v0.6.6Open SourceReady

Context Graph &Decision Intelligence
Engine for  AI Agents, LangGraph Workflows, Claude Code, CrewAI Teams, LlamaIndex Pipelines, AutoGen, Production AI, Enterprise AI, Any AI Framework

The Glass-Box Alternative to Black-Box Intelligence

The open-source, graph-native infrastructure that gives AI systems structured context, causal reasoning, and full decision provenance— so every answer can be traced back to why it was given.

$pipinstallsemantica
Open Source

Built in the open. Trusted in production.

MIT licensed. Zero lock-in. Fully auditable from day one.

Loved by developers at
BDT & MSD PartnersPrivate Merchant Bank · $50B+ AUM
AusgridEnergy Infrastructure · 4M+ Customers
Siemens logoIndustrial Technology · Global Engineering
Regulatory StartupsMarine Ecology · Climate Tech · HealthTech · LegalTech
Planetary IntelligenceGeospatial Risk · Earth Observation · Climate Resilience
Open Source CommunityMIT licensed · Free forever
BDT & MSD PartnersPrivate Merchant Bank · $50B+ AUM
AusgridEnergy Infrastructure · 4M+ Customers
Siemens logoIndustrial Technology · Global Engineering
Regulatory StartupsMarine Ecology · Climate Tech · HealthTech · LegalTech
Planetary IntelligenceGeospatial Risk · Earth Observation · Climate Resilience
Open Source CommunityMIT licensed · Free forever
semantica-agi%2Fsemantica | Trendshift
Platform Tour

See Semantica
in Action

A complete walkthrough of Knowledge Explorer, Context Graphs, Reasoning Engine, Decision Intelligence, and Ontology Hub.

getsemantica.ai/demo
v0.6.6
Live Demo
1:53
Knowledge ExplorerContext GraphsReasoning EngineDecision IntelligenceOntology Hub
Explainability, Precisely Scoped

System-level explainability,
not foundation-model explainability

Semantica doesn't expose the LLM's internal reasoning. It explains what's outside the model.

What Semantica Explains

System-level explainability: everything outside the model, recorded as a first-class, auditable object.

  • Context fed into the model
  • The decision produced
  • Full provenance & lineage
  • Complete audit trail

What Semantica Doesn't Explain

Foundation-model explainability: Semantica doesn't expose or reconstruct an LLM's internal reasoning, which stays opaque, as it does for any external system.

  • The LLM's internal weights
  • Its hidden chain-of-thought
  • Attention or activation internals
  • Any foundation-model interpretability
The Problems We Solve

Why AI deployments fail
and how Semantica fixes each one

Six critical failure modes in production AI, and the exact mechanisms Semantica uses to eliminate each one.

01
Data Silo Problem

Critical knowledge locked in silos your AI can't connect

Problem

Databases, PDFs, APIs, and internal tools sit in isolation. Your AI only sees what's explicitly handed to it, blind to the relationships that make data meaningful.

Solution

Semantic extraction ingests from any source, resolves entities, and unifies everything into a single queryable context graph your AI reasons across in real time.

Semantic ExtractionEntity ResolutionMulti-SourceKnowledge Graph
02
Black Box Problem

Your AI makes decisions you can't explain

Problem

When your AI recommends, classifies, or acts, do you actually know why? Without causal tracking, every answer is a mystery that erodes trust and blocks compliance.

Solution

Every decision is recorded as a first-class object with inputs, confidence score, and full reasoning chain. Always answer 'why did the AI do that?'

This is the AI system's decision trail, not the foundation model's internal chain-of-thought — Semantica records what went in, what came out, and why, at the system level.

Causal ChainsDecision LogsAudit TrailsW3C PROV-O
03
Missing Context Layer

Your AI stack has no structured context layer

Problem

Vector stores retrieve similar text but carry no understanding of entities, relationships, or temporal change. There's no context layer, only nearest-neighbour lookups.

Solution

Semantica delivers that context layer and more: a live, queryable graph of entities, relationships, and decisions, with reasoning and governance built in, shared across every agent and session.

Context GraphsDecision IntelligenceReasoning EngineCross-Agent
04
The Debugging Wall

Tracing AI failures feels impossible

Problem

Something went wrong. The AI gave a bad answer. You're staring at logs with no idea which fact, rule, or model call caused the failure.

Solution

Every fact links to its source: algorithm, document, or inference. Trace any output to the exact data and reasoning step that produced it.

Source AttributionFull ProvenanceGraph TraversalRoot Cause
05
Hallucination Risk

Your AI confidently makes things up

Problem

LLMs fabricate facts, mix real data with plausible fictions, and deliver wrong answers with full confidence, which is especially dangerous in high-stakes domains.

