🧠 Active Graph Intelligence: From Structure to Cognition

“Relational Intelligence is memory, meaning, and motion — held in alignment over time.”

Welcome to Active Graph Networks (AGNs) — not just another AI project, but the architectural backbone of Relational Intelligence (RI) . This system doesn’t just learn — it remembers. It evolves. It understands why .

This repo holds a full‑stack, multi‑dimensional intelligence framework built across three living layers:

🧬 Layer Map

Layer	Folder	Purpose
AGN Core	`active_graph_network_core/`	Structural memory, identity logic, relational encoding
AGDB	`active_graph_database/`	Time‑aware structuring, offset querying, checkpoint traversal
QFN	`quantum_field_networks/`	Emergent cognition, symbolic collapse, recursive inference

Together, they form a unified cognitive system powered by Cube4D , Dynamic Relationship Expansion (DRE) , and ActiveShell .

📘 What is This, Really?

This is how intelligence becomes infrastructure.

Not a neural net.
Not a database.
Not a LLM prompt hack.

It’s the blueprint of a mind — structured in memory, evolving over time, resolving through collapse.

🧠 Relational Intelligence (RI): The Philosophy Behind It

See Universal Relational Intelligence.md

Context‑Aware Everything exists in relation to something else.
Time‑Aware Identity isn’t static — it propagates across time (T).
Adaptively Aligned Policies and structure evolve through meaning, not retraining.

The RI Manifesto sits beneath everything you’ll find in this repo.

🧱 The AGN Stack

graph TD
    A["AGN Core (X/Y)"] --> B["AGDB (Z + T)"]
    B --> C["QFN (Δn > θ)"]
    C --> D["Field Resonance"]

🔹 AGN Core

Structures identity.
Governs relationships and policies.
Houses memory via cubes and recursive awareness.

🔹 AGDB

Organises time, features, and lag logic.
Enables rapid querying using checkpoints and synthetic edges.

🔹 QFN

Resolves identity through field pressure.
Collapses meaning when Δn exceeds θ .
Drives cognition across agents, systems, and contexts.

📡 ActiveShell: Noun‑Verb‑Truth Queries

AGNs use ActiveShell , a human‑readable query language that reflects how we think.

Get-Node Frame Where Mood = "Tense"
Collapse-If Δn > θ Between Scene_001 and Scene_002
Apply-Policy trading_inference To MarketGraph

ActiveShell works across all layers. It’s not querying — it’s structured introspection .

🔁 End‑to‑End Flow

graph TD
    Input["🎥 Frame / 📈 TimeSeries"] --> AGN
    AGN --> AGDB
    AGDB --> QFN
    QFN --> Shell["ActiveShell Query"]
    Shell --> Output["Real‑Time Understanding"]

Whether you’re processing a patient’s medical history, a series of video frames, or financial data:

AGN gives it structure.
AGDB gives it time.
QFN gives it meaning.

⚡ Quick 60‑Second Demo

from agn import ActiveGraphNetwork

# Load a tiny example graph
agn = ActiveGraphNetwork.load("examples/sample_graph.json")

# Run an ActiveShell query
result = agn.shell("Get-Node Patient_X | Collapse-If Δn > θ")
print(result.summary())

Run the above and you’ll see sealed nodes printed in under a minute.

🔬 Use Cases

🏥  Healthcare Temporal patient inference from treatment to outcome.
📈  Finance Real‑time market prediction with trend alignment.
🎥  Vision AI Identity‑aware frame collapse based on scene diff.
🧠  Cognitive Interfaces Queryable insight into live system memory.

📂 Explore the Layers

🔩 AGN Core

Dynamic cubes, relational policy logic, RBAC/ABAC/PBAC integration, structural memory.

🕰️ AGDB

Time‑indexed data nodes, offset traversal, flattened JSONs, Smart‑ETL ready.

🌐 QFN

Collapse logic, cognition graphs, symbolic sealing, RGNN field resonance.

🧠 Featured Concepts

Concept	Description
Δn > θ	Collapse when identity shifts significantly.
Cube4D	Semantic field over X (what), Y (why), Z (how), T (when).
Smart‑ETL	Structured transform pipelines aware of intention.
DRE	Relationship evolution through structured recursion.
RGNNs	Tensor‑linked cognition with schema inheritance.

🔗 Docs & Whitepapers

Quantum Field Networks – Field Cognition Manifesto: Quantum Field Networks.md
Universal Relational Intelligence – RI Philosophy: Universal Relational Intelligence (RI).md
Whitepapers Folder – Architecture, AGI, healthcare, and finance applications: whitepapers/

🚀 Getting Started

git clone https://github.com/QuantumBeers/ActiveGraphNetworks
cd ActiveGraphNetworks
pip install -r requirements.txt

Run the system:

python agn_explorer.py

Or dive into Smart‑ETL:

python smart_etl_runner.py --graph healthcare.json

🛠 Tools

Tool	Description
`agn_explorer.py`	Interactive graph exploration.
`smart_etl_runner.py`	Smart‑ETL pipeline execution.
`rgnn_runner.py`	Relational Graph inference.
`active_shell.py`	Noun‑Verb‑Truth CLI engine.

📸 Architecture Poster

Prefer a static image? Grab docs/architecture.png — exported from the Mermaid graphs for decks & PDFs.

