### 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)
```bash
pip install semantica
```
---
*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
- **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](#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
- **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
```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 (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](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, Databricks, 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,
)
```
```python
# 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.
```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)** | 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](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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*Knowledge Explorer · Context Graphs · Reasoning Engine · Decision Intelligence · Ontology Hub*
**[▶ Watch the full platform walkthrough](https://www.youtube.com/watch?v=QfnNZg4-dZA)**
