Runtime authorization for Claude, Cursor, Codex and OpenClaw
Kastra is the runtime authorization layer for AI agents. It decides what agents can and cannot do before actions execute, enforcing policies with sub-1 ms latency across tools, prompts, inputs, and outputs. Use one control plane to govern agents and policies across Claude Code, Cursor, Codex, OpenClaw, the Anthropic SDK, the OpenAI SDK, and more. Prevent unauthorized tool use, prompt injection, and exposure of sensitive data before they become incidents. Trust the rules, not the agents.
Here's what kept happening. We'd deploy AI agents in real environments: a coding agent, a support agent, and an infra agent. It had credentials and access, and there was nothing structural stopping it from running any action it decided to take, even in production.
Most teams have authentication, logging, monitoring, and evals. Almost none have a layer that decides "this specific action cannot execute" before it executes. The controls that exist look at what the agent already did. We wanted something that decides on the input and output to cover most of the execution level risks. This required a new infra layer for autonomous AI around the authorization angle.
That's Kastra. Every agent action is evaluated against policy and gets an allow or deny decision in under a millisecond. When something needs a human review, that approval now completes in about a second, across our desktop app, web console, and macOS notifications. We reduce that number down every week, and it changes how the product feels. Sub 1ms latency means human-in-the-loop authorization is practical, not theoretical. Kastra is neutral and deterministic across the stack and works with the tools you already use.
We integrate via 1 click to Claude Code, Codex, OpenClaw, and Cursor, and other regulated enterprise use cases right now. The runtime and policy pack library are open source. The enterprise control plane is commercial. The product is free to try, if you are wondering if you need Kastra run these 2 commands below on your coding agent to scan for potential risks your agent has already done that should have policies built. Everything stays locally on your machine, so you can do this safely.
Run "brew install kastra-labs/tap/kastra-edge" and then once connected to your account, you can run "kastra-edge scan".
Happy to go into the policy engine architecture, the latency story, how we think about fail-open vs. fail-closed, probabilistic vs deterministic rules, or anything else. We'll be in the comments all day.
Carlos & Fernando
About Kastra on Product Hunt
“Runtime authorization for Claude, Cursor, Codex and OpenClaw”
Kastra launched on Product Hunt on July 22nd, 2026 and earned 298 upvotes and 127 comments, earning #3 Product of the Day. Kastra is the runtime authorization layer for AI agents. It decides what agents can and cannot do before actions execute, enforcing policies with sub-1 ms latency across tools, prompts, inputs, and outputs. Use one control plane to govern agents and policies across Claude Code, Cursor, Codex, OpenClaw, the Anthropic SDK, the OpenAI SDK, and more. Prevent unauthorized tool use, prompt injection, and exposure of sensitive data before they become incidents. Trust the rules, not the agents.
On the analytics side, Kastra competes within Developer Tools, Artificial Intelligence, GitHub and Security — topics that collectively have 1M followers on Product Hunt. The dashboard above tracks how Kastra performed against the three products that launched closest to it on the same day.
Who hunted Kastra?
Kastra was hunted by Ben Lang. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Kastra including community comment highlights and product details, visit the product overview.
Ben Lang
Hi PH. I'm Carlos. Fernando and I built Kastra.
Here's what kept happening. We'd deploy AI agents in real environments: a coding agent, a support agent, and an infra agent. It had credentials and access, and there was nothing structural stopping it from running any action it decided to take, even in production.
Most teams have authentication, logging, monitoring, and evals. Almost none have a layer that decides "this specific action cannot execute" before it executes. The controls that exist look at what the agent already did. We wanted something that decides on the input and output to cover most of the execution level risks. This required a new infra layer for autonomous AI around the authorization angle.
That's Kastra. Every agent action is evaluated against policy and gets an allow or deny decision in under a millisecond. When something needs a human review, that approval now completes in about a second, across our desktop app, web console, and macOS notifications. We reduce that number down every week, and it changes how the product feels. Sub 1ms latency means human-in-the-loop authorization is practical, not theoretical. Kastra is neutral and deterministic across the stack and works with the tools you already use.
We integrate via 1 click to Claude Code, Codex, OpenClaw, and Cursor, and other regulated enterprise use cases right now. The runtime and policy pack library are open source. The enterprise control plane is commercial. The product is free to try, if you are wondering if you need Kastra run these 2 commands below on your coding agent to scan for potential risks your agent has already done that should have policies built. Everything stays locally on your machine, so you can do this safely.
Run "brew install kastra-labs/tap/kastra-edge" and then once connected to your account, you can run "kastra-edge scan".
Happy to go into the policy engine architecture, the latency story, how we think about fail-open vs. fail-closed, probabilistic vs deterministic rules, or anything else. We'll be in the comments all day.
Carlos & Fernando