Search Results for "computer system optimization" - Page 2
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Showing 116 open source projects for "computer system optimization"
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Agent Zero
Agent Zero AI framework
...It is designed to be dynamic, organically growing, and learning as you use it. Agent Zero is fully transparent, readable, comprehensible, customizable and interactive. Agent Zero uses the computer as a tool to accomplish its (your) tasks. Agents can communicate with their superiors and subordinates, asking questions, giving instructions, and providing guidance. Instruct your agents in the system prompt on how to communicate effectively. The terminal interface is real-time streamed and interactive. You can stop and intervene at any point. ... -
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hCaptcha Challenger
Gracefully face hCaptcha challenge with multimodal llms
hCaptcha Challenger is an open-source automation framework designed to solve hCaptcha verification challenges using computer vision models and multimodal reasoning techniques. The project integrates machine learning models capable of analyzing visual captcha tasks and identifying the correct responses required to pass the verification process. Instead of relying on third-party captcha-solving services or browser scripts, the system operates independently by using pretrained neural networks that can classify images, detect objects, and interpret spatial relationships. ... -
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Vibe-Trading
Vibe-Trading: Your Personal Trading Agent
...It features a swarm-based architecture with prebuilt expert agent teams for research, trading, and risk management. Advanced backtesting engines provide statistical validation, optimization, and performance metrics. The system also includes persistent memory, enabling it to learn from past interactions and refine strategies over time. Overall, it delivers an end-to-end AI-driven trading environment for both research and execution. -
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Youtu-Agent
A simple yet powerful agent framework that delivers with models
Youtu-Agent is an open-source framework developed to simplify the creation, execution, and evaluation of autonomous AI agents. The system focuses on reducing the complexity traditionally involved in configuring large language model agents by providing a modular architecture that separates execution environments, tools, and context management. This structure allows developers to rapidly assemble agent systems capable of performing tasks such as research, file processing, and data analysis.... -
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Google Workspace MCP Server
Control Gmail, Google Calendar, Docs, Sheets, Slides, Chat, Forms
Google Workspace MCP is an open-source server that connects AI assistants to Google Workspace services through the Model Context Protocol (MCP), allowing large language models to interact directly with productivity tools. The project exposes a wide set of Google services including Gmail, Google Drive, Docs, Sheets, Slides, Calendar, Chat, and other Workspace components as structured tools that an AI system can call programmatically. By acting as a bridge between AI clients and the Google... -
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DocETL
A system for agentic LLM-powered data processing and ETL
DocETL is an open-source system designed to build and execute data processing pipelines powered by large language models, particularly for analyzing complex collections of documents and unstructured datasets. The platform allows developers and researchers to construct structured workflows that extract, transform, and organize information from sources such as reports, transcripts, legal documents, and other text-heavy data. Instead of relying on single prompts or ad-hoc scripts, DocETL... -
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torchvision
Datasets, transforms and models specific to Computer Vision
The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision. We recommend Anaconda as Python package management system. Torchvision currently supports Pillow (default), Pillow-SIMD, which is a much faster drop-in replacement for Pillow with SIMD, if installed will be used as the default. Also, accimage, if installed can be activated by calling torchvision.set_image_backend('accimage'), libpng, which can be installed via conda conda install libpng or any of the package managers for debian-based and RHEL-based Linux distributions, and libjpeg, which can be installed via conda conda install jpeg or any of the package managers for debian-based and RHEL-based Linux distributions. ... -
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face.evoLVe
High-Performance Face Recognition Library on PaddlePaddle & PyTorch
face.evoLVe is a high-performance face recognition library designed for research and real-world applications in computer vision. The project provides a comprehensive framework for building and training modern face recognition models using deep learning architectures. It includes components for face alignment, landmark localization, data preprocessing, and model training pipelines that allow developers to construct end-to-end facial recognition systems. The repository supports multiple neural... -
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Desloppify
Agent harness to make your slop code well-engineered and beautiful
Desloppify is a utility-focused project aimed at improving the quality, structure, and clarity of generated or written text by removing redundancy, noise, and unnecessary verbosity. It is designed to “clean up” outputs, particularly those produced by AI systems, making them more concise, readable, and professional. The system likely applies heuristics or transformation rules to identify repetitive patterns, filler content, and stylistic inconsistencies. This makes it especially useful in... -
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E2B Desktop Sandbox
E2B Desktop Sandbox for LLMs. E2B Sandbox
E2B Desktop is an open-source sandboxed virtual desktop environment designed to enable secure “computer use” by large language models and automated agents. The platform provides isolated virtual machines where applications can be executed safely without affecting the host system. Each sandbox runs independently and can be configured with custom dependencies or tools required by an AI agent or automation workflow. The system allows developers to programmatically create and control these virtual desktops through SDKs available in languages such as Python and JavaScript. ... -
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AI Marketing Skills
Open-source AI marketing skills for Claude Code
...Instead of simple prompts, the project provides complete operational modules that include scripts, scoring systems, and decision-making logic, allowing AI tools like Claude Code to execute complex marketing tasks end-to-end. The system is organized into multiple domains such as growth experimentation, sales pipeline generation, content production, outbound marketing, SEO optimization, and financial analysis, effectively covering the entire revenue lifecycle of a business. Each skill functions as an executable capability that can be invoked on demand, enabling users to perform tasks like running A/B tests, generating high-quality content, or analyzing conversion funnels with minimal manual effort. -
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Hephaestus
Semi-Structured Agentic Framework. Workflows build themselves
...Instead of relying entirely on predefined workflows, the framework allows agents to dynamically create tasks as they explore a problem space. Developers define high-level phases such as analysis, implementation, and testing, while agents generate specific subtasks within those phases. The system continuously monitors agent behavior and task progression, allowing workflows to evolve as new discoveries are made. For example, if an agent detects a bug or optimization opportunity, it can automatically create a new task and integrate it into the workflow. The framework also includes monitoring mechanisms that track agent trajectories and ensure that tasks remain aligned with overall objectives. -
