Open-source alternatives to XLeRobot

jetson-inference is a set of libraries and tools for executing optimized deep learning models on embedded GPU hardware. Its primary purpose is to enable real-time computer vision and AI inference at the edge with low latency and high throughput. The project distinguishes itself through high-performance streaming analytics and the ability to execute concurrent AI pipelines on auto-grade silicon. It provides specialized support for multi-sensor stream processing, utilizing zero-copy data transport to load camera frames directly into GPU memory. The codebase covers a broad surface of capabiliti

OpenCat-Quadruped-Robot is a development framework and motion control API for building four-legged robots. It provides a comprehensive environment for quadruped robot development, featuring tools for locomotion gait design, inverse kinematics, and a layered control architecture that separates high-level intelligence from low-level motion. The project distinguishes itself as an embedded AI deployment tool, allowing users to train, quantize, and deploy machine learning models to vision modules for tasks such as object detection and visual target tracking. It further supports complex human-robot

Mobile Alohas is a bimanual mobile manipulation framework designed to learn whole-body robot control. It provides an integrated imitation learning pipeline that manages the process of collecting human demonstration data and training behavior models to automate complex physical tasks. The system features a robotic teleoperation interface that maps human movements to a mobile robot with dual arms. It includes a whole-body motion dataset tool used for recording, visualizing, and replaying joint and sensor data from manipulation sessions. The framework covers several capability areas, including

Genesis is an embodied AI simulation platform and parallelized robotics simulator designed for training general-purpose robotic agents. It integrates a physics engine for robotics that calculates collisions and movements for rigid bodies, soft tissues, and fluids, alongside a photorealistic 3D rendering engine. The platform features a domain randomization framework to vary environment parameters across parallel simulations, aiding in sim-to-real transfer. It supports the integration of real-world captured light fields and Gaussian splatting to provide photorealistic backgrounds within simulat

GenericAgent is an LLM agent framework and autonomous system controller designed to manage local systems, web browsers, and hardware interfaces through action and observation loops. It functions as a tool orchestrator that routes model calls to local executors, enabling the automation of complex tasks on a host machine. The project is distinguished by its self-evolving AI agent capabilities, which convert successful execution paths into reusable procedural scripts and skill trees to reduce future reasoning overhead. It employs a context optimization engine that utilizes layered memory hierarc

Habitat-sim is a high-performance 3D simulation platform designed for training and benchmarking embodied AI agents within photorealistic indoor and outdoor environments. It serves as a simulator for AI and robotics, providing a system for generating synthetic data and simulating physical interactions. The project is distinguished by a native C++ core that enables high-throughput simulation and a rendering pipeline using physically based rendering and baked global illumination. It features a navigation system based on pre-computed navigation meshes to ensure collision-free traversal and a rigi

Auto-GPT is an autonomous agent framework designed for creating and deploying AI agents that use large language models to plan and execute complex goals independently. The system provides a comprehensive environment for managing the entire agent lifecycle, from initial design and testing to live production deployment. The project features a low-code workflow designer that allows users to define agent behaviors by connecting functional blocks in a visual interface. It includes an agent marketplace for discovering and deploying pre-configured agent templates and a standardized evaluation tool t

ReAct is an agentic workflow template and prompting framework for large language models. It implements a logic pattern that integrates chain-of-thought reasoning with external tool execution to solve complex, multi-step tasks. The framework uses an interleaved reasoning and acting logic, forcing the model to document its internal thought process before executing an action. This cycle of planning and acting allows the system to interact with external APIs or databases and inject real-world data back into the model context to refine reasoning paths. The project covers autonomous task execution

Auto-GPT is an autonomous agent framework that uses large language models to decompose complex goals and execute multi-step tasks without human intervention. It functions as a workflow automation tool that chains language model tasks and manages memory to achieve specific objectives. The project features a visual agent designer that allows users to define behaviors and goals by connecting functional blocks through a graphical interface. It employs a vector database memory system to recall information across different sessions and a sliding-window buffer for immediate short-term context. The

dm_control is a physics-based simulation framework and robot control simulation toolkit designed for creating and interacting with continuous control tasks. It serves as a suite of reinforcement learning environments and a benchmarking tool for evaluating autonomous agents within virtual physics spaces. The framework provides a collection of environments based on MuJoCo physics bindings to simulate rigid body dynamics and contact forces. It features hardware-accelerated rendering and interactive viewers for the visualization of physics environments and agent behavior. The system supports the

