Pmll Memory MCP

Name: Pmll Memory MCP
Author: drQedwards

by drQedwardsversion 0.1.1i★ –iRuns on your computer

Registry nameio.github.drQedwards/pmll-memory-mcp

Install

Overview

Description from the maker (original English)

PMLL Memory MCP — persistent KV context memory and Q-promise deduplication.

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": {
    "pmll-memory": {
      "command": "npx",
      "args": [
        "-y",
        "pmll-memory-mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add pmll-memory -- npx -y pmll-memory-mcp

Open in Cursor

Or copy the settings yourself

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

{
  "mcpServers": {
    "pmll-memory": {
      "command": "npx",
      "args": [
        "-y",
        "pmll-memory-mcp"
      ]
    }
  }
}

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": {
    "pmll-memory": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "pmll-memory-mcp"
      ]
    }
  }
}

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 drQedwards, 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 7275 servers in this category
Published to the registry more recently than 4% of the other 7275 servers in this category
Basic checks equal to or better than 100% of the other 7275 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 Pmll Memory MCP 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 (0.1.1) 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.

PPM — Python Package Manager

pypm packaging CLI with shared PMLL memory + Q-promise. Sibling: drQedwards/pmll.

Live Stellar pmll-anchor IDs: docs/STELLAR.md · skills: SKILL.md · promises: Q_promise_lib/README.md.

TL;DR: PPM packages Python (C CLI). Shared PMLL owns memory/state (memory_silo_t, exact peek + semantic peek_semantic,

SAT bridge, init_pml). Q-promise owns temporal/control-flow (qpromise_*, libqpromise.so)

— it does not replace the silo. Optional Stellar commitments hash off-chain

CodeworkPayloads into 32-byte digests via pmll-anchor (ABI: init/store/get/bump).

Companion packaging CLI + MCP memory server live in-tree.

PMLL provides durable structured memory for agent workflows, alongside a PPM packaging CLI (hermetic bundles, plugins, signing) and the PMLL Memory MCP server. Long-term graph tools adapt Context+.

Layers (post-merge)
Features
Building from Source
CLI Commands
GPU & Security Features
Plugin System
Configuration
PMLL Memory MCP Server
Stellar commitments
Architecture
Release Notes
Roadmap
Contributing & Sponsors

Layers (post-merge)

Two complementary layers — not substitutes for each other:

Layer	Owns	Key surface
PMLL	Durable memory / state	`memory_silo_t`, `peek` / `peek_semantic`, `silo_set`, SAT bridge, `init_pml`
Q-promise	Temporal / control-flow	`qpromise_*` API, `libqpromise.so`, deferred `qpromise_drain()`

Q-promise does not replace the silo. A promise may *reference* a memory key (and optional SAT state id); results are committed back with silo_set / qpromise_resolve_commit.

Intended loop

retrieve (peek / peek_semantic)

→ compute / request work → promise PENDING → resolve / reject (optionally resolve_commit → silo) → continuation (then / catch / finally via qpromise_drain) → memory update → retrieve again

Thread safety: the C Q-promise drain is single-threaded. Continuations run only during qpromise_drain() on the calling thread; callers must externally serialize access. Details: Q_promise_lib/README.md.

C core (PMLL)

Headers: PMLL.h · implementation: PMLL.c

Symbol	Role
`memory_silo_t`	Associative/semantic silo: `tree`, `slots[]`, `embed_dim` (`PMLL_EMBED_DIM=32`), `slot_count`
`silo_slot_t`	`key`, `content`, `embedding`, `resolved`
`init_silo` / `free_silo` / `silo_set`	Allocate, free, write key/content + embedding
`peek`	Exact key, else index
`peek_semantic`	Cosine ≥ `min_sim` over embeddings (`silo_embed_text`, `silo_cosine_similarity`)
`init_pml`	Sets every `assignment[i] = -1` (undecided)
`check_conflict`	Treats `-1` as undecided (not sticky false from zero-fill)
`sat_bridge_*`	Map 3SAT tokens → associative string literals in the silo

Q-promise library

Build / test / API: [`Q_promise_lib/README.md`](Q_promise_lib/README.md) (authoritative). Public symbols are qpromise_*; shared object is libqpromise.so. The old prototype (QMemNode / q_mem_* / memory-chain walker) is removed; Q_promises.h is a compatibility include redirecting to qpromise.h.

