Pmll Memory Mcp

PPM — Python Package Manager

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

[CI] [License] [Version] [npm] [Sponsor]

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+.

Table of Contents

1. Layers (post-merge)
2. Features
3. Building from Source
4. CLI Commands
5. GPU & Security Features
6. Plugin System
7. Configuration
8. PMLL Memory MCP Server
9. Stellar commitments
10. Architecture
11. Release Notes
12. Roadmap
13. Contributing & Sponsors

Layers (post-merge)

Two complementary layers — not substitutes for each other:

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

Q-promise library

Build / test / API: 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

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.gitcd PPMcc -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_libmake clean && make testmake shared   # → libqpromise.socd ..

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 packageppm import transformers
# Import with a specific versionppm import transformers==4.43.3
# Import multiple packagesppm import transformers torch numpy
# Scan a Python file for imports and install themppm import --from-file my_script.py
# Verbose — watch what's happeningppm 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 snapshotsppm 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 directoryppm 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 CUDAppm ensure transformers --gpu cu121  # force CUDA 12.1ppm ensure transformers --gpu cpu    # CPU-only

Ed25519 cryptographic signing

ppm keygen --out-priv ed25519.priv --out-pub ed25519.pubppm sign   --sk ed25519.priv --file torch-2.4.0-*.whl --gpu ./libbreath_gpu.soppm verify --receipt torch-2.4.0-*.whl.receipt.json   --file torch-2.4.0-*.whl

Provenance & SBOM

ppm sbom        --out project-sbom.jsonppm provenance  --out provenance.jsonppm graph --dot | dot -Tpng -o deps.png

🔌 Plugin System

# Install a pluginppm plugin add auditwheel https://cdn.example.com/auditwheel.so
# Run itppm 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.cmv 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 detectionexport PYP_DEBUG=1                           # enable debug outputexport 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.

[npm] [PyPI] [MCP Registry]

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 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 semantic retrieval across sessions (SQLite-backed graph).

Tool counts differ by implementation (TS 15 incl. graphql; Python 14 — see note above). Historical docs below list the TypeScript tool surface.

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 navigatingconst 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 SQLite-backed 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-mcppmll-memory-mcp          # starts the stdio MCP server

Via pip (Python)

pip install pmll-memory-mcppmll-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 state 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 stable hashing 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 rootdocker build -f mcp/Dockerfile -t pmll-memory-mcp .
# Rundocker run --rm -i pmll-memory-mcp:latest
# Run with persistent KV memory via volumedocker 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

⚓ Stellar commitments (pmll-anchor)

Memory / codework payloads stay off-chain. Optional 32-byte SHA-256 commitments are live on Stellar mainnet via Soroban pmll-anchor (shared with drQedwards/pmll). ABI unchanged: init / store / get / bump only — do not invent fields or contract IDs. This PPM tree may not include the full pmll-anchor/ contract sources; typed payload is still mirrored as skill.ts. Invoke docs / contract source: pmll/SKILL.md. Local summary: SKILL.md.

Admin: GBFOFCD3XDANQWSGMHKJJ2V3YXS2QQD7RNC4LMDBVNBTUJOQZ3RLSB3E · wasm hash 1b6ad9c574e0f5c9e39968f836a410c03adcf057afa93a63d2710bd30fdd53ba

Skills summary: SKILL.md · full invoke docs: pmll/SKILL.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)       (qpromise_* / libqpromise.so)

Key components

📝 Release Notes

Post-merge (2026-08) — semantic silo + Q-promise

• PMLL C core: associative/semantic silo (slots + embeddings), peek + peek_semantic, SAT bridge, init_pml assignments start at -1 (undecided).
• Q-promise evolved to qpromise_* + libqpromise.so; prototype QMemNode / q_mem_* API removed.
• Stellar skills / skill.ts aligned for off-chain CodeworkPayload hashing into existing pmll-anchor (ABI unchanged).
• MCP servers prefer MCPServer with FastMCP fallback; Q-promise MCP demos load libqpromise.so.

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).
• Stable hashing embeddings (fixed dimension, no shared vocabulary) 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 (historical); current C API is qpromise_* / libqpromise.so.
• 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:

[GitHub Sponsors] [Buy Me a Coffee] [CoinGecko Portfolio]

Built by Dr. Q Josef Kurk Edwards — making Python packaging fast, deterministic, and hackable, with a shared PMLL memory stack.