diff --git a/README.md b/README.md
index ab59edcd..d1a25632 100644
--- a/README.md
+++ b/README.md
@@ -4,22 +4,50 @@
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 [![PyPI version](https://badge.fury.io/py/semanticore.svg)](https://badge.fury.io/py/semanticore)
 [![Downloads](https://pepy.tech/badge/semanticore)](https://pepy.tech/project/semanticore)
+[![Tests](https://github.com/yourusername/semanticore/workflows/Tests/badge.svg)](https://github.com/yourusername/semanticore/actions)
 
-**Transform unstructured data into structured semantic layers for LLMs, Agents, RAG systems, and Knowledge Graphs.**
+**The Ultimate Semantic Layer & Context Engineering Toolkit for LLMs, RAG Systems, and AI Agents**
 
-SemantiCore bridges the gap between raw unstructured data and intelligent AI systems by providing a comprehensive toolkit for semantic extraction, schema generation, and knowledge representation.
+SemantiCore transforms raw, unstructured data into intelligent semantic layers and formal ontologies that power next-generation AI applications. Built for developers who need reliable, scalable, and contextually-aware data processing pipelines with deep semantic understanding.
 
 ---
 
-## 🌟 Why SemantiCore?
+## 🔮 What Makes SemantiCore Unique?
 
-Modern AI systems require structured, semantically rich data to perform effectively. SemantiCore solves the fundamental challenge of converting messy, unstructured information into clean, schema-compliant semantic layers that power:
+SemantiCore isn't just another data processing library—it's a **complete semantic intelligence platform** that bridges the gap between raw data and AI-ready knowledge:
 
-- **🤖 Intelligent Agents** - With type-safe, validated input/output schemas
-- **🔍 RAG Systems** - Enhanced with semantic chunking and enriched metadata
-- **🕸️ Knowledge Graphs** - Automatically extracted entities, relations, and triples
-- **🛠️ LLM Tools** - Wrapped with semantic contracts for reliable operation
-- **📊 Data Pipelines** - Consistent, validated data flows across your stack
+### 🎯 **Semantic Layer Architecture**
+- **Automated Ontology Generation** - Transform raw data into formal OWL/RDF ontologies
+- **Context-Aware Processing** - Understand data meaning, not just structure
+- **Multi-Modal Semantic Fusion** - Unify text, images, documents, and structured data
+- **Dynamic Schema Evolution** - Adapt to changing data patterns intelligently
+
+### 🧠 **Advanced Context Engineering**
+- **Intelligent Context Windows** - Optimize LLM context with semantic relevance
+- **Contextual Memory Systems** - Maintain semantic continuity across conversations
+- **Context Compression** - Preserve meaning while reducing token usage
+- **Semantic Routing** - Direct queries to optimal processing paths
+
+### 🔗 **Knowledge Graph Intelligence**
+- **Automated Graph Construction** - Build knowledge graphs from any data source
+- **Temporal Reasoning** - Track entities and relationships over time
+- **Semantic Inference** - Derive new knowledge from existing relationships
+- **Cross-Domain Linking** - Connect concepts across different knowledge domains
+
+---
+
+## 🌟 Why Choose SemantiCore?
+
+Modern AI systems are only as intelligent as the semantic understanding they possess. SemantiCore solves the fundamental challenge of creating **semantically rich, contextually aware data layers** that enable:
+
+- **🤖 Autonomous Agents** - With validated ontologies and semantic contracts
+- **🔍 Advanced RAG Systems** - Enhanced with contextual chunking and metadata enrichment
+- **🧠 LLM Optimization** - Through intelligent prompt engineering and context management
+- **🕸️ Knowledge Graphs** - Automatically constructed from unstructured data
+- **🛠️ AI Tool Integration** - Type-safe, semantically validated tool calling
+- **📊 Intelligent Pipelines** - Context-aware data flows with semantic validation
+- **🔄 Ontology Engineering** - Transform raw data into formal knowledge representations
+- **🎯 Semantic Search** - Beyond keyword matching to true meaning-based retrieval
 
 ---
 
@@ -31,542 +59,892 @@ Modern AI systems require structured, semantically rich data to perform effectiv
 # Install via pip
 pip install semanticore
 
-# Or install with all dependencies
+# Install with all dependencies
 pip install "semanticore[all]"
 
+# Install specific providers
+pip install "semanticore[openai,neo4j,pinecone,ontology]"
+
 # Development installation
 git clone https://github.com/yourusername/semanticore.git
 cd semanticore
 pip install -e ".[dev]"
 ```
 
-### Basic Usage
+### 30-Second Demo: Raw Data → Ontology
 
 ```python
 from semanticore import SemantiCore
 
-# Initialize the core engine
-core = SemantiCore()
+# Initialize with your preferred LLM provider
+core = SemantiCore(
+    llm_provider="openai",  # or "anthropic", "huggingface", "local"
+    embedding_model="text-embedding-3-large",
+    ontology_mode=True  # Enable advanced ontological reasoning
+)
 
-# Extract semantic information from text
+# Transform unstructured text into formal ontology
 text = """
-OpenAI released GPT-4 in March 2023, which significantly improved 
-reasoning capabilities over GPT-3.5. The model was trained using 
-reinforcement learning from human feedback (RLHF).
+Microsoft announced the acquisition of GitHub for $7.5 billion in June 2018. 
+The deal was completed in October 2018, making GitHub a subsidiary of Microsoft.
+Satya Nadella, CEO of Microsoft, emphasized that GitHub would remain an open platform.
+The acquisition was part of Microsoft's strategy to embrace open-source development.
 """
 
-# One-line semantic extraction
-result = core.extract(text)
+# Extract semantic information with ontological structure
+result = core.extract(text, 
+    include_ontology=True,
+    reasoning_depth=3,
+    temporal_modeling=True
+)
 
