Awesome

🗓️ Updates

• 11/12/2024: OmAgent v0.2.0 is officially released! We have completely rebuilt the underlying framework of OmAgent, making it more flexible and easy to extend. The new version added the concept of devices, making it easier to develop quickly for smart hardware.
• 10/20/2024: We are actively engaged in developing version 2.0.0 🚧 Exciting new features are underway! You are welcome to join us on X and Discord~
• 09/20/2024: Our paper has been accepted by EMNLP 2024. See you in Miami!🏝
• 07/04/2024: The OmAgent open-source project has been unveiled. 🎉
• 06/24/2024: The OmAgent research paper has been published.

📖 Introduction

OmAgent is an open-source agent framework designed to streamlines the development of on-device multimodal agents. Our goal is to enable agents that can empower various hardware devices, ranging from smart phone, smart wearables (e.g. glasses), IP cameras to futuristic robots. As a result, OmAgent creates an abstraction over various types of device and simplifies the process of connecting these devices to the state-of-the-art multimodal foundation models and agent algorithms, to allow everyone build the most interesting on-device agents. Moreover, OmAgent focuses on optimize the end-to-end computing pipeline, on in order to provides the most real-time user interaction experience out of the box.

In conclusion, key features of OmAgent include:

• Easy Connection to Diverse Devices: we make it really simple to connect to physical devices, e.g. phone, glasses and more, so that agent/model developers can build the applications that not running on web page, but running on devices. We welcome contribution to support more devices!

• Speed-optimized SOTA Mutlimodal Models: OmAgent integrates the SOTA commercial and open-source foundation models to provide application developers the most powerful intelligence. Moreover, OmAgent streamlines the audio/video processing and computing process to easily enable natural and fluid interaction between the device and the users.

• SOTA Multimodal Agent Algorithms: OmAgent provides an easy workflow orchestration interface for researchers and developers implement the latest agent algorithms, e.g. ReAct, DnC and more. We welcome contributions of any new agent algorithm to enable more complex problem solving abilities.

• Scalability and Flexibility: OmAgent provides an intuitive interface for building scalable agents, enabling developers to construct agents tailored to specific roles and highly adaptive to various applications.

🛠️ How To Install

1. Deploy the Workflow Orchestration Engine

OmAgent utilizes Conductor as its workflow orchestration engine. Conductor is an open-source, distributed, and scalable workflow engine that supports a variety of programming languages and frameworks. By default, it uses Redis for persistence and Elasticsearch (7.x) as the indexing backend.
It is recommended to deploy Conductor using Docker:

docker-compose -f docker/conductor/docker-compose.yml up -d

• Once deployed, you can access the Conductor UI at http://localhost:5001. (Note: Mac system will occupy port 5000 by default, so we use 5001 here. You can specify other ports when deploying Conductor.)
• The Conductor API can be accessed via http://localhost:8080.
• More details about the deployment can be found here.

2. Install OmAgent

• Python Version: Ensure Python 3.10 or higher is installed.

• Install omagent_core:

  pip install -e omagent-core
  

• Install dependencies for the sample project:

  pip install -r requirements.txt
  

• Install Optional Components:

  • Install Milvus VectorDB for enhanced support of long-term memory. OmAgent uses Milvus Lite as the default vector database for storing vector data related to long-term memory. To utilize the full Milvus service, you may deploy the Milvus vector database via Docker.
  • Pull git lfs files. We provide sample image files for our examples in the examples/step4_outfit_with_ltm/wardrobe_images directory. To use them, ensure Git LFS is installed. You can install it with the following command:

    git lfs install
    

    Then, pull the files by executing:

    git lfs pull
    

3. Connect Devices

If you wish to use smart devices to access your agents, we provide a smartphone app and corresponding backend, allowing you to focus on agent functionality without worrying about complex device connection issues.

• Deploy the app backend
  The APP backend comprises the backend program, along with two middleware components: the MySQL database and MinIO object storage. For installation and deployment instructions, please refer to this link.
• Download, install, and debug the smartphone app
  At present, we offer an Android APP available for download and testing. For detailed instructions on acquiring and using it, please refer to here. The iOS version is currently under development and will be available soon.

🚀 Quick Start

Hello World

1、Configuration

The container.yaml file is a configuration file that manages dependencies and settings for different components of the system. To set up your configuration:

1. Generate the container.yaml file:

   cd examples/step2_outfit_with_switch
   python compile_container.py
   

   This will create a container.yaml file with default settings under examples/step2_outfit_with_switch.

