Open Source Python Artificial Intelligence Software - Page 36
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Python Artificial Intelligence Software
View 17570 business solutionsBrowse free open source Python Artificial Intelligence Software and projects below. Use the toggles on the left to filter open source Python Artificial Intelligence Software by OS, license, language, programming language, and project status.
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Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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Host LLMs in Production With On-Demand GPUsDeploy your model, get an endpoint, pay only for compute time. No GPU provisioning or infrastructure management required.
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Prompt Engineering Interactive Tutorial
Anthropic's Interactive Prompt Engineering Tutorial
Prompt-eng-interactive-tutorial is a comprehensive, hands-on tutorial that teaches the craft of prompt engineering with Claude through guided, executable lessons. It starts with the anatomy of a good prompt and moves into techniques that deliver the “80/20” gains—separating instructions from data, specifying schemas, and setting evaluation criteria. The course leans heavily on realistic failure modes (ambiguity, hallucination, brittle instructions) and shows how to iteratively debug prompts the way you would debug code. Lessons include building prompts from scratch for common tasks like extraction, classification, transformation, and step-by-step reasoning, with checkpoints that let you compare your outputs against solid baselines. You’ll also practice advanced patterns such as tool use, constrained generation, and response validation so outputs are trustworthy and machine-consumable. -
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Prompt flow
Build high-quality LLM apps
Prompt flow is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, and evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality. -
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Protenix
A trainable PyTorch reproduction of AlphaFold 3
Protenix is an open-source, trainable PyTorch reimplementation of AlphaFold 3, developed by ByteDance with the goal of democratizing high-accuracy protein structure prediction for computational biology and drug-discovery research. Protenix provides a complete pipeline for turning protein sequences (with optional MSA / sequence alignment) or structural inputs (e.g. PDB/CIF) into full 3D atomic-level structure predictions. It supports both “full” models and lightweight variants such as “Protenix-Mini,” offering a trade-off between speed/compute cost and predictive accuracy — making structure prediction accessible even in resource-constrained environments. The project also includes support for constraints (e.g., specifying residue- or atom-level contact constraints, or pocket constraints) to guide predictions toward biologically or experimentally relevant conformations, which enhances its utility for tasks like modeling complexes, ligands, or antibody–antigen interactions. -
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PyBroker
Algorithmic Trading in Python with Machine Learning
Are you looking to enhance your trading strategies with the power of Python and machine learning? Then you need to check out PyBroker! This Python framework is designed for developing algorithmic trading strategies, with a focus on strategies that use machine learning. With PyBroker, you can easily create and fine-tune trading rules, build powerful models, and gain valuable insights into your strategy’s performance. -
PRTG Catches Network Issues Before They Cause DowntimeReactive troubleshooting usually means hearing about a problem from frustrated users, not your monitoring tool. PRTG sets threshold-based alerts across devices, servers and applications, notifying your team by email, SMS or push the moment a metric crosses a set limit. That means catching a failing disk or overloaded server before it becomes an outage and getting time back from firefighting. Start a free trial and set your first alerts today.
