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.

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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

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, 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)

Tool	Input	Output	Description
init	session_id: str, silo_size: int = 256	{status, session_id, silo_size}	Set up PMLL silo + Q-promise chain for session
peek	session_id: str, key: str	{hit, value?, index?} or {hit, status, promise_id}	Non-destructive cache + promise check
set	session_id: str, key: str, value: str	{status: "stored", index}	Store KV pair in the silo
resolve	session_id: str, promise_id: str	{status: "resolved"|"pending", payload?}	Check/resolve a Q-promise continuation
flush	session_id: str	{status: "flushed", cleared_count}	Clear all silo slots at task completion

GraphQL (1 tool)

Tool	Input	Output	Description
graphql	query: str, variables?: object, operationName?: str	{data} or {errors}	Execute GraphQL queries/mutations against the memory store

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.

Tool	Input	Output	Description
upsert_memory_node	session_id, type, label, content, metadata?	{node}	Create or update a memory node with auto-generated TF-IDF embeddings
create_relation	session_id, source_id, target_id, relation, weight?, metadata?	{edge}	Create typed edges (relates_to, depends_on, implements, references, similar_to, contains)
search_memory_graph	session_id, query, max_depth?, top_k?, edge_filter?	{direct, neighbors, totalNodes, totalEdges}	Semantic search with graph traversal — direct matches + neighbor walk
prune_stale_links	session_id, threshold?	{removed, remaining}	Remove decayed edges (e^(-λt) below threshold) and orphan nodes with low access
add_interlinked_context	session_id, items[], auto_link?	{nodes, edges}	Bulk-add nodes with auto-similarity linking (cosine ≥ 0.72 creates edges)
retrieve_with_traversal	session_id, start_node_id, max_depth?, edge_filter?	[{node, depth, pathRelations, relevanceScore}]	Walk outward from a node — returns reachable neighbors scored by decay & depth

Solution engine (3 tools)

Tool	Input	Output	Description
resolve_context	session_id, key	{source, value, score}	Unified context lookup: short-term KV → long-term graph → miss
promote_to_long_term	session_id, key, value, node_type?, metadata?	{promoted, nodeId}	Promote a short-term KV entry to the long-term memory graph
memory_status	session_id	{shortTerm, longTerm, promotionThreshold}	Unified view of short-term KV and long-term graph memory status

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

{
  "tools": [
    {
      "name": "init",
      "description": "Set up PMLL silo and Q-promise chain for a session. Call once at task start.",
      "inputSchema": {
        "type": "object",
        "properties": {
          "session_id": { "type": "string" },
          "silo_size": { "type": "integer", "default": 256 }
        },
        "required": ["session_id"]
      }
    },
    {
      "name": "peek",
      "description": "Non-destructive cache lookup + Q-promise check. Call before every expensive MCP tool invocation.",
      "inputSchema": {
        "type": "object",
        "properties": {
          "session_id": { "type": "string" },
          "key": { "type": "string" }
        },
        "required": ["session_id", "key"]
      }
    },
    {
      "name": "set",
      "description": "Store a key-value pair in the session silo. Call after a cache miss.",
      "inputSchema": {
        "type": "object",
        "properties": {
          "session_id": { "type": "string" },
          "key": { "type": "string" },
          "value": { "type": "string" }
        },
        "required": ["session_id", "key", "value"]
      }
    },
    {
      "name": "resolve",
      "description": "Check or resolve a Q-promise continuation by promise ID.",
      "inputSchema": {
        "type": "object",
        "properties": {
          "session_id": { "type": "string" },
          "promise_id": { "type": "string" }
        },
        "required": ["session_id", "promise_id"]
      }
    },
    {
      "name": "flush",
      "description": "Clear all silo slots for a session. Call at task completion.",
      "inputSchema": {
        "type": "object",
        "properties": {
          "session_id": { "type": "string" }
        },
        "required": ["session_id"]
      }
    },
    {
      "name": "graphql",
      "description": "Execute GraphQL queries or mutations against the memory store.",
      "inputSchema": {
        "type": "object",
        "properties": {
          "query": { "type": "string" },
          "variables": { "type": "object" },
          "operationName": { "type": "string" }
        },
        "required": ["query"]
      }
    },
    {
      "name": "upsert_memory_node",
      "description": "Create or update a memory node with auto-generated TF-IDF embeddings.",
      "inputSchema": {
        "type": "object",
        "properties": {
          "session_id": { "type": "string" },
          "type": { "type": "string" },
          "label": { "type": "string" },
          "content": { "type": "string" },
          "metadata": { "type": "object" }
        },
        "required": ["session_id", "type", "label", "content"]
      }
    },
    {
      "name": "create_relation",
      "description": "Create a typed, weighted edge between two memory nodes.",
      "inputSchema": {
        "type": "object",
        "properties": {
          "session_id": { "type": "string" },
          "source_id": { "type": "string" },
          "target_id": { "type": "string" },
          "relation": {
            "type": "string",
            "enum": ["relates_to", "depends_on", "implements", "references", "similar_to", "contains"]
          },
          "weight": { "type": "number" },
          "metadata": { "type": "object" }
        },
        "required": ["session_id", "source_id", "target_id", "relation"]
      }
    },
    {
      "name": "search_memory_graph",
      "description": "Semantic search with graph traversal — returns direct matches and neighbor walk.",
      "inputSchema": {
        "type": "object",
        "properties": {
          "session_id": { "type": "string" },
          "query": { "type": "string" },
          "max_depth": { "type": "integer" },
          "top_k": { "type": "integer" },
          "edge_filter": { "type": "string" }
        },
        "required": ["session_id", "query"]
      }
    },
    {
      "name": "prune_stale_links",
      "description": "Remove decayed edges (e^(-λt) below threshold) and orphan nodes with low access count.",
      "inputSchema": {
        "type": "object",
        "properties": {
          "session_id": { "type": "string" },
          "threshold": { "type": "number" }
        },
        "required": ["session_id"]
      }
    },
    {
      "name": "add_interlinked_context",
      "description": "Bulk-add nodes with auto-similarity linking (cosine >= 0.72 creates edges).",
      "inputSchema": {
        "type": "object",
        "properties": {
          "session_id": { "type": "string" },
          "items": {
            "type": "array",
            "items": {
              "type": "object",
              "properties": {
                "type": { "type": "string" },
                "label": { "type": "string" },
                "content": { "type": "string" },
                "metadata": { "type": "object" }
              },
              "required": ["type", "label", "content"]
            }
          },
          "auto_link": { "type": "boolean" }
        },
        "required": ["session_id", "items"]
      }
    },
    {
      "name": "retrieve_with_traversal",
      "description": "Walk outward from a node, returning reachable neighbors scored by temporal decay and depth.",
      "inputSchema": {
        "type": "object",
        "properties": {
          "session_id": { "type": "string" },
          "start_node_id": { "type": "string" },
          "max_depth": { "type": "integer" },
          "edge_filter": { "type": "string" }
        },
        "required": ["session_id", "start_node_id"]
      }
    },
    {
      "name": "resolve_context",
      "description": "Unified context lookup: short-term KV -> long-term graph -> miss. Returns source and score.",
      "inputSchema": {
        "type": "object",
        "properties": {
          "session_id": { "type": "string" },
          "key": { "type": "string" }
        },
        "required": ["session_id", "key"]
      }
    },
    {
      "name": "promote_to_long_term",
      "description": "Promote a short-term KV entry to the long-term memory graph.",
      "inputSchema": {
        "type": "object",
        "properties": {
          "session_id": { "type": "string" },
          "key": { "type": "string" },
          "value": { "type": "string" },
          "node_type": { "type": "string" },
          "metadata": { "type": "object" }
        },
        "required": ["session_id", "key", "value"]
      }
    },
    {
      "name": "memory_status",
      "description": "Unified view of short-term KV and long-term graph memory status for a session.",
      "inputSchema": {
        "type": "object",
        "properties": {
          "session_id": { "type": "string" }
        },
        "required": ["session_id"]
      }
    }
  ]
}

