Neuromcp

Name: Neuromcp
Author: AdelElo13

by AdelElo13version 0.30.2i★ –iRuns on your computer

Registry nameio.github.AdelElo13/neuromcp

Install

Overview

Description from the maker (original English)

Semantic memory for AI agents with hybrid search, knowledge graph, and consolidation

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

npmNode.js installed (for the npx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "neuromcp": {
      "command": "npx",
      "args": [
        "-y",
        "neuromcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add neuromcp -- npx -y neuromcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "neuromcp": {
      "command": "npx",
      "args": [
        "-y",
        "neuromcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "neuromcp": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "neuromcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account AdelElo13, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 7235 servers in this category
Published to the registry more recently than 63% of the other 7235 servers in this category
Basic checks equal to or better than 100% of the other 7235 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Neuromcp safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.30.2) reached the registry 22 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated 3 days ago

Install

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 20 days ago

Install

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated 44 days ago

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

neuromcp — Sovereign Memory for AI agents

Any model. Your memory. Stays local.

neuromcp is the first Sovereign Memory layer for AI: an open-source MCP server that gives Claude, GPT, Gemini, and Ollama persistent, searchable memory — stored entirely on your machine. No API keys. No cloud sync. No subscription required to remember who you are.

Sovereign Memory = data that you own outright, lives on hardware you control, and is portable across every model you use. Cloud memory products own your data; Sovereign Memory means *you* do.

npx neuromcp-init   # one command: detects your MCP clients, writes configs, sets up the wiki

Or run the bare server without any setup: npx neuromcp. Something not working? npx neuromcp-doctor diagnoses the daemon, Ollama, embeddings and the database in one run.

*The built-in memory browser: the most-connected entities in a namespace and the relations between them, as a force-directed graph you can drag, zoom and click — served from localhost, never from a cloud. See Memory browser & Obsidian.*

Why neuromcp

The LLM is a commodity. Your memory is the moat. GPT-5, Claude 4, Gemini — they all converge. The model you use next year will differ. The memory of every conversation, decision, and preference you build is yours. neuromcp keeps that layer on your machine and makes it portable across any MCP-compatible client.

Local-first is a design choice, not a limitation. No telemetry. No data leaves your laptop. No vendor has a copy of your conversations. Audit every line of code that touches your memory. SQLite + local embeddings; everything fits on one disk.

One install. Every client. Claude Desktop, Cursor, Windsurf, Codex CLI, Continue, LibreChat, Open WebUI — neuromcp speaks MCP, so it works wherever MCP is supported. Switch models tomorrow; your memory follows.

Real recall, not keyword matching. Hybrid retrieval combines vector search (nomic-embed-text, 768-dim), BM25 full-text, graph links, and a learned usefulness prior. At 500 distractors on LongMemEval, R@5 holds at 93.3%. Your context window gets the right memory, not just the most recent.

LongMemEval-S accuracy

Run	Score	Sample	Config
v7 (current)	96.08% (98/102)	n=102	Opus generator + Opus judge, single-model
v6	95.10% (97/102)	n=102	Same as v7, prior hint set

Repro: OMB_ANSWER_LLM=claude OMB_ANSWER_MODEL=opus OMB_JUDGE_LLM=claude OMB_JUDGE_MODEL=opus uv run omb run --dataset longmemeval -s s -m neuromcp -c "single-session-user,single-session-assistant,multi-session,temporal-reasoning,knowledge-update,single-session-preference" --query-limit 17

Sample size honesty. n=102 (17 per category × 6 categories). Wilson 95% CI for 98/102 ≈ 90.5–98.7%. Full 500q run with the same config is the next milestone before any "top-tier" claim.

Benchmarks (v0.18.0)

Oracle split (clean — easy mode)

Mode	R@5	R@10	Hit Rate
Extracted (hybrid)	100%	100%	100%

Oracle-split LongMemEval isolates the correct memory in a small corpus. Every local MCP memory system claims ~99% here. It measures "does the ranker work on clean inputs" — nothing more.

Distractor split (v0.18.0, honest)

Same 30 questions + 1000 random distractor memories drawn from other questions' haystacks. The correct memory now competes against real noise.

Embedder	Distractors	N	R@5	R@10	MRR
Ollama `nomic-embed-text`	0 (oracle)	30	100%	100%	100%
Ollama `nomic-embed-text`	200	5	100%	100%	100%
Ollama `nomic-embed-text`	500	30	93.3%	93.3%	80.3%
Ollama `nomic-embed-text`	1000	5	100%	100%	74%

Reproduce: npx tsx eval/longmemeval-distractor-runner.ts --limit 5 --distractors 1000

Sample sizes. The 500-distractor row is n=30 (Wilson 95% CI for

28/30 ≈ 78-99% R@5). The 1000-distractor row is n=5 — preliminary,

Wilson 95% CI [57%, 100%]. The 1000-distractor n=30 run takes ~36 min

on a single Ollama instance; cached-distractor batching is v0.19.0

work. Treat 500-distractor numbers as defensible, 1000-distractor as

directionally positive but underpowered.

Head-to-head comparison is explicit v0.19.0 work. Hindsight (local

OSS MCP, ~94.6% LongMemEval claimed) and Mem0/Zep publish their own

numbers on their own harnesses. Until we run all of them against the

same corpus + embedder, calling any local MCP server "state of the art"

is marketing, not measurement. neuromcp publishes its numbers with

sample-size caveats so you can judge direction; don't read absolute

superiority into them yet.

Hybrid ranker (BM25 + vector + attention + graph + usefulness prior) keeps R@5 = 100% at 1000:1 distractor:target ratio on the observed sample. MRR drops to 74% because the correct memory is sometimes not rank-1 but always rank ≤ 5 in what we saw. Earlier v0.18.0 numbers (R@5 23%) were from a test FakeEmbedder — fixed in v0.18.1.

