Pmll Memory MCP

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

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag — we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

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

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Documentation Overview

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

Code

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 (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, 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

bash

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

bash

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

Optional: CUDA-accelerated build

server.ts

nvcc -O3 CLI/CLI.cu -lcuda -o ppm-gpu
./ppm-gpu import transformers torch --verbose

🖥 CLI Commands

Import packages

server.ts

# 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

bash

ppm init
# Creates:
# .ppm/
# ├── ledger.jsonl      ← append-only operation log
# ├── state.json        ← current state
# ├── lock.json         ← dependency lockfile
# └── snapshots/        ← rollback points

Dependency resolution & locking

bash

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

bash

ppm snapshot --name "before-upgrade"
ppm snapshots
ppm rollback before-upgrade

Environment diagnostics

bash

ppm doctor
# ✅ Python dev headers found
# ✅ C compiler available
# ✅ CUDA toolkit available
# 🏁 Diagnostics complete (0 issues found)

Sandbox

bash

ppm sandbox                 # ephemeral temp directory
ppm sandbox -d /tmp/mydir   # custom directory

Hermetic packaging

bash

ppm pypylock -o production-env.tar.gz

🔐 GPU & Security Features

GPU-accelerated hash verification

server.ts

ppm import torch --verbose
# 🚀 GPU hash verification: SHA-256 computed on device
# ✅ Integrity verified: e3b0c44298fc1c149afbf4c8996fb924...

GPU backend selection

bash

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

bash

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

bash

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

🔌 Plugin System

bash

# 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;
}

bash

cc -shared -fPIC -o hello.so hello.c
mv hello.so ~/.pypm/plugins/
pypm plugin run hello

⚙️ Configuration

`pypm.toml`

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

Read the full README →View source on GitHub →

Related MCP Servers

View all in Knowledge & Memory View all alternatives

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Pmll Memory MCP

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "pmll-memory-mcp": { "command": "npx", "args": ["-y", "pmll-memory-mcp"] } }

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Technical Specs & Signals

CategoryKnowledge & Memory

37Quality signal: Fair · 37/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools15/30

Adoption & activity3/15

Community engagement0/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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More in 🧠 Knowledge & Memory →Best MCP servers for Memory & Knowledge →Alternatives to Pmll Memory MCP →Install in Claude Desktop Install in Cursor Install in VS Code

Pmll Memory MCP

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

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag — we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

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

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Documentation Overview

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

Code

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

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

✨ 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

bash

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

bash

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

Optional: CUDA-accelerated build

server.ts

nvcc -O3 CLI/CLI.cu -lcuda -o ppm-gpu
./ppm-gpu import transformers torch --verbose

🖥 CLI Commands

Import packages

server.ts

# 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

bash

ppm init
# Creates:
# .ppm/
# ├── ledger.jsonl      ← append-only operation log
# ├── state.json        ← current state
# ├── lock.json         ← dependency lockfile
# └── snapshots/        ← rollback points

Dependency resolution & locking

bash

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

bash

ppm snapshot --name "before-upgrade"
ppm snapshots
ppm rollback before-upgrade

Environment diagnostics

bash

ppm doctor
# ✅ Python dev headers found
# ✅ C compiler available
# ✅ CUDA toolkit available
# 🏁 Diagnostics complete (0 issues found)

Sandbox

bash

ppm sandbox                 # ephemeral temp directory
ppm sandbox -d /tmp/mydir   # custom directory

Hermetic packaging

bash

ppm pypylock -o production-env.tar.gz

🔐 GPU & Security Features

GPU-accelerated hash verification

server.ts

ppm import torch --verbose
# 🚀 GPU hash verification: SHA-256 computed on device
# ✅ Integrity verified: e3b0c44298fc1c149afbf4c8996fb924...

GPU backend selection

bash

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

bash

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

bash

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

🔌 Plugin System

bash

# 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;
}

bash

cc -shared -fPIC -o hello.so hello.c
mv hello.so ~/.pypm/plugins/
pypm plugin run hello

⚙️ Configuration

`pypm.toml`

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

Read the full README →View source on GitHub →

Related MCP Servers

View all in Knowledge & Memory View all alternatives

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Pmll Memory MCP

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "pmll-memory-mcp": { "command": "npx", "args": ["-y", "pmll-memory-mcp"] } }

AllMCPs Directory Badge

Full Badge Customizer

Showcase your server listing on GitHub or your project documentation. Embed this dynamic SVG badge to highlight official listing status and live engagement.

Badge Style:

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<a href="https://allmcps.com/mcp/pmll-memory-mcp"><img src="https://allmcps.com/api/badge/pmll-memory-mcp?style=directory" alt="Pmll Memory MCP on AllMCPs" /></a>

Technical Specs & Signals

CategoryKnowledge & Memory

37Quality signal: Fair · 37/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools15/30

Adoption & activity3/15

Community engagement0/10

★ Featured

AllMCPs Server

Explore Server →

Own this project?

Free dofollow backlink: add your website and place the AllMCPs badge on it — no claim needed. We detect it automatically and keep it verified as long as the badge stays live.

Claim & get free dofollow

Share & Embed

Add our SVG badge (dark/light directory styles) or embeddable widget to your site.

Explore more

More in 🧠 Knowledge & Memory →Best MCP servers for Memory & Knowledge →Alternatives to Pmll Memory MCP →Install in Claude Desktop Install in Cursor Install in VS Code

Pmll Memory MCP

Quick Install

Client Config & Setup

Documentation Overview

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

Project initialization

Dependency resolution & locking

Snapshot & rollback

Environment diagnostics

Sandbox

Hermetic packaging

🔐 GPU & Security Features

GPU-accelerated hash verification

GPU backend selection

Ed25519 cryptographic signing

Provenance & SBOM

🔌 Plugin System

Writing a plugin (C)

⚙️ Configuration

pypm.toml

Environment variables

Related MCP Servers

Reviews

Frequently Asked Questions about Pmll Memory MCP

AllMCPs Directory Badge

Technical Specs & Signals

AllMCPs Server

Own this project?

Share & Embed

Explore more

Pmll Memory MCP

Quick Install

Client Config & Setup

Documentation Overview

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

Project initialization

Dependency resolution & locking

Snapshot & rollback

Environment diagnostics

Sandbox

Hermetic packaging

🔐 GPU & Security Features

GPU-accelerated hash verification

GPU backend selection

Ed25519 cryptographic signing

Provenance & SBOM

🔌 Plugin System

Writing a plugin (C)

⚙️ Configuration

pypm.toml

Environment variables

Related MCP Servers

Reviews

Frequently Asked Questions about Pmll Memory MCP

`pypm.toml`

`pypm.toml`