Anthropic Integrations

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.

Flint AI serves as a local-first and framework-agnostic AgentOps command-line interface designed to assist developers in assessing the reliability of AI agents prior to their deployment in production environments. By executing the command flintai scan, users can evaluate Python source code for various issues such as security flaws, misconfigurations, and inadequate safety measures, while also employing AI reasoning to filter out potential false positives. Additionally, the command flintai eval tests a running agent by sending both functional and adversarial prompts, grading its responses against over 35 established criteria, which encompass aspects like factual accuracy, adherence to instructions, and resilience against prompt injections and jailbreak attempts. Each evaluated agent is assigned a reliability score, with the results linked to the OWASP Agentic Security Initiative risk categories ASI01 through ASI10 and severity assessed via CVSS v4.0 metrics. Flint AI is compatible with several agent frameworks and SDKs, including Claude Agents SDK, LangChain, CrewAI, Anthropic SDK, OpenAI SDK, MCP servers, and AutoGen, ensuring a broad range of applications in the development ecosystem. Furthermore, this versatile tool not only enhances the security and quality of AI agents but also streamlines the evaluation process, ultimately fostering greater confidence in AI deployment.

AICostGuardian serves as a comprehensive platform for managing AI expenses, enabling organizations to monitor, enhance, and regulate their spending on over 25 different AI service providers through a centralized interface. It meticulously tracks every API interaction with millisecond accuracy, providing instantaneous cost calculations and integrating provider data into comprehensive analytics, automated reporting, forecasting, and visual dashboards. Teams can evaluate spending patterns, benchmark usage against peers, pinpoint areas for cost savings, and leverage machine-learning insights alongside intelligent recommendations to minimize avoidable AI costs. With predictive alerts and anomaly detection features, users receive timely notifications about unusual spikes in usage and potential budget exceedances, while customizable spending thresholds ensure that consumption remains manageable. Additionally, department-specific cost tracking, team performance analytics, detailed permission settings, and role-based access facilitate clearer ownership accountability and regulation of AI resource utilization throughout the organization, ensuring informed decision-making and strategic oversight. As organizations increasingly adopt AI technologies, AICostGuardian stands out as a vital tool for fostering financial prudence and operational efficiency.

SatGate functions as a governance and accountability layer for AI agents, regulating their access, expenditure, delegation, and execution capabilities prior to any interaction with APIs, models, MCP tools, or external paid services. Operating as an HTTP reverse proxy and MCP proxy, it implements scoped authority, individual agent budgets, routing policies, and next-request revocation directly within the request workflow. Agents initiate their access by authenticating through established systems like Kubernetes, AWS, or OIDC, after which SatGate Mint converts that identity into a cryptographically signed Macaroon that delineates limits regarding scope, budget, expiration, and delegation depth. The architecture ensures that permissions can only tighten as requests traverse through agent chains, effectively stopping sub-agents from exceeding their authorized capabilities. In addition, the Observe mode tracks requests and analyzes resource usage categorized by agent, team, tool, route, and cost center while preserving existing workflows, whereas the Control mode imposes strict budgetary limits to prevent unauthorized or costly actions from being executed. This dual functionality allows organizations to maintain oversight while granting necessary freedoms to their AI agents.

Cloptima is an innovative platform that integrates AI and cloud FinOps, offering governance for LLM expenditures, insights into multicloud costs, optimization for Kubernetes, analysis of queries, and controls on engineering costs within a unified framework. Through its AI gateway, teams can securely utilize their own credentials from OpenAI, Anthropic, Gemini, Vertex AI, and Amazon Bedrock, applying a range of protections like encrypted controls, virtual keys, model policies, token limits, budgets, guardrails, and attribution before any requests are sent to the providers. The platform's spend analytics provide a comprehensive breakdown of usage categorized by provider, model, team, application, environment, user, agent session, tool, workflow, and other dimensions, while the agent controls monitor retries, loops, tool interactions, and the potential for runaway costs. Additionally, exact and semantic response caching can help minimize redundant usage, whereas intelligent routing capabilities allow for the redirection of eligible traffic to more cost-effective or faster models, with the option for canary rollout and rollback if there are regressions in quality, latency, or error rates. This holistic approach ensures that organizations can effectively manage their AI-related expenditures while maximizing efficiency and performance across their operations.

AICosts.ai serves as a comprehensive platform for managing AI-related expenses, consolidating billing and usage information from over 50 different providers into a single dashboard. Users can easily upload invoices and data exports in various formats such as PDF, CSV, or JSON, or they can utilize the developer API to send usage events, with the platform efficiently parsing this information into a standardized format without needing any proxy setups or alterations to production requests. It accommodates a wide array of services including OpenAI, Anthropic, Google Gemini, AWS Bedrock, Azure OpenAI, Vertex AI, Cohere, Groq, Hugging Face, Pinecone, RunwayML, Make, Zapier, and n8n. Daily insights break down expenditures by platform, model, and billed units, which encompass tokens, operations, characters, and other specific metrics from providers, enabling users to compare different services and understand the origins of their charges. Additionally, users can set budgets that may either encompass the entire AI landscape or focus on specific platforms or features, while also receiving email notifications whenever their rolling 30-day expenses surpass predetermined thresholds, ensuring they stay informed and within their financial limits. This level of detail and control empowers teams to manage their AI costs more effectively.

Burnwise serves as a financial assistant powered by AI, providing insights into an organization's AI expenditure, the reasons behind spending fluctuations, and strategies for cost reduction without compromising on product quality. It monitors usage metrics across large language models, image generation, video, and audio services from leading providers through a consolidated SDK and a cohesive dashboard. Rather than merely presenting aggregate token statistics, Burnwise breaks down costs by specific product features, users, sessions, teams, and agent workflows, allowing teams to gain a clearer understanding of the actual expenses associated with functions such as chat support, document assessment, summaries, or translation services. The platform's usage intelligence uncovers discrepancies between cost and value, while real-time anomaly alerts detect unexpected surges and excessive prompt usage. Additionally, Burnwise provides a concise set of prioritized decision cards that outline potential savings, risk factors, and quality implications, suggesting actions such as changing models, activating semantic caching, imposing limits, or altering feature operations. By offering these insights, Burnwise empowers organizations to make informed decisions that enhance efficiency and optimize resource allocation.

