Search Results for "without code" - Page 7
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Showing 379 open source projects for "without code"
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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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$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
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OpenAI Agent Skills
Skills Catalog for Codex
...It organizes reusable, task-specific workflows, instructions, scripts, and resources into modular skill folders so that an AI agent can reliably perform complex tasks without repeated custom prompting, making agent behavior more predictable and composable. Each skill is defined with clear metadata and instructions organizing how an AI assistant should complete specific tasks ranging from project management to code generation and documentation assistance. The repository supports community contributions, allowing developers to add new skills or update existing ones to keep the catalog relevant and practical for evolving use cases. -
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A.I.G
Full-stack AI Red Teaming platform
AI-Infra-Guard is a powerful open-source security platform from Tencent’s Zhuque Lab designed to assess the safety and resilience of AI infrastructures, codebases, and components through automated scanning and evaluation tools. It brings together AI infrastructure vulnerability scanning, MCP server risk analysis, and jailbreak evaluation into a unified workflow so that enterprises and individuals can identify critical security issues without relying on external services. Users can deploy it... -
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Trellis AI
All-in-one AI framework & toolkit for Claude Code & Cursor
Trellis is an advanced workflow and agent orchestration framework designed for building, managing, and scaling intelligent applications that coordinate numerous autonomous components. At its core, Trellis lets developers define units of work — called tasks or agents — and compose them into rich workflows that can operate with concurrency, conditional logic, and dynamic branching, all without sacrificing readability or control. It emphasizes modular design, encouraging users to encapsulate... -
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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. It also includes scripting capabilities, enabling users to run Python code before and after requests to automate workflows. ... -
99.99% Uptime for MySQL and PostgreSQL DatabasesCloud SQL Enterprise Plus delivers near-zero downtime with 35 days of point-in-time recovery. Supports MySQL, PostgreSQL, and SQL Server.
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Stable Baselines3
PyTorch version of Stable Baselines
Stable Baselines3 (SB3) is a set of reliable implementations of reinforcement learning algorithms in PyTorch. It is the next major version of Stable Baselines. You can read a detailed presentation of Stable Baselines3 in the v1.0 blog post or our JMLR paper. These algorithms will make it easier for the research community and industry to replicate, refine, and identify new ideas, and will create good baselines to build projects on top of. We expect these tools will be used as a base around... -
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glom
Python's nested data operator
...It is designed for real-world data structures where dictionaries, objects, and lists are deeply nested and difficult to handle cleanly. Developers can use path-based access to retrieve values without writing long chains of fragile indexing code. The library also provides readable error messages, which makes debugging broken paths much easier. Its declarative specification style lets users describe the output shape they want instead of manually building transformation logic. glom is especially useful for data processing, API response cleanup, configuration handling, and scripts that need reliable nested data manipulation. -
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furl
The easiest way to parse and modify URLs in Python
furl is a small Python library that makes it easier to parse, inspect, build, and modify URLs without relying on tedious low-level URL handling code. It wraps URL components into convenient objects, so developers can work directly with schemes, usernames, passwords, hosts, ports, paths, queries, and fragments. The library supports simple path editing, query argument changes, fragment manipulation, inline method chaining, and URL joining. -
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smolagents
Agents write python code to call tools and orchestrate other agents
This library is the simplest framework out there to build powerful agents. We provide our definition in this page, where you’ll also find tips for when to use them or not (spoilers: you’ll often be better off without agents). smolagents is a lightweight framework for building AI agents using large language models (LLMs). It simplifies the development of AI-driven applications by providing tools to create, train, and deploy language model-based agents. -
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fastai
Deep learning library
fastai is a deep learning library which provides practitioners with high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains, and provides researchers with low-level components that can be mixed and matched to build new approaches. It aims to do both things without substantial compromises in ease of use, flexibility, or performance. This is possible thanks to a carefully layered architecture, which expresses common underlying... -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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H2O LLM Studio
Framework and no-code GUI for fine-tuning LLMs
Welcome to H2O LLM Studio, a framework and no-code GUI designed for fine-tuning state-of-the-art large language models (LLMs). You can also use H2O LLM Studio with the command line interface (CLI) and specify the configuration file that contains all the experiment parameters. To finetune using H2O LLM Studio with CLI, activate the pipenv environment by running make shell. With H2O LLM Studio, training your large language model is easy and intuitive. First, upload your dataset and then start... -
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Ansible-lint
Best practices checker for Ansible
Ansible Lint is a command-line tool for linting playbooks, roles and collections aimed towards any Ansible users. Its main goal is to promote proven practices, patterns and behaviors while avoiding common pitfalls that can easily lead to bugs or make code harder to maintain. Ansible lint is also supposed to help users upgrade their code to work with newer versions of Ansible. Due to this reason we recommend using it with the newest version of Ansible, even if the version used in production... -
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Agent Control
Centralized agent control plane for governing runtime agent behavior
Agent Control is a centralized control plane for governing AI agent behavior at runtime across different frameworks and deployment environments. It lets teams define controls once and apply them consistently to agents without rewriting the agent’s core code. The platform evaluates agent inputs and outputs against configurable policies to reduce risks such as prompt injection, unsafe responses, sensitive data exposure, and policy drift. It is designed for production environments where organizations need observability, enforcement, and governance around autonomous or semi-autonomous AI systems. ... -
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CUDA Python
Performance meets Productivity
