What is OmAgent
OmAgent is a multimodal language agent framework jointly open-sourced by Om AI and the Zhejiang University Binjiang Research Institute, which simplifies the development of on-device intelligent agents. OmAgent supports reusable agent components, helping developers build complex multimodal agents that handle multiple input forms such as text, images, video, and audio. OmAgent empowers various hardware devices, such as smartphones, smart wearables, IP cameras, etc., simplifying the process of connecting to advanced multimodal models and algorithms by abstracting different device types. OmAgent optimizes the computational workflow, provides a real-time interactive experience, and features key characteristics such as easy connection to diverse devices, integration of cutting-edge multimodal models, and support for complex problem-solving algorithms, providing an intuitive interface for building scalable agents that adapt to various application needs.
OmAgent's Main Features
- Multi-Device Connection: OmAgent makes connecting physical devices very simple, such as phones, glasses, etc., enabling the construction of applications that run directly on devices, providing a smartphone application and corresponding backend. Users do not need to worry about complex device connection issues and can focus on developing agent functionality.
- Efficient Model Integration: Integrates SOTA models，Integrates the most advanced commercial and open-source foundation models, providing application developers with the most powerful intelligent support.
- Provides Algorithm Implementation Interfaces: Provides researchers and developers with an easy-to-use process orchestration interface, making it convenient for them to implement the latest agent algorithms, such as ReAct, DnC, etc. This enables OmAgent to support more complex tasks and problem-solving, expanding the functional boundaries of agents.
OmAgent's Technical Principles
- Video Preprocessing and Storage：
- Scene Detection: Splits the video into independent video chunks, records the start and end timestamps of each chunk, and uniformly samples frames from them.
- Visual Prompting: Annotates video frames using algorithms such as facial recognition to provide additional visual information.
- Audio-Text Representation: Converts speech in the video into text based on ASR algorithms and distinguishes different speakers.
- Scene Description: Uses MLLMs to generate detailed descriptions of each video segment, including information such as time, location, people, and events.
- Encoding and Storage: Vectorizes the generated scene descriptions and stores them in a knowledge database, while also saving the original text and timestamp information.
- Divide and Conquer Loop (DnC Loop)：
- Task Decomposition: Recursively decomposes complex tasks into executable subtasks until the subtasks are simple enough to be handled directly.
- Tool Invocation: During processing, calls external tools (such as the video playback tool "rewinder") as needed to supplement information and solve the problem of information loss.
- Task Execution: Stores task execution paths based on a recursive tree structure, ensuring the smooth execution of tasks and the merging of results.
- Tool Invocation Mechanism：
- Autonomous Invocation: Autonomously generates tool invocation request parameters based on task information, and calls external tools (such as internet search, facial recognition, file processing, etc.) to complete complex tasks.
- Video Playback Tool: Specifically provides a "rewinder" tool to replay video from a specific time period when needed and extract detailed information.
- Query Processing and Retrieval：
- Timestamp Extraction: Extract time information from the query to use in filtering retrieval results.
- Text Encoding and Retrieval: Encode the query text into embedding vectors to use in retrieving relevant video segment information from the knowledge database.
- Task Passing: Pass the retrieved video segment information and the original task to the DnC Loop for processing.
- Result Synthesis and Output：
- Subtask Execution: The DnC Loop recursively executes subtasks, handles complex tasks, and invokes tools to supplement information when necessary.
- Result Synthesis: Synthesize the execution results of all subtasks into the final answer, and output the final result based on a dedicated node.
OmAgent's official project website
- Official Project Website：https://www.om-agent.com/
- GitHub Repository：https://github.com/om-ai-lab/OmAgent
- arXiv Technical Paper：https://arxiv.org/pdf/2406.16620
OmAgent's Application Scenarios
- Video Surveillance: Analyze surveillance video in real time, detect abnormal events and issue alerts, improving security and response speed.
- Content Recommendation: Recommend personalized video content to users, combining multimodal information to provide more comprehensive recommendations.
- Education: Parse educational videos, generate summaries and notes, and enhance the user experience through interactive learning.
- Entertainment and Film & Television: Analyze film and television content, provide plot summaries and character introductions, and enhance viewers' viewing experience.
- Intelligent Customer Service and Support: Query video content through natural language, provide detailed answers and multimodal interactive services.