semantica

Tue, 11 Aug 2026 16:23:08 +0800

semantica-agi/semantica

Graph-Native Infrastructure for Context and Accountable AI Systems

The Open Source Palantir for AI Agents

Ingest your enterprise data, extract what matters, build a Context Graph and knowledge graph (KG), and run graph analytics and causal reasoning over all of it, with full decision provenance baked in. Explainable, traceable, and trustworthy by design.

Decision Intelligence · Context Management · Deterministic Reasoning · Ontology Management · Knowledge Modeling · End-to-End Traceability

Open Source · Self-Hostable · Auditable · Governed · Zero Vendor Lock-In

Polyglot Graph Storage · RDF & LPG Support · W3C Standards · Interoperable

Built for High-Stakes, Regulated Domains

`1`	`pip install semantica`

Knowledge Explorer · Context Graphs · Reasoning Engine · Decision Intelligence · Ontology Hub

▶ Watch the full platform walkthrough

Most AI agents act without a trail. They store embeddings, not meaning: context that can’t be explained, decisions that can’t be audited. In lending, that gap is a compliance exposure, not an inconvenience: an underwriting agent’s approval has to survive a regulator’s “why” months later.

Semantica sits underneath your LLM, vector store, and agent framework as a deterministic infrastructure layer: no LLM required for graph construction, reasoning, or provenance.

Who it’s for:

AI/ML platform teams shipping agents that make consequential decisions and need structured, queryable context built from fragmented raw data, not just a vector index
Data platform teams on Databricks or Snowflake who need to turn tables already sitting in Unity Catalog or a Snowflake warehouse into a governed, lineage-tracked knowledge graph, without exporting that data to a third-party SaaS first
Compliance, risk, and audit teams who need a straight answer to “why did the AI do that?” in a format a regulator will actually accept
Regulated enterprises (finance, healthcare, legal, government, defense) that can’t ship a black box, and can’t send their data to someone else’s SaaS to get one
Platform and infra engineers who want the KG, reasoning, and provenance stack self-hosted and swappable, not locked to one vendor’s backend
Data and knowledge engineers building a KG from messy, multi-source data: entities and relationships get extracted, conflicting or contradictory facts are flagged instead of silently overwritten, and duplicates are merged before they turn into noise

Quick Start · Architecture · What You Get · Why Semantica · Decision Intelligence · Context Graphs · Recipe: Audit Trail · Module Reference · Integrations · CLI · Performance · Install

What Semantica Gives You

Context Graphs: A structured, queryable graph of everything your agent knows, decides, and reasons about
Decision Intelligence: Every decision is a first-class object: traceable, searchable by precedent, and causally linked
AI Governance & Ontology: SHACL constraints, conflict detection, compliance rules, OWL generation, and SKOS vocabulary management with a visual editor
Full Auditability: W3C PROV-O provenance on every fact, with audit trails exportable to JSON, CSV, or RDF
Deterministic Reasoning: Forward chaining, Rete network, Datalog, and SPARQL with fully explainable paths, not black boxes
Knowledge Pipeline: Multi-source ingestion, entity-aware chunking, NER/relation/event extraction, and knowledge graph construction, with semantic deduplication and provenance-preserving merges throughout
Enterprise Data Platforms: Native connectors for Databricks (Unity Catalog + Delta Lake, PAT/OAuth M2M auth, catalog/schema/table/lineage introspection) and Snowflake (warehouse/database/schema, key-pair and OAuth auth), so tables already living in your lakehouse or warehouse become graph nodes with provenance, not another export/import hop
Graph Analytics: Centrality, community detection, link prediction, and shortest-path queries over the graph you just built
Polyglot Graph Storage: Native RDF (embedded Oxigraph, Blazegraph, Apache Jena, Eclipse RDF4J via SPARQL) and Labeled Property Graphs (Neo4j, FalkorDB, Apache AGE, AWS Neptune via Cypher), plus vector stores, all swappable without touching your code
Visualization: Explore any graph, ontology, or timeline in an interactive browser workbench
Drop-in Integrations: Native Agno support, a full-featured MCP server, a comprehensive CLI, a REST API, and plugins across major editors

