Skip to content

Commit

Permalink
Change references from agenext to autogen (#610)
Browse files Browse the repository at this point in the history
  • Loading branch information
jackgerrits authored Sep 23, 2024
1 parent 8ea0931 commit 93e7127
Show file tree
Hide file tree
Showing 23 changed files with 49 additions and 54 deletions.
2 changes: 1 addition & 1 deletion python/packages/agbench/pyproject.toml
Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,7 @@ dynamic = ["version"]
authors = [
{ name="Adam Fourney", email="[email protected]" },
]
description = "AGNext Benchmarking Tools"
description = "AutoGen Benchmarking Tools"
readme = "README.md"
requires-python = ">=3.8, <3.13"
classifiers = [
Expand Down
2 changes: 1 addition & 1 deletion python/packages/agbench/src/agbench/res/Dockerfile
Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
FROM python:3.11
MAINTAINER AGNext
MAINTAINER AutoGen

# Install packages
# ffmpeg and exiftool are needed for mdconvert
Expand Down
2 changes: 1 addition & 1 deletion python/packages/agbench/src/agbench/run_cmd.py
Original file line number Diff line number Diff line change
Expand Up @@ -486,7 +486,7 @@ def run_scenario_in_docker(

if agnext_repo_base is None:
raise ValueError(
"Could not find AGNext repo base. Please set the environment variable AGNEXT_REPO_BASE to the correct value."
"Could not find AutoGen repo base. Please set the environment variable AGNEXT_REPO_BASE to the correct value."
)

agnext_repo_base = os.path.join(agnext_repo_base, "python")
Expand Down
4 changes: 2 additions & 2 deletions python/packages/autogen-core/docs/README.md
Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
## Building the AGNext Documentation
## Building the AutoGen Documentation

AGNext documentation is based on the sphinx documentation system and uses the myst-parser to render markdown files. It uses the [pydata-sphinx-theme](https://pydata-sphinx-theme.readthedocs.io/en/latest/) to style the documentation.
AutoGen documentation is based on the sphinx documentation system and uses the myst-parser to render markdown files. It uses the [pydata-sphinx-theme](https://pydata-sphinx-theme.readthedocs.io/en/latest/) to style the documentation.

### Prerequisites

Expand Down
2 changes: 1 addition & 1 deletion python/packages/autogen-core/docs/src/conf.py
Original file line number Diff line number Diff line change
Expand Up @@ -63,7 +63,7 @@
# -- Options for HTML output -------------------------------------------------
# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output

html_title = "AGNext"
html_title = "AutoGen"

html_theme = "pydata_sphinx_theme"
html_static_path = ["_static"]
Expand Down
19 changes: 7 additions & 12 deletions python/packages/autogen-core/docs/src/index.md
Original file line number Diff line number Diff line change
Expand Up @@ -2,16 +2,16 @@
myst:
html_meta:
"description lang=en": |
Top-level documentation for AGNext, a framework for building multi-agent applications with AI agents.
Top-level documentation for AutoGen, a framework for building multi-agent applications with AI agents.
html_theme.sidebar_secondary.remove: false
---

# AGNext
# AutoGen

AGNext is a OSS framework for developing intelligent applications using AI Agents patterns.
AutoGen is an OSS framework for developing intelligent applications using AI Agents patterns.
It offers an easy way to quickly build event-driven, distributed, scalable, resilient AI agent systems. Agents are developed by using the [Actor model](https://en.wikipedia.org/wiki/Actor_model). You can build and run your agent system locally and easily move to a distributed system in the cloud when you are ready.

Key features of AGNext are summarized below.
Key features of AutoGen are summarized below.

```{gallery-grid}
:grid-columns: 1 2 2 3
Expand All @@ -31,7 +31,7 @@ Key features of AGNext are summarized below.
```

