Business Software for Ollama

Interpreter is an innovative desktop AI solution that enables users to collaborate with smart assistants for tasks such as document editing, PDF form completion, and spreadsheet management all within a cohesive AI-driven platform. It accommodates both interactive and non-interactive PDF forms, allowing users to efficiently populate and process documents in real-time, eliminating the need for manual data input. Featuring a comprehensive AI-powered spreadsheet interface, it supports advanced functions like pivot tables, charts, and complex data manipulation, thereby serving as a contemporary substitute for conventional Excel methods. Additionally, Interpreter includes an integrated Word editor equipped with features like tracked changes, diverse formatting options, and image embedding, which facilitates document creation and modification with AI support directly within the software. Users have the flexibility to log in using OpenAI, utilize their own API keys, or operate the system offline through Ollama for local model execution, ensuring adaptability in deploying AI functionalities. This combination of features positions Interpreter as a versatile tool that enhances productivity while simplifying various administrative tasks.

Qwen3.5 represents a major advancement in open-weight multimodal AI models, engineered to function as a native vision-language agent system. Its flagship model, Qwen3.5-397B-A17B, leverages a hybrid architecture that fuses Gated DeltaNet linear attention with a high-sparsity mixture-of-experts framework, allowing only 17 billion parameters to activate during inference for improved speed and cost efficiency. Despite its sparse activation, the full 397-billion-parameter model achieves competitive performance across reasoning, coding, multilingual benchmarks, and complex agent evaluations. The hosted Qwen3.5-Plus version supports a one-million-token context window and includes built-in tool use for search, code interpretation, and adaptive reasoning. The model significantly expands multilingual coverage to 201 languages and dialects while improving encoding efficiency with a larger vocabulary. Native multimodal training enables strong performance in image understanding, video processing, document analysis, and spatial reasoning tasks. Its infrastructure includes FP8 precision pipelines and heterogeneous parallelism to boost throughput and reduce memory consumption. Reinforcement learning at scale enhances multi-step planning and general agent behavior across text and multimodal environments. Overall, Qwen3.5 positions itself as a high-efficiency foundation for autonomous digital agents capable of reasoning, searching, coding, and interacting with complex environments.

Agent Zero is an innovative open source framework for AI agents that enables the development of autonomous assistants capable of executing intricate tasks through direct interaction with computer systems. This platform offers a unique setting where AI agents can access real system functions, empowering them to run commands, write and execute code, navigate the internet, analyze data, and oversee workflows as part of comprehensive automation solutions. Unlike a standard chat interface, Agent Zero operates within its isolated virtual environment, enabling it to engage with the operating system, install necessary tools, run scripts, and manage tasks across various components seamlessly. The framework prioritizes transparency and developer control, allowing users to monitor, adjust, and personalize agent behavior, tool accessibility, and information processing methods. With a modular architecture, Agent Zero facilitates the dynamic creation and utilization of tools, all while maintaining a consistent memory for enhanced performance. This makes it an ideal choice for developers aiming to build highly customizable and efficient AI-driven workflows.

GLM-5-Turbo represents a rapid iteration of Z.ai’s GLM-5 model, engineered to offer both efficient and stable performance specifically tailored for agent-driven scenarios, all while preserving robust reasoning and programming abilities. This model is fine-tuned to handle high-throughput demands, especially in complex long-chain agent tasks that necessitate a series of sequential steps, tools, and decisions executed reliably and with minimal latency. With its support for sophisticated agentic workflows, GLM-5-Turbo enhances multi-step planning, tool utilization, and task execution, delivering superior responsiveness compared to larger flagship models in the lineup. Drawing from the foundational strengths of the GLM-5 family, it maintains strong capabilities in reasoning, coding, and processing extensive contexts, but prioritizes the optimization of essential aspects like speed, efficiency, and stability within production settings. Furthermore, it is crafted to seamlessly integrate with agent frameworks such as OpenClaw, allowing it to proficiently coordinate actions, manage inputs, and carry out tasks effectively. This ensures that users benefit from a responsive and reliable tool that can adapt to various operational demands and complexities.

