Best Artificial Intelligence Software for Python - Page 17
View:
Compare the Top Artificial Intelligence Software that integrates with Python as of November 2025 - Page 17
Sort By:
This a list of Artificial Intelligence software that integrates with Python. Use the filters on the left to add additional filters for products that have integrations with Python. View the products that work with Python in the table below.
-
1
Ludwig
Uber AI
Ludwig is a low-code framework for building custom AI models like LLMs and other deep neural networks. Build custom models with ease: a declarative YAML configuration file is all you need to train a state-of-the-art LLM on your data. Support for multi-task and multi-modality learning. Comprehensive config validation detects invalid parameter combinations and prevents runtime failures. Optimized for scale and efficiency: automatic batch size selection, distributed training (DDP, DeepSpeed), parameter efficient fine-tuning (PEFT), 4-bit quantization (QLoRA), and larger-than-memory datasets. Expert level control: retain full control of your models down to the activation functions. Support for hyperparameter optimization, explainability, and rich metric visualizations. Modular and extensible: experiment with different model architectures, tasks, features, and modalities with just a few parameter changes in the config. Think building blocks for deep learning. -
2
AutoKeras
AutoKeras
An AutoML system based on Keras. It is developed by DATA Lab at Texas A&M University. The goal of AutoKeras is to make machine learning accessible to everyone. AutoKeras supports several tasks with an extremely simple interface. -
3
HACARUS Check
HACARUS Inc.
HACARUS' AI Core is uniquely differentiated in that it is able to create highly accurate defect detection models from small data sets, trained with only good data, and can be adapted to changing production conditions with simple adjustments. HACARUS Check AI software is a complete inspection software suite, built with the human operator in mind. The intuitive application features a full graphical interface and allows for the creation of powerful AI models with ease. Training a new inspection model requires only a small, good only, data set, and predictions are near instantaneous. Defects detected are highlighted with helpful features such as bounding boxes and heat maps. -
4
Prompt flow
Microsoft
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. With Prompt Flow, you can create flows that link LLMs, prompts, Python code, and other tools together in an executable workflow. It allows for debugging and iteration of flows, especially tracing interactions with LLMs with ease. You can evaluate your flows, calculate quality and performance metrics with larger datasets, and integrate the testing and evaluation into your CI/CD system to ensure quality. Deployment of flows to the serving platform of your choice or integration into your app’s code base is made easy. Additionally, collaboration with your team is facilitated by leveraging the cloud version of Prompt Flow in Azure AI. -
5
Tumeryk
Tumeryk
Tumeryk Inc. specializes in advanced generative AI security solutions, offering tools like the AI trust score for real-time monitoring, risk management, and compliance. Our platform empowers organizations to secure AI systems, ensuring reliable, trustworthy, and policy-aligned deployments. The AI Trust Score quantifies the risk of using generative AI systems, enabling compliance with regulations like the EU AI Act, ISO 42001, and NIST RMF 600.1. This score evaluates and scores the trustworthiness of generated prompt responses, accounting for risks including bias, jailbreak propensity, off-topic responses, toxicity, Personally Identifiable Information (PII) data leakage, and hallucinations. It can be integrated into business processes to help determine whether content should be accepted, flagged, or blocked, thus allowing organizations to mitigate risks associated with AI-generated content. -
6
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 systems. The framework is designed to be user-friendly, requiring only a few lines of code to define and execute agents. It features secure execution environments, such as sandboxed spaces, for safe code running. Smolagents also promotes collaboration by integrating deeply with the Hugging Face Hub, allowing users to share and import tools. It supports a variety of use cases, from simple tasks to multi-agent workflows, offering flexibility and performance improvements. -
7
Dendrite
Dendrite
