PPM — Python Package Manager

pypm – the "npm-style" package manager for Python
C-powered core · reproducible installs · plugin-friendly · workspace-aware

TL;DR: pypm aims to be a single command that handles everything from creating a
virtual-env to publishing wheels—fast, deterministic, and hackable.
The current release is ~500 LOC of portable C that already boots a shell, diagnoses
broken build chains, runs dynamically-loaded plugins, and produces hermetic bundles
for air-gapped deploys.

PPM is a next-generation Python package manager featuring hermetic packaging,
GPU-accelerated verification, cryptographic signing, and a companion
PMLL Memory MCP server for Claude agent tasks —
now integrating Context+ long-term
semantic memory graph for 99% accuracy.

Table of Contents

1. Features
2. Building from Source
3. CLI Commands
4. GPU & Security Features
5. Plugin System
6. Configuration
7. PMLL Memory MCP Server
8. Architecture
9. Release Notes
10. Roadmap
11. Contributing & Sponsors

✨ Features

Road-mapped: SAT dependency solver, 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

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 <stdio.h>
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
semantic memory graph for 99% accuracy in Claude Sonnet/Opus agent tasks.

pmll-memory-mcp (v1.0.1) is a Model Context Protocol (MCP) server that gives
Claude Sonnet / Opus agents a persistent memory logic loop with two complementary
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 persistent property graph with typed nodes, weighted edges, temporal decay scoring (e^(-λt)), and semantic search via TF-IDF embeddings.
• Solution engine (3 tools) — bridges both layers with unified context resolution (short-term → long-term → miss), auto-promotion of frequently accessed entries, and unified memory status views.

The server is designed to be the 3rd initializer alongside Playwright and other MCP tools — loaded once at the start of every agent task. Agents call init once at task start, then use peek before any expensive MCP tool invocation to avoid redundant calls. Frequently accessed entries are promoted to the long-term memory graph for persistent semantic retrieval.

The server exposes 15 tools total across four categories.

Why it's a premium 3rd initializer

Modern Claude agent tasks routinely call Playwright, file-system tools, and other MCP servers. Without a shared memory layer, every subtask re-initializes the same context from scratch. pmll-memory-mcp eliminates this overhead with two complementary memory layers:

Agent task start
  ├── 1st init: Playwright MCP
  ├── 2nd init: Unstoppable Domains MCP  (see unstoppable-domains/)
  └── 3rd init: pmll-memory-mcp   ← this server
        ├── Short-term: all tool calls go through peek() first
        └── Long-term: frequently accessed entries auto-promote to graph

The peek() pattern

Before every expensive MCP tool invocation, agents call peek to check the cache:

// Pseudocode — what the agent does automatically via MCP tool calls

// 1. Check cache before navigating
const result = mcp.call("pmll-memory-mcp", "peek", { session_id: sid, key: "https://example.com" });
if (result.hit) {
    const pageContent = result.value;          // ← served from PMLL silo, no browser needed
} else {
    // 2. Cache miss — do the real work
    const pageContent = mcp.call("playwright", "navigate", { url: "https://example.com" });
    // 3. Populate the cache for future agents / subtasks
    mcp.call("pmll-memory-mcp", "set", {
        session_id: sid,
        key: "https://example.com",
        value: pageContent,
    });
}

Tools reference (15 tools)

Short-term KV memory (5 tools)

GraphQL (1 tool)

Long-term memory graph (6 tools — adapted from Context+)

These tools are adapted from Context+ by @ForLoopCodes, providing persistent semantic memory with graph traversal, decay scoring, and cosine similarity search.

Solution engine (3 tools)

Installation

Via npx (recommended — no install needed)

npx pmll-memory-mcp

Via npm

npm install -g pmll-memory-mcp
pmll-memory-mcp          # starts the stdio MCP server

Via pip (Python)

pip install pmll-memory-mcp
pmll-memory-mcp          # starts the stdio MCP server

Claude Desktop / MCP config (claude_desktop_config.json)

NPX

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

Docker

{
  "mcpServers": {
    "pmll-memory-mcp": {
      "command": "docker",
      "args": [
        "run", "-i",
        "-v", "pmll_data:/app/data",
        "-e", "MEMORY_FILE_PATH=/app/data/memory.jsonl",
        "--rm", "pmll-memory-mcp"
      ]
    }
  }
}

VS Code MCP configuration

Add to .vscode/mcp.json (or open MCP: Open User Configuration from the Command Palette):

NPX

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

Docker

{
  "servers": {
    "pmll-memory-mcp": {
      "command": "docker",
      "args": [
        "run", "-i",
        "-v", "pmll_data:/app/data",
        "-e", "MEMORY_FILE_PATH=/app/data/memory.jsonl",
        "--rm", "pmll-memory-mcp"
      ]
    }
  }
}

Docker

# Build from the repository root
docker build -f mcp/Dockerfile -t pmll-memory-mcp .

# Run
docker run --rm -i pmll-memory-mcp:latest

# Run with persistent KV memory via volume
docker run --rm -i \
  -v pmll_data:/app/data \
  -e MEMORY_FILE_PATH=/app/data/memory.jsonl \
  pmll-memory-mcp:latest