DockClaw serves as a managed hosting solution for OpenClaw, facilitating the rapid deployment and operation of autonomous AI agents in mere seconds, all without the complexities of server management, Docker, or DevOps configurations. This platform empowers users to effortlessly launch AI-driven agents capable of integrating with various messaging services like Telegram and other communication avenues, enabling them to function continuously for automating workflows, interacting with users, and performing various tasks. With one-click deployment options available on dedicated virtual machines or isolated containers, DockClaw guarantees 24/7 uptime, persistent storage, and health monitoring, ensuring that agents stay consistently operational and reliable. Users benefit from the flexibility of selecting from a range of AI models, such as Claude, GPT, Gemini, Llama, and other systems compatible with OpenAI, with the ability to switch models easily without any vendor lock-in. Furthermore, DockClaw incorporates native configuration tools that allow for the fine-tuning of agent behavior, memory management, and system prompts, while also ensuring secure API key management through encrypted environments and a zero-knowledge architecture. This comprehensive approach not only enhances user experience but also fosters a versatile environment for AI development and deployment.

MiniMax M2.7 is a powerful AI model built to drive real-world productivity across coding, search, and office-based workflows. It is trained using reinforcement learning across a wide range of real-world environments, enabling it to execute complex, multi-step tasks with precision and efficiency. The model demonstrates strong problem-solving capabilities by breaking down challenges into structured steps before generating solutions across multiple programming languages. It delivers high-speed performance with rapid token output, ensuring faster completion of demanding tasks. With optimized reasoning, it reduces token usage and execution time, making it more efficient than previous models. M2.7 also achieves state-of-the-art results in software engineering benchmarks, significantly improving response times for technical issues. Its advanced agentic capabilities allow it to work seamlessly with tools and support complex workflows with high skill accuracy. The model is designed to handle professional tasks, including multi-turn interactions and high-quality document editing. It also provides strong support for office productivity, enabling efficient handling of structured data and business tasks. With competitive pricing, it delivers high performance while remaining cost-effective. Overall, it combines speed, intelligence, and versatility to meet the needs of modern professionals and teams.

Octrafic is a command-line tool that leverages AI and is available as open source, aimed at simplifying the process of automated API testing and exploration by allowing users to communicate with APIs in natural language rather than having to write complex scripts or set up intricate testing frameworks. By simply directing the tool to any HTTP API or OpenAPI specification, users can articulate their testing requirements in straightforward English, prompting the integrated AI agent to create test scenarios, perform actual HTTP requests, verify responses, and generate organized results. This tool streamlines the entire testing process, encompassing endpoint discovery, request formulation, schema checks, and error identification, which enables developers to prioritize testing logic without getting bogged down by the underlying implementation specifics. Additionally, it accommodates real-time execution against live APIs, ensuring the accuracy of status codes and behaviors without the need for mock setups, and it can also produce aesthetically formatted PDF reports for effective communication with teams or stakeholders. With its user-friendly approach, Octrafic represents a significant advancement in making API testing more accessible and efficient.

Qwen3.5-35B-A3B is a member of the Qwen3.5 "Medium" model series, meticulously crafted as an effective multimodal foundation model that strikes a balance between robust reasoning capabilities and practical application needs. Utilizing a Mixture-of-Experts (MoE) architecture, it boasts a total of 35 billion parameters, yet activates only around 3 billion for each token, enabling it to achieve performance levels similar to much larger models while significantly cutting down on computational expenses. The model employs a hybrid attention mechanism that merges linear attention with traditional attention layers, which enhances its ability to handle extensive context and boosts scalability for intricate tasks. As an inherently vision-language model, it processes both textual and visual data, catering to a variety of applications, including multimodal reasoning, programming, and automated workflows. Furthermore, it is engineered to operate as a versatile "AI agent," proficient in planning, utilizing tools, and systematically solving problems, extending its functionality beyond mere conversational interactions. This capability positions it as a valuable asset across diverse domains, where advanced AI-driven solutions are increasingly required.