TokenAtlas is an innovative platform focused on AI FinOps and cost intelligence, designed to assist teams in comprehending, predicting, and managing AI expenses before they escalate. Users can define their workload by providing details such as model specifications, token input and output volumes, request frequencies, and anticipated growth, allowing TokenAtlas to evaluate the scenario against a curated list of API pricing. The cost modeling dashboard consolidates all configured workloads into a single interface, while the model comparison feature juxtaposes various provider and model options using clear and transparent assumptions. Additionally, the what-if scenario planning tool assesses the potential financial impact of introducing a new prompt, switching models, modifying retrieval pipelines, or increasing traffic prior to actual implementation. Moreover, cost risk analysis pinpoints the workloads that are particularly vulnerable to fluctuations in volume, prompt size, or model selection, while benchmark comparisons reveal how the configured model mix stands in relation to standard AI product and infrastructure profiles. This comprehensive approach empowers teams to make informed financial decisions, enhancing overall efficiency and cost-effectiveness in AI operations.

ZenLLM serves as an AI-driven platform focused on optimizing costs for engineering teams that deploy LLM applications in live environments. By linking provider invoices to the underlying application activities, it identifies which specific prompts, workflows, models, customers, retries, and request paths contribute to financial expenditures. Teams can utilize the ZenLLM SDK to transmit request-level telemetry, allowing them to incorporate relevant business context—such as workflow, owner, customer, team, or product feature—without having to store the content of prompts or responses. In addition, it keeps track of token consumption, model selection, latency, errors, retries, and overall costs, revealing wasteful patterns that provider dashboards often obscure. The platform is capable of recognizing instances of context accumulation when conversations or agents repeatedly send extended histories, excessive use of premium models for low-risk tasks, retry loops that lead to unnecessary expenses, outdated system prompts, routing errors, anomalies, and a lack of accountability regarding costs. Furthermore, ZenLLM empowers teams to make informed decisions that can significantly enhance cost efficiency in their LLM application operations.

LLMeter is a comprehensive open-source platform designed for monitoring AI costs, allowing developers to manage their expenditures across various providers like OpenAI, Anthropic, DeepSeek, OpenRouter, Mistral, and Azure OpenAI from a single dashboard. By simply connecting read-only provider keys, teams can instantly access detailed insights into actual costs, daily usage trends, model-specific analytics, and potential areas for optimization, all within approximately 30 seconds and without any need for SDK installation, endpoint modifications, or rerouting production traffic through a proxy. Since it facilitates direct communication with model providers, LLMeter introduces no additional latency, avoids becoming a single point of failure, and does not access or store any user prompts or completions. Additionally, budget alerts notify teams prior to exceeding their daily or monthly spending thresholds, while anomaly detection features help catch unexpected usage surges before they escalate. The platform's dashboard provides a clear overview of the costs associated with various providers, models, endpoints, customers, and environments, and its integration with OpenRouter enhances transparency by covering over 500 models, ensuring users have a robust tool for managing their AI-related expenditures efficiently. Ultimately, LLmeter empowers teams to make informed financial decisions regarding their AI usage.

LLMetrics serves as a comprehensive cost tracking solution for teams involved in the development of AI products, integrating model expenses, token consumption, feature attribution, and usage notifications into a single, interactive dashboard. This powerful tool accommodates over 100 models from various providers, including OpenAI, Anthropic, Google Gemini, Mistral, Cohere, Together AI, and Groq, with pricing information updated on a daily basis. Teams can label each model interaction with details such as feature name, provider, model type, input tokens, and output tokens, enabling them to pinpoint which specific functionalities—be it a chatbot, summarizer, search tool, or lesson creator—are contributing to their expenditures. The platform offers real-time updates and daily trend visualizations, illustrating how costs fluctuate in response to software releases, modifications to prompts, increases in traffic, or transitions between models. Additionally, it includes spend thresholds and spike-detection features that can alert teams via email or Slack when unusual usage patterns are identified, aiding them in preventing runaway loops and unforeseen cost surges prior to receiving the provider invoice. By leveraging these insights, teams can make informed decisions regarding their AI product strategies and budget management.

AI Cost Board serves as a comprehensive platform for monitoring AI API usage and managing associated costs, consolidating important metrics like expenses, requests, tokens, latency, errors, and overall usage from various model providers into a unified real-time dashboard. By directing LLM traffic through a single proxy endpoint, applications can efficiently forward requests to the designated provider while capturing detailed logs that include model information, token usage, status, timing, costs, input, output, and raw JSON context. Typically, teams only need to adjust the base URL of the provider and utilize an AI Cost Board project key, thereby maintaining the integrity of the original request structure. This platform accommodates a variety of providers such as OpenAI, Anthropic, and Google Gemini, offering a standardized setup that harmonizes usage data across different integrations. Cost analytics provide a breakdown of spending categorized by project, provider, model, and timeframe, enabling users to identify trends, calculate cost per request, assess success rates, and evaluate operational performance. Moreover, the searchable request logs empower developers to analyze payloads, address failures, compare various models, and probe into instances of slow or costly API calls. Overall, AI Cost Board enhances transparency and control over AI API expenditures, facilitating informed decision-making for teams utilizing AI technology.

