Search Results for "loops"
Sort By:
Showing 414 open source projects for "loops"
View related business solutions-
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.
-
$300 Free Credits to Build on Google CloudPut your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.
-
1
LoopVectorization.jl
Macro(s) for vectorizing loops
LoopVectorization.jl is a Julia package for accelerating numerical loops by automatically applying SIMD (Single Instruction, Multiple Data) vectorization and other low-level optimizations. It analyzes loops and generates highly efficient code that leverages CPU vector instructions, making it ideal for performance-critical computing in fields such as scientific computing, signal processing, and machine learning. -
2
Loop Library
A library of practical AI-agent loops and an installable skill
Loop Library is a library and installable skill for designing repeatable AI-agent workflows. It is built around the idea of loops, which are bounded playbooks that tell an agent what to do, how to check progress, what to try next, and when to stop. The repository contains both a public loop library website and the Loopy skill that agents can use to discover, audit, adapt, run, and prepare loops. It helps turn open-ended prompts into measurable workflows with evidence, stopping criteria, and human approval points. ... -
3
Twisted
Event-driven networking engine written in Python
Twisted is an event-based framework for internet applications, supporting Python 3.6+. It includes modules for many different purposes. Twisted supports all major system event loops, select (all platforms), poll (most POSIX platforms), epoll (Linux), kqueue (FreeBSD, macOS), IOCP (Windows), and various GUI event loops (GTK+2/3, Qt, wxWidgets). Third-party reactors can plug into Twisted, and provide support for additional event loops. -
4
AIBuildAI
An AI agent that automatically builds AI models
...It provides a structured environment for orchestrating agents that can plan, execute, and refine tasks such as code generation, system design, and iterative improvement loops. The framework is designed to support experimentation with self-improving AI pipelines, allowing developers to test concepts like automated architecture search or adaptive system evolution. It integrates multiple components including prompt management, execution control, and feedback loops to ensure that generated outputs can be evaluated and improved over time. -
Cut Data Warehouse Costs by 54%BigQuery delivers 54% lower TCO with exabyte scale and flexible pricing. Free migration tools handle the SQL translation automatically.
-
5
Auto Claude
Autonomous multi-session AI coding
Auto-Claude is an autonomous, multi-agent coding framework that organizes software work into a structured workflow where agents plan, build, and validate code with minimal manual micromanagement. Instead of relying on a single chat thread to do everything, it uses coordinated agents and a task-driven approach so multiple steps—like investigation, implementation, and testing—can be executed systematically. The project aims to make “agentic software engineering” feel like running a small... -
6
atpbar
Progress bars for threading and multiprocessing tasks on terminal
Progress bars for threading and multiprocessing tasks on the terminal and Jupyter Notebook. atpbar can display multiple progress bars simultaneously growing to show the progresses of iterations of loops in threading or multiprocessing tasks. atpbar can display progress bars on the terminal and Jupyter Notebook. atpbar can be used with Mantichora. atpbar started its development in 2015 as part of Alphatwirl. atpbar prevented physicists from terminating their running analysis codes, which would take many hours to complete, by showing progress bars indicating their codes were actually running. ... -
7
ProgressMeter.jl
Progress meter for long-running computations
ProgressMeter.jl is a lightweight Julia package that provides customizable progress bars for long-running loops and computations. It allows developers to track the progress of tasks with real-time visual feedback in the terminal, making it easier to monitor performance, debug slow operations, or report computational progress in user-facing applications. -
8
LLMs-from-scratch
Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
LLMs-from-scratch is an educational codebase that walks through implementing modern large-language-model components step by step. It emphasizes building blocks—tokenization, embeddings, attention, feed-forward layers, normalization, and training loops—so learners understand not just how to use a model but how it works internally. The repository favors clear Python and NumPy or PyTorch implementations that can be run and modified without heavyweight frameworks obscuring the logic. Chapters and notebooks progress from tiny toy models to more capable transformer stacks, including sampling strategies and evaluation hooks. ... -
9
julep
A new DSL and server for AI agents and multi-step tasks
Julep is a platform for creating AI agents that remember past interactions and can perform complex tasks. It offers long-term memory and manages multi-step processes. Julep enables the creation of multi-step tasks incorporating decision-making, loops, parallel processing, and integration with numerous external tools and APIs. While many AI applications are limited to simple, linear chains of prompts and API calls with minimal branching, Julep is built to handle more complex scenarios. -
Veeam Data Platform v13.1 - Get Your Free TrialTry Veeam Data Platform today. Experience the unified platform that's secure by design, portable by default, and proven to recover clean, fast, and anywhere.
