stg-engine 0.5.0a6

Semantic Tension Graph — cognitive memory system for AI agents (BSL 1.1, free for noncommercial use, commercial license required for businesses)

STG Engine

Semantic Tension Graph — a cognitive memory system for AI agents, grounded in Density Monism and Hebbian-inspired learning.

STG Engine gives AI agents a memory that learns, forgets, and generalizes the way cognitive science says memories should — built on a graph where nodes carry activation, edges carry both confidence and salience, and propagation is gravity-aware (community-structure amplifies the right concepts).

This is not another vector store. It is an executable cognitive architecture.

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Why STG?

	Vector DB	LLM context	STG Engine
Storage	Embeddings	Tokens	Graph (nodes + edges)
Retrieval	Cosine similarity	None (re-prompt)	Spreading activation
Learning	None	None	Hebbian, salience decay
Forgetting	Manual delete	Context window	Synaptic pruning
Structure-aware	No	No	Yes (gravity propagation)
Provenance	Limited	None	Per-edge confidence + source
Determinism	Yes	No	Yes

STG is for AI agents that need to remember across sessions, update their world model, and resolve conflicting information without retraining.

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Quickstart (5 minutes)

Install

pip install stg-engine

Pure-Python distribution — one wheel, all platforms (Linux, macOS, Windows) and all supported Python versions (3.10, 3.11, 3.12). No compilation needed.

An optional Rust acceleration core for hot-path algorithms lives in the source repository under rust/ and is picked up automatically if compiled locally; the alpha PyPI release ships the pure-Python path only.

First propagation

from stg_engine import STGEngine

engine = STGEngine()

# Ingest knowledge as STL (Semantic Tension Language)
engine.ingest_stl('[Newton] -> [Calculus] ::mod(rule="historical", confidence=0.95)')
engine.ingest_stl('[Leibniz] -> [Calculus] ::mod(rule="historical", confidence=0.95)')
engine.ingest_stl('[Calculus] -> [Physics] ::mod(rule="enables", confidence=0.9)')
engine.ingest_stl('[Physics] -> [Engineering] ::mod(rule="enables", confidence=0.85)')

# Activate from a query and let it spread through the graph
results = engine.propagate("Newton")
print(results)
# ['Newton', 'Calculus', 'Physics', 'Engineering', 'Leibniz']

# Persist
engine.save("my_memory.stg")

# Reload later
engine2 = STGEngine.load("my_memory.stg")

Hebbian learning

After each propagate() call, edges between co-activated nodes are strengthened — exactly like neurons that fire together.

from stg_engine.learning import HebbianLearner

learner = HebbianLearner()

for _ in range(10):
    activations = engine.propagate("Newton")
    activation_map = {n: engine._nodes[n].activation for n in activations}
    learner.learn_from_propagation(engine, activation_map)

# The Newton → Calculus path is now stronger than before.

See the examples/ directory for more.

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Architecture

stg_engine/                Pure Python — no compilation required
├── engine.py              The main STGEngine class
├── types.py               Node, Edge, Tension data structures
├── formulas.py            Ψ (system stability), tension, activation
├── gravity.py             Gravitational propagation + community detection
├── learning.py            Hebbian learner + synaptic pruner
├── persistence.py         .stg file format (SQLite-backed)
└── cli.py                 The `stg` command-line tool

The source repository additionally contains an optional Rust acceleration core (rust/) implementing three hot-path algorithms — propagate_inner_loop, hebbian_update, compute_elevations. If the extension is compiled locally (maturin develop), stg_engine auto-detects it and uses it; otherwise the pure-Python path handles everything. PyPI ships the pure-Python path only during the alpha phase.

Three things STG does that vector DBs cannot

1. Structure-aware retrieval (gravity propagation)

Nodes that bridge multiple communities are amplified. Nodes buried inside small fragment clusters are suppressed. The graph's topology is the prior — no manual labeling needed.

2. Hebbian learning over time

Edges between co-activated nodes get stronger. Edges that never co-activate weaken and eventually get pruned. The graph adapts to actual usage.

3. Confidence vs salience separation

• Confidence = "how true is this?" — never auto-decays
• Salience = "how easily can I recall it?" — modified by use

Reading a fact 100 times makes it more retrievable but not more true. This separation is critical and is missing from every vector store.

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CLI

# Stats
stg stats

# Add knowledge
stg ingest '[A] -> [B] ::mod(confidence=0.9, salience=0.7)'

# Spreading activation
stg propagate "your query here"

# Find paths between concepts
stg paths Newton Engineering

# Inspect a node
stg node Newton

# Save/load
stg save my_memory.stg

Full CLI reference: stg --help

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Precision recall (default in 0.4+)

stg propagate does more than spread activation — it now converges. Four cooperating mechanisms sharpen retrieval without ever filtering an edge:

