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Cloudflare Workspace is a durable agent workspace platform built on Cloudflare Workers. It gives AI agents a persistent working directory backed by SQLite inside a Durable Object, exposed as an async, Node-style virtual filesystem that survives restarts. Agents work in it through file and command tools, a shell, and optionally a Linux container.
The main features of cloudflare/computer are: Agent Working Computers, SQLite-Row Virtual Filesystems, Durable Workspace Mounters, AI Agent Development Tools, Agent Workspace Toolsets, Tool-Calling Agent Loops, Workspace Container Mounts, Host-Backend Workspace Syncing.
Projects with overlapping indexed features include: cloudflare/sandbox-sdk — The sandbox-sdk is a development kit designed for building secure, isolated execution environments on a global edge… zenml-io/zenml — ZenML is an orchestration platform designed for building, deploying, and monitoring reproducible machine learning… superradcompany/microsandbox — Microsandbox is a runtime for creating and managing lightweight, hardware-isolated virtual machines — called sandboxes… alexzhang13/rlm — rlm is an LLM code execution engine and orchestration framework designed to coordinate multiple language model calls… zerocore-ai/microsandbox — microsandbox is a platform that runs untrusted code inside hardware-isolated microVMs, each with its own kernel,… memodb-io/acontext — Acontext is an LLM orchestration backend and agent memory framework designed to manage session state and knowledge for…
The sandbox-sdk is a development kit designed for building secure, isolated execution environments on a global edge network. It provides a framework for creating ephemeral, containerized workspaces that allow developers to run untrusted code, manage build tasks, and host automated scripts without compromising host system security. By leveraging a serverless runtime, the platform enables the deployment of these environments directly at the network edge to ensure low-latency performance. The platform distinguishes itself by integrating language models with sandboxed execution, facilitating the
ZenML is an orchestration platform designed for building, deploying, and monitoring reproducible machine learning pipelines and agentic workflows. It provides a unified framework that manages the entire lifecycle of machine learning assets, from data processing and model training to the deployment of persistent inference services. By decoupling pipeline logic from underlying compute and storage, the platform enables teams to transition workflows seamlessly from local development environments to production-grade cloud infrastructure. The platform distinguishes itself through a service-oriented
Microsandbox is a runtime for creating and managing lightweight, hardware-isolated virtual machines — called sandboxes — that boot directly from standard OCI container images. Each sandbox runs as its own host process with a separate kernel, filesystem, and network stack, providing process-per-sandbox isolation. The project includes a command-line tool and multi-language SDKs (Rust, TypeScript, Python, Go) for programmatic lifecycle control, and it communicates with sandbox agents over Unix sockets using a CBOR-encoded protocol. What distinguishes Microsandbox is its combination of host-manag
rlm is an LLM code execution engine and orchestration framework designed to coordinate multiple language model calls and recursive sub-tasks through a programmable environment. It provides a sandboxed REPL environment and a recursive context processor to handle inputs that exceed standard token limits by programmatically decomposing prompts. The project differentiates itself through a reinforcement learning training harness used to teach models how to utilize recursive calls and code execution. It includes a reasoning visualization system that records and renders execution trajectories to ana