Alternatives to Agent Substrate

BAND creates robust interaction frameworks designed for enterprise-level applications of distributed AI agents. The platform facilitates immediate, collaborative interactions among both agents and humans, incorporating a runtime control plane that upholds policies, defines authority limits, and ensures transparency across diverse systems. Additionally, BAND empowers developers, engineering teams, and leaders of enterprise platforms who are managing multi-agent ecosystems spanning internal infrastructures, SaaS solutions, and environments shared with partners. This support enhances operational efficiency and fosters innovation within complex organizational structures.

Agent Compute Engine is a fully managed compute service offered by Google Cloud that creates execution environments specifically for AI agents. Each agent operates within its own hardware-isolated microVM, complete with a dedicated guest kernel and root-level management capabilities. Users can benefit from features such as rapid startup, pausing, checkpointing, creating snapshots, and resuming workflows. Persistent workspaces are available to maintain agent files and context throughout different sessions, and the platform utilizes copy-on-write memory sharing to enable parallel environments derived from shared images. Agents have the capability to execute code, run shell commands, and perform browser actions within secure sandboxes, with carefully regulated access to enterprise data through Google Cloud's networking and identity frameworks. Developers have the flexibility to customize environment images, manage identity protocols, control network access, and oversee the lifecycle of their agents, all while Google takes care of the underlying compute infrastructure. The platform is particularly well-suited for interactive assistants, coding agents, browser-based tasks, and extensive evaluation and reinforcement learning tasks, showcasing its versatility and efficiency in handling a wide range of AI applications. This comprehensive management allows developers to focus on building and optimizing their AI solutions without the burden of infrastructure concerns.

Sandbox0 serves as an Apache-2.0 compliant runtime environment designed for executing AI agents and services that handle untrusted code over extended periods. It offers secure, isolated Linux environments capable of command execution, maintaining stateful contexts, performing file operations, facilitating SSH access, managing supervised sessions, and enforcing network controls. The architecture allows sandboxes to preserve a durable writable RootFS through successful pause-and-resume checkpoints, alongside features such as snapshots, restoring capabilities, and forks that enable the creation of reusable workspaces. Developers can leverage the Python, JavaScript/TypeScript, and Go SDKs, along with an HTTP API and the s0 CLI, to efficiently create and manage their sandboxes. For those seeking hosted solutions, Sandbox0 Cloud offers convenient access, while organizations have the option to self-host the runtime on dedicated Nomad infrastructure. By default, pausing the system preserves the filesystem state but does not maintain live process memory or external connections, although experimental memory recovery is available, contingent upon the necessary deployment infrastructure. This multi-faceted approach ensures flexibility and security for various user needs when working with AI technologies.

Sprites by Fly.io is a stateful sandbox environment platform designed to run arbitrary code in isolated Linux computers. Each Sprite is a hardware-isolated execution environment that can persist across runs, making it useful for AI agents, uploaded binaries, developer tools, and application workloads. The platform is built around checkpoint and restore, allowing workloads to pause, resume, and keep state instead of rebuilding the environment repeatedly. Sprites provide a fast NVMe filesystem that syncs continuously to durable external object storage. This tiered storage approach gives users compatibility with normal Linux filesystem behavior while only charging for the data they actually use. Developers can start with the Sprites CLI by logging in, creating a Sprite, executing commands, and connecting to the console. The platform also supports REST API access and SDKs for JavaScript, Go, Elixir, and Python. Pricing is usage-based across CPU time, memory time, hot storage, and cold storage. By combining sandbox VMs, persistent filesystems, checkpointing, APIs, network policy, and pay-as-you-go pricing, Sprites gives developers a practical runtime for AI agents and untrusted code execution.

Daytona is a modern cloud-based runtime designed to let developers and AI systems launch secure, isolated workspaces for any project in seconds. Each environment runs inside a lightweight microVM that includes full Linux support, networking, and persistent storage. Through Daytona’s Python and TypeScript SDKs, users can automate code execution, file uploads, and environment lifecycle management directly from their apps. By shifting development to the cloud, Daytona eliminates the need for complex local setups and enables fully reproducible sandboxes accessible via SSH, APIs, or live preview URLs. Built for speed, automation, and scalability, it supports everything from simple prototypes to production-grade agent workloads.

AgentComputer is an innovative cloud infrastructure platform tailored for operating AI agents in secure and fully operational virtual spaces. It offers "cloud computers" as lightweight Ubuntu-based sandboxes that can be set up in less than a second, enabling developers to swiftly create, access, and control their environments through a command-line interface. These virtual spaces come with persistent storage, ensuring that any installed applications, files, or settings are preserved even after restarts, which facilitates ongoing and consistent workflows. The platform is founded on an agent-first design, allowing AI agents to perform tasks directly within these spaces via SSH, which reduces the barriers between issuing commands and executing them. Furthermore, it features a built-in AI harness that accommodates various agents, including Claude, Codex, and additional coding assistants, thus promoting seamless collaborative multi-agent operations within the same environment. This integration not only enhances productivity but also streamlines the development process for AI-driven projects.