FlowRunner stands out as a visual workflow automation platform that integrates native AI agent orchestration, allowing agents to execute workflows independently while pausing for necessary human input. What sets FlowRunner apart is its unique approach to oversight; while most platforms treat approval as a workflow step, FlowRunner empowers agents to reach out to humans as callable tools during execution. It effectively gathers the necessary context and options, then communicates with a designated individual via email, Slack, WhatsApp, or phone, resuming the workflow once a decision is made. Notably, a run can remain on hold for up to a year, ensuring that time-sensitive decisions, like those requiring three days, do not have a deadline. In addition to this, features like audit trails, role-based access control (RBAC), and service level agreement (SLA) tracking are available starting from the mid-tier package, rather than being confined to custom enterprise agreements, and a business associate agreement (BAA) can also be acquired. Every subscription level allows for unlimited users, unlimited workflows, and full access to the integration catalog, with no limitations based on user count or connectors. Billing is structured around the number of workflow runs rather than the individual action steps taken, ensuring a transparent pricing model. Agents are also able to utilize their own AI provider keys, with no additional charges for inference. Furthermore, users can choose between cloud-hosted and self-hosted options, providing flexibility to fit various operational needs.

Archestra serves as an open-source, self-hosted AI platform designed for the deployment and management of agents within an organization. It features agentic chat functionalities tailored for non-developers, along with applications, skills, collaborative projects, a server-side agent runtime, MCP orchestration, permission-aware RAG, LLM and MCP proxies, security guardrails, and comprehensive observability, all integrated into a single platform. Users can authenticate through SSO, ensuring that every tool interaction occurs under the individual’s personal identity rather than through a common service account. Projects are organized to consolidate chats, files, scheduled tasks, and instructions, while agents operate within isolated containers, triggered by schedules, emails, or webhooks. MCP servers are hosted within the organization's Kubernetes environment, navigating through security-reviewed promotion processes that enforce distinct credentials and network policies. Furthermore, knowledge bases can interface with Confluence, Jira, drives, and internal documents while maintaining source-system ACLs, ensuring that users can access only the content for which they possess permissions. This comprehensive suite of features makes Archestra an invaluable resource for organizations looking to streamline their AI deployments and governance.

BetterClaw serves as a no-code platform for teams seeking effective AI agents without the need for complex infrastructure. Users can simply describe tasks through a chat interface, link various tools and LLM providers, and deploy autonomous agents without the hassles of Docker, YAML, configuration files, or VPS hosting. These agents can operate on set schedules across platforms like Telegram, Slack, Discord, Gmail, and custom webhooks, while the system provides visibility into task states, categorizing them as backlog, ready, running, successful, or failed, with the ability to re-queue unsuccessful attempts. The platform supports any skill compatible with OpenClaw and features a selection of curated skills that have undergone a rigorous four-layer security audit. Teams can transform successful interactions into reusable skills, integrate with over 30 LLM providers, and have agents generate tangible outputs such as PDFs, DOCX files, XLSX/CSV spreadsheets, images, audio, video, and Markdown, eliminating the need for manual text copying. Enhanced security measures are in place, including AES-256 encryption for credentials, dedicated containers for each agent, automatic secret purging every five minutes, specific credential grants per agent, and comprehensive access audit logs to ensure safe operations. Overall, BetterClaw empowers teams to streamline their workflows while maintaining a high level of security and efficiency.

ZGI is an open-source AI platform designed for enterprises to create business-oriented agents that leverage their specific data, tools, workflows, models, and expertise. With its Agent Runtime feature, agents can efficiently load Skills, access real-time company insights, utilize various tools, and quickly deliver valuable results. The Model Gateway integrates multiple global and domestic model providers, such as OpenAI, Anthropic, Google, DeepSeek, and Qwen, enabling teams to select the most suitable model for each agent based on criteria like quality, availability, cost, or geographic location while maintaining centralized control over access, quotas, routing policies, and fallback options. The comprehensive product workspace encompasses the entire agent development lifecycle: Agent Studio merges Skills, knowledge, tools, and models; Workflows facilitate complex, multi-step processes and allow users to monitor each execution closely; the Database enables natural-language queries over real-time business information while adhering to access governance; Model Management oversees provider connections and enterprise-level routing; and Knowledge Assets transform company documents into searchable, context-aware knowledge repositories. By integrating these features, ZGI aims to streamline the deployment and management of AI agents within enterprises.

Agiloop is a platform designed for AI-driven product development that seamlessly integrates the entire software lifecycle, spanning from conceptualization and requirement gathering to execution, evaluation, and ongoing enhancement. The INVENT feature leverages AI-based interviews and repository assessments to convert fresh ideas or pre-existing applications into organized functional and technical specifications, comprehensive features, user narratives, and criteria for acceptance. The IMPLEMENT phase takes the validated specifications and directly constructs complete features within your Git repository, incorporating integrated code reviews, test scaffolding, validation of acceptance criteria, security assessments, and compatibility with current CI/CD workflows. During the INSPECT phase, it instruments the created features at build time, delivering real-time analytics on usage, user journeys, event tracking, metric goals, and AI-driven insights without needing a separate tracking configuration. Finally, the ITERATE component assesses business performance, application stability, system architecture, and technical liabilities, offering actionable recommendations for improvements that can be implemented straight away, ensuring a continuous cycle of enhancement and adaptation. This comprehensive approach not only streamlines the development process but also enhances collaboration and transparency throughout the software lifecycle.

Slack Code offers a shared coding platform that unites team members and AI agents to collaboratively develop software transparently. By mentioning an agent, a temporary code channel is initiated for a designated task, which allows the team to have a focused space for tracking progress, providing guidance, reviewing modifications, and approving outcomes, all while keeping main channels uncluttered. The agents leverage the conversations and knowledge they are authorized to access, enabling them to utilize relevant team context right from the outset. Team members can articulate their development needs, allowing an agent to generate functional code as everyone observes, contributes feedback, and makes decisions on what gets deployed. Once the task is completed, these code channels are automatically archived, yet their context remains searchable for later use. Users can find and manage agents within the Agents tab, where they can monitor ongoing sessions, check live statuses, and identify when their input is required. Additionally, enhanced thread features create more descriptive titles, making it simpler to locate and continue previous work, thereby promoting a more organized workflow. This innovative approach not only enhances collaboration but also streamlines the entire coding process for teams.