Dendrite is a framework-agnostic platform that empowers developers to create web-based tools for AI agents, enabling them to authenticate, interact with, and extract data from any website. By simulating human-like browsing behavior, Dendrite facilitates seamless web navigation and data retrieval for AI applications. The platform offers a Python SDK, providing developers with the necessary tools to build AI agents capable of performing tasks such as interacting with web elements and extracting information. Dendrite's flexibility allows it to integrate with any tech stack, making it a versatile solution for developers aiming to enhance their AI agents' web interaction capabilities. Your Dendrite client syncs with website authentication sessions in your local browser, no need to share or store login credentials. Use our Chrome Extension, Dendrite Vault, to securely share authentication sessions from your browser with the Dendrite client. -
8
Gemini 2.0 Flash-Lite
Google
Gemini 2.0 Flash-Lite is Google DeepMind's lighter AI model, designed to offer a cost-effective solution without compromising performance. As the most economical model in the Gemini 2.0 lineup, Flash-Lite is tailored for developers and businesses seeking efficient AI capabilities at a lower cost. It supports multimodal inputs and features a context window of one million tokens, making it suitable for a variety of applications. Flash-Lite is currently available in public preview, allowing users to explore its potential in enhancing their AI-driven projects. -
9
Gemini 2.0 Pro
Google
Gemini 2.0 Pro is Google DeepMind's most advanced AI model, designed to excel in complex tasks such as coding and intricate problem-solving. Currently in its experimental phase, it features an extensive context window of two million tokens, enabling it to process and analyze vast amounts of information efficiently. A standout feature of Gemini 2.0 Pro is its seamless integration with external tools like Google Search and code execution environments, enhancing its ability to provide accurate and comprehensive responses. This model represents a significant advancement in AI capabilities, offering developers and users a powerful resource for tackling sophisticated challenges. -
10
TextBlob
TextBlob
TextBlob is a Python library for processing textual data, offering a simple API to perform common natural language processing tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, and classification. It stands on the giant shoulders of NLTK and Pattern, and plays nicely with both. Key features include tokenization (splitting text into words and sentences), word and phrase frequencies, parsing, n-grams, word inflection (pluralization and singularization) lemmatization, spelling correction, and WordNet integration. TextBlob is compatible with Python versions 2.7 and above, and 3.5 and above. It is actively developed on GitHub and is licensed under the MIT License. Comprehensive documentation, including a quick start guide and tutorials, is available to assist users in implementing various NLP tasks. -
11
Artelys Knitro
Artelys
Artelys Knitro is a leading solver for large-scale nonlinear optimization problems, offering a suite of advanced algorithms and features to address complex challenges across various industries. It provides four state-of-the-art algorithms: two interior-point/barrier methods and two active-set/sequential quadratic programming methods, enabling efficient and robust solutions for a wide range of optimization problems. Additionally, Knitro includes three algorithms specifically designed for mixed-integer nonlinear programming, incorporating heuristics, cutting planes, and branching rules to effectively handle discrete variables. Key features of Knitro encompass parallel multi-start capabilities for global optimization, automatic and parallel tuning of option settings, and smart initialization strategies for rapid infeasibility detection. The solver supports various interfaces, including object-oriented APIs for C++, C#, Java, and Python. -
12
Navie AI
AppMap
AppMap Navie is an AI-powered development assistant designed to enhance software development by providing actionable insights and troubleshooting support. It combines static and runtime application analysis to guide developers in understanding and optimizing their codebases more effectively. Navie integrates seamlessly with development environments, offering flexible deployment configurations and support for enterprise-grade security, including options for using GitHub Copilot or custom language models. The platform provides valuable context for AI-driven suggestions, such as HTTP requests, function parameters, and database queries, improving code quality and accelerating problem-solving. Navie is ideal for developers looking to streamline workflows, solve complex coding issues, and enhance overall application performance. -
13
Augoor
Augoor