...It integrates tightly with the broader Python GPU ecosystem, including Numba for kernel compilation and CCCL for parallel primitives, allowing developers to write performant code without leaving Python. The toolkit also includes utilities for profiling, memory management, distributed computing, and numerical operations, making it suitable for scientific computing, AI, and data processing workloads. -
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nanochat
The best ChatGPT that $100 can buy
...Its north star is approachability and speed: you can boot a fresh GPU box and drive the whole pipeline via a single script, producing a usable chat model in hours and a clear markdown report of what happened. The code is written to be read—concise training loops, transparent configs, and minimal wrappers—so you can audit each step, tweak it, and rerun without getting lost in framework indirection. -
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Swirl
Swirl queries any number of data sources with APIs
Swirl queries any number of data sources with APIs and uses spaCy and NLTK to re-rank the unified results without extracting and indexing anything! Includes zero-code configs for Apache Solr, ChatGPT, Elastic Search, OpenSearch, PostgreSQL, Google BigQuery, RequestsGet, Google PSE, NLResearch.com, Miro & more! SWIRL adapts and distributes queries to anything with a search API - search engines, databases, noSQL engines, cloud/SaaS services etc - and uses AI (Large Language Models) to re-rank the unified results without extracting and indexing anything. ... -
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AWS Neuron
Powering Amazon custom machine learning chips
...Using Neuron developers can easily train their machine learning models on any popular framework such as TensorFlow, PyTorch, and MXNet, and run it optimally on Amazon EC2 Inf1 instances. You can continue to use the same ML frameworks you use today and migrate your software onto Inf1 instances with minimal code changes and without tie-in to vendor-specific solutions. Neuron is pre-integrated into popular machine learning frameworks like TensorFlow, MXNet and Pytorch to provide a seamless training-to-inference workflow. It includes a compiler, runtime driver, as well as debug and profiling utilities with a TensorBoard plugin for visualization. -
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Story Flicks
Generate high-definition story short videos with one click using AI
Story Flicks is another open-source project in the AI-assisted video generation / editing space, focused on creating short, story-style videos from script or prompt inputs. It aims to let users generate high-definition short movies or video stories with minimal manual effort, using AI models under the hood to assemble visuals, timing, and possibly narration or subtitles. For creators who want to produce narrative short-form content — whether for social media, storytelling, or prototyping... -
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kagglehub
Python library to access Kaggle resources
kagglehub is a Python library for accessing Kaggle resources directly from Python code. It provides a simple API for downloading datasets, models, competition files, and notebook outputs without requiring users to manually manage every URL or file path. The library is designed to work both inside and outside Kaggle Notebooks, with native behavior that can adapt when it runs in Kaggle’s hosted notebook environment. It is useful for machine learning workflows where data, models, and notebook artifacts need to be pulled into scripts, experiments, or pipelines. kagglehub also supports authentication so users can access private or restricted resources when their account has permission. ... -
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GPTImage2Skill
GPT Image 2 prompt gallery, image prompt library, agentic skill
...It provides reusable image prompts across creative, technical, academic, interface, design, photography, typography, gaming, anime, map, tattoo, and reference-editing use cases. The project is designed to help agents and users produce stronger visual outputs without starting from a blank prompt every time. Its gallery is organized into category files so an agent can load only the relevant prompt references instead of overwhelming the context window. It also includes installation paths for skill-capable environments such as Claude Code, Codex, OpenClaw, and other agent runtimes. Overall, it is useful as both a learning resource for prompt structure and a practical toolkit for repeatable image generation workflows. -
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Writer Framework
No-code in the front, Python in the back. An open-source framework
Writer Framework is an open source platform designed to help developers build AI-powered applications by combining a visual interface builder with a Python-based backend architecture. It follows a hybrid approach where user interfaces are created using a drag-and-drop editor while business logic is implemented in Python, allowing teams to balance speed and flexibility without sacrificing control. The framework is particularly focused on AI use cases, enabling developers to integrate large... -
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AndroidNativeEmu
Allows you to partly emulate an Android native library
AndroidNativeEmu is a Python-based framework designed to partially emulate Android native libraries, enabling developers and researchers to analyze and execute native code outside of a full Android environment. Instead of emulating an entire operating system, it focuses specifically on the native layer, allowing for targeted testing and reverse engineering of shared libraries (.so files). This makes it particularly useful in security research, malware analysis, and debugging of Android... -
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ZML
Any model. Any hardware. Zero compromise
...The system allows models to be compiled and executed across multiple types of accelerators, including GPUs and TPUs, even when distributed across different machines or locations. One of its key strengths is cross-compilation, enabling developers to build once and deploy across various platforms without rewriting code. zml provides example implementations of models and workflows, demonstrating how to run inference tasks such as image classification or large language models. It is designed to handle complex distributed setups, including scenarios where model components are split across devices connected via networks. -
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PySpur
Visual tool for building, testing, and deploying AI agent workflows
...It supports iterative experimentation, allowing developers to rapidly improve agents without rebuilding systems from scratch. PySpur also enables deployment of finalized workflows after testing, making it suitable for both development and production use. Overall, it acts as an integrated environment for designing, evaluating, and managing AI-driven processes. -
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SimpleHTR
Handwritten Text Recognition (HTR) system implemented with TensorFlow
...The system uses a combination of convolutional neural networks and recurrent neural networks to extract visual features and model sequential character patterns in handwriting. It also employs connectionist temporal classification (CTC) to align predicted character sequences with input images without requiring character-level segmentation. The repository provides code for training models, performing inference on handwritten text images, and evaluating recognition accuracy. SimpleHTR is commonly used as an educational example for understanding how modern handwriting recognition systems operate. -
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GPU Puzzles
Solve puzzles. Learn CUDA
...It can be run in cloud environments such as Google Colab, making it easy for beginners to start experimenting without configuring local GPU hardware.