Inflowave serves as a comprehensive CRM and marketing solution designed specifically for niche agencies, enabling them to efficiently manage content, advertisements, communications, leads, and client accounts on a large scale. This platform features a consolidated inbox that allows both setters and closers to manage direct messages, comments, and communications from all linked client accounts seamlessly in one location, while its intelligent AI system automatically tags, updates custom fields, and advances leads through various stages of the pipeline. Additionally, the AI-powered chatbot adapts to a brand’s unique conversational tone, providing responses to inquiries, qualifying leads, scheduling appointments, and ensuring follow-ups are conducted automatically across platforms like Instagram and others. With AI Workflows, users can automate processes involving over 34 triggers and 50 actions, facilitating the management of Instagram direct messages, emails, SMS, voice communications, forms, appointment scheduling, payment processing, CRM updates, and webhooks, all through a visual interface or simple natural language commands. Furthermore, the platform aggregates multiple advertising accounts, which empowers teams to evaluate campaign success, pinpoint effective messaging strategies, and correlate ad performance with customer feedback gathered from interactions. This integrated approach not only streamlines operations but also enhances overall agency productivity and client satisfaction.

Assistable is a comprehensive AI conversation platform that facilitates the creation, testing, deployment, and monitoring of hybrid AI agents capable of responding to calls, texts, WhatsApp messages, and web chats while maintaining a unified memory across all channels. These agents effectively manage entire customer dialogues, interpret intent, retrieve information, qualify leads, and perform tasks such as booking, rescheduling, or canceling appointments, as well as creating support tickets, updating contact details, adding notes and tasks, and escalating issues to human agents when necessary. Thanks to the continuity of memory across channels, a customer can initiate a conversation via phone and seamlessly transition to text or chat later without needing to repeat any previous exchanges. Users can easily outline the desired functionalities of an agent using simple English, connect Assistable through MCP, and have the platform automatically generate the agent, attach relevant tools, connect various communication channels, and designate a phone number. Furthermore, these agents can access real-time calendar availability, follow up with potential leads, and facilitate both incoming and outgoing conversations, ensuring a seamless customer experience throughout the interaction process. This versatility ensures that businesses can efficiently manage customer inquiries while maximizing engagement across different communication platforms.

The Model Hardware Standard (MHS) serves as a universal specification facilitating the safe operation of AI agents with physical tools utilized in scientific exploration and high-tech manufacturing. By establishing a common framework, it allows these agents to identify, comprehend, and command programmable devices such as microscopes, liquid handlers, robotic arms, and other instruments found in labs or factories, eliminating the need for customized integrations for each piece of equipment. MHS incorporates standardized drivers that revolve around straightforward commands like reading and writing, which allows for the capabilities of devices to be easily identified within a unified format across various networks. Additionally, these drivers can encompass natural-language metadata detailing machine specifications, adjustable settings, measurements, and mandatory safety restrictions, equipping agents with essential context to handle unfamiliar machinery effectively. Once the connection is made, agents have the ability to manage devices through MCP, command-line interfaces, or APIs, coordinate actions across several instruments, track outcomes, and fine-tune parameters to optimize performance. This comprehensive approach not only enhances efficiency but also fosters safer interactions between AI systems and complex equipment.

Switch is an innovative collaborative workspace where human teams and AI agents jointly accomplish tasks in a unified setting. Instead of confining knowledge within a single individual or agent, it integrates people, agents, decisions, documents, references, instructions, conversations, and work history, ensuring that context endures through transitions and allowing work to progress smoothly without redundant repetition of prior activities. The Switch rooms seamlessly connect to popular messaging platforms such as Slack, Microsoft Teams, Discord, Mattermost, and Telegram, enabling participants to engage with agents effortlessly without the need for additional installations. Agents can easily be invited into these rooms, communicated with directly, and utilized across various teams without the necessity of rebuilding them for each new workspace. The platform is compatible with existing agent frameworks and providers, including Claude Code, LangChain, Google ADK, OpenAI, Amazon Bedrock, and custom agents, facilitating coordination within the same shared environment while avoiding migration issues or vendor lock-in. With its user-friendly design, Switch promotes a more efficient and collaborative atmosphere where human ingenuity and artificial intelligence can thrive together.

OpenTag serves as a versatile AI collaborator within Slack and Microsoft Teams, seamlessly integrating with the company’s context to alleviate the workload of teams rather than merely responding to queries. By mentioning it in a conversation thread, users can have it organize tasks, utilize integrated tools, execute the necessary work, and deliver the results back to the original conversation. Operating on an independent cloud infrastructure, each of its operations is contained and restricted to the tools and permissions established by the user who initiated the request. OpenTag is compatible with various services including GitHub, Stripe, Zendesk, Notion, PostHog, HubSpot, Gmail, and even internal databases, allowing it to manage repetitive tasks by recommending automation whenever it detects frequent inquiries. Additionally, it contributes to a continuously updated company wiki by converting decisions from team discussions into well-sourced pages and modifying them as policies evolve. This ensures that shared knowledge remains accessible across the team, effectively preserving valuable information even amid employee turnover, thus fostering a culture of continuous improvement and collaboration.

Kilo Gateway serves as a versatile AI inference conduit, allowing developers to send Large Language Model (LLM) requests to various providers via a single, standardized endpoint, thus granting them access to a multitude of hosted and open models without the need to modify their applications for different services. It offers seamless access to models from well-known providers, including Anthropic, OpenAI, and Mistral, and accommodates bring-your-own-key setups that empower teams to utilize their existing provider credentials within a centralized framework. The gateway is designed to work with standard AI SDKs, enabling developers to switch providers effortlessly while maintaining the same integration surface. By managing routing intricacies and load balancing between direct providers and external gateways, it enhances system availability and resilience. Additionally, the Auto Model feature intelligently directs each request to the most suitable model, ensuring that routing choices, model performance, and usage metrics remain transparent and manageable for users. This not only streamlines the development process but also provides flexibility as the landscape of AI models continues to evolve.

