NEWRead our new paper

Logical reasoning for Al coding agents

CodeLogician makes AI coding agents structure their reasoning in logic and uses a dedicated reasoning engine to systematically analyze what the code can actually do. So you catch edge cases, surface hidden behaviors, and ship with confidence instead of guesswork.

Start now
7-day free trialpip installableWorks with Claude, GPT, Cursor, Gemini, Grok
Start now How to start
Start now

How to start

1Sign up and get an API key
Your first step is to obtain an Imandra Universe API key that your instance of CodeLogician will use for connecting.

2Install
curl -fsSL codelogician.dev/codelogician/install.sh | sh

Includes Python 3.13

3Start reasoning
Grok/Gemini/ChatGPT/ Claude use CodeLogician to find edge cases in this |

Your agent calls CodeLogician to analyze, verify, and reason about code.

Read the full getting started guide here.

Mechanism

How CodeLogician works

AI coding agents normally reason statistically. CodeLogician makes them build a mathematical mental model and analyze it.

LLM Agent only

Without CodeLogician — AI samples likely paths
  • Samples likely paths
  • Edge cases are easy to miss
  • Humans must manually validate behavior

LLM Agent + CodeLogician

With CodeLogician — decision logic formalized
  • Decision logic is formalized
  • Reasoning engine explores behavior
  • Artifacts expose boundaries and edge cases

CodeLogician turns AI coding from “looks right” into “behavior understood.”

Scope

Tackle Complexity with Math and Logic

CodeLogician activates when code starts encoding rules, states, or business logic — forcing the agent to translate its understanding into structured logic and using a reasoning engine to surface edge cases, decision boundaries, and invariants that LLMs alone struggle to uncover.

Not all code needs deep reasoning. But systems that encode decisions — payments, access control, workflows, distributed services — create behavioral complexity that statistical AI alone can't fully explore.

LLMs Alone Are Great For

  • UI rendering and formatting
  • CRUD services and wrappers
  • Data transformations
  • Simple scripts

CodeLogician Activates For

  • State machines and workflows
  • Financial and rule engines
  • Access control and policy systems
  • Distributed system coordination
  • Complex integration logic

Read the full Tackle Complexity with Math and Logic guide here, or the Managing Complexity with Math and Logic article on Medium.

Use Cases

What developers use it for

From day-to-day code review to high-stakes system verification.

Refactor safely

Prove your refactored function is behaviorally identical to the original — or see precisely where behavior changes.

Catch bugs before production

Give CodeLogician the function and it exhaustively maps all inputs that cause unexpected outputs — before a single line ships.

Equivalence checking

Compare two implementations for semantic equivalence — useful for migrations, API versioning, and shadow deployments.

Prove invariants

Establish formal guarantees that hold across all inputs — account balances, state machine transitions, security properties.

Built for systems that can't be wrong

Payment flows

Charge logic, refunds, idempotency

Pricing logic

Discounts, tiers, edge conditions

Auth & sessions

Token expiry, permission boundaries

Matching engines

Order books, fairness, settlement

Agent plans

Agentic loop invariants, tool safety

Explore demos and examples in the repo.

Research & Media

In the press and on paper

Peer-reviewed research, press coverage, and video walkthroughs of CodeLogician and the reasoning engine behind it.

ArticleMar 2026

Managing complexity with math and logic: changing Stripe payment flow with Claude and CodeLogician

How to use CodeLogician with Claude to refactor and verify a Stripe payment flow — managing complexity with formal logic and automated reasoning instead of guesswork.

Medium
VideoMar 2026

Formal AI Reasoning with ImandraX & CodeLogician | Session 1 of AI Reasoning Weekly

Grant Passmore and Denis Ignatovich introduce ImandraX, a theorem prover and proof assistant built for engineers, and CodeLogician, which plugs formal reasoning directly into AI coding agents like Claude Code.

YouTube
ArticleFeb 2026

Vibe Coding was phase 1. Logic-first AI is phase 2. It is here now.

How CodeLogician makes AI coding agents reason explicitly instead of guessing — the shift from probabilistic generation to structured, verifiable logic.

Medium
PaperJan 2026

How Formal Reasoning Extends LLM Capabilities for Software Analysis

We introduce a benchmark targeting mathematical reasoning about real software logic. Across all tested models, augmenting LLMs with formal reasoning via CodeLogician closes a 41–47 percentage point accuracy gap compared to LLM-only reasoning.

arXiv
VideoJun 2025

A Gentle Intro to ImandraX and CodeLogician

Denis Ignatovich (Co-Founder, Imandra) walks through the core ideas behind ImandraX and CodeLogician — how automated reasoning integrates with AI coding agents.

YouTube
ArticleJun 2025

Imandra Unveils Imandra Universe: The Platform for Neurosymbolic AI Agents

Imandra launches Imandra Universe — a unified platform bringing neurosymbolic AI and logical reasoning to software development teams at scale.

Imandra

For more research, press, and videos, see the media page.