HiClaw is a multi-agent operating system that is open source and operates on the Matrix framework, allowing various AI agents to work together within Matrix rooms, where their activities are fully accessible to humans in real-time. The system features a Manager Agent that oversees multiple Worker Agents, efficiently breaking down complex tasks and facilitating simultaneous execution, which enhances the management of these intricate operations. Designed with a focus on enterprise-level security and collaborative capabilities, HiClaw utilizes the open Matrix instant messaging protocol, ensuring that all communications between agents are transparent, easily auditable, and fit for distributed systems and federated environments. Humans have the ability to join any Matrix room whenever they wish, which allows them to monitor agent discussions, intervene as necessary, or adjust agent actions in real-time, thereby safeguarding oversight and control. This structured two-tier system, consisting of Manager and Worker Agents, delineates clear responsibilities for each agent, simplifying the process of integrating custom Worker Agents tailored for various applications, while also promoting adaptability within the architecture. Consequently, the design of HiClaw not only enhances operational efficiency but also paves the way for innovative uses of AI collaboration across diverse scenarios.

PyGPT is a versatile open-source AI assistant designed for personal use on desktop systems such as Linux, Windows, and Mac, and it is developed using Python. It operates in a manner akin to ChatGPT but functions locally on your computer, providing features like chat, image and video generation, vision capabilities, voice control, and more. Supporting a variety of models, PyGPT includes options like OpenAI's GPT-5, GPT-4, o1, o3, o4, Google Gemini, Anthropic Claude, xAI Grok, Perplexity Sonar, DeepSeek, Mistral AI, alongside models from Ollama and LlamaIndex. Users can choose from 12 operational modes, including chatting with files, real-time audio interactions, research, completion tasks, and various imaging capabilities. With integrated LlamaIndex support, users can engage with their personal files and data seamlessly. Additionally, PyGPT features built-in vector database capabilities, automated embedding of files and data, and maintains full conversation context alongside both short- and long-term memory. The assistant is equipped with internet access through platforms like Google, Microsoft Bing, and DuckDuckGo, enhancing its functionality, which also includes speech synthesis and recognition, making it a comprehensive tool for productivity. Overall, PyGPT stands out as an innovative solution for those seeking a powerful local AI assistant.

OllaCoder serves as a private AI coding assistant tailored for VS Code, catering specifically to developers who prefer not to upload their source code to external servers. Operating locally, it utilizes your personal Ollama models and integrates features such as agent mode, inline edits, codebase chat, intelligent autocomplete, MCP servers, and a local-first runtime all within a single editor interface. The core philosophy behind OllaCoder emphasizes the notion that software development is a personal endeavor, asserting that your code should remain under your control while providing an AI assistant that is robust, transparent, and unobtrusive. It primarily communicates with your local Ollama instance, ensuring that prompts, completions, and modifications remain on your device; cloud services are optional, with API keys securely stored in the OS keychain. OllaCoder's agent mode is capable of planning tasks, modifying files, executing terminal commands, and confirming the accuracy of its work, allowing users to approve, reject, or revert any action taken. Additionally, the inline edits feature enables users to select a function, specify the desired change, and examine a real diff change by change, enhancing the coding experience. Overall, OllaCoder represents a significant step forward in maintaining code privacy while providing powerful AI-assisted development tools.

Dock serves as a collaborative AI workspace designed for you, your team, and the various agents you deploy. It enables both humans and AI agents to share a unified cloud environment, allowing everyone to access and modify the same information in real-time, rather than navigating through disjointed chats, files, and isolated outputs. The platform is structured around tables with defined columns, rich-text documents, and recognizes agents as primary entities, each equipped with their own API keys, permissions, and audit trails, eliminating the need for delegated human tokens. Teams can leverage Dock for a multitude of tasks, including planning, researching, decision-making, and executing projects, all within a shared interface that accommodates both human and AI contributions. Use cases for Dock span various domains, including engineering, go-to-market strategies, research, operations, individual projects, and agency tasks. Engineering teams can utilize Dock to facilitate sprint planning, create specification documents, and respond to incidents efficiently; marketing teams can streamline content calendars, manage sales pipelines, and enhance customer success initiatives; research teams can effectively document interviews, identify themes, and analyze competitive intelligence; and operations teams can oversee runbooks, manage recruitment processes, ensure compliance, and coordinate onboarding efforts. In essence, Dock fosters a seamless collaboration environment that enhances productivity and innovation across all team functions.

