AI regulation now changes faster than any team can track manually. The EU's Digital Omnibus just reshuffled the AI Act — and the gap between the headline and what's actually in force is exactly where teams over-comply or get exposed. (Concrete example: Article 50 transparency duties still take effect on 2 August 2026 despite the reshuffle.)
So I built AI Law Tracker — one API for AI law, designed to live inside other products.
What's in it
- Coverage: 50 US states + DC + federal + EU + other jurisdictions
- Refreshed daily, provenance + official source URL on every record
- An interpreted layer: obligations, penalties and effective dates — not just raw statute text
- Human-audited and sourced, not LLM-generated. A hallucinated deadline is worse than no answer, so the interpreted data is verified, not generated.
The part I care about most: trust
- Public accuracy ledger — every record is checkable.
- Bug bounty for wrong data.
If you're building anything downstream of the regulation layer (AI governance, compliance tooling, legal engineering), you shouldn't have to trust a black box.
MCP connector
There's a Model Context Protocol connector with 24 tools, so an agent inside Claude or ChatGPT can query laws, obligations, penalties and deadlines directly.
Try it (no card)
- Free API key: ai-law-tracker.com/developers#get-key
- Live Omnibus breakdown: ai-law-tracker.com/omnibus
I'd love feedback on the data model and the MCP tool design — is 24 tools the right granularity for an agent reasoning over regulation, or too fine-grained?
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