<!--
Key features of AGNext include:
Key features of AutoGen include:
- Asynchronous messaging: Agents communicate with each other through asynchronous messages, enabling event-driven and request/response communication models.
- Scalable & Distributed: Enable complex scenarios with networks of agents across org boundaries
Expand All @@ -40,7 +40,7 @@ Key features of AGNext include:
- Observable, traceable & debuggable -->

```{seealso}
To start quickly, read the [Quick Start](user-guide/guides/quickstart) guide and follow the tutorial sections. To learn about the core concepts of AGNext, begin with [Agent and Multi-Agent Application](user-guide/core-concepts/agent-and-multi-agent-application).
To start quickly, read the [Quick Start](user-guide/guides/quickstart) guide and follow the tutorial sections. To learn about the core concepts of AutoGen, begin with [Agent and Multi-Agent Application](user-guide/core-concepts/agent-and-multi-agent-application).
```

```{toctree}
Expand All @@ -52,7 +52,7 @@ user-guide/index

<!-- ## Community
Information about the community that leads, supports, and develops AGNext.
Information about the community that leads, supports, and develops AutoGen.
```{toctree}
:maxdepth: 2
Expand All @@ -72,8 +72,3 @@ packages/index
reference/index
```

<!-- ````{toctree}
:hidden:
Changelog <https://github.com/your-org/agnext/releases>
``` -->
4 changes: 2 additions & 2 deletions python/packages/autogen-core/docs/src/packages/index.md
Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@
myst:
html_meta:
"description lang=en": |
AGNext packages provide a set of functionality for building multi-agent applications with AI agents.
AutoGen packages provide a set of functionality for building multi-agent applications with AI agents.
---

<style>
Expand All @@ -23,7 +23,7 @@ myst:
:::{card} {fas}`cube;pst-color-primary` AutoGen Core
:class-title: card-title