-
10
mini SWE-agent
The 100 line AI agent that solves GitHub issues
...The agent operates by interpreting software issues, analyzing repository context, and executing actions such as editing code, running commands, and validating fixes through iterative reasoning loops. It integrates seamlessly with language models, enabling flexible deployment with different providers while maintaining a consistent workflow for automated debugging and code modification. -
11
Vision Transformer Pytorch
Implementation of Vision Transformer, a simple way to achieve SOTA
...The code is intentionally compact and modular, which makes it easy to tinker with hyperparameters, depth, width, and attention dimensions. Because it stays close to vanilla PyTorch, you can integrate custom datasets and training loops without framework lock-in. It’s widely used as an educational reference for people learning transformers in vision and as a lightweight baseline for research prototypes. The project encourages experimentation—swap optimizers, change augmentations, or plug the transformer backbone into downstream tasks. -
12
NFH Self-Improvement Loop
Minimal adversarial framework for AI agent self-modification
NFH Self-Improvement Loop is a conceptual framework and implementation designed to model continuous self-improvement cycles using AI systems. It focuses on creating feedback loops where outputs are evaluated, refined, and reintroduced into the system for further improvement. The project emphasizes iterative learning, allowing systems to evolve over time through repeated evaluation and adjustment. It can be applied to areas such as content generation, decision-making, and personal productivity systems. The framework encourages structured reflection and optimization, ensuring that each iteration builds upon previous results. ... -
13
clawchief
Turn your OpenClaw into a Chief of Staff
...This approach allows for more predictable and organized multi-agent behavior compared to decentralized systems. The architecture likely includes task planning, delegation logic, and feedback loops that enable iterative refinement of outputs. It is particularly useful in scenarios where multiple agents must collaborate on interdependent tasks, such as coding, research, or automation pipelines. The system may also include monitoring tools to track agent performance and identify failures or inefficiencies. -
14
autoresearch-win-rtx
AI agents running research on single-GPU nanochat training
autoresearch-win-rtx is a Windows-based implementation of the autoresearch framework designed to run autonomous AI research loops on consumer NVIDIA RTX GPUs. It adapts the original autoresearch concept to a Windows environment, enabling users to perform iterative machine learning optimization without requiring specialized Linux or data center setups. The system revolves around a small set of core files, including a training script that is continuously modified by an AI agent, along with supporting utilities for data preparation and evaluation. ... -
15
TNT
A lightweight library for PyTorch training tools and utilities
TNT is a lightweight training framework developed by Meta that simplifies the process of building and managing machine learning training loops using PyTorch. The project focuses on providing a flexible yet structured environment for implementing training pipelines without the complexity of large deep learning frameworks. It introduces modular abstractions that allow developers to organize training logic into reusable components such as trainers, evaluators, and callbacks. -
16
LangGraph.js
Framework to build resilient language agents as graphs
...This structure makes it easier to implement long-running agents, multi-step reasoning pipelines, and workflows that require persistent state. LangGraphJS supports advanced capabilities such as branching logic, loops, and conditional execution, enabling developers to build sophisticated AI systems that can adapt to dynamic conditions. The framework integrates seamlessly with language models, tools, and external APIs, allowing agents to retrieve information and perform actions across different systems. Developers can also build applications that maintain conversation history and state across multiple interactions. -
17
Hello Python
Comprehensive tutorial repository aimed at teaching the Python program
...It includes over 100 classes and about 44 hours of video instruction, combined with code samples, projects, and a chat community for support. The material covers the fundamentals—variables, data types, loops, functions—as well as intermediate topics like date handling, list comprehensions, file IO, regular expressions, modules, and packages. The course is designed to be accessible: no prior programming experience required, and the resources are freely available. In addition, it is accompanied by a practical coding approach (projects) and is maintained as an open-source repository under Apache-2.0 license. ... -
