semantica/PLATFORM_REFERENCE.md

Platform Reference

The full module-by-module API reference, additional recipes, the complete integrations matrix, and the capability table. If you're new to Semantica, start with the README; this doc is the deep end.

Jump to: Module Reference · More Recipes · Features at a Glance · Integrations · MCP Server · REST API · Plugin Bundles

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Module Reference

Every module is independently importable and composable. Below are working examples for each.

semantica.ingest: Multi-Source Ingestion

Ingest from files, web, databases, APIs, streams, email, Git repos, Parquet, Databricks, Snowflake, or MCP servers, all through a unified interface.

from semantica.ingest import FileIngestor, WebIngestor, ParquetIngestor, DBIngestor

# Ingest an entire directory of contracts (PDF, DOCX, HTML, TXT)
docs = FileIngestor().ingest_directory("./contracts/", recursive=True)

# Ingest live web content with robots.txt compliance
pages = WebIngestor().ingest_url("https://example.com/reports/annual-2024.html")

# Ingest structured data from Parquet with Snappy compression
records = ParquetIngestor().ingest("./data/transactions.parquet")

# Ingest from a SQL database - specify which tables to pull
rows = DBIngestor().ingest_database(
    connection_string="postgresql://user:pass@localhost/mydb",
    include_tables=["customer_events"],
    max_rows_per_table=50_000,
)

Supported sources: Local files (PDF, DOCX, PPTX, HTML, TXT, CSV, JSON, YAML, Excel, XML) · Web pages · RSS/Atom feeds · REST APIs · Databases (PostgreSQL, MySQL, SQLite, Oracle, SQL Server) · Parquet datasets · Snowflake · Git repositories · Email (IMAP/POP3) · Message streams (Kafka, RabbitMQ, Kinesis, Pulsar) · MCP resources

---

semantica.semantic_extract: NER, Relations, Events, Triplets

Extract structured knowledge from raw text in one pass.

from semantica.semantic_extract import (
    NamedEntityRecognizer,
    RelationExtractor,
    EventDetector,
    TripletExtractor,
)

text = """
Anthropic CEO Dario Amodei announced a $7.3B Series E funding round in partnership
with Google and Spark Capital, valuing the company at $61.5B as of Q4 2024.
"""

# Named entity recognition with confidence thresholding
ner = NamedEntityRecognizer(confidence_threshold=0.7)
entities = ner.extract_entities(text)
# → [Entity(name="Dario Amodei", type="PERSON"), Entity(name="Anthropic", type="ORG"),
#    Entity(name="Google", type="ORG"), Entity(name="$7.3B", type="MONEY"), ...]

# Relationship extraction - bidirectional support
rel_extractor = RelationExtractor(confidence_threshold=0.6, bidirectional=True)
relations = rel_extractor.extract_relations(text, entities=entities)
# → [Relation(subject="Dario Amodei", predicate="ceo_of", object="Anthropic"),
#    Relation(subject="Anthropic", predicate="raised", object="$7.3B Series E"), ...]

# Event detection with temporal processing
events = EventDetector(extract_participants=True, extract_time=True).detect_events(text)
# → [Event(type="FUNDING", participants=["Anthropic","Google","Spark Capital"],
#          amount="$7.3B", date="Q4 2024")]

# RDF triplets with optional provenance metadata
triplets = TripletExtractor(include_temporal=True, include_provenance=True).extract_triplets(text)
# → [("Anthropic", "valuation", "$61.5B"), ("Dario Amodei", "is_ceo_of", "Anthropic"), ...]

---

semantica.kg: Knowledge Graph Construction & Analysis

Build a production knowledge graph from documents and run graph algorithms over it.

from semantica.ingest import FileIngestor
from semantica.kg import (
    GraphBuilder,
    GraphAnalyzer,
    CentralityCalculator,
    CommunityDetector,
    PathFinder,
    LinkPredictor,
    BiTemporalFact,
)
from datetime import datetime

# Build KG - merge duplicate entities, track temporal edges
sources = FileIngestor().ingest_directory("./contracts/", recursive=True)
kg = GraphBuilder(merge_entities=True, enable_temporal=True).build(sources)

