Invariant

Invariant

DataGrout’s Invariant is a tool for testing and validating AI system behaviors, ensuring outputs remain consistent and reliable across different inputs and scenarios.

AI testingvalidationreliabilityconsistency
Open site
image for InvariantOpen site

What is Invariant?

Invariant is a neuro-symbolic code intelligence stack that combines LLM-powered semantic understanding with deterministic Prolog reasoning. It extracts structural facts from source code, queries for patterns, and verifies that code changes match stated goals. Developers use Invariant to catch drift, scope creep, and unintended side effects before shipping code. The tool suite includes four components: lens, query, diff, and review.

Application scenarios

  • Agent self-correction loops

    Add two lines to an agent's system prompt so it autonomously revises code changes when alignment_score drops below 0.8 or unexpected changes are detected.

  • Automated PR review gates

    Drop Invariant into your CI pipeline to get strict pass/fail verdicts with per-criterion reasoning for automated merge decisions.

  • Security and compliance checks

    Query for security_concerns and intent_mismatches across your codebase before each release to catch unauthorized functions handling user input, SQL, or shell execution.

  • Codebase analysis

    Build a queryable semantic model of any codebase before running queries or diffs, with facts persisted per repo and commit for temporal analysis.

  • Version-aware code review

    Track changes across commits with structured facts about functions, callers, dependencies, and side effects.

Core Features

  • Semantic fact extraction

    Parse source code with tree-sitter and enrich it with LLM-powered semantic analysis to extract structured facts about functions, calls, dependencies, intent, side effects, and patterns.

  • Multi-language support

    Works with Python, Rust, TypeScript, JavaScript, Go, Elixir, and Ruby.

  • Version-aware persistence

    Facts are stored per repo_id and commit_sha, enabling temporal querying across code changes.

  • Flexible credit system

    Structural-only extraction costs 2 credits; extraction with intent analysis costs 4 credits.

  • Local CLI extraction

    Perform extraction locally via tree-sitter, then upload facts through Conduit with mTLS security.

  • Deterministic Prolog queries

    Use Prolog rules to query patterns with deterministic verification, avoiding LLM hallucination risks.

  • Diff analyzer

    Compare code changes against stated goals with alignment scores and unexpected change detection.

  • Automated review gate

    Returns structured pass/fail verdicts with per-criterion reasoning suitable for CI pipeline integration.

Target users

Invariant is built for software developers, AI/ML engineers, and DevOps teams who need deterministic verification of AI-generated code. It's especially useful for teams building agentic systems, running automated PR reviews, or enforcing security and compliance checks across multi-language codebases.

How to use Invariant?

Start by visiting the GitHub repository or reading the documentation at library.datagrout.ai/invariant-tools. Install the CLI tool for local extraction via tree-sitter, then run invariant.code_lens to extract semantic facts from your codebase. Use invariant.code_query to search for patterns, invariant.diff_analyzer to compare changes against goals, and invariant.review to set up automated PR gates in your CI pipeline. No credit card is required to get started.

Pricing and free trial

Invariant is free to start with no credit card required. Extraction costs 4 credits per run with intent analysis, or 2 credits for structural-only extraction. Specific pricing tiers beyond the free tier are not detailed on the site.

Effect review

Invariant delivers a genuinely novel approach to code verification by bridging LLM semantics with deterministic Prolog reasoning—a combination that addresses a real pain point in AI-assisted development. The multi-language support and version-aware fact persistence make it practical for real-world codebases, while the credit-based pricing keeps initial experimentation low-risk. Its strength lies in catching drift and scope creep that traditional linters or static analyzers would miss. For teams building agentic systems or automating code review pipelines, Invariant offers a rare blend of flexibility and deterministic certainty.

Frequently Asked Questions

What is Invariant?
Invariant is a tool by DataGrout for testing and validating AI system behaviors, ensuring consistent and reliable outputs across various inputs and scenarios.
How does Invariant help with AI testing?
It allows users to define expected behaviors and invariants, then automatically tests AI outputs to detect inconsistencies, drifts, or failures.
What types of AI systems can Invariant test?
Invariant is designed for any AI system, including LLMs, recommendation engines, and classification models, where output reliability is critical.
Is Invariant free to use?
Pricing details are not specified; contact DataGrout for licensing and subscription options.
Can Invariant integrate with existing workflows?
Yes, it offers APIs and SDKs for seamless integration into CI/CD pipelines and development environments.

Invariant - AI Tool Detail

DataGrout’s Invariant is a tool for testing and validating AI system behaviors, ensuring outputs remain consistent and reliable across different inputs and scenarios.

Category：Agents

Visit Link：https://datagrout.ai/tools/invariant

Tags：AI testing、validation、reliability、consistency