Open Source Python Software - Page 54
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Python Software
Browse free open source Python Software and projects below. Use the toggles on the left to filter open source Python Software by OS, license, language, programming language, and project status.
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AskUI Vision Agent
Enable AI to control your desktop, mobile and HMI devices
AskUI’s Vision Agent is an automation framework that allows you—and AI agents—to control real desktops, mobile devices, and HMI systems by perceiving the UI and performing actions like clicking, typing, scrolling, and drag-and-drop. It is designed for multi-platform compatibility and supports multiple AI models so you can tailor perception and decision-making to your workload. The repository presents a feature overview, sample media, and frequent release notes, which show ongoing improvements such as CORS checks and other operational tweaks. The broader AskUI documentation covers the Python Vision Agent along with suite services and inference APIs, indicating a productized ecosystem rather than a single library. Community-curated lists also recognize Vision Agent as part of the broader “GUI agents” landscape, placing it among other computer-use agents. -
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AsrTools
AsrTools: Smart Voice-to-Text Tool
AsrTools is a voice-to-text desktop utility designed to convert audio and video into subtitle or plain-text files without requiring a local GPU. Its graphical interface is built with PyQt5 and qfluentwidgets for a straightforward desktop workflow. Users can select an ASR engine, add individual files or folders, and process them in batches. Multi-threaded execution speeds up large transcription jobs by handling several tasks concurrently. Output formats include SRT, TXT, and ASS, making the tool suitable for subtitles, transcripts, and later editing. Video files can be imported directly and converted to audio automatically before recognition. Windows users can run a packaged executable, while developers can also launch the Python source directly. -
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Autobahn|Python
WebSocket and WAMP in Python for Twisted and asyncio
Autobahn Python is a networking library for building Python applications that use WebSocket and WAMP. It supports both Twisted and asyncio, giving developers flexibility depending on their preferred Python event framework. The WebSocket layer enables bidirectional real-time messaging between clients and servers. The WAMP layer adds routed remote procedure calls and publish-subscribe messaging for distributed applications. Autobahn Python is useful for dashboards, IoT systems, collaborative apps, backend services, messaging platforms, and applications that need real-time communication across components. It works especially well with a WAMP router such as Crossbar.io when building routed multi-service systems. -
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CS-Ebook
Curated list of classic, high-quality computer science books
CS-Ebook is a curated repository that compiles high-quality and classic computer science books across a wide range of software-related fields. It focuses on depth over volume, selecting only well-regarded titles that support structured learning and long-term skill development. It spans core areas such as computer fundamentals, programming languages, software engineering, mathematics, data science, and artificial intelligence, making it suitable for learners at different stages. Rather than hosting files, the project serves as a discovery guide, helping users identify essential reading materials and build a strong technical foundation. CS-Ebook is actively maintained and updated to reflect relevant and modern resources while preserving foundational texts. Its organized structure allows users to navigate topics efficiently and follow a progressive learning path. Contributions are encouraged, ensuring the list evolves with community input and continues to highlight valuable resources. -
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Chitu
High-performance inference framework for large language models
Chitu is a high-performance inference engine designed to deploy and run large language models efficiently in production environments. The framework focuses on improving efficiency, flexibility, and scalability for organizations that need to run LLM inference workloads across different hardware platforms. It supports heterogeneous computing environments, including CPUs, GPUs, and various specialized AI accelerators, allowing models to run across a wide range of infrastructure configurations. Chitu is designed to scale from small single-machine deployments to large distributed clusters that handle high volumes of concurrent inference requests. The system also includes performance optimizations for large models, including support for quantized formats and efficient computation operators that reduce memory usage and latency. Its architecture aims to support enterprise adoption by ensuring stable long-term operation under production workloads. -
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Claw Code
AI agent harness for AI coding agents
