Memory for AI MCP server

memory-for-ai

An MCP server that turns a codebase into a persistent knowledge graph — functions, classes, call chains, HTTP routes, cross-service links — so an AI coding agent answers structural questions with graph queries instead of reading file after file.

One self-contained native executable. 162 languages via vendored tree-sitter grammars, refined by embedded Hybrid-LSP type resolution. 23 MCP tools. No language runtime, no Docker, no API key, no telemetry — everything runs locally.

• Are you an AI agent? Read docs/AGENT_GUIDE.md — the complete operating manual (tool catalog, task→tool playbooks, correctness protocol, per-project tuning). docs/llms.txt is the machine-readable index.
• Installing for a specific project? Jump to Per-project install — one command, zero global config, one isolated graph named after the repo.
• Want proof it pays off before adopting? docs/MEASURING.md — a 15-minute spot check and a full A/B protocol to measure token and tool-call savings on your own repository. A worked example with real numbers (and real caveats), measured on this repository itself: docs/AB-RESULTS.md.

Research — design and evaluation are described in Codebase-Memory: Tree-Sitter-Based Knowledge Graphs for LLM Code Exploration via MCP (arXiv:2603.27277): across 31 real repositories, 10× fewer tokens and 2.1× fewer tool calls vs. file-by-file exploration, at 83% answer quality (92% for the file-by-file baseline).

Quick start

30-second machine check first (details: docs/INSTALL.md — Preflight):

1. Platform is macOS / Linux / Windows on amd64 or arm64, with ~2 GB free disk.
2. Windows: powershell -Command "$PSVersionTable.PSVersion" must print a version — search_code shells out to PowerShell at runtime; if the WindowsPowerShell\v1.0 directory is missing from PATH, add it before installing.
3. git --version works (watcher freshness + detect_changes).
4. Already installed once? memory-for-ai --version tells you — re-running the installer is the update, indexes survive.

macOS / Linux (one line):

curl -fsSL https://raw.githubusercontent.com/LonelyTraderBay/memory-for-ai/main/install.sh | bash

Windows (PowerShell):

Invoke-WebRequest -Uri https://raw.githubusercontent.com/LonelyTraderBay/memory-for-ai/main/install.ps1 -OutFile install.ps1
Unblock-File .\install.ps1        # remove Mark-of-the-Web
.\install.ps1

Then restart your coding agent and say "Index this project". Done.

Shortened. The full README is on GitHub.