VibeView is a cloud-based simulator accessible through browsers on iOS, Android, Apple TV, and Android TV, featuring real-time connection capabilities, AI-driven mobile app testing, an SDK that can be embedded into devices, and the ability to generate instantly shareable app previews. This innovative tool empowers developers, QA teams, and product managers to execute mobile applications directly from their browsers, eliminating the need for a Mac, Xcode, Android Studio, or physical devices. Users can simply upload their iOS or Android builds and within moments, stream a live simulator, sharing links that allow teammates, clients, beta testers, or sales prospects to engage with the app immediately—without requiring any installations, TestFlight, or provisioning profiles. Additionally, VibeView serves as a versatile development tool as well, where executing the command vibeview dev allows your React Native application to stream to a browser simulator with complete hot reload capabilities—enabling immediate feedback on code edits made locally, all without the necessity of a Mac, Xcode, Android Studio, or a physical device. As a result, both testing and development processes become streamlined and more efficient, enhancing collaboration and productivity.

Rune is an efficient, keyboard-centric integrated development environment designed for advanced users, seamlessly integrating code, terminals, command-line tools, language intelligence, debugging capabilities, and AI agents within a flexible, multi-workspace framework. Adhering to the Unix philosophy, it ensures that robust development resources are readily accessible, avoiding the need for a mouse-based workflow. The IDE features three distinct editing modes: a standard editor aimed at users familiar with VS Code, Cursor, or Sublime; a modal editing option for Vim and Neovim enthusiasts; and an Emacs-style interface, with consistent keybindings across the platform. Workspaces are designed to amalgamate files, terminals, tasks, debugging tools, search functionalities, and language intelligence, capable of operating both locally and on remote servers. Rune Agent operates within the same workspace as the code, enabling it to read and modify files, search through the codebase, execute commands, utilize various tools, and engage in comprehensive dialogues. Moreover, it offers support for a range of AI models including OpenAI, Codex, Anthropic, Claude, Gemini, Amazon Bedrock, local models, and custom OpenAI-compatible endpoints, enhancing its versatility for developers. This combination of features makes Rune a powerful ally for programmers looking to streamline their development processes.

Sprinklr is a Unified-CXM platform that enables enterprises to manage every customer interaction from a single AI-powered system. It combines marketing, social media management, customer service, and consumer insights into one cohesive platform. Sprinklr uses advanced AI to analyze unstructured data from millions of conversations to uncover actionable insights. Its AI copilots and intelligent agents help automate workflows and enhance team efficiency. Marketing teams can run compliant global campaigns while maintaining brand consistency across channels. Customer service teams benefit from omnichannel support tools and real-time context for every interaction. Sprinklr enables human-AI collaboration to deliver more empathetic and personalized experiences. The platform integrates seamlessly with existing enterprise technology stacks. Built for scale, it supports global teams with high customization and governance. Sprinklr helps organizations transform customer experience into a competitive advantage.

Analytify AI seamlessly connects with any database, transforming queries into visually appealing, easily shareable insights while avoiding the pitfalls of BI bloat—no need for a credit card or fear of vendor lock-in. This contemporary GenBI tool harnesses cutting-edge AI technology, simplifying the process of data analysis and visualization for more informed decision-making. As an open-source platform, it provides users with both cloud-hosted and self-hosted alternatives, guaranteeing maximum flexibility and control over their data management. With its user-friendly interface, Analytify AI empowers organizations to harness their data effectively.

WEM builds intelligent enterprise software — no code required. Founded in 2012 in Amsterdam, our no-code platform empowers organizations to develop applications, automate processes, and deploy governed agentic AI at speed and scale. Trusted across Europe by enterprises in financial services, government, logistics, and manufacturing, WEM delivers digital transformation through expert teams and a certified partner network.

LayerLens serves as an autonomous platform dedicated to evaluating AI models, providing insights into their performance through verified benchmarks, prompt-specific outcomes, agentic comparisons, and audit-ready assessments across different vendors. This platform enables teams to conduct side-by-side comparisons of over 200 AI models, utilizing transparent benchmarks and consistent evaluation techniques focused on accuracy, latency, behavior, and practical application in real-world scenarios. Designed for comprehensive model analysis, LayerLens features Spaces that allow teams to organize benchmarks and evaluations, identify strengths in tasks, and monitor performance trends in relevant contexts. The platform also facilitates ongoing evaluations by continuously assessing model updates, prompt modifications, judge changes, and live traces, thereby empowering teams to identify issues like quality regressions, drift, silent failures, contamination, and policy concerns before they impact production. By prioritizing transparency and collaboration, LayerLens ensures that teams can make informed decisions about their AI model choices.

AccessOwl serves as a comprehensive tool for Access Governance and SaaS management, streamlining the process of managing employee access to various SaaS applications throughout their tenure, from onboarding to offboarding. Acting as the primary platform for overseeing SaaS access, it removes the confusion about who is responsible for specific tools and what approvals are necessary, while meticulously logging every application, user access, and the permissions utilized within the organization. By automating the processes of user account creation, access requests, approvals, and audits, along with detecting Shadow IT, AccessOwl enables teams to move away from spreadsheets and establish a reliable source of truth, significantly minimizing the chances of overlooking offboarding tasks. Furthermore, its integration with Slack allows employees to conveniently request access in the environment they already use, and HRIS integrations automate the onboarding and offboarding processes while keeping employee information such as job title, department, and manager up to date. Notably, AccessOwl has the capability to provision and revoke user access across a multitude of SaaS applications without the necessity for SCIM or SAML, ensuring flexibility and ease of use for organizations. This allows for a more efficient management of software access, ultimately enhancing security and compliance efforts.

RouterBase serves as a comprehensive API gateway, allowing developers and teams to utilize over 200 AI models, including well-known options like GPT, Claude, Gemini, Llama, Mistral, and DeepSeek, all through one OpenAI-compatible endpoint. This eliminates the need for managing different keys and billing systems for each model, as switching between them is as simple as changing a single configuration line. Additionally, RouterBase enhances functionality with intelligent routing, built-in failover capabilities across various providers, and consolidated billing, ensuring that your application remains operational even in the event of an upstream provider failure. Moreover, a free tier is offered with no requirement for a credit card, making it accessible for users to explore the service. With RouterBase, developers can streamline their workflow and focus on building innovative applications without the hassle of juggling multiple integrations.