Why Semantica

	Vector DB + RAG	Plain LLM Memory	Semantica
Recall method	Embedding similarity	Token window	Graph traversal + semantic search
Decision history	Not stored	Not stored	First-class queryable objects
Provenance	None	None	W3C PROV-O, source-linked
Reasoning	None	Black box	Forward chain, Rete, Datalog, SPARQL
Conflict detection	Silent overwrite	Silent overwrite	Detected, flagged, resolved
Time travel	No	No	Point-in-time graph snapshots
Compliance export	None	None	PROV-O, SHACL, OWL, RDF
Policy enforcement	None	None	Built-in rule engine + SHACL
Entity resolution	No	No	Blocking + semantic deduplication
Multi-agent context	Separate per agent	Separate per agent	Single shared intelligence layer

Semantica complements your existing stack rather than replacing it. Keep your LLM, vector store, and agent framework exactly as they are; Semantica adds the decision records, causal reasoning, provenance, ontology governance, conflict detection, and audit trails on top. The reasoning engines, KG construction, and provenance layer are fully deterministic; no LLM is required to use them.

Quick Start

`1`	`pip install semantica`

from semantica.context import ContextGraph

graph = ContextGraph(advanced_analytics=True)

# Every agent decision becomes a queryable, auditable knowledge node
decision_id = graph.record_decision(
    category="vendor_selection",
    scenario="Choose cloud provider for HIPAA workload",
    reasoning="AWS offers BAA, mature HIPAA tooling, and existing team expertise",
    outcome="selected_aws",
    confidence=0.93,
)

# Ask "why did this happen?" and get a real, structured answer
chain     = graph.trace_decision_chain(decision_id)       # full causal ancestry
similar   = graph.find_similar_decisions("cloud vendor", max_results=5)  # precedents
impact    = graph.analyze_decision_impact(decision_id)    # downstream influence map
compliant = graph.check_decision_rules({"category": "vendor_selection"})  # policy gate

Verify your install in 5 seconds:

semantica doctor
# Python 3.11.9         pass
# semantica 0.6.0       pass
# faiss vector store    pass
# Config file           pass    ~/.semantica/config.yaml

If Semantica solves a real problem for you, a star helps others find it.

⭐ Star on GitHub · Join Discord

Architecture

Semantica is a real end-to-end pipeline, not a single library with a marketing name. Every stage below is a shipping module, independently importable:

1
2
3

Sources → Ingest → Parse → Normalize → Split → Extract → Conflict Detection → Deduplication
   → Knowledge Graph → [ Ontology · Reasoning · Provenance · Decisions ] → Enriched KG
   → Vector Store + Polyglot Graph Store (RDF & LPG) → Export / Visualize / REST · MCP · CLI

Ingest: files, web, databases, enterprise data platforms (Databricks, Snowflake), cloud (Google Drive, Elasticsearch), streams (Kafka, Kinesis), Git, email, MCP
Parse → Normalize → Split: document parsing, text/entity/date normalization, GraphRAG-native entity-aware chunking
Extract → Conflict Detection → Deduplication: NER, relations, events, triplets; conflicting facts flagged and resolved before they merge
Knowledge Graph: GraphBuilder constructs the graph; bi-temporal facts and full graph analytics (centrality, communities, link prediction) run on top of it
Ontology · Reasoning · Provenance · Decisions: the intelligence layer sitting on the KG, with SHACL/OWL governance, Rete/Datalog/SPARQL inference, W3C PROV-O lineage, and first-class decision records
Storage: polyglot by design, with RDF triple stores (embedded Oxigraph, Blazegraph, Apache Jena, Eclipse RDF4J), Labeled Property Graphs (Neo4j, FalkorDB, Apache AGE, AWS Neptune), and vector stores, all swappable without touching your code
Outputs: export (RDF, OWL, Parquet, Cypher, JSON-LD), interactive visualization, and access via REST API, MCP server, or CLI

→ Full Mermaid diagrams for the pipeline and the decision intelligence lifecycle

Decision Intelligence

Decision Intelligence turns every AI choice from an ephemeral inference into a permanent, auditable, queryable record. It answers “what did your AI decide, why, and what happened next?”: the question regulators and enterprise risk teams ask with increasing urgency.