-print(result.entities)     # [Entity(name="OpenAI", type="ORGANIZATION"), ...]
-print(result.relations)    # [Relation(subject="OpenAI", predicate="released", object="GPT-4"), ...]
-print(result.schema)       # Auto-generated Pydantic schema
-print(result.metadata)     # Enriched contextual information
+# Rich semantic output with formal ontology
+print(result.entities)         # [Entity(name="Microsoft", type="Organization", properties=...)]
+print(result.relations)        # [Relation(subject="Microsoft", predicate="acquired", object="GitHub")]
+print(result.events)          # [Event(type="Acquisition", temporal_bounds=...)]
+print(result.ontology)        # Formal OWL ontology with classes, properties, and axioms
+print(result.knowledge_graph) # Neo4j/RDF-compatible graph structure
+print(result.context_summary) # Contextual summary for LLM consumption
 ```
 
 ---
 
 ## 🧩 Core Features
 
-### 🧠 Semantic Extraction Engine
+### 🏗️ Ontology Engineering & Formal Semantics
 
-Advanced NLP pipeline that extracts meaningful structure from unstructured data:
+Transform raw data into structured knowledge representations:
 
 ```python
-from semanticore.extract import EntityExtractor, RelationExtractor, TopicClassifier
-
-# Named Entity Recognition with custom models
-extractor = EntityExtractor(
-    model="en_core_web_trf",  # spaCy model
-    custom_labels=["MALWARE", "THREAT_ACTOR", "VULNERABILITY"]
+from semanticore.ontology import (
+    OntologyBuilder, 
+    SemanticReasoner, 
+    ConceptMapper,
+    OntologyEvolution
 )
 
-entities = extractor.extract("APT29 used FrostBite malware against critical infrastructure")
-
-# Relation and Triple Extraction
-rel_extractor = RelationExtractor(llm_provider="openai")
-relations = rel_extractor.extract_relations(text, entities)
-
-# Topic Classification and Categorization
-classifier = TopicClassifier()
-topics = classifier.classify(text, categories=["cybersecurity", "technology", "politics"])
-```
-
-### 🧱 Dynamic Schema Generation
-
-Automatically generate type-safe schemas from extracted data:
-
-```python
-from semanticore.schema import SchemaGenerator, validate_data
-
-# Generate Pydantic models from extracted entities
-generator = SchemaGenerator()
-schema = generator.from_entities(entities)
-
-# Export to various formats
-schema.to_pydantic()    # Python Pydantic model
-schema.to_json_schema() # JSON Schema
-schema.to_yaml()        # YAML Schema
-schema.to_typescript()  # TypeScript interfaces
-
-# Validate new data against generated schema
-is_valid = validate_data(new_data, schema)
-```
-
-### 🔌 Universal Connectors
-
-Seamlessly connect to any data source:
-
-```python
-from semanticore.connectors import FileConnector, WebConnector, APIConnector
-
-# File processing (PDF, DOCX, CSV, JSON, Markdown)
-file_conn = FileConnector()
-documents = file_conn.load("./documents/*.pdf")
-semantic_docs = core.process_documents(documents)
-
-# Web scraping and RSS feeds
-web_conn = WebConnector()
-pages = web_conn.scrape_urls(["https://example.com/news"])
-web_semantics = core.extract_from_web(pages)
-
-# REST API integration
-api_conn = APIConnector(base_url="https://api.example.com")
-api_data = api_conn.fetch("/endpoints")
-structured_data = core.structure_api_response(api_data)
-```
-
-### 🧪 Validation & Quality Assurance
-
-Ensure data quality and consistency across your pipeline:
-
-```python
-from semanticore.validation import SchemaValidator, ConsistencyChecker, QualityMetrics
-
-# Schema validation
-validator = SchemaValidator(schema)
-validation_result = validator.validate(data)
-
-if not validation_result.is_valid:
-    print(f"Validation errors: {validation_result.errors}")
-
-# Consistency checking across multiple extractions
-checker = ConsistencyChecker()
-consistency_score = checker.check_consistency([result1, result2, result3])
-
-# Quality metrics and confidence scoring
-metrics = QualityMetrics()
-quality_report = metrics.assess(extraction_result)
-print(f"Extraction confidence: {quality_report.confidence}")
-```
-
-### 📐 Intelligent Chunking & Embedding
-
-RAG-optimized document processing with semantic awareness:
-
-```python
-from semanticore.vectorizer import SemanticChunker, EmbeddingEngine
-
-# Semantic-aware chunking
-chunker = SemanticChunker(
-    chunk_size=512,
-    overlap=50,
-    respect_boundaries=True,  # Don't split entities/relations
-    add_metadata=True
+# Automated ontology construction
+ontology_builder = OntologyBuilder(
+    base_ontologies=["dublin_core", "foaf", "schema_org"],
+    reasoning_engine="pellet",  # or "hermit", "fact++"
+    consistency_checking=True,
+    axiom_learning=True
 )
 