DeepInfra is a cloud-based AI inference platform designed to effortlessly execute a wide range of the latest machine learning models at scale, such as large language models, vision models, embeddings, and various forms of media generation including images and videos. The platform offers serverless inference via straightforward APIs, enabling developers to seamlessly incorporate production-ready AI models into their applications without the burden of managing GPU resources, auto-scaling, complex deployments, or model hosting logistics. Supporting OpenAI-compatible APIs allows for an easier transition from existing OpenAI-style integrations, while also providing access to an extensive library of both open-source and commercial models. With its Native API, users can access every type of model available on the platform, covering tasks such as image generation, speech recognition, object detection, token classification, fill-mask, image classification, zero-shot image classification, and text classification. DeepInfra is designed for optimal performance, ensuring scalable, low-latency inference powered by state-of-the-art GPU infrastructure, which ultimately enhances the efficiency of AI-driven applications. This focus on performance makes it an ideal choice for businesses looking to leverage advanced AI technologies.

JackHamr functions as an AI-driven software solution that manages the entire development process from start to finish. Rather than relying on a singular AI assistant, it coordinates a team of specialized agents: a specification agent formulates requirements, a planning agent divides them into tasks, a coding agent develops the software, a testing agent performs evaluations, a quality reviewer ensures standards are met, and a deployment agent launches the product. These agents operate within hosted cloud development environments utilizing tools such as VS Code, Docker, SSH, and WireGuard-encrypted networking, allowing them to continue functioning even when your laptop is closed. Communication with these agents can be conducted through push-to-talk voice chat or natural typing, making interactions seamless. GitHub integration is built-in, offering features like one-click cloning, automatic branch creation for each task, real-time commit synchronization, and the ability to create pull requests effortlessly. Users have the flexibility to utilize their own LLM keys from providers like OpenAI, Anthropic, Google, or opt for the cost-effective options available through JackHamr, with the ability to switch models during the workflow. The pricing model is pay-as-you-go with detailed billing, charging only for infrastructure costs and LLM tokens without any additional fees. To encourage new users, there is a $10 credit available at the outset, and no credit card is required for registration, allowing anyone to experiment with the platform without financial commitment. This innovative approach not only streamlines software development but also enhances collaboration among various specialists in the field.

OpenParser AI serves as a cutting-edge truth hub for organizations that handle extensive documentation, designed to consolidate information from documents, databases, and web content into a secure and unified knowledge layer. By posing a question, users can allow the AI to retrieve precise answers from their documents, databases, or websites, with each response directly linked to the specific document page, database entry, or web segment from which it originated. OpenParser enhances document management through features like straightforward document Q&A, seamless database querying without the need for SQL, comprehensive website content searches, and cross-document insights, all while ensuring role-based access and implementing document approval workflows. Additionally, it offers instant validation and generates structured outputs that teams can efficiently act upon. Users have the flexibility to upload documents or link to platforms like Google Drive, SharePoint, SQL databases, and website URLs, with an auto-sync feature that keeps all information up to date. The OpenParser AI Engine is capable of comprehensively analyzing and reasoning through all available data, utilizing Llama by default or allowing integration with the user's preferred LLM, which can include options such as OpenAI, Anthropic, Gemini, and others. This powerful combination ensures that teams have access to the most relevant information in real-time, ultimately improving decision-making processes and operational efficiency.

Flo2 serves as a gateway and router that connects users to leading AI model providers such as OpenAI, Anthropic, Groq, Cerebras, and DeepInfra via a single, unified API that is compatible with OpenAI. It intelligently selects the most cost-effective or quickest model for each request through smart routing capabilities. To ensure reliability, automatic fallback mechanisms maintain application functionality even if one provider experiences downtime. Additionally, racing mode allows for simultaneous processing of requests across multiple providers, enhancing efficiency. Comprehensive cost tracking is available, detailing expenses for each request, model, and project. Developers are able to utilize their own provider keys on flo2.com, and RapidAPI's testing tier offers free tokens for preliminary evaluations. This seamless integration is aimed at simplifying the development process while maximizing performance and minimizing costs.

Claude for K-12 teachers is designed to help educators reconnect with their passion for teaching by minimizing the time allotted to planning, preparation, and paperwork, enabling a greater focus on their students' needs. Tailored specifically for K-12 educators, Claude can quickly generate a variety of materials such as lessons, quizzes, assessments, student resources, presentations, emails, newsletters, and customized resources for different learning levels, all while allowing teachers to maintain oversight and make necessary adjustments for their specific class dynamics. By integrating with Learning Commons, Claude has access to educational standards from all 50 states, ensuring that lesson plans are grounded in evidence-based curricula, including resources from organizations like Illustrative Mathematics and OpenSciEd. Educators can utilize Claude to design lessons that meet standards, revise existing materials, and create tools like do-nows, worked examples, exit tickets, and presentations, all enhanced by the connected curriculum framework that makes the outputs more applicable for the classroom environment. This innovative approach not only streamlines the preparation process but also empowers teachers to spend more meaningful time engaging with their students.

Command Code is an advanced coding assistant that operates within the terminal, enabling the creation of comprehensive full-stack applications, deploying new features, troubleshooting issues, writing test cases, and optimizing code, all while adapting to the unique workflows of individual developers. It harnesses the power of the meta neuro-symbolic taste-1 model alongside continuous reinforcement learning, interpreting every suggestion, rejection, and modification as valuable feedback, which allows it to identify and cultivate recurring preferences, structures, patterns, and tools into enduring skills and memories for each project. Rather than simply adhering to standard best practices, it assimilates developers' code review techniques, stylistic inclinations, architectural choices, as well as their preferred package managers and libraries, even those minor conventions that often go undocumented, thereby applying this contextual understanding in future interactions. Command Code is equipped with features that facilitate interactive command-line interface operations, headless prompts, automated task execution, planning capabilities, background sandboxes, customizable agents, checkpoints, and memory retention across different sessions, providing a truly personalized coding experience. This innovative tool not only streamlines the development process but also empowers developers to enhance their productivity and maintain consistency in their coding practices over time.

