Claude Code Integrations

display.dev serves as a secured publishing platform for artifacts generated by agents, ensuring that every HTML report, dashboard, specification, design prototype, or document has a reliable and verified location. While agents are capable of producing high-quality artifacts featuring interactive charts, dynamic filters, hover states, and authentic layouts, the process of sharing these creations often diminishes the user experience due to reliance on screenshots, raw HTML files, collapsed documents, public links, or complex deployment systems. To address this issue, display.dev allows users to publish any HTML or Markdown artifact securely behind company authentication using a single command, a brief sentence within an agent's workflow, or an easy web upload. Once published, viewers can access a permanent link, log in with their Google or Microsoft work accounts or a one-time password, and view the artifact precisely as it was designed. Additionally, the platform seamlessly integrates with Claude Code, Codex, Cursor, Claude Desktop, shell scripts, and any other tools capable of generating HTML or Markdown content, making it a versatile solution for various users. This means that sharing and collaboration around these artifacts can occur without compromising integrity or usability, enhancing overall workflow efficiency.

Whim is a cloud-based development workspace designed for deploying AI coding agents with remarkable speed and efficiency. It provides developers the ability to operate AI coding agents such as Claude Code and Codex within isolated cloud containers, rather than relying on local machines. Each assignment is allocated a dedicated sandboxed Ubuntu environment, offering complete shell access, isolated git branches, and real-time terminal streaming. This setup facilitates seamless integration of AI coding agents into the daily operations of developers and teams, promoting parallelism, collaboration, and eliminating the need for local configuration. Users can easily link a repository, compose a prompt, and the AI agent begins its tasks within a protected cloud container that is accessible from any device. Additionally, multiple tasks can be executed at once, enabling experimentation with various strategies, focusing on different features, or allowing an orchestrator to manage a team of agents without interference. Whim also supports native CLI runtimes for Claude and GPT models, with plans to incorporate more models via OpenRouter in the future. This versatility positions Whim as a powerful tool for enhancing productivity in software development environments.

Proof is a collaborative document editor designed for both agents and humans to work together seamlessly. It allows teams to have a unified document where AI and human contributors can write, suggest modifications, leave comments, and monitor contributions. With each character's authorship clearly identified through a colored gutter, users can easily differentiate between text written by humans and that generated by AI. When AI makes changes, Proof presents these as suggestions similar to the track changes feature, enabling users to review and accept or reject each edit individually. Additionally, AI agents can comment on specific sections to provide explanations, raise queries, highlight concerns, or engage in discussions directly within the document. Users have the ability to create a document and share a link with various agents such as Claude Code, ChatGPT, Codex, or OpenClaw, facilitating collaboration through a shared workspace rather than exchanging .md files. Whether for bug reports, product requirement documents, implementation strategies, research summaries, growth analytics, content evaluations, strategic plans, memos, or proposals, Proof effectively supports a wide range of uses. This innovative tool is particularly beneficial for teams looking to enhance their collaborative processes while maintaining clear authorship and feedback mechanisms.

MemClaw serves as a durable memory service tailored for LLM-driven agents and functions as a regulated shared memory layer among fleets of agents. Its core purpose is to facilitate collaborative learning among AI agents by transforming their isolated contexts into a collective Company Brain, complete with integrated memory features, governance, provenance tracking, contradiction detection, and predefined visibility scopes from the outset. The architecture of MemClaw effectively distinguishes an organization’s agents—including tenants, fleets, nodes, and individual agents—from the managed memory layer via components such as the MCP Server, REST API, OpenClaw plugin, MemClaw Core, and persistent storage solutions. Agents can access and contribute to the Company Brain using MCP-compatible tools, direct HTTPS requests, or integrations through OpenClaw, while the MemClaw Core processes enhancements like entity extraction, contradiction identification, PII screening, and lifecycle management prior to any data being saved. Each memory entry can be labeled with a specific visibility scope and categorized automatically into various types including fact, episode, decision, preference, rule, plan, commitment, action, and outcome. Additionally, this structured approach not only enhances the organization of information but also improves the overall efficiency and effectiveness of AI agent interactions within the network.

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.

Ornold serves as an MCP server that facilitates AI-driven browser automation, allowing AI agents to gain comprehensive control over anti-detect browsers via an open protocol. This platform is specifically designed for large-scale browser automation and integrates features like vision-centric interactions, automatic CAPTCHA resolution, simultaneous multi-browser operations, human-like behavior, and tools for recovery, all within a unified system. Unlike traditional methods that depend on fragile CSS selectors or XPath, Ornold employs a vision mode that takes screenshots and analyzes web pages similarly to a human, accurately identifying interactive elements with pixel-precise coordinates and executing clicks based on normalized coordinates, thereby enhancing the automation's robustness amid layout changes. It interfaces with browser profiles using the Chrome DevTools Protocol and is compatible with various anti-detect browsers, including Dolphin Anty, Octo Browser, Linken Sphere, AdsPower, Multilogin, GoLogin, Incogniton, Vision, Undetectable, MoreLogin, Indigo, and any browser that supports CDP. Furthermore, Ornold's innovative approach positions it as a versatile solution in the realm of automated web interactions, making it an essential tool for developers seeking efficiency and reliability in their automation tasks.

Wandesk is a free local AI desktop application that empowers users to create customized tools simply by describing their needs. Rather than viewing AI as an external chatbot disconnected from daily tasks, Wandesk integrates AI directly into the desktop environment, creating a cohesive workspace where applications, conversations, documents, tasks, and user memory coexist seamlessly. Users can define various tools like a calorie tracker, reading list, invoice generator, bill splitter, lightweight CRM, or even a research dashboard, with Wandesk capable of producing fully functional local applications that feature a React interface, backend API, and SQLite database for storage. The apps generated by Wandesk are built to remain alongside the user's files and workflows, allowing them to be accessed, modified, and refined over time instead of becoming lost in a transient chat conversation. Furthermore, each Wandesk app is equipped to utilize AI natively, facilitating features such as automatically categorized ledgers, summarized notes, and tools for fictional writing that can query lore while ensuring character consistency. This innovative approach not only enhances productivity but also fosters creativity by allowing users to develop and iterate on their applications in real-time.