-chunks = chunker.chunk_document(document, semantic_info=result)
-
-# Multi-modal embedding support
-embedder = EmbeddingEngine(
-    provider="sentence-transformers",  # or "openai", "huggingface"
-    model="all-MiniLM-L6-v2"
-)
-
-embedded_chunks = embedder.embed_chunks(chunks)
-
-# Direct vector database integration
-from semanticore.vector_stores import FAISSStore, PineconeStore
-
-store = FAISSStore()
-store.add_embeddings(embedded_chunks)
-```
-
-### 📚 Knowledge Graph Export
-
-Transform extracted semantics into graph databases:
-
-```python
-from semanticore.kg import Neo4jExporter, RDFExporter, KuzuExporter
-
-# Neo4j export with Cypher generation
-neo4j_exporter = Neo4jExporter(
-    uri="bolt://localhost:7687",
-    user="neo4j",
-    password="password"
-)
-
-# Create nodes and relationships
-neo4j_exporter.export_entities(entities)
-neo4j_exporter.export_relations(relations)
-
-# RDF triple export
-rdf_exporter = RDFExporter(format="turtle")
-triples = rdf_exporter.to_triples(entities, relations)
-
-# Query the generated knowledge graph
-from semanticore.kg.query import GraphQuerier
-
-querier = GraphQuerier(neo4j_exporter)
-results = querier.cypher("MATCH (n:ORGANIZATION)-[r:RELEASED]->(m:PRODUCT) RETURN n, r, m")
-```
-
-### 📡 Semantic Routing
-
-Intelligently route queries and tasks to appropriate handlers:
-
-```python
-from semanticore.routing import SemanticRouter, IntentClassifier
-
-# Set up routing rules
-router = SemanticRouter()
-
-# Intent-based routing
-router.add_intent_route("question_answering", qa_agent)
-router.add_intent_route("data_extraction", extraction_pipeline)
-router.add_intent_route("summarization", summary_agent)
-
-# Keyword and pattern-based routing
-router.add_keyword_route(["threat", "malware", "vulnerability"], security_agent)
-router.add_pattern_route(r"CVE-\d{4}-\d+", vulnerability_lookup)
-
-# LLM-powered semantic routing
-router.add_semantic_route(
-    description="Handle complex analytical queries about financial data",
-    handler=financial_analysis_agent,
-    examples=["What's the trend in quarterly revenue?", "Analyze the risk factors"]
-)
-
-# Route incoming requests
-query = "What are the latest cybersecurity threats targeting healthcare?"
-handler = router.route(query)
-response = handler.process(query)
-```
-
----
-
-## 🎯 Use Cases & Examples
-
-### 🔐 Cybersecurity Threat Intelligence
-
-```python
-from semanticore.domains.cyber import ThreatIntelExtractor
-
-# Specialized cybersecurity extraction
-threat_extractor = ThreatIntelExtractor()
-threat_report = """
-APT29 (Cozy Bear) launched a sophisticated spear-phishing campaign 
-targeting US government agencies using a previously unknown malware 
-variant called FrostBite. The attack exploited CVE-2024-1234 in 
-Microsoft Exchange servers.
-"""
-
-intel = threat_extractor.extract(threat_report)
-print(intel.threat_actors)    # ["APT29", "Cozy Bear"]
-print(intel.malware)          # ["FrostBite"]
-print(intel.vulnerabilities)  # ["CVE-2024-1234"]
-print(intel.attack_patterns)  # ["spear-phishing", "server exploitation"]
-
-# Export to STIX format for threat intelligence platforms
-stix_bundle = intel.to_stix()
-```
-
-### 🧬 Biomedical Research Assistant
-
-```python
-from semanticore.domains.biomedical import BiomedicalExtractor
-
-bio_extractor = BiomedicalExtractor()
-research_text = """
-The study investigated the efficacy of remdesivir in treating COVID-19 
-patients. Results showed a 31% reduction in recovery time compared to 
-placebo (p<0.001). Side effects included nausea in 12% of patients.
-"""
-
-bio_data = bio_extractor.extract(research_text)
-print(bio_data.drugs)         # ["remdesivir"]
-print(bio_data.conditions)    # ["COVID-19"]
-print(bio_data.outcomes)      # ["31% reduction in recovery time"]
-print(bio_data.side_effects)  # ["nausea"]
-
-# Generate structured clinical data
-clinical_schema = bio_data.to_clinical_schema()
-```
-
-### 📊 Financial Document Analysis
-
-```python
-from semanticore.domains.finance import FinancialExtractor
-
-fin_extractor = FinancialExtractor()
-earnings_report = """
-Q4 2024 revenue increased 15% YoY to $2.3B, driven by strong performance 
-in the cloud computing segment. Operating margin improved to 23.5% from 
-21.2% in the prior year. The company announced a $1B share buyback program.
-"""
-
-financial_data = fin_extractor.extract(earnings_report)
-print(financial_data.metrics)     # {"revenue": "$2.3B", "margin": "23.5%"}
-print(financial_data.periods)     # ["Q4 2024"]
-print(financial_data.events)      # ["$1B share buyback program"]
-
-# Export to financial analysis tools
-financial_json = financial_data.to_standardized_json()
-```
-
----
-
-## 🔧 Advanced Configuration
-
-### Custom Model Integration
-
-```python
-from semanticore.models import CustomLLMProvider
-
-# Integrate your own models
-class MyCustomLLM(CustomLLMProvider):
-    def __init__(self, model_path):
-        self.model = load_model(model_path)
-    
-    def extract_entities(self, text):
-        return self.model.predict(text)
-
-# Use custom model in SemantiCore
-core = SemantiCore(llm_provider=MyCustomLLM("./my_model"))
-```
-
-### Pipeline Customization
-
-```python
-from semanticore.pipeline import Pipeline, Step
-
-# Build custom processing pipeline
-pipeline = Pipeline([
-    Step("preprocess", text_cleaner),
-    Step("extract_entities", entity_extractor),
-    Step("extract_relations", relation_extractor),
-    Step("enrich_metadata", metadata_enricher),
-    Step("validate", schema_validator),
-    Step("export", knowledge_graph_exporter)
+# Build ontology from multiple data sources
+ontology = ontology_builder.build_from_sources([
+    "documents/*.pdf",
+    "databases/customer_data.db",
+    "apis/product_catalog",
+    "existing_schemas/*.xsd"
 ])
 
-# Process data through pipeline
-results = pipeline.run(input_data)
+# Advanced semantic reasoning
+reasoner = SemanticReasoner(ontology)
+inferred_knowledge = reasoner.reason(
+    query="What are all possible relationships between customers and products?",
+    reasoning_types=["transitive", "symmetric", "functional"],
+    explanation_depth=3
+)
+
+# Concept mapping and alignment
+mapper = ConceptMapper()
+aligned_ontology = mapper.align_concepts(
+    source_ontology=ontology,
+    target_ontologies=["industry_standard.owl", "domain_specific.owl"],
+    similarity_threshold=0.8,
+    manual_mappings="concept_mappings.yaml"
+)
+
+# Ontology evolution and versioning
+evolution = OntologyEvolution(ontology)
+evolved_ontology = evolution.evolve(
+    new_data_sources=recent_data,
+    evolution_strategy="conservative",  # or "aggressive", "guided"
+    backward_compatibility=True,
+    change_tracking=True
+)
+
+print(f"Ontology classes: {len(ontology.classes)}")
+print(f"Object properties: {len(ontology.object_properties)}")
+print(f"Data properties: {len(ontology.data_properties)}")
+print(f"Axioms: {len(ontology.axioms)}")
 ```
 
-### Configuration Management
+### 🎯 Advanced Context Engineering
+
+Purpose-built tools for optimizing LLM interactions with semantic context:
 
