Search Results for "loops" - Page 2
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Showing 84 open source projects for "loops"
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AGI
The first distributed AGI system
...It aims to provide a foundation for creating agents that can reason, plan, and execute tasks across diverse domains by integrating multiple AI capabilities into a unified system. The project typically explores concepts such as agent orchestration, memory systems, task decomposition, and decision-making loops, enabling the development of more generalized and adaptive AI behaviors. It is designed to be extensible, allowing developers to plug in different models, tools, and data sources to enhance agent performance. The framework encourages experimentation with AGI-like architectures, making it useful for researchers and developers interested in advancing beyond narrow AI applications. -
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Vibecraft
Manage Claude Code in style
...It uses generative modeling techniques to interpret input descriptors such as genre, mood, tempo, instrument palette, and creative themes, then outputs sequences that can serve as sketches, loops, or full musical ideas. The workflow prioritizes interactivity, letting users refine prompts, adjust musical fingerprints, and iterate on compositions in near real-time, making it suitable for ideation, prototyping, or inspiration workflows. The application also emphasizes usability by exporting to common audio formats and DAW-friendly stems, enabling seamless integration into existing music production environments. -
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How to Train Your GPT
Build a modern LLM from scratch. Every line commented
...The project covers the same broad family of architecture behind systems such as GPT-style models, LLaMA-style models, Claude-style systems, and Mistral-style models. It includes chapters and topic explainers on tokenizers, embeddings, attention, RoPE, RMSNorm, SwiGLU, KV cache, AdamW, mixed precision, training loops, and inference. The guide emphasizes writing every important component manually rather than only calling high-level APIs. Its purpose is to make the internals of language models understandable through runnable code and step-by-step explanations. -
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AutoAgent AI
Autonomous harness engineering
AutoAgent is an experimental AI framework focused on autonomous agent engineering, where a meta-agent iteratively improves another agent’s architecture without direct human intervention. Instead of manually tuning prompts or workflows, developers define high-level goals in a configuration file, and the system continuously modifies its own tools, orchestration, and logic based on benchmark performance. It operates through a loop of testing, analyzing failures, and refining the agent’s... -
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Claude Autoresearch
Claude Autoresearch Skill, autonomous goal-directed iteration
Claude Autoresearch is an autonomous research assistant system that automates the process of exploring, collecting, and synthesizing information across multiple iterations. It is designed to mimic human research behavior by generating queries, evaluating results, and refining its approach based on previous findings. The system likely integrates with external data sources, allowing it to gather information from diverse inputs and organize it into structured outputs. Its iterative loop enables... -
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EvoAgentX
Self-evolving AI agent framework for automated workflows
EvoAgentX is an open source framework for building, evaluating, and continuously improving LLM-based agents and multi-agent workflows. It moves beyond static pipelines by introducing a self-evolving system where agents are automatically generated, tested, and optimised through iterative feedback. Developers can define goals in natural language, while the framework handles workflow creation, execution, and refinement. Its modular architecture supports layered components for agents, workflows,... -
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Dexter
An autonomous agent for deep financial research
...It uses a multi-agent architecture with components such as a planning agent (to decompose queries), an action agent (to run tasks & fetch data), and self-validation mechanisms: after getting results, Dexter checks its own outputs and refines them until it is confident about its answer. This means it's more than a simple script — it’s a research assistant that loops through analysis steps until convergence. -
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Ragas
Supercharge Your LLM Application Evaluations
Objective metrics, intelligent test generation, and data-driven insights for LLM apps. Ragas is your ultimate toolkit for evaluating and optimizing Large Language Model (LLM) applications. Say goodbye to time-consuming, subjective assessments and hello to data-driven, efficient evaluation workflows. Don't have a test dataset ready? We also do production-aligned test set generation. -
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Hello-Agents
Building an Intelligent Agent from Scratch