What this benchmark does NOT prove: end-to-end answer correctness, long-horizon multi-session reasoning, or superiority over commercial cloud systems (Mem0, Zep) on their own benchmarks. Those comparisons need their numbers on the same distractor split, which hasn't been published.

Why

AI agents forget everything between sessions. Existing solutions either store flat key-value pairs (useless for real knowledge) or require cloud infrastructure and API keys.

neuromcp gives you two layers of memory:

MCP Server — hybrid search (vector + full-text + graph), verbatim recall, memory governance, automatic consolidation, all in a single SQLite file
Wiki Knowledge Base — compiled Markdown knowledge that survives crashes, compounds over sessions, and gives your agent project-aware context at every startup

Inspired by Karpathy's LLM Wiki, Mastra's Observational Memory, and Zep's temporal knowledge graphs — but simpler than all of them. No vector DB, no embeddings pipeline, no cloud. Just Markdown files + Git + hooks.

Architecture

~/.neuromcp/

├── memory.db ← SQLite: hybrid search, MCP tools ├── wiki/ ← Compiled knowledge (git-tracked) │ ├── index.md ← Routekaart — LLM reads this FIRST │ ├── schema.md ← Operating rules for the LLM │ ├── log.md ← Append-only changelog │ ├── people/ ← User profiles, preferences │ ├── projects/ ← Project knowledge (stack, auth, URLs) │ ├── systems/ ← Infrastructure (tools, MCP servers) │ ├── patterns/ ← Reusable patterns (error fixes, routing) │ ├── decisions/ ← Architecture decisions with context │ └── skills/ ← Repeatable procedures └── raw/sessions/ ← Raw session logs (auto-generated)

How the wiki works

When	What happens
Session start	Hook injects `index.md` + user profile + auto-detected project page (~1300 tokens)
During session	LLM updates wiki pages when learning something persistent
Every 8 tool calls	Hook reminds LLM to update the wiki
Session end	Hook writes raw session log + git auto-commits all wiki changes
Crash	Checkpoint every 5 tool calls to file. Git history for rollback.

Self-healing consolidation pipeline (v0.15.0+)

Every ~4h the launchd agent runs run-consolidation.sh, which orchestrates four steps end-to-end:

`consolidate-sessions.py` — batches raw sessions per project, asks Claude for a factual summary, and fact-checks it against the raw sources. When the auditor flags specific unsupported claims the consolidator now auto-strips those lines and re-audits once — so one speculative sentence no longer kills a whole batch.
`rescue-rejected.py` — any batch that still fails is parsed, the unsupported claims are removed, and the cleaned summary is appended to its wiki page. Pure text surgery, no LLM calls.
`entity-linker.py` — cross-links every page: a bare-word mention of another registered entity (people/, projects/, systems/) is added to the page's related: frontmatter. Makes the wiki act like a graph without a separate graph database.
`rebuild-index.py` — regenerates index.md and per-category -index.md files. Categories over 10 pages are auto-split so the session-start router stays compact as the wiki scales.

The pipeline is idempotent — safe to re-run at any time.

What the LLM knows at session start

Schema (operating rules) → How to maintain the wiki

Index (knowledge map) → What knowledge exists User profile → Who you are, how you work Project page → Current project details (auto-detected from cwd) Last session → What happened last time

Quick Start

One command (recommended)

npx neuromcp-init

Detects your installed MCP clients (Claude Desktop, Claude Code, Cursor, Windsurf), writes the neuromcp entry into each config (with a backup of the original), initializes the wiki + hooks, and checks whether Ollama is available. --dry-run previews everything without writing.

For GUI clients (Claude Desktop, Codex Desktop) install permanently first: npm install -g neuromcp && neuromcp-init. Only a permanent install lets init write the absolute node + script paths those clients need — from the bare-npx cache (garbage-collected) it deliberately falls back to a PATH-dependent npx entry, which GUI clients usually cannot resolve.

Manual steps (what init does under the hood)

1. Start the MCP server

npx neuromcp

2. Initialize the wiki + hooks (required for closed-loop attribution)

npx neuromcp-init-wiki

This creates the wiki structure, installs hooks (Claude Code) and rules (other editors), and configures everything automatically. Without this step, npx neuromcp still runs as a plain MCP server with 46 tools, but the critic hook that closes the attribution loop is not installed — retrieval works but usefulness scores never accumulate. Safe to run multiple times — won't overwrite existing config.

Editor Compatibility

neuromcp works with any MCP-compatible editor. Two tiers of integration:

Feature	Claude Code	Cursor / Windsurf / Cline / Copilot / JetBrains / Zed
MCP tools (46)	Full	Full
Context at session start	Hooks (automatic)	Rules (LLM-driven, best-effort)
Persist at session end	Hooks (automatic)	Rules (LLM-driven, best-effort)
Wiki reminders	Every 8 tool calls	No
Crash-resilient checkpoints	Yes	No

Claude Code gets the full experience via native hooks — context injection and persistence happen automatically, even if the LLM forgets.

Other editors get rules files that instruct the LLM to call neuromcp tools at session start/end. This depends on LLM compliance — it works well in practice but is not guaranteed like hooks.

# Auto

Only the beginning is shown; read the rest in the repo

Neuromcp

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

neuromcp — Sovereign Memory for AI agents

Why neuromcp

LongMemEval-S accuracy

Benchmarks (v0.18.0)

Oracle split (clean — easy mode)

Distractor split (v0.18.0, honest)

Why

Architecture

How the wiki works

Self-healing consolidation pipeline (v0.15.0+)

What the LLM knows at session start

Quick Start

One command (recommended)

Manual steps (what init does under the hood)

Editor Compatibility