In Semantica, a decision is not a log line. It is a first-class graph node with a full lifecycle. In regulated domains, every AI decision must be traceable to a source and defensible to an auditor: record_decision() creates a permanent, structured record exportable as W3C PROV-O, the format most compliance frameworks accept for regulator submission.

record_decision()             → stored as a graph node with full structured context
add_causal_relationship()     → linked to upstream causes and downstream effects
find_similar_decisions()      → semantic precedent search across all past decisions
trace_decision_chain()        → full causal ancestry back to root causes
analyze_decision_impact()     → downstream influence map - everything this decision affected
check_decision_rules()        → policy compliance gate against configurable rule sets
export / audit trail          → W3C PROV-O, CSV, or JSON for regulator submission

from semantica.context import ContextGraph

graph = ContextGraph(advanced_analytics=True)

# Record decisions with full structured context
app_id = graph.record_decision(
    category="credit_application",
    scenario="Personal loan, $85k income, 31% DTI, 3yr employment",
    reasoning="Income meets threshold; employment stable; no adverse credit events",
    outcome="proceed_to_underwriting",
    confidence=0.88,
    metadata={"applicant_id": "A-7291"},
)
uw_id = graph.record_decision(
    category="loan_underwriting",
    scenario="Underwriting review for A-7291",
    reasoning="DTI within policy; clean 36-month credit history",
    outcome="approved",
    confidence=0.94,
)
rate_id = graph.record_decision(
    category="interest_rate",
    scenario="Rate assignment for approved loan A-7291",
    outcome="rate_set_8.9pct",
    reasoning="Prime + 2.4% based on risk tier B2",
    confidence=0.99,
)

# Build the auditable causal chain - relationship_type must be one of
# CAUSED, INFLUENCED, or PRECEDENT_FOR
graph.add_causal_relationship(app_id, uw_id,   relationship_type="CAUSED")
graph.add_causal_relationship(uw_id,  rate_id, relationship_type="INFLUENCED")

# Query the intelligence
chain     = graph.trace_decision_chain(rate_id)
similar   = graph.find_similar_decisions("personal loan approval, 31% DTI", max_results=5)
impact    = graph.analyze_decision_impact(uw_id)
compliant = graph.check_decision_rules({"category": "loan_underwriting", "confidence": 0.94})
insights  = graph.get_decision_insights()

Context Graphs

A Context Graph is the structured memory layer that traditional RAG is missing. Instead of flat embeddings that answer “what is similar?”, a Context Graph answers “what is connected, why, and how?” Every entity, relationship, decision, and fact is a first-class node, queryable by graph traversal. Entities link to source documents, decisions link to evidence and consequences, facts carry full provenance, and conflicts are detected, not silently overwritten.

from semantica.context import ContextGraph, AgentContext
from semantica.vector_store import VectorStore

graph = ContextGraph(advanced_analytics=True)

# Add nodes with typed properties
graph.add_node("acme_corp",    "Organization", name="Acme Corp", industry="SaaS")
graph.add_node("alice_chen",   "Person",       name="Alice Chen", role="CTO")
graph.add_node("contract_001", "Contract",     value=2_400_000, currency="USD")

# Add typed, weighted edges (extra kwargs become edge metadata)
graph.add_edge("alice_chen", "acme_corp",    edge_type="works_for",  since="2019-03-01")
graph.add_edge("acme_corp",  "contract_001", edge_type="party_to",   signed="2024-01-15")

# BFS traversal - hop through the graph from any node
neighbors = graph.get_neighbors("acme_corp", hops=2)

# Point-in-time snapshot - the graph as it existed on any past date
snapshot  = graph.state_at("2024-01-01")

# AgentContext - high-level API for agent memory workflows
vs  = VectorStore(backend="faiss")
ctx = AgentContext(vector_store=vs, knowledge_graph=graph)
ctx.store("Alice approved the Acme renewal in Q1 2024", conversation_id="conv_001")
retrieved = ctx.retrieve("who approved the Acme contract?")