 ```python
-# semanticore.yaml
-extractors:
-  entity:
-    model: "en_core_web_trf"
-    confidence_threshold: 0.8
-  relation:
-    llm_provider: "openai"
-    model: "gpt-4"
+from semanticore.context import (
+    ContextEngineer, 
+    PromptOptimizer, 
+    SemanticMemory,
+    ContextualCompressor,
+    RelevanceScorer
+)
+
+# Intelligent context management with semantic understanding
+context_engineer = ContextEngineer(
+    max_context_length=128000,
+    compression_strategy="semantic_preserving",
+    relevance_threshold=0.7,
+    ontology_aware=True,  # Use ontological structure for context
+    temporal_coherence=True
+)
+
+# Advanced context compression
+compressor = ContextualCompressor(
+    compression_ratio=0.3,  # Compress to 30% while preserving meaning
+    preservation_priority=["entities", "relationships", "temporal_info"],
+    semantic_coherence_check=True,
+    information_density_optimization=True
+)
+
+# Semantic-aware prompt optimization
+optimizer = PromptOptimizer()
+optimized_prompt = optimizer.optimize(
+    base_prompt="Analyze this security incident",
+    context_data=extraction_result,
+    target_model="gpt-4",
+    optimization_objectives=["accuracy", "token_efficiency", "semantic_richness"],
+    domain_knowledge=security_ontology
+)
+
+# Advanced semantic memory with forgetting curves
+memory = SemanticMemory(
+    embedding_model="text-embedding-3-large",
+    vector_store="pinecone",
+    ontology_integration=True,
+    memory_decay=True,  # Implement forgetting curves
+    associative_memory=True,  # Connect related memories
+    episodic_memory=True  # Maintain temporal episode structure
+)
+
+# Store and retrieve with semantic context
+memory.store_interaction(
+    user_query=user_query,
+    agent_response=agent_response,
+    context=extraction_result,
+    importance_score=0.8,
+    emotional_valence=0.2,
+    epistemic_certainty=0.9
+)
+
+# Contextual retrieval with relevance scoring
+scorer = RelevanceScorer(ontology=domain_ontology)
+relevant_memories = memory.retrieve_contextual(
+    query=new_query,
+    context_window=conversation_history,
+    relevance_scorer=scorer,
+    temporal_weighting=True,
+    top_k=5
+)
+```
+
+### 🔄 Multi-Modal Semantic Integration
+
+Unify semantic understanding across different data modalities:
+
+```python
+from semanticore.multimodal import (
+    MultiModalSemanticProcessor,
+    CrossModalAlignment,
+    SemanticFusion
+)
+
+# Process multiple data types with unified semantics
+multimodal_processor = MultiModalSemanticProcessor(
+    text_extractor="advanced_nlp",
+    image_extractor="clip_vit_large",
+    document_extractor="layout_aware",
+    audio_extractor="whisper_large",
+    video_extractor="videomae",
+    cross_modal_fusion=True
+)
+
+# Extract and align semantics across modalities
+result = multimodal_processor.process({
+    "text": document_text,
+    "images": image_files,
+    "audio": audio_transcript,
+    "structured_data": database_records
+})
+
+# Cross-modal semantic alignment
+aligner = CrossModalAlignment()
+aligned_semantics = aligner.align_modalities(
+    result,
+    alignment_strategy="ontology_based",
+    confidence_threshold=0.8
+)
+
+# Semantic fusion with conflict resolution
+fusion_engine = SemanticFusion(
+    conflict_resolution="ontology_guided",
+    fusion_strategy="weighted_consensus",
+    uncertainty_handling=True
+)
+
+unified_semantics = fusion_engine.fuse(aligned_semantics)
+```
+
+### 🎯 Intelligent Chunking & Semantic Boundaries
+
+RAG-optimized processing with ontology-aware chunking:
+
+```python
+from semanticore.chunking import (
+    SemanticChunker, 
+    OntologyAwareChunker,
+    HierarchicalChunker, 
+    ContextualChunker
+)
+
+# Ontology-aware semantic chunking
+ontology_chunker = OntologyAwareChunker(
+    ontology=domain_ontology,
+    concept_coherence=True,        # Keep related concepts together
+    relationship_preservation=True, # Don't split related entities
+    hierarchical_awareness=True,   # Respect conceptual hierarchies
+    temporal_coherence=True        # Maintain temporal relationships
+)
+
+# Advanced semantic chunking with meaning preservation
+semantic_chunker = SemanticChunker(
+    chunk_size=1024,
+    overlap_strategy="semantic_overlap",  # Overlap based on meaning
+    boundary_detection="ontological",     # Use ontological boundaries
+    preserve_entities=True,
+    preserve_relationships=True,
+    add_contextual_headers=True,
+    generate_semantic_summaries=True,
+    cross_reference_links=True
+)
+
+# Hierarchical chunking with ontological structure
+hierarchical_chunker = HierarchicalChunker(
+    levels=["document", "section", "concept_cluster", "paragraph"],
+    ontology_guided=True,
+    maintain_conceptual_hierarchy=True,
+    cross_reference_generation=True,
+    semantic_index_creation=True
+)
+
+chunks = hierarchical_chunker.chunk_document(
+    document=complex_document,
+    extract_ontological_structure=True,
+    generate_concept_map=True,
+    create_semantic_index=True
+)
+
+# Each chunk includes rich semantic metadata
+for chunk in chunks:
+    print(f"Chunk ID: {chunk.id}")
+    print(f"Semantic Concepts: {chunk.concepts}")
+    print(f"Ontological Relations: {chunk.relations}")
+    print(f"Context Summary: {chunk.context_summary}")
+    print(f"Semantic Embedding: {chunk.embedding}")
+    print(f"Cross-references: {chunk.cross_refs}")
+```
+
+### 🔍 Semantic Retrieval & Context-Aware Search
+
+Advanced retrieval with ontological understanding:
+
+```python
+from semanticore.retrieval import (
+    SemanticRetriever,
+    OntologyQueryEngine,
+    ContextualSearchEngine
+)
+
+# Ontology-powered semantic retrieval
+retriever = SemanticRetriever(
+    vector_store="pinecone",
+    ontology=domain_ontology,
+    reranking_model="cross-encoder/ms-marco-MiniLM-L-12-v2",
+    query_expansion="ontological",     # Expand using ontology
+    concept_reasoning=True,            # Use reasoning for retrieval
+    temporal_awareness=True,           # Consider temporal relevance