Claw Code is an open-source AI agent harness project focused on building better tools for orchestrating and managing autonomous coding agents. It originated as a clean-room reimplementation inspired by the architecture of Claude Code, aiming to replicate core concepts without using proprietary code. The project provides a Python-based foundation for experimenting with agent workflows, tool integration, and task execution pipelines. It emphasizes harness engineering—how agents are structured, how they interact with tools, and how they maintain context during execution. The system is being actively expanded, with a Rust-based runtime in development to improve performance and memory safety. Overall, Claw Code serves as a research-driven platform for advancing agent-based software development systems. -
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CodeGeeX2
CodeGeeX2: A More Powerful Multilingual Code Generation Model
CodeGeeX2 is the second-generation multilingual code generation model from ZhipuAI, built upon the ChatGLM2-6B architecture and trained on 600B code tokens. Compared to the first generation, it delivers a significant boost in programming ability across multiple languages, outperforming even larger models like StarCoder-15B in some benchmarks despite having only 6B parameters. The model excels at code generation, translation, summarization, debugging, and comment generation, and it supports over 100 programming languages. With improved inference efficiency, quantization options, and multi-query/flash attention, CodeGeeX2 achieves faster generation speeds and lightweight deployment, requiring as little as 6GB GPU memory at INT4 precision. Its backend powers the CodeGeeX IDE plugins for VS Code, JetBrains, and other editors, offering developers interactive AI assistance with features like infilling and cross-file completion. -
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CodeGen
Open-source model for program synthesis
CodeGen is a family of open-source large language models designed specifically for program synthesis and code generation tasks. Developed by Salesforce Research, the models are trained on large datasets containing both natural language and programming language content. This allows them to translate natural language descriptions into functional code across a variety of programming languages. CodeGen supports multi-turn program synthesis, meaning it can generate complex programs through a sequence of prompts that progressively refine the solution. The project also includes training infrastructure and model checkpoints that allow researchers to experiment with different model sizes and training configurations. Its architecture and training approach enable the models to perform competitively with proprietary coding models on benchmark tasks. -
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Codex gpt-5.6 Instruct
A Codex CLI jailbreak prompt and test pack for gpt-5.6-sol
Codex gpt-5.6 Instruct is an unofficial prompt research and evaluation package created for the gpt-5.6-sol model in Codex CLI. It contains instruction files designed to alter refusal behavior across technical and fictional request categories. Two prompt variants are provided: a simpler recommended edition and a more elaborate edition for compound tasks. A Python deployment utility can preview changes, install a selected configuration, create backups, and restore earlier settings. The repository includes bilingual prompt banks, recorded model outputs, pass-or-fail results, historical reports, and candidate comparisons. Its evaluation workflow organizes scenarios by language, length, and task family. The project is intended for adversarial prompt testing rather than ordinary application development. -
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Cognita
Open source RAG framework for building scalable modular AI apps
Cognita is an open source framework designed to help developers build, organize, and deploy Retrieval-Augmented Generation (RAG) applications in a structured and production-ready way. It addresses the gap between quick experimentation in notebooks and the complexity of deploying scalable AI systems by introducing a modular and API-driven architecture. Cognita provides reusable components such as parsers, data loaders, embedders, retrievers, and query controllers, allowing teams to customize each stage of the RAG pipeline independently. It includes both a backend service and a frontend interface, enabling users to upload documents, experiment with configurations, and perform question-answering tasks interactively. Cognita supports incremental indexing, meaning it processes only new or updated data to reduce computational overhead and improve efficiency. -
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ComfyUI InstantID
Native InstantID support for ComfyUI
ComfyUI_InstantID is a ComfyUI extension that adds native InstantID support for face-guided image generation. Unlike other implementations, it does not rely on diffusers and instead integrates InstantID directly into ComfyUI workflows. The extension is designed for SDXL and uses InsightFace, ONNX Runtime, the antelopev2 face model, an InstantID model, and a ControlNet model. It lets users generate images guided by a reference face while controlling pose through face keypoints. The project includes basic workflows, video guidance, noise injection options, additional ControlNet support, IPAdapter styling, Multi-ID workflows, and an advanced node for separate model and ControlNet weights. It is now maintained only for crucial updates, making it most useful for users who want a stable native InstantID workflow inside ComfyUI. -
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D2L.ai
Interactive deep learning book with multi-framework code
