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Test Automation Frameworks
Test automation frameworks are sets of tools, components, and practices that automate the process of testing software applications. These frameworks enable testers to write, execute, and manage test scripts for various types of software testing, including functional, regression, load, and performance testing. They often provide features such as reusable test scripts, integration with continuous integration/continuous deployment (CI/CD) tools, reporting, and test result tracking. Test automation frameworks help improve test efficiency, reduce manual errors, and speed up the overall testing process, especially in large and complex software environments.
Development Frameworks
Development frameworks are code libraries and development tools that streamline the development process for developers that build applications. Development frameworks simplify the process of programming in different languages. There are a variety of different types of development frameworks including web development frameworks, mobile app development frameworks, frontend and backend frameworks, and more.
Agentic Frameworks Software
Agentic frameworks are systems designed to build and manage autonomous or semi-autonomous artificial intelligence (AI) agents that can make decisions, interact with their environment, and perform tasks without constant human oversight. These frameworks provide the underlying structure for designing, training, and optimizing AI agents, enabling them to learn from experience, adapt to new situations, and make decisions based on predefined goals or objectives.
View more categories (16) for "framework python"
45 Products for "framework python" with 2 filters applied:
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1
PydanticAI
Pydantic
PydanticAI is a Python-based agent framework designed to simplify the development of production-grade applications using generative AI. Built by the team behind Pydantic, the framework integrates seamlessly with popular AI models such as OpenAI, Anthropic, Gemini, and others. It offers type-safe design, real-time debugging, and performance monitoring through Pydantic Logfire. PydanticAI also provides structured responses by leveraging Pydantic to validate model outputs, ensuring consistency...Starting Price: Free -
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Horovod
Horovod
Horovod was originally developed by Uber to make distributed deep learning fast and easy to use, bringing model training time down from days and weeks to hours and minutes. With Horovod, an existing training script can be scaled up to run on hundreds of GPUs in just a few lines of Python code. Horovod can be installed on-premise or run out-of-the-box in cloud platforms, including AWS, Azure, and Databricks. Horovod can additionally run on top of Apache Spark, making it possible to unify data...Starting Price: Free -
3
Pathway
Pathway
Pathway is a Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG. Pathway comes with an easy-to-use Python API, allowing you to seamlessly integrate your favorite Python ML libraries. Pathway code is versatile and robust: you can use it in both development and production environments, handling both batch and streaming data effectively. The same code can be used for local development, CI/CD tests, running batch jobs, handling stream replays, and processing... -
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SuperAGI SuperCoder
SuperAGI
SuperAGI SuperCoder is an open-source autonomous system that combines AI-native dev platform & AI agents to enable fully autonomous software development starting with python language & frameworks SuperCoder 2.0 leverages LLMs & Large Action Model (LAM) fine-tuned for python code generation leading to one shot or few shot python functional coding with significantly higher accuracy across SWE-bench & Codebench As an autonomous system, SuperCoder 2.0 combines software guardrails specific...Starting Price: Free -
5
Towhee
Towhee
... favorite libraries, tools, and frameworks, making development quick and easy. Towhee includes a pythonic method-chaining API for describing custom data processing pipelines. We also support schemas, making processing unstructured data as easy as handling tabular data.Starting Price: Free -
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JarvisLabs.ai
JarvisLabs.ai
We have set up all the infrastructure, computing, and software (Cuda, Frameworks) required for you to train and deploy your favorite deep-learning models. You can spin up GPU/CPU-powered instances directly from your browser or automate it through our Python API.Starting Price: $1,440 per month -
7
Steamship
Steamship
Ship AI faster with managed, cloud-hosted AI packages. Full, built-in support for GPT-4. No API tokens are necessary. Build with our low code framework. Integrations with all major models are built-in. Deploy for an instant API. Scale and share without managing infrastructure. Turn prompts, prompt chains, and basic Python into a managed API. Turn a clever prompt into a published API you can share. Add logic and routing smarts with Python. Steamship connects to your favorite models and services... -
8
Agenta
Agenta
....) and model providers (OpenAI, Cohere, Huggingface, self-hosted models, etc.). Gain visibility into your LLM app's costs, latency, and chain of calls. You have the option to create simple LLM apps directly from the UI. However, if you would like to write customized applications, you need to write code with Python. Agenta is model agnostic and works with all model providers and frameworks. The only limitation at present is that our SDK is available only in Python.Starting Price: Free -
