Best Artificial Intelligence Software for Linux - Page 13
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Compare the Top Artificial Intelligence Software for Linux as of October 2025 - Page 13
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1
Kiota
Microsoft
Kiota is a client, plugin, and manifest generator for HTTP REST APIs described by OpenAPI. Available as a command-line tool and a Visual Studio Code (VS Code) extension, Kiota enables developers to search for API descriptions, filter and select specific endpoints, and generate models and a chained method API surface in various programming languages. This approach eliminates the need to depend on different API clients for each API and allows for precise generation of the required API surface area. Additionally, Kiota facilitates participation in the Microsoft Copilot ecosystem by enabling the generation of API plugins. The VS Code extension enhances the Kiota experience with a rich user interface, supporting features such as searching for API descriptions, filtering endpoints, and generating API clients. Users can select desired endpoints and generate clients, plugins, or other outputs, with completion notifications and easy access to generated outputs within the workspace.Starting Price: Free -
2
AutoRest
Microsoft
The AutoRest tool generates client libraries for accessing RESTful web services. Input to AutoRest is a spec that describes the REST API using the OpenAPI specification format and streamlines the creation of client code across multiple programming languages, including C#, Java, Python, TypeScript, and Go. This automation enhances consistency and efficiency in API consumption, reducing the manual effort required to develop and maintain client libraries. AutoRest operates through a flexible pipeline that processes OpenAPI input files, transforming them into a code model which is then utilized by language-specific generators to produce client code adhering to each language's design guidelines. The tool supports both OpenAPI 2.0 and 3.0 specifications, ensuring compatibility with a wide range of APIs. Developers can install AutoRest on Windows, macOS, or Linux systems, with installation facilitated via Node.js.Starting Price: Free -
3
Defang
Defang
Defang is a developer-centric platform that simplifies the process of developing, deploying, and debugging cloud applications. By leveraging AI-assisted tooling, Defang enables developers to swiftly transition from an idea to a deployed application on their preferred cloud provider. The platform supports multiple programming languages, including Go, JavaScript, and Python, allowing developers to start with sample projects or generate project outlines using natural language prompts. With a single command, Defang builds and deploys applications, handling configurations for computing, storage, load balancing, networking, logging, and security. The Defang Command Line Interface (CLI) facilitates interactions with the platform, offering installation options via shell scripts, Homebrew, Winget, Nix, or direct download. Developers can define services using compose.yaml files, which Defang utilizes to deploy applications to the cloud.Starting Price: $10 per month -
4
KitchenAI
KitchenAI
KitchenAI is a developer-centric framework that streamlines the process of transforming AI Jupyter Notebooks into production-ready APIs. It bridges the gap between AI developers, application developers, and infrastructure developers by providing a fully featured API server with default routes, a command-line interface for quick setup, and an extensible plugin framework. This design enables users to author multiple AI techniques, rapidly test and iterate, and seamlessly build and share their work. For AI developers, KitchenAI manages scalability within familiar environments, converting notebooks into robust applications. Application developers benefit from intuitive SDKs and tools that facilitate the integration of AI through simple APIs, allowing for quick testing to determine the most suitable AI techniques for their applications. Infrastructure developers can integrate with AI tooling.Starting Price: $17 per month -
5
SmythOS
SmythOS
Say goodbye to manual coding and build agents faster than ever. Describe what you need, and SmythOS builds it from your chat or image, using the best AI models and APIs for your task. Use any AI model or API. Integrate with OpenAI, Hugging Face, Amazon Bedrock, and hundreds of vendors without a line of code. A pre-built agent template library gives you agents that already work out of the box for dozens of use cases. Just hit the button and connect with your own API keys. Because your marketing team should not have access to agents that work with your code. We got you covered. Create a space for each client, team, and project with full user and permission management. Deploy on-prem or to AWS. Integrate with Bedrock, Vertex, Adobe, Salesforce, etc. Explainable AI with full control over data flows, audit logs, encryption, and auth. Chat with your agents, give them bulk work, inspect their work logs, assign them work schedules, and more.Starting Price: $30 per month -
6
MLJAR Studio
MLJAR