✨ Features

Command	What it does
`pypm doctor`	Checks Python headers, C compiler, OpenSSL, WASI toolchain, GPU, …
`pypm sandbox [-d DIR]`	Drops you into an ephemeral temp dir (or custom DIR) with a full shell
`pypm plugin add NAME SRC`	Downloads a `.so` plugin (from URL or path) into `~/.pypm/plugins/`
`pypm plugin run NAME …`	Executes `pypm_plugin_main()` inside the named plugin
`pypm pypylock [-o FILE]`	Bundles every wheel + interpreter into `dist/venv.tar.gz` (or FILE)
`pypm version`	Prints the current CLI version
`ppm import PKG`	Import and cache a package with GPU-accelerated hash verification
`ppm add PKG --lock`	Add packages and update the lockfile
`ppm plan` / `ppm apply`	Plan dependency changes, then apply them with an audit trail
`ppm snapshot` / `ppm rollback`	Snapshot the environment; roll back to any prior state
`ppm sign` / `ppm verify`	Sign artifacts (Ed25519) and verify cryptographic receipts
`ppm sbom`	Generate a Software Bill of Materials (SBOM)

*Road-mapped:* SAT dependency solver (the standalone SAT.c CDCL solver exists but is not yet used for resolution), parallel wheel cache, workspaces with single lockfile, WASM wheel resolution, Conda & Poetry import plugins.

🔧 Building from Source

System dependencies

C11 compiler (gcc, clang, or MSVC)
libcurl (plugin downloads)
libdl (dynamic loading — standard on Linux / macOS)
tar / libarchive (optional, for pypylock bundles)

Build & first run

git clone https://github.com/drQedwards/PPM.git

cd PPM cc -Wall -Wextra -ldl -lcurl -o pypm Ppm.c ./pypm doctor # Diagnose your dev box ./pypm sandbox # Spin up a throw-away REPL playground

Q-promise shared library

cd Q_promise_lib

make clean && make test make shared # → libqpromise.so cd ..

Optional: CUDA-accelerated build

nvcc -O3 CLI/CLI.cu -lcuda -o ppm-gpu

./ppm-gpu import transformers torch --verbose

🖥 CLI Commands

Import packages

# Import a single package

ppm import transformers

Import with a specific version

ppm import transformers==4.43.3

Import multiple packages

ppm import transformers torch numpy

Scan a Python file for imports and install them

ppm import --from-file my_script.py

Verbose — watch what's happening

ppm import transformers --verbose

🔍 Resolving transformers...

⬇️ Downloading transformers-4.43.3-py3-none-any.whl

🔐 GPU integrity check: PASSED

✅ transformers==4.43.3 imported successfully

Project initialization

ppm init

Creates:

.ppm/

├── ledger.jsonl ← append-only operation log

├── state.json ← current state

├── lock.json ← dependency lockfile

└── snapshots/ ← rollback points

Dependency resolution & locking

ppm add transformers torch==2.4.0 --lock

ppm plan

{ "plan": "install", "packages": { "transformers": "4.43.3", ... } }

ppm apply --note "Added ML stack"

Snapshot & rollback

ppm snapshot --name "before-upgrade"

ppm snapshots ppm rollback before-upgrade

Environment diagnostics

ppm doctor

✅ Python dev headers found

✅ C compiler available

✅ CUDA toolkit available

🏁 Diagnostics complete (0 issues found)

Sandbox

ppm sandbox                 # ephemeral temp directory

ppm sandbox -d /tmp/mydir # custom directory

Hermetic packaging

ppm pypylock -o production-env.tar.gz

🔐 GPU & Security Features

GPU-accelerated hash verification

ppm import torch --verbose

🚀 GPU hash verification: SHA-256 computed on device

✅ Integrity verified: e3b0c44298fc1c149afbf4c8996fb924...