9
Writer AI Studio
Writer
The fastest way to build AI apps. Build AI apps and workflows that are fully integrated with the Writer full-stack generative AI platform. Writer API: Integrate enterprise-grade generative AI directly into your own tools and services. Writer Framework: Visual editor in the front, Python in the back Build feature-rich AI apps quickly with an open-source, Python framework. Build with no code Ship AI apps easily without writing a single line of code. Stop stitching together a stack... -
10
UnionML
Union
Creating ML apps should be simple and frictionless. UnionML is an open-source Python framework built on top of Flyte™, unifying the complex ecosystem of ML tools into a single interface. Combine the tools that you love using a simple, standardized API so you can stop writing so much boilerplate and focus on what matters: the data and the models that learn from them. Fit the rich ecosystem of tools and frameworks into a common protocol for machine learning. Using industry-standard machine... -
11
Griptape
Griptape AI
Build, deploy, and scale end-to-end AI applications in the cloud. Griptape gives developers everything they need to build, deploy, and scale retrieval-driven AI-powered applications, from the development framework to the execution runtime. 🎢 Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. ☁️ Griptape Cloud is a one...Starting Price: Free -
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Smolagents
Smolagents
Smolagents is an AI agent framework developed to simplify the creation and deployment of intelligent agents with minimal code. It supports code-first agents where agents execute Python code snippets to perform tasks, offering enhanced efficiency compared to traditional JSON-based approaches. Smolagents integrates with large language models like those from Hugging Face, OpenAI, and others, enabling developers to create agents that can control workflows, call functions, and interact with external... -
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MindMeld
Cisco DevNet
The MindMeld Conversational AI Platform is a Python-based machine learning framework that encompasses all of the algorithms and utilities required for building production-quality conversational applications. Evolved over several years of building and deploying dozens of advanced interfaces, MindMeld is optimized for building conversational assistants which demonstrate deep understanding of a particular use case or domain while providing highly useful and versatile conversational experiences... -
14
Semantic Kernel
Microsoft
Semantic Kernel is a lightweight, open-source development kit that lets you easily build AI agents and integrate the latest AI models into your C#, Python, or Java codebase. It serves as an efficient middleware that enables rapid delivery of enterprise-grade solutions. Microsoft and other Fortune 500 companies are already leveraging Semantic Kernel because it’s flexible, modular, and observable. Backed with security-enhancing capabilities like telemetry support, hooks, and filters you’ll feel...Starting Price: Free -
15
Keepsake
Replicate
Keepsake is an open-source Python library designed to provide version control for machine learning experiments and models. It enables users to automatically track code, hyperparameters, training data, model weights, metrics, and Python dependencies, ensuring that all aspects of the machine learning workflow are recorded and reproducible. Keepsake integrates seamlessly with existing workflows by requiring minimal code additions, allowing users to continue training as usual while Keepsake saves...Starting Price: Free -
16
OpenAI Agents SDK
OpenAI
The OpenAI Agents SDK enables you to build agentic AI apps in a lightweight, easy-to-use package with very few abstractions. It's a production-ready upgrade of our previous experimentation for agents, Swarm. The Agents SDK has a very small set of primitives, agents, which are LLMs equipped with instructions and tools; handoffs, which allow agents to delegate to other agents for specific tasks; and guardrails, which enable the inputs to agents to be validated. In combination with Python...Starting Price: Free -
17
Metaflow
Metaflow
Successful data science projects are delivered by data scientists who can build, improve, and operate end-to-end workflows independently, focusing more on data science, less on engineering. Use Metaflow with your favorite data science libraries, such as Tensorflow or SciKit Learn, and write your models in idiomatic Python code with not much new to learn. Metaflow also supports the R language. Metaflow helps you design your workflow, run it at scale, and deploy it to production. It versions... -
18
MakerSuite
Google
MakerSuite is a tool that simplifies this workflow. With MakerSuite, you’ll be able to iterate on prompts, augment your dataset with synthetic data, and easily tune custom models. When you’re ready to move to code, MakerSuite will let you export your prompt as code in your favorite languages and frameworks, like Python and Node.js. -
19
MLlib
Apache Software Foundation
Apache Spark's MLlib is a scalable machine learning library that integrates seamlessly with Spark's APIs, supporting Java, Scala, Python, and R. It offers a comprehensive suite of algorithms and utilities, including classification, regression, clustering, collaborative filtering, and tools for constructing machine learning pipelines. MLlib's high-quality algorithms leverage Spark's iterative computation capabilities, delivering performance up to 100 times faster than traditional Map... -
