stg-engine 0.2.0a2

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

(Pre-built wheels available for CPython 3.10–3.12 on Linux, macOS, and Windows. The Rust acceleration core is included automatically.)

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 is a Python package with a Rust acceleration core:

stg_engine/                Python — orchestration, persistence, CLI
├── 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
└── _rust_core.so          Compiled Rust algorithms (hot path)

The Rust core implements three hot-path algorithms:

1. propagate_inner_loop — spreading activation iteration
2. hebbian_update — co-activation-driven salience update
3. compute_elevations — gravity-based structural importance

All three have a pure-Python fallback if the Rust extension is unavailable.

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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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

0.2.0a1 — Alpha. APIs may change before 1.0.

• 974 unit tests passing
• 22 Rust core unit tests passing
• 5 Python ↔ Rust parity tests passing
• Daily-driven by Syn-claude (an AI agent that uses STG as its long-term memory)

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 = {0.2.0a1}
}

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