Business Software for Python

EODHD serves as a robust financial data API platform, granting developers and analysts seamless access to an extensive array of global market information through a single, cohesive interface. The platform boasts more than three decades of historical data and provides real-time and intraday feeds from over 60 stock exchanges, encompassing in excess of 150,000 tickers, which include stocks, ETFs, mutual funds, bonds, Forex pairs, and digital currencies. Users can benefit from a diverse selection of datasets, such as end-of-day prices, real-time OHLCV data, corporate actions including splits and dividends, financial news, technical indicators, macroeconomic statistics, and stock screening tools, all conveniently accessible through REST APIs and WebSocket connections. Additionally, EODHD facilitates the integration of this wealth of data into various applications, enhancing decision-making and analytical processes for its users.

Cosyra offers a mobile-centric cloud development platform where users can access AI-driven coding utilities via a comprehensive Linux terminal right on their smartphones. Developers benefit from a suite of pre-installed tools including Claude Code, Codex CLI, OpenCode, and Gemini CLI, which can be easily activated by entering an API key and launching the terminal. It features an isolated Ubuntu environment equipped with key development resources like Node.js, Python, Git, tmux, and vim, along with 30 GB of persistent storage that retains data across sessions. Cosyra aims to emulate the functionality of a local development setup, enabling users to create, test, and oversee projects entirely through their mobile devices. The platform accommodates various workflows such as cloning repositories, reviewing pull requests, executing tests, and deploying code, all while maintaining a persistent session that can be paused and resumed without any disruption. By enhancing mobile productivity, Cosyra empowers developers to work flexibly and efficiently, breaking the limitations typically associated with traditional coding environments.

Subconscious is a platform tailored for developers that simplifies the creation, deployment, and scaling of production-ready AI agents by automating the most challenging aspects of agent architecture. By offering a comprehensive agent system, it takes care of context management, tool orchestration, and facilitates long-term reasoning, allowing developers to concentrate on setting objectives and defining functionalities instead of dealing with intricate infrastructure setups. The platform features a cohesive inference engine that combines a jointly designed model and runtime, enabling the breakdown of complex tasks, dynamic workflow generation, and the execution of multi-step reasoning without the need for manual context management or coordination among multiple agents. In contrast to conventional methods that depend on linking various APIs and frameworks, Subconscious empowers agents to receive goals and tools and then independently plan, reason, and act with minimal human oversight. This innovation effectively results in systems that can autonomously accomplish tasks, streamlining the development process for AI applications. As a result, developers can realize their visions more efficiently and with greater ease.

Kubbi serves as a secure handoff layer specifically designed for AI agents, facilitating the secure transmission of data, files, and execution results among various systems, agents, or individuals via temporary, encrypted claim links. Rather than transmitting sensitive information directly through workflows, prompts, or logs, kubbi empowers a producer to generate a payload that encapsulates content or packaged files, assigns a time-to-live, and creates a unique claim URL for sharing with the next participant in the workflow. The recipient can then access the payload at their convenience, with the option for the data to either automatically expire or be permanently deleted according to predefined retrieval limits. This methodology significantly minimizes the risk of sensitive data exposure across systems and inhibits any unintentional logging, caching, or replaying of that information. Furthermore, kubbi is versatile, accommodating various use cases like the transfer of configuration files, datasets, reports, tokens, and any other transient or sensitive data that should not persist within systems. By implementing such a secure transfer process, kubbi enhances data integrity and confidentiality throughout the entire workflow.

InventDB is a JSON database that utilizes encryption while offering schema-free data storage alongside SQL querying capabilities, allowing developers to manage flexible data structures with the robust functionality of relational queries. This system is engineered to uphold full ACID transactions, which guarantees the consistency, reliability, and durability of data operations, even in environments that are complex or experience high concurrency. With its implementation of row-level encryption, InventDB ensures that sensitive information is safeguarded at a granular level, rather than depending solely on security measures that cover the entire database, thus enhancing both data privacy and user control. Beyond its primary database capabilities, InventDB features an integrated key-value cache that boosts performance and accelerates access to frequently utilized data. Furthermore, it offers semantic functionalities that facilitate more sophisticated data retrieval and user interactions that surpass the limitations of conventional query techniques. This combination of features positions InventDB as a versatile solution for modern data management challenges.

