Search Results for "memory" - Page 29
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Showing 3192 open source projects for "memory"
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AGiXT
AGiXT is a dynamic AI Automation Platform
AGiXT is a dynamic Artificial Intelligence Automation Platform engineered to orchestrate efficient AI instruction management and task execution across a multitude of providers. Our solution infuses adaptive memory handling with a broad spectrum of commands to enhance AI's understanding and responsiveness, leading to improved task completion. The platform's smart features, like Smart Instruct and Smart Chat, seamlessly integrate web search, planning strategies, and conversation continuity, transforming the interaction between users and AI. ... -
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Reaktive
Kotlin multi-platform implementation of Reactive Extensions
Kotlin multiplatform implementation of Reactive Extensions. There are a number of modules published to Maven Central. This functionality is provided by the coroutines-interop module. Reaktive provides an easy way to manage subscriptions: DisposableScope. Reaktive provides Plugin API, something similar to RxJava plugins. The Plugin API provides a way to decorate Reaktive sources. A plugin should implement the ReaktivePlugin interface, and can be registered using the registerReaktivePlugin... -
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Tokio
A runtime for writing reliable asynchronous applications with Rust
...These include a multithreaded, work-stealing based task scheduler; a reactor that's supported by the operating system's event queue; and asynchronous TCP and UDP sockets. Tokio is reliable in that its APIs are memory-safe, thread-safe, and misuse-resistant. Thanks to its task scheduler, it is also incredibly fast. It is capable of processing hundreds of thousands of requests per second with little to no overhead. -
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CloudCompare
CloudCompare main repository
...It relies on an octree structure that is highly optimized for this particular use-case. It was also meant to deal with huge point clouds (typically more than 10 million points, and up to 120 million with 2 GB of memory). -
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Loop Engineering
Practical patterns, starters & CLI tools for loop engineering with AI
...The project is aimed at developers using tools such as Grok, Claude Code, Codex, Cursor, and other coding agents. It explains core building blocks such as scheduling, worktrees, skills, plugins, connectors, sub-agents, and persistent memory or state. The repository includes production loop patterns, quickstarts, starters, checklists, safety notes, and real-world stories. It also provides CLI tools such as loop-audit, loop-init, and loop-cost to scaffold, evaluate, and estimate agent loop workflows. -
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multi-agent-shogun
Samurai-inspired multi-agent system for Claude Code
multi-agent-shogun is a multi-agent orchestration system designed to coordinate multiple AI coding agents working in parallel. Inspired by the hierarchy of a feudal Japanese military structure, the system organizes agents into roles such as Shogun, Karo, and Ashigaru, which correspond to strategist, coordinator, and worker agents. A user interacts primarily with the Shogun agent by issuing natural language instructions that describe the desired tasks. The system then automatically... -
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tt-metal
TT-NN operator library, and TT-Metalium low level kernel programming
...Instead of following a traditional GPU model centered on massive thread parallelism, the platform is built around a grid of specialized compute nodes called Tensix cores, each with local SRAM, dedicated compute units, and multiple RISC-V control processors. The SDK provides the abstractions and APIs needed to manage data movement, compute kernels, memory coordination, and execution flow across this architecture. -
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nndeploy
An Easy-to-Use and High-Performance AI Deployment Framework
...The system supports multiple inference engines and hardware accelerators, allowing the same AI workflow to run on different platforms without significant modifications. nndeploy also includes performance optimization techniques such as parallel execution, memory reuse, and hardware-accelerated operations to improve inference speed. -
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Xtuner
A Next-Generation Training Engine Built for Ultra-Large MoE Models
...The engine supports training models with hundreds of billions of parameters and enables long-context training with sequence lengths reaching tens of thousands of tokens. Its architecture incorporates memory-efficient optimizations that allow researchers to train large models even when computational resources are limited. XTuner is also designed to integrate with modern AI ecosystems, supporting multimodal training, reinforcement learning optimization, and instruction tuning pipelines. -
Fully Managed MySQL, PostgreSQL, and SQL ServerCloud SQL handles your database ops end to end, so you can focus on your app.