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PyG
Graph Neural Network Library for PyTorch
PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, DataPipe support, distributed graph learning via Quiver, a large number of common benchmark datasets (based on simple interfaces to create your own), the GraphGym experiment manager, and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. All it takes is 10-20 lines of code to get started with training a GNN model (see the next section for a quick tour). -
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PyTorch Image Models
The largest collection of PyTorch image encoders / backbones
timm (PyTorch Image Models) is a premier library hosting a vast collection of state-of-the-art image classification models and backbones such as ResNet, EfficientNet, NFNet, Vision Transformer, ConvNeXt, and more. Created by Ross Wightman and now maintained by Hugging Face, it includes pretrained weights, data loaders, augmentations, optimizers, schedulers, and reference scripts for training, evaluation, inference, and model export. It's an essential toolkit for vision research and production workflows. -
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Pytorch Points 3D
Pytorch framework for doing deep learning on point clouds
Torch Points 3D is a framework for developing and testing common deep learning models to solve tasks related to unstructured 3D spatial data i.e. Point Clouds. The framework currently integrates some of the best-published architectures and it integrates the most common public datasets for ease of reproducibility. It heavily relies on Pytorch Geometric and Facebook Hydra library thanks for the great work! We aim to build a tool that can be used for benchmarking SOTA models, while also allowing practitioners to efficiently pursue research into point cloud analysis, with the end goal of building models which can be applied to real-life applications. Task driven implementation with dynamic model and dataset resolution from arguments. Core implementation of common components for point cloud deep learning - greatly simplifying the creation of new models. 4 Base Convolution base classes to simplify the implementation of new convolutions. Each base class supports a different data format. -
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Pytorch-toolbelt
PyTorch extensions for fast R&D prototyping and Kaggle farming
A pytorch-toolbelt is a Python library with a set of bells and whistles for PyTorch for fast R&D prototyping and Kaggle farming. Easy model building using flexible encoder-decoder architecture. Modules: CoordConv, SCSE, Hypercolumn, Depthwise separable convolution and more. GPU-friendly test-time augmentation TTA for segmentation and classification. GPU-friendly inference on huge (5000x5000) images. Every-day common routines (fix/restore random seed, filesystem utils, metrics). Losses: BinaryFocalLoss, Focal, ReducedFocal, Lovasz, Jaccard and Dice losses, Wing Loss and more. Extras for Catalyst library (Visualization of batch predictions, additional metrics). By design, both encoder and decoder produces a list of tensors, from fine (high-resolution, indexed 0) to coarse (low-resolution) feature maps. Access to all intermediate feature maps is beneficial if you want to apply deep supervision losses on them or encoder-decoder of object detection task. -
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Qwen-Agent
Agent framework and applications built upon Qwen>=3.0
Qwen-Agent is a framework for building applications / agents using Qwen models (version 3.0+). It provides components for instruction following, tool usage (function calling), planning, memory, RAG (retrieval augmented generation), code interpreter, etc. It ships with example applications (Browser Assistant, Code Interpreter, Custom Assistant), supports GUI front-ends, backends, server setups. Agent workflow can maintain context / memory to perform multi-turn or more complex logic over time. It acts as the backend for Qwen Chat among other use cases. Built-in Code Interpreter tool that can execute code (locally) as part of agent workflows. -
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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Qwen-VL
Chat & pretrained large vision language model
Qwen-VL is Alibaba Cloud’s vision-language large model family, designed to integrate visual and linguistic modalities. It accepts image inputs (with optional bounding boxes) and text, and produces text (and sometimes bounding boxes) as output. The model variants (VL-Plus, VL-Max, etc.) have been upgraded for better visual reasoning, text recognition from images, fine-grained understanding, and support for high image resolutions / extreme aspect ratios. Qwen-VL supports multilingual inputs and conversation (e.g. Chinese, English), and is aimed at tasks like image captioning, question answering on images (VQA, DocVQA), grounding (detecting objects or regions from textual queries), etc. -
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Qwen2-Audio
Repo of Qwen2-Audio chat & pretrained large audio language model
Qwen2-Audio is a large audio-language model by Alibaba Cloud, part of the Qwen series. It is trained to accept various audio signal inputs (including speech, sounds, etc.) and perform both voice chat and audio analysis, producing textual responses. It supports two major modes: Voice Chat (interactive voice only input) and Audio Analysis (audio + text instructions), with both base and instruction-tuned models. It is evaluated on many benchmarks (speech recognition, translation, sound classification, emotion, etc.), and offers pretrained models (e.g. 7B) released via ModelScope and Hugging Face. Code & examples provided with Hugging Face transformers, and usage via AutoProcessor, model classes etc. High performance on many standard benchmarks: ASR, speech-emotion recognition, vocal sound classification, speech translation etc. -
