SaaS Metrics
Product Positioning & Context
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.
Related Ecosystem & Alternatives
Discover adjacent products, open-source repositories, and developer tools sharing similar technical architecture.
Deep-Dive FAQs
What is Kastra?
Kastra is a digital product or tool described as: Runtime authorization for Claude, Cursor, Codex and OpenClaw
Where did Kastra originate?
Data for Kastra was aggregated directly from the Product Hunt community ecosystem, representing raw developer and early-adopter sentiment.
When was Kastra publicly launched?
The initial public indexing or launch date for Kastra within our tracked developer communities was recorded on July 22, 2026.
How popular is Kastra?
Kastra has achieved measurable traction, logging over 313 traction score and facilitating 128 recorded discussions or engagements.
Which technical categories define Kastra?
Based on metadata extraction, Kastra is categorized under topics such as: Developer Tools, Artificial Intelligence, GitHub.
What are some commercial alternatives to Kastra?
Our semantic intelligence engine identifies potential commercial alternatives in the SaaS space, such as Muse Wonder, which offers overlapping value propositions.
How does the creator describe Kastra?
The original author or development team describes the product as follows: "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..."
Community Voice & Feedback
Checking shell commands and tool calls before they run is the part that clicks for me. The local policy checks and scan of past agent activity make this feel practical, not just another monitoring dashboard.
the part that keeps me from letting my coding agents run fully unattended is exactly this. they will happily do something you didn't want and you only find out after the fact. a layer that decides no before the action runs is the piece i keep wishing i had. how granular can the policies get per tool?
Congrats on the launch!
Congrats on the launch. The "trust the rules, not the agents" framing is the right instinct — we've landed on the same philosophy internally. One thing I'm curious about: when a policy check fails, is deny the only outcome, or can a policy route the action to a human approval queue instead? In practice I've found agents need three outcomes, not two — allow, deny, and "a human decides this one." Sub-1ms is impressive for the allow path; wondering what the escalation path looks like.
congrats on hitting product hunt @carlosjimenez1 keeping dev velocity high while putting hard guardrails around shell commands is pure leverage.
This is the unglamorous layer everyone is about to need. The moment you let Claude or Codex actually run things, "what is this agent allowed to do right now" stops being a nice-to-have. Runtime is the key word for me. Static permissions are easy, but authorizing per action while the agent works is the hard part. How do you handle an agent that needs to escalate mid-task without a human sitting there approving every step?
Congrats on the launch. Deciding before the action executes, rather than reviewing what already happened, is the right place to sit.The thing I would want as a builder is every denial landing as a structured event I can line up with the agent's own trace. When an agent quietly stops making progress, the hardest case to debug is not knowing whether it hit a policy or just reasoned itself into a dead end. If the authorization decision and the step that triggered it share an id, that is a five minute answer instead of an afternoon.
Congrats team! 🚀 Runtime authorization before execution is the piece most agent stacks are missing, and covering Claude Code, Cursor, and both major SDKs from one control plane is a smart wedge. Quick technical question: how do you intercept actions across such different runtimes? Is it a proxy sitting between the agent and its tools, SDK-level hooks, or MCP middleware? Curious whether an agent could bypass enforcement by shelling out directly, and how Kastra closes that gap. Excited to dig into the docs!
So basically it prevents to disasters made by AI agents?
Finally something that treats agent permissions like a real problem. The sub-1ms latency claim seems legit - I didn't notice any lag in tool calls when I tested it with Claude Code.
Congratulations on your launch! This feels like the missing piece for regulated AI adoption. Enterprise‑ready - control, neutral, deterministic, and fast.
Runtime authorization for coding agents is something I didnt know I needed till now. Using OpenClaw quite a bit and the permission layer is always something I have to handle manually. Nice solve
The important line here is "before actions execute." For teams using agents around real repos or customer systems, post-hoc logs are useful but they are not a control boundary. Policy has to sit in the path of the work.
how granular do the policies get in practice - is it more like broad allow/deny categories per tool, or can you actually write a rule like "deny this specific SQL write pattern but allow reads"? asking because the false positive rate on anything doing semantic judgment on agent intent tends to be the real blocker to running this in prod, not the latency.
How does kastra prevent prompt injection attacks where an agent attempts to bypass tool parameters by obfuscating CLI commands? huge congrats for shipping 🙌@carlosjimenez1
Discovery Source
Product Hunt Aggregated via automated community intelligence tracking.
Tech Stack Dependencies
No direct open-source NPM package mentions detected in the product documentation.
Media Tractions & Mentions
No mainstream media stories specifically mentioning this product name have been intercepted yet.
Deep Research & Science
No direct peer-reviewed scientific literature matched with this product's architecture.