Solution

Every answer is grounded in the knowledge graph. Before the LLM responds, it queries verified, sourced facts, turning hallucination into a controlled retrieval problem.

GraphRAGFact GroundingConfidence ScoringSource Verification
06
Compliance Nightmare

Regulators ask how it decided. You have no answer.

Problem

GDPR, EU AI Act, HIPAA, and financial regulators demand explainability. Most teams can't produce a satisfying audit trail for even a single AI decision.

Solution

W3C PROV-O compliant lineage records who acted, what data was used, and which rules fired, producing a complete, exportable audit trail for every output.

W3C PROV-OGDPR AlignedEU AI ActExportable Audits
Built to Be Trusted

Engineered for the domains
where mistakes aren't an option

No proprietary format, no closed pipeline, no black box. Just infrastructure regulated teams can stand behind.

01
Regulatory Ready

Built for High-Stakes, Regulated Domains

Energy, Finance, Healthcare, Pharma and Life Sciences, Climate and Marine Ecology, Nature Intelligence, Sovereign Infrastructure, and Legal teams run Semantica where GDPR, EU AI Act, and HIPAA audits are non-negotiable.

GDPREU AI ActHIPAASovereign Infrastructure
02
Zero Lock-in

Zero Vendor Lock-in

Swap between Neo4j, FalkorDB, RDF, and labeled property graph backends behind one API. Your data, your infrastructure, your call.

Neo4jFalkorDBRDF & LPGOne API
03
Full Provenance

Full, Exportable Provenance

W3C PROV-O compliant lineage on every decision, producing an audit trail a regulator can actually read.

W3C PROV-OSource AttributionExportable AuditsDecision Logs
04
Infrastructure Ownership

You Own Your Infrastructure

Run entirely inside your own VPC or on-premise, on infrastructure you own — open source, self-hosted, or a custom enterprise deployment. Migrate critical data in from wherever it lives today, and export your full graph back out cleanly if you ever want to leave.

HostingCustom Enterprise DeploymentData MigrationClean Export
Comprehensive Feature Set

Everything You Need for
Trustworthy AI Systems

13 production-ready modules spanning context graphs, reasoning engines, compliance lineage, and graph algorithms.

01

Context Graphs

Foundation of Trustworthy AI

Structured, queryable graph of entities, relationships, and decisions. Causal, persistent, and fully traceable with temporal validity windows.

Entity ManagementTyped RelationshipsTemporal ValiditySPARQL Queries
02

Decision Intelligence

Track Every Choice, Trace Every Outcome

Record decisions as first-class objects with add_decision(). Find precedents, analyze impact, and maintain causal chains across your entire AI system.

Causal ChainsPrecedent SearchImpact AnalysisPolicy Engine
03

Full Provenance

W3C PROV-O Compliant Lineage

Every fact links to its source with ProvenanceTracker. Algorithm provenance, graph builder provenance, and audit trails for compliance.

Source AttributionAlgorithm TrackingAudit TrailsExport to RDF
04

Reasoning Engines

Rules to Explainable Inferences

Forward chaining with IF/THEN rules, Rete networks for high-throughput matching, Datalog, and SPARQL for RDF triples.

Forward ChainingRete NetworkDatalogSPARQL Reasoning
05

Ontology Management

Schema-First Knowledge Engineering

Auto-generate OWL ontologies, import OWL/RDF/Turtle/JSON-LD schemas, validate with HermiT/Pellet, and generate SHACL shapes automatically.

OWL GenerationSHACL ValidationSKOS VocabMulti-Format Import
06

Knowledge Explorer

Visualize, Navigate, Understand

Real-time visual interface via Sigma.js. Timeline scrubbing, decision audit trails, entity resolution viewer, and interactive graph navigation.

Sigma.js GraphsTimeline ScrubbingAudit ViewerEntity Resolution
07

Semantic Extraction

Raw Text to Structured Knowledge

Named entity recognition, relation extraction, LLM-typed extraction with confidence scores, and advanced deduplication strategies.

NER PipelineRelation ExtractionLLM TypingDeduplication
08

Vector Store Integration

Semantic & Hybrid Search

Native support for FAISS, Pinecone, Weaviate, Qdrant, Milvus, and PgVector. Combine vector similarity with graph traversal for hybrid retrieval.

6+ BackendsHybrid SearchSemantic SimilarityGraph-Aware
09

Polyglot Graph Storage

One API, Any Backend

Swap between RDF triple stores and labeled property graphs without touching your code. Native SPARQL and Cypher support across every major backend.

RDF & LPGSPARQL + CypherNeo4j & FalkorDBZero Migration
10

Temporal Intelligence

Point-in-Time Reasoning

Temporal GraphRAG, Allen Interval Algebra with 13 relations, bi-temporal provenance, and TemporalNormalizer for intelligent date parsing.

