doorway-memory 0.2.0

Geometric memory engine. Store knowledge as shapes. Retrieve by containment. Verify by chain.

doorway-memory

Geometric memory engine. Store knowledge as shapes. Retrieve by containment. Verify by chain.

---

What This Is

A memory system where knowledge is geometry. Not embeddings. Not vectors. Not key-value pairs. Shapes.

A shape is a region in dimensional space with defined boundaries. Store a shape and you're saying "I know what happens inside these boundaries." Query a point and the system answers a binary question: is this point inside known territory, or is it in the void?

That's a different question than "what's similar to this?" Vector databases answer similarity. doorway-memory answers containment. Similarity is fuzzy — close enough counts. Containment is precise — you're inside or you're not.

pip install doorway-memory

from doorway_memory import Memory, Shape, Dimension

mem = Memory()

# Define a region of known territory
shape = Shape(dimensions={
    "temperature": Dimension("temperature", 90.0, 110.0),
    "pressure": Dimension("pressure", 0.9, 1.1),
})
mem.store(shape)

# Is this point inside known territory?
mem.is_known({"temperature": 100.0, "pressure": 1.0})  # True

# Is this point in the void?
mem.is_void({"temperature": 200.0, "pressure": 1.0})   # True — unknown territory

No AI required. No specific domain. Any system that operates in a dimensional space can use this.

---

Scanner — Zero Cold Start

Point doorway-memory at your existing system. It reads the structure and mints shapes automatically.

from doorway_memory import Memory, scan

mem = Memory()

# Scan a pandas DataFrame
import pandas as pd
df = pd.read_csv("orders.csv")
mem.scan_and_store(df)
# Every numeric column becomes a dimension.
# Observed min/max become boundaries. Done.

# Scan a database
mem.scan_and_store("postgresql://user:pass@localhost/mydb")
# Each table becomes a shape. Numeric columns become dimensions.

# Scan a JSON file or API response
mem.scan_and_store("data.json")
# Walks nested structure. Extracts numeric fields with ranges.

# Scan an OpenAPI spec
mem.scan_and_store("openapi.yaml")
# Each endpoint with numeric parameters becomes a shape.

# Scan a Python codebase
mem.scan_and_store("./src")
# Functions with typed numeric parameters become shapes.

Day one: your library is populated with the geometric territory of your own infrastructure. No manual shape definition. No cold start.

The scan() function auto-detects source type. One function, any source.

---

Confidence — How Deeply Known

Containment isn't binary when you need nuance. confidence() tells you how deep inside known territory a point sits.

shape = Shape(dimensions={
    "x": Dimension("x", 0.0, 100.0),
})
mem.store(shape)

# Center of shape — maximum confidence
shape.confidence({"x": 50.0})   # 1.0

# Near the edge — low confidence
shape.confidence({"x": 95.0})   # 0.1

# On the boundary — zero
shape.confidence({"x": 100.0})  # 0.0

# Outside — zero
shape.confidence({"x": 110.0})  # 0.0

# Per-dimension breakdown
shape.confidence_breakdown({"x": 50.0, "y": 90.0})
# {"x": 1.0, "y": 0.2} — strong on x, weak on y

Edge knowledge and core knowledge are distinguishable.

---

Growth — Shapes That Learn

Shapes expand from use. When points consistently land just outside a boundary, the shape grows to absorb them.

mem = Memory(growth=True)
shape = Shape(dimensions={"x": Dimension("x", 0.0, 10.0)})
mem.store(shape)

# Point at 10.5 is a near-miss — just outside the boundary
mem.recall({"x": 10.5})  # Empty — in void
mem.recall({"x": 10.5})  # Still tracking...
mem.recall({"x": 10.5})  # Near-miss count: 3
mem.recall({"x": 10.5})  # Near-miss count: 4
mem.recall({"x": 10.5})  # Threshold hit — shape expands

mem.is_known({"x": 10.5})  # True — boundary grew

The library calibrates itself from actual queries. No manual tuning.