AnduinOS is a tailored Linux distribution based on Ubuntu, crafted to deliver a user-friendly experience for those migrating from different operating systems, especially those familiar with Windows. The system is designed to be lightweight and efficient, featuring a compact installation image that allows for swift setup and immediate functionality without the need for complicated configurations. With a GNOME-based desktop environment, AnduinOS presents a clean and user-friendly interface that mirrors common usage patterns, thereby minimizing the learning curve and facilitating rapid adoption. As it is built on the robust Ubuntu ecosystem, AnduinOS supports the majority of Ubuntu packages, providing users with access to a diverse array of applications and tools while ensuring both stability and flexibility. Furthermore, it prioritizes user privacy and security by steering clear of any tracking, profiling, or data collection practices, allowing individuals to navigate the system with complete anonymity. Additionally, AnduinOS regularly updates its features to enhance user experience and maintain security, making it a compelling choice for both newcomers and seasoned Linux users alike.

TinyFish is an innovative enterprise AI platform that creates and manages "enterprise web agents" capable of executing intricate workflows across the internet at an extensive scale. Rather than depending on APIs or manual interventions, these agents emulate human behavior by navigating websites, extracting valuable information, and performing multi-step operations across numerous platforms simultaneously. This approach addresses the increasing complexity of the modern online landscape, where valuable data is often scattered, secured behind logins, or in a state of constant flux, rendering traditional automation methods ineffective. TinyFish's agents are supported by a sophisticated infrastructure that allows them to learn, adapt, and scale efficiently, ensuring their accuracy and reliability amidst the evolving web environments. The platform is structured around achieving specific outcomes rather than merely completing isolated tasks, allowing agents to carry out comprehensive processes such as pricing intelligence, inventory management, or market surveillance from beginning to end. As a result, TinyFish not only streamlines operations but also enhances the potential for businesses to extract actionable insights from diverse data sources.

GPT-5.5 Pro is a next-generation AI model built for execution-heavy tasks across coding, research, business analysis, and scientific workflows. It can interpret complex instructions, break them into steps, and carry work through to completion using tools and automation. The model supports tasks such as generating documents, building applications, analyzing datasets, and navigating software environments. It is designed to operate across tools, enabling seamless workflows from idea to output. In addition, GPT-5.5 Pro integrates with workspace agents—customizable AI agents that automate recurring and multi-step processes across teams. These agents can handle tasks like lead research, reporting, and workflow automation, running independently or on schedules. Built with enterprise-grade safeguards, the model ensures secure and controlled automation. It helps organizations improve productivity by reducing manual effort and accelerating decision-making. GPT-5.5 Pro is ideal for teams looking to scale operations and handle complex workloads efficiently.

Qwen3.6-27B is an open-source, dense multimodal language model from the Qwen3.6 series, engineered to provide top-tier performance in areas such as coding, reasoning, and agent-driven workflows, all while maintaining an efficient parameter count of 27 billion. This model is recognized for its ability to outperform or compete closely with much larger counterparts on essential benchmarks, particularly excelling in agent-based coding tasks. It features dual operational modes—thinking and non-thinking—that enable it to effectively adapt its reasoning depth and response speed based on the specific requirements of each task. Additionally, it supports a variety of input types, including text, images, and video, showcasing its versatility. As part of the Qwen3.6 lineup, this model prioritizes practical usability, consistency, and the enhancement of developer productivity, reflecting advancements inspired by community insights and real-world application demands. Its innovative design not only responds to immediate user needs but also anticipates future trends in AI development.

ZenMux serves as a robust AI gateway tailored for enterprises, facilitating a seamless interface to access and manage various top-tier large language models via a single account and API. By consolidating multiple providers into one platform, users can interact with leading models from firms such as OpenAI, Anthropic, and Google without the hassle of juggling different keys and integrations. This streamlined approach is designed to enhance efficiency by providing intelligent routing capabilities that automatically determine the optimal model for each specific task, taking into account factors like cost, performance, and reliability. ZenMux prioritizes direct engagement with official providers and certified cloud partners, guaranteeing that all generated outputs originate from credible, high-quality sources, free from proxies or inferior alternatives. Among its standout features is an integrated AI model insurance mechanism that identifies and addresses potential issues, thereby ensuring a smoother user experience. Furthermore, this innovative solution significantly reduces administrative burdens, allowing organizations to focus on leveraging AI technology effectively.

