Best Artificial Intelligence Software for Python - Page 24
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Compare the Top Artificial Intelligence Software that integrates with Python as of July 2026 - Page 24
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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.
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1
Raven
Raven
Raven is a runtime application security platform designed to protect cloud-native applications by operating directly inside the application during execution, rather than relying on external defenses. It provides real-time visibility into how code actually runs, allowing it to understand execution flows, libraries, and function-level behavior in order to detect and stop malicious activity before it occurs. Unlike traditional tools such as WAF or EDR that monitor from the outside, Raven embeds itself within the application, enabling it to prevent exploits, supply chain attacks, and zero-day threats even when no known vulnerability or CVE exists. It continuously monitors runtime behavior, identifies abnormal patterns or misuse of legitimate logic, and responds immediately to block harmful execution. It also helps teams prioritize security efforts by filtering out the majority of irrelevant vulnerabilities and focusing only on those that are truly exploitable. -
2
Open Wallet
Open Wallet
OpenWallet is an open standard designed for secure local wallet storage and seamless agent access, providing a unified interface that works across all blockchain networks, tools, and autonomous agents. It focuses on simplifying how digital wallets interact with modern systems by creating a consistent layer that allows developers and AI agents to access, manage, and utilize wallet data locally without relying on fragmented integrations. The standard enables interoperability across multiple chains, ensuring that a single interface can handle different blockchain environments without requiring custom implementations for each one. By prioritizing local storage, it enhances security and control, reducing exposure to external vulnerabilities while allowing direct interaction between wallets and applications. OpenWallet is built to support emerging agent-based ecosystems, where AI tools and automation systems need reliable, standardized access to financial or blockchain assets. -
3
Simaril
Simaril
Silmaril is a self-healing prompt injection defense designed to protect AI systems from increasingly complex, multi-step attacks that traditional guardrails fail to stop. It operates by wrapping inference calls and evaluating whether an execution sequence is leading toward a harmful outcome, rather than simply filtering inputs. It uses a multihead classifier that analyzes user intent, application context, and execution states together, enabling it to detect indirect injection, multi-turn attack chains, context poisoning, and tool abuse before damage occurs. Silmaril continuously strengthens its defenses through autonomous threat hunting agents that probe systems, discover vulnerabilities, and generate synthetic training data from real attack scenarios. These insights are used to retrain the model automatically, deploying updated protections in under an hour and propagating anonymized defenses across all deployments. -
4
LakeSail
LakeSail
LakeSail is a unified, cloud-native data and AI platform designed to transform how organizations process, analyze, and act on large-scale data by combining all workloads into a single, high-performance system. At its core is Sail, a Rust-native distributed computation engine that serves as a drop-in replacement for Apache Spark, enabling teams to run existing SQL and Python workloads without rewriting code while eliminating JVM overhead and improving efficiency. It unifies batch processing, stream processing, ad-hoc queries, and AI workloads into one runtime, allowing data pipelines and intelligent systems to operate seamlessly on the same infrastructure. It introduces a multimodal lakehouse architecture capable of handling structured and unstructured data, including PDFs, images, and video, within a single environment, making it suitable for modern AI-driven use cases. -
5
GraphBit
GraphBit
GraphBit is an enterprise-grade agentic AI framework built to run critical AI systems with security, governance, and predictable production performance. It combines a Rust execution core with a Python wrapper to give developers high-performance orchestration with the accessibility of Python, helping teams build reliable multi-agent workflows with minimal CPU and memory usage. GraphBit is designed around the layers that reduce risk, including interfaces, configuration, models, tools, actions, memory, orchestration, and observability. It integrates into existing apps, powers custom AI interfaces, and lets users interact through familiar workflows with controlled actions. Teams can define policies, rules, and guardrails centrally, while GraphBit enforces behavior without changing application code. It supports LLMs and multimodal models from multiple providers, allowing teams to swap models freely without breaking workflows or governance. -
