Melchizedek

Name: Melchizedek
Author: louis49

by louis49version 1.0.2i★ –iRuns on your computer

Registry nameio.github.louis49/melchizedek

Install

Overview

Description from the maker (original English)

Persistent memory for Claude Code — offline, single-file, hybrid search.

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

Run this in your terminal

claude mcp add melchizedek -- npx -y melchizedek

Open in Cursor

Or copy the settings yourself

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

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

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

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 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account louis49, 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 7277 servers in this category
Published to the registry more recently than 4% of the other 7277 servers in this category
Basic checks equal to or better than 100% of the other 7277 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 Melchizedek 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 (1.0.2) reached the registry 8 months 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 yesterday

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 21 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 last month

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.

Melchizedek

Persistent memory for Claude Code. Automatically indexes every conversation and provides production-grade hybrid search (BM25 + vectors + reranker) via MCP tools. 100% local, zero config, zero API keys, zero invoice.

Why Melchizedek?

Claude Code forgets everything between sessions - and knows nothing about your other projects. Melchizedek fixes both.

It runs silently in the background - indexing your conversations as you work - then gives Claude the ability to search across your entire history, across all projects: past debugging sessions, architectural decisions, error solutions, code patterns.

No cloud. No API keys. No config. Plug and ask.

How it works

~/.claude/projects/**/*.jsonl       (your conversation transcripts - read-only)

Search pipeline - 4 levels of graceful degradation

Every layer is optional. The plugin works with BM25 alone and gets better as more components are available.

Level	Component	What it adds	Dependency
1	BM25 (FTS5)	Keyword search with stemming	None (always active)
2	Dual vectors (sqlite-vec)	Semantic search - text (MiniLM 384d) + code (Jina 768d)	`@huggingface/transformers` (optional)
3	RRF fusion	Merges BM25 + text vectors + code vectors via Reciprocal Rank Fusion	Vectors enabled
4	Reranker	Cross-encoder re-scoring of top results	Transformers.js or node-llama-cpp (optional)

Performance

Measured with npm run bench - 100 sessions, 1 000 chunks, on a single SQLite file.

Metric	Result	Target
Indexation (100 sessions)	~80 ms	< 10 s
BM25 search (mean)	~0.2 ms	< 50 ms
DB size (100 sessions)	~1.4 MB	< 30 MB
Tokens per search	~125	< 2 000

Quick Start

npm (recommended)

npm install -g melchizedek

Add the MCP server to Claude Code:

claude mcp add --scope user melchizedek -- melchizedek-server

npx (no install)

claude mcp add --scope user melchizedek -- npx melchizedek-server

From source

git clone https://github.com/louis49/melchizedek.git

cd melchizedek && npm install && npm run build claude --mcp-config .mcp.json

Claude Code plugin marketplace (coming soon)

Plugin review pending. In the meantime, use npm or npx install above.

claude plugin install melchizedek   # not yet available

Setting up hooks (automatic indexing)

The MCP server provides search tools, but hooks trigger automatic indexing. Without hooks, you'd need to manually index sessions.

For marketplace installs, hooks are configured automatically. For npm/npx/source installs, add hooks to ~/.claude/settings.json.

See docs/installation.md for the full JSON configuration, hook reference, and troubleshooting.

After setup, restart Claude Code. Indexing starts automatically.

MCP Tools

Search (start here)

Tool	Description
`m9k_search`	Search indexed conversations. Returns compact snippets. Current project boosted. Supports `since`/`until` date filters and `order` (score, date_asc, date_desc).
`m9k_context`	Get a chunk with surrounding context (adjacent chunks in the same session).
`m9k_full`	Retrieve full content of chunks by IDs.

Progressive retrieval pattern - search returns ~50 tokens/result, context ~200-300, full ~500-1000. Start with m9k_search, drill down only when needed. 4x token savings vs loading everything.

Context-aware ranking - results from your current project (×1.5) and current session (×1.2) are automatically promoted. Cross-project results remain visible.

Specialized search

Tool	Description
`m9k_file_history`	Find past conversations that touched a specific file.
`m9k_errors`	Find past solutions for an error message.
`m9k_similar_work`	Find past approaches to similar tasks. Prioritizes rich metadata.