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

Composio is an advanced platform designed to empower AI agents with the ability to execute real-world tasks across multiple applications. It connects agents to over 1,000 tools, enabling seamless interaction with platforms like Slack, Gmail, Notion, and GitHub. The platform automates key processes such as authentication, permission management, and tool execution, reducing development complexity. Composio uses intelligent tool selection to match user intent with the appropriate actions, improving accuracy and efficiency. It also provides secure sandbox environments where workflows can run safely and independently. Developers can create multi-step workflows and automate complex tasks with minimal setup. The platform supports parallel execution, allowing agents to perform multiple operations simultaneously. Composio is model-agnostic, enabling flexibility in choosing AI models without reworking integrations. Its context-aware sessions ensure agents maintain continuity across tasks and interactions. Overall, Composio transforms AI agents into fully functional systems capable of executing real-world workflows.

Ovrin serves as a cloud-based computing platform tailored for developers who are creating AI agents and other software that necessitates execution within regulated environments. Users can leverage a REST API, alongside Python and TypeScript SDKs, or an MCP server to establish temporary Linux sandboxes, execute shell commands and stateful code, handle file management, configure scoped API keys, and access execution results. The platform features preconfigured setups such as Claude Code, OpenAI Codex, Gemini CLI, and DeepSeek Harness, while browser workflows are compatible with Chrome and Playwright. Additionally, it provides functionalities like persistent memory, controls for sandbox lifecycles, usage tracking, and limits on concurrency. Common applications of Ovrin include executing coding agents, automating browser tasks, and running code generated by models. Notably, Ovrin offers a free tier alongside several paid options that provide increased concurrency and usage-based compute costs. Overall, the platform aims to streamline the development process for AI solutions by providing a robust and flexible environment.

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.

E2B is an open-source runtime that provides a secure environment for executing AI-generated code within isolated cloud sandboxes. This platform allows developers to enhance their AI applications and agents with code interpretation features, enabling the safe execution of dynamic code snippets in a regulated setting. Supporting a variety of programming languages like Python and JavaScript, E2B offers software development kits (SDKs) for easy integration into existing projects. It employs Firecracker microVMs to guarantee strong security and isolation during code execution. Developers have the flexibility to implement E2B on their own infrastructure or take advantage of the available cloud service. The platform is crafted to be agnostic to large language models, ensuring compatibility with numerous options, including OpenAI, Llama, Anthropic, and Mistral. Among its key features are quick sandbox initialization, customizable execution environments, and the capability to manage long-running sessions lasting up to 24 hours. With E2B, developers can confidently run AI-generated code while maintaining high standards of security and efficiency.

The Agents API enables developers to create and operate cloud agents utilizing the same framework and infrastructure that supports Codex, all under the management of OpenAI. By making a simple API request, users can establish a fully operational agent by defining the specific task, model, tools, and environment, with OpenAI overseeing the agent's infrastructure. Developers have the flexibility to select where their agents are deployed: whether in an OpenAI-managed sandbox, on personal servers, or via authorized sandbox partners. The OpenAI-managed sandboxes offer secure environments for agents to execute code, interact with files, utilize packages, skills, and plugins, and generate outputs. Designed for tasks that require extended operation, the API features automatic context compaction to retain pertinent information across multiple context windows within sessions. Additionally, the tool search function efficiently loads relevant definitions only as needed, while programmatic tool invocation allows agents to execute calls simultaneously, chain related tasks, and filter or merge results seamlessly in code. This comprehensive approach not only enhances efficiency but also empowers developers to create more sophisticated applications.

Epho transforms coding agents into a versatile API, enabling developers to execute Claude Code, Codex, or OpenCode in secure cloud sandboxes via a singular HTTP endpoint. Users can submit prompts, select a harness and model, link repositories and files, connect to MCP servers, and provide environment variables or provider credentials; Epho will then initiate the environment, replicate the code, integrate the necessary tools, and stream the agent's progress in real-time. The system supports both synchronous runs, which can deliver live events, tool calls, edits, final outputs, and artifacts, as well as asynchronous execution featuring polling and webhooks. Importantly, chat sessions are persistent, allowing subsequent interactions to continue from the same filesystem, checkout, agent session, system prompt, model, and MCP setup, even in the absence of the original sandbox. It accommodates private repositories from GitHub, GitLab, and Bitbucket, with agents capable of reading code, implementing changes, executing tests, and refining errors similarly to a local environment. Furthermore, every event is securely stored, ensuring that interrupted streams can reconnect seamlessly without any loss of data during a run. This robust architecture not only enhances productivity but also fosters a more efficient coding workflow.

