Gru Sandbox

Executes Model Context Protocol (MCP) servers in isolated sandbox environments with resource constraints and lifecycle management. Implements process-level isolation to prevent malicious or buggy MCP implementations from affecting the host system, with configurable memory limits, CPU quotas, and timeout enforcement. Manages server startup, health monitoring, and graceful shutdown through a containerized or process-based runtime.

Maintains a centralized registry of available tools/MCP servers with JSON Schema validation for tool definitions, input parameters, and output contracts. Validates tool schemas at registration time and runtime to ensure type safety and prevent malformed requests from reaching sandboxed servers. Supports dynamic tool discovery and registration with conflict detection for duplicate tool names across multiple MCP servers.

Routes tool requests from AI agents to appropriate MCP servers based on tool name, capability matching, or load-balancing policies. Implements request multiplexing across multiple MCP server instances, with automatic failover and retry logic. Abstracts away the complexity of managing multiple MCP server connections, allowing agents to call tools without knowing which server provides them.

Executes arbitrary code (Python, JavaScript, shell scripts) within isolated sandbox environments triggered by agent tool calls. Implements filesystem isolation, network restrictions, and resource limits to prevent code from accessing sensitive data or consuming excessive resources. Captures stdout/stderr and execution results, with timeout enforcement and crash recovery.

Captures and persists all agent requests, tool invocations, and responses with full context including timestamps, parameters, results, and execution metadata. Implements structured logging with queryable audit trails for compliance, debugging, and performance analysis. Supports filtering, searching, and exporting logs for external analysis or compliance reporting.

Provides containerized deployment configurations (Docker, Docker Compose, Kubernetes manifests) for running Gru Sandbox in self-hosted environments. Includes pre-built container images, environment variable configuration, and orchestration templates for scaling across multiple nodes. Supports both single-machine and distributed deployments with persistent storage backends.

Manages sandbox execution policies through declarative configuration (YAML/JSON) including resource limits (CPU, memory, disk), network access rules, filesystem permissions, and timeout settings. Applies policies at sandbox creation time and enforces them throughout execution. Supports policy inheritance and overrides for different tool categories or user groups.

Continuously monitors MCP server health through configurable health check endpoints and liveness probes. Detects server crashes, hangs, or degraded performance and triggers automatic recovery actions (restart, failover, alerting). Exposes health metrics and status for external monitoring systems and dashboards.

Implements a middleware pipeline for transforming agent requests before sending to MCP servers and transforming responses before returning to agents. Supports custom middleware for authentication, request validation, response formatting, caching, and rate limiting. Middleware executes in configurable order with early termination on errors.

Implements comprehensive error handling for tool execution failures including timeouts, crashes, invalid responses, and network errors. Provides fallback strategies (retry with backoff, alternative tool selection, graceful degradation) and error context for agent decision-making. Distinguishes between transient and permanent failures to inform recovery strategies.