Memory management

Tool	Description
`m9k_save`	Manually save a memory note for future recall.
`m9k_sessions`	List all indexed sessions, optionally filtered by project.
`m9k_info`	Show memory index info: corpus size, search pipeline, embedding worker, usage metrics.
`m9k_config`	View or update plugin configuration.
`m9k_forget`	Permanently remove a chunk from the index.
`m9k_delete_session`	Delete a session from the index.
`m9k_ignore_project`	Exclude a project from indexing. Future sessions won't be indexed, existing ones optionally purged.
`m9k_unignore_project`	Re-enable indexing for a previously ignored project. Purged data is not restored.
`m9k_restart`	Restart the MCP server to load fresh code after `npm run build`. Supports `force: true` for stuck processes.

Usage guide

Tool	Description
`__USAGE_GUIDE`	Phantom tool. Its description teaches Claude the retrieval pattern and available tools.

Configuration

Zero config by default. Everything is tunable via m9k_config or environment variables.

Setting	Default	Env var
Database path	`~/.melchizedek/memory.db`	`M9K_DB_PATH`
Daemon mode	enabled	`M9K_NO_DAEMON=1` to disable
Log level	`warn`	`M9K_LOG_LEVEL`
Embeddings enabled	`true`	`M9K_EMBEDDINGS=false` to disable
Reranker enabled	`true`	`M9K_RERANKER=false` to disable

See docs/configuration.md for the full settings reference (20+ options, env vars, config file examples).

Enhanced Search

Melchizedek works out of the box with BM25 keyword search. Text embeddings (MiniLM) download automatically on first use for semantic search.

For GPU-accelerated code embeddings (Ollama), cross-encoder reranking (GGUF models), platform-specific setup guides, and the full model reference, see [Enhanced Search Setup](docs/enhanced-search.md).

How is this different?

	Melchizedek	claude-historian-mcp	claude-mem	episodic-memory	mcp-memory-service

Philosophy	Search engine - indexes everything, you search	Search engine - scans JSONL on demand	Notebook - AI compresses & saves	Search engine	Notebook - AI decides what to store
Indexes raw conversations	Yes (JSONL transcripts)	Yes (direct JSONL read, no persistent index)	Compressed summaries	Yes (JSONL)	No (manual `store_memory`)
Retroactive on install	Yes (backfills all history)	Yes (reads existing files)	No	Yes	No (empty at start)
Search	BM25 + vectors + RRF + reranker	TF-IDF + fuzzy matching	FTS5 + ChromaDB	Vectors only	BM25 + vectors
Progressive retrieval	3 layers (search/context/full)	No	No	No	No
100% offline	Yes	Yes	No (needs API for compression)	Yes	Yes
Single-file storage	SQLite	None (reads raw JSONL)	SQLite + ChromaDB	SQLite	SQLite-vec
Zero config	Yes	Yes	Yes	Yes	Yes
MCP tools	16	10	4	2	12
License	MIT	MIT	AGPL-3.0	MIT	Apache-2.0
Dual embedding (text + code)	Yes (MiniLM + Jina Code)	No	No	No	No
Configurable models	Yes (Transformers.js or Ollama)	No	No (Chroma internal)	No (hardcoded)	Yes (ONNX, Ollama, OpenAI, Cloudflare)
Reranker	Cross-encoder (ONNX, GGUF, or HTTP)	No	No	No	Quality scorer (not search reranker)
Privacy	All local, tag redaction	All local	Sends data to Anthropic API	All local	All local
Multi-instance	Singleton daemon - N Claude windows share 1 process (Unix socket / Windows named pipe, local fallback)	N separate processes	Shared HTTP worker (:37777)	N separate processes	Shared HTTP server

Inspirations

This project stands on the shoulders of others. Key ideas borrowed from:

Project	What we took
CASS	RRF hybrid fusion, SHA-256 dedup, auto-fuzzy fallback
claude-historian-mcp	Specialized MCP tools (file_history, error_solutions)
claude-diary	PreCompact hook (archive before `/compact`)

Known issues

Session boost inactive - Claude Code currently send

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

Melchizedek

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

Melchizedek

Why Melchizedek?

How it works

Search pipeline - 4 levels of graceful degradation

Performance

Quick Start

npm (recommended)

npx (no install)

From source

Claude Code plugin marketplace *(coming soon)*

Setting up hooks (automatic indexing)

MCP Tools

Search (start here)

Specialized search

Memory management

Usage guide

Configuration

Enhanced Search

How is this different?

Inspirations

Known issues

Claude Code plugin marketplace (coming soon)