Search Results for "loop" - Page 7
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Showing 435 open source projects for "loop"
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MLE-bench
AI multi-agent framework for automating data-driven R&D workflows
RD-Agent is an open source AI framework designed to automate research and development workflows in data-driven domains. It uses large language models and multiple collaborating agents to simulate the typical cycle of research, experimentation, and improvement that human data scientists follow. It separates the process into two core phases: a research stage that proposes hypotheses and ideas, and a development stage that implements and evaluates them through code execution and experiments. By... -
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Deep Search Agent
Implement a concise and clear Deep Search Agent from 0
Deep Search Agent is an experimental demonstration project that showcases an autonomous AI agent designed to perform multi-step research and information gathering tasks. The repository illustrates how large language models can be orchestrated with tools and planning logic to execute complex search workflows rather than single-prompt responses. It typically combines reasoning, retrieval, and iterative refinement so the agent can break down questions, gather evidence, and synthesize structured... -
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Sandstorm
One API call, pull Claude agent, completely sandboxed
Sandstorm is an open-source project that wraps a powerful Claude-based AI agent within a completely sandboxed, ephemeral API service designed to make agentic AI workflows easy to deploy and scale without infrastructure complexity. The core idea is to provide “one API call” access to a robust Claude agent loop that runs inside a secure sandbox, so you can upload files, connect tools, and run long-running tasks — all managed behind a simple REST-style interface that disappears when the work is done. This approach lowers the friction of building autonomous agents by removing the need to provision servers, orchestrate distributed agents, or manage persistent tooling; agents can be spun up in parallel without manual setup and shut down when complete. ... -
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Dash Data Agent
Self-learning data agent that grounds its answers in layers of content
Dash is a self-learning data agent built by the Agno AI community that generates grounded answers to English queries over structured data by synthesizing SQL and reasoning based on six layers of context, improving automatically with each run. It sidesteps common limitations of simple text-to-SQL agents by incorporating multiple context layers — including schema structure, human annotations, known query patterns, institutional knowledge from docs, machine-discovered error patterns, and live... -
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AIPex
AI browser automation assistant, no migration and privacy first
AIPex is an AI-augmented development toolkit and workflow platform that aims to accelerate software productivity by integrating intelligent assistants, code generation tools, and customizable automation patterns directly into developer workflows. Rather than treating AI as a separate helper, AIPex embeds AI capabilities into common tasks like scaffolding components, generating tests, analyzing code quality, and performing refactors, allowing developers to stay in flow while benefiting from... -
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Stremio Core
Types, addon system, UI models, core logic
Stremio Core is the Rust engine that powers Stremio’s apps by centralizing all reusable logic behind discovery, catalogs, metadata, streams, add-ons, and user/library state. It exposes a clean set of modules—types, addon_transport, and state_types—so apps can talk to add-ons, model UI state, and react to events without duplicating code. The architecture is inspired by Elm: immutable state, message-driven updates, and explicit side-effects (“effects”) keep behavior predictable and testable.... -
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Perf Book
The book "Performance Analysis and Tuning on Modern CPU"
...It also demonstrates tool-driven workflows—using profilers and PMU events—to locate true bottlenecks and validate that changes actually help. Throughout, the emphasis is on a methodical loop of hypothesize → measure → change → re-measure, rather than folklore or premature micro-optimizations. -
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Mojolicious (Mojo)
Perl real-time web framework
Mojolicious is a modern, real-time web framework for Perl that ships batteries-included for building both APIs and dynamic web apps. It features a fast non-blocking I/O loop, a built-in HTTP/1.1 and WebSocket server, and first-class support for JSON, templates, and routing. Developers can start small with Mojolicious::Lite, a concise DSL for single-file apps, then grow into a full MVC structure with controllers, plugins, and reusable components. The framework emphasizes developer ergonomics: hot-reloading dev server, powerful testing tools, and a consistent Mojo:: ecosystem for clients, servers, and utilities. ... -
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TraceRoot
Find the Root Cause in Your Code's Trace