RLinf is a distributed reinforcement learning orchestrator and embodied AI training framework. It provides the infrastructure to train vision-language-action models and robotic policies using a combination of reinforcement learning and supervised fine-tuning. The system is designed for scaling workloads across GPU clusters, managing the placement of actors, rollout workers, and environment components. It features a specialized robotics data collection pipeline for gathering teleoperated demonstrations and simulation trajectories into standardized replay buffers, alongside a hardware interface

This project is a development framework for building autonomous agents that utilize language models to reason through multi-step tasks. It functions as an orchestrator that manages iterative loops of thought, action, and observation, allowing systems to process information and reach solutions without manual intervention. The framework distinguishes itself through a modular tool abstraction that connects language models to external data sources and code execution environments. By injecting tool-binding metadata into the prompt context, the system enables models to dynamically invoke custom fun

This project is a comprehensive framework for building and managing autonomous agent systems. It provides a unified architecture for orchestrating multi-agent societies, where specialized agents collaborate through roleplay to decompose and solve complex tasks. The system integrates language models with external environments, enabling agents to perform real-world actions through a standardized tool-calling abstraction layer. The framework distinguishes itself through its focus on iterative reasoning and data reliability. It employs automated feedback loops to refine agent outputs and self-eva

OpenPi is a vision-language-action robot control framework designed to generate physical control actions for robotic systems. It functions as a distributed robot model trainer, a model format converter, and a robot action streaming server. The framework provides tools for transforming model checkpoints between different framework formats to ensure interoperability across various development environments. It also includes a server that uses websocket connections to stream model-generated control actions from remote inference servers to physical robot hardware in real-time. The system supports

OM1 is a multimodal AI agent runtime and orchestration framework designed to connect large language models to physical robot hardware and sensors. It provides an execution environment that processes audio, video, and sensor data to drive autonomous decisions and actions in real-world settings. The system integrates a robotics SLAM and navigation stack with a hardware abstraction layer, allowing high-level AI commands to be translated into low-level motor and actuator instructions. It distinguishes itself by incorporating blockchain-based governance to enforce immutable operational rules and p

SO-ARM100 is an open-source robot arm hardware project providing 3D-printable designs and assembly guides for building affordable robotic arms. It includes calibration software to synchronize motor communication parameters and arm positions via USB, alongside hardware designs for tactile sensing robotic grippers. The project distinguishes itself through the integration of touch-sensing and flexible filaments for adaptive grasping. It also provides a dedicated imitation learning dataset tool, featuring a web interface for labeling and visualizing robotics data to train machine learning models

This project provides a complete set of design files and hardware specifications for a 3D-printable industrial-style robot arm. The system includes a CAN bus robot controller for managing stepper motors and sensors, a kinematics engine for calculating joint angles and poses, and a UDP-based Python API for sending motion commands and monitoring telemetry. The system features a force-controlled robotic gripper that utilizes field-oriented control on stepper motors to enable compliant grasping and precise force sensing. It also includes a 3D position visualization tool for real-time telemetry tr

PaddleDetection is an object detection framework designed for the end-to-end development, training, and deployment of computer vision models. It provides a comprehensive library of modular neural network architectures and pipelines that support object detection, instance segmentation, and multi-object tracking tasks. The project distinguishes itself through a configuration-driven approach that decouples model components like backbones and heads, allowing for the flexible assembly of custom vision workflows. It incorporates advanced techniques such as anchor-free detection logic, joint detecti

Ultralytics is a comprehensive computer vision framework designed for training, validating, and deploying deep learning models across a wide range of visual recognition tasks. It provides a unified interface for core operations including object detection, instance segmentation, pose estimation, and image classification. By utilizing a modular architecture, the platform allows users to swap model components to balance inference speed and accuracy requirements for diverse applications. The framework distinguishes itself through its support for real-time processing and flexible deployment. It in