# Graph analytics
analyzer    = GraphAnalyzer()
analysis    = analyzer.analyze_graph(kg)             # full graph metrics

centrality  = CentralityCalculator()
degree      = centrality.calculate_degree_centrality(kg)    # most-connected entities
betweenness = centrality.calculate_betweenness_centrality(kg)

communities = CommunityDetector().detect_communities(kg, method="louvain")  # natural clusters
path        = PathFinder().find_shortest_path(kg, "alice_chen", "contract_001")
predictions = LinkPredictor().predict_links(kg, top_k=10)   # relationship predictions

# Bi-temporal facts - track valid time vs. recorded time independently
fact = BiTemporalFact(
    valid_from=datetime(2024, 3, 1),
    valid_until=datetime(2025, 1, 1),
    recorded_at=datetime(2024, 3, 5),
)

---

semantica.reasoning: Forward Chaining, Rete, Datalog, SPARQL

Run explainable rule-based inference, not a black box.

from semantica.reasoning import ReteEngine, Rule, Fact, RuleType

rete = ReteEngine()
rete.build_network([
    Rule(
        rule_id="aml_flag",
        name="Flag high-risk transactions",
        conditions=[
            {"field": "amount",  "operator": ">",  "value": 10_000},
            {"field": "country", "operator": "in", "value": ["IR", "KP", "SY"]},
        ],
        conclusion="flag_for_compliance_review",
        rule_type=RuleType.IMPLICATION,
    ),
    Rule(
        rule_id="velocity_check",
        name="Flag rapid sequential transfers",
        conditions=[
            {"field": "transfers_in_1h", "operator": ">", "value": 5},
            {"field": "total_amount",    "operator": ">", "value": 50_000},
        ],
        conclusion="flag_velocity_breach",
        rule_type=RuleType.IMPLICATION,
    ),
])

rete.add_fact(Fact("tx_001", "transaction", [{"amount": 15_000, "country": "IR"}]))
flagged = rete.match_patterns()
# → [{"rule": "aml_flag", "matched_facts": ["tx_001"], "conclusion": "flag_for_compliance_review"}]

# Recursive Datalog - natural language for graph queries
from semantica.reasoning import DatalogReasoner

engine = DatalogReasoner()
engine.add_fact("parent(tom, bob)")
engine.add_fact("parent(bob, ann)")
engine.add_fact("parent(ann, pat)")
engine.add_rule("ancestor(X, Y) :- parent(X, Y).")
engine.add_rule("ancestor(X, Z) :- parent(X, Y), ancestor(Y, Z).")
ancestors = engine.query("ancestor(tom, ?X)")
# → [{"X": "bob"}, {"X": "ann"}, {"X": "pat"}]

# Explainable reasoning - trace the path, not just the answer
from semantica.reasoning import ExplanationGenerator, Reasoner

reasoner = Reasoner()
result   = reasoner.infer(kg, rules=[...])

explainer = ExplanationGenerator()
explanation = explainer.generate(result)
# → Explanation(conclusion="...", steps=[ReasoningStep(...)], justification=Justification(...))

---

semantica.vector_store: Hybrid & Filtered Semantic Search

Drop-in vector store with multiple backends, hybrid search, and decision-aware retrieval.

from semantica.vector_store import VectorStore, HybridSearch

# Works with FAISS, Qdrant, Weaviate, Milvus, Pinecone, PgVector, or in-memory
vs = VectorStore(backend="qdrant", dimension=1536)

# Store a decision with scenario description and outcome
vs.store_decision(
    scenario="Personal loan A-7291, $85k income, 31% DTI, 3yr employment",
    outcome="approved",
    confidence=0.94,
    category="loan_underwriting",
)

# Semantic similarity search
results = vs.search(
    query="personal loan approval with low DTI",
    limit=10,
)

# Hybrid search - dense + sparse retrieval in one pass with RRF fusion
hs   = HybridSearch(vector_store=vs)
hits = hs.search("high-risk transactions 2024")

# Explain why a decision was retrieved
explanation = vs.explain_decision(results[0]["id"])

---

semantica.split: GraphRAG-Native Document Chunking

KG-aware splitting that preserves entity boundaries, relation triplets, and ontology concepts, essential for GraphRAG pipelines.

from semantica.split import TextSplitter, EntityAwareChunker, RelationAwareChunker

text = open("contracts/master_agreement.txt").read()

