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omo; the best agent harness - previously oh-my-opencode
Run OpenClaw more securely inside NVIDIA OpenShell with managed inference
A set of ready to use Agent Skills for research, science, engineering, analysis, finance and writing.
Agent0 Series: Self-Evolving Agents from Zero Data
Fully autonomous & self-evolving research from idea to paper. Chat an Idea. Get a Paper. 🦞
CLI-Anything: Making ALL Software Agent-Native
OpenClaw-RL: Train any agent simply by talking
🦞 Just talk to your agent — it learns and EVOLVES 🧬.
AI agents running research on single-GPU nanochat training automatically
Optimize prompts, code, and more with AI-powered Reflective Text Evolution
Toolkit for Seamlessly Enabling RL Training on Any Agent with Bedrock AgentCore.
Symphony turns project work into isolated, autonomous implementation runs, allowing teams to manage work instead of supervising coding agents.
A curated list of state-of-the-art research in embodied AI, focusing on vision-language-action (VLA) models, vision-language navigation (VLN), and related multimodal learning approaches.
MAT: Multi-modal Agent Tuning 🔥 ICLR 2025 (Spotlight)
MineStudio: A Streamlined Package for Minecraft AI Agent Development
Your own personal AI assistant. Any OS. Any Platform. The lobster way. 🦞
PaperBanana: Automating Academic Illustration For AI Scientists
Official Repository of VisGym: Diverse, Customizable, Scalable Environments for Multimodal Agents
The Open-Source Multimodal AI Agent Stack: Connecting Cutting-Edge AI Models and Agent Infra
Open-source, local-first alternative to Cowork-style computer assistants: real PTY terminal ops, explicit approvals, JSONL audit logs. Windows + Linux + macOS. Model agnostic.
A Plugin-Based Multi-Agent System for In-Editor Academic Writing, Review, and Editing
The repository provides code for running inference with the Meta Segment Anything Audio Model (SAM-Audio), links for downloading the trained model checkpoints, and example notebooks that show how t…
[ACL2025 Oral & Award] Evaluate Image/Video Generation like Humans - Fast, Explainable, Flexible
We introduce new approach, Token Reduction using CLIP Metric (TRIM), aimed at improving the efficiency of MLLMs without sacrificing their performance.
Natural language workflows that enable AI agents to perform complex, multi-step tasks with consistency and reliability.