dratify

Check a DRAT/DRUP proof of unsatisfiability, inside your Python process.

A SAT solver that answers "unsatisfiable" is asking to be trusted. A DRAT proof is how it stops asking: the solver logs every clause it derives, and a checker that shares no code with it replays the log and confirms the empty clause really follows.

Emitting those proofs is routine. PySAT exposes them from six solver families with with_proof=True / get_proof(). Checking them from Python has not been practical: drat-trim is C you compile and shell out to, and the only checker on PyPI installs on Linux x86-64 under Python ≤ 3.10 alone.

pip install dratify              # pure Python, zero dependencies

from dratify import parse_dimacs, check_proof

formula = parse_dimacs(open("problem.cnf").read())
result = check_proof(formula, open("proof.drat").read())

print(result.ok)              # True  -- the refutation is genuine
print(result.reached_empty)   # True  -- the empty clause was derived

No subprocess, no compiler, no temporary files.

Use this if:

• ✅ You have a DRAT or DRUP proof and need to verify it
• ✅ You are about to act on "no solution exists"
• ✅ You want it in-process, from Python or Rust, on any platform
• ✅ You want two independent implementations that agree

Use something else if:

• ❌ You want to solve rather than check → PySAT, or cdclkit
• ❌ You only ever look at SAT answers — then you do not need this at all

The same checker is published as a Rust crate, for use from Rust directly:

cargo add dratify                # https://crates.io/crates/dratify

Want the Rust checker from Python? This package deliberately ships no compiled extension of its own -- installing a proof checker should never need a toolchain. Instead it exposes a seam, register_native(), and anything that already embeds the crate can supply an implementation. Today that is cdclkit-native:

pip install "cdclkit[native]"    # brings wheels that register with dratify

After that, engine="auto" uses the Rust checker (~15x faster; see Speed) with no further configuration.

Why you would want this

Something reported that no solution exists, and you are about to act on it. Ship the hardware. Declare the configuration impossible. Sign off the safety argument. Close the bug as "cannot happen".

That report is a bare assertion from a large, aggressively optimised program. A "yes" answer from a solver checks itself — walk the clauses, confirm each has a true literal. A "no" answer does not: it is a claim about all 2^n assignments, and nothing about it is verifiable unless the solver shows its work.

DRAT is how it shows its work, and this is what reads it. Emitting proofs is routine; PySAT exposes them from six solver families. Checking them from Python has not been practical until now, and checking them from Rust meant shelling out to a C program.

When not to. If you want to solve rather than check, this is not that — use PySAT or, from Rust, an established solver crate. If you never look at UNSAT answers, you do not need this at all.

Check a proof from PySAT

from pysat.formula import CNF as PyCNF
from pysat.solvers import Glucose42
from dratify import parse_dimacs, check_proof

cnf = PyCNF(from_file="problem.cnf")
with Glucose42(bootstrap_with=cnf, with_proof=True) as s:
    assert not s.solve()
    proof = s.get_proof()

result = check_proof(parse_dimacs(cnf.to_dimacs()), "\n".join(proof))
print(result.ok)          # True -- the refutation is genuine

No subprocess, no compiler, no temporary files.

Two engines, and that is the point

Proof checking is the one domain where two independent implementations agreeing is the evidence. This ships both:

Neither is the "real" one. They have been differentially tested against each other on acceptances and on rejections, and they agree.

check_proof(formula, proof, engine="python")   # always available
check_proof(formula, proof, engine="native")   # once an implementation is registered
check_proof(formula, proof, engine="auto")     # default: native when present

What it checks

• RUP (reverse unit propagation) — the common case.
• RAT (resolution asymmetric tautology) — the "A" in DRAT, checked properly: pivot on the first literal, resolvent against every clause containing its negation, RUP on each.
• Deletion, applied by default. Deletion is monotone-safe for RUP; RAT is not monotone, so RAT steps are checked against exactly the clauses present.

Checking is forward: every step is verified, rather than working backwards from the empty clause as drat-trim does. That makes it slower on large proofs and means a corrupted step is caught where it occurs.

Speed

Every number here is regenerated by python bench/repro.py, which builds the instances, obtains proofs from whichever solvers are on PATH, times both engines, and cross-checks drat-trim when it is installed. It writes the whole result set, with the host and versions attached, to bench/results.json.

Measured on Darwin arm64, Python 3.14, proofs from CaDiCaL and kissat:

Across all 12 measurements the Rust engine's geometric mean is about 15x over pure Python, and the ratio grows with proof size.

Read the per-instance numbers as a range, not as values. Two runs of the script on this machine gave geometric means of 14.7x and 14.9x — stable — while individual ratios moved between 11x and 22x, one of them by a third. The absolute times are worse: uuf250-01 took 43.9s of pure Python in one run and 63.1s in another, because the second shared the laptop with a compile. Run repro.py on an otherwise idle machine, and treat the geometric mean as the figure and any single row as an illustration.

Against drat-trim the pattern is the one forward checking implies: Rust wins by 20-50x on small proofs, where drat-trim pays process startup and cannot be called in-process at all, and loses about 2x on the largest, where the backward pass gets to skip work.

What "skip work" means

drat-trim verifies only the lemmas its refutation needs. On uuf100-01 it reports "304 of 403 lemmas in core" — the other 99 are never examined. So corrupting one of them is a proof drat-trim still calls VERIFIED, and this checker rejects at the step where the corruption is.

bench/repro.py demonstrates exactly that: of 36 perturbed proofs, the two tools agree on 33, and the 3 they split on are all this direction. Zero are the direction that would matter — this checker accepting something drat-trim rejects — and the script fails the run if one ever appears.

That is the trade the speed table is buying, in both directions.

Tests

105 tests and 87% statement coverage, of which the enforced floor is 80% — the percentage is a snapshot, the floor is the gate. Ten of the tests need a native checker and skip without one, so the dependency-free job runs 95 of them and a separate job (below) runs the rest. The negative cases carry most of the weight: a checker that accepts everything passes any suite that only feeds it valid proofs, so there are tests for truncated proofs, bogus refutations of satisfiable formulas, clauses that are neither RUP nor RAT, and the tautological-resolvent case that makes some surprising clauses vacuously RAT.

make test        # or: PYTHONPATH=src python3 -m unittest discover -s tests
make coverage    # floor is 80%
make fuzz        # randomised; SEED=n reproduces a run

The package lives under src/, so it has to be on the path to be imported without installing — make handles that.

The coverage tool uses the standard library's trace module, so checking this package needs no more dependencies than using it.

A separate CI job installs a native checker and runs tests/test_differential.py, which compares the two implementations on every verdict field over a 150-instance random sweep plus hand-written cases. That job fails if those tests skip — the agreement claim above is only worth making while something is checking it.

Honest limitations

• Forward checking only. Backward checking would close the large-proof gap.
• No binary DRAT format yet; text proofs only.
• Not formally verified. It is carefully written, differentially tested against a second implementation, and cross-validated against drat-trim — which is not the same thing as a machine-checked proof of the checker itself. If you need that, cake_lpr is CakeML-verified and consumes LRAT.

Related

cdclkit (source) is a from-scratch CDCL SAT solver by the same author that uses this package to check its own refutations. You do not need it to use dratify — checking a proof should never require installing a solver, which is why these are separate packages.

Generating code against this?

AGENTS.md lists the API's sharp edges — the mistakes that have actually been made, not hypothetical ones. Worth reading before writing a line.

Licence

Apache-2.0. See LICENSE.