# Standard recursive chunking
chunks = TextSplitter(method="recursive", chunk_size=1000, chunk_overlap=200).split(text)

# Entity-aware chunking - never splits a named entity across chunks (GraphRAG)
chunks = TextSplitter(method="entity_aware", ner_method="llm", chunk_size=1000).split(text)

# Relation-aware chunking - preserves (subject, predicate, object) triplets intact
chunks = RelationAwareChunker(chunk_size=1000, preserve_triplets=True).chunk(text)

# Graph-based chunking - uses centrality to find natural community boundaries
chunks = TextSplitter(method="graph_based", chunk_size=1000).split(text)

# Hierarchical chunking - multi-level (section → paragraph → sentence)
chunks = TextSplitter(method="hierarchical", levels=["section", "paragraph"]).split(text)

Supported methods: recursive · token · sentence · paragraph · semantic_transformer · entity_aware · relation_aware · graph_based · ontology_aware · hierarchical · community_detection · centrality_based · llm

---

semantica.provenance: W3C PROV-O Lineage

Every fact is linked to its source. No black boxes, no mystery outputs.

from semantica.provenance import ProvenanceManager

prov = ProvenanceManager(storage_path="./provenance.db")

# Track where every entity came from
prov.track_entity(
    entity_id="acme_corp",
    source="contracts/acme_master_agreement_2024.pdf",
    metadata={"page": 1, "confidence": 0.97, "extractor": "NamedEntityRecognizer"},
)

prov.track_relationship(
    relationship_id="alice_works_for_acme",
    source_entity_id="alice_chen",
    target_entity_id="acme_corp",
    source="hr_records/employees_q1_2024.csv",
)

# Answer "where did this come from?"
lineage = prov.get_lineage("acme_corp")
trail   = prov.trace_lineage("alice_chen")   # full ancestor chain
entry   = prov.get_provenance("acme_corp")

---

semantica.ontology: OWL Generation, SHACL Validation

Generate ontologies from data, validate shapes, and manage your vocabulary.

from semantica.ontology import OntologyGenerator, OntologyValidator

data = {
    "entities": [
        {"id": "acme_corp",  "type": "Organization", "industry": "SaaS", "founded": 2012},
        {"id": "alice_chen", "type": "Person",        "role": "CTO",     "since": 2019},
    ],
    "relationships": [
        {"source": "alice_chen", "target": "acme_corp", "type": "works_for"},
    ],
}

gen       = OntologyGenerator(base_uri="https://semantica.dev/ontology/")
ontology  = gen.generate_ontology(data)
classes   = gen.infer_classes(data)
props     = gen.infer_properties(data, classes)
optimized = gen.optimize_ontology(ontology)

# Validate against SHACL shapes
validator = OntologyValidator()
report    = validator.validate(ontology)
# → ValidationResult(conforms=True, errors=[], warnings=[])

---

semantica.conflicts: Conflict Detection & Resolution

Detect and resolve conflicting facts from multiple sources before they corrupt your knowledge base.

from semantica.conflicts import ConflictDetector, ConflictResolver, SourceTracker

entities_from_source_a = [
    {"id": "alice_chen", "role": "CTO",   "salary": 250_000, "start_date": "2019-03-01"},
]
entities_from_source_b = [
    {"id": "alice_chen", "role": "VP Eng", "salary": 275_000, "start_date": "2019-03-01"},
]

# Detect all conflict types: value, type, relationship, temporal, logical
detector   = ConflictDetector()
conflicts  = detector.detect_conflicts(entities_from_source_a + entities_from_source_b)
# → [Conflict(entity="alice_chen", field="role",   values=["CTO","VP Eng"], severity="HIGH"),
#    Conflict(entity="alice_chen", field="salary",  values=[250000,275000],   severity="MEDIUM")]

# Resolve using multiple strategies
resolver = ConflictResolver()
resolved = resolver.resolve(conflicts, strategy="credibility_weighted")  # weighted by source trust
resolved = resolver.resolve(conflicts, strategy="temporal")              # prefer most recent
resolved = resolver.resolve(conflicts, strategy="voting")                # majority wins

# Track source credibility over time
tracker = SourceTracker()
tracker.track("source_a", credibility=0.85)
tracker.track("source_b", credibility=0.72)