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

Server / Integration	Directory / Source	Transport	Description
Unstoppable Domains	unstoppable-domains/	HTTP (remote)	Search, purchase, and manage Web3 domain names via natural conversation.
Context+	github.com/ForLoopCodes/contextplus	Integrated	Long-term semantic memory graph, adapted into memory-graph.ts and solution-engine.ts. By @ForLoopCodes.

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

File / Directory	Purpose
Ppm.c	C-core CLI v0.0.3-dev — integrated single-file build (~500 LOC)
Pypm.c	PyPM 0.3.x front-door dispatcher; delegates to module sources
PMLL.c / PMLL.h	Persistent Memory Logic Loop — KV silo primitives
SAT.c / SAT.h	Boolean SAT solver used for dependency resolution
Q_promise_lib/	Q-promise / async continuation chain (mirrors JS Promises in C)
mcp/	TypeScript PMLL Memory MCP server (15 tools)
mcp/src/memory-graph.ts	Long-term memory graph adapted from Context+
mcp/src/solution-engine.ts	Solution engine bridging short-term KV + long-term graph
mcp/src/embeddings.ts	TF-IDF embeddings and cosine similarity for semantic search
CLI/	Extended CLI interface
Panda-lib/ Torch-lib/ Numpy-lib/	Library integration shims
scripts/	Build helpers and automation scripts

---

📝 Release Notes

pypm 0.0.3-dev (25 Jun 2025)

New & Improved

Area	What's new
Unified source	v0.0.1 + v0.0.2 merged into a single pypm.c file to simplify builds.
Version bump	CLI now reports 0.0.3-dev.
Workspace override	Honors PYP_WORKSPACE_ROOT and still climbs for pypm-workspace.toml.
Doctor v2.1	Counts issues and exits with that count; inline Python probe via here-doc.
Sandbox v2.1	-d <DIR> flag; default remains mkdtemp.
Plugin fetcher hardening	Creates ~/.pypm/plugins safely; CURLOPT_FAILONERROR for HTTP 4xx/5xx; preserves plugin exit code.
Hermetic bundle flag	pypylock -o <file> works regardless of flag order; default dist/venv.tar.gz.
Error surfacing	fatal() now shows errno via perror; dlopen/curl errors bubble up.

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)

If you did …	Do this now
./pypm doctor && echo OK	Check [[ $? -eq 0 ]] or parse the numeric count.
Used pypm_v002.c / pypm_v001.c	Switch to pypm.c, make clean && make.
Hard-coded dist/venv.tar.gz path	Pass -o flag for custom output paths.

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/.

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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.

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🗺 Roadmap

Version	Planned features
0.0.4	Lockfile parser + wheel copier for real hermetic bundles
0.0.5	libsolv-backed dependency resolver
0.1.0	Cross-platform shims (Windows / macOS)
0.1.1	WASI toolchain detection & wheel preference
future	SAT dependency solver, parallel wheel cache, workspaces, WASM resolution

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🤝 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:

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