It's a desktop app with Jupyter Notebook and Python built in, installed with just one click. It includes interactive code snippets and an AI assistant to make coding faster and easier, perfect for data science projects. We manually hand crafted over 100 interactive code recipes that you can use in your Data Science projects. Code recipes detect packages available in the current environment. Install needed modules with 1-click, literally. You can create and interact with all variables available in your Python session. Interactive recipes speed-up your work. AI Assistant has access to your current Python session, variables and modules. Broad context makes it smart. Our AI Assistant was designed to solve data problems with Python programming language. It can help you with plots, data loading, data wrangling, Machine Learning and more. Use AI to quickly solve issues with code, just click Fix button. The AI assistant will analyze the error and propose the solution.Starting Price: $20 per month -
7
Aide
CodeStory
Aide proactively proposes fixes or asks to include files that may be missing in the context. Our agent can do so by iterating on linter errors and pulling in relevant context using LSP tools. Go ahead, and do AI-edits on top of your coding session. We keep slim, VS Code-native checkpoints (we don’t use git) to easily roll back to previous states, in case the agent made any mistake. We try to make Aide feel like a real engineer to pair-program with. Chat about a problem by @’ting the file(s) and then jump into edits, or go from a smaller set of edits and discuss their side effects. Taking inspiration from MacOS spotlight, we created a floating widget you can invoke with CMD + K. If you have a text selection active, you quickly prompt a change for it. We ship a binary called Sidecar which takes care of preparing and sending prompts to LLMs, as well as giving them access to editor features.Starting Price: Free -
8
Llama 3.3
Meta
Llama 3.3 is the latest iteration in the Llama series of language models, developed to push the boundaries of AI-powered understanding and communication. With enhanced contextual reasoning, improved language generation, and advanced fine-tuning capabilities, Llama 3.3 is designed to deliver highly accurate, human-like responses across diverse applications. This version features a larger training dataset, refined algorithms for nuanced comprehension, and reduced biases compared to its predecessors. Llama 3.3 excels in tasks such as natural language understanding, creative writing, technical explanation, and multilingual communication, making it an indispensable tool for businesses, developers, and researchers. Its modular architecture allows for customizable deployment in specialized domains, ensuring versatility and performance at scale.Starting Price: Free -
9
Janus-Pro-7B
DeepSeek
Janus-Pro-7B is an innovative open-source multimodal AI model from DeepSeek, designed to excel in both understanding and generating content across text, images, and videos. It leverages a unique autoregressive architecture with separate pathways for visual encoding, enabling high performance in tasks ranging from text-to-image generation to complex visual comprehension. This model outperforms competitors like DALL-E 3 and Stable Diffusion in various benchmarks, offering scalability with versions from 1 billion to 7 billion parameters. Licensed under the MIT License, Janus-Pro-7B is freely available for both academic and commercial use, providing a significant leap in AI capabilities while being accessible on major operating systems like Linux, MacOS, and Windows through Docker.Starting Price: Free -
10
DeepSeekMath
DeepSeek
DeepSeekMath is a specialized 7B parameter language model developed by DeepSeek-AI, designed to push the boundaries of mathematical reasoning in open-source language models. It starts from the DeepSeek-Coder-v1.5 7B model and undergoes further pre-training with 120B math-related tokens sourced from Common Crawl, alongside natural language and code data. DeepSeekMath has demonstrated remarkable performance, achieving a 51.7% score on the competition-level MATH benchmark without external tools or voting techniques, closely competing with the likes of Gemini-Ultra and GPT-4. The model's capabilities are enhanced by a meticulous data selection pipeline and the introduction of Group Relative Policy Optimization (GRPO), which optimizes both mathematical reasoning and memory usage. DeepSeekMath is available in base, instruct, and RL versions, supporting both research and commercial use, and is aimed at those looking to explore or apply advanced mathematical problem-solving in AI contexts.Starting Price: Free -
11
DeepSeek-V2
DeepSeek
DeepSeek-V2 is a state-of-the-art Mixture-of-Experts (MoE) language model introduced by DeepSeek-AI, characterized by its economical training and efficient inference capabilities. With a total of 236 billion parameters, of which only 21 billion are active per token, it supports a context length of up to 128K tokens. DeepSeek-V2 employs innovative architectures like Multi-head Latent Attention (MLA) for efficient inference by compressing the Key-Value (KV) cache and DeepSeekMoE for cost-effective training through sparse computation. This model significantly outperforms its predecessor, DeepSeek 67B, by saving 42.5% in training costs, reducing the KV cache by 93.3%, and enhancing generation throughput by 5.76 times. Pretrained on an 8.1 trillion token corpus, DeepSeek-V2 excels in language understanding, coding, and reasoning tasks, making it a top-tier performer among open-source models.Starting Price: Free -