Phinite offers a comprehensive shared infrastructure designed for the efficient construction, deployment, and governance of AI agents, encompassing orchestration, security, observability, lifecycle management, and environment promotion, which allows engineering teams to avoid the repetitive task of rebuilding these foundational layers for each new agent application. Key features include orchestration capabilities for multi-agent systems that facilitate agent-to-agent interactions and nested calls, in-depth session-level observability that tracks execution timelines, decision variables, tool usages, and associated latency and cost metrics. Additionally, Phinite boasts a Private Agent Registry to enhance skill discoverability, an evaluation suite for assessing accuracy and safety benchmarks, and a streamlined Dev-to-Production workflow that supports seamless environment promotion. Moreover, it enables Kubernetes-native deployments and VPC-internal deployability while ensuring adherence to SOC 2 Type 2 compliance standards, ultimately providing a robust environment for developing AI agents efficiently. This combination of features not only enhances productivity but also fosters innovation within engineering teams.

Molted serves as a managed operating environment specifically designed for autonomous AI agents, enabling teams to effortlessly deploy, host, monitor, recover, and scale agents based on OpenClaw without needing to create their own cloud infrastructure, DevOps, integration, or recovery frameworks. With features that include agent-ready runtimes equipped with persistent workspaces, browser automation capabilities, over 1,000 application integrations, dedicated email and voice functionalities for each agent, comprehensive monitoring, automatic recovery solutions, and efficient lifecycle management, Molted empowers agents to utilize various tools, access websites lacking APIs, and communicate via email, voice, or SMS, ensuring their ongoing operational capability. Targeted at AI agencies, SaaS developers, OpenClaw consultants, and organizations managing fleets of agents for both customer-oriented and internal processes, Molted also offers robust support for managing multiple agents, versioned filesystems, restore points, REST API controls, and deployment options across cloud, on-premise, or sovereign environments. Importantly, Molted distinguishes itself from generic hosting solutions by serving as the foundational run layer specifically tailored for production-grade AI agents, ensuring optimal performance and reliability for diverse applications. In this way, it significantly enhances the operational efficiency of teams utilizing AI technologies.

ComputeSDK is an open-source toolkit available at no cost, specifically crafted to empower developers to execute external or user-generated code within their applications through a cohesive and standardized interface. With a TypeScript-native API, it simplifies the process by seamlessly integrating various compute providers, enabling developers to transition between platforms such as E2B, Vercel, Daytona, Modal, and others while keeping their primary codebase intact. This toolkit is constructed around isolated sandbox environments, which guarantee that the executed code operates securely without affecting the host infrastructure, thereby making it ideal for applications that necessitate controlled execution of potentially untrusted code. Additionally, ComputeSDK offers essential functionalities, including the execution of code and shell commands, filesystem management, the ability to create and dismantle sandboxes, and compatibility with modern web frameworks like Next.js, Nuxt, and SvelteKit. Furthermore, its design ensures that developers can focus on building robust applications without worrying about security vulnerabilities associated with running external code.

Substrate serves as the foundation for agentic AI, featuring sophisticated abstractions and high-performance elements, including optimized models, a vector database, a code interpreter, and a model router. It stands out as the sole compute engine crafted specifically to handle complex multi-step AI tasks. By merely describing your task and linking components, Substrate can execute it at remarkable speed. Your workload is assessed as a directed acyclic graph, which is then optimized; for instance, it consolidates nodes that are suitable for batch processing. The Substrate inference engine efficiently organizes your workflow graph, employing enhanced parallelism to simplify the process of integrating various inference APIs. Forget about asynchronous programming—just connect the nodes and allow Substrate to handle the parallelization of your workload seamlessly. Our robust infrastructure ensures that your entire workload operates within the same cluster, often utilizing a single machine, thereby eliminating delays caused by unnecessary data transfers and cross-region HTTP requests. This streamlined approach not only enhances efficiency but also significantly accelerates task execution times.

AI can reason, but real work often stops at a login, unsupported interface, paid service, missing software, or decision that needs a person. Solid gives that missing layer a name: the operating system for always-on AI agents. Its persistent AI agent runtime keeps approved context and work moving after the chat closes. Subject to permissions and system policies, a Solid-powered agent can use APIs and MCP connections, operate browsers, desktops, phones, and software interfaces, or write and test code to create the missing path. Solid can coordinate models and tools, recover where the workflow permits, check completion, and route sensitive or out-of-policy actions through human-in-the-loop approvals. Supported computers, approved identities and accounts, credentials, phone numbers, cloud resources, and paid services remain subject to customer permissions, budgets, policies, and availability. Developers can use Solid's API to place an agent inside their own product.

