Walkthroughs for the most common “what do I actually do?” questions, organized by who you are and what you’re trying to solve. Each page is command-driven — copy, run, done.
| Use case | Best for | Primary goal |
|---|---|---|
Index any repo with just pip (no graphify) |
First-timers, CI, locked-down machines | Nothing → queryable index in one install + one build |
| Does it work on your code? (5-minute benchmark) | Evaluating whether to install at all | Measured before/after on YOUR codebase |
| Claude Code user | You use Claude Code daily and want full two-phase optimization | Cheapest + smartest agent sessions |
| Cost optimization | Teams or solos watching LLM spend climb | Measure, reduce, and report savings |
| Any LLM (ChatGPT / Gemini / local) | You use non-MCP chats or a model-agnostic workflow | Get NeuralMind context into any chat window |
| Offline / regulated work | Regulated industries, air-gapped machines | 100% local retrieval with zero telemetry |
| Growing monorepo | Codebase where old context goes stale fast | Keep the index fresh with minimal effort |
| Multi-agent codebase | You use multiple AI tools (Claude Code + Cursor + Hermes + OpenClaw) on the same project | One shared associative memory across every agent; v0.6.0 live graph shows the union |
| Slim & sovereign: ChromaDB-free local stack | Security-sensitive teams, tiny-footprint installs (v0.21.0+) | Embed + search with zero ChromaDB — smaller deps, smaller index, fewer advisories |
| Branch-isolated memory & team baselines | Heavy branchers, teams onboarding new devs (v0.24.0+) | Keep feature-branch learning out of main’s memory; ship a shared baseline as a versioned bundle |
Not sure which applies? Start with the symptom / goal table in the main README.