Outcron is an innovative hosted job scheduler crafted to offer a more dependable alternative to conventional cron configurations, eliminating the hassle of server maintenance and the cumbersome process of integrating scripts. This platform centralizes the scheduling and execution of essential jobs, delivering built-in reliability that guarantees tasks are carried out consistently without requiring manual intervention or the management of underlying infrastructure. With support for a variety of job types, Outcron features an adaptable scheduling system that transcends the limitations of standard cron syntax, facilitating the creation of intricate and flexible workflows. One of its standout functionalities is misfire recovery, which automatically identifies and retries missed or failed jobs, thereby minimizing the likelihood of unnoticed failures that can plague self-managed cron setups. Additionally, Outcron comes equipped with tools for execution history and observability, empowering users to monitor every job run, review logs, and gain insights into job performance over time. This comprehensive solution not only enhances reliability but also streamlines job management, making it an invaluable resource for teams looking to optimize their workflow automation.

Abliteration.ai serves as an AI platform tailored for developers, granting access to large language models without constraints, all while incorporating a governance layer that empowers teams to dictate model behavior instead of depending on preset limitations from providers. The platform features an API compatible with OpenAI, promoting effortless integration into current tools, SDKs, and processes without necessitating significant infrastructure modifications. Central to Abliteration.ai’s philosophy is the idea of being “unrestricted, not ungoverned,” which allows developers to leverage models with fewer censorships while implementing their own regulatory measures through a Policy Gateway that manages outputs in real-time, enabling actions like allowing, blocking, redacting, or escalating based on tailored policies. These policies are designed as code, facilitating auditing, simulation, and deployment, complemented by capabilities such as shadow testing and rollback measures for added security. Furthermore, Abliteration.ai caters to sophisticated applications, including security assessments, red teaming, synthetic data generation, and research workflows tailored to specific needs, thereby expanding the potential for innovative solutions in various fields. With its comprehensive approach, Abliteration.ai not only enhances the flexibility of AI usage but also ensures that developers maintain control over the ethical implications of their models.

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.

RailPush is an innovative deployment service that effortlessly transitions your code from a repository to a fully operational application, complete with builds, HTTPS support, and a default URL right from the start. By simply connecting your repository, users can deploy their applications in a matter of minutes and receive a live URL, while RailPush efficiently manages deployments, data, and routing all in one place, allowing integration with GitHub and customization of build requirements. Each code push triggers a build process that checks the application's health and implements updates, providing instant URLs secured with TLS and offering preview links for testing. Users can initiate their first web service by linking their GitHub repository, choosing a specific repo and branch, and defining any necessary build or start commands, leading to an automatic deployment service. RailPush simplifies the development process by automatically identifying the runtime and creating an optimized Dockerfile, offering compatibility with popular languages such as Node.js, Python, Go, Ruby, Rust, Elixir, Java, and Docker. For those who prefer a hassle-free setup, pre-configured stacks are available, allowing users to deploy popular combinations like Next.js with Postgres, Django coupled with Redis, and Rails, among others, using straightforward templates that streamline the process even further. This all-in-one platform not only enhances productivity but also empowers developers to focus more on coding rather than the complexities of deployment.

Zavu serves as a unified API for managing all your messaging needs, allowing you to send and receive SMS, WhatsApp messages, Email, Voice calls, Telegram, and other forms of communication through a single integration. This solution enables developers to streamline the messaging process rather than deal with the complexities of maintaining multiple messaging infrastructures, effectively replacing various APIs, SDKs, authorization models, webhook structures, sender configurations, template approvals, and error management with just one SDK, a single webhook format, and an integrated dashboard. Designed to accommodate AI agents from the outset, Zavu empowers these agents to communicate globally via SMS, WhatsApp, Email, and Voice, while offering developers access to production-ready SDKs in programming languages like Node.js, Python, Ruby, Go, PHP, and Java. The platform's Smart Send and AI Routing capabilities intelligently select the appropriate messaging channel—be it SMS, WhatsApp, or Email—based on the recipient, optimizing delivery by leveraging platform-wide delivery intelligence and automatically redirecting to alternative channels when necessary. In this process, context is consistently maintained, duplicate deliveries are eliminated, and costs are meticulously monitored throughout the entire fallback sequence, ensuring both efficiency and clarity for users. Additionally, Zavu's comprehensive approach allows for seamless integration into existing workflows, enhancing the overall user experience.

