Showing 727 open source projects for "smtp-test"

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    Ship Agents Faster

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  • 1
    Posting

    Posting

    The modern API client that lives in your terminal

    Posting is an open-source, terminal-based API client designed for developers who prefer a fast, keyboard-driven workflow. It allows users to create, test, and manage HTTP requests directly from the command line without relying on graphical tools. The interface is highly interactive, offering features like command palettes, jump navigation, and real-time editing for efficient API exploration. Posting supports saving requests in a readable, version-control-friendly format, making it ideal for collaboration and reproducibility. ...
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  • 2
    Strawberry GraphQL

    Strawberry GraphQL

    A GraphQL library for Python that leverages type annotations

    Python GraphQL library based on dataclasses. Strawberry's friendly API allows to create GraphQL API rather quickly, the debug server makes it easy to quickly test and debug. Django and ASGI support allow having your API deployed in production in a matter of minutes. The quick start method provides a server and CLI to get going quickly. Strawberry comes with a mypy plugin that enables statically type-checking your GraphQL schema. A Django view is provided for adding a GraphQL endpoint to your application. ...
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  • 3
    Graphene-Django

    Graphene-Django

    Integrate GraphQL into your Django project

    Graphene-Django is built on top of Graphene. Graphene-Django provides some additional abstractions that make it easy to add GraphQL functionality to your Django project. First time? We recommend you start with the installation guide to get set up and the basic tutorial. It is worth reading the core graphene docs to familiarize yourself with the basic utilities. Graphene Django has a number of additional features that are designed to make working with Django easy. Our primary focus in this...
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  • 4
    Amazon Braket Default Simulator

    Amazon Braket Default Simulator

    An implementation of a quantum simulator that you can run locally

    The Amazon Braket Default Simulator is a Python open-source library that provides an implementation of a quantum simulator that you can run locally. You can use the simulator to test quantum tasks that you construct for the Amazon Braket SDK before you submit them to the Amazon Braket service for execution. You must have the Amazon Braket SDK installed to use the local simulator. Follow the instructions in the README for setup. If you want to contribute to the project, be sure to run unit tests and get a successful result before you submit a pull request. ...
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    Demo Series - Small Business Backup By Veeam

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  • 5
    DjangoBlog

    DjangoBlog

    A blog system based on python3.8 and Django3.0

    Articles, pages, categories, tags (add, delete, edit), etc. Articles and pages support Markdown and highlighting. Articles support full-text search. Complete comment feature, include posting reply comment and email notification. Markdown supporting. Sidebar feature, new articles, most readings, tags, etc. OAuth Login supported, including Google, GitHub, Facebook, Weibo, QQ. Memcache supported, with cache auto refresh. Simple SEO Features, notify Google and Baidu when there was a new article...
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  • 6
    Full Stack FastAPI and PostgreSQL

    Full Stack FastAPI and PostgreSQL

    Full stack, modern web application generator

    ...Celery worker that can import and use models and code from the rest of the backend selectively. REST backend tests based on Pytest, integrated with Docker, so you can test the full API interaction, independent on the database. As it runs in Docker, it can build a new data store from scratch each time.
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  • 7
    MiniStack

    MiniStack

    Ministack: Free, open-source local AWS emulator

    MiniStack is an open-source local AWS emulator designed as a lightweight, fully free alternative to tools like LocalStack, enabling developers to replicate cloud environments directly on their machines. It emulates over 35 AWS services through a single unified endpoint, allowing developers to test applications, infrastructure, and CI/CD pipelines without needing real cloud resources. One of its defining characteristics is its use of “real infrastructure” where possible, meaning services like databases and container orchestration are backed by actual technologies such as PostgreSQL, MySQL, Redis, and Docker instead of pure mocks. ...
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  • 8
    LongBench

    LongBench

    LongBench v2 and LongBench (ACL 25'&24')

    ...It supports bilingual evaluation in English and Chinese to assess multilingual capabilities across extended contexts. Newer versions of the benchmark introduce extremely long context windows ranging from thousands to millions of tokens, enabling researchers to test the limits of modern long-context models.
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  • 9
    LLM Colosseum