Implements the core functionality of the AGNext framework, providing basic building blocks for creating multi-agent systems.
Implements the core functionality of the AutoGen framework, providing basic building blocks for creating multi-agent systems.

```sh
pip install autogen-core
Expand Down
2 changes: 1 addition & 1 deletion python/packages/autogen-core/docs/src/reference/index.md
Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@
myst:
html_meta:
"description lang=en": |
AGNext is a community-driven project. Learn how to get involved, contribute, and connect with the community.
AutoGen is a community-driven project. Learn how to get involved, contribute, and connect with the community.
---

<style>
Expand Down
Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@
"and [Azure OpenAI Assistant](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/assistant)\n",
"are server-side APIs for building\n",
"agents.\n",
"They can be used to build agents in AGNext. This cookbook demonstrates how to\n",
"They can be used to build agents in AutoGen. This cookbook demonstrates how to\n",
"to use OpenAI Assistant to create an agent that can run code and Q&A over document.\n",
"\n",
"## Message Protocol\n",
Expand Down
Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
# Application Stack

AGNext is designed to be an unopinionated framework that can be used to build
AutoGen core is designed to be an unopinionated framework that can be used to build
a wide variety of multi-agent applications. It is not tied to any specific
agent abstraction or multi-agent pattern.

Expand Down
Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@
"source": [
"# Agent and Agent Runtime\n",
"\n",
"In this and the following section, we focus on the core concepts of AGNext:\n",
"In this and the following section, we focus on the core concepts of AutoGen:\n",
"agents, agent runtime, messages, and communication.\n",
"You will not find any AI models or tools here, just the foundational\n",
"building blocks for building multi-agent applications."
Expand All @@ -16,12 +16,12 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"An agent in AGNext is an entity defined by the base class {py:class}`autogen_core.base.BaseAgent`.\n",
"An agent in AutoGen is an entity defined by the base class {py:class}`autogen_core.base.BaseAgent`.\n",
"It has a unique identifier of the type {py:class}`autogen_core.base.AgentId`,\n",
"a metadata dictionary of the type {py:class}`autogen_core.base.AgentMetadata`,\n",
"and method for handling messages {py:meth}`autogen_core.base.BaseAgent.on_message`.\n",
"\n",
"An agent runtime is the execution environment for agents in AGNext.\n",
"An agent runtime is the execution environment for agents in AutoGen.\n",
"Similar to the runtime environment of a programming language,\n",
"an agent runtime provides the necessary infrastructure to facilitate communication\n",
"between agents, manage agent lifecycles, enforce security boundaries, and support monitoring and\n",
Expand Down
Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
# Logging

AGNext uses Python's built-in [`logging`](https://docs.python.org/3/library/logging.html) module.
AutoGen uses Python's built-in [`logging`](https://docs.python.org/3/library/logging.html) module.

There are two kinds of logging:

Expand Down
Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@
"source": [
"# Message and Communication\n",
"\n",
"An agent in AGNext can react to, send, and publish messages,\n",
"An agent in AutoGen core can react to, send, and publish messages,\n",
"and messages are the only means through which agents can communicate\n",
"with each other."
]
Expand Down Expand Up @@ -263,7 +263,7 @@
"source": [
"## Direct Messaging\n",
"\n",
"There are two types of communication in AGNext:\n",
"There are two types of communication in AutoGen core:\n",
"\n",
"- **Direct Messaging**: sends a direct message to another agent.\n",
"- **Broadcast**: publishes a message to a topic.\n",
Expand Down
Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@
"source": [
"# Model Clients\n",
"\n",
"AGNext provides the {py:mod}`autogen_core.components.models` module with a suite of built-in\n",
"AutoGen provides the {py:mod}`autogen_core.components.models` module with a suite of built-in\n",
"model clients for using ChatCompletion API.\n",
"All model clients implement the {py:class}`~autogen_core.components.models.ChatCompletionClient` protocol class."
]
Expand Down
Original file line number Diff line number Diff line change
Expand Up @@ -13,6 +13,6 @@ For example, the [tool-equiped agent](./tools.ipynb#tool-equipped-agent) in
the previous section employs a design pattern called ReAct,
which involves an agent interacting with tools.

You can implement any multi-agent design pattern using AGNext agents.
You can implement any multi-agent design pattern using AutoGen agents.
In the next two sections, we will discuss two common design patterns:
group chat for task decomposition, and reflection for robustness.
Original file line number Diff line number Diff line change
Expand Up @@ -381,7 +381,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"AGNext also supports distributed agent runtime, which can host agents running on\n",
"AutoGen also supports a distributed agent runtime, which can host agents running on\n",
"different processes or machines, with different identities, languages and dependencies.\n",
"\n",
"To learn how to use agent runtime, communication, message handling, and subscription, please continue\n",
Expand Down
Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,7 @@
"For example, given a task to write code, the first LLM can generate a code snippet,\n",
"and the second LLM can generate a critique of the code snippet.\n",
"\n",
"In the context of AGNext and agents, reflection can be implemented as a pair\n",
"In the context of AutoGen and agents, reflection can be implemented as a pair\n",
"of agents, where the first agent generates a message and the second agent\n",
"generates a response to the message. The two agents continue to interact\n",
"until they reach a stopping condition, such as a maximum number of iterations\n",
Expand All @@ -22,7 +22,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's implement a simple reflection design pattern using AGNext agents.\n",
"Let's implement a simple reflection design pattern using AutoGen agents.\n",
"There will be two agents: a coder agent and a reviewer agent, the coder agent\n",
"will generate a code snippet, and the reviewer agent will generate a critique\n",
"of the code snippet.\n",
Expand Down Expand Up @@ -259,7 +259,7 @@
"- It stores message histories for different `CodeWritingTask` in a dictionary,\n",
"so each task has its own history.\n",
"- When making an LLM inference request using its model client, it transforms\n",
"the message history into a list of {py:class}`agnext.components.models.LLMMessage` objects\n",
"the message history into a list of {py:class}`autogen_core.components.models.LLMMessage` objects\n",
"to pass to the model client.\n",
"\n",
"The reviewer agent subscribes to the `CodeReviewTask` message and publishes the `CodeReviewResult` message."
Expand Down
Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
# Open Telemetry

AGNext has native support for [open telemetry](https://opentelemetry.io/). This allows you to collect telemetry data from your application and send it to a telemetry backend of your choosing.
AutoGen has native support for [open telemetry](https://opentelemetry.io/). This allows you to collect telemetry data from your application and send it to a telemetry backend of your choosing.

These are the components that are currently instrumented:
- Runtime (Single Threaded Agent Runtime, Worker Agent Runtime)
Expand All @@ -12,7 +12,7 @@ To instrument your application, you will need an sdk and an exporter. You may al
pip install opentelemetry-sdk
```

Depending on your open telemetry collector, you can use grpc or http to export your telemetry.
Depending on your open telemetry collector, you can use grpc or http to export your telemetry.

```bash
# Pick one of the following
Expand All @@ -35,7 +35,7 @@ def configure_oltp_tracing(endpoint: str = None) -> trace.TracerProvider:
processor = BatchSpanProcessor(OTLPSpanExporter())
tracer_provider.add_span_processor(processor)
trace.set_tracer_provider(tracer_provider)

return tracer_provider
```