Multica is an innovative open-source project management platform designed for collaboration between human teams and AI agents, transforming coding agents into collaborative partners instead of merely being viewed as separate tools. This platform offers a unified workspace where both humans and AI can interact seamlessly; agents are capable of taking on tasks, providing updates, engaging in discussions, addressing obstacles, delivering code, and showcasing their presence along with profiles, avatars, and issue queues. Users can delegate tasks to agents as casually as they would to a fellow teammate, or they can initiate a chat to request issue drafting, inquiries, or to manage one-off tasks. Furthermore, Multica's shared context layer ensures that comments, attachments, reports, task histories, and workspace knowledge remain readily available to both agents and users, while the implementation of skills serves as comprehensive playbooks that empower all agents to utilize consistent definitions and operational guidelines. This integration not only enhances productivity but also fosters a more cohesive working relationship between humans and AI in the project environment.

AionUi serves as a desktop environment where AI agents reside directly on the user's computer, collaborating seamlessly on various daily tasks including coding, slide creation, file organization, data analysis, photo editing, report writing, academic paper drafting, and automating processes around the clock. Users have the flexibility to engage with a single agent, operate multiple agents simultaneously, delegate tasks to the most suitable assistant, or combine them within a cohesive workspace. This innovative platform automatically identifies and integrates with a variety of tools already available on the user's machine, including Claude Code, Codex, Gemini CLI, Aion CLI, OpenCode, OpenClaw, Goose, and many more, allowing for the efficient use of existing resources without the need for reinstallation. AionUi comes equipped with over twenty pre-built assistants designed for various applications such as presentations, Excel spreadsheets, financial modeling, document creation, academic writing, diagramming, UI/UX design, gaming, creative writing, project management, recruitment, setup processes, and complete autonomous workflows. Additionally, users have the option to develop custom assistants that are specifically designed to enhance their individual workflows, making the platform highly adaptable to different user needs. This level of customization ensures that every user can optimize their productivity while leveraging the power of AI.

Kimchi serves as a centralized platform designed for overseeing both SaaS and self-hosted AI models, enabling teams to deploy, route, optimize, and scale their LLM infrastructure seamlessly, all while maintaining their established developer workflows. This solution provides a unified control layer for managing AI coding agents, open-source models, commercial offerings, and internal inference, allowing organizations to blend cost-effective open-source solutions with premium providers like Claude, OpenAI, and Gemini when necessary. By prioritizing the reduction of LLM costs, Kimchi enhances the autonomy of development processes through efficient model routing, coding-focused inference, integration with multi-cloud platforms, support for multi-agent workflows, and the ability to interchange OSS and commercial models, all with minimal setup friction. Additionally, it facilitates the operation of the Kimchi coding agent across various teams, thereby broadening access to AI coding capabilities for engineering organizations while ensuring transparency in usage attribution, visibility into costs, and maintained operational governance. This comprehensive approach not only streamlines AI integration but also empowers teams to leverage the best resources available for their specific needs.

Vokal serves as a collaborative hub designed for teams and AI agents, enabling founders and product teams to manage agent tasks in a transparent environment where they can observe, evaluate, and repurpose important work. This platform ensures that human-agent collaborations have a centralized starting point, maintaining visibility and facilitating the reuse of contextual information, rather than relegating agent activities, assumptions, and decisions to isolated sessions across various tools like Claude Code, Codex, Cursor, and ChatGPT. By integrating channels, tasks, documents, files, applications, agents, memory, a Knowledge Base, identity, access rights, runtime, and event logs, Vokal empowers teams to keep their outputs synchronized, reviewed, controlled, and easily reusable. Agents operate within shared channels, which have designated owners, specified roles, clear instructions, reliable sources, defined statuses, permission scopes, application permissions, allocated memory, local project-file access, and observable activities. In addition, teams can utilize pre-defined roles tailored for engineering, product development, growth, customer support, operations, research, and other areas, or can opt to integrate their own local tools like Codex, Claude Code, and Hermes to suit their specific needs. This flexibility not only enhances collaboration but also fosters a more efficient workflow among team members and AI agents alike.

oh-my-claudecode is an advanced plugin for Claude Code that adds multi-agent and multi-AI orchestration to software development workflows. The platform allows Claude to coordinate with Gemini and Codex workers, each contributing strengths such as design review, large-context analysis, architecture validation, security review, and code inspection. It includes 19 specialized agents and 39 skills that help developers handle planning, implementation, debugging, testing, documentation, quality review, and other technical tasks. Users can activate workflows through simple keywords such as autopilot, ralph, ulw, plan, ralplan, deep-interview, and team. The Autopilot mode supports autonomous execution from a high-level idea to tested code, while Team mode enables coordinated parallel work across multiple agents. oh-my-claudecode also includes MCP-powered tools such as LSP integration, AST Grep, a persistent Python REPL, and project memory. The plugin is built to reduce the need for memorizing commands by detecting user intent and routing work to the right mode or specialist. It supports installation through the Claude Code plugin marketplace or npm for users who want direct CLI access. oh-my-claudecode gives developers a stronger Claude Code workflow for building, reviewing, and managing software projects with AI assistance.