...Lightweight SDKs for Python and TypeScript enable seamless instrumentation using OpenTelemetry, with support for both self-hosted and cloud deployment. Human-in-the-loop interaction is central: developers can guide reasoning by selecting relevant spans or logs, then verify agent reasoning through traceable context. -
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PML
The easiest way to use deep metric learning in your application
This library contains 9 modules, each of which can be used independently within your existing codebase, or combined together for a complete train/test workflow. To compute the loss in your training loop, pass in the embeddings computed by your model, and the corresponding labels. The embeddings should have size (N, embedding_size), and the labels should have size (N), where N is the batch size. The TripletMarginLoss computes all possible triplets within the batch, based on the labels you pass into it. Anchor-positive pairs are formed by embeddings that share the same label, and anchor-negative pairs are formed by embeddings that have different labels. ... -
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Rescript Compiler
The compiler for ReScript
...ReScript is a robustly typed language that compiles to efficient and human-readable JavaScript. It comes with a lightning-fast compiler toolchain that scales to any codebase size. ReScript cares about a consistent and fast feedback loop for any codebase size. Refactor code, pull complex changes or switch to feature branches as you please. No sluggish CI builds stale caches, wrong type hints, or memory-hungry language servers that slow you down. Every ReScript app is fully typed and provides reliable type information for any given value in your program. We prioritize simpler types over complex types for the sake of clarity and easy debugability. ... -
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Hoppscotch
Open source API development ecosystem
...Retrieve response headers identical to those of a GET request, but without the response body. Establishes a tunnel to the server identified by the target resource. Describe the communication options for the target resource. Performs a message loop-back test along the path to the target resource. Some APIs use custom request methods such as LIST. Type in your custom methods. Choose a theme, System (default), Light, Dark, and Black. Choose accent color, Green (default), Teal, Blue, Indigo, Purple, Yellow, Orange, Red, and Pink. Distraction-free Zen mode. Receive a stream of updates from a server over an HTTP connection without resorting to polling. ... -
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Concourse
Concourse is a container-based continuous thing-doer written in Go
...Your pipeline is then visualized in the web UI, taking only one click to get from a failed job to seeing why it failed. The visualization provides a "gut check" feedback loop: if it looks wrong, it probably is wrong. Jobs can depend on other jobs by configuring passed constraints. The resulting chain of jobs and resources is a dependency graph that continuously pushes your project forward, from source code to production. -
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Micro Agent
AI CLI agent that writes code by iterating until tests pass
Micro Agent is a command-line tool designed to generate and refine code using a test-driven approach powered by large language models. Instead of producing one-shot code outputs, it creates or uses test cases and repeatedly iterates on the generated code until those tests pass successfully. This workflow emphasizes reliability by using structured feedback from failing tests to guide improvements, reducing the need for manual debugging and iteration. Micro Agent intentionally limits its scope... -
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Monoio
Rust async runtime based on io-uring
Monoio is a Rust asynchronous runtime designed for high-performance I/O-bound servers and applications, built around native OS async I/O primitives (e.g. io_uring on Linux, epoll / kqueue on other Unix-like systems), rather than layering atop an existing runtime. Its design philosophy centers on a “thread-per-core” model where each core runs its own event loop, minimizing cross-thread synchronization needs, avoiding the overhead and complexity of task scheduling, and letting developers write efficient, low-overhead asynchronous networking or I/O code. Because tasks do not need to be Send or Sync and can make use of thread-local data safely, Monoio simplifies certain concurrency paradigms while delivering performance benefits for workloads like high-throughput network servers, proxies, or real-time services. ... -
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DeepSeekMath-V2
Towards self-verifiable mathematical reasoning
...Under the hood, Math-V2 uses a massive Mixture-of-Experts (MoE) architecture (activated parameter count reportedly in the hundreds of billions) derived from DeepSeek’s experimental base architecture. For math problems, it employs a generator-verifier loop: it first generates a candidate proof (or solution path), then runs a verifier that assesses correctness and completeness. -
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Valdi
Cross-platform UI framework that delivers native performance