Augoor transforms static code into dynamic knowledge, enabling teams to navigate, document, and optimize complex systems effortlessly. By extracting structures, relationships, and context, Augoor builds a living knowledge graph that accelerates the development lifecycle. Its AI-driven code navigation tool accelerates new developer productivity, integrating them into projects from day one. Augoor reduces maintenance efforts and enhances code integrity by pinpointing problematic code segments, saving costs, and reinforcing your codebase. It automatically generates clear, updated code explanations, preserving knowledge, especially for complex legacy systems. The AI navigation system cuts down time spent searching through code, allowing developers to focus more on coding, speeding up feature development, and fostering innovation in large codebases. Augoor's advanced AI-driven visualizations uncover hidden patterns, map complex dependencies, and reveal critical relationships. -
14
Undrstnd
Undrstnd
Undrstnd Developers empowers developers and businesses to build AI-powered applications with just four lines of code. Experience incredibly fast AI inference times, up to 20 times faster than GPT-4 and other leading models. Our cost-effective AI services are designed to be up to 70 times cheaper than traditional providers like OpenAI. Upload your own datasets and train models in under a minute with our easy-to-use data source feature. Choose from a variety of open source Large Language Models (LLMs) to fit your specific needs, all backed by powerful, flexible APIs. Our platform offers a range of integration options to make it easy for developers to incorporate our AI-powered solutions into their applications, including RESTful APIs and SDKs for popular programming languages like Python, Java, and JavaScript. Whether you're building a web application, a mobile app, or an IoT device, our platform provides the tools and resources you need to integrate our AI-powered solutions seamlessly. -
15
Mistral OCR
Mistral AI
Mistral AI's Document Capabilities provide a powerful set of tools for understanding, summarizing, and generating content from complex documents using advanced AI models. Designed for developers and businesses, these capabilities allow users to process large volumes of text efficiently, extracting key information, generating concise summaries, and even drafting new content based on the original document. By leveraging state-of-the-art language models, Mistral enables organizations to automate document-heavy workflows, from legal reviews and contract analysis to research paper summaries and business reports. The API allows seamless integration into existing systems, enabling real-time document processing and analysis. Mistral’s Document capabilities are especially suited for scenarios where quick comprehension of lengthy or technical materials is critical, reducing the time spent on manual reading and review. -
16
ERNIE X1
Baidu
ERNIE X1 is an advanced conversational AI model developed by Baidu as part of their ERNIE (Enhanced Representation through Knowledge Integration) series. Unlike previous versions, ERNIE X1 is designed to be more efficient in understanding and generating human-like responses. It incorporates cutting-edge machine learning techniques to handle complex queries, making it capable of not only processing text but also generating images and engaging in multimodal communication. ERNIE X1 is often used in natural language processing applications such as chatbots, virtual assistants, and enterprise automation, offering significant improvements in accuracy, contextual understanding, and response quality.Starting Price: $0.28 per 1M tokens -
17
Codoki
Codoki
🚀 Codoki is an AI-powered engineering assistant that helps teams fix bugs, refactor code, and reduce tech debt—up to 50x faster. Unlike AI code assistants that just suggest snippets, Codoki integrates with your workflow, detects issues, automates fixes, and even acts as a 24/7 AI on-call engineer—reducing downtime and saving developer time. Engineering teams using Codoki ship faster, cut operational costs, and spend more time building instead of fixing. -
18
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 MapReduce implementations. It is designed to operate across diverse environments, running on Hadoop, Apache Mesos, Kubernetes, standalone clusters, or in the cloud, and accessing various data sources such as HDFS, HBase, and local files. This flexibility makes MLlib a robust solution for scalable and efficient machine learning tasks within the Apache Spark ecosystem. -
19
JAX
JAX
JAX is a Python library designed for high-performance numerical computing and machine learning research. It offers a NumPy-like API, facilitating seamless adoption for those familiar with NumPy. Key features of JAX include automatic differentiation, just-in-time compilation, vectorization, and parallelization, all optimized for execution on CPUs, GPUs, and TPUs. These capabilities enable efficient computation for complex mathematical functions and large-scale machine-learning models. JAX also integrates with various libraries within its ecosystem, such as Flax for neural networks and Optax for optimization tasks. Comprehensive documentation, including tutorials and user guides, is available to assist users in leveraging JAX's full potential. -