PRFlow is an innovative AI-driven code review tool designed to identify bugs before they reach production. It efficiently indexes your entire codebase, examines dependencies across different files, and generates a comprehensive security review in less than three minutes for every pull request. Tailored to address the intricacies of complex codebases, PRFlow leverages semantic memory to grasp cross-repo dependencies and internal structures prior to analyzing the pull request. Instead of merely focusing on the differences or the entire file, it extracts pertinent context, including the modified function and its related dependencies. With a security-centric approach, PRFlow highlights vulnerabilities such as XSS, SSRF, SQL injection, authentication bypass, and race conditions by monitoring the flow of code across files. The tool reviews the entire pull request in one go, delivering a thorough structured analysis that includes a score, walkthrough, issues categorized by file, severity ratings, strengths, and suggestions for code improvements presented as inline comments on GitHub. Additionally, it facilitates ongoing conversations within the pull request thread, allowing for collaborative troubleshooting and enhancement of code quality.

Parsebridge is an innovative PDF parsing API designed to convert PDFs into well-structured Markdown format. This tool efficiently extracts text, tables, and various data from PDF files, catering specifically to developers who require dependable document parsing capabilities at scale. It can adeptly manage complex PDFs, including those with intricate tables, multi-column layouts, nested structures, and scanned pages—all within a single API call, effectively transforming challenging elements that often confuse other parsers into usable Markdown. With the ability to accurately parse merged cells, nested headers, and sophisticated layouts, users can expect clear and precise outputs rather than jumbled results. Additionally, Parsebridge offers the convenience of live testing, allowing users to either paste a PDF URL or upload a document directly to the preview page to generate Markdown without the need for an account. Currently, it exclusively supports PDF files, prioritizing high extraction quality for documents up to 100MB in size. Utilizing Docling, an open-source parser renowned for its excellence in table extraction and layout preservation, Parsebridge manages the necessary infrastructure, OCR, scaling, and the API layer, ensuring a seamless user experience. This comprehensive approach makes Parsebridge a valuable tool for anyone needing reliable PDF parsing solutions.

BrowserQL serves as a specialized scraping language and browser automation solution designed to effectively navigate bot detection systems while leaving minimal traces of automation. It features inherent anti-detection capabilities that require no configuration, enabling users to circumvent services like Cloudflare and Datadome without the need for additional plugins or setups. Additionally, BrowserQL can seamlessly handle common CAPTCHA challenges, even those embedded within iframes or shadow DOMs, utilizing techniques such as auto-humanized clicking, scrolling, and typing patterns, as well as hidden debugging protocols and automatic fingerprint evasion, all complemented by residential proxy integration for a more authentic browsing experience. In contrast to traditional DIY setups utilizing Playwright, which demand the use of stealth plugins and frequent manual interventions for mouse or keyboard simulations, BrowserQL provides a more efficient and streamlined process that significantly reduces the chances of detection by automation libraries. This allows users to focus on their scraping tasks without the constant worry of being flagged or blocked by sophisticated bot detection mechanisms.

Exmergo Viz is an innovative Dashboard AI Agent that transforms chaotic data into trustworthy dashboards in just minutes rather than weeks. Users can easily upload or link their datasets, utilize AI for exploration, and create engaging visualizations all from a single interface. The platform accommodates various structured files like CSV and Excel, offers live data connectors, and features a centralized Data Hub that allows teams to efficiently reuse data across different dashboards and analyses while maintaining complete traceability. Rather than having to write complex SQL queries, Python scripts, or create charts manually, users can simply pose questions in natural language, request specific rankings or comparisons, and rely on Exmergo’s AI to analyze datasets in real-time, providing clear and contextual insights. With Exmergo Viz, teams benefit from the creation of dynamic interactive charts and analytical views, along with built-in analysis and actionable insights that facilitate the discovery of trends, metric comparisons, and quicker sharing of findings. This seamless integration of data processing and visualization empowers organizations to make informed decisions swiftly and effectively.

InstaVM offers a robust production sandbox and cloud solution designed specifically for AI agents, equipping them with immediate access to computing resources including runtime, storage, networking, secrets, and policy management. Unlike traditional sandboxes, it utilizes hardware-isolated virtual machines instead of containers, ensuring that teams can provide AI agents with secure environments that feature complete Linux filesystems, networking capabilities, package management, RESTful API access, and the ability to maintain persistent states. The platform's support for snapshots enables users to create forks of any sandbox and revert to previous states, while its persistent volumes ensure that data is retained across executions. Additionally, egress control allows teams to manage which connections can be made externally, and features like secrets injection and Vault work to safeguard sensitive information from potential prompt injections. Moreover, public URL deployments can make any port accessible on the public web, enhancing its versatility. It is specifically tailored for various agent patterns, including code interpreters, deployment agents, deep research agents, AI evaluations, reinforcement learning applications, computer usage, and vibe coding applications, making it a comprehensive tool for developers. With its innovative architecture and extensive features, InstaVM solidifies its position as a leading solution for AI-driven projects.