Runx is a command-line and open-source runtime tool designed for the creation, discovery, and execution of governed skills tailored for AI agents. It allows developers to establish defined workflows, assign specific permissions and credentials, and execute skills on their own provider accounts locally. The system is capable of pausing runs for necessary approvals or inputs, and it retains a signed receipt that can be reviewed and validated. Skills can be sourced from a public registry and integrated with various compatible agents, including Claude Code, Cursor, and Codex. Additionally, an optional hosted service can facilitate managed OAuth connections, scoped grants, API-based run submissions and inspections, handling of approvals, and notarization of receipts. The command-line interface can function in both local and self-hosted setups. Runx is specifically designed for developers and platform teams focused on building and managing AI-agent workflows that require clear authority boundaries along with auditable execution processes. Furthermore, its capabilities support enhanced collaboration and security measures for the development of AI-driven solutions.

OpenComputer provides a cloud-based platform designed specifically for creating and launching TypeScript AI agents. Developers can customize the behavior of these agents, choose from various models and tools, and deploy them with features like persistent sessions, real-time data streaming, scheduled tasks, memory management, and version-controlled environments. Furthermore, the integration of APIs and service connections enables teams to seamlessly incorporate these agents into their applications and workflows, facilitating tasks like coding support and automated background processes. This flexibility allows for a diverse range of applications, enhancing productivity and efficiency in software development.

AGBCLOUD is a cloud-based sandbox platform designed for AI that offers developers and organizations secure and isolated environments to create and manage autonomous software agents. This platform provides agents with fully-equipped cloud development environments that facilitate multilingual code generation, compilation, and debugging through easily accessible browser sandboxes. By allowing advanced functionalities such as web browsing, computer interactions, and data analysis, AGBCLOUD ensures that AI systems can engage with files, applications, and the internet safely within a controlled space. Furthermore, it incorporates plug-and-play MCP tools alongside LLM-driven analytics to convert raw data into meaningful insights and dynamic applications. The sandbox architecture supports cross-platform capabilities, enabling agents to transition effortlessly between coding, browsing, and system-level tasks, all while upholding stringent security and isolation measures. This versatility opens up new possibilities for developers seeking to enhance their AI solutions.

KERNEL serves as a cloud-based browser infrastructure solution tailored for developers who create AI agents and automate web interactions. By offering managed browser sessions, it enables these agents to explore websites, engage with content, and execute tasks through programming rather than depending on traditional local browser configurations. The platform is optimized for production use, featuring persistent browser profiles, live session monitoring, recording, and replay functionalities, along with tools to oversee browser access effectively. Teams can seamlessly integrate their agent workflows using KERNEL's APIs, SDKs, and hosted MCP server, making it ideal for web research, data collection, and various activities that necessitate web engagement. Additionally, KERNEL offers various subscription options for individuals, startups, and large enterprises, each providing different levels of browser concurrency, usage credits, and access to advanced features like managed authentication, stealth mode, and region-specific browsers. Ultimately, KERNEL stands out as a comprehensive solution catering to a wide range of web automation needs.

Cosine provides an enterprise-ready AI engineering platform designed to keep software development moving beyond the limits of local devices, time zones, and developer availability. The platform enables AI agents to execute long-running coding tasks, perform large-scale refactoring projects, manage software migrations, and coordinate parallel workstreams without conflicts through isolated work environments. Developers can seamlessly transition between desktop, command-line, and cloud interfaces while leveraging support for multiple AI models, remote execution environments, and approval-based deployment workflows. With features such as MicroVM isolation, SSH connectivity, model flexibility, and controlled write operations, Cosine empowers engineering teams to automate development processes while maintaining security and governance standards.

NVIDIA OpenShell serves as an open and secure runtime environment specifically designed for autonomous AI agents, regulating their execution, access rights, and the pathways for inference traffic. Instead of embedding security within the model or application, it maintains safety measures in the surrounding environment, meaning that nothing is allowed by default; permissions are assigned through established policies, and enforcement mechanisms operate outside the agent process to prevent any circumvention via prompts. Each autonomous agent operates within a confined sandbox, which restricts direct network connectivity, limits file access, and implements kernel-level monitoring of system calls to ensure rigorous oversight. Acting as the control plane, a gateway is responsible for user authentication, lifecycle management of sandboxes, and the provision of policies, settings, credentials, and inference configurations to the agents. Additionally, a supervisor operates externally to each sandbox, scrutinizing network requests according to policies at various levels—including binary, destination, method, and path—and only grants access to credentials when explicitly permitted, thus enhancing the overall security framework. This layered architecture ensures that the agents function effectively while maintaining robust security protocols throughout their operations.

