NWcortex 1.2.2

NeuroWeave Cortex (NWC) — Graph-first cognitive memory runtime for AI agents. Hippocampal-inspired architecture with domain routing, spreading activation, edge budget management, 4-layer compression, thermal storage, personality modeling, and 8-phase sleep consolidation.

NeuroWeave Cortex (NWC) — External Cognitive Cortex for AI Agents

Not a vector database. Not a graph database. Not RAG. An external cognitive cortex — a cognitive runtime that perceives, activates concepts, maintains working memory, forms reasoning chains, tracks goals, manages attention, and evolves a self-model. It remembers, forgets, strengthens, connects, abstracts, and reasons — the way biological cognition does.

v1.0.8 | 112 modules | Phase 1-6 complete | Cognitive Cortex Architecture

What makes it different

Vector databases retrieve. Graph databases traverse. NeuroWeave Cortex runs a full cognitive lifecycle:

Capability	Vector DB	Graph DB	NW Cortex
Semantic retrieval	yes	no	yes
Graph traversal	no	yes	yes
Automatic forgetting (survival decay)	no	no	yes
Memory strengthening (rehearsal)	no	no	yes
Conflict detection & resolution	no	no	yes
Multi-strategy RRF retrieval fusion	no	no	yes
Cross-Encoder reranking	no	no	yes
Explainable reasoning paths	no	no	yes
Memory revision engine (sleep)	no	no	yes
Hot/Warm/Cold memory tiering	no	no	yes
Multimodal (text+image+audio)	no	no	yes
Fuzzy recall ("I think I remember...")	no	no	yes
Emergent abstraction (pattern discovery)	no	no	yes
Temporal context (TimeSpine-indexed)	no	no	yes
8-phase sleep consolidation	no	no	yes
Ghost revival (savings effect)	no	no	world-first
Hebbian edge learning	no	no	yes
4-layer memory pyramid	no	no	yes
Goal-driven inference	no	no	yes
Working Memory Workspace	no	no	yes
Concept Cortex	no	no	yes
Spreading Activation Engine	no	no	yes
Salience-based attention	no	no	yes
Self Model → LLM prompt injection	no	no	yes
Autonomous reasoning loop	no	no	yes
Full memory lifecycle (9 stages)	no	no	yes

Quick Start

Classic API (Phase 1-5)

from star_graph import MemoryManager
from star_graph.scheduler import AgentContext

mgr = MemoryManager()

# Remember
mgr.remember("User debugs Redis connection timeout — pool was 10, fixed to 20",
             tags=["redis", "debug", "timeout"])
mgr.remember("User prefers type hints and concise code",
             tags=["preference", "style"])

# Working memory
mgr.remember_working("Currently debugging auth middleware timeout",
                     tags=["debug", "auth"])

# Context-aware recall
ctx = AgentContext(task_type="debugging", active_goals=["fix Redis connection"])
memories = mgr.recall("Redis connection pool config", context=ctx)
print(memories.memory_summary)

# Sleep consolidation
report = mgr.sleep()

# Persist
mgr.save("agent_memory.json")

Cognitive Cortex API (Phase 6)

from star_graph import (
    PerceptionLayer, CognitiveWorkspace, ConceptCortex,
    ActivationEngine, GoalSystem, SalienceEngine, SelfModel
)

# ── Full cognitive pipeline ──
pl = PerceptionLayer()
ws = CognitiveWorkspace(max_items=20)
cc = ConceptCortex()
ae = ActivationEngine(graph=graph)
gs = GoalSystem()
se = SalienceEngine()

# 1. Perceive user input
frame = pl.perceive("I need to debug a Redis memory leak urgently!")
# → PerceptionFrame(intent="command", valence=-0.4, concepts=["redis","debug","leak"])

# 2. Admit to working memory workspace
ws.on_perception(frame)

# 3. Activate concepts
for concept in frame.extracted_concepts:
    cc.get_or_create_concept(concept)

# 4. Spread activation through memory graph
result = ae.activate_from_perception(frame)

# 5. Track goals
goal = gs.add_goal("Fix Redis pipeline memory leak", priority=0.9)
gs.activate_goal(goal.id)

# 6. Compute salience — decide what enters attention
signals = [se.compute_salience(item.id, "thought",
    context={"task_relevance": item.relevance_to_current_task})
    for item in ws.get_items()]
winners = se.compete(signals, max_winners=5)

# 7. Build self-model → LLM prompt injection
sm = SelfModel(workspace=ws, goal_system=gs, concept_cortex=cc, salience_engine=se)
prompt = sm.get_prompt_injection()
# → "# Cognitive State Summary\n## Current Focus: ...\n## Active Goals: ..."

