Best AI Control Planes for OpenCode

Preloop serves as an open-source control plane designed for AI agents that perform tangible actions. It integrates a multi-layered security approach featuring an MCP firewall for managing tool access, an AI model gateway that ensures cost-effectiveness, safety, and accountability, along with policy-as-code that incorporates human oversight, all while providing runtime session visibility and audit trails—all within a self-hosted environment. Given the rapid capabilities of AI agents to deploy code, modify infrastructure, manage financial transactions, access production data, and incur model costs almost instantaneously, Preloop empowers teams to regulate agent activities, monitor expenditures, and determine which actions necessitate human consent. It is compatible with a variety of tools such as OpenClaw, Hermes, Claude Code, Codex CLI, Cursor, Gemini CLI, Windsurf, Cline, OpenCode, and any agents that adhere to MCP standards. Additionally, access rules can evaluate not only the tool names but also arguments and context, utilizing CEL expressions to establish detailed conditions. Furthermore, teams have the flexibility to initiate with observability features and progressively introduce approval and denial protocols without the need for SDKs or extensive modifications to existing applications, thus streamlining the implementation process. This comprehensive approach ensures that organizations remain in control of their AI agents' functionalities and impacts.

SuperBased serves as a local-first control hub for AI coding agents, enabling developers to monitor, manage, and optimize agent performance from a single binary installed on their personal computers. It seamlessly integrates with 40 coding tools by directly reading native session data, eliminating the need for proxies, SDK modifications, or complicated setups, and supports various agents including Claude Code, Codex, Cursor, GitHub Copilot, OpenCode, Gemini CLI, Kilo Code, Qwen Code, Aider, Devin, and others. The intuitive dashboard provides insights into token usage reported by providers, cache operations, expenses, session tracking, and forecasts for future messaging costs across tools that typically maintain isolated data. Developers can initiate over 20 CLI agents as terminal sessions, manage multiple repositories from a single interface, connect to an active agent, take control of the keyboard, and relinquish control when necessary. Additionally, model routing capabilities enable teams to effectively align tasks with the most suitable models, while egress gates allow for pre-execution command holds, giving users the power to halt or redirect actions that may be costly or pose risks. This comprehensive solution empowers developers to enhance their workflow and maintain better oversight over their coding agents.

Bevel serves as a vendor-neutral, Git-integrated control plane tailored for enterprise AI agents, allowing organizations to define their agents, context, skills, tools, permissions, and identities as owned files within their infrastructure, which can then be accessed by any agent runtime through MCP. The context is organized as typed knowledge nodes, each with documented provenance detailing its source, the last modification, and verification timestamps, and this information is compiled into a navigable graph that can be updated and utilized for creating dashboards. Skills are articulated as straightforward Markdown procedures, enabling process owners to easily read, review changes, and transfer them across different runtimes. Additionally, tool manifests outline the capabilities available, while sensitive information is stored securely in a vault, governed by access rules that dictate which agents can read certain files or invoke specific endpoints. Each agent is assigned a unique identity and credentials, ensuring that all actions can be traced back to their source. This comprehensive framework not only enhances security and organization but also promotes transparency and accountability in AI operations.