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Omagent

Omagent is an open-source Python library for building multimodal language agents with reusable components, workflow orchestration, and task-queue support.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Omagent — OpenViST project profile in Agent Frameworks.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Omagent when building a code-based agent system that needs reusable components and multimodal input handling, especially for visual or video question-answering examples.

Who it is for

  • Developers building multimodal language-agent prototypes or applications
  • Researchers comparing or composing agent workflows
  • Teams that want to use local models or configure an OpenAI-compatible endpoint

Documented uses

  • Define an agent using reusable components.
  • Compose components into more complex agent workflows.
  • Build agents that reason over text, images, video, or audio.
  • Create visual question-answering or video question-answering applications.
  • Configure a local model through Ollama or LocalAI, or an OpenAI-compatible endpoint.
Before you choose

Requirements & limits

The public repository specifies an Apache-2.0 license. The documented installation requires Python 3.10 or later.

  • The framework is a Python library, so using it involves installing and configuring code rather than signing up for a hosted agent interface.
  • The simple VQA quick start documents an OpenAI key and compatible endpoint, with a locally deployed Ollama model as an alternative.
  • The repository also describes local model deployment through Ollama or LocalAI and a Lite mode that removes the need for middleware deployment.
  • The framework’s stated multimodal scope includes VLM models, video processing, computer vision models, and mobile-device connection.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up the simple visual question-answering demo

Prepare the documented VQA example, configure an LLM connection, and open its local webpage interface.

Before you start

  • Python 3.10 or later
  • Install the omagent-core package
  • An OpenAI API key and compatible endpoint, or a locally deployed Ollama model

Steps

  1. Install omagent-core with pip.

  2. From examples/step1_simpleVQA, run python compile_container.py to generate container.yaml.

  3. Configure LLM settings in configs/llms/gpt.yml, using the documented environment variables for an OpenAI key and endpoint, or follow the Ollama model tutorial.

  4. From examples/step1_simpleVQA, run python run_webpage.py and open http://127.0.0.1:7860.

Expected outcome

The example presents a webpage interface for visual question answering, with input and output in the webpage.

Guide limitations

  • The quick start describes the simple VQA example; it does not establish setup steps for every agent project.
  • The OpenAI configuration requires a key and endpoint; the Ollama option is described as a locally deployed model.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
13 October 2026
Original website title
om-ai-lab/OmAgent · Official homepage title · retained in its source language
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