# This is what the LLM sees — NOT raw memory dumps!

Architecture

Four-layer cognitive design. Phase 6 adds the Cortex layer above Behavior.

Layer 4: Cortex      │  Perception, Working Memory Workspace, Concept Cortex,
    (Phase 6)        │  Spreading Activation, Goal System, Salience Engine,
    perception.py,   │  Self Model, Autonomous Reasoning, Cognitive Compression,
    cognitive_workspace.py,│  Memory Lifecycle Manager
    concept_cortex.py,│  "What am I thinking right now? What should I focus on?"
    activation_engine.py, │
    goal_system.py,  │
    salience.py,     │
    self_model.py,   │
    autonomous_reasoning.py,│
    cognitive_compression.py,│
    memory_lifecycle.py)│
                     │
Layer 3: Behavior    │  Cortex routing, memory gating, working memory,
    (cortex.py,      │  dual-channel retrieval, scheduler, quality scoring,
     router.py,      │  memory budget, stability control, cognitive priority
     gate.py,        │  "What should I recall right now, at what detail level?"
     working_memory.py,│
     scheduler.py,   │
     quality_score.py,│
     memory_budget.py,│
     stability_control.py,│
     cognitive_priority.py)│
                     │
Layer 2: Cognitive   │  Hub abstraction, cascade recall, TimeSpine temporal index,
    (retriever.py,   │  sleep consolidation, evolution, ghost revival,
     sleep.py,       │  abstraction, community detection, competition,
     evolution.py,   │  conflict detection, memory revision, cross-encoder,
     ghost.py,       │  Hebbian learning, domain routing, context routing
     abstraction.py, │  "How do memories connect, strengthen, and fade?"
     community.py,   │
     competition.py, │
     hebbian_learning.py,│
     domain_graph.py,│
     context_routing.py)│
                     │
Layer 1: Storage     │  CRUD, persistence, ANN indexing, tiered storage,
    (graph.py,       │  BM25 keyword index, multi-level caching, typed memory,
     anchor.py,      │  4-layer memory pyramid, abstraction chain
     storage.py,     │  "Where is this memory stored?"
     index.py,       │
     bm25.py,        │
     typed_memory.py,│
     memory_layers.py,│
     abstraction_chain.py)│

Core modules (Phase 1-4 foundation)

Module	Role
manager.py	High-level facade — remember(), recall(), sleep(), save()
runtime.py	Dependency container — manages all subsystem lifecycles
scheduler.py	Context-aware retrieval with memory type selection
working_memory.py	Short-term buffer (15-item, 1h TTL) — auto-promotes to long-term
sleep.py	8-phase sleep: N1_Replay → N2_Merge → N2b_Conflict → N2c_Revision → N3_Compression → N3d_Rebuild → REM_Emotion → N4_Prune
evolution.py	Survival-based decay (Ebbinghaus/Power-law/Exponential), belief transitions
retriever.py	HybridFusion + OscillationResonance + VectorSimilarity + Personalized PageRank
bm25.py	Sparse keyword retrieval with RRF fusion
cross_encoder.py	Cross-Encoder reranking for precision improvement
ghost.py	Ghost traces, fuzzy recall, savings effect revival
abstraction.py	Emergent category discovery from anchor clusters
community.py	Louvain community detection with centroid routing
anchor.py	Memory unit with 6-state lifecycle, 10-dim AnchorVector
graph.py	Star graph with RichEdge, Schema, ReflectionNode
compression.py	Multi-level session compression (episodic/strategic/meta)
forget_certificate.py	Ed25519-signed JWS deletion certificates — GDPR Article 17
multimodal.py	Cross-modal text/image/audio memory
zero_llm_pipeline.py	7-stage zero-LLM ingestion pipeline
batch_vectorizer.py	Deferred batch embedding writes with SQLite WAL
markdown_export.py	GBrain-aligned Markdown memory export

Phase 5 — Cognitive Depth

Module	Role
memory_budget.py	Token + anchor budget management with eviction policies
quality_score.py	7-dimension memory quality scoring (usage, reasoning, feedback, etc.)
stability_control.py	Long-term stability with exponential/linear decay, drift monitoring
memory_layers.py	4-layer pyramid: Working → Episodic → Semantic → Core Identity
typed_memory.py	7 memory types (code/task/dialogue/tool_call/knowledge/event/preference)
abstraction_chain.py	Event → Summary → Pattern → Identity abstraction pipeline
domain_graph.py	Domain-based graph partitioning with soft isolation
context_routing.py	6-dimension context-aware retrieval routing
hebbian_learning.py	Hebbian edge learning — "neurons that fire together wire together"
agent_state.py	Agent state memory: goal tree, tool calls, checkpoints
cognitive_closure.py	Closed-loop feedback: recall → use → learn → improve
cognitive_priority.py	5-level priority assignment with forced injection for core identity