20
AlphaCodium
Qodo
AlphaCodium is a research-driven AI tool developed by Qodo to enhance coding with iterative, test-driven processes. It helps large language models improve their accuracy by enabling them to engage in logical reasoning, testing, and refining code. AlphaCodium offers an alternative to basic prompt-based approaches by guiding AI through a more structured flow paradigm, which leads to better mastery of complex code problems, particularly those involving edge cases. It improves performance on coding challenges by refining outputs based on specific tests, ensuring more reliable results. AlphaCodium is benchmarked to significantly increase the success rates of LLMs like GPT-4o, OpenAI o1, and Sonnet-3.5. It supports developers by providing advanced solutions for complex coding tasks, allowing for enhanced productivity in software development. -
21
Amazon Nova Act
Amazon
Amazon Nova Act is an AI model designed to perform actions within web browsers, enabling the development of agents capable of completing tasks such as submitting out-of-office requests, scheduling calendar events, and setting up 'away from office' emails. Unlike traditional large language models that primarily generate natural language responses, Nova Act focuses on executing tasks in digital environments. The Nova Act SDK allows developers to decompose complex workflows into reliable atomic commands (e.g., search, checkout, answer questions about the screen) and incorporate detailed instructions where necessary. It also supports API calls and direct browser manipulation through Playwright to enhance reliability. Developers can integrate Python code, including tests, breakpoints, asserts, or thread pools for parallelization, to manage web page load times effectively. -
22
NetsPresso
Nota AI
NetsPresso is a hardware-aware AI model optimization platform. NetsPresso powers on-device AI across industries, and it's the ultimate platform for hardware-aware AI model development. Lightweight models of LLaMA and Vicuna enable efficient text generation. BK-SDM is a lightweight version of Stable Diffusion models. VLMs combine visual data with natural language understanding. NetsPresso resolves Cloud and server-based AI solutions-related issues, such as limited network, excessive cost, and privacy breaches. NetsPresso is an automatic model compression platform that downsizes computer vision models to a size small enough to be deployed independently on the smaller edge and low-specification devices. Optimization of target models being key, the platform combines a variety of compression methods which enables it to downsize AI models without causing performance degradation. -
23
Gemini 2.5 Flash
Google
Gemini 2.5 Flash is a powerful, low-latency AI model introduced by Google on Vertex AI, designed for high-volume applications where speed and cost-efficiency are key. It delivers optimized performance for use cases like customer service, virtual assistants, and real-time data processing. With its dynamic reasoning capabilities, Gemini 2.5 Flash automatically adjusts processing time based on query complexity, offering granular control over the balance between speed, accuracy, and cost. It is ideal for businesses needing scalable AI solutions that maintain quality and efficiency. -
24
Gymnasium
Gymnasium
Gymnasium is a maintained fork of OpenAI’s Gym library, providing a standard API for reinforcement learning and a diverse collection of reference environments. The Gymnasium interface is simple, pythonic, and capable of representing general RL problems, and has a compatibility wrapper for old Gym environments. At the core of Gymnasium is the Env class, a high-level Python class representing a Markov Decision Process (MDP) from reinforcement learning theory. The class provides users the ability to generate an initial state, transition to new states given an action, and visualize the environment. Alongside Env, Wrapper classes are provided to help augment or modify the environment, particularly the agent observations, rewards, and actions taken. Gymnasium includes various built-in environments and utilities to simplify researchers’ work, along with being supported by most training libraries. -
25
TF-Agents
Tensorflow
TensorFlow Agents (TF-Agents) is a comprehensive library designed for reinforcement learning in TensorFlow. It simplifies the design, implementation, and testing of new RL algorithms by providing well-tested modular components that can be modified and extended. TF-Agents enables fast code iteration with good test integration and benchmarking. It includes a variety of agents such as DQN, PPO, REINFORCE, SAC, and TD3, each with their respective networks and policies. It also offers tools 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. -