Ask Sage is a robust generative AI platform designed for government, defense, and regulated entities that require the handling of sensitive information within mission-critical workflows. It offers a single multimodal workspace that integrates over 150 different models, including commercial, frontier, and open-source options, and facilitates the generation and analysis of text, code, images, videos, and audio, enabling flexibility without binding teams to a sole provider. Users can input their organizational data just once and leverage it across multiple models for various tasks such as grounded document Q&A, drafting, summarization, knowledge management, policy validation, data analysis, and outputs tailored to specific roles. The platform features Ask Sage Chat, which provides a conversational interface, while Workbook consolidates sources, memos, shared chat histories, and team collaboration into a unified document-oriented space. Furthermore, Agent Builder offers a visual, node-based canvas that allows users to create, preview, reuse, monitor, and orchestrate automated workflows without requiring coding skills, promoting accessibility for all team members. This comprehensive approach streamlines processes and enhances productivity across diverse organizational functions.

Cloudgov.ai serves as an intelligent AI-driven FinOps platform designed for ongoing management of costs and policy adherence across various environments, including cloud, multicloud, data systems, containers, and artificial intelligence. By integrating major platforms such as AWS, Azure, Google Cloud, Oracle Cloud, Snowflake, Databricks, Kubernetes, OpenAI, Anthropic, and Gemini into a unified control panel, it enables teams to monitor expenses, allocation, policies, and associated risks in real time. Its Continuous Multicloud Observability feature links accounts, reviews past expenditures, categorizes costs based on region, account, and service, and projects future spending based on historical data. With AI-generated insights, the platform uncovers areas of waste and potential optimization, and its anomaly detection functionality alerts users to unexpected spikes in spending along with their financial implications. Furthermore, it provides ready-to-use Infrastructure as Code snippets for remediation, which allows engineering teams to implement suggested adjustments seamlessly, and integrates with Jira to convert insights and anomalies into actionable tasks for team members, thereby streamlining the workflow for cost management. Overall, Cloudgov.ai empowers organizations to maintain financial control while enhancing efficiency across their cloud operations.

Waterfall serves as a credit infrastructure tailored for platforms that leverage large language models, enabling the transformation of AI applications into profitable business ventures without the need for teams to develop a proprietary billing system. It offers each user, agent, or team a credit wallet secured by stablecoins, meticulously tracking every model interaction based on provider, model, token count, and associated costs. Users can either route their requests through the Waterfall Gateway or utilize TypeScript and Python SDKs for integration, ensuring that usage is accurately attributed to the appropriate wallet in real time. Each API request is settled instantly against the wallet, leading to a decrease in credits while allowing for immediate revenue recognition for every request, eliminating the delays associated with traditional invoicing and manual accounting processes. With support for over 300 models from various providers, including OpenAI, Anthropic, DeepSeek, and xAI, Waterfall enables products to seamlessly deploy multiple AI services while managing a unified accounting framework. This innovative approach simplifies financial management for AI-driven applications, making it easier for businesses to scale their operations efficiently.

FinOps LLM serves as an advanced platform for AI cost management and observability, specifically designed for engineering teams utilizing production GenAI. It enables transparency in token expenditures across a variety of providers such as OpenAI, Anthropic, Amazon Bedrock, Google Gemini, Azure, and Groq, while also aligning internal usage data with invoices from these providers. Users can filter token-level expenses based on provider, model, feature, team, customer, environment, and other custom metrics, ensuring that each dollar spent has a designated owner. Additionally, the platform includes attribution and chargeback functionalities that correlate usage with product interfaces and customer demographics, facilitating showback processes and allowing for data exports to systems like NetSuite, QuickBooks, CSV, or through APIs. Furthermore, real-time anomaly detection features track spending, latency, and quality, comparing them against dynamic feature baselines, and issue alerts via Slack, PagerDuty, email, or webhooks whenever notable changes occur. To further enhance cost control, optional budget enforcement and auto-throttling measures can prevent excessive spending due to runaway agents, excessive retries, or unexpected model shifts. This comprehensive approach ensures that engineering teams can manage their AI resources effectively while maintaining financial oversight.

Mongrel serves as a comprehensive desktop database systems workbench that integrates over 30 database engines, terminals, file transfers, container management, Kubernetes functionalities, and an API client into a single native application compatible with Windows, macOS, and Linux platforms. You can execute queries on various databases such as PostgreSQL, MySQL, MongoDB, SQL Server, Oracle, Redis, ClickHouse, Snowflake, BigQuery, and others using native SQL or respective engine dialects. The application allows for inline data editing, navigation through foreign keys, running EXPLAIN plans, designing schemas using a visual ER canvas, synchronizing data across different databases, and scheduling encrypted backups with ease. It replaces a multitude of tools by offering built-in SSH, Mosh, Telnet, Serial terminals, as well as SFTP and SCP file transfers, management for Docker and Podman, Kubernetes functionalities, and support for HTTP, GraphQL, WebSocket, and gRPC clients. An optional AI assistant is available, compatible with OpenAI, Anthropic, and Gemini providers, and it securely stores keys in your operating system's keychain while ensuring that all writes are subject to approval. Mongrel is designed to prioritize user privacy, ensuring that your queries, data, and logs remain exclusively on your local machine. All subscription plans provide access to every feature, and you can initiate a 7-day trial without needing to enter any payment information. Additionally, the user-friendly interface and rich functionality make it an essential tool for database management.