Why graph over embeddings: traversal finds connections embeddings miss (a person 3 hops from a contract); every node carries provenance so you can always ask “where did this come from?”; conflicts are flagged before they corrupt your knowledge base; point-in-time snapshots let you replay history without reprocessing.

Recipe: Audit Trail for a Regulated Decision

The flagship pattern: record a causally-linked decision chain, attach provenance to every entity, and export a regulator-ready audit trail.

from semantica.context import ContextGraph
from semantica.provenance import ProvenanceManager
from semantica.export import RDFExporter

graph = ContextGraph(advanced_analytics=True)
prov  = ProvenanceManager(storage_path="./audit.db")

# Record the decision chain
d1 = graph.record_decision(
    category="drug_interaction_check", scenario="Patient P-4821: warfarin + amiodarone co-prescribed",
    reasoning="Amiodarone potentiates warfarin's anticoagulant effect", outcome="flag_for_review", confidence=0.91,
)
d2 = graph.record_decision(
    category="dosage_adjustment", scenario="INR monitoring plan for P-4821",
    reasoning="Reduce warfarin dose per interaction severity; recheck INR in 5 days", outcome="dose_reduced_30pct", confidence=0.87,
)
# relationship_type must be one of CAUSED, INFLUENCED, or PRECEDENT_FOR
graph.add_causal_relationship(d1, d2, relationship_type="CAUSED")

# Track provenance for every entity
prov.track_entity("patient_P4821", source="ehr/medication_orders_2024.json",
                  metadata={"extractor": "NamedEntityRecognizer"})

# Export W3C PROV-O for regulator submission - RDFExporter expects
# {"entities": [...], "relationships": [...]}, so map ContextGraph.to_dict()'s
# {"nodes": [...], "edges": [...]} shape onto it first
graph_dict = graph.to_dict()
kg = {
    "entities": [{"id": n["id"], "type": n["type"], "text": n["content"]} for n in graph_dict["nodes"]],
    "relationships": [
        {"source_id": e["source"], "target_id": e["target"], "type": e["type"]}
        for e in graph_dict["edges"]
    ],
}
RDFExporter().export(kg, "audit_trail.ttl", format="turtle")

More recipes (GraphRAG pipelines, an AML rules engine, ontology-to-KG in one pass) are in More Recipes below.

Explore the Platform

Every module below is independently importable, with working code samples verified against the current source tree; use one or all of them.

Module	What it does
`semantica.ingest`	Files, web, databases, APIs, streams, email, Git, Parquet, Databricks, Snowflake, MCP
`semantica.semantic_extract`	NER, relation extraction, event detection, triplet generation
`semantica.kg`	Graph construction, centrality, communities, link prediction
`semantica.reasoning`	Forward chaining, Rete, Datalog, SPARQL, fully explainable
`semantica.vector_store`	FAISS, Qdrant, Weaviate, Milvus, Pinecone, PgVector, hybrid search
`semantica.split`	Entity-aware, relation-aware, ontology-aware chunking for GraphRAG
`semantica.provenance`	W3C PROV-O lineage on every fact
`semantica.ontology`	OWL generation, SHACL validation, SKOS vocabularies
`semantica.conflicts`	Detect and resolve conflicting facts across sources
`semantica.deduplication`	Entity resolution at scale
`semantica.normalize`	Text, entity, date, and number normalization; dataset cleaning
`semantica.pipeline`	Declarative, parallel pipeline DSL for ingest → extract → build → export
`semantica.export`	RDF, OWL, Parquet, Cypher, JSON-LD
`semantica.visualization`	Force-directed graphs, ontology hierarchies, temporal dashboards
Temporal Intelligence	Bi-temporal facts, Allen interval algebra, time travel
Multi-Agent (Agno)	One shared context graph across every agent on a team

↓ Expand Module Reference below for every module’s working example, or jump to More Recipes, the full Integrations matrix, MCP tool list, and REST endpoints.

Module Reference

Expand any module below for its runnable example.