+    result_fusion="semantic_weighted", # Fuse results semantically
+    explanation_generation=True        # Explain why results are relevant
+)
+
+# Advanced ontological querying
+query_engine = OntologyQueryEngine(
+    ontology=domain_ontology,
+    reasoning_engine="pellet",
+    query_language="sparql",  # Support SPARQL queries
+    natural_language_interface=True
+)
+
+# Natural language to formal query translation
+results = query_engine.query(
+    natural_query="Find all security incidents that involved APT groups and resulted in data exfiltration",
+    reasoning_depth=2,
+    include_inferred_results=True,
+    confidence_scoring=True
+)
+
+# Context-aware search with conversation understanding
+search_engine = ContextualSearchEngine(
+    retriever=retriever,
+    context_window=10,
+    user_profile_aware=True,
+    session_state_tracking=True,
+    intent_understanding=True
+)
+
+# Search with full contextual awareness
+search_results = search_engine.search(
+    query="How do we prevent similar attacks?",
+    conversation_context=conversation_history,
+    user_expertise_level="intermediate",
+    domain_focus="cybersecurity",
+    result_personalization=True
+)
+```
+
+### 🧪 Dynamic Schema Generation & Ontological Validation
+
+AI-powered schema and ontology generation with validation:
+
+```python
+from semanticore.schema import (
+    AISchemaGenerator,
+    OntologyValidator,
+    SchemaOntologyBridge,
+    SemanticConstraintEngine
+)
+
+# AI-powered ontological schema generation
+generator = AISchemaGenerator(
+    llm_provider="gpt-4",
+    ontology_integration=True,
+    validation_rounds=3,
+    formal_verification=True,
+    constraint_learning=True,
+    example_generation=True
+)
+
+# Generate schemas with ontological grounding
+schema = generator.generate_from_sources([
+    "incident_reports/*.json",
+    "threat_intel/*.xml", 
+    "security_logs/*.csv",
+    "existing_ontologies/*.owl"
+])
+
+# Advanced ontological validation
+validator = OntologyValidator(
+    schema=schema,
+    ontology=domain_ontology,
+    reasoning_engine="hermit"
+)
+
+validation_result = validator.validate(
+    data=new_incident_data,
+    consistency_checking=True,
+    completeness_verification=True,
+    semantic_constraint_validation=True,
+    temporal_consistency_checking=True
+)
+
+# Bridge between schemas and ontologies
+bridge = SchemaOntologyBridge()
+ontology_aligned_schema = bridge.align_schema_with_ontology(
+    schema=generated_schema,
+    ontology=domain_ontology,
+    mapping_strategy="semantic_similarity",
+    preserve_schema_structure=True
+)
+
+# Semantic constraint engine
+constraint_engine = SemanticConstraintEngine(ontology=domain_ontology)
+constraints = constraint_engine.derive_constraints(
+    data_patterns=historical_data_patterns,
+    business_rules=business_rules,
+    domain_knowledge=expert_knowledge
+)
+```
+
+### 🔄 Temporal Reasoning & Event Processing
+
+Advanced temporal understanding and event-based processing:
+
+```python
+from semanticore.temporal import (
+    TemporalReasoner,
+    EventStreamProcessor,
+    TimelineConstructor,
+    CausalityAnalyzer
+)
+
+# Advanced temporal reasoning
+temporal_reasoner = TemporalReasoner(
+    ontology=temporal_ontology,
+    temporal_logic="interval_algebra",  # Allen's interval algebra
+    uncertainty_handling=True,
+    temporal_constraint_solving=True
+)
+
+# Process temporal relationships
+temporal_result = temporal_reasoner.analyze(
+    events=event_sequence,
+    temporal_queries=[
+        "What events happened before the security breach?",
+        "Which actions were causally related to the incident?",
+        "What is the temporal pattern of similar attacks?"
+    ]
+)
+
+# Real-time event stream processing
+event_processor = EventStreamProcessor(
+    ontology=domain_ontology,
+    temporal_reasoning=True,
+    pattern_detection=True,
+    causality_inference=True,
+    anomaly_detection=True
+)
+
+# Timeline construction with semantic understanding
+timeline_constructor = TimelineConstructor(
+    event_extractor=event_processor,
+    temporal_reasoner=temporal_reasoner,
+    visualization_engine="plotly",
+    interactive_exploration=True
+)
+
+timeline = timeline_constructor.construct_timeline(
+    data_sources=multi_source_data,
+    granularity="minute",
+    include_inferred_events=True,
+    semantic_clustering=True
+)
+
+# Causality analysis with ontological reasoning
+causality_analyzer = CausalityAnalyzer(
+    ontology=causal_ontology,
+    causal_models=["pearl_causality", "granger_causality"],
+    intervention_analysis=True
+)
+
+causal_graph = causality_analyzer.analyze_causality(
+    events=event_sequence,
+    background_knowledge=domain_knowledge,
+    confidence_threshold=0.8
+)
+```
+
+### 🕸️ Advanced Knowledge Graph Intelligence
+
+Enterprise-grade knowledge graph construction and reasoning:
+
+```python
+from semanticore.kg import (
+    KnowledgeGraphBuilder, 
+    GraphReasoner, 
+    SemanticQuerier,
+    GraphEvolution,
+    MultiGraphFusion
+)
+
+# Intelligent knowledge graph construction
+kg_builder = KnowledgeGraphBuilder(
+    ontology_integration=True,      # Use ontologies for structure
+    entity_linking=True,            # Link entities across documents
+    relationship_inference=True,    # Infer implicit relationships
+    temporal_modeling=True,         # Model time-based relationships
+    confidence_scoring=True,        # Score relationship confidence
+    provenance_tracking=True,       # Track source information
+    multi_lingual_support=True      # Support multiple languages
+)
+
+# Build knowledge graph from heterogeneous sources
+kg = kg_builder.build_from_sources([
+    "documents/*.pdf",
+    "databases/incidents.db",
+    "apis/threat_intel",
+    "ontologies/*.owl",
+    "structured_data/*.json"
+])
+
+# Advanced graph reasoning with ontological inference
+reasoner = GraphReasoner(
+    kg=kg,
+    ontology=domain_ontology,
+    reasoning_engines=["sparql", "cypher", "gremlin"],