AIOS is an LLM agent operating system and orchestration kernel designed to manage memory, resource scheduling, and tool execution for multiple autonomous AI agents. It serves as a comprehensive framework for developing and deploying agents, featuring a dedicated resource manager that coordinates model backends, GPU memory, and isolated kernel instances. The system distinguishes itself through a semantic memory engine that uses vector search and autonomous clustering for long-term knowledge management, and a semantic file system that allows users to control computer files and system operations

This repository serves as a comprehensive research platform and toolkit for advancing machine learning, quantum computing, and large-scale scientific data analysis. It provides foundational frameworks for developing complex algorithmic systems, offering the necessary infrastructure for distributed training, computational graph execution, and high-performance model development. The project distinguishes itself by integrating specialized research domains with robust, privacy-preserving methodologies. It supports diverse scientific discovery through tools for quantum simulation, physics-informed

Genesis World is an embodied AI simulation platform designed for training robotic agents through physics-based interactions. It centers on a multi-physics simulation engine that integrates rigid body, particle, and finite element method dynamics, supported by a parallel simulation kernel compiler that translates Python functions into optimized GPU and CPU kernels. The platform features a photorealistic robot renderer that utilizes path-tracing and Gaussian Splatting to generate synthetic training data. It includes a domain randomization framework to vary lighting and physical parameters acros

This project is a collection of implementation guides, recipes, and developer resources for building applications with Llama models. It serves as a comprehensive kit for developing autonomous agents, establishing retrieval-augmented generation systems, and executing model fine-tuning. The resource provides specific patterns for multimodal workflows that process text, images, and audio. It includes specialized guidance on adapting pre-trained model weights for targeted tasks and implementing tool-calling orchestration to connect models with external APIs and functions. The codebase covers a b

Hermes-agent is an autonomous AI agent framework and runtime designed to execute complex tasks and synthesize new skills from execution traces. It includes a provider-agnostic gateway for routing requests across multiple model backends and a serverless runtime that suspends idle agent instances and resumes them on demand across containers and virtual machines. The project provides a desktop automation toolset that controls native GUI workflows on Linux by querying accessibility APIs and injecting input events. It further distinguishes itself with the ability to generate procedural skills from

This project is a research-focused toolkit designed for building autonomous agent systems, multi-agent workflows, and security governance frameworks. It provides a platform for coordinating specialized sub-agents through structured communication protocols and phased task delegation to complete complex technical objectives. The framework distinguishes itself by integrating a dedicated security policy engine that validates autonomous tool execution against user-defined permissions and safety rules. It also features a research-oriented approach to prompt engineering, enabling the dynamic assembl

Agentscope is a comprehensive toolkit for developing and orchestrating autonomous multi-agent systems. It provides a unified framework for building agents that can reason, execute tools, and manage memory, enabling the creation of complex, collaborative workflows where multiple specialized agents interact to solve multi-step objectives. The platform distinguishes itself through a robust orchestration engine that supports both sequential and concurrent agent pipelines. It utilizes a centralized event bus for real-time telemetry, allowing developers to track agent reasoning, tool usage, and sys

This project is a TypeScript software development kit designed for building and orchestrating autonomous agents that interact with codebases and system environments. It provides a programmatic interface for constructing agents capable of executing complex workflows, such as automated code refactoring, file system manipulation, and shell command execution, by leveraging large language models. The framework distinguishes itself through a focus on secure, governed agent operations. It includes granular access control systems that allow developers to define specific permissions for tools and exte

This project is an LLM financial agent framework and multi-agent orchestration system designed to execute complex investment banking and wealth management workflows. It provides a financial data integration layer using a standardized context protocol to connect autonomous agents to real-time market data and third-party feeds. The system utilizes a multi-agent architecture that coordinates specialized worker agents through a steering event bus to handle task delegation and secure handoffs. It includes an enterprise AI deployment manifest for provisioning agent personas, prompts, and skill sets

Ironclaw is an LLM orchestration framework and AI agent gateway designed to connect large language models with external tools, messaging interfaces, and persistent memory systems. It functions as a communication layer that routes interactions between users and AI models via HTTP webhooks and various messaging channels. The system focuses on secure tool execution through a WebAssembly sandbox and isolated containers, which allows the framework to run untrusted code and dynamically generate new tools from natural language descriptions. Security middleware provides prompt injection defense and s