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,
)

# Enterprise data platforms - pull tables straight out of your lakehouse
# or warehouse, with lineage, instead of exporting to CSV first
from semantica.ingest import DatabricksIngestor, SnowflakeIngestor

# pip install "semantica[db-databricks]"
databricks = DatabricksIngestor(
    host="https://adb-xxx.azuredatabricks.net",
    token="dapi-xxxxxxxx",              # or client_id/client_secret for OAuth M2M
    http_path="/sql/1.0/warehouses/xxxxxxxx",
    catalog="main",
)
customers    = databricks.ingest_table("customers", limit=10_000)
sales        = databricks.ingest_query("SELECT * FROM sales WHERE region = 'EMEA'")
table_lineage = databricks.get_table_lineage("customers", catalog="main", schema="default")  # Unity Catalog lineage

# pip install semantica[db-snowflake]
snowflake = SnowflakeIngestor(
    account="myaccount",
    user="myuser",
    password="mypassword",              # or private_key=... for key-pair; use authenticator="oauth", token=... for OAuth
    warehouse="COMPUTE_WH",
    database="MYDB",
)
orders = snowflake.ingest_table("ORDERS", limit=10_000)

Security Note: Never hardcode credentials (token, password, private_key) in production code; pass them via environment variables (e.g., DATABRICKS_TOKEN, SNOWFLAKE_PASSWORD) or a secrets manager.

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 · Databricks (Unity Catalog + Delta Lake) · Snowflake · Git repositories · Email (IMAP/POP3) · Message streams (Kafka, RabbitMQ, Kinesis, Pulsar) · MCP resources · Apache Arrow/Feather/IPC (ArrowIngestor)

DuckDB, Elasticsearch, Google Drive, HuggingFace, MongoDB, and Pandas ingestion also ship (DuckDBIngestor, ElasticIngestor, GDriveIngestor, HuggingFaceIngestor, MongoIngestor, PandasIngestor) but aren’t re-exported from the top-level semantica.ingest namespace yet — import them directly: from semantica.ingest.duckdb_ingestor import DuckDBIngestor.

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"), ...]

Batch processing across many documents uses ner.process_batch([...]), not a per-call extract_entities_batch on the facade class.

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"}]

Current limitation: ReteEngine’s alpha-node condition matcher is intentionally simple in this release — validate match_patterns() output against your actual rule set before wiring it into a production compliance gate; more selective condition evaluation is on the roadmap.

# 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()
reasoner.add_fact("parent(tom, bob)")
reasoner.add_rule("ancestor(X, Y) :- parent(X, Y)")
result = reasoner.forward_chain()

explainer = ExplanationGenerator()
explanation = explainer.generate_explanation(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

# In-memory backend shown here: HybridSearch and explain_decision() work out of the box.
# Swap backend="qdrant" / "weaviate" / "milvus" / "pinecone" / "pgvector" / "faiss" once you
# scale past a single process — search() and store_decision() work identically on all of them.
vs = VectorStore(backend="inmemory", 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"])

Backends: faiss · qdrant · weaviate · milvus · pinecone · pgvector · sqlite · inmemory

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"},
)

# Track a relationship's provenance - entity linkage travels in metadata
prov.track_relationship(
    relationship_id="alice_works_for_acme",
    source="hr_records/employees_q1_2024.csv",
    metadata={"source_entity_id": "alice_chen", "target_entity_id": "acme_corp"},
)

# 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(valid=True, consistent=True, satisfiable=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(conflicts, strategy="credibility_weighted")  # weighted by source trust
resolved = resolver.resolve_conflicts(conflicts, strategy="most_recent")          # prefer most recent
resolved = resolver.resolve_conflicts(conflicts, strategy="voting")               # majority wins

# Track source credibility over time
tracker = SourceTracker()
tracker.register_source("source_a", source_type="document", credibility_score=0.85)
tracker.register_source("source_b", source_type="document", credibility_score=0.72)

semantica.deduplication: Entity Resolution at Scale

Block, cluster, and merge duplicates with semantic similarity.

