Open Source Python Software Development Software - Page 40
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Python Software Development Software
View 6286 business solutionsBrowse free open source Python Software Development Software and projects below. Use the toggles on the left to filter open source Python Software Development Software by OS, license, language, programming language, and project status.
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Fully Managed MySQL, PostgreSQL, and SQL ServerCloud SQL handles your database ops end to end, so you can focus on your app.
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Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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ADP
ADP is a bare-bones Python Debugger
Another Debugger for Python was written in Python3 using PySimpleGui and has a window showing your script and some buttons and another window showing variables and their values. The buttons entirely control ADP, they are accompanied by help buttons. ADP stores details of the path taken by your script and variables and their values in a SQLITE3 database. The current release supports LIVE running. A future release will support DEAD running. In DEAD runs you’ll be able to replay a LIVE run from the database recording, which means that you can start at any point and travel in either direction and the value of the variables will be those that were current at that point of time in the LIVE run. Imagine your script crashed and you can replay from there backwards, and see how you got there and the values the variables had. Extra functionality will allow running queries forwards and backwards. -
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AI Berkshire
AI-era Berkshire: a value investing research framework
AI Berkshire is an AI-assisted value investing research framework designed for Claude Code and Codex workflows. It turns the investment methods of Warren Buffett, Charlie Munger, Duan Yongping, and Li Lu into structured research agents. The project is meant to improve research depth, decision discipline, and analytical consistency compared with asking a general AI model for a one-off stock opinion. It uses parallel agent analysis, adversarial viewpoints, financial rigor checks, and repeatable report formats to reduce shallow or overly balanced conclusions. The framework covers company research, earnings review, industry screening, portfolio thinking, management analysis, and investment checklists. It is best understood as a research and decision-support system rather than a source of financial advice. -
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AI-Agent-Host
The AI Agent Host is a module-based development environment.
The AI Agent Host integrates several advanced technologies and offers a unique combination of features for the development of language model-driven applications. The AI Agent Host is a module-based environment designed to facilitate rapid experimentation and testing. It includes a docker-compose configuration with QuestDB, Grafana, Code-Server and Nginx. The AI Agent Host provides a seamless interface for managing and querying data, visualizing results, and coding in real-time. The AI Agent Host is built specifically for LangChain, a framework dedicated to developing applications powered by language models. LangChain recognizes that the most powerful and distinctive applications go beyond simply utilizing a language model and strive to be data-aware and agentic. Being data-aware involves connecting a language model to other sources of data, enabling a comprehensive understanding and analysis of information. -
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Data server for user intelligent agents. Written in Python, using XML-RPC as client/server communication and RDBMS as storage.
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The Automatic Model Optimization Reference Implementation, AMORI, is a framework that integrates the modelling and the optimization processes by providing a plug-in interface for both. A genetic algorithm and Markov simulations are currently implemented.
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API Correios
API correios.com.br in Python
pycorreios is a Python library aimed at interacting with Brazil’s postal service (Correios) APIs, making it easier for developers to track shipments, calculate postage, query service availability, and integrate with Brazilian e-commerce flows. The library abstracts the raw SOAP or REST endpoints exposed by Correios, providing Pythonic methods to perform common tasks like tracking a package by its code or computing shipping cost/lead time between postal codes. It handles serialization and mapping of API responses into Python objects so developers don’t manually parse raw XML or JSON. With this tool, developers building Brazilian market e-commerce or logistics solutions can integrate postal services smoothly. Because it is open source, improvements can be contributed to support new endpoints, changes in the postal service API, or additional features like caching or async requests. -
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Simplified interface to the Augmented Reality library ARToolKit for Python and other programming languages.
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ARREA
Collaborative editor, requirements management tool and much more
ARREA (A Reasonable Requirements Engineering Application) is a simple yet powerful requirements management tool that uses arbitrary files to store information and Subversion to allow team collaboration. Its simple conventions make it suitable for much other purposes. In fact, for any document-oriented tasks focused on collaborative work and exhaustive change and traceability management. At its heart, ARREA provides a thin layer on top of SVN that allows it to be used as a powerful time-aware NoSQL database, accessible through an easy REST API. -
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ART ASCII Library
ASCII art library for Python
ASCII art is also known as "computer text art". It involves the smart placement of typed special characters or letters to make a visual shape that is spread over multiple lines of text. ART is a Python lib for text converting to ASCII art fancy. -
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ASDRAD is a source code generator which reads a configuration file and generates *.c, *.h, makefiles etc. It's written in Python and uses templates. Output files are for AmigaOS and derivates like AROS and MorphOS.