---

semantica.deduplication: Entity Resolution at Scale

Block, cluster, and merge duplicates with semantic similarity. 6.98× faster than baseline.

from semantica.deduplication import DuplicateDetector, EntityMerger

entities = [
    {"id": "e1", "name": "Acme Corporation",  "domain": "acme.com"},
    {"id": "e2", "name": "Acme Corp.",         "domain": "acme.com"},
    {"id": "e3", "name": "ACME Corp",          "domain": "acme.co"},
    {"id": "e4", "name": "Globex Industries",  "domain": "globex.com"},
]

detector   = DuplicateDetector(similarity_threshold=0.75, use_clustering=True)
candidates = detector.detect_duplicates(entities)
groups     = detector.detect_duplicate_groups(entities)
# → DuplicateGroup(entities=["e1","e2","e3"], confidence=0.91, strategy="semantic+blocking")

merger  = EntityMerger(preserve_provenance=True)
ops     = merger.merge_duplicates(entities, strategy="keep_most_complete")
history = merger.get_merge_history()

---

semantica.normalize: Data Normalization & Cleaning

Standardize text, entities, dates, numbers, and encodings before building your knowledge graph.

from semantica.normalize import (
    TextNormalizer,
    EntityNormalizer,
    DateNormalizer,
    NumberNormalizer,
    DataCleaner,
)

# Unicode, whitespace, casing, HTML tags, smart quotes
text  = TextNormalizer().normalize("  Acme Corp.’s Q4 report…  ")
# → "Acme Corp.'s Q4 report..."

# Alias resolution + entity disambiguation with confidence scores
names = EntityNormalizer().normalize_entity("ACME Corp.")
# → NormalizedEntity(canonical="Acme Corporation", type="Organization", confidence=0.91)

# Natural language date parsing with timezone conversion
dt    = DateNormalizer().normalize_date("3 weeks ago")
# → datetime(2026, 5, 22, tzinfo=UTC)

# Unit conversion and currency normalization
price = NumberNormalizer().normalize("$1.25M USD")
# → NormalizedNumber(value=1_250_000, currency="USD")

# Deduplicate and impute missing values across a dataset
clean = DataCleaner().clean(records, dedup_threshold=0.9, fill_missing="mean")

---

semantica.pipeline: Pipeline DSL

Compose ingestion, extraction, and graph-building into a declarative, parallel pipeline.

from semantica.pipeline import PipelineBuilder, ExecutionEngine

pipeline = (
    PipelineBuilder()
    .add_step("ingest",      step_type="ingest",           source="./contracts/", recursive=True)
    .add_step("extract",     step_type="ner_extract")
    .add_step("relations",   step_type="relation_extract")
    .add_step("build_kg",    step_type="kg_build",         merge_entities=True)
    .add_step("deduplicate", step_type="deduplicate",      threshold=0.75)
    .add_step("export",      step_type="export",           format="turtle", output="kg.ttl")
    .connect_steps("ingest",      "extract")
    .connect_steps("extract",     "relations")
    .connect_steps("relations",   "build_kg")
    .connect_steps("build_kg",    "deduplicate")
    .connect_steps("deduplicate", "export")
    .set_parallelism(4)
    .build(name="contracts_pipeline")
)

engine   = ExecutionEngine()
result   = engine.execute(pipeline)
status   = engine.get_status(pipeline)
progress = engine.get_progress(pipeline)

---

Temporal Intelligence: Bi-Temporal Graphs & Time Travel

Track when facts were true in the world vs. when they were recorded, and query either axis.

from semantica.context import ContextGraph
from semantica.kg import (
    BiTemporalFact,
    TemporalGraphQuery,
    TemporalVersionManager,
    TemporalNormalizer,
)
from datetime import datetime

graph = ContextGraph(advanced_analytics=True)
graph.add_node("alice_chen", "Person",       role="VP Engineering")
graph.add_node("acme_corp",  "Organization", valuation=1_200_000_000)

# Point-in-time snapshots - replay history without reprocessing
snapshot_2023 = graph.state_at("2023-06-01")
snapshot_2024 = graph.state_at("2024-01-01")