Expand Down
Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,7 @@
"In the context of AI agents, tools are designed to be executed by agents in\n",
"response to model-generated function calls.\n",
"\n",
"AGNext provides the {py:mod}`autogen_core.components.tools` module with a suite of built-in\n",
"AutoGen provides the {py:mod}`autogen_core.components.tools` module with a suite of built-in\n",
"tools and utilities for creating and running custom tools."
]
},
Expand Down
4 changes: 2 additions & 2 deletions python/packages/autogen-core/docs/src/user-guide/index.md
Original file line number Diff line number Diff line change
Expand Up @@ -2,12 +2,12 @@
myst:
html_meta:
"description lang=en": |
User Guide for AGNext, a framework for building multi-agent applications with AI agents.
User Guide for AutoGen, a framework for building multi-agent applications with AI agents.
---

# User Guide

AGNext is a flexible framework for building multi-agent systems. Begin with the [installation](guides/installation.md) guide to set up the framework on your machine. Then, follow the [quickstart](guides/quickstart) guide to get started with building your first multi-agent application.
AutoGen is a flexible framework for building multi-agent systems. Begin with the [installation](guides/installation.md) guide to set up the framework on your machine. Then, follow the [quickstart](guides/quickstart) guide to get started with building your first multi-agent application.

```{danger}
This project and documentation is a work in progress. If you have any questions or need help, please reach out to us on GitHub.
Expand Down
22 changes: 11 additions & 11 deletions python/packages/autogen-core/samples/README.md
Original file line number Diff line number Diff line change
@@ -1,26 +1,26 @@
# Examples

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

- `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.
- `core`: Contains examples that illustrate the core concepts of AutoGen core.
- `tool-use`: Contains examples that illustrate tool use in AutoGen core.
- `patterns`: Contains examples that illustrate how multi-agent patterns can be implemented in AutoGen core.
- `demos`: Contains interactive demos that showcase applications that can be built using AutoGen core.

See [Running the examples](#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.
We provide examples to illustrate the core concepts of AutoGen core: agents, runtime, and message passing.

- [`one_agent_direct.py`](core/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`](core/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`](core/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:
We provide examples to illustrate how to use tools in AutoGen core:

- [`coding_direct.py`](tool-use/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`](tool-use/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.
Expand All @@ -29,7 +29,7 @@ We provide examples to illustrate how to use tools in AGNext:

## Pattern examples

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

- [`coder_executor.py`](patterns/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`](patterns/coder_reviewer.py): An example of how to create a coder-reviewer reflection pattern.
Expand All @@ -39,7 +39,7 @@ We provide examples to illustrate how multi-agent patterns can be implemented in

## Demos

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

- [`assistant.py`](demos/assistant.py): a demonstration of how to use the OpenAI Assistant API to create
a ChatGPT agent.
Expand All @@ -63,7 +63,7 @@ We provide examples on how to integrate other agents with the platform:

### Prerequisites

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

### Using Azure OpenAI API

Expand All @@ -75,7 +75,7 @@ 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.
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:
Expand Down
Original file line number Diff line number Diff line change
Expand Up @@ -318,7 +318,7 @@ async def ainput(prompt: str = "") -> str:
# import logging

# logging.basicConfig(level=logging.WARNING)
# logging.getLogger("agnext").setLevel(logging.DEBUG)
# logging.getLogger("autogen_core").setLevel(logging.DEBUG)

# if os.path.exists("state.json"):
# os.remove("state.json")
Expand Down
2 changes: 1 addition & 1 deletion python/packages/team-one/readme.md
Original file line number Diff line number Diff line change
Expand Up @@ -160,7 +160,7 @@ Team One agents can emit several log events that can be consumed by a log handle
- OrchestrationEvent : emitted by a an [Orchestrator](src/team_one/agents/base_orchestrator.py) agent.
- WebSurferEvent : emitted by a [WebSurfer](src/team_one/agents/multimodal_web_surfer/multimodal_web_surfer.py) agent.

In addition, developers can also handle and process logs generated from the AGNext core library (e.g., LLMCallEvent etc). See the example log handler in [utils.py](src/team_one/utils.py) on how this can be implemented. By default, the logs are written to a file named `log.jsonl` which can be configured as a parameter to the defined log handler. These logs can be parsed to retrieved data agent actions.
In addition, developers can also handle and process logs generated from the AutoGen core library (e.g., LLMCallEvent etc). See the example log handler in [utils.py](src/team_one/utils.py) on how this can be implemented. By default, the logs are written to a file named `log.jsonl` which can be configured as a parameter to the defined log handler. These logs can be parsed to retrieved data agent actions.


# Setup
Expand Down

0 comments on commit 93e7127

Please sign in to comment.