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ASN.1 types and codecs (BER, CER, DER) implementation in Python programming language. A collection of various ASN.1-based protocols data structures is supplied in a dedicated Python package. Project moved to GitHub: https://github.com/etingof/pyasn1
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AT Managed File is a project originally developed by Enfold Systems company for Plone 2.5. This is product version patched to work in Plone version 3.x. AT Managed File product stores files in local path insted of ZOBD while maintains a Plone instance.
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Automated Test Framework for Embedded Software: A system to perform test scripts 'in system' on embedded software. It uses Python as a high level description language and interfaces with GDB to communicate with the embedded platform.
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AWS Chalice
Python Serverless Microframework for AWS
Chalice is a Python microframework for writing and deploying serverless applications on AWS with minimal ceremony. You define routes, event handlers, and background tasks in plain Python, and Chalice turns them into AWS Lambda functions wired to API Gateway, Amazon EventBridge schedulers, S3/SNS/SQS triggers, and more. A single command builds your app, bundles dependencies, generates infrastructure templates, and deploys to your account with sensible defaults. The framework includes local development tools so you can iterate on HTTP routes and inspect logs before shipping to the cloud. It also manages per-stage configuration, environment variables, IAM permissions scaffolding, and packaging optimizations to keep cold starts small. While it’s intentionally lightweight, it supports common production needs like CORS, custom authorizers, binary responses, and fine-grained IAM so you can grow from prototype to production without rewriting your stack. -
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AWS Deep Learning Containers
A set of Docker images for training and serving models in TensorFlow
AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet. Deep Learning Containers provide optimized environments with TensorFlow and MXNet, Nvidia CUDA (for GPU instances), and Intel MKL (for CPU instances) libraries and are available in the Amazon Elastic Container Registry (Amazon ECR). The AWS DLCs are used in Amazon SageMaker as the default vehicles for your SageMaker jobs such as training, inference, transforms etc. They've been tested for machine learning workloads on Amazon EC2, Amazon ECS and Amazon EKS services as well. This project is licensed under the Apache-2.0 License. Ensure you have access to an AWS account i.e. setup your environment such that awscli can access your account via either an IAM user or an IAM role. -
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AWS IoT Device SDK for Python
SDK for connecting to AWS IoT from a device using Python
The AWS IoT Device SDK for Python allows developers to write Python script to use their devices to access the AWS IoT platform through MQTT or MQTT over the WebSocket protocol. By connecting their devices to AWS IoT, users can securely work with the message broker, rules, and the device shadow (sometimes referred to as a thing shadow) provided by AWS IoT and with other AWS services like AWS Lambda, Amazon Kinesis, Amazon S3, and more. It is a complete rework, built to improve reliability, performance, and security. We invite your feedback! The SDK is built on top of a modified Paho MQTT Python client library. Developers can choose from two types of connections to connect to AWS IoT. For MQTT over TLS (port 8883 and port 443), a valid certificate and a private key are required for authentication. For MQTT over the WebSocket protocol (port 443), a valid AWS Identity and Access Management (IAM) access key ID and secret access key pair are required for authentication. -
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AWS IoT Device SDK v2 for Python
Next generation AWS IoT Client SDK for Python
Next-generation AWS IoT Client SDK for Python using the AWS Common Runtime. This document provides information about the AWS IoT Device SDK v2 for Python. This SDK is built on the AWS Common Runtime, a collection of libraries (aws-c-common, aws-c-io, aws-c-mqtt, aws-c-compression, aws-c-http, aws-c-cal, aws-c-auth, s2n ...) written in C to be cross-platform, high-performance, secure, and reliable. The libraries are bound to Python by the awscrt package (PyPI). AWS IoT provides the cloud services that connect your IoT devices to other devices and AWS cloud services. AWS IoT provides device software that can help you integrate your IoT devices into AWS IoT-based solutions. If your devices can connect to AWS IoT, AWS IoT can connect them to the cloud services that AWS provides. AWS IoT lets you select the most appropriate and up-to-date technologies for your solution. -
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AWS IoT Greengrass Core SDK
SDK to use with functions running on Greengrass Core using Python
The AWS IoT Greengrass Core SDK is meant to be used by AWS Lambda functions running on an AWS IoT Greengrass Core. It will enable Lambda functions to invoke other Lambda functions deployed to the Greengrass Core, publish messages to the Greengrass Core and work with the local Shadow service. To use the AWS IoT Greengrass Core SDK, you must first import the AWS IoT Greengrass Core SDK in your Lambda function as you would with any other external libraries. You then need to create a client for ‘iot-data’ or ‘lambda’. Use ‘iot-data’ if you wish to publish messages to the local Greengrass Core and interact with the local Shadow service. Use ‘lambda’ if you wish to invoke other Lambda functions deployed to the same Greengrass Core. As new features are added to AWS IoT Greengrass, newer versions of the AWS IoT Greengrass SDK may be incompatible with older versions of the AWS IoT Greengrass core. -