    LLM Colosseum

    Benchmark LLMs by fighting in Street Fighter 3

    LLM-Colosseum is an experimental benchmarking framework designed to evaluate the capabilities of large language models through gameplay interactions rather than traditional text-based benchmarks. The system places language models inside the environment of the classic video game Street Fighter III, where they must interpret the game state and decide which actions to perform during combat. This setup creates a dynamic environment that tests reasoning, situational awareness, and decision-making...
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  • 10
    Agentless

    Agentless

    An agentless approach to automatically solve software development

    Agentless is an open-source framework that applies large language models to automatically resolve software development issues without relying on complex autonomous agent systems. The project proposes an alternative approach to AI-driven code repair that avoids the overhead of multi-agent orchestration by using a structured pipeline for identifying and fixing bugs. When solving a problem, the system first performs localization to determine which files, functions, or code segments are most...
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  • 11
    Unsloth-MLX

    Unsloth-MLX

    Bringing the Unsloth experience to Mac users via Apple's MLX framework

    ...It supports loading and training Hugging Face models with fine-tuning strategies like SFT, DPO, ORPO, and GRPO and even handles exporting models to formats like GGUF for downstream use, although some limitations apply with quantized models. Users can write and test training pipelines directly on macOS before scaling up, accelerating development cycles and lowering entry barriers for model refinement.
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  • 12
    Ansible for DevOps

    Ansible for DevOps

    Ansible for DevOps examples

    Ansible for DevOps is a collection of Ansible playbooks, roles, and infrastructure-as-code examples that accompany the book Ansible for DevOps by Jeff Geerling. Rather than being theoretical, the examples span real-world infrastructure setups: multi-server orchestration, LAMP stacks, Docker deployments, Kubernetes cluster spins, rolling updates, and security hardening. You can clone the repo and play with actual scenarios using Vagrant, VirtualBox, or cloud hosts, making it ideal for both...
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  • 13
    HunyuanWorld-Mirror

    HunyuanWorld-Mirror

    Fast and Universal 3D reconstruction model for versatile tasks

    ...The project sits within a broader family of Hunyuan models that explore world generation and 3D-consistent understanding, and this mirror variant makes the reconstruction stack easier to test. It’s attractive for rapid prototyping of scenes, environment scans, or reference assets when you need repeatable 3D results from ordinary media.
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  • 14
    Multimodal

    Multimodal

    TorchMultimodal is a PyTorch library

    ...The design emphasizes composability: you can mix and match encoder, fusion, and decoder components rather than starting from monolithic models. The repository also includes example scripts and datasets for common multimodal tasks (e.g. retrieval, visual question answering, grounding) so you can test and compare models end to end. Installation supports both CPU and CUDA, and the codebase is versioned, tested, and maintained.
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  • 15
    MetaCLIP

    MetaCLIP

    ICLR2024 Spotlight: curation/training code, metadata, distribution

    MetaCLIP is a research codebase that extends the CLIP framework into a meta-learning / continual learning regime, aiming to adapt CLIP-style models to new tasks or domains efficiently. The goal is to preserve CLIP’s strong zero-shot transfer capability while enabling fast adaptation to domain shifts or novel class sets with minimal data and without catastrophic forgetting. The repository provides training logic, adaptation strategies (e.g. prompt tuning, adapter modules), and evaluation...
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  • 16
    AWS X-Ray SDK for Python

    AWS X-Ray SDK for Python

    AWS X-Ray SDK for the Python programming language

    ...If customer wants to enable generating secure random trace and entity id for all the (sampled/unsampled) requests (this is applicable for trace id injection into logs use case) then they should set the AWS_XRAY_NOOP_ID environment variable as False. Oftentimes, it may be useful to be able to disable X-Ray for specific use cases, whether to stop X-Ray from sending traces at any moment or to test code functionality that originally depended on X-Ray instrumented packages to begin segments prior to the code call. For example, if your application relied on an XRayMiddleware to instrument incoming web requests, and you have a method that begins subsegments based on the segment generated.
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  • 17
    Harbor LLM

