Open Source Python Software - Page 73
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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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Datapizza AI
Build reliable Gen AI solutions without overhead
Datapizza AI is a lightweight framework for building modular, multi-agent AI systems that collaborate to solve complex tasks through orchestration and tool usage. The project focuses on simplicity and transparency, enabling developers to construct agent-based workflows without the heavy abstractions and dependencies often found in larger AI frameworks. It provides a flexible architecture where individual agents can be assigned specialized roles, such as web search, reasoning, or domain-specific expertise, and can communicate with each other to complete tasks collaboratively. The framework supports integration with external APIs and tools, allowing agents to perform actions like retrieving data, executing functions, or interacting with external services. It is particularly well-suited for building retrieval-augmented generation pipelines, automation systems, and experimental AI applications that require coordination between multiple components. -
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Datasets
Hub of ready-to-use datasets for ML models
Datasets is a library for easily accessing and sharing datasets, and evaluation metrics for Natural Language Processing (NLP), computer vision, and audio tasks. Load a dataset in a single line of code, and use our powerful data processing methods to quickly get your dataset ready for training in a deep learning model. Backed by the Apache Arrow format, process large datasets with zero-copy reads without any memory constraints for optimal speed and efficiency. We also feature a deep integration with the Hugging Face Hub, allowing you to easily load and share a dataset with the wider NLP community. There are currently over 2658 datasets, and more than 34 metrics available. Datasets naturally frees the user from RAM memory limitation, all datasets are memory-mapped using an efficient zero-serialization cost backend (Apache Arrow). Smart caching: never wait for your data to process several times. -
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DeepSeed
Deep learning optimization library making distributed training easy
DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective. DeepSpeed delivers extreme-scale model training for everyone, from data scientists training on massive supercomputers to those training on low-end clusters or even on a single GPU. Using current generation of GPU clusters with hundreds of devices, 3D parallelism of DeepSpeed can efficiently train deep learning models with trillions of parameters. With just a single GPU, ZeRO-Offload of DeepSpeed can train models with over 10B parameters, 10x bigger than the state of arts, democratizing multi-billion-parameter model training such that many deep learning scientists can explore bigger and better models. Sparse attention of DeepSpeed powers an order-of-magnitude longer input sequence and obtains up to 6x faster execution comparing with dense transformers. -
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DeepSeek VL
Towards Real-World Vision-Language Understanding
DeepSeek-VL is DeepSeek’s initial vision-language model that anchors their multimodal stack. It enables understanding and generation across visual and textual modalities—meaning it can process an image + a prompt, answer questions about images, caption, classify, or reason about visuals in context. The model is likely used internally as the visual encoder backbone for agent use cases, to ground perception in downstream tasks (e.g. answering questions about a screenshot). The repository includes model weights (or pointers to them), evaluation metrics on standard vision + language benchmarks, and configuration or architecture files. It also supports inference tools for forwarding image + prompt through the model to produce text output. DeepSeek-VL is a predecessor to their newer VL2 model, and presumably shares core design philosophy but with earlier scaling, fewer enhancements, or capability tradeoffs. -
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Deepchecks
Test Suites for validating ML models & data
Deepchecks is the leading tool for testing and for validating your machine learning models and data, and it enables doing so with minimal effort. Deepchecks accompany you through various validation and testing needs such as verifying your data’s integrity, inspecting its distributions, validating data splits, evaluating your model and comparing between different models. While you’re in the research phase, and want to validate your data, find potential methodological problems, and/or validate your model and evaluate it. To run a specific single check, all you need to do is import it and then to run it with the required (check-dependent) input parameters. More details about the existing checks and the parameters they can receive can be found in our API Reference. An ordered collection of checks, that can have conditions added to them. The Suite enables displaying a concluding report for all of the Checks that ran. -
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Dendrite
Tools to build web AI agents that can authenticate