# Bi-temporal facts - valid_time is when true in the world;
# recorded_at is when you learned about it
fact = BiTemporalFact(
    valid_from=datetime(2024, 3, 1),
    valid_until=datetime(2025, 1, 1),
    recorded_at=datetime(2024, 3, 5),
)

# Allen interval algebra - 13 temporal relations (before, during, overlaps, etc.)
tq = TemporalGraphQuery(graph)
facts_in_window = tq.query_time_range("2024-01-01", "2024-12-31")

# Normalize natural language temporal expressions
norm = TemporalNormalizer()
dt   = norm.normalize("last quarter")  # → datetime range for Q1 2026

---

semantica.export: RDF, OWL, Parquet, Cypher, JSON-LD

Export to any format required by regulators, graph databases, or downstream systems.

from semantica.export import (
    RDFExporter,
    JSONExporter,
    ParquetExporter,
    LPGExporter,
    ReportGenerator,
)

kg = {"entities": [...], "relationships": [...]}

rdf = RDFExporter()
turtle_str = rdf.export_to_rdf(kg, format="turtle")     # returns string
jsonld_str = rdf.export_to_rdf(kg, format="json-ld")

rdf.export(kg, "kg_audit.ttl",    format="turtle")
rdf.export(kg, "kg_audit.jsonld", format="json-ld")
rdf.export(kg, "kg_audit.nt",     format="n-triples")

# Columnar analytics - Snappy-compressed Parquet
ParquetExporter().export(kg, "kg_snapshot.parquet", compression="snappy")

# JSON knowledge graph
JSONExporter().export_knowledge_graph(kg, "kg.json")

# Neo4j / Memgraph Cypher statements for graph database import
LPGExporter().export(kg, "kg_import.cypher", method="cypher")

# Human-readable HTML / Markdown report
ReportGenerator().generate(kg, "audit_report.html", format="html")

---

semantica.visualization: Interactive Graph Workbench

Render force-directed graphs, community maps, ontology hierarchies, and temporal dashboards.

from semantica.visualization import (
    KGVisualizer,
    OntologyVisualizer,
    EmbeddingVisualizer,
    TemporalVisualizer,
)
import numpy as np

kg = {"entities": [...], "relationships": [...]}

# Interactive force-directed graph (opens in browser)
viz = KGVisualizer(layout="force", color_scheme="default")
viz.visualize_network(kg, output="interactive", file_path="kg.html")
viz.visualize_communities(kg, communities, output="interactive")
viz.visualize_centrality(kg, centrality, centrality_type="degree")
viz.visualize_entity_types(kg, output="html", file_path="entity_types.html")

# Ontology class hierarchy
OntologyVisualizer().visualize_hierarchy(ontology, output="interactive")

# 2D embedding projection (UMAP / t-SNE / PCA)
EmbeddingVisualizer().visualize_2d_projection(
    embeddings=np.array([...]),
    labels=["entity_a", "entity_b"],
    method="umap",
)

# Timeline scrubber - watch the graph evolve
TemporalVisualizer().visualize_timeline(kg, output="interactive")

---

Multi-Agent Shared Context with Agno

One shared intelligence layer. All agents read and write to the same context graph.

# pip install semantica[agno]
from agno.agent import Agent
from agno.team import Team
from agno.models.anthropic import Claude
from semantica.context import ContextGraph
from semantica.vector_store import VectorStore
from integrations.agno import AgnoSharedContext, AgnoDecisionKit, AgnoKGToolkit

shared = AgnoSharedContext(
    vector_store=VectorStore(backend="faiss"),
    knowledge_graph=ContextGraph(advanced_analytics=True),
    decision_tracking=True,
)

researcher = Agent(
    name="Researcher",
    model=Claude(id="claude-sonnet-4-6"),
    memory=shared.bind_agent("researcher"),
    tools=[AgnoKGToolkit(context=shared)],
)
analyst = Agent(
    name="Analyst",
    model=Claude(id="claude-sonnet-4-6"),
    memory=shared.bind_agent("analyst"),
    tools=[AgnoDecisionKit(context=shared)],
)

team = Team(agents=[researcher, analyst], mode="coordinate")
# Researcher's findings are instantly available to the Analyst - no copy, no sync

→ runnable notebooks in the cookbook, each self-contained and runnable in under 5 minutes

---

More Recipes

The README covers the flagship audit-trail recipe. Here are three more common patterns.