Scalebrowser provides a self-hosted infrastructure designed specifically for AI agents that require the capability to accomplish genuine tasks. Each unique profile maintains consistency in identity, cookies, passkeys, memory, and tasks, regardless of whether the system is restarted or if the user switches devices. It utilizes a modified Chromium engine alongside authentic local GPU values, enabling human-like interactions and effective handling of captchas, email and SMS verification codes, and TOTP, all while allowing browser-level prompt control to ensure agents can navigate beyond the limitations faced by standard automation tools. Users can connect through various methods including MCP, REST, Node.js, Python, or even direct CDP for versatile integration. This level of functionality empowers AI agents to perform in environments that typically resist automation attempts.

StateFabric is a compact infrastructure layer designed for AI agents that require additional context beyond just the chat history. When an agent begins to utilize tools and operates over extended sessions without restarting, relying solely on retained messages is insufficient; it becomes essential to track the following aspects: what events transpired, what changes occurred in the state, which tools were utilized, and what context should be factored into the upcoming model iteration. To address these needs, StateFabric maintains an append-only event log throughout agent operations, enabling the extraction of relevant context from this data. Currently, it offers a range of features, including durable session management and event storage, user/model/tool event timelines, the ability to reconstruct session states from recorded events, and a streamlined model-facing context. Additionally, it includes a user-friendly dashboard for analyzing sessions, inspecting raw payloads, reviewing compaction artifacts, and monitoring usage patterns. Furthermore, StateFabric supports integration with Google ADK through the package @statefabric/adk and facilitates direct usage in Node/REST environments via @statefabric/client, making it adaptable for custom runtimes. This versatility ensures that AI agents can operate more efficiently and effectively in complex environments.

Amazon Bedrock AgentCore allows for the secure deployment and management of advanced AI agents at scale, featuring infrastructure specifically designed for dynamic agent workloads, robust tools for agent enhancement, and vital controls for real-world applications. It is compatible with any framework and foundation model, whether within or outside of Amazon Bedrock, thus eliminating the burdensome need for specialized infrastructure. AgentCore ensures complete session isolation and offers industry-leading support for prolonged workloads lasting up to eight hours, with seamless integration into existing identity providers for smooth authentication and permission management. Additionally, a gateway is utilized to convert APIs into tools that are ready for agents with minimal coding required, while built-in memory preserves context throughout interactions. Furthermore, agents benefit from a secure browser environment that facilitates complex web-based tasks and a sandboxed code interpreter, which is ideal for functions such as creating visualizations, enhancing their overall capability. This combination of features significantly streamlines the development process, making it easier for organizations to leverage AI technology effectively.

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

Reflexio serves as a platform for developers focused on the creation and management of AI agents, utilizing a variety of input methods such as an SDK, REST API, CLI, or connections to supported tracing platforms to gather data from agent interactions, execution traces, tool calls, and user corrections. By identifying common failures, it generates reusable playbooks and user profiles that enhance the learning process. When an agent is in operation, it can access pertinent guidance derived from these learnings prior to formulating a response. The Hosted Enterprise portal provides teams with the capability to explore, modify, approve, reject, search, and assess the acquired knowledge effectively. Reflexio enables agents to implement necessary corrections across various interactions without needing to alter the underlying model weights. Additionally, teams have the flexibility to utilize the managed cloud service, set up their own model-provider keys, or opt for self-hosting on platforms like AWS, Google Cloud, or Azure. Furthermore, enterprise trace imports are supported from sources such as Braintrust, Langfuse Cloud, and LangSmith Cloud, while Reflexio also features a mem0 wrapper and integration with OpenClaw, thus offering a comprehensive suite of tools for enhancing agent performance. This multifaceted approach ensures that developers can optimize their AI agents while leveraging the flexibility of various deployment options.

The NVIDIA Open Agent Safety Platform serves as an open reference framework designed to monitor and manage the behavior of AI agents continuously, ensuring that organizations can maintain agents that are isolated, observable, auditable, and operate within set boundaries. This platform integrates real-time governance, ongoing threat detection, and the enforcement of policies through hardware isolation, providing robust protection for enterprise AI agents from the initial testing phases all the way to deployment. The NVIDIA OpenShell contributes to this by offering an open-source runtime that differentiates the execution of agents from their access to data, tools, and outside systems, utilizing sandboxed environments and a zero-trust policy approach to strictly regulate agents' visibility, actions, and interactions. Policies are enforced externally to the agent process, effectively mitigating the risks associated with unpredicted behavior. Furthermore, NVIDIA Sentry enhances security by adding an extra layer that meticulously monitors agent requests and responses, verifies agent identities, and continuously manages access to data, tools, APIs, and services while also having the capability to isolate agents when necessary. This comprehensive approach ensures that organizations can safeguard their AI agents while promoting a secure and controlled operational environment.