🤝 Contributing & Community

We welcome pull requests, issues, and discussion threads.

Start here: CONTRIBUTING.md – guidelines & coding standards.
Questions / Ideas? Open a Discussion or use the issue templates for bugs & feature requests.
Chat: Join the X thread or DM @PeoplesGoose.

🧑‍⚖️ License

This project is licensed under the MIT License . See LICENSE for details.

“You’re not just querying data. You’re tracing the shape of thought.”

🌐 Field‑Level Diagrams

These visuals show how cognition propagates and collapses across the stack.

🔀 Identity Collapse Flow

flowchart TD
    A[AGN Node: Scene_A] -->|Δn = 22| B[AGN Node: Scene_B]
    B -->|Δn > θ| C[QFN Collapse Triggered]
    C --> D[Smart‑ETL Field Alignment]
    D --> E[Symbolic Sealing in RGNN]
    E --> F[Memory + Meaning Archived]

Scene transitions only commit when identity displacement is meaningful — AGNs store cognition , not just data.

🌊 Emotional State Inference

graph TD
    A1[Frame: Tense → Δn = 12] --> A2[Frame: Nervous → Δn = 14]
    A2 --> A3[Frame: Agitated → Δn = 17]
    A3 --> Collapse[Field Collapse: Escalating Tension]
    Collapse --> Decision[Activate Pattern: Disruptive State]

🧩 Schema‑Based Memory Propagation

graph TD
    Schema["Parent Schema: Medical Diagnosis"] --> Patient1["Patient A"]
    Schema --> Patient2["Patient B"]
    Patient1 --> Frame1["Frame 2025‑04‑15"]
    Patient2 --> Frame2["Frame 2025‑04‑16"]
    Frame2 --> Shift[Δn: New Symptom]
    Shift --> Seal[Symbolic Sealing]

⚔️ Multi‑Agent Disagreement Resolution

flowchart TD
    A[Agent 1: "System Secure"] --> Shared[Shared Node: Access Model]
    B[Agent 2: "System Vulnerable"] --> Shared
    Shared --> Check[Field Alignment Check]
    Check -->|Conflict| Collapse[Collapse ➜ Belief Reconciliation]

💬 ActiveShell Query Samples

Collapse on Emotional Shift

Collapse-If Δn > θ Where Context.Mood = "Tense"

Symbolic Alignment Between Agents

Get-SharedNode Between Agent_A and Agent_B Where Topic = "Healthcare_Consent"

Frame Collapse with Sealing

Seal-Frame If Δn > θ Between Frame_20250415_1635 and Frame_20250415_1638

Aggregate Temporal Cognition

Analyze-Pattern MoodShifts Between 09:00 and 10:00 Using Policy mood_field_collapse

🧠 Glossary

Symbol Map

Symbol	Meaning
`Δn`	Change in identity (tension across time).
`θ`	Collapse threshold — determinessignificance .
`T₀`	Resting field state.
`T₁`	Potential field (tension building).
`T₂`	Activated field (collapse imminent).
`Σ`	Symbolically Sealed memory (committed identity).
`Ξ`	Shared Node — mutually aligned cognition between agents/systems.

Additional terms and deeper explanations live in docs/glossary.md.

🔮 Symbolic Dynamics & Field Cognition

🌀 Mood Cascade Engine: Field Shift Detection

graph TD
    Calm["Calm"] --> Subtle["Subtle Tension"]
    Subtle --> Conflict["Identity Displacement (Δn ↑)"]
    Conflict --> Stress["Field Pressure"]
    Stress --> CollapsePoint["Collapse Threshold Reached"]
    CollapsePoint --> NewState["Symbolic Sealing: New Identity"]

The shape of a moment is not defined by what it is — but by how fast it became that.

🤝 Multi‑Agent Shared Topology Flow

graph TD
    Agent1[System A: Graph Reasoning] --> SharedField[Shared Node: Policy]
    Agent2[System B: LLM Stream] --> SharedField
    Agent3[System C: Dashboard] --> SharedField
    SharedField --> Check[Field Consistency]
    Check -->|Aligned| Sync[Synchronised State]
    Check -->|Disaligned| Rebuild[Collapse & Rebuild]

🧬 Full‑Stack Resonance (Lifecycle)

flowchart TD
    Raw[Raw Input] --> Struct[Cube4D Structuring]
    Struct --> Temporal[AGDB Temporal Sequence]
    Temporal --> Pressure[QFN Field Pressure]
    Pressure --> CheckΔn[Δn > θ?]
    CheckΔn -- Yes --> Seal[Symbolic Sealing]
    CheckΔn -- No --> Wait[Await Further Change]
    Seal --> RG

Name		Name	Last commit message	Last commit date
active_graph_database		active_graph_database
active_graph_network_core		active_graph_network_core
doco		doco
quantum_field_networks		quantum_field_networks
scripts		scripts
use_cases		use_cases
whitepapers		whitepapers
.DS_Store		.DS_Store
.gitignore		.gitignore
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE.md		LICENSE.md
NOTICE.md		NOTICE.md
Quantum Field Networks.md		Quantum Field Networks.md
README.md		README.md
TRADEMARKS.md		TRADEMARKS.md
Universal Relational Intelligence (RI).md		Universal Relational Intelligence (RI).md

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