6
PromptUnit
PromptUnit
PromptUnit is an AI inference proxy that reduces AI costs automatically by sitting between an app and its AI providers with no code changes required. Teams swap the base URL, keep the same SDK, endpoints, response parsing, and error handling, then PromptUnit handles routing, failover, cost tracking, and quality validation. It logs every API call by model, feature, user segment, token count, latency, and cost, giving real-time visibility into where AI spend is going before any routing changes go live. In observation mode, PromptUnit watches traffic, shadow-classifies requests, forecasts savings, and explains routing decisions so teams can see exact savings before enabling live routing. Once enabled, Smart Routing uses task classification to route each request to the cheapest model that clears the configured quality bar. PromptUnit also includes prompt compression, token inflation defense, prompt efficiency scoring, semantic request caching, and multi-model consensus. -
7
Google GenAI SDK
Google
The Gemini API libraries provide official, production-ready Google GenAI SDKs for building with the Gemini API in popular programming languages. Google recommends using the Google GenAI SDK when building with Gemini, since these libraries are developed and maintained by Google, used across official documentation and examples, and are generally available for production use. The SDKs are available for Python, JavaScript/TypeScript, Go, Java, and C#, with installation through standard package managers such as pip install google-genai, npm install google/genai, Maven dependencies for google genai, and dotnet add package Google GenAI. They provide access to the latest Gemini API features and are designed to offer the best performance when working with Gemini models. Google strongly recommends migrating from legacy libraries to the new Google GenAI SDK because the legacy libraries are not actively maintained. -
8
Gemini 3.5 Pro
Google
Gemini 3.5 Pro is Google’s anticipated next-generation Pro model in the Gemini 3.5 series, designed for advanced reasoning, coding, multimodal understanding, and agentic workflows. It is expected to build on Google’s Gemini 3 family with stronger performance for complex tasks that require planning, context handling, tool use, and deep problem solving. The model is aimed at users who need more power than faster Flash models for demanding development, research, automation, and enterprise AI use cases. Gemini 3.5 Pro is expected to support sophisticated workflows across text, code, files, multimodal inputs, and connected tools. Developers and organizations will likely use it through Google’s AI platforms for building assistants, agents, coding tools, analysis systems, and productivity applications. As an upcoming Pro-tier model, Gemini 3.5 Pro is positioned for high-value workloads where accuracy, reasoning quality, and advanced task execution matter more than maximum speed. -
9
Qwen3.7-Plus
Alibaba
Qwen3.7-Plus is a multimodal agent model that unifies vision and language into a single, versatile agent foundation. Building on Qwen3.7’s agentic intelligence, it extends Qwen’s capabilities into visual understanding, visual reasoning, grounded interaction, and multimodal tool use, enabling agents to perceive, analyze, and act across text, images, documents, screens, and complex real-world contexts. It is designed for tasks that require more than static question answering, including visual search, document comprehension, chart and table analysis, screen understanding, GUI interaction, image-grounded reasoning, and agent workflows that combine perception with planning and execution. Qwen3.7-Plus strengthens the connection between language reasoning and visual evidence, allowing users to ask questions about images, interpret dense multimodal inputs, extract structured information, and generate responses that reflect both context and visual details. -
10
Gray Swan
Gray Swan
Gray Swan is an enterprise AI security and evaluation platform that helps organizations deploy AI with confidence by protecting LLM applications, agents, and model deployments from emerging threats, policy violations, and harmful content. It integrates with any LLM provider to add security without disrupting existing workflows, combining automated adversarial testing, continuous red teaming, runtime monitoring, and adaptive protections. Gray Swan tests beyond known attacks by using threat intelligence from 15,000+ adversarial researchers and more than three million attack attempts generated through its Arena, helping teams discover vulnerabilities before they appear in public databases. Its core products include Shade, an advanced AI vulnerability assessment platform that continuously probes LLMs like a security researcher working 24/7, and Cygnal, a runtime monitoring and protection layer for AI interactions. -
11
Rapidminer AI Studio
Siemens