Graphify serves as an innovative open source knowledge graph engine that converts diverse inputs such as code, documentation, research papers, meetings, images, browser tabs, and commits into a single, navigable graph with full recall capabilities. Designed to function as a persistent memory for AI coding assistants, it empowers tools like Claude Code, Codex, OpenCode, Cursor, Gemini CLI, GitHub Copilot CLI, Aider, Factory Droid, Kimi Code, Kiro, Pi, and Google Antigravity with a queryable grasp of a project, thereby eliminating the need for them to continuously search through files. Users can direct Graphify to any directory, where it generates an initial corpus through AST extraction, semantic analysis, and Leiden clustering, effectively converting an entire codebase or document collection into a comprehensive graph in a single operation. Unlike traditional RAG pipelines that require re-embedding for every modification, Graphify sustains a dynamic graph that only updates the affected nodes and edges when files are altered, allowing the remainder of the corpus to remain stable even at an enterprise scale. This capability not only enhances efficiency but also facilitates seamless collaboration among various AI tools, significantly improving the overall workflow for developers and researchers alike.

MemPalace is a storage and retrieval system that prioritizes local-first principles for AI workflows, ensuring that users retain control over their conversations while providing AI with a form of memory. Instead of summarizing dialogues, it stores them in their entirety and organizes this information into a navigable "palace" structure, drawing inspiration from the classical memory palace method. Users can categorize conversations into designated wings based on individuals, projects, or themes, while utilizing rooms and drawers to facilitate easy access and retrieval of information. This system is tailored for those who value ownership of their words, featuring local-first storage, no telemetry, and a strong emphasis on privacy by keeping all memory on the user's device. Additionally, MemPalace enhances AI functionalities through MCP tooling, which includes features for reading and writing within the palace, performing knowledge-graph operations, navigating across wings, managing drawers, and maintaining agent diaries. Ultimately, MemPalace serves as a bridge between user agency and AI memory, creating a seamless experience that respects personal privacy.

OpenViking is an open-source context database tailored for AI agents, utilizing a file-system architecture to streamline the management of memories, resources, and skills. Rather than viewing context as disjointed pieces in a fragmented vector store, OpenViking consolidates agent context into a virtual file system through the viking protocol, allowing agents to effectively store, navigate, retrieve, and observe the necessary information. This system is designed to alleviate the burdens of manual context management for developers, offering agents a simplified interaction model akin to file operations. Furthermore, OpenViking facilitates hierarchical context loading, semantic and recursive retrieval, session management, metrics tracking, and observability, enabling AI agents to efficiently access pertinent information without overwhelming prompts. By adopting this approach, developers can enhance the efficiency and effectiveness of their AI systems.

Hindsight is an innovative memory framework designed to enhance AI agents by enabling them to learn progressively rather than resetting their knowledge with each new interaction. Unlike traditional memory systems that primarily focus on recalling past conversations, Hindsight prioritizes the learning process, equipping agents with a persistent long-term memory through advanced biomimetic data structures. This functionality allows AI agents to keep track of essential facts, access relevant context, and engage in reflective reasoning based on their experiences. Hindsight is particularly beneficial for agents that require a deep understanding of user identities, previous discussions, evolving preferences, decision-making histories, and necessary behavioral adjustments across different sessions. To achieve this, it incorporates three fundamental operations: retain, which captures new information; recall, which accesses appropriate memories when required; and reflect, which aids agents in synthesizing observations, developing mental frameworks, and gaining insights from earlier interactions. By implementing these features, Hindsight ensures a more personalized and context-aware experience for users.

MythOS serves as a collaborative memory platform that connects you with every AI you interact with, aiming to eliminate the need for repetitive explanations across various models, agents, and communication channels. Tailored for individuals who engage in writing as a form of thinking, it provides a modular framework for organizing structured notes, memos, contextual maps, and workflows enhanced by artificial intelligence. With MythOS, users can efficiently record what they read, link their thoughts, and disseminate their key insights, all while keeping their resource library easily accessible to any AI. Functioning as a personal knowledge management system, it allows for the systematic organization of memory, notes, concepts, resources, and context into coherent documents that maintain their relevance over time. By considering knowledge as an ongoing process rather than a static achievement, MythOS enables users to create living documents that adapt, develop, and interconnect with relevant individuals, projects, themes, and concepts. Additionally, it features tools for constructing contextual maps, sharing public memos, managing private knowledge, leveraging AI-compatible memory, and facilitating exportable workflows that assist users in establishing a resilient framework of context. This approach not only enhances personal productivity but also fosters a deeper understanding of complex ideas through interconnectedness.

claude-mem serves as an offline-first cloud memory solution for AI agents, centered around an open source engine along with a cloud synchronization layer that connects agent memories universally through a single private MCP link. Its design ensures that coding agents and AI assistants do not begin from scratch in each session, regardless of the machine or editor in use. As agents work, claude-mem efficiently records notes that encapsulate decisions, solutions, obstacles, environmental insights, architectural choices, and a variety of structured observations within a temporal database. The CMEM Cloud then replicates this local memory through a private Model Context Protocol endpoint, enabling any compatible agent or integrated development environment to access and modify the same memory across various platforms such as Claude Code, Cursor, Windsurf, OpenCode, Codex CLI, Gemini CLI, and VS Code. Operating primarily in a local setting, it maintains functionality whether or not a network connection is available, and ensures that memory is kept in sync whenever cloud access is present. This innovative approach enhances the continuity of AI interactions, facilitating a smoother experience for developers and users alike.

CMEM Cloud serves as the synchronization layer for claude-mem, designed to connect AI agent memory universally via a single private MCP link. The open-source engine, claude-mem, records notes while an agent performs tasks, while CMEM Cloud replicates that local memory, enabling agents to access it seamlessly across different sessions, devices, editors, and any MCP-compatible client. This innovative system eliminates the need for users to repetitively clarify context, copy previous notes, or start from scratch by automatically logging decisions, bug fixes, dead ends, environmental observations, architectural decisions, and other structured insights as the agent operates. These valuable insights are preserved in a temporal database, allowing for meaning-based searches through vector recall, and are accessible via a private MCP endpoint that any compatible agent can utilize for reading and writing. The process initiates with the installation of the local engine, followed by allowing a secondary model to generate structured notes independently, syncing the local database with CMEM Cloud, and finally enabling memory recall from any location. This approach not only enhances efficiency but also fosters a more collaborative environment among agents by sharing insights effortlessly.

