AI Law Tracker Review 2026: A Source-Linked API for AI Laws Your Agents Can Actually Cite

Ask a general-purpose chatbot which AI laws apply to your company and you will usually get a fluent, confident answer — occasionally a statute that does not exist. That gap between plausible and provable is exactly what AI Law Tracker is built to close. It is not another governance-workflow suite; it is the underlying regulated data, served as a versioned, read-only JSON API and an MCP connector, where every record carries the primary government source it came from.

AI Law Tracker launched as a Show HN in July 2026 and has been quietly building out what is, to my knowledge, one of the few machine-readable, agent-facing datasets of AI regulation. The pitch is simple and honest: a query returns the same answer every time, because there is no large language model anywhere in the request path. You get the law, the deadline, the penalty, and the URL — not a paraphrase that might be wrong.

What AI Law Tracker Does

At its core, AI Law Tracker is a curated corpus of AI laws, rules and enforcement actions across US state and federal regimes, the EU, and other national jurisdictions — roughly 1,435 source-linked records spanning 104 jurisdictions, with about 205 in force today, updated daily. Each record is retrieved verbatim from a real source and stamped with the government URL it originated from.

The data is exposed three ways. First, a versioned REST API: 32 endpoints across four scopes — laws and search, obligations and penalties, changes and webhooks, and meta/account. Second, official SDKs for Python (pip install ai-law-tracker) and Node/TypeScript (@ailawtracker/ai-law-tracker), both with zero runtime dependencies. Third, an MCP connector with 30 tools that lets Claude and ChatGPT query the dataset directly — search laws, pull deadlines, compare jurisdictions, and cite primary sources without leaving the conversation.

The interpreted layer is where it becomes more than a raw dataset: a compliance assessment endpoint takes a company profile and sector and returns what you must do, by when, and what you owe if you don’t. That layer, along with full change-feed history and signed webhooks, lives behind the paid tiers.

Use Cases

• Agentic compliance assistants. Because it speaks MCP, an assistant can answer “what AI-law deadlines hit Colorado HR next quarter?” with cited records instead of invented ones.
• In-product regulatory features. A startup building an AI tool can embed live, source-linked law lookups rather than hard-coding a static list that goes stale.
• Change monitoring. Poll the diff feed or subscribe to HMAC-signed webhooks so your system knows the moment a relevant statute moves.
• Legal-ops research. Replace part of a manual “what applies to us” exercise with a deterministic, auditable data pull your team can re-run.

Key Features

Source-linked, deterministic answers

Every obligation, deadline and penalty is pulled verbatim from a real record and carries its government URL. Same input, same output — the explicit antidote to hallucinated statutes.

Versioned API with a real free tier

Thirty-two versioned endpoints, a self-serve API key that needs no card, and 60 requests/min at 300/day on free — usable for evaluation and side projects, not a crippled teaser.

Official SDKs, zero dependencies

Typed Python and TypeScript clients with async iterators and typed errors mean you drop the data into a backend without writing your own HTTP layer.

MCP connector for agents

Thirty tools across Claude and ChatGPT let agents search, compare and cite AI-law records inline — the feature that actually differentiates it for the 9bests audience of builders.

Change feed and signed webhooks

A pollable diff feed plus HMAC-signed webhooks keep integrations current as regulations shift, with a reproducible risk score attached.

Pricing

The free tier is genuinely useful: $0, no card, 60 req/min and 300/day. Starter is $49/mo (150 req/min, 5,000/day), Pro is $99/mo (300 req/min, 10,000/day, change-feed history, the obligations layer, webhooks, priority support), and Business is $299/mo (1,000 req/min, 15,000/day, multiple keys, SLA). Notably, pricing is public — unlike most governance vendors that hide subscription costs. One caveat: the npm SDK readme references a $29 starter that the developer page lists as $49, so confirm the live tier before committing.

Common Questions

Is the data free to use? A meaningful slice is: the free tier serves the full record quality at low volume. What you pay for is headroom, the interpreted obligations layer, change history and webhooks.

Can my AI assistant use it? Yes — that is the point. The MCP connector exposes 30 tools to Claude and ChatGPT, and the REST API plus SDKs cover everything else.

Does it replace a compliance platform or a lawyer? No. It is the data layer, not a governance workflow or legal advice. Pair it with a platform like Credo AI or OneTrust, or with counsel, when you need assessments and sign-off.

Verdict

AI Law Tracker is a focused, genuinely differentiated product. In a “AI compliance” market crowded with heavy governance suites, it ships the regulated data itself as a deterministic, source-linked API and MCP connector — the right architecture for developers and agents who need provable AI-law facts without standing up a legal-research function. The honest downsides: the dataset and API are closed-source with no public repository (no LICENSE to audit — an enterprise hard gate), traction is modest for a Show HN, and the interpreted risk layer that makes it more than raw data sits behind the $99 Pro tier. For the 9bests audience this is a solid 7.0/10 — recommended as your AI-law data layer, not as a compliance certification or legal counsel.