ASL V6 vs RiskKernel

ASL V6 and RiskKernel are both guardrails & safety tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

ASL V6

ASL V6 combines AST-based static analysis with Docker-isolated runtime verification to find and confirm exploitable vulnerabilities in AI agent frameworks before they ship. The dual-layer approach means a finding isn't just flagged — it's verified in a sandboxed execution environment, which cuts the false-positive rate that burns security team time. It runs entirely offline with no external API calls, so sensitive proprietary code never leaves your machine. The ceiling appears quickly on non-Python codebases and on teams that need ticketing integrations or cloud-native CI pipelines baked in rather than assembled by hand.

RiskKernel

Deployed as a single Go binary, it sits in front of your existing OpenAI, Anthropic, or LangChain stack via a one-variable proxy — no rewrite required. Every call is metered and checkpointed, so a killed or crashed run resumes from the last saved state instead of re-spending from zero. The human-approval gate routes irreversible tool calls for sign-off over CLI, web, or webhook before they fire, and the LLM cannot bypass it because the gate lives in compiled code, not a prompt. The hosted dashboard is private beta only; teams that need a UI today are self-managing.

Attribute	ASL V6	RiskKernel
Pricing	Free	Free
Free trial	No	No
Open source	Yes	Yes
Has API	No	Yes
Self-hosted option	Yes	Yes
Platforms	Linux, macOS, Windows (with Docker)	Linux, macOS, Windows (Go binary)
Pros	AST static analysis paired with Docker runtime verification confirms exploitability before surfacing a finding, so your team spends time fixing real vulnerabilities rather than triaging false positives. 100% local execution with no external API calls, which means auditing proprietary or pre-release AI code without the legal and compliance risk of sending source to a third-party service. Remediation patch generation alongside confirmed findings, so developers receive an actionable fix rather than a vulnerability description they have to decode into a code change. MIT license with self-hosted deployment, so security teams can run it inside air-gapped environments or modify detection rules to match their specific AI framework stack without vendor approval. Detection rules derived from confirmed, disclosed CVEs in production AI systems (AutoGPT, FlowiseAI), which means the tool targets attack patterns that have already caused real damage rather than theoretical edge cases.	Hard per-run dollar and token ceilings enforced in compiled code, which means the kill switch fires before the overspend registers rather than after the invoice cycle closes. Crash-resumable checkpointing, so a process killed mid-run restarts from the last saved state instead of replaying every prior API call and paying for them again. Human-approval gate for side-effecting tool calls that the LLM cannot route around, so irreversible actions — deleting records, sending messages, writing to external systems — wait for a human decision before executing. One-variable proxy adoption with no code rewrite required, so existing agents running against OpenAI or Anthropic get metering and enforcement without refactoring the application. Self-hosted Apache 2.0 binary with BYO provider keys and no telemetry, so teams in regulated or air-gapped environments get full auditability without exporting run data to a third-party service.
Cons	Coverage is scoped entirely to Python — teams auditing AI systems with Node.js tool-calling layers, Go-based infrastructure, or polyglot agent frameworks get no static or dynamic analysis for the non-Python surface, and there is no documented path to extend language support without forking the project. Docker is a hard runtime dependency for the dynamic verification layer; teams in environments where Docker is restricted by policy (common in enterprise security tooling reviews) lose the exploit-confirmation step entirely and fall back to static-only output, which is where false positives return. There is no native integration with vulnerability management platforms, ticketing systems, or SIEM pipelines — teams that need findings routed into Jira, Defect Dojo, or Splunk build that plumbing themselves, and when the integration maintenance cost grows, teams with existing platform investments switch to commercial SAST tools that ship those connectors out of the box.	The hosted dashboard is private beta only, so teams that need a web UI to monitor, review, or manage runs across agents have no production-ready option yet — they operate through CLI or build their own view against the OpenTelemetry export. SDK adapters are scoped to LangChain, the Claude Agent SDK, and the OpenAI Agents SDK; teams running CrewAI, AutoGen, or any other framework hit the proxy layer only and lose loop-count and tool-level controls until they write their own adapter. The project is maintained by a single developer with no enterprise support tier listed; teams whose compliance posture requires a support contract or SLA will find nothing on offer and will move to a vendor-backed observability or guardrails product instead.

Bottom line

Only RiskKernel exposes a public API. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between ASL V6 and RiskKernel?

ASL V6 is Free and open source, while RiskKernel is Free and open source. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is ASL V6 better than RiskKernel?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.