26
DeepSeek-Coder-V2
DeepSeek
DeepSeek-Coder-V2 is an open source code language model designed to excel in programming and mathematical reasoning tasks. It features a Mixture-of-Experts (MoE) architecture with 236 billion total parameters and 21 billion activated parameters per token, enabling efficient processing and high performance. The model was trained on an extensive dataset of 6 trillion tokens, enhancing its capabilities in code generation and mathematical problem-solving. DeepSeek-Coder-V2 supports over 300 programming languages and has demonstrated superior performance on benchmarks such surpassing other models. It is available in multiple variants, including DeepSeek-Coder-V2-Instruct, optimized for instruction-based tasks; DeepSeek-Coder-V2-Base, suitable for general text generation; and lightweight versions like DeepSeek-Coder-V2-Lite-Base and DeepSeek-Coder-V2-Lite-Instruct, designed for environments with limited computational resources. -
27
Scottie
Scottie
Describe what you need in plain English. Scottie turns it into a working agent you can run on our cloud or export to your own hosting service. Join our waitlist today to secure your spot and get exclusive early access to premium features. Everything you need to build, test, and deploy AI agents in minutes. Pick from today's leading language models and switch anytime without rebuilding (OpenAI, Gemini, Anthropic, Llama, and more). Bring your company knowledge together from Slack, Google Drive, Notion, Confluence, GitHub, and more. Your data stays private and secure. Scottie supports models from all top vendors. Switch models anytime without rebuilding your agents. Scottie agents adapt to different roles and industries, operating exactly how you need them to. The AI tutor analyzes student responses, provides personalized feedback, and adapts difficulty based on performance. -
28
Upsonic
Upsonic
Upsonic is an open source framework that simplifies AI agent development for business needs. It enables developers to build, manage, and deploy agents with integrated Model Context Protocol (MCP) tools across cloud and local environments. Upsonic reduces engineering effort by 60-70% with built-in reliability features and service client architecture. It offers a client-server architecture that isolates agent applications, keeping existing systems healthy and stateless. It provides more reliable agents, scalability, and a task-oriented structure needed for completing real-world cases. Upsonic supports autonomous agent characterization, allowing self-defined goals and backgrounds, and integrates computer-use capabilities for executing human-like tasks. With direct LLM call support, developers can access models without abstraction layers, completing agent tasks faster and more cost-effectively. -
29
Airweave
Airweave
Airweave is an open source platform that transforms application data into agent-ready knowledge, enabling AI agents to semantically search across various apps, databases, and document stores. It simplifies the process of building intelligent agents by offering no-code solutions, instant data synchronization, and scalable deployment options. Users can connect their data sources using OAuth2, API keys, or database credentials, initiate data synchronization with minimal configuration, and provide agents with a unified search endpoint to access the necessary information. Airweave supports over 100 connectors, including integrations with Google Drive, Slack, Notion, Jira, GitHub, and Salesforce, allowing agents to access a wide range of data sources. It handles the entire data pipeline, from authentication and extraction to embedding and serving, automating tasks such as data ingestion, enrichment, mapping, and syncing to vector stores and graph databases. -
30
Beam Cloud
Beam Cloud
Beam is a serverless GPU platform designed for developers to deploy AI workloads with minimal configuration and rapid iteration. It enables running custom models with sub-second container starts and zero idle GPU costs, allowing users to bring their code while Beam manages the infrastructure. It supports launching containers in 200ms using a custom runc runtime, facilitating parallelization and concurrency by fanning out workloads to hundreds of containers. Beam offers a first-class developer experience with features like hot-reloading, webhooks, and scheduled jobs, and supports scale-to-zero workloads by default. It provides volume storage options, GPU support, including running on Beam's cloud with GPUs like 4090s and H100s or bringing your own, and Python-native deployment without the need for YAML or config files.
