Pmll Memory Mcp

io.github.drQedwardsPublished by GitHub account drQedwardsv0.1.1Updated Oct 27, 2026

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

The publisher's own server: Memory.fit

VerifiedSTDIODesktop onlyKnowledge & MemoryDeveloper Tools

Overview

AI-generated overview

Local stdio MCP server giving coding agents a session-scoped key-value memory cache plus a SQLite-backed long-term memory graph with semantic search.

What you can do
  • Initialize, peek, set, resolve and flush session-isolated short-term key-value memory with promise deduplication
  • Create and update memory nodes and typed weighted relations, then search the graph semantically with neighbor traversal
  • Prune decayed links, bulk-add interlinked context, and walk outward from a starting node
  • Resolve context across short-term and long-term layers, promote entries to the graph, and view unified memory status
  • Run GraphQL queries and mutations against the memory store (TypeScript build only)
When to use it
Useful when an agent repeats expensive tool calls or loses context between subtasks and you want a shared cache and persistent memory graph loaded alongside other MCP servers. Best suited to coding agents that can be told to check memory before costly calls.
Requirements
Runs as a local stdio process, launched via npx, a global npm install, pip, or Docker. The Docker setup uses a volume and the MEMORY_FILE_PATH environment variable for persistence. No accounts, API keys or headers are declared.
Before you install
The server writes, updates and deletes stored memory: init resets the session silo, flush clears all slots, and prune removes nodes and edges. GraphQL mutations can change the store. Memory content persists in a SQLite or JSONL file, so avoid storing secrets in it. The README also documents an optional Stellar anchoring contract and a package manager CLI that are separate from the MCP server.

Installation

In SourceWeft

  1. Open Pmll Memory Mcp in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

README

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:

LayerOwnsKey surface
PMLLDurable memory / statememory_silo_t, peek / peek_semantic, silo_set, SAT bridge, init_pml
Q-promiseTemporal / control-flowqpromise_* 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

text
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

SymbolRole
memory_silo_tAssociative/semantic silo: tree, slots[], embed_dim (PMLL_EMBED_DIM=32), slot_count
silo_slot_tkey, content, embedding, resolved
init_silo / free_silo / silo_setAllocate, free, write key/content + embedding
peekExact key, else index
peek_semanticCosine ≥ min_sim over embeddings (silo_embed_text, silo_cosine_similarity)
init_pmlSets every assignment[i] = -1 (undecided)
check_conflictTreats -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

CommandWhat it does
pypm doctorChecks 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 SRCDownloads 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 versionPrints the current CLI version
ppm import PKGImport and cache a package with GPU-accelerated hash verification
ppm add PKG --lockAdd packages and update the lockfile
ppm plan / ppm applyPlan dependency changes, then apply them with an audit trail
ppm snapshot / ppm rollbackSnapshot the environment; roll back to any prior state
ppm sign / ppm verifySign artifacts (Ed25519) and verify cryptographic receipts
ppm sbomGenerate 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

bash
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

bash
cd Q_promise_libmake clean && make testmake shared   # → libqpromise.socd ..

Optional: CUDA-accelerated build

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

🖥 CLI Commands

Import packages

bash
# 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

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 snapshotsppm 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 directoryppm sandbox -d /tmp/mydir   # custom directory

Hermetic packaging

bash
ppm pypylock -o production-env.tar.gz

🔐 GPU & Security Features

GPU-accelerated hash verification

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

Ed25519 cryptographic signing

bash
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

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

🔌 Plugin System

bash
# 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)

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.cmv 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

bash
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:

typescript
// 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)
ToolInputOutputDescription
initsession_id: str, silo_size: int = 256{status, session_id, silo_size, cleared}Set up or reset the session silo + Q-promise state (cleared is always true)
peeksession_id: str, key: str{hit, value?, index?} or {hit, status, promise_id}Non-destructive cache + promise check
setsession_id: str, key: str, value: str{status: "stored", index}Store KV pair in the silo
resolvesession_id: str, promise_id: str{status: "resolved"|"pending", payload?}Check/resolve a Q-promise continuation
flushsession_id: str{status: "flushed", cleared_count}Clear all silo slots at task completion
GraphQL (1 tool)
ToolInputOutputDescription
graphqlquery: 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 SQLite-backed semantic memory with graph traversal, decay scoring, and cosine similarity search.

