AIエージェント
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Rivet is Ironclad's visual IDE and TypeScript library for building complex AI agents and prompt chains
★ 4.7k スターMIT
ガイドを見る →OmAgentは、om-ai-labが公開しているオープンソースのAIエージェントプロジェクトです（GitHub: om-ai-lab/OmAgent）。GitHubスターは2,667、フォークは291で、Apache-2.0ライセンスの下で公開されています。GitHubでの最終更新は2025年3月19日です。
Python library for building multimodal language agents — VLM, video and mobile, fast to prototype and ship
Pick a multimodal task — e.g. answering questions about a video, a visual QA web app, or a phone-connected assistant.
pip install omagent-core, or clone the repo to work from the examples.
Generate container.yaml for an example with compile_container.py, and set your LLM key/endpoint (or point it at a local Ollama model) in configs/llms.
Run the simple VQA example's webpage demo and ask a question about an image.
A flexible architecture with graph-based workflow orchestration and various memory types for contextual reasoning.
Built-in support for VLM models, real-time APIs, computer vision models, video processing and mobile device connection.
State-of-the-art unimodal and multimodal agent algorithms beyond simple LLM reasoning, e.g. ReAct, CoT and SC-CoT, packaged as reusable operators.
Deploy your own models locally with Ollama or LocalAI instead of hosted APIs.
A fully distributed architecture with custom scaling, or Lite mode that eliminates middleware deployment.
Video QA agent (Gradio app), mobile personal assistant, and an agentic-operators comparison project are included.
Clone OmAgent to use its examples; the library itself installs from PyPI in step 2.
git clone https://github.com/om-ai-lab/OmAgent.git
cd OmAgentInstall omagent_core from PyPI (Python >= 3.10).
pip install omagent-coreGenerate the example config and launch the webpage demo on http://127.0.0.1:7860.
cd examples/step1_simpleVQA
python compile_container.py
python run_webpage.pyom-ai-lab maintains it; the framework paper is Zhang et al., 2024 (arXiv:2406.16620), presented in the EMNLP 2024 context.
Yes, Apache-2.0 licensed.
Only if you choose a hosted LLM/VLM — set your OpenAI-compatible key in the config, or run local models with Ollama/LocalAI instead.
The authors aim for a simpler interface with less overhead, while natively covering multimodal inputs (video, vision, mobile) that text-first frameworks treat as add-ons.
pip install omagent-core, then run the step1_simpleVQA example: compile_container.py, configure your LLM, and python run_webpage.py.
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