Ejentum serves as a structured reasoning framework tailored for agentic AI, enhancing the reliability, auditability, and discipline of LLM agents during intricate or protracted tasks. This innovative tool can be invoked by agents mid-task, facilitating precise cognitive operations tailored to the specific challenges they face, allowing for real-time corrections in reasoning rather than depending solely on static prompts. Designed to prevent AI agents from deviating, flattering, fabricating, or fixating on incorrect hypotheses, Ejentum also ensures they don’t settle for superficial answers or lose vital context over successive steps. The framework boasts 679 capabilities organized into four cognitive harnesses: reasoning, code, anti-deception, and memory. Within the reasoning harness, analytical capabilities are directed towards understanding causality, time, space, simulation, abstraction, and metacognition, which aids agents in steering clear of merely recognizing surface patterns. By integrating these diverse functionalities, Ejentum empowers AI to maintain a deeper engagement with tasks, ultimately enhancing the quality of their outputs.

PageAI is the best AI website builder, ranked as the top AI website generator by multiple publications. It's powered by the latest state of the art AI models that transform a simple prompt into a fully functional, production-ready website within minutes. It employs specialized AI agents to meticulously plan, research, design, and code each individual page, generating wireframes, selecting themes, creating visual assets, and producing professional-quality copy, ultimately delivering clean and maintainable code in Next.js + TypeScript built on Shadcn UI and Tailwind CSS. Users have access to a drag-and-drop editor with live preview capabilities, enabling them to personalize various elements such as themes, fonts, text, images, icons, and the arrangement of sections according to their preferences. Each website is designed with SEO best practices in mind, equipped with essential features like meta tags, JSON-LD, canonical URLs, sitemaps, RSS feeds, dynamic social images, favicons, dark mode support, and a built-in MDX blog that includes search functionality, tags, and multiple layout options, as well as terms and privacy pages. With a simple one-click deployment to Vercel or the option to download the complete codebase, users enjoy a hassle-free experience. There are no subscription fees or long-term commitments involved; you pay only once for each website, and the code is yours to keep indefinitely, with unlimited edits included, offering both flexibility and ownership. Additionally, this model allows users to focus on their creativity without worrying about ongoing costs or restrictions.

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

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

Sakana Fugu is a multi-agent AI platform and AI model that gives users access to coordinated model intelligence through one API. Instead of relying on one frontier model, Fugu dynamically selects, routes, and coordinates multiple expert models to complete complex tasks more effectively. The system is based on research into learned model orchestration, including the TRINITY and Conductor approaches for assembling agents and guiding collaboration patterns. Fugu is designed for coding, code review, reasoning, research, paper reproduction, cybersecurity analysis, patent investigation, and other work that benefits from multiple specialized agents. Users can access Fugu and Fugu Ultra through an OpenAI-compatible API, making integration easier for existing workflows and developer tools. Fugu is positioned as the default option for everyday use because it balances performance and latency. Fugu Ultra is built for difficult, high-value tasks where maximum quality matters more than speed. The platform also gives organizations the ability to opt out of specific models or providers for data, privacy, compliance, or internal policy reasons. Sakana Fugu helps users reduce dependence on a single AI vendor while gaining a flexible orchestration layer for advanced multi-step AI work.

DanubeData is an advanced managed services platform tailored for European cloud infrastructure, integrating compute, databases, caches, storage, and applications into a cohesive namespace. Designed to ensure your data navigates optimally, it enables the deployment of VPS instances powered by AMD Zen4, along with managed databases such as PostgreSQL, MySQL, and MariaDB, as well as Redis-compatible caches and S3-compatible object storage, all accessible from a single dashboard. Operating entirely within a German datacenter, the platform benefits from zero-latency internal networking, eliminating cross-region delays and simplifying overall architecture. Virtual machines can be provisioned in less than 45 seconds, featuring AMD EPYC Zen 4 cores, NVMe Gen4 storage, DDR5 memory, full root access, cloud-init compatibility, SSH key support, DDoS protection, and real-time resource monitoring. Managed databases come fully equipped for production with features like automated backups, point-in-time recovery, read replicas, automatic failover, and default SSL/TLS encryption, along with performance insights and additional tools for efficiency. This comprehensive setup not only enhances the speed of deployment but also ensures a robust and secure environment for all your cloud needs.

Backgrind is a versatile overlay window designed for AI coding agents, tailored for both vibe coders and professional developers on macOS and Windows platforms. It allows for AI tools like Claude Code, Cursor, or its proprietary Grindy model to remain in an always-visible window that hovers above your active tasks, enabling you to initiate a task, engage in coding or leisure activities, and only receive notifications when the AI requires your input. This innovative solution serves as a lightweight interface for the command-line tools you already utilize, preserving your login credentials, history, hooks, and overall workflow while seamlessly integrating with your editor, web browser, documentation, or even during immersive full-screen gaming sessions. The window is highly customizable; you can drag it around, resize it, change its colors, adjust its transparency, summon it using a hotkey, or activate a click-through mode that allows your clicks and keystrokes to interact directly with the applications beneath it. Notifications are subtle, with the window only flashing or chiming when your AI agent requests approval, poses a question, or completes a task. Additionally, Backgrind functions like a compact integrated development environment (IDE), featuring a file tree, syntax-highlighted previews, rendered Markdown content, and clickable links to files, making it an invaluable tool for developers. This unique blend of functionality and accessibility ensures that you stay focused on your projects without being overwhelmed by interruptions.