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RA.Aid
Develop software autonomously
RA.Aid is an AI-powered assistant designed to enhance the efficiency of software development workflows. It integrates seamlessly with various development environments, providing intelligent code suggestions, automated documentation generation, and real-time error detection. By leveraging advanced machine learning models, RA.Aid aims to reduce development time and improve code quality. -
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RL Games
RL implementations
rl_games is a high-performance reinforcement learning framework optimized for GPU-based training, particularly in environments like robotics and continuous control tasks. It supports advanced algorithms and is built with PyTorch. -
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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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ReCall
Learning to Reason with Search for LLMs via Reinforcement Learning
ReCall is an open-source framework designed to train and evaluate language models that can reason through complex problems by interacting with external tools. The project builds on earlier work focused on teaching models how to search for information during reasoning tasks and extends that idea to a broader system where models can call a variety of external tools such as APIs, databases, or computation engines. Instead of relying purely on static knowledge stored inside the model, ReCall allows the language model to dynamically decide when it should retrieve information or invoke external capabilities during the reasoning process. The framework uses reinforcement learning to train models to perform these tool calls effectively while solving multi-step reasoning tasks. -
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Reader 3
Quick illustration of how one can easily read books together with LLMs
This project is a minimalist, self-hosted EPUB reader designed to help users browse and read EPUB books one chapter at a time through a lightweight local server, making it especially easy to extract or work with chapters in external tools like large language models. It was created primarily as a simple demonstration of how to combine local book reading with LLM workflows without heavy dependencies or complicated setup, and it runs with just a small Python script and a basic HTTP server. The interface focuses on clarity and ease of use, offering straightforward navigation of book chapters rather than full-featured e-reading capabilities. While it lacks advanced features like built-in annotations or rich media support, its simplicity is intentional, enabling users to quickly load EPUBs, view them in a browser, and even repurpose text for downstream tasks. -
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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. This approach is particularly useful for complex reasoning tasks, coding challenges, and decision-making scenarios where initial outputs may be incomplete or incorrect. Reflexion also emphasizes transparency by making intermediate reasoning steps explicit, allowing developers to inspect how conclusions are reached and where improvements occur. -
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Resemblyzer
A python package to analyze and compare voices with deep learning
Resemblyzer is a Python package for analyzing and comparing voices with deep learning. It works by turning speech audio into a compact voice embedding that represents the speaker’s vocal characteristics. These embeddings can then be used for speaker similarity, clustering, diarization experiments, voice comparison, and audio dataset exploration. The project is useful for researchers and developers who need a practical way to reason about speaker identity without building a voice encoder from scratch. It can help identify whether two recordings sound like the same speaker or visualize voice relationships across many samples. Its main value is making speaker representation accessible through a simple Python workflow. -
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Rhino
On-device Speech-to-Intent engine powered by deep learning
Rhino is Picovoice's Speech-to-Intent engine. It directly infers intent from spoken commands within a given context of interest, in real-time. The end-to-end platform for embedding private voice AI into any software in a few lines of code. Design with no limits on top of a modular platform. Create use-case-specific voice AI models in seconds. Develop voice features with a few lines of code using intuitive and cross-platform SDKs. Deliver voice AI everywhere: on-device, mobile, web browsers, on-premise, or cloud. Measure adoption, learn, and iterate. Continuously re-design and re-train to optimize engagement. Building accurate, responsive, and private voice technology is difficult. We learned the hard way, so you don’t have to. Picovoice heavily invests in R&D to offer superior voice AI that surpasses even Big Tech in accuracy and efficiency. Picovoice researchers do not follow recent frameworks and techniques but build them. -
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SEO Machine
A specialized Claude Code workspace for creating long-form