RapidMiner AI Studio is a dedicated environment for rapidly developing and prototyping AI solutions, helping teams unify the complete data science lifecycle from data exploration and machine learning to model operations and visualization. It allows data scientists and engineers to build, train, and test AI models locally, giving organizations full control and flexibility for initial exploration and development. It connects directly to enterprise data sources, including files, databases, data lakes, cloud data platforms, warehouses, SQL databases, and Internet of Things data streams, helping teams unify data, prevent errors, and power accurate, explainable AI. RapidMiner AI Studio supports both domain experts and technical teams: users without coding experience can quickly build effective machine learning models with an intuitive drag-and-drop canvas, while data scientists can create complex models in a fully integrated notebook environment using Python and R. -
12
Concentrate AI
Concentrate AI
Concentrate AI is the LLM gateway for fast-growing teams, one API for every major LLM provider, with routing, spend, logs, and controls in one place. It helps teams securely access, use, and manage AI through a single API, so every request can find the smarter, faster, cheaper model for the workflow or task. Teams can access 130+ models, benchmark speed, quality, and cost, and route each workload to the best fit without wiring separate provider APIs into every environment. Support bots, coding agents, internal tools, chat, and batch jobs do not need the same model or the same route, so Concentrate lets teams pick a model slug, limit allowed providers, sort by live latency, use fallbacks, and reroute traffic when a provider slows down, errors, or hits a rate limit. It also gives engineering, finance, security, and leadership a shared view of AI usage with request-level logs, models, provider, duration, token counts, spend, error rates, alerts, and exports. -
13
Seed Audio 1.0
BytePlus
Seed Audio 1.0 is a non-streaming audio generation API based on HTTP, designed to generate complete audio from text prompts, reference audio, or reference images. It supports text-only generation, where audio is created directly from the prompt; reference-audio generation, where uploaded reference clips guide the output; and reference-image generation, where an image reference can be passed to generate audio from the text to be synthesized. Built as part of BytePlus Seed Speech, Audio 1.0 uses the seed-audio-1.0 model version and is positioned as an audio creation capability rather than a standard speech-only endpoint. It can generate voice, music, and sound effects in a single pass, making it useful for producing richer audio scenes without separately creating and mixing every track. The API is intended for developers building audio generation into applications, workflows, and production systems, with a request-based structure that lets teams submit prompts. -
14
Gemini 3.5 Flash Cyber
Google
Gemini 3.5 Flash Cyber is a specialized cyber-focused model built on Gemini 3.5 Flash and fine-tuned to find, validate, and fix cybersecurity vulnerabilities efficiently at scale. It is designed for defensive security workflows where organizations need to identify critical weaknesses faster and generate reliable patches before those issues can be exploited. Flash’s combination of performance and efficiency makes it a strong foundation for scanning code, reasoning about security flaws, validating whether findings are real, and proposing targeted remediations across large software environments. Within CodeMender, multiple Gemini 3.5 Flash Cyber agents work together and combine their findings into a single report, helping the system investigate vulnerabilities from different angles and improve the quality of the final result. This coordinated agent setup delivers competitive frontier performance on CyberGym, a benchmark for evaluating cybersecurity capabilities. -
15
Jedi
Jedi
Jedi is a static analysis tool for Python that is typically used in IDEs/editors plugins. Jedi has a focus on autocompletion and goto functionality. Other features include refactoring, code search and finding references. Jedi has a simple API to work with. There is a reference implementation as a VIM-Plugin. Autocompletion in your REPL is also possible, IPython uses it natively and for the CPython REPL you can install it. Jedi is well tested and bugs should be rare. A Script is the base for completions, goto or whatever you want to do with Jedi. The counter part of this class is Interpreter, which works with actual dictionaries and can work with a REPL. This class should be used when a user edits code in an editor. Most methods have a line and a column parameter. Lines in Jedi are always 1-based and columns are always zero based. To avoid repetition they are not always documented. -
16
Stenography
Stenography
No need to Google it. Hydrate responses with Stack Overflow Suggestions and documentation from across the web. Extensions, extensions, extensions. Wherever code can be found, Stenography integrates. Stenography uses a passthrough API and does not store code. Your code stays on your system. -
17
CodeT5
Salesforce