12
Falcon Mamba 7B
Technology Innovation Institute (TII)
Falcon Mamba 7B is the first open-source State Space Language Model (SSLM), introducing a groundbreaking architecture for Falcon models. Recognized as the top-performing open-source SSLM worldwide by Hugging Face, it sets a new benchmark in AI efficiency. Unlike traditional transformers, SSLMs operate with minimal memory requirements and can generate extended text sequences without additional overhead. Falcon Mamba 7B surpasses leading transformer-based models, including Meta’s Llama 3.1 8B and Mistral’s 7B, showcasing superior performance. This innovation underscores Abu Dhabi’s commitment to advancing AI research and development on a global scale.Starting Price: Free -
13
Falcon 2
Technology Innovation Institute (TII)
Falcon 2 11B is an open-source, multilingual, and multimodal AI model, uniquely equipped with vision-to-language capabilities. It surpasses Meta’s Llama 3 8B and delivers performance on par with Google’s Gemma 7B, as independently confirmed by the Hugging Face Leaderboard. Looking ahead, the next phase of development will integrate a 'Mixture of Experts' approach to further enhance Falcon 2’s capabilities, pushing the boundaries of AI innovation.Starting Price: Free -
14
Falcon 3
Technology Innovation Institute (TII)
Falcon 3 is an open-source large language model (LLM) developed by the Technology Innovation Institute (TII) to make advanced AI accessible to a broader audience. Designed for efficiency, it operates seamlessly on lightweight devices, including laptops, without compromising performance. The Falcon 3 ecosystem comprises four scalable models, each tailored to diverse applications, and supports multiple languages while optimizing resource usage. This latest iteration in TII's LLM series achieves state-of-the-art results in reasoning, language understanding, instruction following, code, and mathematics tasks. By combining high performance with resource efficiency, Falcon 3 aims to democratize access to AI, empowering users across various sectors to leverage advanced technology without the need for extensive computational resources.Starting Price: Free -
15
Qwen2.5-Max
Alibaba
Qwen2.5-Max is a large-scale Mixture-of-Experts (MoE) model developed by the Qwen team, pretrained on over 20 trillion tokens and further refined through Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF). In evaluations, it outperforms models like DeepSeek V3 in benchmarks such as Arena-Hard, LiveBench, LiveCodeBench, and GPQA-Diamond, while also demonstrating competitive results in other assessments, including MMLU-Pro. Qwen2.5-Max is accessible via API through Alibaba Cloud and can be explored interactively on Qwen Chat.Starting Price: Free -
16
Qwen2.5-VL
Alibaba
Qwen2.5-VL is the latest vision-language model from the Qwen series, representing a significant advancement over its predecessor, Qwen2-VL. This model excels in visual understanding, capable of recognizing a wide array of objects, including text, charts, icons, graphics, and layouts within images. It functions as a visual agent, capable of reasoning and dynamically directing tools, enabling applications such as computer and phone usage. Qwen2.5-VL can comprehend videos exceeding one hour in length and can pinpoint relevant segments within them. Additionally, it accurately localizes objects in images by generating bounding boxes or points and provides stable JSON outputs for coordinates and attributes. The model also supports structured outputs for data like scanned invoices, forms, and tables, benefiting sectors such as finance and commerce. Available in base and instruct versions across 3B, 7B, and 72B sizes, Qwen2.5-VL is accessible through platforms like Hugging Face and ModelScope.Starting Price: Free -
17
Hathr AI
Hathr AI
HIPAA-compliant AI Chat Tool, API, and Enterprise Solutions powered by Anthropic's Claude, Hathr AI empowers healthcare providers, insurers, and anyone who needs to deal with HIPAA Controlled Data to automate and streamline operations without compromising on data security. Hosted in AWS GovCloud's FedRAMP high environment and Hathr AI helps teams ensure that all data interactions remains confidential and protected against unauthorized access. It allows users to automate tasks such as patient note summarization, pre-authorization writing, and insurance claim submissions on a unified interface. Leveraging models, such as Claude 3.5 Sonnet, Hathr AI provides a private, HIPAA-compliant AI environment, ensuring that sensitive data remains within control. Teams can retrieve and summarize information from extensive medical records, enabling informed clinical decisions.Starting Price: $45/month -
18
R1 1776
Perplexity AI
Perplexity AI has open-sourced R1 1776, a large language model (LLM) based on DeepSeek R1 designed to enhance transparency and foster community collaboration in AI development. This release allows researchers and developers to access the model's architecture and codebase, enabling them to contribute to its improvement and adaptation for various applications. By sharing R1 1776 openly, Perplexity AI aims to promote innovation and ethical practices within the AI community.Starting Price: Free -