Scalekit is an authentication and integration platform designed specifically for AI agents that need to securely connect with SaaS applications, APIs, MCP servers, databases, and internal systems. The platform enables agents to act on behalf of individual users instead of using shared service accounts, ensuring every action follows the user's identity, permissions, and approved access scopes. Scalekit manages delegated OAuth authentication, secure credential vaulting, token lifecycle management, authorization checks, and API communication without requiring developers to build that infrastructure themselves. Developers can connect to more than 100 prebuilt integrations or create custom connectors for proprietary services and internal APIs. Built-in handling for pagination, retries, rate limiting, and error management simplifies tool execution while improving application reliability. Every request is logged with a complete delegation chain that records the user, agent, connector, permissions, and resulting actions for auditing and compliance. The platform supports encrypted credential storage, regional deployments, private cloud options, SIEM integrations, and enterprise-grade security controls. Scalekit also extends beyond AI agents by providing SaaS authentication capabilities such as SSO, SCIM provisioning, RBAC, passwordless authentication, and user management. By combining delegated identity, secure authentication, and production-ready connector infrastructure, Scalekit enables organizations to build trustworthy AI agents without the complexity of managing authentication themselves.

Chutes represents a revolutionary advancement in serverless computing tailored for AI at scale, serving as a premier open source and decentralized platform designed for the deployment, scaling, and execution of open-source models in real-world applications. Engineered for the demands of hyperscaling AI-driven products, it empowers developers with high-performance AI inference capabilities across a range of cutting-edge open source models, along with support for ephemeral and batch processing tasks. Operating continuously, Chutes ensures that the latest open-source models are available within minutes of their release, enabling builders to be at the forefront of innovation as new models emerge. There exists a Chute for nearly every application, extending beyond just the expected large language models to include functionalities for image, video, speech, music, embeddings, content moderation, and custom workloads, all consistently available and poised to scale. With Chutes, teams simply need to provide their code while the platform efficiently manages all other aspects, leveraging swift APIs, the Chutes SDK, or one-click deployment options to seamlessly operate serverless AI applications without any infrastructure concerns. This innovative approach not only streamlines development but also enhances productivity, allowing teams to focus more on their creative solutions rather than on the complexities of deployment.

FetchSandbox provides a safe environment for testing the AI integrations you create, allowing you to avoid any impact on production systems. It facilitates the entire journey from API requests to full-fledged integrations, empowering developers to construct, test, and confirm API integrations in realistic scenarios prior to actual deployment, which helps identify errors early, minimize debugging efforts, and ensures a confident rollout. Rather than depending on static mocks, artificial success responses, or potentially hazardous production keys, FetchSandbox offers teams operational API environments that mimic authentic services. Its stateful API sandbox retains state across multiple requests, adheres to lifecycle transitions, triggers genuine webhook events, and manages multi-step workflows, which are often points of failure for integrations. Developers can leverage tools like the MCP Server, CLI, web application, OpenAPI import, and monitoring solutions to validate their integrations with various APIs, including Stripe, Resend, Clerk, GitHub, Paddle, and Shopify. Overall, this approach not only enhances the reliability of integrations but also streamlines the development process by providing insights into potential issues before they reach the production phase.

Type develops AI teammates that seamlessly integrate with your existing tools like Slack and email, enhancing collaboration among coworkers. In this multiplayer work environment, your AI capabilities are expanded, as Type consolidates knowledge, skills, and integrations into collective agents that foster teamwork rather than confining AI interactions to isolated chats or individual prompts. Tailored for various sectors including marketing, sales, support, and operations, Type empowers teams to either create custom AI teammates or import their own, while securely linking them to the platforms where their work takes place. These agents can be easily mentioned in Slack, organized by channel, and granted access through OAuth and secure API keys that can be deactivated whenever necessary. With extensive support for over 1000 native integrations spanning categories like developer tools, analytics, and productivity, Type ensures that teams have the comprehensive support they need. By enabling collaboration on shared AI, Type not only enhances productivity but also transforms the way teams interact with technology and each other.

Unabyss serves as a comprehensive context layer for AI applications, seamlessly integrating and maintaining live, structured context across all AI interactions a user engages with. It aggregates data from various daily work platforms, including Slack, Gmail, Notion, Google Calendar, GitHub, Linear, Google Drive, and meeting applications, while automatically extracting, organizing, tagging, and updating that context in real time. Rather than confining knowledge within a single chatbot or relying on outdated context files, Unabyss empowers tools such as Claude, ChatGPT, Cursor, Codex, Gemini, Perplexity, OpenCode, VS Code, and OpenClaw to draw from a unified knowledge base. Each AI can access only the pertinent context based on criteria like topic, source, sensitivity, project, and account, ensuring that users are not burdened with the need to repeatedly communicate their roles, preferences, decisions, clients, repositories, or current priorities. This innovative approach enhances productivity and collaboration, allowing users to focus on their tasks without the hassle of redundant explanations.

Maguyva is an innovative agent-first code intelligence system that provides AI coding tools with a prioritized map of a repository even before any modifications are made. Teams integrate their GitHub repositories, while cloud pipelines effectively parse, rank, and index various elements such as symbols, dependencies, imports, semantic relationships, and cross-file structures, ensuring that the index remains up-to-date as code evolves. With a single remote MCP integration, agents within platforms like Claude Code, Cursor, VS Code, Windsurf, Codex, Gemini CLI, and other compatible clients can access a shared grounded context without the need for a local indexer or altering their preferred editors. The system's 11 MCP tools utilize a combination of semantic, structural, graph, and text retrieval across five different search modalities, delivering ranked results rather than just raw grep output. Users can pose questions in plain language, pinpoint crucial symbols, identify patterns with AST-aware searches, trace dependencies, detect orphaned code, evaluate the impact of changes, and compile task context prior to engaging with a file, enhancing overall productivity and collaboration within development teams. This streamlined process not only simplifies coding tasks but also fosters better team communication and efficiency.