Valdi is a cross-platform UI framework built to deliver native runtime performance without giving up the fast iteration loop that teams want from higher-level frameworks. You write your UI once in declarative TypeScript (TSX-style components), and Valdi compiles those components directly into platform-native views for iOS, Android, and macOS, instead of relying on web views or a JavaScript bridge. Its design focuses heavily on performance characteristics that matter in real apps, like keeping scrolling smooth, minimizing view creation overhead, and limiting unnecessary re-renders so updates stay incremental and cheap. ... -
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Poetiq
Reproduction of Poetiq's record-breaking submission to the ARC-AGI-1
...Instead of relying on a single prompt or fixed strategy, their solver dynamically adapts the reasoning path, selecting what to ask or analyze next depending on intermediate results — effectively compositing reasoning, perception, and program synthesis (or symbolic manipulation) in a loop. The repository allows others to reproduce their results, experiment with different LLM backends (e.g. the user may supply keys for supported models), and observe how their adaptive meta-system handles the logic and abstraction challenges. -
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Agentex
Open source codebase for Scale Agentex
AgentEX is an open framework from Scale for building, running, and evaluating agentic workflows, with an emphasis on reproducibility and measurable outcomes rather than ad-hoc demos. It treats an “agent” as a composition of a policy (the LLM), tools, memory, and an execution runtime so you can test the whole loop, not just prompting. The repo focuses on structured experiments: standardized tasks, canonical tool interfaces, and logs that make it possible to compare models, prompts, and tool sets fairly. It also includes evaluation harnesses that capture success criteria and partial credit, plus traces you can inspect to understand where reasoning or tool use failed. ... -
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LLM Course
Course to get into Large Language Models (LLMs)
...The materials also cover inference optimization and quantization to make serving LLMs feasible on commodity GPUs or even CPUs, which is crucial for side projects and startups. Evaluation is treated as a first-class topic, with examples of automatic and human-in-the-loop methods to catch regressions and verify quality beyond simple loss values. By the end, students have a mental model and a practical toolkit for iterating on datasets, training configs, etc. -
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Pry
A runtime developer console and IRB alternative
...An extensible command system and a rich plugin ecosystem add profiling, colorized output, paging, and Git integration. In day-to-day development, Pry shortens the feedback loop and turns debugging into an exploratory, conversational workflow. -
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Habitat-Lab
A modular high-level library to train embodied AI agents
Habitat-Lab is a modular high-level library for end-to-end development in embodied AI. It is designed to train agents to perform a wide variety of embodied AI tasks in indoor environments, as well as develop agents that can interact with humans in performing these tasks. Allowing users to train agents in a wide variety of single and multi-agent tasks (e.g. navigation, rearrangement, instruction following, question answering, human following), as well as define novel tasks. Configuring and... -
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libhv
Network library for developing TCP/UDP/SSL/HTTP/WebSocket/MQTT client
Like libevent, libev, and libuv, libhv provides event loop with non-blocking IO and timer, but simpler api and richer protocols. Cross-platform (Linux, Windows, MacOS, BSD, Solaris, Android, iOS) High-performance EventLoop (IO, timer, idle, custom) TCP/UDP client/server/proxy. TCP supports heartbeat, reconnect, upstream, MultiThread-safe write and close, etc. Built-in common unpacking modes (FixedLength, Delimiter, LengthField) RUDP support: WITH_KCP. -
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MacDriver
Native Mac APIs for Go
...Webkit types: WKWebView and configuration classes. Core types: NSData, NSDictionary, NSNumber, NSPoint, NSRect, NSSize, NSString, NSURL, more. Core also allows dispatching Go functions in the Cocoa run loop. Many constants/enums are defined. -
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NannyML
Detecting silent model failure. NannyML estimates performance
...Built for data scientists, NannyML has an easy-to-use interface, and interactive visualizations, is completely model-agnostic, and currently supports all tabular classification use cases. NannyML closes the loop with performance monitoring and post deployment data science, empowering data scientist to quickly understand and automatically detect silent model failure. By using NannyML, data scientists can finally maintain complete visibility and trust in their deployed machine learning models. When the actual outcome of your deployed prediction models is delayed, or even when post-deployment target labels are completely absent, you can use NannyML's CBPE-algorithm to estimate model performance.