19
txtai
NeuML
txtai is an all-in-one open source embeddings database designed for semantic search, large language model orchestration, and language model workflows. It unifies vector indexes (both sparse and dense), graph networks, and relational databases, providing a robust foundation for vector search and serving as a powerful knowledge source for LLM applications. With txtai, users can build autonomous agents, implement retrieval augmented generation processes, and develop multi-modal workflows. Key features include vector search with SQL support, object storage integration, topic modeling, graph analysis, and multimodal indexing capabilities. It supports the creation of embeddings for various data types, including text, documents, audio, images, and video. Additionally, txtai offers pipelines powered by language models that handle tasks such as LLM prompting, question-answering, labeling, transcription, translation, and summarization.Starting Price: Free -
20
LexVec
Alexandre Salle
LexVec is a word embedding model that achieves state-of-the-art results in multiple natural language processing tasks by factorizing the Positive Pointwise Mutual Information (PPMI) matrix using stochastic gradient descent. This approach assigns heavier penalties for errors on frequent co-occurrences while accounting for negative co-occurrences. Pre-trained vectors are available, including a common crawl dataset with 58 billion tokens and 2 million words in 300 dimensions, and an English Wikipedia 2015 + NewsCrawl dataset with 7 billion tokens and 368,999 words in 300 dimensions. Evaluations demonstrate that LexVec matches or outperforms other models like word2vec in terms of word similarity and analogy tasks. The implementation is open source under the MIT License and is available on GitHub.Starting Price: Free -
21
GloVe
Stanford NLP
GloVe (Global Vectors for Word Representation) is an unsupervised learning algorithm developed by the Stanford NLP Group to obtain vector representations for words. It constructs word embeddings by analyzing global word-word co-occurrence statistics from a given corpus, resulting in vector spaces where the geometric relationships reflect semantic similarities and differences among words. A notable feature of GloVe is its ability to capture linear substructures within the word vector space, enabling vector arithmetic to express relationships. The model is trained on the non-zero entries of a global word-word co-occurrence matrix, which records how frequently pairs of words appear together in a corpus. This approach efficiently leverages statistical information by focusing on significant co-occurrences, leading to meaningful word representations. Pre-trained word vectors are available for various corpora, including Wikipedia 2014.Starting Price: Free -
22
fastText
fastText
fastText is an open source, free, and lightweight library developed by Facebook's AI Research (FAIR) lab for efficient learning of word representations and text classification. It supports both unsupervised learning of word vectors and supervised learning for text classification tasks. A key feature of fastText is its ability to capture subword information by representing words as bags of character n-grams, which enhances the handling of morphologically rich languages and out-of-vocabulary words. The library is optimized for performance and capable of training on large datasets quickly, and the resulting models can be reduced in size for deployment on mobile devices. Pre-trained word vectors are available for 157 languages, trained on Common Crawl and Wikipedia data, and can be downloaded for immediate use. fastText also offers aligned word vectors for 44 languages, facilitating cross-lingual natural language processing tasks.Starting Price: Free -
23
Gensim
Radim Řehůřek
Gensim is a free, open source Python library designed for unsupervised topic modeling and natural language processing, focusing on large-scale semantic modeling. It enables the training of models like Word2Vec, FastText, Latent Semantic Analysis (LSA), and Latent Dirichlet Allocation (LDA), facilitating the representation of documents as semantic vectors and the discovery of semantically related documents. Gensim is optimized for performance with highly efficient implementations in Python and Cython, allowing it to process arbitrarily large corpora using data streaming and incremental algorithms without loading the entire dataset into RAM. It is platform-independent, running on Linux, Windows, and macOS, and is licensed under the GNU LGPL, promoting both personal and commercial use. The library is widely adopted, with thousands of companies utilizing it daily, over 2,600 academic citations, and more than 1 million downloads per week.Starting Price: Free -
24
NLTK
NLTK