Laguna XS.2 represents Poolside’s innovative open-weight coding model, distinguished as the lightest and quickest member of the Laguna series. This model features a total of 33 billion parameters in a Mixture of Experts setup, with 3 billion parameters activated, and has been meticulously trained in-house using 30 trillion tokens. As the latest generation model accessible to the public, it embodies a second-generation architecture and marks Poolside’s inaugural open-weight offering, drawing from insights gained during the training of Laguna M.1 with synthetic data and reinforcement learning techniques. Specifically designed to enhance agentic coding workflows, Laguna XS.2 excels in coding, acting, and rapidly iterating, particularly within Poolside’s coding agent environment. This model is particularly advantageous for developers and teams seeking a lightweight, efficient coding solution rather than a more cumbersome frontier system. Released under the permissive Apache 2.0 license, it empowers the community to assess, fine-tune, quantize, and build upon its weights, fostering a collaborative development atmosphere. In essence, Laguna XS.2 not only provides a robust platform for agentic coding but also encourages innovation and experimentation among its users.

Laguna M.1 stands out as Poolside's most proficient model for agentic coding, meticulously developed in-house specifically for enhancing software development workflows. This model features a total of 225 billion parameters, utilizing a Mixture of Experts architecture with 23 billion activated parameters, and has been trained entirely within the organization on a dataset consisting of 30 trillion tokens, leveraging the power of 6,144 interconnected NVIDIA H200 GPUs. Poolside undertook the task of training Laguna M.1 from the ground up, employing its proprietary data, dedicated training codebase, and an asynchronous on-policy reinforcement learning approach within its agent framework, all tailored for agentic coding applications. The design of the model ensures optimal performance within Poolside's coding agent, enabling it to effectively reason through software tasks, interact with various tools, edit code, execute tests, and facilitate extended autonomous development sessions. Specifically crafted for developers and teams tackling intricate coding challenges, Laguna M.1 offers enhanced capabilities in reasoning, architectural comprehension, terminal operations, and multi-step execution, surpassing what lighter models can achieve. Ultimately, its robust feature set positions it as an essential asset for those engaged in demanding software projects.

AI Workers are independent agents crafted specifically for your organization; these advanced AI entities function like genuine colleagues rather than mere chatbots awaiting instructions. Each comes equipped with its own professional identity, including an email, phone number, and presence on platforms like Slack and Teams, demonstrating initiative and the capacity to operate around the clock without needing prompts. Rather than dictating every detail of their tasks, you simply define the objectives, and they autonomously develop the necessary workflows, accommodating a variety of tasks such as generating daily reports, conducting weekly follow-ups, managing CRM updates, handling client interactions, performing research, overseeing content creation, executing finance responsibilities, coordinating HR activities, supporting design projects, and assisting with development operations. This system includes a diverse range of specialized AI Workers tailored for functions in marketing, development, design, HR, finance, and more, with each worker crafted for a specific role and practical applications. Furthermore, AI Workers can integrate seamlessly with over 3,000 tools, such as Slack, Microsoft Teams, Gmail, Notion, HubSpot, Salesforce, and various other business applications, ensuring they fit effortlessly into your existing workflows. Their versatility and adaptability make them an invaluable asset for enhancing productivity and streamlining processes in any business environment.

OpenWorker serves as an open-source, locally-focused AI assistant designed to complete various daily tasks from initiation to conclusion rather than merely providing answers. Users can request specific results like a renewal brief, incident report, follow-up message, calendar update, sprint summary, or finalized document, and OpenWorker seamlessly operates across multiple platforms where the relevant data is stored. It offers integration with a range of services including Slack, Gmail, Outlook, Google Calendar, Notion, HubSpot, GitHub, Attio, Google Drive, Jira, Linear, Asana, Dropbox, Box, and an array of other applications through both one-click and manual connections. The platform accommodates cloud, open-weight, and fully local models, supporting providers such as OpenAI, Anthropic, Google, xAI, Mistral, DeepSeek, Kimi, Qwen, and Ollama, allowing users the flexibility to switch models based on task requirements. OpenWorker excels at researching, gathering necessary context, executing multi-step tasks, and generating refined outputs in various formats like chat, Slack, Markdown, PDF, images, or files, all while ensuring to check in prior to making significant decisions. This comprehensive suite of functionalities empowers users to streamline their workflows and enhances overall productivity.

