 #### Static and dynamic conversations
 
 By adopting the conversation-driven control with both programming language and natural language, AutoGen inherently allows dynamic conversation. Dynamic conversation allows the agent topology to change depending on the actual flow of conversation under different input problem instances, while the flow of a static conversation always follows a pre-defined topology. The dynamic conversation pattern is useful in complex applications where the patterns of interaction cannot be predetermined in advance. AutoGen provides two general approaches to achieving dynamic conversation:
 
-- Registered auto-reply. With the pluggable auto-reply function, one can choose to invoke conversations with other agents depending on the content of the current message and context. A working system demonstrating this type of dynamic conversation can be found in this code example, demonstrating a [dynamic group chat](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_groupchat.ipynb). In the system, we register an auto-reply function in the group chat manager, which lets LLM decide who will the next speaker be in a group chat setting.
+- Registered auto-reply. With the pluggable auto-reply function, one can choose to invoke conversations with other agents depending on the content of the current message and context. A working system demonstrating this type of dynamic conversation can be found in this code example, demonstrating a [dynamic group chat](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_groupchat.ipynb). In the system, we register an auto-reply function in the group chat manager, which lets LLM decide who the next speaker will be in a group chat setting.
 
 - LLM-based function call. In this approach, LLM decides whether or not to call a particular function depending on the conversation status in each inference call.
 By messaging additional agents in the called functions, the LLM can drive dynamic multi-agent conversation. A working system showcasing this type of dynamic conversation can be found in the [multi-user math problem solving scenario](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_two_users.ipynb), where a student assistant would automatically resort to an expert using function calls.