ToolInputOutputDescription
upsert_memory_nodesession_id, type, label, content, metadata?{node}Create or update a memory node with auto-generated stable hashing embeddings
create_relationsession_id, source_id, target_id, relation, weight?, metadata?{edge}Create typed edges (relates_to, depends_on, implements, references, similar_to, contains)
search_memory_graphsession_id, query, max_depth?, top_k?, edge_filter?{direct, neighbors, totalNodes, totalEdges}Semantic search with graph traversal — direct matches + neighbor walk
prune_stale_linkssession_id, threshold?{removed, remaining}Remove decayed edges (e^(-λt) below threshold) and orphan nodes with low access
add_interlinked_contextsession_id, items[], auto_link?{nodes, edges}Bulk-add nodes with auto-similarity linking (cosine ≥ 0.72 creates edges)
retrieve_with_traversalsession_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)
ToolInputOutputDescription
resolve_contextsession_id, key{source, value, score}Unified context lookup: short-term KV → long-term graph → miss
promote_to_long_termsession_id, key, value, node_type?, metadata?{promoted, nodeId}Promote a short-term KV entry to the long-term memory graph
memory_statussession_id{shortTerm, longTerm, promotionThreshold}Unified view of short-term KV and long-term graph memory status

Installation

Via npx (recommended — no install needed)
bash
npx pmll-memory-mcp
Via npm
bash
npm install -g pmll-memory-mcppmll-memory-mcp          # starts the stdio MCP server
Via pip (Python)
bash
pip install pmll-memory-mcppmll-memory-mcp          # starts the stdio MCP server

Claude Desktop / MCP config (claude_desktop_config.json)

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

bash
# 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

Server / IntegrationDirectory / SourceTransportDescription
Unstoppable Domainsunstoppable-domains/HTTP (remote)Search, purchase, and manage Web3 domain names via natural conversation.
Context+github.com/ForLoopCodes/contextplusIntegratedLong-term semantic memory graph, adapted into memory-graph.ts and solution-engine.ts. By @ForLoopCodes.

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.

NetworkContract IDExplorer
mainnetCCF3B64AXLS4OLY5RN4H4K2CFZAYNZCJQY5MKCKCVAKMZNH7G7F7XUUFstellar.expert
testnetCDLQR24LLFWXTNGGJVJCRXAF3ZRDWFZRUFTDZ5SJOT2J33CS7DDYP7IUstellar.expert

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

File / DirectoryPurpose
Ppm.cC-core CLI v0.0.3-dev — integrated single-file build (~500 LOC)
Pypm.cPyPM 0.3.x front-door dispatcher; delegates to module sources
PMLL.c / PMLL.hAssociative/semantic silo, peek/peek_semantic, SAT bridge, init_pml=-1
SAT.c / SAT.hStandalone Boolean SAT solver (CDCL with first-UIP learning, optional DRUP proofs; tested by tests/test_sat_c.py). Not wired into dependency resolution yet (road-mapped). SAT.py / SAT.pyx: the same algorithm in Python and Cython; see docs/SAT_SOLVERS.md
Q_promise_lib/Promise/continuation library (qpromise_*, libqpromise.so; single-threaded drain)
skill.ts / SKILL.mdOff-chain CodeworkPayload + Stellar skills entry
pmll-anchor/Soroban commitment contract (source on pmll; may be absent here)
lattice/On pmll only — playable Stellar skills graph / ARC-AGI-3 play test
mcp/TypeScript PMLL Memory MCP server (15 tools)
mcp/src/memory-graph.tsLong-term memory graph adapted from Context+
mcp/src/solution-engine.tsSolution engine bridging short-term KV + long-term graph
mcp/src/embeddings.tsStable hashing embeddings and cosine similarity for semantic search (TF-IDF class kept as legacy)
CLI/Extended CLI interface
Panda-lib/ Torch-lib/ Numpy-lib/Library integration shims
scripts/Build helpers and automation scripts

📝 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

AreaWhat's new
Unified sourcev0.0.1 + v0.0.2 merged into a single pypm.c file to simplify builds.
Version bumpCLI now reports 0.0.3-dev.
Workspace overrideHonors PYP_WORKSPACE_ROOT and still climbs for pypm-workspace.toml.
Doctor v2.1Counts issues and exits with that count; inline Python probe via here-doc.
Sandbox v2.1-d <DIR> flag; default remains mkdtemp.
Plugin fetcher hardeningCreates ~/.pypm/plugins safely; CURLOPT_FAILONERROR for HTTP 4xx/5xx; preserves plugin exit code.
Hermetic bundle flagpypylock -o <file> works regardless of flag order; default dist/venv.tar.gz.
Error surfacingfatal() 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 OKCheck [[ $? -eq 0 ]] or parse the numeric count.
Used pypm_v002.c / pypm_v001.cSwitch to pypm.c, make clean && make.
Hard-coded dist/venv.tar.gz pathPass -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).
  • 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

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

🤝 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.

Source: README.md at commit e5cf8be

Tools

0
SourceWeft has not started this package in its sandbox yet, so its tools are not known.

Version history

1
  1. v0.1.1LatestSep 16, 2026

Score

50% of the applicable checks met

How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.

  • Reached in the last 30 days25 pts

    Keep the endpoint answering the MCP handshake; SourceWeft probes it every 30 days.

  • Auth declared10 pts

    Declare the credential headers in the registry entry, or keep the endpoint answering the probe so its auth can be classified.

  • Tools listed15 pts

    Answer tools/list without a credential, or ship a package that starts in a sandbox without one.

  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts
  • License10 pts

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