FluentDB is an advanced database client designed specifically for Apple Silicon Macs, emphasizing an AI-centric approach by integrating a schema-aware assistant, a contemporary SQL editor, a user-friendly keyboard navigator, efficient data tables, and essential safety measures within a singular, streamlined interface. It offers seamless connectivity to various databases, including PostgreSQL, MySQL, SQLite, and Microsoft SQL Server, allowing users to pose inquiries in plain English and receive accurately generated SQL statements tailored to their actual tables and columns. The AI prioritizes schema interpretation over row data, refrains from executing any statements autonomously, and seeks user consent prior to executing any write queries, ensuring a high level of control. Features like read-only connections, local query history, server-side cancellation for unresponsive queries, and transparent SQL execution empower users to maintain oversight of their operations. The editor enhances productivity with schema-aware autocomplete, formatting options, saved queries, instant previews, and the ability to effortlessly toggle between manual SQL and AI-driven modes without any loss of context. Users can access a comprehensive range of database components—such as tables, views, functions, sequences, types, and extensions—by using Command + P, while the intuitive native grid allows for smooth scrolling, editing, sorting, copying, and exporting of data, enhancing the overall user experience. This innovative tool is designed to elevate database management by merging advanced AI capabilities with user-friendly functionality.

Antalogy is a Markdown editor designed to function like a conventional word processor, featuring a Word-style Ribbon for a familiar experience. You can type freely while the interface remains unobtrusive, ensuring that clean Markdown is saved in the background. - Completely Local Documents: Your files are stored exclusively on your device, devoid of cloud syncing, telemetry, or any risk of document format dependence. - Import from Word .docx: With just one click, you can transform .docx files into .md formats while retaining tables, lists, and converting embedded images to PNG. - Comprehensive Mermaid Diagrams and AI Capabilities: It fully accommodates Mermaid syntax for creating visual data representations and charts directly from text. The built-in AI Assistant can analyze your document's text and automatically generate the corresponding Mermaid code to create accurate visual flowcharts and architecture diagrams instantly. - AI Assistant for Custom LLM Integration: This feature allows you to link to any OpenAI-compatible API for your own local quantized LLMs, whether they are hosted through LMStudio, Ollama, or other private cloud and on-premises inference servers, enhancing your document's functionality even further. This flexibility opens up a world of possibilities for users who want to leverage advanced AI tools in their writing process.

LensHH-LT is a powerful optical design software tailored for engineers focused on refractive and reflective imaging systems, providing essential functionality at a price that doesn't require an enterprise-level investment. The software offers comprehensive analyses, including ray tracing, Seidel aberrations, ray and OPD fans, as well as FFT and geometric MTF, PSF, wavefront maps, spot diagrams, field curvature, distortion, and relative illumination. Users can optimize designs through both local and global methods—utilizing multi-start approaches, basin-hopping searches, glass substitutions from complete catalogs, stock-lens matching, and saddle-point construction for discovering topologies. It supports seamless integration by reading and writing files from Zemax, Code V, OSLO, Optalix, and Optiland, ensuring that existing designs can be imported without loss. In addition to its user-friendly GUI, the software features an API, a command-line interface, and an open MCP server, enabling AI agents like Claude to automate design processes, perform optimizations, and retrieve analysis results. Priced at a one-time fee of $385, it does not require a subscription and includes a full-featured 45-day trial without the need for an account. For users with air-gapped systems, offline activation is also available, offering flexibility for various working environments. Overall, LensHH-LT stands out for its robust functionality and accessibility to engineers seeking a cost-effective solution.

AtomCode is an innovative open-source AI coding assistant that operates directly within the terminal, enabling it to autonomously read and edit files, run commands, search the web, conduct tests, and verify its own work until each task is accomplished. Serving as a multi-model alternative to platforms like Claude Code and Cursor Agent, it is compatible with a range of models including Claude, OpenAI, DeepSeek, GLM, Qwen, Ollama, SiliconFlow, and any other API that aligns with OpenAI's standards. The agent's advanced code graph functionalities facilitate symbol indexing, reference lookup, caller and callee tracing, dependency analysis, and blast-radius analysis, allowing it to navigate extensive codebases with a depth of understanding that transcends simple text searches. Additionally, developers have the capability to attach screenshots and images, with vision preprocessing available to derive valuable context when the primary model lacks direct image support. AtomCode also features seamless integration with AtomGit for managing OAuth logins, repositories, issue tracking, and pull requests, while further enhancing its utility with support for MCP, reusable Skills, plugins, custom slash commands, hooks, and workflows. This comprehensive set of features makes AtomCode a robust tool for developers seeking efficiency and versatility in their coding tasks.