+    inference_rules="custom_rules.ttl"
+)
+
+# Complex reasoning queries
+reasoning_results = reasoner.reason(
+    queries=[
+        "What are potential attack paths to critical assets?",
+        "Which threat actors have similar TTPs?",
+        "What are the cascading effects of this vulnerability?"
+    ],
+    reasoning_depth=3,
+    include_probabilities=True,
+    generate_explanations=True
+)
+
+# Natural language graph querying with ontological understanding
+querier = SemanticQuerier(
+    kg=kg,
+    ontology=domain_ontology,
+    llm_provider="gpt-4",
+    query_planner=True,
+    result_synthesis=True
+)
+
+results = querier.query(
+    natural_query="Show me all security incidents involving APT groups in the last 6 months that targeted financial institutions",
+    return_subgraph=True,
+    explain_reasoning=True,
+    confidence_scoring=True
+)
+
+# Multi-graph fusion and alignment
+fusion_engine = MultiGraphFusion()
+unified_kg = fusion_engine.fuse_graphs([
+    internal_kg,
+    external_threat_intel_kg,
+    industry_knowledge_kg
+], alignment_strategy="ontology_guided")
+```
+
+### 🎛️ Semantic Routing & Intelligent Orchestration
+
+Advanced request routing with deep contextual understanding:
+
+```python
+from semanticore.routing import (
+    SemanticRouter, 
+    ContextualRouter, 
+    AgentOrchestrator,
+    IntentClassifier,
+    ComplexityAnalyzer
+)
+
+# Multi-dimensional semantic routing
+router = SemanticRouter(
+    ontology=domain_ontology,
+    routing_dimensions=["intent", "domain", "complexity", "urgency", "user_expertise"],
+    learning_enabled=True,          # Learn from routing outcomes
+    load_balancing=True,            # Balance load across handlers
+    circuit_breaker=True,           # Prevent cascade failures
+    semantic_similarity_routing=True # Route based on semantic similarity
+)
+
+# Intent classification with ontological understanding
+intent_classifier = IntentClassifier(
+    ontology=intent_ontology,
+    multi_intent_detection=True,
+    confidence_scoring=True,
+    intent_hierarchy_support=True
+)
+
+# Complexity analysis for optimal routing
+complexity_analyzer = ComplexityAnalyzer(
+    ontology=domain_ontology,
+    complexity_dimensions=["computational", "semantic", "temporal"],
+    resource_estimation=True
+)
+
+# Advanced routing with semantic understanding
+router.add_semantic_route(
+    pattern_ontology_class="ThreatAnalysisRequest",
+    handler=threat_analysis_agent,
+    conditions={
+        "confidence": "> 0.8",
+        "domain_match": "cybersecurity",
+        "complexity_level": "< high",
+        "data_availability": True
+    },
+    preprocessing_steps=["entity_extraction", "context_enhancement"]
+)
+
+# Contextual routing with conversation and user awareness
+contextual_router = ContextualRouter(
+    context_window=10,              # Consider last 10 interactions
+    user_profile_aware=True,        # Adapt to user expertise level
+    session_state_tracking=True,    # Maintain session context
+    semantic_coherence_checking=True, # Ensure semantic consistency
+    adaptive_routing=True           # Adapt routes based on outcomes
+)
+
+# Advanced agent orchestration with semantic coordination
+orchestrator = AgentOrchestrator(
+    agents={
+        "researcher": research_agent,
+        "analyzer": analysis_agent,
+        "synthesizer": synthesis_agent,
+        "validator": validation_agent
+    },
+    coordination_strategy="semantic_handoff",  # Semantic handoffs
+    ontology=coordination_ontology,
+    parallel_execution=True,
+    result_fusion="semantic_weighted",
+    conflict_resolution="ontology_guided"
+)
+
+# Execute complex workflows with semantic understanding
+result = orchestrator.execute_workflow(
+    query="Analyze the security posture of our cloud infrastructure",
+    workflow_ontology="security_analysis_workflow.owl",
+    max_iterations=5,
+    quality_gates=["consistency_check", "completeness_check"],
+    semantic_validation=True
+)
+```
+
+---
+
+## 🎯 Advanced Use Cases
+
+### 🔐 Enterprise Security Operations Center (SOC)
+
+```python
+from semanticore.domains.cybersecurity import (
+    ThreatHuntingAgent, 
+    IncidentAnalyzer, 
+    ThreatIntelligence,
+    SecurityOntologyManager
+)
+
+# Initialize security-specific ontology
+security_onto_manager = SecurityOntologyManager()
+security_ontology = security_onto_manager.load_ontologies([
+    "mitre_attack.owl",
+    "stix_objects.owl", 
+    "cti_ontology.owl",
+    "company_assets.owl"
+])
+
+# Automated threat hunting with ontological reasoning
+threat_hunter = ThreatHuntingAgent(
+    data_sources=[
+        "siem://splunk",
+        "edr://crowdstrike", 
+        "network://zeek_logs"
+    ],
+    ontology=security_ontology,
+    hunting_rules="mitre_attack_patterns.sparql",
+    ml_detection=True,
+    temporal_analysis=True,
+    attribution_modeling=True
+)
+
+# AI-powered incident analysis with semantic understanding
+incident_analyzer = IncidentAnalyzer(
+    knowledge_base=security_kg,
+    ontology=security_ontology,
+    response_playbooks="ontology_driven_playbooks/",
+    escalation_rules="semantic_escalation.owl",
+    auto_response=True,
+    impact_assessment=True,
+    timeline_reconstruction=True
+)
+
+# Threat intelligence with ontological integration
+threat_intel = ThreatIntelligence(
+    feeds=["misp", "taxii", "commercial_feeds"],
+    ontology=security_ontology,
+    ioc_extraction=True,
+    attribution_analysis=True,
+    predictive_analysis=True,
+    threat_landscape_modeling=True,
+    campaign_tracking=True
+)
+
+# Orchestrate SOC operations with semantic coordination
+soc_orchestrator = AgentOrchestrator({
+    "hunter": threat_hunter,
+    "analyzer": incident_analyzer,
+    "intel": threat_intel
+}, ontology=security_ontology)
+
+# Process security events with full semantic understanding
+for event in security_event_stream:
+    # Enrich event with ontological context
+    enriched_event = security_onto_manager.enrich_event(event)
     