CodeSandbox aims to make it easier for you to express your ideas with code, and to validate them. It also removes the hassles of setting up development tooling and sharing your project. Join us to help build the future of web coding. Over 4M developers use the platform each month. This includes organizations like Shopify and Atlassian. Since its launch, creators have created over 35M apps. It's used in thousands of open-source projects like React, Vue and Babel. You can invite your friends, colleagues, or team to join you or simply view your creation by using a URL. Use any of 1M+ packages for building real, powerful applications quickly and efficiently. Import and run repos directly from GitHub or choose from hundreds of templates to start in seconds. Boxy, CodeSandbox's AI-powered coding assistant, is now available to all Pro subscriptions.

Flowise is an open-source agentic development platform designed to help teams build AI agents and LLM-powered applications using a visual workflow interface. The platform allows users to design intelligent workflows through modular components that can be combined to create chatbots, automation systems, and autonomous AI agents. Developers can build both single-agent chat assistants and multi-agent systems that collaborate to complete complex tasks. Flowise integrates with more than 100 large language models, embedding models, and vector databases, providing flexibility in selecting AI technologies. The platform also supports retrieval-augmented generation (RAG), enabling applications to retrieve knowledge from documents and data sources. Built-in features such as human-in-the-loop workflows allow users to review and validate agent actions before execution. Observability tools provide detailed execution traces and compatibility with monitoring systems like Prometheus and OpenTelemetry. Developers can integrate Flowise with existing applications using APIs, SDKs, or embedded chat widgets. The platform supports both cloud and on-premises deployment environments for enterprise scalability. By providing visual tools and flexible integrations, Flowise accelerates the development and deployment of advanced AI-driven applications.

nono is a novel open-source sandbox that utilizes kernel enforcement to create a secure environment for AI coding agents and LLM tasks. In contrast to traditional policy-based guardrails that merely monitor and filter operations, nono leverages operating system security features—specifically Landlock on Linux and Seatbelt on macOS—to render unauthorized operations impossible at the syscall level. With just a single command, you can encapsulate any AI agent, including Claude Code, OpenCode, OpenClaw, or any command-line interface process. The system automatically enforces a default-deny policy for filesystem access, restricts harmful commands (such as rm, dd, chmod, and sudo), isolates sensitive credentials and API keys, and extends all imposed restrictions to any child processes, ensuring there's no avenue for escape once limitations are set. Built-in profiles allow for rapid deployment, and secrets can be injected from the system keystore in a secure manner, with automatic zeroization upon exit. Additionally, future enhancements such as audit logging, atomic rollbacks, and Sigstore-attested policy signing are planned, offering robust tracking and security features. It operates under the Apache 2.0 license and is developed by the same creator behind Sigstore, further emphasizing its credibility and reliability in securing AI workloads.

Mirantis Container Runtime (MCR), which was previously known as Docker Engine Enterprise, serves as a secure and robust container runtime designed for enterprise use, allowing development teams to create and manage containers on both Linux and Windows platforms while utilizing the familiar Docker CLI, Dockerfiles, and APIs essential for mission-critical applications. This solution is fully aligned with Docker-centric workflows and toolchains, ensuring a smooth transition from development to production with rigorously tested and validated releases across various operating systems, accompanied by comprehensive CVE patching and bug fixes that maintain workload reliability. Furthermore, MCR emphasizes top-tier security through FIPS 140-2 certified cryptographic modules, implements mandatory access controls such as AppArmor and SELinux, and incorporates image signature verification, alongside support for sandboxed runtimes like Kata and gVisor, all aimed at maintaining trusted and compliant containers. The combination of these features positions MCR as a leading choice for organizations seeking to enhance their container management capabilities while adhering to strict security standards.

Calljmp is a developer-first AI runtime for building and running long-lived, stateful agent workflows in production. Unlike AI agent frameworks that focus mainly on authoring logic in code, Calljmp provides a managed runtime that handles execution concerns by default. This includes durable state persistence, pause and resume for human-in-the-loop workflows, safe retries with idempotency, and built-in observability across every step of an agent’s execution. Calljmp is designed for teams using TypeScript who want to ship production-grade AI systems without stitching together queues, databases, custom state machines, and monitoring infrastructure. Developers write agent workflows as code, while the runtime guarantees reliable execution over time, even across crashes, restarts, and long waits. Calljmp targets the gap between developer-first agent frameworks and heavy workflow engines, offering a practical path from prototype to production for real-world AI agents.