    Harbor LLM

    Run a full local LLM stack with one command using Docker

    ...Built on Docker, Harbor allows services to run in isolated containers while communicating over a local network. It is intended for local development and experimentation rather than production deployment, giving developers a flexible way to explore AI systems, test configurations, and manage complex LLM stacks without manual wiring or setup overhead.
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  • 18
    CycleGAN and pix2pix in PyTorch

    CycleGAN and pix2pix in PyTorch

    Image-to-Image Translation in PyTorch

    CycleGAN and pix2pix in PyTorch repository is a PyTorch implementation of two influential image-to-image translation frameworks: CycleGAN (for unpaired translation) and pix2pix (for paired translation). This repo gives developers and researchers a convenient, modern (PyTorch-based) platform to train and test these methods — supporting both paired datasets (input to output) and unpaired datasets (domain-to-domain) with minimal changes. The code supports standard training and inference pipelines, and as of recent updates, compatibility with the latest Python and PyTorch versions (e.g. Python 3.11, PyTorch 2.4) as well as support for distributed/multi-GPU training for scalable workflows. ...
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  • 19
    openbench

    openbench

    Provider-agnostic, open-source evaluation infrastructure

    ...With a simple CLI interface (e.g. bench eval <benchmark> --model <model-id>), you can quickly evaluate any model supported by Groq or other providers (OpenAI, Anthropic, HuggingFace, local models, etc.). openbench also supports private/local evaluations: you can integrate your own custom benchmarks or data (e.g. internal test suites, domain-specific tasks) to evaluate models in a privacy-preserving way.
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  • 20
    OpenAGI

    OpenAGI

    When LLM Meets Domain Experts

    OpenAGI is a package for AI agent creation designed to connect large language models with domain-specific tools and workflows in the AIOS (AI Operating System) ecosystem. It provides a structured Python framework, pyopenagi, for defining agents as modular units that encapsulate execution logic, configuration, and dependency metadata. Agents are organized in a well-defined folder structure that includes code (agent.py), configuration (config.json), and extra requirements...
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  • 21
    FastKoko

    FastKoko

    Dockerized FastAPI wrapper for Kokoro-82M text-to-speech model

    ...The server also offers per-word timestamped captions, which makes it useful for creating subtitles or aligning audio with text. A built in web UI, API documentation, and debug endpoints for monitoring system status help users explore voices, test requests, and integrate the service into larger systems.
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  • 22
    Jenkins-Zero-To-Hero

    Jenkins-Zero-To-Hero

    Install Jenkins and configure Docker

    ...From there, it covers installing plugins like Docker Pipeline, configuring Docker as an agent, and wiring up multi-stage and multi-agent pipelines. The folder structure includes practical examples such as java-maven-sonar-argocd-helm-k8s and python-jenkins-argocd-k8s, showing real CI/CD flows that build, test, analyze, containerize, and deploy apps to Kubernetes via Argo CD in a GitOps style. The README walks through detailed step-by-step commands and screenshots, making it accessible to beginners who are unfamiliar with Jenkins, AWS, or pipelines.
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  • 23
    Agentex

    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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  • 24
    Janus

    Janus

    Unified Multimodal Understanding and Generation Models

    Janus is a sophisticated open-source project from DeepSeek AI that aims to unify both visual understanding and image generation in a single model architecture. Rather than having separate systems for “look and describe” and “prompt and generate”, Janus uses an autoregressive transformer framework with a decoupled visual encoder—allowing it to ingest images for comprehension and to produce images from text prompts with shared internal representations. The design tackles long-standing...
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  • 25
    Playground Cheatsheet for Python

    Playground Cheatsheet for Python

    Playground and cheatsheet for learning Python

    learn-python is another repository by Oleksii Trekhleb that serves as both a playground and an interactive cheatsheet for learning Python. It contains numerous Python scripts organized by topic (lists, dictionaries, loops, functions, classes, modules, etc.), each with code examples, explanations, test assertions, and links to further readings. The design supports “learn by doing”: you can modify the code, run the tests, see how behavior changes, and thus internalize Python language features, idioms, and good style practices (including linting and PEP8). Because it is organized in bite-sized chunks, it’s ideal for beginners or people refreshing their Python skills who want to revisit syntax and common patterns before moving into larger frameworks or applications. ...
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