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QWED v7.2.0 is live — you get fail-closed security hardening across expression parsing, auth, sandbox, and event loop, plus a new float-precision advisory. No breaking wire changes. See what’s new →

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What is QWED?

QWED (Query With Evidence & Determinism) is a trust boundary for AI systems:
  • LLMs can translate user intent into structured claims.
  • QWED verifies those claims with deterministic engines before execution or response.
  • You get proof-backed outcomes instead of probability-only confidence.
QWED is designed for LLM verification, AI agent security, verified tool calls, prompt injection defense, and deterministic transaction verification in high-stakes workflows.
“Do not trust generated output. Verify it.”

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Explore common verification problems

LLM verification

Learn how formal verification for LLMs differs from prompting, RAG, and output formatting.

AI agent security

Add pre-execution checks, policy enforcement, and budget controls for agent actions.

Prompt injection defense

Harden your stack against prompt injection, exfiltration, and unsafe execution paths.

MCP security

Secure Model Context Protocol tools with deterministic verification and tool schema checks.

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When to use QWED first

Math and logic

Verify equations, constraints, and logical claims before they reach users.

Code and SQL

Catch unsafe patterns, injection risks, and structural errors before execution.

Agent tool calls

Inspect actions and payloads before external systems are touched.

High-stakes workflows

Add deterministic checkpoints for finance, legal, tax, and regulated systems.

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Quick start (5 minutes)

1

Install and configure

Follow Installation and set your provider with LLM configuration.
2

Run first verification

Use Quick start to validate math, logic, code, and SQL.
3

Integrate into production flow

Use Integration getting started and Production deployment.

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Verification engines at a glance

Math

SymPy-based symbolic verification.

Logic

Z3 SAT/SMT verification with models.

Code

AST and symbolic checks for risky behavior.

SQL

Parser-backed SQL safety and validation.

Schema

Type and shape validation for structured outputs.

Taint

Data-flow tracking for untrusted inputs.

See all engines

Explore core, analysis, and specialized engines.

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What’s new in v7.2.0

Security hardening batch

v7.2.0 hardens expression parsing, auth, sandbox containment, and event-loop safety against RCE, auth DoS, and resource exhaustion — with no breaking wire changes. Learn more →
When you verify expressions with binary floating-point constants, you will see a precision.float-constants advisory in developer_fields.advisory_checks. It never affects your verdict.
  1. Core concepts
  2. Architecture overview
  3. SDKs overview
  4. API overview
  5. Integration guide