Qualflare is a platform that leverages artificial intelligence for managing tests and enhancing observability, turning unrefined test data into valuable insights. It gathers results from your continuous integration pipeline, identifies flaky tests based on historical data, categorizes failures by their underlying causes, monitors trends in reliability, and evaluates the risk associated with each release. Its AI assistant, Quo, provides straightforward answers about your testing suite in easily understandable language. The platform features an integrated approach to test management, encompassing test cases, execution runs, milestones, and defect tracking alongside observability dashboards, enabling both QA and engineering teams to comprehend not only what failed but also the reasons behind those failures. Additionally, it is compatible with a wide array of testing frameworks, including Playwright, Cypress, Jest, pytest, and JUnit, and offers seamless integration with CI/CD tools such as GitHub Actions, GitLab CI, Jenkins, CircleCI, and Azure DevOps. A free plan is also available, allowing teams to experience its capabilities without initial investment. This comprehensive solution is designed to streamline the testing process and improve overall software quality.

Kastra serves as the crucial authorization framework for AI systems, determining the permissions of agents, models, and tools prior to their execution. Positioned along the execution path of all interactions such as prompts, tool calls, shell commands, database operations, and API requests, it evaluates each action based on deterministic, attribute-driven policies, rendering decisions to allow, deny, redact, or escalate in less than a millisecond. In contrast to monitoring solutions that only track AI actions post-execution, Kastra proactively prevents unauthorized activities before they can impact any tool, API, database, or production environment. Its comprehensive control plane integrates a policy engine, edge decision-making capabilities, various integrations, and a tamper-proof evidence vault that securely signs each decision for auditing and replay purposes. Furthermore, with Kastra Edge, local enforcement is extended to developer environments, safeguarding coding agents such as Claude Code, Cursor, and Codex CLI from harmful commands, unauthorized data extraction, unsafe file modifications, and improper tool usage. This proactive approach to authorization not only enhances security but also ensures compliance and accountability in AI-driven processes.

Heard is a comprehensive financial back office designed exclusively for therapists, seamlessly integrating bookkeeping, taxes, payroll, and business assistance into a single cohesive platform. This service empowers private-practice owners to track their financial flow, maintain organization throughout the year, and confidently make informed fiscal choices without the hassle of managing spreadsheets or juggling various service providers. By linking their business bank and credit card accounts, users allow Heard to sort transactions, reconcile accounts, and generate straightforward monthly financial statements that detail income, expenses, profits, and cash flow. Additionally, it offers support for quarterly tax projections, preparation and submission of annual federal and state taxes, identification of business deductions, tax extensions, and continuous access to financial professionals well-versed in the unique challenges of therapy practices. Furthermore, the integrated payroll system ensures that owners can accurately compensate themselves, handle employee payroll, manage contractor payments, and keep payroll records in sync with their bookkeeping and tax obligations, enhancing overall financial management for practitioners. This holistic approach not only simplifies financial tasks but also allows therapists to focus more on their clients and less on administrative burdens.

Prefactor is a cutting-edge platform designed for real-time assessment, monitoring, and reliability of production AI agents. It evaluates each execution instantly based on metrics such as quality, drift, cost, and data risk, seamlessly integrating these assessments into actionable responses to ensure that any failing agent is detected in real time rather than merely reflected on a post-execution dashboard. Teams are equipped to monitor every model invocation, tool usage, and decision-making process through structured traces and spans, allowing them to conduct evaluations using LLM-as-judge, technical assessments, qualitative analyses, and custom metrics at every phase of the process. Additionally, context can be incorporated from various sources, including GitHub, Linear, Jira, databases, and internal APIs, serving as ground truth for evaluations. When a run exceeds predefined limits, Prefactor is capable of blocking or throttling it, pausing sensitive actions, or routing the decision to a person for approval, modification, or rejection prior to execution, with meticulous logging of each choice made. The command-line interface allows for the discovery of agents without the need for platform migration, while the TypeScript and Python SDKs ensure seamless integration with LangChain, Claude, Vercel AI, OpenClaw, and LiveKit, enhancing the overall functionality and adaptability of the platform. This comprehensive approach not only optimizes agent performance but also fosters collaboration among teams by providing clear visibility and control over the AI processes.

Memmy serves as a local-first AI memory framework that ensures all AI tools maintain a unified representation of the user. Designed for individuals who frequently collaborate with multiple assistants, it seamlessly interprets authorized collaboration histories from platforms like Cursor, Claude, and Codex, transforming disjointed dialogues, user preferences, project details, technical choices, achievements, and common challenges into an organized memory system. The workflow consists of three distinct phases: Scan reviews chosen histories stored on the user's device; Organize processes and refines the information by deduplication, categorization, and indexing; and Inject provides the active AI with only the most pertinent memories through targeted, real-time matching rather than overwhelming it with excessive data. This intelligent structure enables users to shift between tools effortlessly while retaining context, consolidate discussions held with various agents, document recent choices made, maintain writing styles, and proceed with tasks that are yet to be completed, all of which enhances productivity. As a result, users can navigate their work with greater efficiency and less disruption.