Phase 6 — Cognitive Cortex

Module	Role
thought_object.py	Unified cognitive base class — activated nodes, not static memory
perception.py	Raw text → structured PerceptionFrame (intent, emotion, goals, concepts)
cognitive_workspace.py	Core: Working memory workspace with reasoning chains, attention, TTL decay
concept_cortex.py	Concept network: activation, fusion, competition, 10 built-in core concepts
activation_engine.py	Multi-source spreading activation (query/goal/concept/emotion seeds)
goal_system.py	Goal hierarchy, conflict detection, goal-driven inference
salience.py	10-component attention competition with lateral inhibition
cognitive_compression.py	Events → Concepts → Identity → World Model compression pipeline
self_model.py	System self-model → compressed CognitiveState → LLM prompt injection
autonomous_reasoning.py	Contradiction → Activation → Resolution loop (no LLM required)
memory_lifecycle.py	Unified 9-stage lifecycle: Perception → Working → ... → Ghost → Dead

Retrieval Benchmarks

LoCoMo Benchmark (real-world conversations)

Evaluated on the LoCoMo-10 dataset: 10 conversations, 5,882 turns across 272 sessions, 1,986 QA pairs across 5 categories. Pure retrieval (no LLM generation).

Method	has_answer	F1	Latency
VectorSimilarity	13.1%	0.026	122.2ms
OscillationResonance	11.9%	0.026	110.4ms
HybridFusion + BM25	15.3%	0.025	<200ms

Per-category has_answer:

Category	#QA	VecSim	OscRes	HybFus
Temporal (1)	282	3.5%	2.8%	4.3%
Short Memory (2)	321	1.9%	2.2%	1.9%
Long Memory (3)	96	2.1%	2.1%	3.1%
Composite (4)	841	18.0%	16.4%	21.0%
Adversarial (5)	446	20.4%	18.4%	23.5%

Internal Benchmark (synthetic multi-session)

6 sessions × 80 turns, 5 categories. 1.7x compression (6,708 → 3,982 tokens) with maintained or improved recall.

Method	C-R@3	C-R@5	Interf
VectorSimilarity	0.933	0.933	N/A
OscillationResonance	0.967	0.967	0.667
HybridFusion	0.900	0.900	0.667

Memory Lifecycle (9 stages)

Every memory moves through a full 9-stage cognitive lifecycle (Phase 6.10 memory_lifecycle.py):

PERCEPTION → WORKING → SHORT_TERM → LONG_TERM → CONSOLIDATED → ARCHIVED → DORMANT → GHOST → DEAD

Stage	Description	Duration
Perception	Initial intake, not yet stored	seconds
Working	In cognitive workspace, high activation	minutes~hours
Short-term	Stored, not yet consolidated	hours~days
Long-term	Consolidated, actively retrievable	days~months
Consolidated	Deeply integrated, high stability	months~years
Archived	Cold storage, low access frequency	indefinite
Dormant	Very low access, near ghost	indefinite
Ghost	Forgotten but potentially revivable	90 days
Dead	Permanently removed	—

Paired with ThermalState (HOT → WARM → COLD → DEAD) for storage tier switching and 4-layer pyramid (Working → Episodic → Semantic → Core Identity).

Sleep Consolidation (8 phases)

Sleep is not cleanup. Sleep changes the graph:

1. N1_Replay — SWR-scored replay of surprising/emotional memories
2. N2_Merge — ANN-accelerated near-duplicate fusion + constellation bridging
3. N2b_Conflict — Semantic contradiction detection (overwrite/coexist/deprecate)
4. N2c_Revision — Surprise-prioritized memory revision engine
5. N3_Compression — Hippocampal→cortical transfer, schema formation
6. N3d_Rebuild — Multi-node fusion, edge rewiring, abstractive pattern discovery
7. REM_Emotion — Emotional decoupling from consolidated memories
8. N4_Prune — Weak anchor/edge removal, ghost trace creation (savings effect)

Dual-Channel Retrieval

System-1 (fast, embedding + BM25 hybrid) and System-2 (deep, hierarchical traversal) with automatic triggering:

• Low-confidence System-1 results (<0.35) automatically trigger System-2
• Structural keywords ("all", "every", "list", "which", "before", "last") trigger exhaustive search
• Results merged via weighted reciprocal rank fusion

Configuration

from star_graph.config import config, override, load_config

# Dot-path access
threshold = config.sleep.merge.default_threshold  # 0.85

# Programmatic override
override('sleep.merge.default_threshold', 0.75)
override('gate.k', 30)

# Schema validation
warnings = config.validate()  # type, range, and cross-section checks

# Load custom YAML
cfg = load_config("my_params.yaml")

See star_graph/defaults.yaml for all 300+ tunable parameters.

Installation

# From PyPI (package name: NWcortex, import as: star_graph)
pip install NWcortex

# With sentence-transformers for semantic embeddings
pip install "NWcortex[embeddings]"

# With MCP server support (for AI agent integration)
pip install "NWcortex[mcp]"

# Everything
pip install "NWcortex[all]"

# Run demo
python examples/emergence_demo.py

Note: Without [embeddings], the system uses a lightweight TF-IDF fallback for text encoding. Install sentence-transformers only if you need semantic-quality embeddings.

MCP Server — AI Agent Integration

NeuroWeave Cortex ships with a Model Context Protocol server. Connect any MCP-compatible AI agent (Claude Desktop, OpenClaw, Cursor, etc.) for persistent cognitive memory across conversations.

Quick start

# Install with MCP support
pip install "NWcortex[mcp]"

# Launch the MCP server on stdio
nwc-mcp

# With persistent storage
nwc-mcp --storage agent_memory.json

# Load a previously saved memory graph
nwc-mcp --load my_memories.json

Tool reference (12 tools)

Tool	Description
remember	Store a long-term memory (tags, importance, emotional valence)
remember_working	Store in fast ephemeral working-memory buffer (current task context)
recall	Context-aware semantic retrieval with task-type routing
forget	Remove a memory, creating a ghost trace for potential fuzzy recall
sleep	Run 5-phase sleep consolidation — merges, prunes, forms schemas
consolidate	Micro-consolidation — incremental, non-blocking
stats	Memory system statistics (anchors, edges, ghosts, schemas, cognitive health)
fuzzy_recall	Low-confidence recall from ghost traces ("I seem to remember...")
get_profile	Inferred user profile from accumulated memories
evolve	Memory evolution cycle (decay, boost, conflict resolution)
save	Persist memory graph to disk (JSON)
load	Load memory graph from disk (JSON)

Claude Desktop config

{
  "mcpServers": {
    "neuroweave-cortex": {
      "command": "nwc-mcp",
      "args": ["--storage", "/path/to/agent_memory.json"]
    }
  }
}

OpenClaw config

{
  "mcpServers": {
    "neuroweave-cortex": {
      "command": "nwc-mcp",
      "args": ["--storage", "/path/to/agent_memory.json"]
    }
  }
}

REST API Server

# Start the REST server
nwc-server --port 8420

# Or via module
python -m star_graph.server --port 8420

Endpoints:

Method	Path	Description
GET	/health	Health check with anchor/edge counts
GET	/metrics	Prometheus-format metrics
GET	/stats	Full memory system statistics
POST	/remember	Store a memory {"text": "...", "tags": [...]}
POST	/recall	Retrieve memories {"query": "...", "max_items": 10}
POST	/sleep	Run sleep consolidation
POST	/consolidate	Run micro-consolidation

Interactive Demo

python examples/emergence_demo.py

# Or use the CLI:
# sg-add "Discussed microservices deployment patterns" --tags architecture --emotional 0.6
# sg-query "database connection pooling best practices"
# sg-query --trace "When did Alice visit Hawaii?"
# sg-stats --schemas --ghosts
# sg-sleep --retention 0.15 --edge-prune 0.1
# nwc-export --output memories/ --organize both
# nwc-forget <anchor_id> --certificate --reason gdpr_art17
# nwc-verify certificates/forget-<id>.jws

Benchmarks

python examples/memory_benchmark.py --quick   # 4 sessions, ~200 turns
python examples/memory_benchmark.py --full    # 12 sessions, ~5000 turns

Running Tests

pip install pytest pytest-cov
pytest tests/ -v

# With coverage report
pytest tests/ --cov=star_graph --cov-report=term

Status: Phase 1-6 complete, 112 modules, v1.0.8.

Roadmap

See ROADMAP.md for planned work.

License

MIT