-schema:
-  auto_generate: true
-  validation_level: "strict"
-  
-export:
-  formats: ["json", "rdf", "cypher"]
-  knowledge_graph:
-    provider: "neo4j"
-    batch_size: 1000
+    # Analyze with semantic reasoning
+    analysis = soc_orchestrator.process_event(enriched_event)
+    
+    if analysis.risk_score > 8.0:
+        # Generate ontology-driven response
+        response_plan = incident_analyzer.generate_response_plan(
+            analysis, 
+            include_attack_graph=True,
+            suggest_countermeasures=True
+        )
+        incident_analyzer.execute_response(response_plan)
+```
 
-# Load configuration
-from semanticore.config import load_config
-config = load_config("semanticore.yaml")
-core = SemantiCore(config=config)
+### 🧬 Scientific Research & Knowledge Discovery
+
+```python
+from semanticore.domains.research import (
+    LiteratureReviewer, 
+    HypothesisGenerator, 
+    ExperimentDesigner,
+    ScientificOntologyManager
+)
+
+# Comprehensive scientific ontology integration
+science_onto_manager = ScientificOntologyManager()
+research_ontology = science_onto_manager.integrate_ontologies([
+    "gene_ontology.owl",
+    "chemical_entities.owl",
+    "experimental_protocols.owl",
+    "publication_metadata.owl"
+])
+
+# AI-powered literature review with semantic understanding
+lit_reviewer = LiteratureReviewer(
+    databases=["pubmed", "arxiv", "google_scholar", "semantic_scholar"],
+    ontology=research_ontology,
+    search_strategy="semantic_expansion",
+    quality_filtering=True,
+    citation_analysis=True,
+    concept_evolution_tracking=True,
+    cross_domain_discovery=True
+)
+
+# Research hypothesis generation with ontological reasoning
+hypothesis_generator = HypothesisGenerator(
+    domain_knowledge=research_kg,
+    ontology=research_ontology,
+    creativity_level=0.7,
+    feasibility_assessment=True,
+    novelty_scoring=True,
+    ethical_consideration=True,
+    resource_requirement_estimation=True
+)
+
+# Experiment design with ontological validation
+experiment_designer = ExperimentDesigner(
+    methodology_database="protocols_kg",
+    ontology=research_ontology,
+    statistical_planning=True,
+    resource_optimization=True,
+    ethics_compliance=True,
+    reproducibility_enhancement=True
+)
+
+# Comprehensive research workflow
+research_query = "Novel approaches to treating Alzheimer's disease using gene therapy"
+literature_review = lit_reviewer.comprehensive_review(
+    query=research_query,
+    temporal_analysis=True,
+    concept_mapping=True,
+    gap_identification=True
+)
+
+hypotheses = hypothesis_generator.generate_hypotheses(
+    literature_review,
+    reasoning_depth=3,
+    interdisciplinary_connections=True
+)
+
+experiments = experiment_designer.design_experiments(
+    hypotheses[0],
+    optimization_objectives=["validity", "efficiency", "cost"],
+    include_pilot_studies=True
+)
+```
+
+### 📈 Financial Intelligence & Risk Management
+
+```python
+from semanticore.domains.finance import (
+    MarketAnalyzer, 
+    RiskAssessment, 
+    RegulatoryMonitor,
+    FinancialOntologyManager
+)
+
+# Financial domain ontology integration
+finance_onto_manager = FinancialOntologyManager()
+financial_ontology = finance_onto_manager.build_ontology([
+    "financial_instruments.owl",
+    "market_entities.owl",
+    "regulatory_frameworks.owl",
+    "risk_factors.owl"
+])
+
+# Multi-source market analysis with semantic understanding
+market_analyzer = MarketAnalyzer(
+    data_sources=[
+        "bloomberg_api",
+        "reuters_feeds",
+        "sec_filings",
+        "social_sentiment",
+        "economic_indicators"
+    ],
+    ontology=financial_ontology,
+    analysis_models=["technical", "fundamental", "sentiment", "behavioral"],
+    real_time_monitoring=True,
+    cross_asset_correlation=True,
+    regime_detection=True
+)
+
+# Comprehensive risk assessment with ontological reasoning
+risk_assessor = RiskAssessment(
+    ontology=financial_ontology,
+    risk_models=["var", "monte_carlo", "stress_testing", "scenario_analysis"],
+    regulatory_compliance=True,
+    systemic_risk_modeling=True,
+    early_warning_system=True,
+    cascading_effect_analysis=True
+)
+
+# Regulatory compliance with semantic monitoring
+regulatory_monitor = RegulatoryMonitor(
+    jurisdictions=["sec", "finra", "mifid2", "basel_iii"],
+    ontology=financial_ontology,
+    regulation_updates=True,
+    compliance_checking=True,
+    reporting_automation=True,
+    impact_assessment=True
+)
+
+# Financial intelligence orchestration
+financial_intel = AgentOrchestrator({
+    "market": market_analyzer,
+    "risk": risk_assessor,
+    "compliance": regulatory_monitor
+}, ontology=financial_ontology)
+
+# Monitor portfolio with semantic intelligence
+portfolio_analysis = financial_intel.analyze_portfolio(
+    portfolio_data=portfolio_data,
+    include_semantic_insights=True,
+    generate_ontology_report=True,
+    predictive_analysis=True
+)
 ```
 
 ---
 
-## 🏗️ Architecture
+## 🔧 Enterprise Features
 
-SemantiCore follows a modular, extensible architecture:
+### 🏢 Multi-Tenant Ontology Management
 
-```
-┌─────────────────────────────────────────────────────────────┐
-│                    SemantiCore Engine                        │
-├─────────────────────────────────────────────────────────────┤
-│  Connectors    │  Extractors   │  Schema      │  Exporters   │
-│  ┌──────────┐  │  ┌─────────┐  │  ┌────────┐  │  ┌────────┐  │
-│  │   File   │  │  │   NER   │  │  │ Pydantic│  │  │ Neo4j  │  │