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 300 universities from 55 countries including Stanford, MIT, Harvard, and Cambridge. This open-source book represents our attempt to make deep learning approachable, teaching you the concepts, the context, and the code. The entire book is drafted in Jupyter notebooks, seamlessly integrating exposition figures, math, and interactive examples with self-contained code. Offers sufficient technical depth to provide a starting point on the path to actually becoming an applied machine learning scientist. -
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DeepCode
DeepCode: Open Agentic Coding
DeepCode is an agentic coding platform built around a multi-agent architecture that turns high-level inputs, including research papers, documents, and natural-language requirements, into working software artifacts. It positions itself as an “open agentic coding” system that can handle tasks like paper-to-code reproduction, frontend generation, and backend implementation by decomposing problems into structured steps and coordinating specialized agents. The system description highlights an orchestration layer that plans, assigns subtasks, and adapts strategies as complexity changes, rather than relying on a single monolithic prompt. It also describes document parsing capabilities aimed at extracting algorithmic and mathematical details from technical materials, translating them into implementable specifications and code. -
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DeepSeek-OCR 2
Visual Causal Flow
DeepSeek-OCR-2 is the second-generation optical character recognition system developed to improve document understanding by introducing a “visual causal flow” mechanism, enabling the encoder to reorder visual tokens in a way that better reflects semantic structure rather than strict raster scan order. It is designed to handle complex layouts and noisy documents by giving the model causal reasoning capabilities that mimic human visual scanning behavior, enhancing OCR performance on documents with rich spatial structure. The repository provides model code and inference scripts that let researchers and developers run and benchmark the system on both images and PDFs, with support for batch evaluation and optimized pipelines leveraging vLLM and transformers. -
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Desloppify
Agent harness to make your slop code well-engineered and beautiful
Desloppify is a utility-focused project aimed at improving the quality, structure, and clarity of generated or written text by removing redundancy, noise, and unnecessary verbosity. It is designed to “clean up” outputs, particularly those produced by AI systems, making them more concise, readable, and professional. The system likely applies heuristics or transformation rules to identify repetitive patterns, filler content, and stylistic inconsistencies. This makes it especially useful in workflows where AI-generated text needs to be refined before publication or use in production. It may also support integration into pipelines, allowing automatic post-processing of outputs. The project reflects a growing need to manage and optimize AI-generated content rather than simply produce it. Overall, desloppify acts as a refinement layer that enhances clarity and usability of textual outputs. -
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Detoxify
Trained models & code to predict toxic comments
Detoxify is a deep learning-based tool for detecting and filtering toxic language in online conversations, leveraging Transformer models for high accuracy. -
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Dulwich
Pure-Python Git implementation
Dulwich is a Python implementation of the Git file formats and protocols, which does not depend on Git itself. All functionality is available in pure Python. Optional C extensions can be built for improved performance. Dulwich takes its name from the area in London where the friendly Mr. and Mrs. Git once attended a cocktail party. Supported Python versions are Python 3.5 and later. Versions of Dulwich prior to 0.20 also supported Python 2.7. Supported platforms include Linux, Mac OS X and Windows. Dulwich comes with both a lower-level API and higher-level plumbing ("porcelain"). By default, Dulwich' setup.py will attempt to build and install the optional C extensions. The reason for this is that they significantly improve the performance since some low-level operations that are executed often are much slower in CPython. -
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ERNIE
The official repository for ERNIE 4.5 and ERNIEKit
ERNIE is an open-source large-model toolkit and model family from the PaddlePaddle ecosystem that focuses on training, fine-tuning, compression, and practical application of ERNIE large language models. The repository positions ERNIEKit as an industrial-grade development toolkit, emphasizing end-to-end workflows that span high-performance pre-training, supervised fine-tuning, and alignment. It supports both full-parameter training and parameter-efficient approaches so teams can choose between maximum quality and lower-cost adaptation depending on their constraints. The project also emphasizes optimization techniques for large-scale training, including mixed-precision and hybrid-parallel strategies that are commonly needed for multi-node GPU clusters. In addition to training, it includes guidance and example materials intended to help developers adopt ERNIE models for real product scenarios rather than only research demonstrations. -
