Sandboxed MCP Server Runtimes

This project provides secure, containerized infrastructure designed for autonomous agents, remote code execution, and cloud development. It functions as a sandboxed environment where AI agents and external processes can execute code, run shell commands, and manage files while remaining isolated from the host system. The system distinguishes itself by implementing the Model Context Protocol, allowing it to act as a standardized tool server that exposes browser and filesystem capabilities to compatible clients. It further integrates headless browser automation, enabling programmatic web navigation and screenshot capture within the isolated workspace. The platform covers a broad capability surface, including multi-runtime command execution, dynamic port forwarding for application previewing, and shared filesystem coordination. It also provides interactive development tools such as web-based editors, terminals, and notebooks for real-time activity inspection.

Nanoclaw is an LLM agent orchestrator and multi-platform chat gateway designed to deploy and manage isolated AI agents. It provides a containerized runtime that executes agents within sandboxed Linux containers, ensuring filesystem and state isolation through dedicated workspaces and host bind-mounts. The project distinguishes itself through a unified routing pipeline that connects agents to diverse messaging platforms, including WhatsApp, Discord, Slack, Telegram, Signal, and iMessage. It integrates the Model Context Protocol to extend agent capabilities via managed external data and functions, and utilizes a secret vault proxy to inject credentials at runtime so that containers never store raw API keys. The system covers broad capability areas including autonomous multi-agent workflow orchestration, asynchronous task scheduling, and network egress lockdown. It includes a comprehensive management CLI for controlling agent lifecycles, monitoring active sessions, and administering host resources. The platform is implemented in TypeScript and provides a command-line interface for all administrative and system monitoring operations.

container-use is a containerized AI execution environment and code sandbox designed to provide a secure space for AI coding agents to execute commands and build applications. It functions as a workspace orchestrator that provisions isolated containers mapped to git branches, allowing multiple agents to operate in parallel without state conflicts or affecting the host system. The project serves as a Model Context Protocol server, bridging AI agents to containerized environments for standardized tool access. It enables a workflow for reviewing and merging changes made by agents within these isolated environments back into a local repository. The system includes capabilities for agentic workflow monitoring through command history logging and provides mechanisms for human intervention via direct terminal tunneling into active sessions. It further supports bidirectional file system syncing to facilitate the review and integration of agent-generated code.

Bytebot is an LLM desktop automation framework and virtual Linux desktop environment. It enables AI agents to plan and execute mouse and keyboard actions on a virtual computer using natural language, allowing for autonomous desktop automation and the integration of legacy systems that lack native APIs. The system operates as an LLM API gateway and a Model Context Protocol server, routing requests across multiple language model providers with integrated load balancing and rate limiting. It provides isolated, containerized environments where agents use visual reasoning to interpret screenshots and translate goals into precise UI actions. The platform includes a comprehensive suite of orchestration tools for managing asynchronous task lifecycles, programmatic desktop control via REST, and real-time state streaming via WebSockets. It supports hybrid control modes, allowing users to monitor agent execution through a browser-based viewer and intervene manually when necessary. Deployment is supported through Docker Compose, Helm charts for Kubernetes orchestration, and one-click cloud templates for private infrastructure hosting.

Devenv is a Nix-based development environment manager that provides declarative definitions for reproducible shells and toolchains. It functions as a declarative task runner for executing dependency-aware pipelines and a service orchestration tool for supervising background processes. The project distinguishes itself by generating OCI container images directly from environment definitions without requiring a separate container engine. It also implements the Model Context Protocol to expose project context and package search to AI agents, and supports AI-assisted scaffolding to generate configuration files from natural language descriptions. The platform covers a broad range of development capabilities, including local service orchestration with health checks, automated shell activation through hooks, and binary caching for accelerated setup. It also includes secret management via provider-agnostic abstractions and integration with Dev Container configurations. The system provides an IDE language server to support configuration files with completion and diagnostics.

Bubblewrap is a Linux sandbox runner that creates lightweight, isolated execution environments for running untrusted applications. It combines Linux user, mount, network, PID, and UTS namespaces with seccomp-BPF system call filtering to restrict filesystem, network, process, and inter-process communication access. The project provides comprehensive process isolation by giving each sandbox its own private tmpfs root with selective bind-mounts, a separate network stack containing only a loopback interface, an independent process ID space, and remapped user and group identifiers. It applies seccomp filters to block dangerous kernel system calls before they execute, and assigns an independent hostname to the sandbox environment through UTS namespace isolation. Bubblewrap enforces read-only and nodev filesystem access by default, preventing sandboxed processes from writing to or creating device nodes on the host. The sandbox environment is created using user namespaces, which hide real user and group identifiers from sandboxed processes by mapping them to different values inside the namespace.

Open Interpreter is an autonomous agent runtime that translates natural language instructions into executable code to interact with local software and operating systems. It functions as an orchestration framework that connects language models to a secure execution environment, enabling the development of agents capable of managing system resources and performing complex tasks. To ensure safety, the system mandates explicit user verification before executing any generated code and provides robust isolation through containerized sandboxing. The project distinguishes itself through its deep integration with local environments and its focus on secure, human-in-the-loop automation. It supports a wide range of hosted and local language models, allowing users to balance privacy and performance requirements. Beyond simple script execution, it features vision-enabled automation that analyzes screen content to simulate mouse and keyboard interactions, effectively allowing the agent to navigate graphical user interfaces as a human would. The system provides a comprehensive suite of computer automation primitives, including tools for managing calendar events, email communications, and clipboard data. It is designed for extensibility, offering support for custom language runtimes and remote sandbox configurations to handle specialized execution needs. Users can manage the interpreter's behavior through detailed configuration settings, including options for stateful conversation persistence and telemetry controls. The software is distributed as a Python-based package and can be installed and configured to run within isolated container environments to maintain host system security.

OpenHuman is an AI application framework for building private intelligence systems and personal AI layers. It provides a system for deploying private AI assistants that execute technical tasks and manage personal knowledge bases. The project features a model-agnostic request proxy that routes AI workloads to different large language models based on requirements for reasoning, speed, or vision. It integrates an OAuth-driven data integrator to synchronize personal information from external services into a local knowledge base composed of hierarchical Markdown summaries. The framework also includes a voice interface with synchronized avatars for participation in video conferencing. The system covers autonomous agent orchestration with sandboxed tool execution for coding, web research, and filesystem manipulation. It implements a headless JSON-RPC server architecture for remote client access and includes a token-reduction pipeline to compress payloads and optimize model context. Security is handled through local data encryption, secure credential storage, and agent execution isolation within containers or OS jails. The core logic can be deployed as a self-hosted containerized server to maintain data privacy and support local model execution.