-│  │   Web    │  │  │ Relations│  │  │  JSON   │  │  │  RDF   │  │
-│  │   API    │  │  │  Topics  │  │  │  YAML   │  │  │ Vector │  │
-│  │   DB     │  │  │   LLM   │  │  │   TS    │  │  │  DB    │  │
-│  └──────────┘  │  └─────────┘  │  └────────┘  │  └────────┘  │
-├─────────────────────────────────────────────────────────────┤
-│              Validation & Quality Assurance                 │
-├─────────────────────────────────────────────────────────────┤
-│                  Semantic Routing                           │
-└─────────────────────────────────────────────────────────────┘
-```
+```python
+from semanticore.enterprise import (
+    TenantOntologyManager, 
+    SemanticResourceIsolation, 
+    OntologyGovernance
+)
 
----
-
-## 📋 Requirements
-
-- **Python**: 3.8+
-- **Core Dependencies**: spaCy, transformers, pydantic, networkx
-- **Optional Dependencies**:
-  - **LLM Providers**: openai, anthropic, huggingface-hub
-  - **Vector Databases**: faiss-cpu, pinecone-client, weaviate-client
-  - **Graph Databases**: neo4j, rdflib, kuzudb
-  - **Document Processing**: PyMuPDF, python-docx, openpyxl
-
----
-
-## 🛣️ Roadmap
-
-### 🚀 Version 1.0 (Current)
-- ✅ Core semantic extraction engine
-- ✅ Schema generation and validation
-- ✅ Basic connectors (file, web, API)
-- ✅ Neo4j and RDF export
-- ✅ Vector database integration
-
-### 🔮 Version 1.1 (Q3 2025)
-- 🔄 **Web-based visual schema editor**
-- 🔄 **Real-time streaming support** (Kafka, MQTT, WebSockets)
-- 🔄 **Advanced semantic routing** with learning capabilities
-- 🔄 **Multi-modal support** (images, audio, video)
-
-### 🔮 Version 1.2 (Q4 2025)
-- ⏳ **Domain-specific modules** (Legal, Healthcare, Finance)
-- ⏳ **Graph reasoning engine** with inference capabilities
-- ⏳ **Distributed processing** support
-- ⏳ **Model fine-tuning** utilities
-
-### 🔮 Version 2.0 (2026)
-- ⏳ **Custom DSL** for semantic pipeline definition
-- ⏳ **AutoML** for extraction model optimization
-- ⏳ **Federated learning** across distributed deployments
-- ⏳ **Enterprise management** console
-
----
-
-## 🤝 Contributing
-
-We welcome contributions from the community! Here's how you can help:
-
-### 🐛 Report Issues
-Found a bug or have a feature request? [Open an issue](https://github.com/yourusername/semanticore/issues) on GitHub.
-
-### 💻 Contribute Code
-1. Fork the repository
-2. Create a feature branch: `git checkout -b feature/amazing-feature`
-3. Make your changes and add tests
-4. Run the test suite: `pytest`
-5. Commit your changes: `git commit -m 'Add amazing feature'`
-6. Push to the branch: `git push origin feature/amazing-feature`
-7. Open a Pull Request
-
-### 📖 Improve Documentation
-Help us improve our documentation by:
-- Fixing typos and clarifying explanations
-- Adding new examples and use cases
-- Creating tutorials and guides
-- Translating documentation
-
-### 🧪 Testing
-Help us maintain quality by:
-- Writing unit tests for new features
-- Testing on different platforms and Python versions
-- Performance testing and optimization
-- Integration testing with external services
-
----
-
-## 📚 Documentation
-
-- **📖 [Full Documentation](https://semanticore.readthedocs.io/)**
-- **🚀 [Quick Start Guide](https://semanticore.readthedocs.io/quickstart/)**
-- **📋 [API Reference](https://semanticore.readthedocs.io/api/)**
-- **💡 [Examples & Tutorials](https://semanticore.readthedocs.io/examples/)**
-- **🔧 [Configuration Guide](https://semanticore.readthedocs.io/configuration/)**
-
----
-
-## 🏆 Community & Support
-
-- **💬 [Discord Community](https://discord.gg/semanticore)** - Chat with users and developers
-- **📧 [Mailing List](https://groups.google.com/g/semanticore)** - Stay updated with announcements
-- **🐦 [Twitter](https://twitter.com/semanticore)** - Follow us for updates
-- **📺 [YouTube Channel](https://youtube.com/c/semanticore)** - Tutorials and demos
-- **❓ [Stack Overflow](https://stackoverflow.com/questions/tagged/semanticore)** - Get help with specific issues
-
----
-
-## 📄 License
-
-SemantiCore is released under the **MIT License**. See the [LICENSE](LICENSE) file for details.
-
-```
-MIT License
-
-Copyright (c) 2025 SemantiCore Contributors
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
-```
-
----
-
-## 🙏 Acknowledgments
-
-SemantiCore is built on the shoulders of giants. We thank the communities behind:
-
-- **🤗 Hugging Face** - For democratizing NLP and ML
-- **🌶️ spaCy** - For industrial-strength NLP
-- **🔗 Neo4j** - For graph database excellence
-- **🐍 Python** - For being an amazing ecosystem
-- **🧠 OpenAI & Anthropic** - For advancing AI capabilities
-
----
-
-## 📊 Project Stats
-
-![GitHub stars](https://img.shields.io/github/stars/yourusername/semanticore?style=social)
-![GitHub forks](https://img.shields.io/github/forks/yourusername/semanticore?style=social)
-![GitHub issues](https://img.shields.io/github/issues/yourusername/semanticore)
-![GitHub pull requests](https://img.shields.io/github/issues-pr/yourusername/semanticore)
-![PyPI downloads](https://img.shields.io/pypi/dm/semanticore)
-
----
-
-**Ready to transform your unstructured data into intelligent, semantic knowledge?**
-
-```bash
-pip install semanticore
-```
-
-**[Get Started Now →](https://semanticore.readthedocs.io/quickstart/)**
+# Multi-tenant ontology architecture
+tenant_onto_manager = TenantOntologyManager(
+    isolation_level="strict",
+    ontology_sharing_policies=True,
+    version_management=True,
+    access_control=True,
+    cross_tenant_reasoning=False  #