Spokenly is an innovative dictation application powered by AI, available for Mac, iPhone, Windows, and Linux, designed to convert spoken words into clear, punctuated text in any working environment. By simply holding a shortcut, users can speak naturally and then release to insert the transcription directly at the cursor across various platforms including browsers, email, chat applications, word processors, IDEs, terminals, and more. This versatile app accommodates over 100 languages, supporting mixed-language dictation, and provides both local and cloud-based speech-to-text models. Users can utilize on-device models like Whisper and Parakeet for offline operation, while cloud services from companies such as OpenAI, Deepgram, Groq, Soniox, and ElevenLabs can be accessed for enhanced accuracy or real-time transcription needs. Additionally, the Local Only Mode ensures that voice data remains solely on the device, preventing any network interactions. The application features modes that allow users to save different transcription models, select AI providers, set prompts, and choose output styles tailored for specific tasks. Furthermore, the AI Instructions feature enables users to eliminate filler words, correct grammar and punctuation, summarize, rewrite, translate, or reformat the dictated text, enhancing the overall functionality and user experience of the app. With its extensive capabilities, Spokenly stands out as a comprehensive solution for anyone looking to streamline their dictation process.

port22 allows you to carry your coding agents with you, making it easy to run the agent from your iPhone while overseeing its actions through a live transcript. You can interact with Claude Code, Codex, or OpenCode from virtually anywhere, approving actions as the agent operates. Instead of initiating a new session, it seamlessly connects to the existing one running in your terminal, ensuring you can maintain continuity with your work from your desk. As the agent thinks, edits, and executes commands, every token is streamed directly to your phone, and you receive push notifications when it requires your input or has completed a task. The current status of the session is readily accessible via the Dynamic Island and lock screen, enabling you to monitor every ongoing process without the hassle of opening the app repeatedly. Additionally, port22 functions over your local network while at your desk, and when you're on the go, it effortlessly transitions to a secure, end-to-end encrypted relay, allowing you to keep the same session active over cellular data as well. This innovative approach ensures that your workflow remains uninterrupted, regardless of your location.

Pikes is an innovative AI platform designed specifically for consumer brands, enabling them to create, modify, and produce product images, advertisements, and videos directly from existing product materials. This tool ensures that every output aligns with the brand identity through features like shared product libraries, saved prompts, and adherence to brand guidelines, which teams can utilize for various applications including product detail pages, Amazon listings, lifestyle imagery, photoshoots, hero sections, and performance marketing strategies. Users can input a product just once to generate high-quality visuals, variations for multiple SKUs, seasonal themes, marketplace content, animations, and video advertisements without the need for traditional filming or reshoots. With a wide range of product-centric templates, the platform allows teams to initiate projects swiftly, and PikesGPT can convert straightforward requests into prompts ready for production. Additionally, shared boards facilitate collaboration by allowing comments and feedback on creative outputs, while the AI agent utilizes brand context and tailored skills to consistently produce new assets according to a predetermined schedule. This seamless integration of collaboration and automation empowers teams to enhance their marketing efforts efficiently and effectively.

Blume serves as a desktop sidecar tailored for AI coding agents, enabling developers to monitor each agent's actions, maintain consistent project context, and identify potential drift before it impacts the codebase. It integrates seamlessly with tools like Cursor, Claude Code, Codex, omp, and Pi, consolidating agent activities, hidden files, skills, hooks, rules, and provider usage into a single interface. The Agents view provides insights into the status of each coding agent, indicating whether they are currently active, have completed their tasks, or are pending approval, while the Setup section reveals the instructions and configuration files that dictate agent behavior. Additionally, usage tracking helps developers keep tabs on remaining plan limits and token usage across various providers like Claude, Codex, and Cursor, thereby minimizing the risk of unexpected disruptions. Blume also securely stores conversation history locally, enabling on-device reviews of rules, skills, and hooks. Its Improve workflow actively identifies recurring points of friction within conversations, groups related issues, and suggests actionable improvements, which may include new rules or the creation of reusable skills, ultimately enhancing the overall coding experience. Furthermore, this continuous feedback loop fosters a more efficient development process, allowing teams to adapt and refine their workflows as needed.

Spendstream enables businesses that interact directly with customers to establish their own customized rewards, cashback, and gift card systems without the need for developing the underlying infrastructure. By utilizing our API and comprehensive white-label services, companies can offer incentives to their users when they shop with prominent UK brands, such as supermarkets and various retailers. These rewards can be given in forms like cashback, account credits, competition entries, loyalty points, or other currencies unique to the brand. The outcome of this service is the creation of an additional revenue source, enhanced customer engagement, increased repeat interactions, and a more attractive value proposition for customers. Spendstream is especially beneficial for companies involved in prize draws, fintech solutions, loyalty programs, membership organizations, publishers, community groups, or any business looking to capitalize on customer spending beyond their own sales transactions. This approach not only fosters customer loyalty but also strengthens partnerships with leading brands, driving mutual growth and success.

bb is an innovative, local-first IDE that allows for extensive customization while interacting with AI coding agents, enabling users to automate, control, and even enhance its functionalities. With just a single prompt, users can effortlessly modify nearly every aspect of the environment, including the addition of panels, CLI commands, skills, plugins, and workflows, which become instantly accessible to their agents. The platform’s various features, such as GitHub integration, agent memory, scheduled tasks, and remote access, are structured as plugins utilizing the same tools that users can employ. Furthermore, its command line interface supports integration with external applications, such as shell scripts, cron jobs, and messaging bots from Telegram, Signal, and Slack, which can initiate tasks that remain visible in the sidebar. bb accommodates several coding agents, like Claude Code, Codex, Cursor, Pi, OpenCode, Grok, omp, and Hermes, allowing users to delegate tasks to the most appropriate agent or enable one agent to create and oversee another in distinct threads. Additionally, all work is executed on the user's own device, providing the flexibility for tasks to persist and operate autonomously until the user decides to resume their interaction. This level of independence enhances productivity and allows for a seamless workflow experience.

Oqoqo serves as a comprehensive platform for creating evaluations and tailored benchmarks for practical tasks requiring agency, enabling teams to conduct large-scale experiments in realistic settings utilizing fully managed cloud services. Users have the flexibility to establish private sets of tasks and criteria, evaluate agents on their ability to interact with various products such as skills, MCP servers, CLIs, SDKs, APIs, documentation, and files, while also facilitating the comparison of agents, models, interventions, and levels of effort under consistent conditions. Each individual task operates in its own separate environment, complete with the necessary project state, context, files, tools, and credentials. Oqoqo meticulously records every aspect of each run, documenting commands, tool interactions, errors, files, and the point at which an agent ceased functioning, ultimately providing metrics such as pass or fail results, pass rates, improvements, token utilization, and areas of friction. With these valuable insights, teams are empowered to pinpoint issues within product interfaces, address token inefficiencies, analyze performance variances, rectify failures, and subsequently re-execute the experiments for further refinement and learning. This iterative process fosters a culture of continuous improvement, ensuring that agents are consistently enhanced for optimal performance.