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
canonical = 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, 7, 1, tzinfo=UTC)

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

# Deduplicate, validate, and impute missing values across a dataset
clean = DataCleaner().clean_data(records, remove_duplicates=True, handle_missing=True)

semantica.pipeline: Pipeline DSL

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

from semantica.pipeline import PipelineBuilder, ExecutionEngine

builder = PipelineBuilder()

# add_step() returns the created PipelineStep, not the builder, so these don't chain
builder.add_step("ingest",      step_type="ingest",           source="./contracts/", recursive=True)
builder.add_step("extract",     step_type="ner_extract")
builder.add_step("relations",   step_type="relation_extract")
builder.add_step("build_kg",    step_type="kg_build",         merge_entities=True)
builder.add_step("deduplicate", step_type="deduplicate",      threshold=0.75)
builder.add_step("export",      step_type="export",           format="turtle", output="kg.ttl")

# connect_steps() and set_parallelism() return the builder, so these do chain
pipeline = (
    builder
    .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(pipeline)
status   = engine.get_pipeline_status(pipeline.name)
progress = engine.get_progress(pipeline.name)

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,
    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)

# A temporally-bounded edge - valid_from/valid_until define when it held true
graph.add_edge(
    "alice_chen", "acme_corp", edge_type="works_for",
    valid_from="2024-03-01T00:00:00", valid_until="2025-01-01T00:00:00",
)

# 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),
)

# Query facts valid within a time window - query_time_range() expects
# {"relationships": [...]} with source_id/target_id keys, which differs from
# ContextGraph.to_dict()'s {"nodes", "edges"} shape, so map it first
graph_dict = graph.to_dict()
kg_relationships = {
    "relationships": [
        {**e, "source_id": e["source"], "target_id": e["target"]}
        for e in graph_dict["edges"]
    ]
}

tq = TemporalGraphQuery()
facts_in_window = tq.query_time_range(
    kg_relationships, query="valid_facts", start_time="2024-01-01", end_time="2024-12-31"
)

# Normalize natural language temporal expressions - returns a (start, end) range
norm = TemporalNormalizer()
start, end = norm.normalize("last quarter")

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 (writes kg_snapshot_entities.parquet
# and kg_snapshot_relationships.parquet)
ParquetExporter(compression="snappy").export_knowledge_graph(kg, "kg_snapshot")

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

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

# Human-readable HTML report
ReportGenerator().generate_report(
    {"title": "KG Audit Report", "summary": "Weekly ingestion summary", "metrics": {"entities": len(kg["entities"])}},
    file_path="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-4-5"),
    memory=shared.bind_agent("researcher"),
    tools=[AgnoKGToolkit(context=shared)],
)
analyst = Agent(
    name="Analyst",
    model=Claude(id="claude-sonnet-4-5"),
    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 flagship audit-trail recipe is above. 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="inmemory")
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,
    ),
])

# Run the rule across a batch of incoming transactions, not just one
for tx in [
    Fact("tx_101", "transaction", [{"amount": 25_000, "country": "IR"}]),
    Fact("tx_102", "transaction", [{"amount": 4_500,  "country": "DE"}]),
    Fact("tx_103", "transaction", [{"amount": 60_000, "country": "KP"}]),
]:
    rete.add_fact(tx)

flagged = rete.match_patterns()

Same condition-matcher caveat as above applies — validate against your rule set before production use.

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.process_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.valid:
    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 · embedding cache
Semantic Extraction	NER · relation extraction · event detection · triplet generation · coreference
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 · multiple 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 · 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)	Oxigraph (embedded) · Blazegraph · Apache Jena · Eclipse RDF4J · unified `TripletStore` interface · SPARQL query & bulk load
Enterprise Data Platforms	Databricks (`DatabricksIngestor`: Unity Catalog + Delta Lake, PAT/OAuth M2M, table/query ingestion, catalog/schema/table/lineage introspection) · Snowflake (`SnowflakeIngestor`: warehouse/database/schema, password/key-pair/OAuth auth)
LLM Providers	All already supported today: OpenAI (GPT-4o, o1, o3) · Anthropic (Claude) · 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

Performance

Benchmarks from v0.5.0 on a 118,000-node production graph:

Operation	Before	After	Improvement
Node search (118k nodes)	24 ms	0.004 ms	6,000× faster
Embedding cache hit	cold load	revision-based cache	10× throughput
Semantic deduplication	baseline	optimized candidate gen	6.98× faster
Candidate generation	baseline	blocking strategy	63.6% faster

Measured on a 118,000-node production graph (AMD EPYC, 64 GB RAM); the deduplication/candidate-generation figures are historical measurements recorded in CHANGELOG.md rather than an automated tests/ assertion. Results vary by hardware, dataset topology, and backend selection — run pytest tests/vector_store/test_performance_benchmarks.py -s to measure your own data.