2. Configure your LLM settings in configs/llms/gpt.yml and configs/llms/text_res.yml:

   • Set your OpenAI API key or compatible endpoint through environment variable or by directly modifying the yml file

   export custom_openai_key="your_openai_api_key"
   export custom_openai_endpoint="your_openai_endpoint"
   

3. Update settings in the generated container.yaml:

   • Configure Redis connection settings, including host, port, credentials, and both redis_stream_client and redis_stm_client sections.
   • Update the Conductor server URL under conductor_config section
   • Adjust any other component settings as needed

4. Websearch gives multiple providers, you can choose one of them by modifying the configs/tools/all_tools.yml file.

   1. [Recommend] Use Tavily as the websearch tool, all_tools.yml file should be like this:

   llm: ${sub|text_res}
   tools:
       - ...other tools...
       - name: TavilyWebSearch
         tavily_api_key: ${env|tavily_api_key, null}
   

   You can get the tavily_api_key from here. It start with tvly-xxx. By setting the tavily_api_key, you can get better search results. 2. Use bing search or duckduckgo search, all_tools.yml file should be like this:

   llm: ${sub|text_res}
   tools:
       - ...other tools...
       - name: WebSearch
         bing_api_key: ${env|bing_api_key, null}
   

   For better results, it is recommended to configure Bing Search setting the bing_api_key.

For more information about the container.yaml configuration, please refer to the container module

2、Running the Example

1. Run the outfit with switch example:

   For terminal/CLI usage: Input and output are in the terminal window

   cd examples/step2_outfit_with_switch
   python run_cli.py
   

   For app/GUI usage: Input and output are in the app

   cd examples/step2_outfit_with_switch
   python run_app.py
   

   For app backend deployment, please refer to here
   For the connection and usage of the OmAgent app, please check app usage documentation

🏗 Architecture

The design architecture of OmAgent adheres to three fundamental principles:

1. Graph-based workflow orchestration;
2. Native multimodality;
3. Device-centricity.

With OmAgent, one has the opportunity to craft a bespoke intelligent agent program.

For a deeper comprehension of OmAgent, let us elucidate key terms:

• Devices: Central to OmAgent's vision is the empowerment of intelligent hardware devices through artificial intelligence agents, rendering devices a pivotal component of OmAgent's essence. By leveraging the downloadable mobile application we have generously provided, your mobile device can become the inaugural foundational node linked to OmAgent. Devices serve to intake environmental stimuli, such as images and sounds, potentially offering responsive feedback. We have evolved a streamlined backend process to manage the app-centric business logic, thereby enabling developers to concentrate on constructing the intelligence agent's logical framework. See client for more details.

• Workflow: Within the OmAgent Framework, the architectural structure of intelligent agents is articulated through graphs. Developers possess the liberty to innovate, configure, and sequence node functionalities at will. Presently, we have opted for Conductor as the workflow orchestration engine, lending support to intricate operations like switch-case, fork-join, and do-while. See workflow for more details.

• Task and Worker: Throughout the OmAgent workflow development journey, Task and Worker stand as pivotal concepts. Worker embodies the actual operational logic of workflow nodes, whereas Task oversees the orchestration of the workflow's logic. Tasks are categorized into Operators, managing workflow logic (e.g., looping, branching), and Simple Tasks, representing nodes customized by developers. Each Simple Task is correlated with a Worker; when the workflow progresses to a given Simple Task, the task is dispatched to the corresponding worker for execution. See task and worker for more details.

Basic Principles of Building an Agent

• Modularity: Break down the agent's functionality into discrete workers, each responsible for a specific task.

• Reusability: Design workers to be reusable across different workflows and agents.

• Scalability: Use workflows to scale the agent's capabilities by adding more workers or adjusting the workflow sequence.

• Interoperability: Workers can interact with various backends, such as LLMs, databases, or APIs, allowing agents to perform complex operations.

• Asynchronous Execution: The workflow engine and task handler manage the execution asynchronously, enabling efficient resource utilization.

Examples

We provide exemplary projects to demonstrate the construction of intelligent agents using OmAgent. You can find a comprehensive list in the examples directory. Here is the reference sequence:

1. step1_simpleVQA illustrates the creation of a simple multimodal VQA agent with OmAgent.

2. step2_outfit_with_switch demonstrates how to build an agent with switch-case branches using OmAgent.

3. step3_outfit_with_loop shows the construction of an agent incorporating loops using OmAgent.

4. step4_outfit_with_ltm exemplifies using OmAgent to create an agent equipped with long-term memory.

5. dnc_loop demonstrates the development of an agent utilizing the DnC algorithm to tackle complex problems.

6. video_understanding showcases the creation of a video understanding agent for interpreting video content using OmAgent.

API Documentation

The API documentation is available here.

🔗 Related works

If you are intrigued by multimodal large language models, and agent technologies, we invite you to delve deeper into our research endeavors:
🔆 How to Evaluate the Generalization of Detection? A Benchmark for Comprehensive Open-Vocabulary Detection (AAAI24)
🏠 GitHub Repository

🔆 OmDet: Large-scale vision-language multi-dataset pre-training with multimodal detection network (IET Computer Vision)
🏠 Github Repository

⭐️ Citation

If you find our repository beneficial, please cite our paper:

@article{zhang2024omagent,
  title={OmAgent: A Multi-modal Agent Framework for Complex Video Understanding with Task Divide-and-Conquer},
  author={Zhang, Lu and Zhao, Tiancheng and Ying, Heting and Ma, Yibo and Lee, Kyusong},
  journal={arXiv preprint arXiv:2406.16620},
  year={2024}
}

Third-Party Dependencies

This project includes code from the following third-party projects:

• conductor-python
  • License: Apache License 2.0
  • Link to Project
  • Link to License

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