Companion servers & integrations

Full MCP server documentation: mcp/README.md

🏛 Architecture

┌───────────────┐
│ pypm (CLI)    │  ← C-based command parser
└───────┬───────┘
        │
        ▼
┌───────────────┐     ┌─────────────┐     ┌──────────────┐
│ Workspace     │◀───▶│ Resolver    │◀───▶│ Wheel Cache  │
│ (TOML / YAML) │     │ (SAT + PEP) │     │ (~/.cache)   │
└───────────────┘     └─────┬───────┘     └─────┬────────┘
                            │                   │
                            ▼                   ▼
                       ┌──────────┐       ┌────────────┐
                       │ Env Mgr  │       │ Plugin Host│
                       │ (.venv)  │       │ (dlopen)   │
                       └──────────┘       └────────────┘

┌─────────────────────────────────────────────────────┐
│                  pmll-memory-mcp v1.0.1              │
│                                                     │
│  ┌──────────── Short-term (5 tools) ──────────┐    │
│  │ index.ts → peekContext() → kv-store.ts      │    │
│  │                  │                          │    │
│  │                  └──────► q-promise-bridge   │    │
│  └─────────────────────────────────────────────┘    │
│                                                     │
│  ┌──────── Long-term — Context+ (6 tools) ────┐    │
│  │ memory-graph.ts → embeddings.ts             │    │
│  │ (nodes, edges, decay, cosine similarity)    │    │
│  └─────────────────────────────────────────────┘    │
│                                                     │
│  ┌──────── Solution Engine (3 tools) ─────────┐    │
│  │ solution-engine.ts                          │    │
│  │ (resolve_context, promote, memory_status)   │    │
│  └─────────────────────────────────────────────┘    │
└─────────────────────────────────────────────────────┘
        │                    │
        ▼                    ▼
  PMLL.c / PMLL.h      Q_promise_lib/
  (memory_silo_t)       (QMemNode chain)

Key components

📝 Release Notes

pypm 0.0.3-dev (25 Jun 2025)

New & Improved

Fixes

• CLI flags after sub-commands were occasionally skipped by getopt → optind = 2 before parsing.
• Plugin loader returned success even when dlsym failed → now returns non-zero and closes handle.
• Workspace scan no longer overwrites cwd for later getcwd() calls.

Breaking changes

1. pypm version is now a sub-command (not --version flag).
2. doctor exit codes can now be >1 (numeric issue count).

Migration (0.0.2 → 0.0.3-dev)

Known issues

• Windows build needs: LoadLibraryW, _mktemp_s, bsdtar.exe fallback (#22).
• pypylock relies on shell tar; libarchive port planned for 0.0.4.
• WASI/Rust/OpenSSL checks are informational stubs only.

pmll-memory-mcp 1.0.1

• Version bump — bumped from 1.0.0 to 1.0.1 to fix PyPI publishing (1.0.0 already existed on PyPI).
• Updated project descriptions — PyPI and npm package descriptions now include "in Claude Sonnet/Opus agent tasks" to match the mcp/README.md tagline.
• README refresh — PPM README.md MCP section updated with full tool reference, peek() pattern with TypeScript example, VS Code MCP configuration, and companion servers table from mcp/README.md.

pmll-memory-mcp 1.0.0

• Context+ integration — 6 long-term memory graph tools adapted from
  Context+ by
  @ForLoopCodes: upsert_memory_node,
  create_relation, search_memory_graph, prune_stale_links,
  add_interlinked_context, retrieve_with_traversal.
• Solution engine — 2 new tools + 1 status tool bridging short-term KV cache
  with long-term memory graph: resolve_context, promote_to_long_term,
  memory_status.
• GraphQL tool — graphql tool for flexible query/mutation access.
• 15 total tools (5 short-term KV + 1 GraphQL + 6 long-term graph + 3 solution engine).
• TF-IDF embeddings with cosine similarity search across the memory graph.
• Temporal decay scoring (e^(-λt)) on graph edges with automatic pruning.
• Auto-similarity linking (cosine ≥ 0.72) on bulk context additions.
• Unified context resolution path: short-term → long-term → miss.

pmll-memory-mcp 0.2.0

• Initial MCP Registry submission.
• Five tools: init, peek, set, resolve, flush.
• TypeScript KV store mirroring PMLL.c::memory_silo_t.
• Q-promise registry mirroring Q_promise_lib::QMemNode.
• Docker multi-stage image with persistent volume support.
• Companion Unstoppable Domains MCP server included in mcp/unstoppable-domains/.

pypm 0.0.2 (25 Jun 2025)

Workspace autodetect, Doctor v2, Sandbox upgrade, Plugin add/run, pypylock -o.
Breaking: --version flag removed; doctor exits non-zero on issues.

pypm 0.0.1 (23 Jun 2025)

Initial proof-of-concept — single-file CLI with doctor, sandbox, plugin, and pypylock.

🗺 Roadmap

🤝 Contributing & Sponsors

Pull requests are welcome! Open issues and PRs at
https://github.com/drQedwards/PPM/issues

If you find PPM or pmll-memory-mcp useful, please consider supporting development:

Built by Dr. Q Josef Kurk Edwards — making Python packaging fast, deterministic, and hackable.