---

Overlap — Cross-Domain Emergence

When two shapes share a region of dimensional space, that intersection is detected automatically and stored as a new derived shape.

shape_a = Shape(
    dimensions={"x": Dimension("x", 0.0, 10.0)},
    metadata={"domain": "physics"}
)
shape_b = Shape(
    dimensions={"x": Dimension("x", 5.0, 15.0)},
    metadata={"domain": "economics"}
)
mem.store(shape_a)
mem.store(shape_b)
# Overlap detected: x=[5.0, 10.0]
# New derived shape stored with both parents linked.
# A cross-domain pattern nobody explicitly defined.

---

Decay — Knowledge That Fades

Shapes that aren't queried shrink over time. Frequently accessed shapes resist decay.

mem = Memory(decay=True)
mem.store(shape)

# Shape is active — accessed regularly
mem.recall({"x": 5.0})  # Access recorded

# Much later — no queries for weeks...
mem.maintain()  # Boundaries contract slightly

# Even later — still no queries...
mem.maintain()  # Shape archived — removed from active library,
                # preserved in chain for history

The library self-prunes. Active knowledge stays strong. Dead knowledge fades gracefully.

---

Merge — Shape Fusion

When growth causes two shapes to touch, they merge into one continuous region.

# Shape A: x=[0, 10]
# Shape B: x=[8, 20]  (overlaps significantly)
# After merge: single shape x=[0, 20]
# Parents archived. Merged shape replaces both.

The library consolidates instead of accumulating redundant overlapping shapes.

---

Narrative — Trajectories Through Knowledge

Shapes stored in sequence form trajectories. The system predicts what comes next.

# Record a learning trajectory
mem.store_in_trajectory(shape_a, "session-1")
mem.store_in_trajectory(shape_b, "session-1")
mem.store_in_trajectory(shape_c, "session-1")

# Another session follows a similar path
mem.store_in_trajectory(shape_a, "session-2")
mem.store_in_trajectory(shape_b, "session-2")

# Predict: after shape_b, what usually comes next?
mem.predict_next(shape_b.id)
# [("shape_c_id", 0.67), ...]

# Find common paths across all sessions
mem.find_common_paths(min_length=2)
# [["shape_a_id", "shape_b_id"]] — this sequence appears in multiple trajectories

Memory has temporal structure. Not just what you know, but how you learned it.

---

Void Mapping — The Shape of What You Don't Know

The void has structure. Boundaries. Size. Neighbors.

# What percentage of the bounded space is unknown?
mem.void_percentage()  # 0.73 — 73% is void

# Where are the biggest gaps?
regions = mem.map_void()
for region in regions:
    print(region.volume())           # How big is this gap?
    print(region.center())           # Where is it?
    print(region.neighboring_shapes) # What borders it?

# What's the single largest gap?
biggest = mem.largest_gap()

The gap detector expressed as persistent geometry.

---

Emergence — Tier 2 Pattern Detection

Patterns that span many shapes across many domains emerge automatically.

# After storing shapes from multiple domains...
tier2_patterns = mem.detect_emergence()

for pattern in tier2_patterns:
    print(pattern.shape)       # The geometric intersection
    print(pattern.parent_ids)  # Which shapes contribute
    print(pattern.domains)     # Which domains it spans
    print(pattern.strength)    # How many shapes participate

# Generative Complexity System — the system recognizing its own growth
gcs = mem.detect_gcs(growth_history)

# Intelligence System — the mechanism recognizing the mechanism
is_pattern = mem.detect_is()

Nobody programs emergence. It's detected from the geometry.

---

Verified Memory — Cryptographic Chain

Every store, every growth event, every merge, every archive is anchored to an xycore cryptographic chain. Optional but powerful.

pip install doorway-memory[anchor]

mem = Memory(anchor=True)
shape_id = mem.store(shape)

# Verify any shape's provenance
proof = mem.verify(shape_id)
# {"anchor_id": "...", "timestamp": ..., "hash": "...", "verified": True}

# Replay memory history
for shape in mem.replay():
    print(shape.id, shape.metadata)

Provable, replayable history of everything the system has ever learned.