End-to-End GraphRAG Pipeline

from semantica.ingest import FileIngestor
from semantica.split import TextSplitter
from semantica.semantic_extract import NamedEntityRecognizer, RelationExtractor
from semantica.kg import GraphBuilder
from semantica.vector_store import VectorStore, HybridSearch
from semantica.context import AgentContext

# 1. Ingest
docs = FileIngestor().ingest_directory("./docs/", recursive=True)

# 2. Entity-aware chunking - never splits an entity across a chunk boundary
splitter = TextSplitter(method="entity_aware", chunk_size=1000)
chunks   = [splitter.split(doc["text"]) for doc in docs]

# 3. Extract entities and relations
ner      = NamedEntityRecognizer(confidence_threshold=0.7)
rel_ext  = RelationExtractor(confidence_threshold=0.6)
entities = [ner.extract_entities(chunk) for chunk_group in chunks for chunk in chunk_group]

# 4. Build KG
kg = GraphBuilder(merge_entities=True, enable_temporal=True).build(docs)

# 5. Hybrid retrieval
vs  = VectorStore(backend="faiss")
ctx = AgentContext(vector_store=vs, knowledge_graph=kg)
ctx.store("Alice approved the Acme renewal in Q1 2024", conversation_id="c1")

results = HybridSearch(vector_store=vs).search("who approved the renewal?")

---

AML Rules Engine

from semantica.reasoning import ReteEngine, Rule, Fact, RuleType

rete = ReteEngine()
rete.build_network([
    Rule(
        rule_id="sanctions_check",
        name="Flag sanctioned-country transactions",
        conditions=[
            {"field": "amount",  "operator": ">",  "value": 10_000},
            {"field": "country", "operator": "in", "value": ["IR", "KP", "SY", "CU"]},
        ],
        conclusion="flag_for_compliance_review",
        rule_type=RuleType.IMPLICATION,
    ),
])
rete.add_fact(Fact("tx_99", "transaction", [{"amount": 25_000, "country": "IR"}]))
matches = rete.match_patterns()
# → [{"rule": "sanctions_check", "matched_facts": ["tx_99"],
#     "conclusion": "flag_for_compliance_review"}]

---

Ontology-to-Knowledge-Graph in One Pass

from semantica.ingest import FileIngestor
from semantica.semantic_extract import NamedEntityRecognizer, RelationExtractor
from semantica.kg import GraphBuilder
from semantica.ontology import OntologyGenerator, OntologyValidator
from semantica.export import RDFExporter

sources   = FileIngestor().ingest_directory("./contracts/")
ner       = NamedEntityRecognizer(confidence_threshold=0.7)
entities  = ner.extract_entities_batch([s["text"] for s in sources])

kg  = GraphBuilder(merge_entities=True).build(sources)
gen = OntologyGenerator(base_uri="https://myco.dev/ontology/")
ont = gen.generate_ontology({"entities": entities[0], "relationships": []})

report = OntologyValidator().validate(ont)
if report.conforms:
    RDFExporter().export({"entities": entities[0]}, "ontology.ttl", format="turtle")

---

Features at a Glance

Capability	Highlights
Context Graphs	Queryable graph of entities, decisions, relationships; causal links; cross-graph navigation
Decision Intelligence	record_decision · trace_decision_chain · find_similar_decisions · analyze_decision_impact · check_decision_rules
Temporal Intelligence	Point-in-time snapshots · Allen interval algebra (13 relations) · TemporalNormalizer · bi-temporal provenance
Distance Intelligence	N×N semantic distance matrices · ego-mode visualization · distance bands · 10× embedding cache
Semantic Extraction	NER · relation extraction · event detection · triplet generation · coreference · 6.98× faster dedup
Reasoning Engines	Forward chaining · Rete · deductive · abductive · SPARQL · Datalog with explainable output
GraphRAG Chunking	Entity-aware · relation-aware · graph-based · ontology-aware · community-detection chunking
Conflict Detection	Value / type / relationship / temporal / logical conflicts · 5 resolution strategies
Provenance	W3C PROV-O · every fact traced to source · audit log export JSON/CSV/RDF
Ontology Hub	SHACL Studio · visual editor · cross-ontology alignments · 5-dimension health dashboard
Vector Store	FAISS · Pinecone · Weaviate · Qdrant · Milvus · PgVector · hybrid + filtered search
Graph Databases (LPG)	Neo4j · FalkorDB · Apache AGE · AWS Neptune
Triple Stores (RDF)	Blazegraph · Apache Jena · Eclipse RDF4J · unified TripletStore interface · SPARQL query & bulk load
LLM Providers	All already supported today: OpenAI (GPT-4o, o1, o3) · Anthropic (Claude 4) · Google Gemini · Mistral · Meta Llama · Groq · Cohere · Azure OpenAI · AWS Bedrock · Ollama · DeepSeek · Perplexity · Together AI · Fireworks AI · Replicate · HuggingFace · via semantica.llms and LiteLLM

