OmAgent - A multimodal language agent framework jointly open-sourced by Om AI and Zhejiang University.
OmAgent is a multimodal language proxy framework jointly open-sourced by Om AI and the Binjiang Research Institute of Zhejiang University. It simplifies the development of intelligent agents on device-side. OmAgent supports reusable proxy components, helping developers build complex multimodal...
What is OmAgent?
OmAgent is a multimodal language agent framework jointly open-sourced by Om AI and the Binjiang Research Institute of Zhejiang University. It simplifies the development of intelligent agents on devices. OmAgent supports reusable agent components, helping developers build complex multimodal agents that handle various input formats such as text, images, video, and audio. OmAgent empowers various hardware devices, such as smartphones, smart wearables, and IP cameras, simplifying the process of connecting to advanced multimodal models and algorithms by abstracting different device types. OmAgent optimizes the computation process, 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. It provides intuitive interfaces for building scalable agents to adapt to various application needs.
OmAgent's main functions
- Multi-device connectionOmAgent makes connecting physical devices, such as mobile phones and glasses, very simple. It can build applications that run directly on the device, providing smartphone applications and corresponding backends. Users do not need to worry about complex device connection issues and can focus on developing agent functions.
- High-efficiency model ensemble: Integrating the SOTA model,It integrates the most advanced commercial and open-source foundational models, providing application developers with the most powerful intelligent support.
- Provide algorithm implementation interfaceIt provides researchers and developers with an easy-to-use workflow orchestration interface, enabling them to implement the latest agent algorithms such as ReAct and DnC. This allows OmAgent to support more complex tasks and problem-solving, expanding the functional boundaries of the agent.
OmAgent's technical principles
- Video preprocessing and storage:
- Scene detectionThe video is divided into independent video blocks, the start and end timestamps of each block are recorded, and frames are sampled evenly from them.
- Visual cuesUsing algorithms such as facial recognition to label video frames provides additional visual information.
- Audio text representationIt uses the ASR algorithm to convert speech in a video into text and distinguish between different speakers.
- Scene DescriptionUse MLLMs to generate detailed descriptions for each video segment, including information such as time, location, people, and events.
- Encoding and storageThe generated scene description is vectorized and stored in a knowledge database, while preserving the original text and timestamp information.
- Divide and Conquer Loop (DnC Loop):
- Task breakdown: Recursively break down complex tasks into executable subtasks until the subtasks are simple enough to be handled directly.
- Tool callDuring the processing, external tools (such as the video playback tool "rewinder") can be called as needed to supplement information and resolve the problem of information loss.
- Task executionThe task execution path is stored based on a recursive tree structure to ensure the smooth execution of tasks and the merging of results.
- Tool Invocation Mechanism:
- Autonomous InvocationBased on task information, it can autonomously generate tool call request parameters and call external tools (such as internet search, facial recognition, file processing, etc.) to complete complex tasks.
- Video playback toolIt provides a special "rewinder" tool to replay videos of a specific time period when needed and extract detailed information.
- Query processing and retrieval:
- Timestamp ExtractionExtract time information from the query and use it to filter search results.
- Text Encoding and RetrievalThe query text is encoded into an embedding vector, which is used to retrieve relevant video clip information from a knowledge database.
- Task transferThe retrieved video segment information and the original task are passed to the DnC Loop for processing.
- Results Synthesis and Output:
- Subtask executionThe DnC Loop recursively executes subtasks, handles complex tasks, and calls tools to supplement information when necessary.
- Result SynthesisCombine the execution results of all subtasks into the final answer, and output the final result based on a dedicated node.
OmAgent's official website
- Project official website:https://www.om-agent.com/
- GitHub repository:https://github.com/om-ai-lab/OmAgent
- arXiv technical paper:https://arxiv.org/pdf/2406.16620
Application scenarios of OmAgent
- Video surveillanceReal-time analysis of surveillance video, detection of abnormal events and issuance of alerts, improving security and response speed.
- Content RecommendationIt recommends personalized video content to users and provides more comprehensive recommendations by combining multimodal information.
- educateIt analyzes educational videos, generates summaries and notes, and enhances the user experience through interactive learning.
- Entertainment and FilmAnalyze film and television content, provide plot summaries and character introductions to enhance the audience's viewing experience.
- Intelligent Customer Service and SupportIt allows users to query video content using natural language, providing detailed answers and multimodal interactive services.