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End-to-End Negotiator
Deal or No Deal? End-to-End Learning for Negotiation Dialogues
End-to-End Negotiator is a PyTorch-based research framework developed by Facebook AI Research to train neural agents capable of conducting strategic negotiations in natural language. The project implements the models presented in two key papers: “Deal or No Deal? End-to-End Learning for Negotiation Dialogues” and “Hierarchical Text Generation and Planning for Strategic Dialogue”. It enables agents to plan, reason, and communicate effectively to maximize outcomes in multi-turn negotiations over shared resources. The framework provides code for both supervised learning (training from human dialogue data) and reinforcement learning (via self-play and rollout-based planning). It introduces a hierarchical latent model, where high-level intents are first clustered and then translated into coherent language, improving dialogue diversity and goal consistency. The repository also includes the Negotiate dataset, comprising over 5,800 dialogues across 2,200 unique scenarios. -
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Face Recognition
World's simplest facial recognition api for Python & the command line
Face Recognition is the world's simplest face recognition library. It allows you to recognize and manipulate faces from Python or from the command line using dlib's (a C++ toolkit containing machine learning algorithms and tools) state-of-the-art face recognition built with deep learning. Face Recognition is highly accurate and is able to do a number of things. It can find faces in pictures, manipulate facial features in pictures, identify faces in pictures, and do face recognition on a folder of images from the command line. It could even do real-time face recognition and blur faces on videos when used with other Python libraries. -
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Fapro
Fake Protocol Server
Fapro is an open-source asset discovery and vulnerability scanning tool developed by Fofa Pro. It assists in identifying and managing network assets, detecting potential vulnerabilities, and enhancing overall security posture -
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Finetune Transformer LM
Code for "Improving Language Understanding by Generative Pre-Training"
finetune-transformer-lm is a research codebase that accompanies the paper “Improving Language Understanding by Generative Pre-Training,” providing a minimal implementation focused on fine-tuning a transformer language model for evaluation tasks. The repository centers on reproducing the ROCStories Cloze Test result and includes a single-command training workflow to run the experiment end to end. It documents that runs are non-deterministic due to certain GPU operations and reports a median accuracy over multiple trials that is slightly below the single-run result in the paper, reflecting expected variance in practice. The project ships lightweight training, data, and analysis scripts, keeping the footprint small while making the experimental pipeline transparent. It is provided as archived, research-grade code intended for replication and study rather than continuous development. -
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Fingerprint Pro Server Python SDK
Python SDK for Fingerprint Pro Server API
Fingerprint Pro Server API allows you to get information about visitors and about individual events in a server environment. It can be used for data exports, decision-making, and data analysis scenarios. Server API is intended for server-side usage, it's not intended to be used from the client side, whether it's a browser or a mobile device. -
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FixRes
Reproduces results of "Fixing the train-test resolution discrepancy"
FixRes is a lightweight yet powerful training methodology for convolutional neural networks (CNNs) that addresses the common train-test resolution discrepancy problem in image classification. Developed by Facebook Research, FixRes improves model generalization by adjusting training and evaluation procedures to better align input resolutions used during different phases. The approach is simple but highly effective, requiring no architectural modifications and working across diverse CNN backbones such as ResNet, ResNeXt, PNASNet, and EfficientNet. FixRes demonstrates that a mismatch between training and testing resolutions often leads to suboptimal accuracy, and fine-tuning the classifier and batch normalization layers at higher test resolutions significantly enhances performance. The repository includes pretrained models, feature embeddings, and evaluation scripts corresponding to the experiments reported in the NeurIPS 2019 paper “Fixing the train-test resolution discrepancy.” -
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Flask-SQLAlchemy
Adds SQLAlchemy support to Flask
Flask-SQLAlchemy is an extension for Flask that adds support for SQLAlchemy to your application. It simplifies using SQLAlchemy with Flask by setting up common objects and patterns for using those objects, such as a session tied to each web request, models, and engines. Flask-SQLAlchemy does not change how SQLAlchemy works or is used. See the SQLAlchemy documentation to learn how to work with the ORM in depth. The documentation here will only cover setting up the extension, not how to use SQLAlchemy.