---

Integrations

Native plugin bundles across major editors, a full-featured MCP server, a comprehensive REST API, and first-class Agno support. All LLM providers already supported: OpenAI · Anthropic · Gemini · Mistral · Llama · Groq · Cohere · Azure · Bedrock · Ollama · DeepSeek · HuggingFace and more via LiteLLM

Native Plugin Bundle			MCP Server + Plugin
Claude Code Skills · agents · hooks	Cursor Skills · agents	Codex CLI Skills · agents	Windsurf plugin	Cline plugin	Continue plugin	VS Code plugin	OpenClaw MCP + plugin
MCP Server	REST API
Claude Desktop MCP server	GitHub Copilot REST API	Roo Code REST API	Goose REST API	Kilo Code REST API	Aider REST API	Amazon Q REST API	Zed REST API

Agentic Frameworks

Native Integration
Agno First-class · pip install semantica[agno]
Already Supported via REST API & MCP
LangChain REST API · MCP	LangGraph REST API · MCP	CrewAI REST API · MCP	LlamaIndex REST API · MCP	AutoGen REST API · MCP	OpenAI Agents REST API · MCP	Google ADK REST API · MCP
Native SDK Integration (Coming Soon)
LangChain Dedicated toolkit	CrewAI Dedicated toolkit	LlamaIndex Dedicated toolkit	AutoGen Dedicated toolkit	OpenAI Agents Dedicated toolkit	Google ADK Dedicated toolkit

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MCP Server

Connect any MCP-compatible client (Claude Desktop, Windsurf, Cline, VS Code) in 30 seconds:

python -m semantica.mcp_server
# or via the installed entry point
semantica-mcp

{
  "mcpServers": {
    "semantica": { "command": "python", "args": ["-m", "semantica.mcp_server"] }
  }
}

Tools exposed over MCP:

Tool	What it does
extract_entities	NER on any text
extract_relations	Relation extraction
record_decision	Persist a decision node
query_decisions	Search decision history
find_precedents	Semantic precedent lookup
get_causal_chain	Full causal ancestry
add_entity	Add a KG node
add_relationship	Add a KG edge
run_reasoning	Execute rule set
get_graph_analytics	Centrality, communities
export_graph	Export to RDF/JSON/Parquet
get_graph_summary	Graph statistics

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REST API

# Start the backend
python -m semantica.server   # port 8000

# Extract entities via REST
curl -X POST http://localhost:8000/api/extract/entities \
  -H "Content-Type: application/json" \
  -d '{"text": "Apple CEO Tim Cook announced record earnings."}'

# Record a decision
curl -X POST http://localhost:8000/api/decisions \
  -H "Content-Type: application/json" \
  -d '{
    "category": "vendor_selection",
    "scenario": "Choose ML cloud provider",
    "reasoning": "Best GPU availability and pricing",
    "outcome": "selected_aws",
    "confidence": 0.91
  }'

# Query the knowledge graph
curl http://localhost:8000/api/graph/neighbors/acme_corp?hops=2

REST endpoints span: extract · kg · decisions · reasoning · provenance · ontology · embeddings · search · export · pipeline · temporal · deduplication

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Plugin Bundles

Domain skills: extract · ingest · query · ontology · validate · deduplicate · embed · reason · decision · causal · temporal · provenance · policy · explain · export · change · visualize

Specialized agents: kg-assistant · decision-advisor · explainability

Bundles for Claude Code, Cursor, Codex, Windsurf, Cline, Continue, VS Code, and OpenClaw in plugins/.

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