SEO Machine is an AI-powered content production system built as a structured workspace for generating long-form, SEO-optimized blog content through automated workflows. It integrates research, writing, analysis, and optimization into a single pipeline, allowing users to produce high-quality articles tailored to search engine performance. The system uses specialized commands and agents to perform tasks such as keyword research, competitor analysis, content drafting, and optimization. It incorporates real data sources like Google Analytics and Search Console to guide decision-making and improve content effectiveness. The architecture emphasizes context-awareness, using brand voice, style guides, and keyword strategies to maintain consistency across outputs. It also includes performance evaluation tools that score content and suggest improvements before publishing. -
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SafeClaw
Chat with it via text and voice
SafeClaw is an open-source, entirely local alternative to cloud-based AI assistants like OpenClaw, enabling users to build a personal assistant that runs on their own machine without incurring API usage charges or exposing data to third-party services. It emphasizes privacy and predictability by using traditional programming, rule-based intent parsing, and established machine learning tools rather than large language models, meaning there are no per-token API costs and deterministic behavior. The assistant offers features such as voice control using fully local speech-to-text (Whisper) and text-to-speech (Piper) capabilities, news aggregation with extractive summarization, and smart home or Bluetooth device control. SafeClaw supports multiple channels, including CLI and Telegram, and avoids prompt injection risk because it doesn’t rely on LLMs for core operations. -
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Seldon Core
An MLOps framework to package, deploy, monitor and manage models
The de facto standard open-source platform for rapidly deploying machine learning models on Kubernetes. Seldon Core, our open-source framework, makes it easier and faster to deploy your machine learning models and experiments at scale on Kubernetes. Seldon Core serves models built in any open-source or commercial model building framework. You can make use of powerful Kubernetes features like custom resource definitions to manage model graphs. And then connect your continuous integration and deployment (CI/CD) tools to scale and update your deployment. Built on Kubernetes, runs on any cloud and on-premises. Framework agnostic, supports top ML libraries, toolkits and languages. Advanced deployments with experiments, ensembles and transformers. Our open-source framework makes it easier and faster to deploy your machine learning models and experiments at scale on Kubernetes. The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes. -
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Self-hosted AI Package
Run all your local AI together in one package
Self-hosted AI Package is an open-source Docker Compose-based starter kit that makes it easy to bootstrap a full local AI and low-code development environment with commonly used open tools, empowering developers to run LLMs and AI workflows entirely on their infrastructure. The stack typically includes Ollama for running local large language models, n8n as a low-code workflow automation platform, Supabase for database and vector storage, Open WebUI for interacting with models, Flowise for agent building, and additional services like SearXNG, Neo4j, and Langfuse for search, knowledge graphs, and observability. This integrated setup allows users to experiment with RAG pipelines, automated workflows, AI agents, and project data management without relying on external hosted services, increasing flexibility and privacy. The repository comes with example workflows (such as Local RAG AI Agent workflows) and environment configurations that help streamline setup and encourage customization. -
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Sepia
De-AI writing skill for any Agent Skills-compatible agent
Sepia is a portable agent skill for reducing recognizable AI-writing patterns in fiction and professional prose. Rather than focusing only on vocabulary substitutions, it targets higher-level narrative structure and document-specific writing habits. Fiction processing emphasizes narrative architecture before sentence-level polishing. Professional modes apply different rules to release notes, technical articles, pull request replies, postmortems, tickets, and other workplace formats. Four workflows are provided: writing from scratch, diagnostic review, minimal refactoring, and complete recreation. The skill follows the standard Agent Skills format and can therefore be installed across many compatible assistants. Native packaging is also provided for selected environments such as Claude Code, Codex, Grok Build, Antigravity, and QwenPaw. -
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Spark TTS
Spark-TTS Inference Code
Spark TTS is an open-source, PyTorch-based text-to-speech inference system that leverages large language models to produce highly natural, intelligible speech from text input. It uses an efficient single-stream architecture where speech tokens are directly reconstructed from the predictions of an LLM, removing the need for external acoustic models or complex vocoders and making the generation pipeline cleaner and faster. The project supports zero-shot voice cloning, meaning it can imitate a new speaker’s voice without dedicated training for that specific voice, and works across languages, including English and Chinese, even in cross-lingual code-switching scenarios. Spark-TTS allows users to control speech characteristics like gender, pitch, and speaking rate to customize synthesized output and support virtual speaker creation.