Dendrite Python SDK is a toolkit for building web AI agents that can authenticate, interact with, and extract data from any website, facilitating web automation tasks. -
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Depth Anything 3
Recovering the Visual Space from Any Views
Depth Anything 3 is a research-driven project that brings accurate and dense depth estimation to any input image or video, enabling foundational understanding of 3D structure from 2D visual content. Designed to work across diverse scenes, lighting conditions, and image types, it uses advanced neural networks trained on large, heterogeneous datasets, producing depth maps that reveal scene depth relationships and object surfaces with strong fidelity. The model can be applied to photography, AR/VR content creation, robotics perception, and 3D reconstruction workflows, making it versatile across industries and research domains. It includes support for high-resolution inputs and post-processing tools that refine depth predictions, helping downstream tasks like segmentation, bounding volume estimation, and mixed reality layering. -
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Diffusers
State-of-the-art diffusion models for image and audio generation
Diffusers is the go-to library for state-of-the-art pretrained diffusion models for generating images, audio, and even 3D structures of molecules. Whether you're looking for a simple inference solution or training your own diffusion models, Diffusers is a modular toolbox that supports both. Our library is designed with a focus on usability over performance, simple over easy, and customizability over abstractions. State-of-the-art diffusion pipelines that can be run in inference with just a few lines of code. Interchangeable noise schedulers for different diffusion speeds and output quality. Pretrained models that can be used as building blocks, and combined with schedulers, for creating your own end-to-end diffusion systems. We recommend installing Diffusers in a virtual environment from PyPi or Conda. For more details about installing PyTorch and Flax, please refer to their official documentation. -
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Django Cachalot
No effort, no worry, maximum performance
Caches your Django ORM queries and automatically invalidates them. Cachalot officially supports Python 3.7-3.10 and Django 2.2, 3.2, and 4.0-4.1 with the databases PostgreSQL, SQLite, and MySQL. Note: an upper limit on Django version is set for your safety. Please do not ignore it. To start developing, install the requirements and run the tests via tox. Currently, benchmarks are supported on Linux and Mac/Darwin. You will need a database called "cachalot" on MySQL and PostgreSQL. Additionally, on PostgreSQL, you will need to create a role called "cachalot." You can also run the benchmark, and it'll raise errors with specific instructions for how to fix it. Use cachalot for cold or modified <50 times per minutes (Most people should stick with only cachalot since you most likely won't need to scale to the point of needing cache-machine added to the bowl). -
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Django RQ
A simple app that provides django integration for RQ
A simple app that provides django integration for RQ (Redis Queue). Django integration with RQ, a Redis-based Python queuing library. Django-RQ is a simple app that allows you to configure your queues in django's settings.py and easily use them in your project. Django-RQ allows you to easily put jobs into any of the queues defined in settings.py. You can provide your own singleton Redis connection object to this function so that it will not create a new connection object for each queue definition. If you have django-redis or django-redis-cache installed, you can instruct django_rq to use the same connection information from your Redis cache. This has two advantages, it's DRY and it takes advantage of any optimization that may be going on in your cache setup (like using connection pooling or Hiredis.) -
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Django-CRM
Open Source CRM based on Django
Django CRM is opensource CRM developed on django framework. It has all the basic features of CRM to start with. We welcome code contributions and feature requests via github. Create and activate a virtual environment. Install the project's dependency after activating env. -
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DocETL
A system for agentic LLM-powered data processing and ETL
DocETL is an open-source system designed to build and execute data processing pipelines powered by large language models, particularly for analyzing complex collections of documents and unstructured datasets. The platform allows developers and researchers to construct structured workflows that extract, transform, and organize information from sources such as reports, transcripts, legal documents, and other text-heavy data. Instead of relying on single prompts or ad-hoc scripts, DocETL provides a declarative pipeline framework that breaks complex document analysis tasks into manageable operations that can be optimized and orchestrated automatically. Pipelines are typically defined using a low-code YAML interface, giving users full control over prompts and processing steps while still simplifying workflow creation. -
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Docker SDK for Python
A Python library for the Docker Engine API