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Intel LLM Library for PyTorch
Accelerate local LLM inference and finetuning
...The framework provides hardware-aware optimizations and low-precision computation techniques that significantly improve the performance of large language models while reducing memory consumption. IPEX-LLM supports a wide range of popular models, including architectures such as LLaMA, Mistral, Qwen, and other transformer-based systems. The library can integrate with common AI frameworks and serving tools such as Hugging Face Transformers, LangChain, and vLLM, allowing developers to incorporate optimized inference into existing pipelines. -
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DINOv2
PyTorch code and models for the DINOv2 self-supervised learning
...The repository includes code for training, evaluating, and feature extraction, with utilities to run k-NN or linear evaluation baselines to assess representation quality. Pretrained checkpoints cover multiple model sizes so practitioners can trade accuracy for speed and memory depending on their deployment constraints. -
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NYC Taxi Data
Import public NYC taxi and for-hire vehicle (Uber, Lyft)
...It collects and preprocesses large-scale trip datasets (fares, pickup/dropoff, timestamps, locations, passenger counts) to enable data analysis, modeling, and visualization efforts. The project includes scripts and notebooks for cleaning and filtering the raw data, memory-efficient processing for large CSV/Parquet files, and aggregation workflows (e.g. trips per hour, heatmaps of pickups/dropoffs). It also contains example analyses—spatial and temporal visualizations like maps, time-series plots, and hotspot detection—highlighting insights such as patterns of demand, peak times, and geospatial distributions. ... -
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DoKit
A platform for the life cycle of pan-front-end product development
DoKit is a debugging and quality-of-experience toolkit for mobile (primarily Android) apps that provides an in-app overlay with tools for inspection, monitoring, and interaction. It offers features like CPU/memory monitoring, frame rate detection, network logging, view hierarchy inspection, and plugin support to expand capabilities. Developers can interactively explore UI layouts, measure layout times, simulate slow networks, or trace touch events—all while the app is running on the device. DoKit aims to shorten feedback loops by surfacing internal state and metrics in situ rather than relying solely on external logging or tooling. ... -
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KWOK
Kubernetes WithOut Kubelet - Simulates thousands of Nodes and Clusters
KWOK is a toolkit that enables setting up a cluster of thousands of Nodes in seconds. Under the scene, all Nodes are simulated to behave like real ones, so the overall approach employs a pretty low resource footprint that you can easily play around with on your laptop. -
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Authelia
The Single Sign-On Multi-Factor portal for web apps
Authelia is an open-source authentication and authorization server and portal fulfilling the identity and access management (IAM) role of information security in providing multi-factor authentication and single sign-on (SSO) for your applications via a web portal. It acts as a companion for common reverse proxies. With a compressed container size smaller than 20 megabytes and observed memory usage normally under 30 megabytes, it's one of the most lightweight solutions available. Written in Go and React, authorization policies and many other backend tasks are completed in mere milliseconds and login portal loading times of 100 milliseconds makes it one of the fastest solutions available. Processors can use a lot of electricity, but when idle usage is basically so low that you can't measure it, and active usage in a small business environment being under 1% you can rest easy (with the exclusion of password hashing). -
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MQTTnet
High performance .NET library for MQTT based communication
...It provides an MQTT client and an MQTT server (broker) and supports the MQTT protocol up to version 5. TLS support for client and server (but not UWP servers). Extensible communication channels (e.g. In-Memory, TCP, TCP+TLS, WS). Lightweight (only the low-level implementation of MQTT, no overhead). Performance optimized (processing ~150.000 messages/second). Uniform API across all supported versions of the MQTT protocol. Tested on the local machine (Intel i7 8700K) with MQTTnet client and server running in the same process using the TCP channel. ... -
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s3fs
FUSE-based file system backed by Amazon S3
s3fs allows Linux, macOS, and FreeBSD to mount an S3 bucket via FUSE. s3fs preserves the native object format for files, allowing use of other tools like AWS CLI. Large subset of POSIX including reading/writing files, directories, symlinks, mode, uid/gid, and extended attributes. Compatible with Amazon S3, and other S3-based object stores. Allows random writes and appends, large files via multi-part upload, and renames via server-side copy. User-specified regions, including Amazon GovCloud.... -
