### 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
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[](https://getsemantica.ai/) [](https://docs.getsemantica.ai/) [](https://discord.gg/sV34vps5hH) [](https://x.com/BuildSemantica) [](https://www.youtube.com/watch?v=QfnNZg4-dZA) [](CHANGELOG.md)
---
*Knowledge Explorer · Context Graphs · Reasoning Engine · Decision Intelligence · Ontology Hub*
**[▶ Watch the full platform walkthrough](https://www.youtube.com/watch?v=QfnNZg4-dZA)**
---
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
- **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](#quick-start)** · **[Architecture](#architecture)** · **[What You Get](#what-semantica-gives-you)** · **[Why Semantica](#why-semantica)** · **[Decision Intelligence](#decision-intelligence)** · **[Context Graphs](#context-graphs)** · **[Recipe: Audit Trail](#recipe-audit-trail-for-a-regulated-decision)** · **[Module Reference](#module-reference)** · **[Integrations](#integrations)** · **[CLI](#cli)** · **[Performance](#performance)** · **[Install](#installation)**
---
## 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
- **Graph Analytics:** Centrality, community detection, link prediction, and shortest-path queries over the graph you just built
- **Polyglot Graph Storage:** Native RDF (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
```bash
pip install semantica
```
```python
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:**
```bash
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](https://github.com/semantica-agi/semantica)** · **[Join Discord](https://discord.gg/sV34vps5hH)**
---
## 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:
```
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 (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](ARCHITECTURE.md)**
---
## 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
```
```python
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.
```python
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.
```python
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](#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`](#semanticaingest-multi-source-ingestion) | Files, web, databases, APIs, streams, email, Git, Parquet, Snowflake, MCP |
| [`semantica.semantic_extract`](#semanticasemantic_extract-ner-relations-events-triplets) | NER, relation extraction, event detection, triplet generation |
| [`semantica.kg`](#semanticakg-knowledge-graph-construction--analysis) | Graph construction, centrality, communities, link prediction |
| [`semantica.reasoning`](#semanticareasoning-forward-chaining-rete-datalog-sparql) | Forward chaining, Rete, Datalog, SPARQL, fully explainable |
| [`semantica.vector_store`](#semanticavector_store-hybrid--filtered-semantic-search) | FAISS, Qdrant, Weaviate, Milvus, Pinecone, PgVector, hybrid search |
| [`semantica.split`](#semanticasplit-graphrag-native-document-chunking) | Entity-aware, relation-aware, ontology-aware chunking for GraphRAG |
| [`semantica.provenance`](#semanticaprovenance-w3c-prov-o-lineage) | W3C PROV-O lineage on every fact |
| [`semantica.ontology`](#semanticaontology-owl-generation-shacl-validation) | OWL generation, SHACL validation, SKOS vocabularies |
| [`semantica.conflicts`](#semanticaconflicts-conflict-detection--resolution) | Detect and resolve conflicting facts across sources |
| [`semantica.deduplication`](#semanticadeduplication-entity-resolution-at-scale) | Entity resolution at scale |
| [`semantica.normalize`](#semanticanormalize-data-normalization--cleaning) | Text, entity, date, and number normalization; dataset cleaning |
| [`semantica.pipeline`](#semanticapipeline-pipeline-dsl) | Declarative, parallel pipeline DSL for ingest → extract → build → export |
| [`semantica.export`](#semanticaexport-rdf-owl-parquet-cypher-json-ld) | RDF, OWL, Parquet, Cypher, JSON-LD |
| [`semantica.visualization`](#semanticavisualization-interactive-graph-workbench) | Force-directed graphs, ontology hierarchies, temporal dashboards |
| [Temporal Intelligence](#temporal-intelligence-bi-temporal-graphs--time-travel) | Bi-temporal facts, Allen interval algebra, time travel |
| [Multi-Agent (Agno)](#multi-agent-shared-context-with-agno) | One shared context graph across every agent on a team |
**↓ Expand [Module Reference](#module-reference) below** for every module's working example, or jump to [More Recipes](#more-recipes), the full [Integrations](#integrations) matrix, [MCP tool list](#mcp-server), and [REST endpoints](#rest-api).
---
## 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.
```python
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 · Apache Arrow/Feather/IPC (`ArrowIngestor`)
Elasticsearch and Google Drive ingestion also ship (`ElasticIngestor`, `GDriveIngestor`) but aren't re-exported from the top-level `semantica.ingest` namespace yet — import them directly: `from semantica.ingest.elastic_ingestor import ElasticIngestor`.
semantica.semantic_extract: NER, Relations, Events, Triplets
Extract structured knowledge from raw text in one pass.
```python
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.
```python
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.
```python
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.
```python
# 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"}]
```
```python
# 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.
```python
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.
```python
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.
```python
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.
```python
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.
```python
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.
```python
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.
```python
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.
```python
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.
```python
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.
```python
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.
```python
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.
```python
# 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](https://github.com/semantica-agi/semantica/tree/main/cookbook), each self-contained and runnable in under 5 minutes
---
## More Recipes
The flagship audit-trail recipe is [above](#recipe-audit-trail-for-a-regulated-decision). Here are three more common patterns.
End-to-End GraphRAG Pipeline
```python
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
```python
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](#semanticareasoning-forward-chaining-rete-datalog-sparql) applies — validate against your rule set before production use.
Ontology-to-Knowledge-Graph in One Pass
```python
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)** | 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) · 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](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.
```bash
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](https://docs.getsemantica.ai/)
---
## 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](#mcp-server) below.
Full integrations matrix (editors, MCP clients, REST clients, agentic frameworks)
---
## Contributing
All contributions are welcome: bug fixes, features, tests, and documentation.
1. Fork the repo and create a branch
2. `pip install -e ".[dev]"`
3. Write tests alongside your changes (`pytest tests/`)
4. Open a PR and tag `@KaifAhmad1` for review
See [CONTRIBUTING.md](CONTRIBUTING.md) for full guidelines.
---
MIT License · Built by [Semantica](https://github.com/semantica-agi)
[GitHub](https://github.com/semantica-agi/semantica) ·
[Discord](https://discord.gg/sV34vps5hH) ·
[Twitter/X](https://x.com/BuildSemantica) ·
[Website](https://getsemantica.ai/) ·
[Docs](https://docs.getsemantica.ai/) ·
[PyPI](https://pypi.org/project/semantica/)
If this project helps you build better AI, a star means a lot.
**[⭐ Star on GitHub →](https://github.com/semantica-agi/semantica)**
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### 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
[](https://github.com/semantica-agi/semantica) [](https://github.com/semantica-agi/semantica/network/members) [](https://github.com/semantica-agi/semantica/graphs/contributors) [](https://pypi.org/project/semantica/) [](https://pepy.tech/project/semantica) [](https://www.python.org/) [](https://opensource.org/licenses/MIT) [](https://github.com/semantica-agi/semantica/actions) [](https://deepwiki.com/semantica-agi/semantica)
[](https://getsemantica.ai/) [](https://docs.getsemantica.ai/) [](https://discord.gg/sV34vps5hH) [](https://x.com/BuildSemantica) [](https://www.youtube.com/watch?v=QfnNZg4-dZA) [](CHANGELOG.md)
