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🧠 NeuralMind Wiki

Reduce Claude, GPT, and Gemini token costs 40–70× on code questions. Local semantic codebase index + MCP server + PostToolUse compression hooks for Claude Code, Cursor, Cline, Continue, and any LLM.

Welcome — this wiki is the in-depth reference. For the fastest orientation, use the two pages at the top of Quick Links.

What's New

v0.4.0 — Brain-like synapse layer. NeuralMind now runs as a second brain alongside the LLM: a persistent SQLite-backed weighted graph that learns associations between code nodes from co-activation, decays unused edges, and answers via spreading activation. Includes the neuralmind watch daemon, three new Claude Code lifecycle hooks (SessionStart, UserPromptSubmit, PreCompact), and a memory exporter that surfaces learned associations to Claude Code's auto-memory system. See the release notes or the Architecture and Learning Guide sections.

Quick Links

Start here

Page When to read it
Setup Guide First-time setup for Claude Code, Cursor, Claude Desktop, or any MCP client
Use Cases Step-by-step walkthroughs by persona: Claude Code user, cost optimization, any-LLM, offline/regulated, growing monorepo
Comparisons Honest "NeuralMind vs X" pages: Cursor, Copilot, Cody, Aider, Claude Projects, LangChain, long context, prompt caching, RAG, tree-sitter
Version Strategy Versioning policy, breaking changes, release schedule, deprecation timeline
Compatibility Matrix Version compatibility, Python support, known issues, upgrade paths
Benchmarks CI-measured reduction ratios, per-model breakdown, community submissions, and how to run it on your own code

Enterprise & Deployment

Page For...
Deployment Guide DevOps/Infrastructure: Architecture patterns, Docker, Kubernetes, PostgreSQL backend, scaling, monitoring
Security Guide Security teams: RBAC, encryption, secrets management, NIST AI RMF, SOC 2, threat models
Upgrading Guide Everyone: How to upgrade between versions, breaking changes, rollback procedures

Reference

Page Contents
Installation Install from PyPI; the default install includes the MCP server
Usage Guide End-to-end examples for every command
CLI Reference All CLI commands, flags, and output shapes
API Reference Python API (NeuralMind, ContextResult, TokenBudget)
Architecture How the 4-layer progressive disclosure system works
Integration Guide MCP, CI/CD, VS Code, JetBrains, any-LLM piping
Scheduling Guide Automate audits with Windows Task Scheduler, GitHub Actions, or cron
Learning Guide Opt-in memory + cooccurrence reranking (v0.3.2) and brain-like synapses (v0.4.0)
Brain-Like Learning Design rationale for the v0.3.x learning system
v0.4.0 Release Notes Brain-like synapse layer: continuous co-activation, spreading activation, lifecycle hooks
Troubleshooting Common issues and fixes
FAQ 30+ frequently asked questions answered

What is NeuralMind?

A two-phase token optimizer for AI coding agents.

  • Phase 1 — Retrieval. A 4-layer progressive-disclosure index surfaces ~800 tokens of structured context for any code question, instead of loading 50,000+ tokens of raw source.
  • Phase 2 — Consumption. PostToolUse hooks (Claude Code) compress Read, Bash, and Grep output before the agent sees it — typically 88–91% smaller.

Combined effect: 5–10× total reduction vs baseline agent usage, offline and model-agnostic.

The core problem

You: "How does authentication work in my codebase?"

❌ Traditional: Load entire codebase → 50,000 tokens → $0.15-$3.75/query
✅ NeuralMind: Smart context → ~800 tokens → $0.002-$0.06/query

When do I reach for it?

Short answer: if any of these describe you, start with the Use Cases page.

  • My Claude Code session hits context limits mid-task
  • My monthly LLM bill is climbing
  • I start every session re-pasting project structure
  • The agent reads a 2,000-line file to answer one question
  • I want to query my codebase from ChatGPT / Gemini / a local model
  • I need AI coding help but code can't leave my machine

Full symptom-and-goal matrix in the main README.

Quick Start

# Install
pip install neuralmind graphifyy

# Setup
cd your-project
graphify update .
neuralmind build .

# Use
neuralmind wakeup .
neuralmind query . "How does authentication work?"
neuralmind skeleton src/auth/handlers.py

Claude Code users, install the lifecycle hooks (PostToolUse compression plus the v0.4.0 brain-like synapse hooks: SessionStart, UserPromptSubmit, PreCompact):

neuralmind install-hooks .
neuralmind init-hook .        # auto-rebuild on every git commit (optional)
neuralmind watch &            # always-on synapse learning from file edits (optional)

Compare to alternatives

Compared against Short verdict
Cursor @codebase Works only in Cursor; NeuralMind works anywhere
GitHub Copilot Copilot is hosted completions; NeuralMind is local context
Claude Projects Projects reload all files every turn; NeuralMind retrieves only what the query needs
Long context windows Possible ≠ cheap — NeuralMind drops per-query cost ~60×
Prompt caching Caching amortizes big prompts; NeuralMind makes them small

Full list: Comparisons.

Prove it on your code

Don't trust fixture numbers — measure it on your own repo:

pip install neuralmind graphifyy
graphify update . && neuralmind build .
neuralmind benchmark . --contribute

This outputs your reduction ratio, tokens per query, and an estimated monthly savings figure at Claude 3.5 Sonnet pricing. The --contribute flag produces a ready-to-share JSON blob you can paste into a PR (or a benchmark submission issue) to add to the public leaderboard.

Full walkthrough: Does NeuralMind work on your codebase?

Support

Clone this wiki locally