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.

ESMC represents the newest advancement in the ESM series of protein language models, pushing the boundaries of representation learning within the field of protein biology. With training on billions of evolutionary sequences, it adeptly captures representations that encapsulate a mechanistic understanding of protein structure and function. The model utilizes a transformer architecture, focusing on sequences as its primary modality, and is trained on a vast dataset comprising up to 6 billion proteins. ESMC is tailored for various protein science applications, such as predicting structures, annotating functions, designing proteins, and exploring evolutionary connections among proteins. Additionally, it possesses the capability to create novel proteins based on partial sequences, structures, or functional constraints, thereby enabling researchers to investigate innovative avenues in protein design and biological discovery. Accessible through the Biohub Platform, ESMC can be utilized via an API and the ESM Python package, which includes quickstart resources for installation, API key generation, and platform connectivity, ensuring a seamless experience for users. This comprehensive accessibility encourages a broader engagement with protein research and enhances collaborative efforts in the scientific community.

ESMFold2 builds upon its predecessor, ESMFold, by establishing a new benchmark in single-sequence structure prediction and facilitating the creation of novel functional proteins via exploration of the latent space within the ESMC model. This advanced model is capable of forecasting high-resolution, all-atom 3D structures of biomolecular complexes straight from the amino acid sequence, and it allows for the incorporation of multiple sequence alignments to improve accuracy on difficult targets. Tailored for predicting structures through both sequence and structure modalities, it employs ESM representations that drive a series of looped folding layers while a diffusion model translates pairwise representations into atomic-resolution outcomes. ESMFold2 excels in predicting protein structures from amino acid sequences, providing detailed structural data, including precise all-atom coordinates for both backbone and side chains, along with confidence metrics and optional distogram predictions for in-depth structural evaluation. Furthermore, its innovative approach enhances the understanding of protein folding dynamics and functional implications, making it a valuable tool for researchers in the field.

OrcaRouter serves as a routing system for AI models that are compatible with OpenAI, efficiently directing prompts to the appropriate models from a wide array, including OpenAI, Anthropic, Gemini, DeepSeek, Qwen, Kimi, and over 200 other leading and open-source models. Its design aims to maintain the high quality of responses while minimizing costs associated with AI inference by evaluating each prompt and directing complex reasoning tasks to premium models while assigning simpler tasks to more economical open-source options. The routing process is meticulously quality-graded, avoiding arbitrary swaps for cheaper models, and every request clearly indicates the difficulty rating, chosen model, provider, and associated costs, ensuring that routes remain transparent, accountable, and reproducible. Developers can easily switch models by updating the API base URL, while previously established SDKs, model names, and streaming functionalities remain operational. Additionally, OrcaRouter features seamless automatic failover capabilities, allowing for traffic rerouting without interruption should a provider experience downtime, thus preventing disruptions for users. It also offers comprehensive API key management that incorporates spending limits, model allowlists, rate restrictions, and budget compliance, among other functionalities, ensuring robust control over resource usage. This combination of features makes OrcaRouter an indispensable tool for optimizing AI model utilization in various applications.

AG2 is an open-source AgentOS that enables the rapid development of production-ready AI agents and multi-agent systems in a matter of minutes rather than months. Previously known as AutoGen, it offers a Python framework for constructing, managing, and scaling AI agents that can effectively collaborate through a shared context while utilizing tools, executing workflows, and accommodating both autonomous and human-in-the-loop processes. This platform is specifically tailored for developers focused on creating systems rather than just prompts, featuring user-friendly syntax, integrated conversation patterns, and a versatile infrastructure for multi-agent automation. In AG2, agents can enhance their functionalities through various tools, enabling them to connect with external systems, retrieve real-time information, run code, conduct web searches, process documents, and tackle intricate tasks that exceed a model's inherent knowledge. The framework is compatible with a wide range of large language model (LLM) providers and local models, such as OpenAI-compatible endpoints, Anthropic Claude, Gemini via Vertex AI, DeepSeek, and LM Studio, making it a flexible choice for developers. By streamlining the development process, AG2 significantly accelerates the innovation of AI solutions across various applications.