Nativ is an entirely open-source application designed for macOS, enabling users to execute OpenAI models locally on Apple Silicon, thereby bringing cutting-edge intelligence directly to your workspace without the need for accounts or cloud infrastructure. It features an intuitive chat interface that facilitates streaming responses, supports Markdown and code highlighting, accepts image inputs, and offers performance metrics for each message, all while ensuring that responses are generated locally on the device. The app includes a curated library of models from various teams, such as Google, Cohere, and Liquid AI, and it intelligently suggests models that align with the specifications of your Mac hardware. Built on the MLX-VLM architecture and optimized for M-series unified memory and Metal, Nativ operates models seamlessly without the need for wrappers or translation layers. Users benefit from live telemetry that provides insights into tokens processed per second, memory usage, thermal conditions, and the time taken to generate the first token, giving a clear view of the inference process. Furthermore, Nativ accommodates diverse workflows, including language processing, vision tasks, video analysis, code assistance, and audio manipulation, allowing users to engage in activities like conversing with LLMs, generating image captions, summarizing video content, auto-completing code snippets, transcribing audio files, and producing speech outputs. This versatility makes Nativ an invaluable tool for developers and creators looking to harness local AI capabilities.

Dograh is a self-hostable voice agent platform that is open source and features a no-code workflow builder designed for developing production-ready voice agents. Teams have the flexibility to select their preferred inbound channels, speech-to-text services, language models, text-to-speech options, and telephony providers, or they can opt for innovative speech-to-speech models that facilitate direct audio interactions with seamless turn-taking, interruption management, and minimal latency. The platform caters to both inbound and outbound calling, offering widgets, telephony integrations, observability, tracing capabilities, real-time analytics, and a hybrid approach that combines pre-recorded voice with TTS, all while supporting over 70 languages. Additionally, the MCP server enables various agent runtimes, including Claude Code, Cursor, OpenClaw, and Codex, to create, modify, and deploy voice agents directly from development environments. Dograh can be operated on personal servers, within a private cloud or virtual private cloud, or in a managed setting, ensuring that models can be hosted entirely within the user's infrastructure. With its extensive features and adaptability, Dograh stands out as a versatile solution for teams looking to innovate in voice technology.

Nuphos serves as a DevOps environment tailored for AI, enabling engineering teams and AI agents to collaboratively manage production systems while maintaining control. These agents are designed to familiarize themselves with your infrastructure, troubleshoot problems, and seamlessly navigate platforms like AWS, GCP, Kubernetes, and Cloudflare, all while adhering to strict IAM permissions, human oversight, and comprehensive audit trails. Each session initiated by an agent can be confined to specific IAM roles and employs temporary, least-privilege credentials, ensuring that any proposed changes to the infrastructure undergo a prior approval process. Agents are capable of examining resources, accessing dashboards, analyzing logs, creating actionable plans, seeking necessary approvals, and performing safe actions, all the while accumulating knowledge about services, environments, workflows, runbooks, and the history of operations. Instead of toggling between different terminals, cloud interfaces, dashboards, and documentation, engineers and agents collaborate within a unified DevOps workspace for enhanced efficiency and coherence. This integrated approach fosters a streamlined workflow, allowing both teams and AI to innovate and respond to challenges more effectively.

Norma consistently verifies AI-generated code against a set of governed rules, delivering the same outcome each time. This verification process occurs in two distinct environments: Livecheck, which assesses a specific file or code snippet upon request from editors such as Cursor, Claude Code, Windsurf, or VS Code, and Full Scan, which conducts a thorough audit of an entire GitHub or Bitbucket repository, identifying the technology stack and frameworks even in the absence of a configuration file, while prioritizing issues based on severity and providing the necessary context for remediation. Evaluations of the rules are conducted through static analysis, ensuring that results remain consistent across different runs. The rules are derived from SOLID design principles and specific conventions for various technology stacks, with the flexibility to disable those that are not applicable. The coverage of this system includes multiple programming languages and frameworks, such as JavaScript, TypeScript, Python, PHP, Java, Node, React, and Supabase. Additionally, the MCP server provides a range of functionalities, including link_repository, get_rulesets, get_rules_for_ruleset, live_check, get_open_issues, and register_applied_actions, all secured by OAuth and listed on the Official MCP Registry. This robust framework ensures that developers can maintain high code quality while adapting to various project requirements seamlessly.

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.

AdAnt serves as an innovative AI tool designed to automate the creation of short-form video advertisements for social media platforms in a scalable manner. Users can input a product link, a website, or a reference video, engage in discussions about their creative vision through chat, and then produce customizable ad variations suitable for platforms like TikTok, Instagram Reels, and YouTube Shorts. It leverages successful social video patterns to suggest effective hooks, templates, avatars, and product narratives that align with the user's brief, subsequently generating numerous variations for potential testing. In the process of recreating a previously successful ad, AdAnt thoroughly examines its components, such as structure, scenes, pacing, hooks, captions, and visual rhythms, and then adapts the format for a new product and target audience. The creation process is structured around three essential dimensions—template, avatar, and product—allowing users to modify each element before generating a series of ads. Additionally, users can refer directly to products, avatars, and templates during their conversations, ensuring that all creative assets and decision-making occur within a single, cohesive workspace. This streamlined approach enhances collaboration and efficiency, making it easier for users to achieve their advertising goals.