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CUDA Containers for Edge AI & Robotics
Machine Learning Containers for NVIDIA Jetson and JetPack-L4T
...The project is particularly useful for developers building edge AI and robotics systems that rely on GPU-accelerated inference and real-time computer vision. By using containerized environments, developers can ensure that their applications run consistently across different Jetson platforms and JetPack versions. The repository also includes build tools and package management utilities that help automate the process of assembling machine learning environments. -
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Claude Cognitive
Persistent context and multi-instance coordination
Claude Cognitive is an advanced memory and context-management extension designed to address the stateless limitations of Claude Code by giving the model a form of persistent “working memory” and multi-instance coordination. It introduces an attention-based context router that prioritizes files and content relevant to the current development discussion — tagging them as HOT, WARM, or COLD based on recency and keyword activation — so Claude Code doesn’t waste token budget rereading irrelevant... -
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RecursiveMAS
Offical Implementation for "Recursive Multi-Agent Systems"
RecursiveMAS is an advanced multi-agent AI framework that introduces a recursive collaboration mechanism to improve reasoning and problem-solving across multiple agents. Instead of treating agents as independent units exchanging text outputs, it connects them through a shared latent computation loop, allowing internal “thought states” to be passed and refined iteratively. This recursive structure enables agents to build on each other’s intermediate reasoning, leading to deeper and more... -
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Upsonic
The most reliable AI agent framework that supports MCP
Upsonic is a reliability-focused AI agent framework designed for real-world applications. It enables the development of trusted agent workflows within organizations by incorporating advanced reliability features, such as verification layers and output evaluation systems. The framework supports the Model Context Protocol (MCP), facilitating integration with various tools and enhancing agent capabilities. -
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PennyLane
A cross-platform Python library for differentiable programming
A cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network. Built-in automatic differentiation of quantum circuits, using the near-term quantum devices directly. You can combine multiple quantum devices with classical processing arbitrarily! Support for hybrid quantum and classical models, and compatible with existing machine learning libraries. Quantum circuits can be set up to interface with either NumPy,... -
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Claw Compactor
14-stage Fusion Pipeline for LLM token compression
Claw Compactor is a utility designed to optimize and manage the context limitations inherent in AI agent systems, particularly those built on OpenClaw-like architectures. It addresses the challenge of finite context windows in language models by compressing or summarizing historical interactions while preserving essential information. The system works by transforming older conversation data into condensed representations that maintain continuity without exceeding token limits. This approach... -
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Chitu
High-performance inference framework for large language models
Chitu is a high-performance inference engine designed to deploy and run large language models efficiently in production environments. The framework focuses on improving efficiency, flexibility, and scalability for organizations that need to run LLM inference workloads across different hardware platforms. It supports heterogeneous computing environments, including CPUs, GPUs, and various specialized AI accelerators, allowing models to run across a wide range of infrastructure configurations.... -
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fairseq2
FAIR Sequence Modeling Toolkit 2
fairseq2 is a modern, modular sequence modeling framework developed by Meta AI Research as a complete redesign of the original fairseq library. Built from the ground up for scalability, composability, and research flexibility, fairseq2 supports a broad range of language, speech, and multimodal content generation tasks, including instruction fine-tuning, reinforcement learning from human feedback (RLHF), and large-scale multilingual modeling. Unlike the original fairseq—which evolved into a... -
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AgentEvolver
Towards Efficient Self-Evolving Agent System
AgentEvolver is an open-source research framework for building self-evolving AI agents powered by large language models. The system focuses on improving the efficiency and scalability of training autonomous agents by allowing them to generate tasks, explore environments, and refine strategies without heavy reliance on manually curated datasets. Its architecture combines reinforcement learning with LLM-driven reasoning mechanisms to guide exploration and learning. The framework introduces... -
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Claude-Flow
The leading agent orchestration platform for Claude
...At its core, Claude-Flow integrates Dynamic Agent Architecture (DAA) for self-organizing agent management, neural pattern recognition accelerated by WebAssembly SIMD, and a SQLite-based memory system for context retention and knowledge persistence across tasks. It automates development workflows via pre- and post-operation hooks, providing seamless coordination, code formatting, validation, and performance optimization. -
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autoresearch-macos
AI agents running research on single-GPU nanochat training
...The project typically includes components such as data preparation scripts, a training loop, and an instruction file that guides the agent’s behavior. By automating experimentation and optimization, it allows continuous improvement without manual intervention, effectively turning research into a self-improving process. -
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OmAgent
Build multimodal language agents for fast prototype and production
...The framework provides abstractions and infrastructure for building AI agents that operate on text, images, video, and audio while maintaining a relatively simple interface for developers. Instead of forcing developers to implement complex orchestration logic manually, the system manages task scheduling, worker coordination, and node optimization behind the scenes. Its architecture uses a graph-based workflow engine where tasks are represented as nodes in a directed workflow, enabling modular composition of complex reasoning pipelines. The framework also includes support for various reasoning strategies commonly used in language agents, such as chain-of-thought prompting, self-consistency reasoning, and ReAct-style decision loops. -
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InternGPT
Open source demo platform where you can easily showcase your AI models
InternGPT is an open-source multimodal AI framework designed to extend large language models beyond text interactions into visual reasoning and image manipulation tasks. The system integrates conversational AI with computer vision models so users can interact with images, videos, and visual environments through natural language instructions. Unlike traditional chat systems that rely solely on text prompts, InternGPT allows users to interact with visual content using both language and nonverbal signals such as pointing or highlighting objects within images. ...