CtrlAI Review (2026): Pricing, Features & Honest Verdict

TLDR

CtrlAI is an open-source HTTP proxy that adds security guardrails to AI coding agents like Claude Code and OpenAI Codex. It blocks dangerous tool calls, logs everything to a tamper-proof audit trail, and requires zero code changes to set up. We think it fills a critical gap in the AI agent security stack. Best for: developers running AI agents with tool use. Price: Free (MIT open source). Rating: 7.5/10.

What is CtrlAI?

We found CtrlAI while looking at the latest developer tools on Product Hunt and it immediately stood out. It solves a problem every developer using AI agents has quietly worried about: what happens when the AI does something dangerous? CtrlAI is a transparent HTTP proxy built in Go that sits between your AI agent SDK and the LLM provider. Every tool call the model attempts passes through CtrlAI’s rule engine before anything executes. Dangerous calls get blocked. Everything gets logged.

The architecture is clever. Your agent SDK thinks it is talking directly to Anthropic or OpenAI. CtrlAI intercepts the responses, inspects the tool calls, and either lets them through or rewrites the response so the SDK thinks the model simply chose not to use that tool. The agent keeps running without crashing. No SDK modifications needed. Just change the base URL in your config and you are protected. It works with any provider that accepts custom base URLs, including Anthropic, OpenAI, Moonshot, Qwen, MiniMax, and Zhipu.

What Are CtrlAI’s Key Features?

19 Built-in Security Rules

Out of the box, CtrlAI blocks access to SSH keys, .env files, credential stores, browser password databases, and private keys. It catches destructive commands like rm -rf /, fork bombs, and credential exfiltration via curl or wget. Privacy rules block camera access, screen recording, GPS location, and remote code execution on paired devices. All 19 rules are enabled by default and can be toggled individually in a YAML config file. You get real protection from minute one.

Custom Rule Engine

The YAML-based rule system is where CtrlAI gets interesting. You can write rules that match on tool names, action types, file paths (with glob patterns), command arguments (with regex), URLs, and specific agent IDs. Multiple fields in a rule are AND logic. Multiple values in a field are OR logic. First match wins. This gives you fine-grained control. Want to block all exec calls for your “intern” agent but allow them for “main”? That is a three-line rule. Want to prevent any file reads outside your project directory? Another three lines.

Multi-Agent Support

CtrlAI routes requests using URL path segments: /provider/anthropic/agent/main/ versus /provider/openai/agent/work/. Each agent gets its own rules, audit trail, and kill switch. You can run a cautious agent through Anthropic with strict rules while a trusted agent hits OpenAI with looser permissions. All through a single proxy instance. For teams running multiple AI agents across different tasks, this is a significant architectural advantage.

Tamper-Proof Audit Trail

Every tool call that passes through the proxy gets logged with a SHA-256 hash chain. Logs rotate daily and are indexed in SQLite for fast searching. The hash chain means nobody can quietly edit or delete log entries after the fact. If an AI agent tried to access your SSH keys at 3:47 AM last Tuesday, you will have a cryptographically verifiable record of it. The dashboard at localhost:3100 gives you a real-time view of all agent activity.

How Much Does CtrlAI Cost?

CtrlAI is completely free. The entire product is MIT licensed, self-hosted, and open to modification. There are no usage limits, no feature gates, and no “community edition” limitations. You get everything.

An enterprise tier exists for teams that need centralized policy management, SSO, RBAC, and managed deployment with SLAs. Enterprise pricing is not published. For context, Guardrails AI (which focuses on LLM output validation rather than tool call blocking) also offers open-source and enterprise tiers. Invariant Labs targets prompt injection specifically. CtrlAI fills a different niche by focusing on the actual actions agents take, not just what they say.

Who is CtrlAI Best For?

Use CtrlAI if you run AI coding agents with tool use enabled. Claude Code, OpenAI Codex, or any agent that can read files, execute commands, or make network requests. It is especially valuable for teams where multiple developers run AI agents, because the multi-agent support and centralized audit trail give you visibility across everyone’s usage. Also use it if you work in a regulated environment where you need an audit trail of AI actions.

Skip CtrlAI if you only use AI for chat completions without tool use. The proxy only inspects tool calls, so there is nothing to protect if your agent cannot take actions. Also skip if you are on a provider that does not support custom base URLs, though most modern providers do.

Best CtrlAI Alternatives

Guardrails AI

Open-source framework focused on validating LLM outputs. Good at catching hallucinations and enforcing output formats. Does not block tool calls or audit agent actions. Different problem, complementary tool. Free tier available.

Invariant Labs Guardrails

Contextual security layer targeting prompt injection and context manipulation in agentic systems. More focused on input safety than action safety. Worth evaluating alongside CtrlAI for comprehensive agent security.

OpenGuardrails

Community-driven guard agent framework. Less mature than CtrlAI but takes an interesting approach by using AI to guard AI. Still early but worth watching for teams who want AI-native security rather than rule-based blocking.

Final Verdict: Is CtrlAI Worth It?

CtrlAI solves a problem that is only getting more urgent. AI agents are writing code, executing commands, and accessing files with increasing autonomy. The gap between “what an agent can do” and “what you want it to do” is where accidents happen. Having a transparent proxy that enforces rules and logs everything is sensible security hygiene. The zero-code-change setup means you can add it to your existing workflow in under five minutes.

The honest caveat: this is a very new project. As of this writing, the GitHub repo has 7 commits and 2 stars. That does not invalidate the technology, but it means the community is tiny and you are an early adopter. The rule system is well-designed, the multi-agent architecture is thoughtful, and the audit trail adds genuine accountability. If you build with AI agents, CtrlAI belongs in your security stack. Install it, enable the defaults, and customize rules as you learn what your agents actually try to do. The price is right (free), the risk is low (it is a proxy, not a dependency), and the protection is real.