CodeLogician Documentation

CodeLogician helps AI coding agents reason about complex software using math and logic.

Instead of only generating code and explanations, CodeLogician forces the agent to build a mathematical mental model of the system and uses automated reasoning to uncover:

• edge cases
• decision boundaries
• invariants
• hidden behavioral interactions

before software is deployed.

What CodeLogician Adds to AI Coding

LLM-based coding assistants are extremely good at generating code, but their reasoning is fundamentally statistical.

CodeLogician introduces a logic-first reasoning layer that systematically analyzes software behavior.

Instead of sampling possible executions, CodeLogician:

• builds a formal behavioral model
• explores the full decision space
• proves correctness properties
• produces concrete counterexamples when assumptions fail

This transforms AI-assisted development from:

"looks correct"

into

"behavior understood"

How CodeLogician Works

LLM-only workflow

AI coding assistants typically operate like this:

• The user asks for code
• The LLM generates an implementation
• The user reviews the result manually

Edge cases and behavioral boundaries are often missed because the model only samples likely paths.

Logic-first workflow

CodeLogician augments the workflow by introducing a formal reasoning step.

The LLM produces a structured model of the system, which is then analyzed by the ImandraX reasoning engine.

This produces artifacts such as:

• counterexamples
• verified invariants
• behavioral decompositions
• high‑coverage test cases

The result is evidence-backed reasoning, not just generated output.

Where CodeLogician Helps Most

CodeLogician is most valuable when software encodes complex behavior, not just simple transformations.

Examples include:

• state machines
• distributed systems
• payment and pricing logic
• access control systems
• compliance and risk rules
• workflow orchestration

These systems often contain combinatorial behavioral complexity that traditional testing or LLM reasoning alone cannot fully explore.

Powered by ImandraX

CodeLogician is built on ImandraX, a high‑performance automated reasoning engine used to analyze complex real‑world systems.

ImandraX enables CodeLogician to:

• systematically explore behavioral state spaces
• prove logical properties of systems
• synthesize executable counterexamples
• reason about complex numerical and structural constraints

This allows AI-generated software to be evaluated with mathematical rigor rather than probability.

Typical CodeLogician Workflows

1. Direct CLI workflow

Invoke reasoning tools directly from the command line.

Typical steps:

• Use codelogician doc to explore the IML language and reference materials
• Convert source code into formal models
• Run eval commands to analyze behavior and verify properties
• Inspect counterexamples and generated tests

2. Agent‑assisted formalization

CodeLogician includes an autoformalization agent that converts source code into analyzable models.

Common usage:

• codelogician agent PATH_TO_FILE
• codelogician multiagent PATH_TO_DIRECTORY

This allows coding agents to iteratively improve formal models and reasoning results.

3. Interactive reasoning server

The CodeLogician server provides a live environment for multi‑agent reasoning.

Features include:

• continuous model updates
• interactive analysis
• integration with the terminal UI (TUI)

This mode is useful for complex projects where models evolve continuously.

Using CodeLogician from your AI tool

Tools like Cursor, Gemini, Claude, and any MCP-compatible agent framework can connect to CodeLogician directly — no CLI invocation required.

The CodeLogician MCP server exposes the Agent and Server workflows as structured tool calls, letting your AI tool drive reasoning, formalization, and analysis from within its own interface.

Install the MCP server from Imandra Universe, then connect it to your tool of choice.

→ See: MCP Server docs

Getting Started

New to CodeLogician? Start here:

• Get your Imandra Universe API key
• Getting Started — install CodeLogician and run your first reasoning example

Tutorials

Step‑by‑step walkthroughs for common workflows:

• CLI Tutorial — working with the CodeLogician CLI
• TUI Tutorial — using the terminal interface

Real‑World Examples

Explore production‑style analyses demonstrating CodeLogician capabilities:

• Case Studies

Highlighted examples:

• Algorithmic trading rules analysis
• Financial market infrastructure verification
• workflow reasoning and system integration analysis

These examples include formal models, reasoning artifacts, and visual workflow diagrams.

Further Help

Need assistance or want to explore enterprise deployments?

Contact the Imandra team