TinyFish is a web infrastructure platform for developers and AI teams building agents and applications that need to access or operate across the web. The platform includes four connected product surfaces: 1. Search provides agent-optimized web search with current results structured for AI consumption. 2. Fetch extracts clean content from specified URLs and returns it as markdown, JSON, or HTML. It supports dynamic, JavaScript-heavy, and single-page application websites. 3. Browser provides managed cloud Chrome sessions, fingerprinting, session infrastructure, scaling, and browser management. 4. Web Agent takes a goal and a URL, navigates a website, works through filters and pagination, fills forms, authenticates when required, and returns the requested result. 5. Vault and Profiles support Web Agent workflows by securely handling credentials and identity for authenticated website operations. Together, these capabilities allow developers to move from web discovery and content retrieval into complete, multi-step web operations without assembling and maintaining separate infrastructure products.

Enhance your contemporary applications with VMware Tanzu Kubernetes Grid, enabling you to operate the same Kubernetes environment across data centers, public cloud, and edge computing, ensuring a seamless and secure experience for all development teams involved. Maintain proper workload isolation and security throughout your operations. Benefit from a fully integrated, easily upgradable Kubernetes runtime that comes with prevalidated components. Deploy and scale clusters without experiencing any downtime, ensuring that you can swiftly implement security updates. Utilize a certified Kubernetes distribution to run your containerized applications, supported by the extensive global Kubernetes community. Leverage your current data center tools and processes to provide developers with secure, self-service access to compliant Kubernetes clusters in your VMware private cloud, while also extending this consistent Kubernetes runtime to your public cloud and edge infrastructures. Streamline the management of extensive, multi-cluster Kubernetes environments to keep workloads isolated, and automate lifecycle management to minimize risks, allowing you to concentrate on more strategic initiatives moving forward. This holistic approach not only simplifies operations but also empowers your teams with the flexibility needed to innovate at pace.

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.

Eve serves as a framework for creating agents, akin to how Next.js functions for web applications, offering a specialized environment for agent development. It employs Markdown to articulate instructions and skills, while TypeScript is utilized for implementing tools, ensuring durable execution by default. An agent is essentially a directory that outlines its instructions and skills using Markdown, defines tools through TypeScript, and facilitates deployment. Eve meticulously compiles this directory, orchestrates durable workflows, and integrates various channels, providing developers with a systematic approach to construct production-ready agents without the need to piece together disparate solutions. An instructions.md file can represent a fully functional agent, and the agent.ts file empowers teams to select a model or adjust the runtime configuration. Skills can be reused as Markdown playbooks that are loaded when needed, allowing the agent to receive targeted guidance without the burden of carrying unnecessary information in every prompt. Tools are introduced as TypeScript files, with their filenames serving as the tool names, eliminating the requirement for any registration process. Each agent operates within its own isolated sandbox and includes file tools, and there is also the option for custom sandbox configurations, enhancing flexibility for developers. This robust framework not only streamlines agent creation but also fosters innovation by allowing developers to focus on building unique functionalities.

Kitesurf is an innovative browser tailored specifically for AI agents, operating entirely on Cloudflare Workers rather than relying on Chromium. This browser prioritizes the essential needs of agents, focusing on aspects such as structured machine-readable content, efficient scalability, minimal CPU and memory consumption, and secure interaction with potentially untrustworthy web pages, all while eschewing unnecessary human-centric features like tabs, themes, extensions, or perfectly smooth scrolling. Treating each page load as potentially untrusted, Kitesurf initiates every session in a clean state, ensuring that components are segregated and have access only to the resources necessary for their operations. Utilizing native Rust compiled to WebAssembly when feasible, the architecture is structured around three main components: the Engine, PageScript, and PageRenderer. The Engine facilitates interaction through Chrome DevTools Protocol WebSocket and REST APIs, while managing session state, and PageScript is responsible for parsing HTML and CSS, executing JavaScript, and running WebAssembly within isolated Workers. Overall, Kitesurf exemplifies a forward-thinking approach to browser design, catering exclusively to the unique requirements of AI-driven tasks.

SEAOTTER serves as a managed control plane specifically designed for the Hermes Agent on Google Cloud, providing isolated and consistently active agents without the need for VPS or SSH access. Users can easily create an agent via the dashboard, and within minutes, SEAOTTER sets up a dedicated namespace for each agent using a gVisor sandbox. The system allows for actions such as pausing, restarting, restoring, and reprovisioning through both API and user interface, all while offering integrated logs and metrics without the necessity of SSH access. Sensitive information is securely managed in a write-only section and stored within Google Secret Manager, with Hermes loading the secrets at startup, ensuring that users should refrain from sharing keys in chat. Upon signing up, users can connect using an organization-scoped so_ MCP key from Cursor, Claude, or Codex, while Hermes acts as the operational hub featuring various tools, memory management, and cron capabilities, all hosted in the us-central1 region. A trial period of seven days is available without requiring a credit card, allowing users to utilize one agent with limited resources (1 CPU / 4Gi / 8Gi). Following the trial, the cost for each always-on agent is $99 per month, with additional agents available for an extra $99 each, and custom solutions can be arranged through a consultation. This streamlined approach makes SEAOTTER an efficient choice for deploying agents in a managed cloud environment.