Traxy serves as a signal-to-leads platform designed for go-to-market strategies driven by LinkedIn; it monitors activities surrounding the accounts and individuals you prioritize, transforms those interactions into meaningful “signals,” and then translates those signals into prioritized, actionable leads for your team to pursue—eliminating the need for you to constantly engage with LinkedIn throughout the day. Additionally, this system streamlines the lead generation process, allowing your team to focus on high-impact opportunities.

Amelia’s Agent is an innovative AI tool that transforms simple English descriptions into a variety of digital products, including websites, applications, games, tools, automations, and code. Users initiate the process with a straightforward sentence, respond to a set of clarifying questions, and then review and approve a proposed plan before any execution occurs. The AI then constructs the project on a secure private cloud server, with a transparent display of all files and commands as they are generated. Every project is archived in a genuine Git repository from the very first build, ensuring that each version is meticulously documented and that the user retains full ownership of the code. Users have the capability to clone, download, upload to GitHub, or execute the project on their local machines. Additionally, Amelia is capable of enhancing existing websites and repositories, analyzing them prior to implementing any accepted modifications. Supporting over 300 AI models, it intelligently directs each task to the most appropriate model while giving advanced users the flexibility to select models directly, utilize their personal API keys, or execute open-source models via Ollama or LM Studio. This versatility makes Amelia's Agent a powerful ally in the realm of AI-driven development.

JustDocz is a desktop application designed for Windows that prioritizes user privacy by enabling the search for information within documents, scanned PDFs, images, email archives, and attachments without the need to upload files to the cloud. It efficiently indexes content locally and provides rapid search capabilities for filenames and full text across various formats including PDFs, Word, Excel, PowerPoint, images, text files, and email archives. With built-in OCR technology, it allows users to make scanned documents and images searchable, and the extracted text can be easily saved in either TXT or DOCX formats. Additionally, JustDocz offers multilingual and phonetic search functionalities, catering to users who handle documents in multiple languages. Users can also benefit from optional local AI features that enable them to pose questions and extract insights from their documents while keeping all data securely on their own machine. This application eliminates the need for cloud storage or login credentials, making it an ideal solution for lawyers, researchers, businesses, professionals, and anyone managing extensive document collections. Overall, JustDocz combines powerful search capabilities with a strong emphasis on user privacy and data security.

Introducing the next iteration of our open-source large language model, this version features model weights along with initial code for the pretrained and fine-tuned Llama language models, which span from 7 billion to 70 billion parameters. The Llama 2 pretrained models have been developed using an impressive 2 trillion tokens and offer double the context length compared to their predecessor, Llama 1. Furthermore, the fine-tuned models have been enhanced through the analysis of over 1 million human annotations. Llama 2 demonstrates superior performance against various other open-source language models across multiple external benchmarks, excelling in areas such as reasoning, coding capabilities, proficiency, and knowledge assessments. For its training, Llama 2 utilized publicly accessible online data sources, while the fine-tuned variant, Llama-2-chat, incorporates publicly available instruction datasets along with the aforementioned extensive human annotations. Our initiative enjoys strong support from a diverse array of global stakeholders who are enthusiastic about our open approach to AI, including companies that have provided valuable early feedback and are eager to collaborate using Llama 2. The excitement surrounding Llama 2 signifies a pivotal shift in how AI can be developed and utilized collectively.

Code Llama is an advanced language model designed to generate code through text prompts, distinguishing itself as a leading tool among publicly accessible models for coding tasks. This innovative model not only streamlines workflows for existing developers but also aids beginners in overcoming challenges associated with learning to code. Its versatility positions Code Llama as both a valuable productivity enhancer and an educational resource, assisting programmers in creating more robust and well-documented software solutions. Additionally, users can generate both code and natural language explanations by providing either type of prompt, making it an adaptable tool for various programming needs. Available for free for both research and commercial applications, Code Llama is built upon Llama 2 architecture and comes in three distinct versions: the foundational Code Llama model, Code Llama - Python which is tailored specifically for Python programming, and Code Llama - Instruct, optimized for comprehending and executing natural language directives effectively.