Formal Verification Python Packages

pyvcg

Verification Condition Generator

certora-cli

The Certora Prover is the state-of-the-art security tool for automated formal verification of smart contracts running on EVM-based chains, Solana and Stellar

velaris-lang

Run code an AI wrote without handing it everything you can reach. Each function declares what it may touch. You grant the run one folder, one host or a number of calls, and the runtime refuses anything else the moment it's tried.

qwed

Open-source AI verification infrastructure for deterministic verification of LLM outputs, tool calls, code, schemas, and agent state before production execution.

certora-cli-alpha-master

The Certora Prover is the state-of-the-art security tool for automated formal verification of smart contracts running on EVM-based chains, Solana and Stellar

certo-math

Between having a mathematical idea and having a proof of it there is a lot of work that is not proving: find the object, break the false claims, measure what survives, reduce it to what it really is, assemble the rest. certo does that work, and every step returns a certificate anyone can re-check without trusting it. CLI + MCP.

darm-guard

Runtime authorization for AI agents: a broker executes only what a decision kernel, compiled from a machine-checked Lean 4 model, admits, and leaves signed evidence of every effect.

certora-cli-beta

The Certora Prover is the state-of-the-art security tool for automated formal verification of smart contracts running on EVM-based chains, Solana and Stellar

acacia-boomslang

A minimal implementation of reactive synthesis via universal co-Buchi automata using antichains

certora-cli-beta-mirror

The Certora Prover is the state-of-the-art security tool for automated formal verification of smart contracts running on EVM-based chains, Solana and Stellar

microvms

A verified Rust client stack and in-VM daemon for AWS Lambda MicroVMs: typed trap closures, cost reporting, and a live conformance suite

emilia-verify

Authority control plane for autonomous work. EMILIA Gate enforces finite customer-owned mandates at protected executor boundaries; the open protocol keeps evidence verifiable.

csl-core

Find, prove and control the AI agents on your machines: discovers every agent and what it can reach, proves policies with Z3 + TLA+, enforces them deterministically outside the model.

nomosguard

Deterministic security reasoning core: hash-chained evidence ledger, attack-path rule engine, fail-closed policy gate. LLMs produce evidence and explain decisions; they never cross the decision boundary.

invar-tools

From AI-generated to AI-engineered code. Guides AI agents to write verifiable code through contracts and structured workflows.

kepler-formal

Digital Design Equivalence Checking

pq-verify

Independent verification of post-quantum crypto implementations: ML-KEM (FIPS 203), ML-DSA (FIPS 204), SLH-DSA (FIPS 205), LMS/XMSS (SP 800-208). 1566/1566 pinned NIST ACVP vectors, audits of vendor libraries, Coq certificates, SARIF + GitHub Action. FN-DSA (draft FIPS 206) on a separate track.

open-ontologies-lite

Plan, apply and roll back changes to a production ontology, with a blast radius report and a proof an auditor can re-check.

sabline-lang

Run code an AI wrote without handing it everything you can reach. Each function declares what it may touch. You grant the run one folder, one host or a number of calls, and the runtime refuses anything else the moment it's tried.

intrepyd

Intrepyd Model Checker

plan-auditor

Make an AI agent's 'done' mean 'here is the command output that proves it.' Deterministic, LLM-free verification for AI coding agents: sealed plans, SHA-256 hash-chained evidence, STRIPS/PDDL reachability, and a fail-closed completion gate.

attest-receipts

Own what you buy: durable, portable proof of purchase you hold. The seller signs a receipt, you keep the file, anyone verifies it offline after the store is gone.

cadl-lang

CADL (Contract Architecture Description Language): a DSL and toolchain for specifying, verifying and generating code from institutional designs in Systems of Systems

dratify

Check DRAT/DRUP proofs of unsatisfiability inside your Python process. Zero dependencies, optional Rust accelerator.