Composio Instant serves as a cloud-based wallet that enables AI agents to access paid tools without the necessity for developers to subscribe to each service provider or individually configure API keys for every tool. By linking Instant to an agent session, developers can select the tools they wish to make available while utilizing a communal organization balance, with all transactions documented in execution logs. Each call deducts from this shared balance at a per-use fee, and newly registered developer organizations benefit from a one-time credit of $2 to get started. The platform offers a variety of services, such as web searching, data enrichment, web scraping, transcription, voice services, weather updates, and document processing. Developers can easily integrate by using Composio’s SDKs and MCP-compatible agent clients like Claude, ChatGPT, Codex, Cursor, Gemini CLI, Grok, and Visual Studio Code. Instant is specifically designed for developers and teams who are creating or managing AI agents that require immediate access to external services, thus streamlining the integration process. This functionality not only enhances efficiency but also allows for greater flexibility in the deployment of AI capabilities.

Flue is an innovative agent framework designed for creating robust AI agents utilizing a customizable TypeScript environment. Developed by the creators of Astro, it incorporates a React-like hooks API for constructing agent functionalities, including persistent state, lifecycle events, various models, tools, sandboxes, subagents, skills, and MCP servers within the codebase. These agents maintain state and can be addressed via HTTP, preserving context throughout interactions and adapting their abilities as tasks evolve. Flue ensures that every session is logged in a reliable stream, allowing for the recovery of accepted tasks even after crashes, restarts, or deployments; this means that interrupted sessions can seamlessly resume, and clients can reconnect without needing to start anew. Developers have the flexibility to operate agents locally, through continuous integration, from their own backend systems, or coordinate them using platforms like Cloudflare Workflows and Inngest. The secure sandboxes allow agents to execute commands, modify files, and perform meaningful tasks, while integrated tools facilitate connections to various APIs and data sources. Flue is powered by Pi and offers compatibility with multiple LLM providers, enabling teams to select the models that best suit their needs. Ultimately, this framework empowers developers to create versatile AI agents that can adapt to changing requirements and environments.

Macswarm offers hosted macOS Pods powered by dedicated Apple silicon specifically designed for Claude Code and Codex, featuring the latest Xcode, iOS runtimes, pre-booted Simulators, and XcodeBuildMCP. These Pods allow coding agents to efficiently build iOS applications, conduct testing, and capture screenshots seamlessly. Each Pod accommodates two to three active agents who work in separate git worktrees, while a two-slot xcodebuild queue manages their workloads. Pods are created fresh for each session and are terminated after use, ensuring security with outbound-only networking and no inbound SSH access. Users are charged based on Pod-hour usage, with metering done per second, and idle Pods automatically stop functioning after ten minutes of inactivity. Macswarm has options in both the EU (Netherlands) and US regions. The free plan provides three Pod-hours for a one-time use, whereas the Pro plan costs $99 per month for 300 Pod-hours, and the Team plan is priced at $399 per month for 1,500 Pod-hours. Additionally, early-access accounts are onboarded based on the order of signup, ensuring a smooth entry for all new users. This system allows developers to scale their efforts while maintaining flexibility and efficiency in their app development processes.

Inquir Compute serves as a cloud-based solution that enables the deployment and execution of server-side code without the need to oversee servers, Kubernetes, CI/CD, or DevOps frameworks. This platform empowers developers to effortlessly build functions, APIs, webhooks, cron jobs, background tasks, and complex workflows using either a browser-based editor or an API interface. Users have the flexibility to code in languages such as Node.js, Python, or Go and can adjust runtime parameters including memory allocation, CPU usage, timeout limits, environment variables, and network permissions before deploying their code in secure containers. The platform allows for functions to be made accessible through an API Gateway, triggered on-demand, scheduled for execution, or integrated into workflows where data seamlessly flows from one function to another. Tailored for extended workloads like AI-driven agents, web scraping, document processing, data enhancement, system integrations, and various automation tasks, Inquir Compute also offers comprehensive features such as logging, tracing, invocation records, error tracking, route management, API key management, tenant isolation, and robust observability tools to facilitate effective monitoring and management. Additionally, its user-friendly interface and extensive capabilities make it an ideal choice for developers seeking efficient solutions for complex backend processing.