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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.

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How to start

1. Get an API key
Your first step is to obtain an Imandra Universe API key that your instance of CodeLogician will use for connecting. No credit card required.

2. Install locally

pip install codelogician# codelogician --help

Install the CodeLogician package via pip (requires Python 3.12).

3. Ask your AI agent

Claude/Gemini/Grok use Codelogician to explain this |

Neurosymbolic AI
for Code

For those who love AI coding, but still want to be in control. CodeLogician™ (CL) helps your AI coding assistant stay grounded in logic, explain its actions and scale beyond training data.

Formal Verification

Mathematically prove the correctness of your algorithms and systems, ensuring they meet specifications with absolute certainty through rigorous formal methods.

Test Case Generation

Automatically generate comprehensive test suites that cover edge cases and critical scenarios, ensuring thorough validation of your code and systems.

Logical Reasoning Booster

Empower your coding LLMs with logical reasoning so they can escape the limits of the training data and answer questions about your code based on logic, not the patters they have seen before.

CodeLogician™ helps coding assistants think logically

CodeLogician™ applies neurosymbolic AI to translate source code into precise mathematical logic, striving to create a formal model of the program's behavior that's functionally equivalent to the original source code. This model can then be analyzed with its reasoning tools (reasoners and agents) to prove deep properties, uncover hidden bugs, and automatically generate rigorous test cases.
Which reasoner or agent is best for reasoning about your code? It depends! At Imandra, we've created ImandraX - automated reasoning engine which we successfully applied to some of the world's most complex software systems in highly regulated environments and government agencies. But there're many other reasoners and tools (e.g. TLA+ and Lean) that we're working on bringing to CodeLogician™. If you have a specific request - please reach out and we will prioritize accordingly!

How it works

From source code to formal model in three steps — each building on the last to give you deep, verifiable insight into your system.

01Neurosymbolic AI at scale

CodeLogician integrates neurosymbolic AI to revolutionize software development, combining the power of machine learning with formal reasoning.

Neurosymbolic AI at scale
02Autoformalization

The latest CodeLogician builds on its agent-only predecessor applying autoformalization techniques to a new scale. It analyzes your entire project and creates a structured logical representation — called the MetaModel.

Autoformalization Process
03MetaModel Build-out

MetaModels capture logical structure and relationships within your project, enabling systematic logical reasoning and analysis.

MetaModel Build-out

Select Features

Purpose-built capabilities powered by the MetaModel — from mathematically proving correctness to planning safe system changes.

Formal Verification