CLI

Every capability is available from the terminal. The CLI ships with the package, no separate install required.

pip install semantica
semantica        # startup dashboard
semantica doctor # health check
semantica --help # full grouped command reference

Start with semantica, verify with doctor, build a graph, and explore the command groups from one terminal.

Command groups: ingest · parse · extract · kg · reason · decision · temporal · provenance · ontology · embed · deduplicate · validate · export · visualize · pipeline · server · explorer · mcp · doctor · shell · init · watch

→ Full CLI reference

Integrations

Native plugin bundles for Claude Code, Cursor, Codex, Windsurf, Cline, Continue, VS Code, and OpenClaw; a full-featured MCP server for any MCP-compatible client; a comprehensive REST API; and first-class Agno support for multi-agent shared context. Every major LLM provider is already supported via semantica.llms and LiteLLM: OpenAI, Anthropic, Gemini, Mistral, Llama, Groq, Cohere, Azure, Bedrock, Ollama, DeepSeek, HuggingFace, and more.

MCP setup takes 30 seconds — see MCP Server below.

Full integrations matrix (editors, MCP clients, REST clients, agentic frameworks)

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}

MCP Server

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

1
2
3

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

REST API

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

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

# List recorded decisions
curl "http://localhost:8000/api/decisions?category=vendor_selection"

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

REST endpoints span: enrich (extract) · graph · decisions · reasoning · provenance · ontology · embeddings · search · export · pipeline · temporal · deduplication

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/.

Knowledge Explorer

A browser-based graph workbench. Pan and zoom live graphs, scrub the timeline, review every decision’s causal chain, resolve duplicates, and author your ontology visually. Built on React 19 + Sigma.js.

Workspace	What you can do
Knowledge Graph	Live Sigma.js canvas with ForceAtlas2 layout, Ego Mode, semantic distance heatmap
Timeline	Scrub through temporal events and watch the graph evolve
Decisions	Browse the causal chain behind every recorded decision
Registry	Live audit log of every graph mutation
Entity Resolution	Review and merge duplicates
Ontology Hub	SHACL Studio, visual editor, cross-ontology alignments, SKOS browser
Lineage	W3C PROV-O provenance visualization for any entity

Quickest way to start (no Node.js required):

1
2
3

pip install "semantica[explorer]"
semantica-explorer --graph my_graph.json
# Dashboard opens at http://127.0.0.1:8000

For contributor / dev-server setup: explorer/README.md: Local Setup Guide

What’s New in v0.6.0

Named-Graph Support for JenaStore: Migrated onto rdflib.Dataset(default_union=False), completing cross-backend named-graph parity across Blazegraph, RDF4J, and Jena; add_triplets() gains a graph= option
SPARQL CONSTRUCT Query Templates: Parameterized, injection-safe CONSTRUCT templates extended from Blazegraph-only to RDF4J and Jena, plus pipeline integration via the construct_template step type
Databricks Connector: DatabricksIngestor for Unity Catalog + Delta Lake ingestion, with PAT/OAuth M2M auth, table/query ingestion, and catalog/schema/table/lineage introspection. Install with pip install "semantica[db-databricks]"
SQLite Vector Store Backend: SQLiteVecStore, a disk-backed local vector store on sqlite-vec’s vec0 virtual tables, with Cosine/L2 metrics, metadata filtering, and WAL mode. Install with pip install semantica[vectorstore-sqlite]

→ Full release notes · Changelog

Built for High-Stakes Domains

Semantica is designed for environments where AI outputs must be explainable, auditable, and defensible, and where the data itself can’t leave your infrastructure. Self-hostable with zero vendor lock-in, it’s built as much for organizations handling confidential or classified data as for regulated industries chasing an audit trail:

Finance: Loan underwriting audit trails, fraud detection, AML compliance, regulatory risk knowledge graphs
Healthcare: Clinical decision support, drug interaction graphs, and patient safety audit trails
Legal: Evidence-backed research, contract analysis, case law reasoning, and privilege tracking
Government & Defense: Policy decision records, classified information governance, and regulatory reporting, fully self-hosted with no data leaving your perimeter
Law Enforcement: Case linkage, evidence provenance chains, and investigative knowledge graphs that hold up under legal scrutiny
Cybersecurity: Threat attribution, incident response timelines, and IOC provenance tracking
Autonomous Systems: Decision logs, safety validation, and explainable AI for certification

Installation

1
2

pip install semantica           # core
pip install semantica[all]      # everything

pip install semantica[agno]                 # Agno multi-agent integration
pip install semantica[llm-litellm]          # OpenAI, Anthropic, Gemini, Mistral, Llama, Groq, Cohere, Bedrock, Ollama, DeepSeek, and more
pip install semantica[graph-neo4j]          # Neo4j graph store (LPG)
pip install semantica[graph-falkordb]       # FalkorDB graph store (LPG)
pip install semantica[graph-apache-age]     # Apache AGE graph store (LPG)
pip install semantica[graph-amazon-neptune] # AWS Neptune graph store (LPG)
pip install semantica[tripletstore-oxigraph] # Embedded in-memory/on-disk RDF store
# RDF triple stores (Blazegraph, Apache Jena, Eclipse RDF4J) need no extra:
# semantica.triplet_store talks SPARQL over HTTP using the core `requests` dependency
pip install semantica[vectorstore-qdrant]   # Qdrant vector store
pip install semantica[vectorstore-pinecone] # Pinecone vector store
pip install semantica[db-snowflake]         # Snowflake
pip install semantica[db-databricks]        # Databricks (SDK + SQL connector)
pip install semantica[ingest-parquet]       # Parquet / PyArrow
pip install semantica[ingest-arrow]        # Apache Arrow, Feather, IPC
pip install semantica[viz]                  # HTML interactive visualization
pip install semantica[watch]                # Directory file watcher
pip install semantica[explorer]             # Knowledge Explorer dashboard

For production deployments, use Docker or Kubernetes rather than a local pip install. Set SEMANTICA_SECRET_KEY, configure a persistent LPG graph store (Neo4j / FalkorDB / Apache AGE / AWS Neptune) and/or RDF triple store (Blazegraph / Apache Jena / Eclipse RDF4J), and point the vector store at a hosted backend (Qdrant / Pinecone). See ARCHITECTURE.md for the full deployment topology.

1
2
3

# From source
git clone https://github.com/semantica-agi/semantica.git
cd semantica && pip install -e ".[dev]" && pytest tests/

Enterprise

On-premises deployment · Private cloud · Custom domain implementations · SLA-backed support · Professional services for regulated industries (finance, healthcare, legal, government).

getsemantica.ai for enterprise solutions and pricing.

Community & Support


Discord	discord.gg/sV34vps5hH: real-time help, showcases, and announcements
GitHub Discussions	Q&A and feature requests
GitHub Issues	Bug reports
Documentation	docs.getsemantica.ai
Cookbook	Runnable Jupyter notebooks
Changelog	CHANGELOG.md · Release Notes

Star History

Contributors

Contributing

All contributions are welcome: bug fixes, features, tests, and documentation.

Fork the repo and create a branch
pip install -e ".[dev]"
Write tests alongside your changes (pytest tests/)
Open a PR and tag @KaifAhmad1 for review

See CONTRIBUTING.md for full guidelines.

MIT License · Built by Semantica

GitHub · Discord · Twitter/X · Website · Docs · PyPI

If this project helps you build better AI, a star means a lot.

⭐ Star on GitHub →

English · Deutsch · Français · Español · Italiano · Português · العربية · اردو · हिन्दी · 中文 · 日本語 · 한국어

Context Graphs on Producthunt daily