ResiMind MCP server

ResiMind — Verified Memory for AI Agents

Catch the error. Keep the insight. Give the next task a checked head start.

Run the demo · Connect MCP · Install the Skill · Take the challenge · 中文

ResiMind is an open-source neuro-symbolic Agent architecture that connects independent verification, residual-guided self-correction and reusable rule memory. Your model, rules or symbolic tools propose a step; domain checks decide what becomes a fact. Failed checks tell the next attempt what still needs fixing.

A checked solution can become a checked rule. Explicit learning extracts candidate rules from completed derivations, independently verifies admission, and rechecks each future application. Knowledge grows outside the model, without changing its weights. When local rules or tools suffice, the supported math routes run with zero model calls.

The core also runs bridge engineering, constrained optimization, customer support and open-ended planning with their own verifiers. Adaptive algebra, rule learning and the five MCP tools currently cover bounded rational polynomials. Core: Python 3.10+, no runtime dependencies. Optional MCP: Python 3.11+.

Created and originally published by @1105216375-alt. Citation · Changelog

Try it in three minutes

With Python installed:

git clone https://github.com/1105216375-alt/resimind.git
cd resimind
python -m venv .venv
source .venv/bin/activate
python -m pip install .
python -m resimind demo --domain lifelong

On Windows PowerShell, activate with .venv\Scripts\Activate.ps1. Installation may download build tools. The demo runs offline, without an API key; both python -m resimind and the installed resimind command work outside the checkout.

The first demo solves two training problems, extracts a checked rule, saves and reloads frozen memory, and transfers that rule to new coefficients. It then solves a long expansion using a sparse certificate. The frozen memory stays unchanged throughout evaluation. No API calls are made.

All demo commands: math, bridges, planning, support and Lean

Choose a different challenge. These commands run offline; the Lean example additionally requires an installed Lean 4.29.0 toolchain:

Shortened. The full README is on GitHub.