Mechanism	What it does	When it fires
R1 Recency × supersede soft weight	Older / superseded edges get softly down-weighted (default 30-day half-life, supersede factor 0.3)	Every propagate
R2 Multi-seed chain intersection	Each query token runs through propagate separately; output is the node intersection across each token's reconstructed chains	When ≥2 query tokens hit node names
R5 active_context anchor	Nodes from your last stg select get a temporary +5.0 elevation boost, pulling propagate energy toward your current focus	When active_context is non-empty (TTL 30 min)
R6 Edge-as-fallback-seed	Tokens that match no node name fall through to scan edge description/lesson/action/role/status/is_a. Returns event edges as a separate "Event-edge" view	When ≥1 query token misses all node names
R7 Community dominance ratio	Communities scoring below dominant/3.0 are folded out — except those containing precise query hits, which are always preserved	Every propagate (community mode)

The memory-never-vanishes principle is hard-coded: superseded edges are softly down-weighted, never deleted. Recall completeness is preserved across all four mechanisms — they shape ranking, not membership.

Disable any mechanism via flag (rarely needed):

stg propagate "your query"                          # default — all five ON
stg propagate "your query" --no-recency-weight      # disable R1
stg propagate "your query" --no-multi-seed          # disable R2
stg propagate "your query" --no-context-anchor      # disable R5
stg propagate "your query" --no-edge-fallback       # disable R6
stg propagate "your query" --no-community-filter    # disable R7
stg propagate "your query" --legacy                 # all five OFF (pre-0.4)

Full design: see STG_PRECISION_RECALL_DESIGN.md.

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Skills: turning Skill nodes into runnable commands

Skill-namespaced nodes (Skill:SomeName) can be registered as executable and invoked via stg use. This makes STG double as a lightweight capability registry — ask what you can do, then do it, all inside one tool.

One-time setup (opt-in)

# Master switch — off by default for safety
stg config set skill.enabled true

# Whitelist the directories where your scripts live
stg config set skill.roots "/home/you/my-tools,/home/you/workshop"

# (optional) Name interpreters you want to re-use across skills
stg config set skill.interpreters.myvenv "/home/you/proj/.venv/bin/python3"

Register a skill

Add a Skill:-namespaced node whose outgoing edge carries path, executable, and interpreter:

stg ingest '[Skill:Greet] -> [Greeting_Target] ::mod(
  rule="empirical", confidence=0.95,
  path="/home/you/my-tools/greet.py",
  description="say hello, optionally to someone",
  executable="true",
  interpreter="python3",
  args_template="[name]",
  timeout_s="10"
)'

Or retrofit invocation fields onto an existing Skill edge:

stg skill configure Greet --executable --interpreter python3 \
  --args-template "[name]" --timeout 10

Browse and run

# See every registered skill, executable ones on top
stg skill list

# Detail on one skill + recent invocations
stg skill show Greet

# Run it — positional args pass through to the script
stg use Greet World

# Dry-run to see the resolved command line without executing
stg use Greet World --dry-run

# Audit log
stg skill history --limit 10

Security model

Execution requires double opt-in: skill.enabled=true in user config AND executable="true" on the edge. The resolved script path must lie under at least one skill.roots entry (symlinks resolved). Subprocess invocation uses list args (no shell), a hard timeout, and a 10 MB output cap. Every call writes one audit row in the skill_invocations SQLite table.

Interpreters resolve in this order: absolute path → named entry in skill.interpreters.<name> → builtin name (python3/python/bash/sh/ node) via shutil.which. No magic names are hardcoded into the engine; portability across machines is the user's responsibility via config.

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License

STG Engine is dual-licensed:

• Free under the Business Source License 1.1 for personal, academic, educational, non-profit, government, freelancer, and open source use.
• Commercial use by for-profit companies requires a separate license. Contact [e-mail] for details.

After 2030-04-07, this version automatically converts to Apache License 2.0.

TL;DR

You are	License you need
Individual developer / hobbyist	Free
Student / academic researcher	Free
Open source project	Free
Non-profit / government	Free
For-profit company (any size)	Commercial license required

This is the JetBrains / Unity model, not the "100% open source" model. We do this because STG represents years of original research and we want it to remain free for the people who advance the field, while companies that profit from it contribute back. Contact [e-mail] for details.

Theoretical foundation

STG is grounded in Density Monism — a research framework that derives quantum field theory, particle masses (Koide formula), and consciousness from a single scalar density field ρ(x,t).

• Project: scos-lab/koide-lcft
• Architecture paper: forthcoming

Status

0.3.0a2 — Alpha. APIs may change before 1.0.

• 1026 unit tests passing
• Dogfooded daily as the long-term memory of an AI agent running inside Claude Code

Contributing

Bug reports, issues, and pull requests are welcome. Note that contributors agree their contributions are licensed under the same BSL 1.1 terms.

For substantial contributions or research collaboration, contact [e-mail].

Naming

"STG" and "Semantic Tension Graph" are names used by wuko / scos-lab.

Citation

If STG helps your research, please cite:

@software{stg_engine_2026,
  author = {wuko},
  title = {STG Engine: A Cognitive Memory System for AI Agents},
  year = {2026},
  url = {https://github.com/scos-lab/stg-engine},
  version = {0.3.0a2}
}

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Copyright (C) 2026 wuko / scos-lab. All rights reserved.