Hello Agents is an open educational project designed to teach developers how to understand, design, and build AI-native agents from the ground up through structured tutorials and practical examples. The project focuses on guiding learners beyond superficial framework usage toward deeper comprehension of agent architecture, reasoning loops, and real-world implementation patterns. It walks users through core concepts such as ReAct-style reasoning, tool usage, memory handling, and multi-step task execution, enabling hands-on experimentation with modern LLM-powered agent systems. The repository is structured as a progressive learning path, combining theory, exercises, and runnable code so users can incrementally build more capable agents. ... -
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LLM From Scratch
Build and train a GPT-style language model
...Instead of relying on high-level abstractions or prebuilt frameworks, the project walks users through implementing every core component manually, including tokenization, transformer architecture, training loops, and autoregressive text generation. The repository is intentionally simplified to focus on conceptual clarity, using a compact model of roughly 10 million parameters that can train on consumer hardware such as laptops within a relatively short time. Inspired by Andrej Karpathy’s nanoGPT, the project emphasizes learning through direct implementation and experimentation rather than black-box usage. ... -
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darwin-skill
Autoresearch-inspired autonomous skill optimization for Claude Code
darwin-skill is an experimental framework designed to automatically improve AI agent “skills” through iterative evaluation and optimization loops inspired by machine learning training processes. Instead of treating prompts or skill definitions as static assets, the system applies a continuous improvement cycle that evaluates performance, proposes changes, tests outcomes, and either retains or reverts modifications. The framework introduces a scoring system across multiple dimensions, enabling quantitative assessment of skill quality and ensuring that only improvements are preserved over time. ... -
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Riffusion App
Stable diffusion for real-time music generation (web app)
...Unlike traditional music generation tools, it treats audio as spectrogram images and applies diffusion techniques to generate continuous sound transitions, allowing users to create evolving musical loops and compositions. The application is built with modern web technologies including Next.js, React, and three.js, providing a responsive and visually engaging interface for experimentation. It relies on a separate inference server to perform model computations, enabling flexible deployment depending on hardware capabilities. Users can input prompts or modify parameters to influence the style, tempo, and characteristics of generated audio, making it useful for creative exploration and prototyping. -
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mosaicml composer
Supercharge Your Model Training
composer is a deep learning training framework built on PyTorch and designed to make large-scale model training more efficient, scalable, and customizable. At the center of the project is a highly optimized Trainer abstraction that simplifies the management of training loops, parallelization, metrics, logging, and data loading. The framework is intended for modern workloads that may span anything from a single GPU to very large distributed training environments, which makes it suitable for both experimentation and production-scale development. It includes built-in support for distributed training strategies such as Fully Sharded Data Parallelism and standard Distributed Data Parallel execution, helping teams scale models without having to assemble as much infrastructure by hand. -
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Auto-Deep-Research
Your Fully-Automated Personal AI Assistant
...Auto-Deep-Research integrates retrieval from academic and web sources, processes document corpora for relevance and key insights, and organizes outputs into coherent chapters or sections according to research standards. It also embeds validation loops, where intermediate drafts are self-checked for consistency, coverage, and alignment with sound reasoning practices, reducing reliance on raw generation alone. -
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AI-Researcher
AI-Researcher: Autonomous Scientific Innovation
...Rather than simply generating text from prompts, AI-Researcher orchestrates sequences of subtasks — such as extracting definitions, identifying key experiments, and tracking citations — and uses self-refinement loops to iteratively improve outputs. -
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SimpleLLM
950 line, minimal, extensible LLM inference engine built from scratch
...Designed to run efficiently on high-end GPUs like NVIDIA H100 with support for models such as OpenAI/gpt-oss-120b, Simple-LLM implements continuous batching and event-driven inference loops to maximize hardware utilization and throughput. Its straightforward code structure allows anyone experimenting with custom kernels, new batching strategies, or inference optimizations to trace execution from input to output with minimal cognitive overhead. -