Code for CodeT5, a new code-aware pre-trained encoder-decoder model. Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation. This is the official PyTorch implementation for the EMNLP 2021 paper from Salesforce Research. CodeT5-large-ntp-py is specially optimized for Python code generation tasks and employed as the foundation model for our CodeRL, yielding new SOTA results on the APPS Python competition-level program synthesis benchmark. This repo provides the code for reproducing the experiments in CodeT5. CodeT5 is a new pre-trained encoder-decoder model for programming languages, which is pre-trained on 8.35M functions in 8 programming languages (Python, Java, JavaScript, PHP, Ruby, Go, C, and C#). In total, it achieves state-of-the-art results on 14 sub-tasks in a code intelligence benchmark - CodeXGLUE. Generate code based on the natural language description. -
18
Betteromics
Betteromics
Betteromics is deployed as a Private SaaS in your VPC so you can draw connections on all your data. Reproducibly validate your structured and unstructured data using configurable rules. Trace and audit your data from input to analysis with complete data provenance. Use natural language processing and large language models to abstract data elements from clinical records for QC, labeling, and analysis. Quickly develop and tune models specific to your task/data: detect anomalies, make predictions, understand your data, and optimize your processes. Enhance and complement your analysis and machine learning with integration-ready public datasets. Clinical-grade security including full encryption, data traceability, and role-based access controls. -
19
Unremot
Unremot
Unremot is a go-to place for anyone aspiring to build an AI product - with 120+ pre-built APIs, you can build and launch AI products 2X faster, at 1/3rd cost. Even, some of the most complicated AI product APIs take less than a few minutes to deploy and launch, with minimal code or even no-code. Choose an AI API that you want to integrate to your product from 120+ APIs we have on Unremot. Provide your API private key to authenticate Unremot to access the API. Use unremot unique URL to connect the product API - the whole process takes only minutes, instead of days and weeks. -
20
Hunch
Hunch
Supercharge your work with Hunch, all the best AI models in one no-code app. Chain together AI tasks in a visual, no-code workspace, share your work as a tool for your whole team to use. Far more than just a workflow automation tool, Hunch is a visual canvas for thinking, experimenting, exploring and working with AI on complex tasks. -
21
CodeGemma
Google
CodeGemma is a collection of powerful, lightweight models that can perform a variety of coding tasks like fill-in-the-middle code completion, code generation, natural language understanding, mathematical reasoning, and instruction following. CodeGemma has 3 model variants, a 7B pre-trained variant that specializes in code completion and generation from code prefixes and/or suffixes, a 7B instruction-tuned variant for natural language-to-code chat and instruction following; and a state-of-the-art 2B pre-trained variant that provides up to 2x faster code completion. Complete lines, and functions, and even generate entire blocks of code, whether you're working locally or using Google Cloud resources. Trained on 500 billion tokens of primarily English language data from web documents, mathematics, and code, CodeGemma models generate code that's not only more syntactically correct but also semantically meaningful, reducing errors and debugging time. -
22
TopK
TopK
TopK is a serverless, cloud-native, document database built for powering search applications. It features native support for both vector search (vectors are simply another data type) and keyword search (BM25-style) in a single, unified system. With its powerful query expression language, TopK enables you to build reliable search applications (semantic search, RAG, multi-modal, you name it) without juggling multiple databases or services. Our unified retrieval engine will evolve to support document transformation (automatically generate embeddings), query understanding (parse metadata filters from user query), and adaptive ranking (provide more relevant results by sending “relevance feedback” back to TopK) under one unified roof. -
23
Grok 4 Fast
SpaceXAI
Grok 4 Fast is the latest AI model from xAI, engineered to deliver rapid and efficient query processing. It improves upon earlier versions with faster response times, lower latency, and higher accuracy across a variety of topics. With enhanced natural language understanding, the model excels in both casual conversation and complex problem-solving. A key feature is its real-time data analysis capability, ensuring users receive up-to-date insights when needed. Grok 4 Fast is accessible across multiple platforms, including Grok, X, and mobile apps for iOS and Android. By combining speed, reliability, and scalability, it offers an ideal solution for anyone seeking instant, intelligent answers. -
24
Grok 4.1
SpaceXAI