A Python library for the Docker Engine API. It lets you do anything the docker command does, but from within Python apps, run containers, manage containers, manage Swarms, etc. The latest stable version is available on PyPI. Either add docker to your requirements.txt file or install with pip. To communicate with the Docker daemon, you first need to instantiate a client. The easiest way to do that is by calling the function from_env(). It can also be configured manually by instantiating a DockerClient class. Run and manage containers on the server. You can also create more advanced networks with custom IPAM configurations. Get and list nodes in a swarm. Before you can use these methods, you first need to join or initialize a swarm. Manage plugins on the server. Both the main DockerClient and low-level APIClient can connect to the Docker daemon with TLS. -
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Dominate
Dominate is a Python library for creating and manipulating HTML docs
Dominate is a Python library for creating and manipulating HTML documents using an elegant DOM API. It allows you to write HTML pages in pure Python very concisely, which eliminates the need to learn another template language, and lets you take advantage of the more powerful features of Python. Dominate can also use keyword arguments to append attributes onto your tags. Most of the attributes are a direct copy from the HTML spec with a few variations. Through the use of the += operator and the .add() method you can easily create more advanced structures. By default, render() tries to make all output human readable, with one HTML element per line and two spaces of indentation. -
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ECommerceCrawlers
Collection of Python ecommerce and website crawler examples projects
ECommerceCrawlers is a collection of practical Python web crawler projects designed to gather data from a variety of ecommerce platforms, websites, and online services. It aggregates many independent crawler examples created by contributors and organized into separate subprojects that target specific sites or data sources. These examples demonstrate how to build and operate web scrapers capable of collecting structured information such as product listings, news content, job postings, social media data, and other publicly available web data. It aims to help developers understand the full workflow of web scraping, including request simulation, data extraction, storage, and handling anti-scraping techniques. It includes crawlers for platforms such as ecommerce marketplaces, blogging platforms, recruitment sites, and social networks, providing real-world practice scenarios. Developers can study the individual project documentation to understand the analysis process. -
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ERAlchemy
Entity Relation Diagrams generation tool
ERAlchemy is a tool that generates Entity-Relationship (ER) diagrams from databases or SQLAlchemy models and vice versa. It’s useful for database documentation, reverse engineering, and understanding complex schemas. ERAlchemy can export diagrams in formats like Graphviz and Mermaid, making it easy to include in reports or markdown files. -
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Elasticsearch MCP Server
A Model Context Protocol (MCP) server implementation
This MCP server implementation provides interaction capabilities with Elasticsearch and OpenSearch, enabling functionalities such as document searching, index analysis, and cluster management through a set of tools. -
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Elia
Terminal-based LLM chat tool with multi-model and local support
Elia is an open source terminal-based interface designed for interacting with large language models in a fast and efficient way. It runs entirely in the command line, offering a keyboard-driven experience that reduces the need for switching between apps. Users can chat with both proprietary models like ChatGPT and Claude, as well as local models such as Llama 3, Mistral, and Gemma. Elia stores conversations in a local SQLite database, making it easy to revisit past interactions. It supports flexible usage with inline and full-screen chat modes, along with simple configuration through a single file. Installation is straightforward via pipx, and users can customize themes, system prompts, and model settings. Elia is built for developers and power users who prefer a streamlined, terminal-first workflow for working with AI models. -
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Emoji for Python
emoji terminal output for Python
Emoji for Python. This project was inspired by kyokomi. The entire set of Emoji codes as defined by the Unicode consortium is supported in addition to a bunch of aliases. By default, only the official list is enabled but doing emoji.emojize(language='alias') enables both the full list and aliases. By default, the language is English (language='en') but also supported languages are Spanish ('es'), Portuguese ('pt'), Italian ('it'), French ('fr'), German ('de'). The utils/get-codes-from-unicode-consortium.py may help when updating unicode_codes.py but is not guaranteed to work. Generally speaking it scrapes a table on the Unicode Consortium's website with BeautifulSoup and prints the contents to stdout in a more useful format. -
