Agent Guardrail MCP Server

This is a policy enforcement layer that sits between your AI agent framework and the actions it wants to take. Instead of filtering LLM outputs, it evaluates every tool call, shell command, or API request against allowlists, denylists, and spend caps before execution. You get a Python library with a PolicyEngine for LangChain, CrewAI, or custom agents, plus an optional HTTP proxy for multi-agent orchestrators. Every action gets logged to a SQLite flight recorder with full replay capability. Ships with three policy templates (restrictive, moderate, permissive) and includes automatic risk scoring. The hosted API mode adds metered billing via Bitcoin for agent-to-agent governance scenarios. Reach for this when you need runtime control over what agents can actually do, not just what they say.