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gops
A tool to list and diagnose Go processes currently running
gops is a command to list and diagnose Go processes currently running on your system. For processes that starts the diagnostics agent, gops can report additional information such as the current stack trace, Go version, memory stats, etc. You could set GOPS_CONFIG_DIR environment variables to assign your config dir. Default, gops will use the current user's home directory(AppData on windows). It is possible to use gops tool both in local and remote mode. Local mode requires that you start the target binary as the same user that runs gops binary. To use gops in a remote mode you need to know target's agent address. ... -
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py-spy
Sampling profiler for Python programs
py-spy is a sampling profiler for Python programs. It lets you visualize what your Python program is spending time on without restarting the program or modifying the code in any way. py-spy is extremely low overhead: it is written in Rust for speed and doesn't run in the same process as the profiled Python program. This means py-spy is safe to use against production Python code. py-spy works from the command line and takes either the PID of the program you want to sample from or the command... -
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gpt-oss
gpt-oss-120b and gpt-oss-20b are two open-weight language models
...The series includes two main models: gpt-oss-120b, a 117-billion parameter model optimized for general-purpose, high-reasoning tasks that can run on a single H100 GPU, and gpt-oss-20b, a lighter 21-billion parameter model ideal for low-latency or specialized applications on smaller hardware. Both models use a native MXFP4 quantization for efficient memory use and support OpenAI’s Harmony response format, enabling transparent full chain-of-thought reasoning and advanced tool integrations such as function calling, browsing, and Python code execution. The repository provides multiple reference implementations—including PyTorch, Triton, and Metal—for educational and experimental use, as well as example clients and tools like a terminal chat app and a Responses API server. -
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image-to-ascii
A Node.js module that converts images to ASCII art
image-to-ascii is a Node.js module that converts raster images into character-based terminal artwork. Input can come from a local file, remote URL, or in-memory Buffer. The converter maps pixel brightness to a configurable sequence of ASCII characters. It can preserve the original colors through ANSI foreground or background styles or generate plain monochrome output. Options control width, height, screen fitting, aspect ratio, transparency, reversed brightness, and custom background colors. ... -
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linkedom
A triple-linked lists based DOM implementation
linkedom is a lightweight DOM implementation designed for server-side rendering and other environments without a browser DOM. Its triple-linked-list structure aims to keep operations linear and avoid recursion, excessive memory use, and call stack failures on very large documents. It provides DOMParser and parseHTML APIs that return documents, window-like objects, events, elements, and custom element support without polluting global scope. Developers can query content with CSS selectors, create and move nodes, serialize documents, and restore nodes through its JSON format. ... -
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react-native-fast-tflite
High-performance TensorFlow Lite library for React Native
react-native-fast-tflite is a high-performance TensorFlow Lite library for React Native applications. It is built with Nitro Modules and uses the low-level C and C++ TensorFlow Lite core API for direct memory access. The library supports zero-copy ArrayBuffers, which helps reduce overhead when passing model inputs and outputs through React Native. It can load and swap TensorFlow Lite models at runtime, making it useful for apps that run multiple machine learning tasks. The project supports GPU-accelerated delegates such as CoreML, Metal, and OpenGL. ... -
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plakar
plakar is a backup solution powered by Kloset and ptar
...Its main value is giving users and organizations a practical backup system that combines simple operation, strong protection, and support for large datasets with limited memory usage. -
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whichllm
Find the local LLM that actually runs and performs best
whichllm is a command-line tool for finding local large language models that can realistically run on a user’s hardware. It detects the machine’s available resources, including GPU, CPU, memory, and storage, then recommends models based on practical fit rather than parameter count alone. The project is useful for users who are unsure which local LLM will perform well on their system. It focuses on real, recency-aware benchmarks so recommendations better reflect current model performance. whichllm is especially helpful for developers, AI hobbyists, and researchers comparing local inference options across NVIDIA, AMD, Apple Silicon, and CPU-only environments. ...