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FR-AI / ANALYSISWhy it is worth attention
It combines exact bigint-cent arithmetic, a full rule corpus for 2025 and 2026 tax years, a solver layer for cliffs and marginal rates, and independent validation against PolicyEngine, all while never guessing—only deriving from encoded rules.
Who it is for
- Tax software developers integrating proof-based tax computation into applications
- AI engineers building LLM agents that require reliable tax figures without hallucination
- Tax professionals and policymakers who need auditable, citation-backed calculations
- Open-source contributors extending tax rule coverage via data-driven contributions
Use cases
- Agent-based tax preparation: LLM calls the engine via MCP tools for accurate answers
- Guardrail for AI-generated tax figures: `opentax check` refutes wrong claims with the correct value
- Policy simulation and education: explore marginal rates, cliffs, and compare filing statuses
- Offline tax verification: run in a browser with a self-contained HTML file, verify proofs without network
Strengths
- Exact to the cent using bigint arithmetic with no floating point errors
- Verifiable proof trees that cite specific statutory references (e.g., 26 U.S.C. § 63)
- Covers all five filing statuses, hundreds of credits/deductions, and both 2025 and 2026 law
- Independently validated against PolicyEngine with 533 of 572 scenarios agreeing exactly
Considerations
- Some approximations: earned income ≈ wages, MAGI ≈ AGI in several phase-outs
- EITC qualifying children approximated by CTC child count; niche interactions may refuse outright
- State returns only partially covered (IL/VA/CA/NY/PA via MCP), and some areas (consolidated returns, REIT/RIC) refuse explicitly
README quick start
opentax-engine
A US tax calculator that shows its work — and can prove it.
Three ideas, that's the whole project:
- Ask it a tax question with plain facts (
--wages 50000). - It answers with a proof: every rule it applied, with the law it came from (
26 U.S.C. § 63(c)…), every assumption it made. - Anyone can verify that proof offline. If it can't derive an answer from its encoded rules, it refuses — it never guesses.
Open source (AGPL-3.0, with commercial licenses available), exact to the cent (no floating point), built to be extended.
Try it
pnpm install && pnpm build
pnpm opentax eval --status mfj --wages 120000 --kids 2
us.federal.income_tax_after_credits $5,640.00
├─ us.federal.income_tax_before_credits $10,040.00 [26 U.S.C. § 1(j); Rev. Proc. 2025-32]
│ └─ us.federal.taxable_income $87,800.00 [26 U.S.C. § 63(b)]
│ ├─ us.federal.agi $120,000.00
│ │ ├─ fact wages = $120,000.00
│ │ └─ assumed taxableInterest = $0.00 (default)
│ └─ us.federal.standard_deduction $32,200.00 [Rev. Proc. 2025-32 § 4.14]
└─ us.federal.ctc $4,400.00 [§ 24, as amended by OBBBA § 70104]
Assumptions (6): taxableInterest = $0.00, isAge65OrOlder = false, …
corpus sha256:6ff44719…
It answers under the law in force on the date you ask about — --as-of defaults to today; pass --as-of 2025-12-31 and the same facts derive under TY2025 rules instead (different standard deduction, different brackets, cited to the other Rev. Proc.).
Money is written in dollars (50000, "$50,000", "1234.56"). Not sure what inputs exist? pnpm opentax facts lists them all. More facts than fit in flags? Use a JSON file: --facts examples/mfj_120k_2kids.json.
Or skip the terminal entirely — the engine is pure TypeScript with zero platform dependencies, so the whole thing runs in a browser:
pnpm -F @invaro/opentax-playground build && open packages/playground/dist/index.html
One 130 KiB self-contained HTML file: engine + full rule corpus + verifier, no server,
no network, works from file://. Compute, read the proof tree, download the proof —
then paste it into the Verify tab and watch your own browser re-derive every step
(alter one byte and it says exactly what broke). Same corpus Merkle root the CLI prints.
Save a proof, verify a proof:
Description
The deterministic tax engine for AI agents. Highest score ever recorded on TaxCalcBench (96% exact returns)
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