18
FernFlower
Decompiler from Java bytecode to Java, used in IntelliJ IDEA
Fernflower is an open-source Java decompiler originally developed by JetBrains that takes compiled Java bytecode and reconstructs readable source code, helping developers understand, debug, and recover lost Java code from .class files. It works by analyzing the structure of bytecode and inferring higher-level constructs like loops, conditionals, generics, and exception handling to produce Java source that is as close as possible to what a human would have written, making it useful for both reverse engineering and educational purposes. Fernflower is integrated into popular Java IDEs and tools where decompilation is needed, and its robust analysis handles a wide range of language features introduced across multiple Java versions. ... -
19
FlowGram
Extensible workflow development framework
...This makes FlowGram highly flexible: you can prototype data-processing pipelines, AI-agent flows, automation scripts, or even business process automation without writing all the plumbing yourself. The framework supports both free-layout canvases (for free-form graphs) and fixed-layout canvases (for more structured flowcharts, including loops, branches, compound nodes), giving you visual freedom depending on your use-case. -
20
AI_memory_Loops
Persistent Memory Logic Loop
-
21
Agent Apprenticeship
The living ecosystem where AI agents complete tasks
Agent Apprenticeship is an open infrastructure project for turning real AI-agent work into reusable learning signals. It lets local agents complete tasks through iterative workflow loops, then records the process as experience that can improve future agents. Apprentice agents can be paired with mentor agents, human reviewers, or domain experts depending on the selected mode. The project supports Codex, Cursor, Claude Code, OpenClaw, OpenCode, Hermes Agent, and custom agent commands. Its seed dataset includes curated tasks, reusable lessons, execution traces, work episodes, and structured experience records. ... -
22
Reflexion
Reflexion: Language Agents with Verbal Reinforcement Learning
Reflexion is a research-oriented AI framework that focuses on improving the reasoning and problem-solving capabilities of language model agents through iterative self-reflection and feedback loops. Instead of relying solely on a single-pass response, Reflexion enables agents to evaluate their own outputs, identify errors, and refine their reasoning over multiple iterations, leading to more accurate and reliable results. The framework introduces a mechanism where agents maintain a memory of past attempts and use that memory to guide future decisions, effectively simulating a learning process without requiring traditional model retraining. ... -
23
Personal AI Infrastructure
Agentic AI Infrastructure for magnifying HUMAN capabilities
...Unlike once-stateless chatbots, this platform captures context, memory, goals, preferences, and feedback to enable an AI that understands you and improves over time, using a full agentic stack rather than simple question-answer loops. PAI blends tools like browsing, code editing, execution, and more into a continuous Observe → Think → Plan → Execute → Verify → Learn cycle, letting the system refine its behavior with each use. Its architecture supports long-term memory, verification of actions, and ongoing self-improvement, blurring the line between “assistant” and persistent, evolving collaborator. -
24
AI Researcher
An autonomous AI researcher
...The system emphasizes modularity, so teams can swap in new reasoning modules, data retrieval strategies, or domain knowledge bases depending on the research topic. Through self-supervised feedback loops, agents adjust their strategies based on prior outcomes, improving both the quality and relevance of results over time. -
25
llm.c
LLM training in simple, raw C/CUDA
...By stripping away heavy frameworks, it exposes the core math and memory flows of embeddings, attention, and feed-forward layers. The code illustrates how to wire forward passes, losses, and simple training or inference loops with direct control over arrays and buffers. Its compact design makes it easy to trace execution, profile hotspots, and understand the cost of each operation. Portability is a goal: it aims to compile with common toolchains and run on modest hardware for small experiments. Rather than delivering a production-grade stack, it serves as a reference and learning scaffold for people who want to “see the metal” behind LLMs.