DeskFerry serves as an intuitive platform designed for assembling an AI team simply by articulating your needs in everyday language. It generates AI agents that can seamlessly integrate with the tools your team is already familiar with, facilitating automated tasks across over 1,500 applications without the necessity of coding. These agents possess the capability to read, write, and interact with various services, including Gmail, Slack, Notion, Salesforce, HubSpot, Jira, and Stripe, among others, while users have the option to select from a variety of AI models provided by OpenAI, Anthropic, Google, xAI, and more. Teams can customize agent skills to align with their brand voice, create branded materials, and organize outputs in appropriate tools and formats. Additionally, the platform features scheduling and trigger functions that support automations running on an hourly, daily, or weekly basis, or in response to events within connected applications. To enhance security and oversight, human-in-the-loop mechanisms can be implemented, requiring approval prior to the execution of significant actions, such as sending emails or updating records. Users can enrich the platform's knowledge base by incorporating documents, databases, and URLs, while a memory feature enables automations to remember previous interactions and preferences over time, ensuring a more personalized user experience. This combination of functionality and ease of use makes DeskFerry a valuable asset for teams looking to leverage AI in their workflows.

Keenable operates as a standalone web search infrastructure tailored for AI laboratories, inference frameworks, agents, and developers seeking quick and reliable access to real-time web content. The Search API equips AI entities with an extensive index comprising over 100 billion documents, specifically designed for rapid retrieval with performance fine-tuned for demanding production agent tasks. Agents are enabled to search through web pages and obtain page content via a REST API, MCP server, or command-line interface, all under a single account and API key. Continuously striving for excellence, Keenable assesses and enhances search quality through its NEEDLE benchmark, which evaluates retrieval efficiency across various search providers and aligns results with an oracle ranking derived from aggregated outcomes. For expansive AI tasks, the platform offers dedicated search capacity alongside options for cloud and on-premises deployment. Additionally, its Time Machine feature enhances retrieval capabilities by allowing users to conduct searches across historical webpage versions, offering a comprehensive view of past content. This dual focus on current and historical data positions Keenable as a versatile tool for modern AI applications.

Claude Computer Use is an advanced capability that allows Claude to operate directly on your computer to perform tasks across applications and files. It works by interacting with your screen, enabling actions like clicking, typing, opening programs, and navigating workflows without requiring manual input. The system prioritizes efficiency by first using direct connectors, then browser automation, and finally full screen interaction when necessary. Claude can handle tasks such as generating reports from local files, filling spreadsheets, testing applications, and navigating internal tools. Users retain control through permission prompts that must be approved before Claude accesses any application. The feature includes built-in safeguards designed to prevent risky actions and flag potential issues. It also captures screenshots to understand the interface, allowing it to adapt to different applications. However, users are advised to avoid exposing sensitive information while using the feature. Claude Computer Use is currently available in research preview and continues to evolve. Overall, it transforms Claude into an active assistant capable of executing real tasks on your machine.

Claude Security is an advanced AI-driven cybersecurity platform designed to help organizations detect and fix vulnerabilities in their codebases. It scans software repositories to identify security risks and uses validation processes to ensure accurate results. The platform provides detailed insights into each vulnerability, including severity, impact, and recommended fixes. It generates patch suggestions that developers can review and approve before applying changes. Claude Security integrates seamlessly into existing development workflows, allowing teams to start scanning without complex setup. It supports both full repository scans and targeted scans for specific sections of code. The system helps reduce false positives by validating findings before presenting them to users. It enables faster resolution by combining detection and remediation in a single workflow. Claude Security is available for enterprise users and supports ongoing security monitoring. It is designed to improve efficiency by reducing manual security analysis. By combining automation and AI, Claude Security helps organizations strengthen their software security posture.

Claude Dispatch is a cross-device productivity feature that enables users to assign tasks to Claude and have them executed on their desktop environment. It maintains a single continuous conversation, allowing users to seamlessly switch between mobile and desktop while preserving context. Claude can access local files, connected apps, and plugins to complete tasks such as generating reports, organizing files, or drafting documents. Once the task is completed, results are delivered directly to the user without requiring step-by-step supervision. This functionality allows users to delegate work and focus on other priorities while Claude handles execution in the background.

Cherry Studio serves as a comprehensive AI assistant and cross-platform desktop application that integrates numerous AI models into one cohesive workspace compatible with Windows, macOS, and Linux. By connecting with leading model providers, it enables users to seamlessly transition between various AI services without the hassle of managing multiple applications, browser tabs, or disjointed workflows. This tool is crafted to function as a robust local AI productivity center, facilitating tasks like everyday chatting, writing, translation, research, coding assistance, document comprehension, image analysis, and multimodal AI workflows all through a single interface. Users have the capability to customize model providers, oversee assistants, organize discussions, and select different models according to their specific tasks, which makes Cherry Studio valuable for both casual users and those engaged in more intricate experimentation. Additionally, its assistant system empowers users to create, subscribe to, and oversee role-based assistants equipped with tailored prompts for various scenarios, including product management, community operations, technical support, and strategic planning, enhancing the overall user experience and efficiency. This flexibility allows individuals and teams to harness AI effectively, adapting to their unique workflows and requirements.

PromptUnit serves as an AI inference intermediary that automatically minimizes AI expenses by acting as a bridge between an application and its AI service providers, requiring no modifications to existing code. Teams simply replace the base URL while maintaining the same SDK, endpoints, response parsing, and error management, allowing PromptUnit to take care of routing, failover, cost monitoring, and quality assessment. It meticulously logs every API interaction, detailing aspects such as model, feature, user segment, token count, latency, and cost, thereby providing immediate insights into AI expenditures before any routing adjustments are implemented. In its observation mode, PromptUnit meticulously monitors traffic, shadow-classifies incoming requests, predicts potential savings, and clarifies routing choices, enabling teams to visualize exact savings prior to activating live routing. After activation, Smart Routing intelligently classifies tasks to direct each request to the most cost-effective model that meets the established quality standards. Additionally, PromptUnit incorporates features like prompt compression, token inflation protection, efficiency scoring for prompts, semantic request caching, and multi-model consensus for enhanced performance. Its comprehensive approach ensures that organizations can optimize their AI usage and manage budgets effectively.