Grok 4.1 is an advanced AI model developed by Elon Musk’s xAI, designed to push the limits of reasoning and natural language understanding. Built on the powerful Colossus supercomputer, it processes multimodal inputs including text and images, with upcoming support for video. The model delivers exceptional accuracy in scientific, technical, and linguistic tasks. Its architecture enables complex reasoning and nuanced response generation that rivals the best AI systems in the world. Enhanced moderation ensures more responsible and unbiased outputs than earlier versions. Grok 4.1 is a breakthrough in creating AI that can think, interpret, and respond more like a human. -
25
GPT-5.4
OpenAI
GPT-5.4 is an advanced artificial intelligence model developed by OpenAI to support complex professional and technical work. The model combines improvements in reasoning, coding, and agent-based workflows into a single system designed for real-world productivity tasks. GPT-5.4 can generate, analyze, and edit documents, spreadsheets, presentations, and other work outputs with greater accuracy and efficiency. It also features improved tool integration, enabling the model to interact with software environments and external tools to complete multi-step workflows. With enhanced context capabilities supporting up to one million tokens, GPT-5.4 can process and reason over very large amounts of information. The model also improves factual accuracy and reduces errors compared to earlier versions. By combining strong reasoning, coding ability, and tool use, GPT-5.4 helps users complete complex tasks faster and with fewer iterations. -
26
OpenAI o3-mini-high
OpenAI
The o3-mini-high model from OpenAI advances AI reasoning by refining deep problem-solving in coding, mathematics, and complex tasks. It features adaptive thinking time with adjustable reasoning modes (low, medium, high) to optimize performance based on task complexity. Outperforming the o1 series by 200 Elo points on Codeforces, it delivers high efficiency at a lower cost while maintaining speed and accuracy. As part of the o3 family, it pushes AI problem-solving boundaries while remaining accessible, offering a free tier and expanded limits for Plus subscribers. -
27
ERNIE X1.1
Baidu
ERNIE X1.1 is Baidu’s upgraded reasoning model that delivers major improvements over its predecessor. It achieves 34.8% higher factual accuracy, 12.5% better instruction following, and 9.6% stronger agentic capabilities compared to ERNIE X1. In benchmark testing, it surpasses DeepSeek R1-0528 and performs on par with GPT-5 and Gemini 2.5 Pro. Built on the foundation of ERNIE 4.5, it has been enhanced with extensive mid-training and post-training, including reinforcement learning. The model is available through ERNIE Bot, the Wenxiaoyan app, and Baidu’s Qianfan MaaS platform via API. These upgrades are designed to reduce hallucinations, improve reliability, and strengthen real-world AI task performance. -
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Grok 4.20
SpaceXAI
Grok 4.20 is an advanced artificial intelligence model developed by xAI to elevate reasoning and natural language understanding. Built on the high-performance Colossus supercomputer, it is engineered for speed, scale, and accuracy. Grok 4.20 processes multimodal inputs such as text and images, with video support planned for future releases. The model excels in scientific, technical, and linguistic tasks, delivering highly precise and context-aware responses. Its architecture supports deep reasoning and sophisticated problem-solving capabilities. Enhanced moderation improves output reliability and reduces bias compared to earlier versions. Overall, Grok 4.20 represents a significant step toward more human-like AI reasoning and interpretation. -
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Autotab
Autotab
Build browser agents for real-world tasks. Autotab makes it easy to create auditable browser automation using AI. Go from a point-and-click demonstration in the browser to live code for those actions in seconds. -
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Amazon CodeWhisperer
Amazon
Build apps faster with ML-powered coding companion. Accelerate application development with automatic code recommendations based on the code and comments in your IDE. Empower developers to use artificial intelligence (AI) responsibly to create syntactically correct and secure applications. Generate entire functions and logical code blocks without having to search and customize code snippets from the web. Stay focused and never leave the IDE, with real-time customized code recommendations for all your Java, Python, and JavaScript projects. Amazon CodeWhisperer is a machine learning (ML)–powered service that helps improve developer productivity by generating code recommendations based on their comments in natural language and code in the integrated development environment (IDE). Accelerate frontend and backend development by empowering developers with automatic code recommendations. Save time and effort by using CodeWhisperer to generate code to build and train your ML models.