GPU backend selection

ppm ensure transformers --gpu auto    # auto-detect CUDA

ppm ensure transformers --gpu cu121 # force CUDA 12.1 ppm ensure transformers --gpu cpu # CPU-only

Ed25519 cryptographic signing

ppm keygen --out-priv ed25519.priv --out-pub ed25519.pub

ppm sign --sk ed25519.priv --file torch-2.4.0-*.whl --gpu ./libbreath_gpu.so ppm verify --receipt torch-2.4.0-*.whl.receipt.json --file torch-2.4.0-*.whl

Provenance & SBOM

ppm sbom        --out project-sbom.json

ppm provenance --out provenance.json ppm graph --dot | dot -Tpng -o deps.png

🔌 Plugin System

# Install a plugin

ppm plugin add auditwheel https://cdn.example.com/auditwheel.so

Run it

ppm plugin run auditwheel repair --wheel torch-2.4.0-cp310-linux_x86_64.whl

Writing a plugin (C)

// hello.c

#include int pypm_plugin_main(int argc, char **argv) { puts("Hello from a plugin 👋"); return 0; }

cc -shared -fPIC -o hello.so hello.c

mv hello.so ~/.pypm/plugins/ pypm plugin run hello

⚙️ Configuration

`pypm.toml`

[tool.ppm]

python = "^3.10" default_gpu = "auto"

[tool.ppm.backends] cpu.index = "https://download.pytorch.org/whl/cpu" cu121.index = "https://download.pytorch.org/whl/cu121" cu122.index = "https://download.pytorch.org/whl/cu122"

torch_prefer = "2.4.*" transformers_prefer = "4.43.*"

Environment variables

export PYP_WORKSPACE_ROOT=/path/to/project   # override workspace detection

export PYP_DEBUG=1 # enable debug output export CUDA_VISIBLE_DEVICES=0 # control GPU usage

🧠 PMLL Memory MCP Server

**Persistent memory logic loop with short-term KV context memory, Q-promise

deduplication, and Context+ long-term

memory graph designed to improve context retention and retrieval for coding agents.**

pmll-memory-mcp is a Model Context Protocol (MCP) server with complementary short-term KV and optional long-term graph layers. Server construction prefers MCPServer (mcp 2.x) and falls back to FastMCP (mcp 1.x) — see mcp/pmll_memory_mcp/server.py.

Ground tool names in code: TypeScript (mcp/src/index.ts) has 15 tools including graphql and names like create_relation; Python (mcp/pmll_memory_mcp/server.py) has 14 tools (no graphql) with several longer names such as create_memory_relation. A ctypes demo in Ppm-lib/pmll_mcp/ loads libqpromise.so via qpromise_*.

Memory layers:

Short-term KV cache (5 tools) — session-isolated key-value memory with Q-promise deduplication, mirroring PMLL.c::memory_silo_t.
Long-term memory graph (6 tools) — adapted from Context+ by @ForLoopCodes, providing a SQLite-backed property graph with typed nodes, weighted edges, temporal decay scoring (e^(-λt)), and semantic search via stable hashing embeddings.
Solution engine (3 tools) — bridges both layers with unified context

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

Pmll Memory MCP

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

PPM — Python Package Manager

Table of Contents

Layers (post-merge)

Intended loop

C core (PMLL)

Q-promise library

✨ Features

🔧 Building from Source

System dependencies

Build & first run

Q-promise shared library

Optional: CUDA-accelerated build

🖥 CLI Commands

Import packages

Import with a specific version

Import multiple packages

Scan a Python file for imports and install them

Verbose — watch what's happening

🔍 Resolving transformers...

⬇️ Downloading transformers-4.43.3-py3-none-any.whl

🔐 GPU integrity check: PASSED

✅ transformers==4.43.3 imported successfully

Project initialization

Creates:

.ppm/

├── ledger.jsonl ← append-only operation log

├── state.json ← current state

├── lock.json ← dependency lockfile

└── snapshots/ ← rollback points

Dependency resolution & locking

{ "plan": "install", "packages": { "transformers": "4.43.3", ... } }

Snapshot & rollback

Environment diagnostics

✅ Python dev headers found

✅ C compiler available

✅ CUDA toolkit available

🏁 Diagnostics complete (0 issues found)

Sandbox

Hermetic packaging

🔐 GPU & Security Features

GPU-accelerated hash verification

🚀 GPU hash verification: SHA-256 computed on device

✅ Integrity verified: e3b0c44298fc1c149afbf4c8996fb924...

GPU backend selection

Ed25519 cryptographic signing

Provenance & SBOM

🔌 Plugin System

Run it

Writing a plugin (C)

⚙️ Configuration

pypm.toml

Environment variables

🧠 PMLL Memory MCP Server

`pypm.toml`