The Natural Language Toolkit (NLTK) is a comprehensive, open source Python library designed for human language data processing. It offers user-friendly interfaces to over 50 corpora and lexical resources, such as WordNet, along with a suite of text processing libraries for tasks including classification, tokenization, stemming, tagging, parsing, and semantic reasoning. NLTK also provides wrappers for industrial-strength NLP libraries and maintains an active discussion forum. Accompanied by a hands-on guide that introduces programming fundamentals alongside computational linguistics topics, and comprehensive API documentation, NLTK is suitable for linguists, engineers, students, educators, researchers, and industry professionals. It is compatible with Windows, Mac OS X, and Linux platforms. Notably, NLTK is a free, community-driven project.Starting Price: Free -
25
SWE-agent
SWE-agent
SWE-agent is an advanced AI-powered tool designed to automate various tasks such as fixing GitHub issues, performing cybersecurity operations like Capture The Flag (CTF) challenges, and solving coding problems. By leveraging language models such as GPT-4 or Claude, it interacts with isolated computer environments to carry out tasks autonomously, providing highly customizable solutions for developers and cybersecurity professionals. The platform supports a wide range of use cases, from improving software repositories to identifying vulnerabilities, and even executing custom tasks. Developed by researchers from Princeton and Stanford University, SWE-agent offers a powerful way to integrate machine learning with practical problem-solving in both software development and security fields.Starting Price: Free -
26
Devika
Devika
Devika is an open-source AI software engineer designed to understand high-level instructions, break them into steps, research relevant information, and write code to complete objectives. Using large language models, reasoning algorithms, and web browsing capabilities, Devika can assist in software development by taking on complex coding tasks with minimal human intervention. The platform supports multiple programming languages and offers key features like advanced AI planning, contextual keyword extraction, and dynamic agent tracking. Devika aims to be a competitive alternative to commercial AI tools, providing an ambitious, open-source solution for developers.Starting Price: Free -
27
FastAgency
FastAgency
FastAgency is an open source framework designed to accelerate the deployment of multi-agent AI workflows from prototype to production. It provides a unified programming interface compatible with various agentic AI frameworks, enabling developers to deploy agentic workflows in both development and production settings. With features like multi-runtime support, seamless external API integration, and a command-line interface for orchestration, FastAgency simplifies the creation of scalable, production-ready architectures for serving AI workflows. Currently, it supports the AutoGen framework, with plans to extend support to CrewAI, Swarm, and LangGraph in the future. Developers can easily switch between frameworks, choosing the best one for their project's specific needs. FastAgency also features a common programming interface that enables the development of core workflows once and reuse them across various user interfaces without rewriting code.Starting Price: Free -
28
SmolLM2
Hugging Face
SmolLM2 is a collection of state-of-the-art, compact language models developed for on-device applications. The models in this collection range from 1.7B parameters to smaller 360M and 135M versions, designed to perform efficiently even on less powerful hardware. These models excel in text generation tasks and are optimized for real-time, low-latency applications, providing high-quality results across various use cases, including content creation, coding assistance, and natural language processing. SmolLM2's flexibility makes it a suitable choice for developers looking to integrate powerful AI into mobile devices, edge computing, and other resource-constrained environments.Starting Price: Free -
29
SmolVLM
Hugging Face
SmolVLM-Instruct is a compact, AI-powered multimodal model that combines the capabilities of vision and language processing, designed to handle tasks like image captioning, visual question answering, and multimodal storytelling. It works with both text and image inputs, providing highly efficient results while being optimized for smaller, resource-constrained environments. Built with SmolLM2 as its text decoder and SigLIP as its image encoder, the model offers improved performance for tasks that require integration of both textual and visual information. SmolVLM-Instruct can be fine-tuned for specific applications, offering businesses and developers a versatile tool for creating intelligent, interactive systems that require multimodal inputs.Starting Price: Free -
30
QwQ-Max-Preview
Alibaba
QwQ-Max-Preview is an advanced AI model built on the Qwen2.5-Max architecture, designed to excel in deep reasoning, mathematical problem-solving, coding, and agent-related tasks. This preview version offers a sneak peek at its capabilities, which include improved performance in a wide range of general-domain tasks and the ability to handle complex workflows. QwQ-Max-Preview is slated for an official open-source release under the Apache 2.0 license, offering further advancements and refinements in its full version. It also paves the way for a more accessible AI ecosystem, with the upcoming launch of the Qwen Chat app and smaller variants of the model like QwQ-32B, aimed at developers seeking local deployment options.Starting Price: Free