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Gemma in PyTorch
The official PyTorch implementation of Google's Gemma models
...The code is organized to be legible and hackable, exposing attention blocks, positional encodings, and head configurations. With standard PyTorch abstractions, it integrates easily into existing training loops, loggers, and evaluation harnesses. -
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Oasis
Inference script for Oasis 500M
Open-Oasis provides inference code and released weights for Oasis 500M, an interactive world model that generates gameplay frames conditioned on user keyboard input. Instead of rendering a pre-built game world, the system produces the next visual state via a diffusion-transformer approach, effectively “imagining” the world response to your actions in real time. The project focuses on enabling action-conditional frame generation so developers can experiment with interactive, model-generated... -
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Mastra
The TypeScript AI agent framework
...Model routing lets you connect to dozens of providers (OpenAI, Anthropic, Gemini, and others) through a single standardized interface, while agents orchestrate LLM calls and tools to solve open-ended tasks with internal reasoning loops. When explicit control is needed, Mastra’s workflow engine uses a graph-style API (.then(), .branch(), .parallel()) to orchestrate multi-step processes. -
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Oh My codeX (OMX)
Your codex is not alone. Add hooks, agent teams, HUDs
Oh My codeX (OMX) is a multi-agent orchestration layer designed to extend the capabilities of OpenAI Codex CLI by introducing structured teamwork, automation, and advanced workflow management. It addresses limitations in the base Codex environment, such as the lack of hooks, agent coordination, and persistent execution, by layering a shell-based system that enables richer interaction patterns. The project transforms a single AI coding assistant into a coordinated system of specialized agents... -
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The AI Scientist-v2
Workshop-Level Automated Scientific Discovery via Agentic Tree Search
...A key innovation is its progressive agentic tree search, which systematically explores experimental paths and is coordinated by an experiment manager agent that guides decision-making. The system also integrates automated review mechanisms, including vision-language feedback loops, to iteratively refine the quality of generated research outputs. -
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CGraph
A general, three-party dependency-free, cross-platform
CGraph is a high-performance, cross-platform Directed Acyclic Graph (DAG) framework implemented in pure C++ with no third-party dependencies, designed for building complex task pipelines and parallel execution workflows. It allows developers to model computational processes as graph structures, where nodes represent tasks and edges define dependencies, enabling efficient scheduling and execution. The framework includes a pipeline system that supports sequential and parallel execution,... -
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Pydantic Logfire
Python observability platform for tracing apps, metrics, and logs
...Pydantic Logfire provides deep visibility into application performance by capturing traces, metrics, and logs through an OpenTelemetry-based architecture. It is particularly strong in Python environments, offering detailed insights into Python objects, event loops, database queries, and validation flows. Logfire also integrates closely with Pydantic models, enabling developers to inspect and analyze how data moves through validation layers. In addition to traditional observability, it supports modern AI and LLM-based applications by tracing full request lifecycles, including model calls and external dependencies. -
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PySpur
Visual tool for building, testing, and deploying AI agent workflows
PySpur is a visual development environment designed to help AI engineers build, test, and iterate on agent-based workflows more efficiently. It provides a structured playground where users can define test cases, construct agents either through Python code or a graphical interface, and continuously refine their behavior. It addresses common challenges in AI agent development such as prompt tuning difficulties and lack of visibility into workflow execution. By offering a visual representation... -
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Neovim 99
Neovim AI agent done right
...Rather than a polished end-product, this repo serves as a playground for testing, iterating, and documenting workflows that integrate AI agents directly into everyday coding tools, emphasizing rapid feedback loops, automation, and minimal friction. The project often includes configuration files, scripts, and examples that show how to coerce modern AI assistants into productive roles within editors, plugins, and terminal workflows, with a focus on “no excuses” productivity. It blends examples from Neovim, agent automation, and developer ergonomics to illustrate how AI can be baked into existing environments.
