Kastra
Runtime authorization for AI agents — decide before actions execute.
Kastra is the first purpose-built authorization layer for AI agents that goes beyond monitoring to enforce policies before actions run. It's ideal for teams using Claude Code, Cursor, or Codex who need fine-grained control and audit trails, but its value depends on the specific agent ecosystems it supports. If you already trust your AI completely, this is overkill; if you've been burned by unintended side effects, it's a must-have.
Verified 8h ago · liveness 77/100 · cite: rightaichoice.com/tools/kastra
- Development teams running AI coding agents like Claude Code or Cursor who need to prevent destructive commands
- Platform teams enforcing consistent governance across all AI agent actions in the organization
- DevSecOps engineers integrating policy-as-code into AI infrastructure pipelines
- Compliance officers requiring signed audit trails and real-time control of AI agent behavior
- Projects not using AI coding agents or autonomous agent workflows
- Teams seeking passive monitoring or observability without real-time enforcement
- Organizations that already trust their AI agents fully and have no compliance requirements
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In short
Kastra — Runtime authorization for AI agents — decide before actions execute. Best for Development teams running AI coding agents like Claude Code or Cursor who need to prevent destructive commands, Platform teams enforcing consistent governance across all AI agent actions in the organization, DevSecOps engineers integrating policy-as-code into AI infrastructure pipelines. Free to use.
What independent users actually report about Kastra
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
75 mentions across 5 sources (Hacker News, YouTube, Product Hunt, Bluesky, Lemmy).
- +Proactive runtime enforcement instead of reactive monitoring.
- +Cross-framework support for Claude, Cursor, Codex, and OpenClaw.
- +Policy-based permissions that don't rely on agent's judgment.
- +Real-time approval workflows for risky operations.
- +Audit logging of all agent actions for compliance.
- −No public evidence against red-teamed or adversarial agents.
- −Unclear how nested tool calls are policed downstream.
- −Community feedback limited to launch platforms only.
- −No long-term reliability or performance data available.
- −Integrations and platform support not yet fully documented.
- • Enterprise pricing not publicly listed — may be expensive for small teams.
- • Potential overage charges for high volume of agent actions beyond free tier limits.
Viability Score
How likely is Kastra to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Intercepts every agent action before execution
- Policy Decision Point (PDP) with sub-millisecond latency
- Define custom policies for shell, file, network, database, browser actions
- Real-time approval workflows for sensitive actions
- Audit trail with ed25519-signed append-only traces
- Streams to SIEM (Datadog, Splunk, S3)
- Kastra Recon scans agent history for past risky actions
- Supports Claude Code, Cursor, Codex CLI, OpenClaw
- macOS GUI app and CLI for local Edge governance
- Cloud control plane with US, EU, AP regions
- Self-hosted and air-gapped deployment options
- Post-inference validation (guard model outputs)
- OpenClaw browser agent governance (clicks, form fills, navigation)
- SDKs for TS, Python, Go, Rust, Java, Swift
- Policy as code — typed, versioned, reviewed like code
About Kastra
Kastra is a runtime authorization layer that intercepts every action an AI agent attempts — shell commands, file edits, API calls, browser clicks — and checks them against policy before execution. It is built for developers, platform teams, and security engineers who run AI coding agents like Claude Code, Cursor, Codex CLI, and OpenClaw, and need to prevent accidental or malicious actions without slowing down workflows. Kastra's Policy Decision Point (PDP) evaluates each request in under a millisecond with four checks: identity, scope, guardrail, and audit. It offers a macOS GUI app and a CLI (`brew install kastra-labs/tap/kastra-edge`) for local governance, plus a cloud control plane with regions in US, EU, and AP. Unlike monitoring-only tools, Kastra enforces decisions in real time, blocking risky operations like destructive shell commands, database writes to production, or unauthorized API calls. It also provides Kastra Recon, which scans agent history to retrospectively identify past risky actions and draft policies for them. Kastra is SOC 2 Type II in audit, ISO 27001 Stage 2, HIPAA BAA available, and supports self-hosted or air-gapped deployments. It positions itself as infrastructure — not a bolt-on monitor — for teams that need audit-ready governance across laptops, agents, browsers, and backend services.
Behind the Verdict
Kastra solves a real problem that most teams don't realize they have until it's too late. AI coding agents can execute shell commands, edit files, and hit APIs autonomously — and there's rarely anything between the model and your infrastructure. Kastra fills that gap with a lightweight, policy-as-code approach that fits into existing CI/CD workflows. We'd reach for this when we want to let agents run without constant supervision, especially on shared or production-adjacent environments. The sub-millisecond latency claim is credible for local edge enforcement, though cloud PDP latency will depend on network proximity. One area where Kastra shines is its design for compliance teams: every decision is signed ed25519, append-only, and streamable to SIEMs like Datadog or Splunk. That's rare in this space. Compared to LLM guardrails (which filter model outputs), Kastra focuses on action-level authorization — a fundamentally different and arguably more critical layer. The closest alternative is manual approval gates or no enforcement at all; Kastra is more surgical than blanket agent sandboxing tools. Where it bites: Kastra currently targets a narrow set of agents — Claude Code, Cursor, Codex CLI, OpenClaw. If you use a less common agent or a custom build, integration may require their API/SDK. The free tier is generous for solo devs, but team pricing isn't visible — you'll need to book a demo. Also, this is infrastructure you need to operate: policies must be written and maintained. It's not a set-and-forget product. For indie builders, Kastra's free local edge agent requires no account and works as an OpenAI-compatible proxy — a low-friction way to test drive enforcement. For enterprises, the self-hosted and air-gapped options make it viable for regulated industries.
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Use Cases
- Prevent AI agents from modifying sensitive configuration files without approval.
- Audit all shell commands executed by an AI assistant during development.
- Allow AI read-only access to production databases while blocking writes.
- Enforce compliance policies by restricting agent network access to approved endpoints.
Limitations
- Pricing details are not publicly available and require contact.
- Specific policy enforcement capabilities may vary per integrated agent.
- Self-hosted and air-gapped deployments are available.
- Post-inference validation can guard model outputs.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
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