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AWS Lambda Python Runtime Interf Client
Seamlessly extend your preferred base images to be Lambda compatible
We have open-sourced a set of software packages, Runtime Interface Clients (RIC), that implement the Lambda Runtime API, allowing you to seamlessly extend your preferred base images to be Lambda compatible. The Lambda Runtime Interface Client is a lightweight interface that allows your runtime to receive requests from and send requests to the Lambda service. The Lambda Python Runtime Interface Client is vended through pip. You can include this package in your preferred base image to make that base image Lambda compatible. To make it easy to locally test Lambda functions packaged as container images we open-sourced a lightweight web-server, Lambda Runtime Interface Emulator (RIE), which allows your function packaged as a container image to accept HTTP requests. You can install the AWS Lambda Runtime Interface Emulator on your local machine to test your function. Then when you run the image function, you set the entry point to be the emulator. -
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AWS Neuron
Powering Amazon custom machine learning chips
AWS Neuron is a software development kit (SDK) for running machine learning inference using AWS Inferentia chips. It consists of a compiler, run-time, and profiling tools that enable developers to run high-performance and low latency inference using AWS Inferentia-based Amazon EC2 Inf1 instances. 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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AWS ParallelCluster Node
Python package installed on the Amazon EC2 instances
aws-parallelcluster-node is the python package installed on the Amazon EC2 instances launched as part of AWS ParallelCluster. AWS ParallelCluster is an AWS-supported Open Source cluster management tool that makes it easy for you to deploy and manage High-Performance Computing (HPC) clusters in the AWS cloud. Built on the Open Source CfnCluster project, AWS ParallelCluster enables you to quickly build an HPC compute environment in AWS. It automatically sets up the required compute resources and a shared filesystem and offers a variety of batch schedulers such as AWS Batch and Slurm. AWS ParallelCluster facilitates both quick start proof of concepts (POCs) and production deployments. You can build higher-level workflows, such as a Genomics portal that automates the entire DNA sequencing workflow, on top of AWS ParallelCluster. -
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AWS Serverless Application Model
An open-source framework for building serverless applications
The AWS Serverless Application Model (SAM) is an open-source framework for building serverless applications. It provides shorthand syntax to express functions, APIs, databases, and event source mappings. With just a few lines per resource, you can define the application you want and model it using YAML. During deployment, SAM transforms and expands the SAM syntax into AWS CloudFormation syntax, enabling you to build serverless applications faster. To get started with building SAM-based applications, use the AWS SAM CLI. SAM CLI provides a Lambda-like execution environment that lets you locally build, test, and debug applications defined by SAM templates or through the AWS Cloud Development Kit (CDK). You can also use the SAM CLI to deploy your applications to AWS. SAM and SAM CLI are open-sourced under the Apache 2.0 license. You can contribute new features and enhancements to SAM on GitHub or SAM CLI on GitHub. -
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AWS Step Functions Data Science SDK
For building machine learning (ML) workflows and pipelines on AWS
The AWS Step Functions Data Science SDK is an open-source library that allows data scientists to easily create workflows that process and publish machine learning models using Amazon SageMaker and AWS Step Functions. You can create machine learning workflows in Python that orchestrate AWS infrastructure at scale, without having to provision and integrate the AWS services separately. The best way to quickly review how the AWS Step Functions Data Science SDK works is to review the related example notebooks. These notebooks provide code and descriptions for creating and running workflows in AWS Step Functions Using the AWS Step Functions Data Science SDK. In Amazon SageMaker, example Jupyter notebooks are available in the example notebooks portion of a notebook instance. To run the AWS Step Functions Data Science SDK example notebooks locally, download the sample notebooks and open them in a working Jupyter instance. -
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Our goal is to create a library extension that has the ability to seamlessly intergrates with the C++ standard template library, but does not subtitute it. This will include standard and proprietary libraries.
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Acid
Python module for parsing and analyzing binary data files
Acid is a module and a simple application for displaying content of binary data files. It relies on plugins for parsing file formats.