20
TF-Agents
Tensorflow
... for building custom environments, policies, and networks, facilitating the creation of complex RL pipelines. TF-Agents supports both Python and TensorFlow environments, allowing for flexibility in development and deployment. It is compatible with TensorFlow 2.x and provides tutorials and guides to help users get started with training agents on standard environments like CartPole. -
21
Chainer
Chainer
A powerful, flexible, and intuitive framework for neural networks. Chainer supports CUDA computation. It only requires a few lines of code to leverage a GPU. It also runs on multiple GPUs with little effort. Chainer supports various network architectures including feed-forward nets, convnets, recurrent nets and recursive nets. It also supports per-batch architectures. Forward computation can include any control flow statements of Python without lacking the ability of backpropagation. It makes... -
22
Databutton
Databutton
Ship your idea in days, not weeks, with Databutton, the world's first fully AI app developer. Describe what you want, and use natural language, screenshots, or diagrams to get React UIs built by AI. Power your product with any service. Connect your app to any API or model to realize its full potential. Prompt Databutton to build Python APIs that scrape websites fetch data across systems, and more. Ship value to your customers continuously, we handle the security and infrastructure for you...Starting Price: $20 per month -
23
ZZZ Code AI
ZZZ Code AI
ZZZ Code AI is an AI-powered coding assistant designed to support developers across various programming tasks. It offers a suite of tools, including AI Code Generator, AI Bug Detector, AI Code Explainer, AI Code Refactor, AI Code Review, AI Code Converter, and AI Code Documentation. It supports multiple programming languages such as Python, C#, C++, Java, JavaScript, HTML, CSS, SQL, and Excel formulas. Users can input their coding requirements or questions, and the AI provides instant responses...Starting Price: Free -
24
Agent Squad
Amazon
Agent Squad is a flexible and powerful open source framework developed by AWS for managing multiple AI agents and handling complex conversations. It enables multi-agent orchestration, allowing seamless coordination and leveraging of multiple AI agents within a single system. It offers dual language support, being fully implemented in both Python and TypeScript. Intelligent intent classification dynamically routes queries to the most suitable agent based on context and content. Agent Squad...Starting Price: Free -
25
Azure Machine Learning
Microsoft
.... Robust MLOps capabilities that integrate with existing DevOps processes and help manage the complete ML lifecycle. Responsible ML capabilities – understand models with interpretability and fairness, protect data with differential privacy and confidential computing, and control the ML lifecycle with audit trials and datasheets. Best-in-class support for open-source frameworks and languages including MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python, and R. -
26
Weights & Biases
Weights & Biases
.... Save every detail of your end-to-end machine learning pipeline — data preparation, data versioning, training, and evaluation. It's never been easier to share project updates. Quickly and easily implement experiment logging by adding just a few lines to your script and start logging results. Our lightweight integration works with any Python script. W&B Weave is here to help developers build and iterate on their AI applications with confidence. -
27
... models through one-click integration. Promote AI governance with fair, explainable AI. Drive better business outcomes by optimizing decisions. Use open source frameworks like PyTorch, TensorFlow and scikit-learn. Bring together the development tools including popular IDEs, Jupyter notebooks, JupterLab and CLIs — or languages such as Python, R and Scala. IBM Watson Studio helps you build and scale AI with trust and transparency by automating AI lifecycle management.
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28
Xilinx
Xilinx
The Xilinx’s AI development platform for AI inference on Xilinx hardware platforms consists of optimized IP, tools, libraries, models, and example designs. It is designed with high efficiency and ease-of-use in mind, unleashing the full potential of AI acceleration on Xilinx FPGA and ACAP. Supports mainstream frameworks and the latest models capable of diverse deep learning tasks. Provides a comprehensive set of pre-optimized models that are ready to deploy on Xilinx devices. You can find... -
29
Tabnine
Tabnine
... completion and an AI-powered chat and supports numerous use cases such as code generation, explanation of code, creating unit tests, generating documentation, debugging code, refactoring and maintaining code. Tabnine is integrated with all the major IDEs such as VS Code, JetBrains, Visual Studio, Eclipse. It supports over 80 programming languages and frameworks including JavaScript, Python, Java, PHP, Go, and more.Starting Price: $12 per user per month -
30
Deeplearning4j
Deeplearning4j
DL4J takes advantage of the latest distributed computing frameworks including Apache Spark and Hadoop to accelerate training. On multi-GPUs, it is equal to Caffe in performance. The libraries are completely open-source, Apache 2.0, and maintained by the developer community and Konduit team. Deeplearning4j is written in Java and is compatible with any JVM language, such as Scala, Clojure, or Kotlin. The underlying computations are written in C, C++, and Cuda. Keras will serve as the Python API...