---

Storage Backends

# In-memory (default — no persistence)
mem = Memory()

# File persistence
mem = Memory(backend="file", path="./memory.json")

# Supabase (cloud persistence)
pip install doorway-memory[supabase]
mem = Memory(backend="supabase", config={
    "url": "https://xxx.supabase.co",
    "key": "your-service-key"
})

---

Configuration

Every threshold is tunable.

from doorway_memory.growth import GROWTH_THRESHOLD, NEAR_MISS_MARGIN
from doorway_memory.decay import DECAY_RATE, DECAY_GRACE_PERIOD
from doorway_memory.merge import MERGE_OVERLAP_RATIO
from doorway_memory.emergence import MIN_SHAPES_FOR_EMERGENCE

Threshold	Default	What It Controls
NEAR_MISS_MARGIN	0.15	How far outside counts as near-miss
GROWTH_THRESHOLD	5	Near-misses before expansion
DECAY_RATE	0.02	Boundary shrink per cycle
DECAY_GRACE_PERIOD	7 days	Time before decay starts
MERGE_OVERLAP_RATIO	0.50	Overlap % to trigger merge
MIN_SHAPES_FOR_EMERGENCE	3	Minimum shapes for Tier 2

---

Full API

from doorway_memory import Memory, Shape, Dimension, scan

mem = Memory()

# ── Store ──────────────────────────────
mem.store(shape)                              # Store a shape
mem.store_in_trajectory(shape, "session-1")   # Store as trajectory step
mem.scan_and_store(source)                    # Scan system, store all shapes

# ── Recall ─────────────────────────────
mem.recall(point)                             # Shapes containing this point
mem.recall_with_confidence(point)             # With confidence gradients
mem.is_known(point)                           # Inside known territory?
mem.is_void(point)                            # In the void?

# ── Predict ────────────────────────────
mem.predict_next(shape_id)                    # What comes after this?
mem.find_common_paths()                       # Shared trajectories

# ── Map ────────────────────────────────
mem.map_void()                                # Characterize unknown territory
mem.void_percentage()                         # % of space that's void
mem.largest_gap()                             # Biggest void region

# ── Emerge ─────────────────────────────
mem.detect_emergence()                        # Find Tier 2 patterns
mem.detect_gcs(history)                       # Growth pattern self-recognition
mem.detect_is()                               # Intelligence mechanism detection

# ── Verify ─────────────────────────────
mem.verify(shape_id)                          # Cryptographic proof
mem.replay()                                  # Walk memory history

# ── Maintain ───────────────────────────
mem.maintain()                                # Run decay cycle
mem.count()                                   # Shape count

---

Not a Vector Database

	Vector DB	doorway-memory
Stores	Embeddings (points)	Shapes (regions)
Retrieves by	Similarity (nearest neighbor)	Containment (inside or not)
Answer type	"Here's what's close"	"You're inside / you're in the void"
Confidence	Distance score	Depth gradient (center to boundary)
Learns from use	No	Yes — growth, decay, merge
Cross-domain	No	Yes — overlap detection
Maps unknown	No	Yes — void mapping
Emergence	No	Yes — Tier 2 pattern detection
Verified	No	Yes — cryptographic chain

---

Part of Doorway

doorway-memory is a standalone package. It does not require Doorway, AI, or any specific domain.

It's also the geometric memory layer of the Doorway reasoning stack.

Package	What It Is
xycore	Cryptographic chain primitive
pruv	Verification infrastructure
doorway-memory	Geometric memory engine (this package)
doorway-agi	AGI reasoning engine
vantagepoint	Structured thinking methodology

---

License

Apache License 2.0 — see for details.

© 2026 Doorway · doorwayagi.com

Created by Luke H