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Encord Active
The toolkit to test, validate, and evaluate your models and surface
Encord Active is an open-source toolkit to test, validate, and evaluate your models and surface, curate, and prioritize the most valuable data for labeling to supercharge model performance. Encord Active has been designed as a all-in-one open source toolkit for improving your data quality and model performance. Use the intuitive UI to explore your data or access all the functionalities programmatically. Discover errors, outliers, and edge-cases within your data - all in one open source toolkit. Get a high level overview of your data distribution, explore it by customizable quality metrics, and discover any anomalies. Use powerful similarity search to find more examples of edge-cases or outliers. -
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EverMemOS
Long-term memory OS for AI with structured recall and context awarenes
EverMemOS is an open-source memory operating system built to give AI agents long-term, structured memory. It captures conversations, transforms them into organized memory units, and enables agents to recall past interactions with context and meaning. Instead of treating each prompt independently, it builds evolving user profiles, tracks preferences, and connects related events into coherent narratives. Its architecture combines memory storage, indexing, and retrieval with agent-level reasoning, allowing AI systems to make informed decisions based on prior interactions. EverMemOS goes beyond simple retrieval by actively applying stored knowledge to current tasks, improving personalization and consistency. EverMemOS uses a multi-stage memory lifecycle to convert raw dialogue into structured semantic data, supporting long-horizon reasoning and adaptive behavior across sessions. -
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EverydayWechat
Python tool that automates WeChat messages, replies, & group utilities
EverydayWechat is a Python-based automation tool designed to enhance and automate interactions on the WeChat messaging platform. Built using Python 3 and the Itchat library, it connects to the web version of WeChat to perform various automated messaging tasks. It allows users to send scheduled messages to friends or group chats, including daily weather updates, reminders, inspirational quotes, and other personalized content. It also supports intelligent automatic replies to incoming messages by integrating with multiple chatbot services. In addition to personal messaging automation, the project includes a group assistant that can respond to queries and provide useful information within chat groups. These group utilities can retrieve data such as weather conditions, calendar details, garbage classification information, movie box office statistics, delivery tracking updates, and air quality reports. -
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Excel MCP Server
A Model Context Protocol server for Excel file manipulation
The Excel MCP Server is a Python-based implementation of the Model Context Protocol that provides Excel file manipulation capabilities without requiring Microsoft Excel installation. It enables workbook creation, data manipulation, formatting, and advanced Excel features. -
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Face Alignment
2D and 3D Face alignment library build using pytorch
Detect facial landmarks from Python using the world's most accurate face alignment network, capable of detecting points in both 2D and 3D coordinates. Build using FAN's state-of-the-art deep learning-based face alignment method. For numerical evaluations, it is highly recommended to use the lua version which uses identical models with the ones evaluated in the paper. More models will be added soon. By default, the package will use the SFD face detector. However, the users can alternatively use dlib, BlazeFace, or pre-existing ground truth bounding boxes. While not required, for optimal performance(especially for the detector) it is highly recommended to run the code using a CUDA-enabled GPU. While here the work is presented as a black box, if you want to know more about the intrisecs of the method please check the original paper either on arxiv or my webpage. -
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FairScale
PyTorch extensions for high performance and large scale training
FairScale is a collection of PyTorch performance and scaling primitives that pioneered many of the ideas now used for large-model training. It introduced Fully Sharded Data Parallel (FSDP) style techniques that shard model parameters, gradients, and optimizer states across ranks to fit bigger models into the same memory budget. The library also provides pipeline parallelism, activation checkpointing, mixed precision, optimizer state sharding (OSS), and auto-wrapping policies that reduce boilerplate in complex distributed setups. Its components are modular, so teams can adopt just the sharding optimizer or the pipeline engine without rewriting their training loop. FairScale puts emphasis on correctness and debuggability, offering hook points, logging, and reference examples for common trainer patterns. Although many ideas have since landed in core PyTorch, FairScale remains a valuable reference and a practical toolbox for squeezing more performance out of multi-GPU and multi-node jobs.