Allen IntervalsBi-TemporalTime-Aware RAGValidity Windows
11

Pipeline Builder

Production-Ready Orchestration

Stage chaining with parallel workers, validation gates, and failure handling with configurable retry policies. Build knowledge pipelines that scale.

Parallel WorkersRetry PoliciesValidation GatesStage Chaining
12

Quality & Deduplication

Clean Data, Reliable Results

Conflict detection, entity resolution with blocking strategies, and pipeline validation built in. Keep your knowledge graph accurate and consistent.

Entity ResolutionBlocking StrategiesConflict DetectionValidation
13

Graph Algorithms

Advanced Network Analysis

PageRank, betweenness centrality, Louvain community detection, Node2Vec embeddings, cosine similarity, and link prediction for network insights.

PageRankCommunity DetectionNode2VecLink Prediction
Complete Capabilities

Deep Dive Into
Every Feature

One graph engine, organized into eight capability areas.

Context & Decision Intelligence

semantica.context

Every AI decision recorded as a traceable, auditable object.

Context Graphs · Decision Tracking · Causal Chains · Policy Engine

Knowledge Graph Engine

semantica.kg

Build and query a knowledge graph from any data source.

Entity Management · Centrality Analysis · Community Detection · Link Prediction

Polyglot Graph Storage

Swap graph databases without touching your code.

RDF Triple Stores · Labeled Property Graphs · Cypher Support · Zero-Code Swaps

Reasoning Engines

semantica.reasoning

Explainable rule-based inference, not a black box.

Forward Chaining · Rete Network · Datalog · SPARQL Reasoning

Temporal Intelligence

semantica.kg (temporal)

Query your graph as it existed at any point in time.

Point-in-Time Snapshots · Bi-Temporal Facts · Allen Interval Algebra · TemporalNormalizer

Provenance & Auditability

semantica.provenance

Every fact linked to its source, no mystery outputs.

Entity Provenance · Relationship Provenance · W3C PROV-O Export · Lineage Tracing

Ontology & Schema Management

semantica.ontology

Auto-generate and validate schemas for your knowledge graph.

OWL Generation · SHACL Validation · Class Inference · SKOS Vocabulary

Enterprise Data Platforms

semantica.ingest

Pull tables straight from your lakehouse or warehouse, with lineage intact.

Databricks · Snowflake · Unity Catalog Lineage · Table & Query Ingestion

Quick start: copy, paste, run

More examples
quick_start.py
Python 3.8+
from semantica.context import ContextGraph

# Initialize context graph with advanced analytics
graph = ContextGraph(advanced_analytics=True)

# Add typed nodes and a causal edge
graph.add_node("user_query",  "Event",    timestamp="2024-01-15T10:30:00Z", source="api")
graph.add_node("decision_01", "Decision", category="approval")
graph.add_edge("user_query", "decision_01", edge_type="triggers")

# Record a decision as a permanent, auditable object
decision_id = graph.record_decision(
    category="approval",
    scenario="User query, risk_level: low, source: api",
    reasoning="Based on historical precedents and policy compliance",
    outcome="approve_request",  confidence=0.95,
)

# Trace the causal chain and find precedents
chain      = graph.trace_decision_chain(decision_id)
precedents = graph.find_similar_decisions(
    scenario="approval, risk_level: low",  max_results=5,
)
Code Examples

Every Module.
Real, Runnable Code.

Production-grade examples drawn directly from the library. Copy, paste, run. Also available as Jupyter notebooks in the cookbook.

Install and build your first context graph in under 60 seconds.

quick_start.py
from semantica.context import ContextGraph

graph = ContextGraph(advanced_analytics=True)

# Record a vendor-selection decision
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,
)

# Trace the full causal ancestry
chain   = graph.trace_decision_chain(decision_id)
similar = graph.find_similar_decisions("cloud vendor", max_results=5)
impact  = graph.analyze_decision_impact(decision_id)
ok      = graph.check_decision_rules({"category": "vendor_selection"})
Universal Compatibility

Works With Every AI Tool

Native plugins, MCP server, and REST API. Integrate in minutes. No configuration required.

Claude CodeCursorCodex CLIWindsurfClineContinueVS CodeOpenClawClaude DesktopGitHub CopilotRoo CodeGooseAiderAmazon QZedClaude CodeCursorCodex CLIWindsurfClineContinueVS CodeOpenClawClaude DesktopGitHub CopilotRoo CodeGooseAiderAmazon QZed
AgnoLangChainLangGraphCrewAILlamaIndexAutoGenOpenAI AgentsGoogle ADKNeo4jAWS NeptuneApache AGEFalkorDBBlazegraphApache JenaEclipse RDF4JFAISSPineconeWeaviateQdrantMilvusPgVectorSnowflakeDatabricksAgnoLangChainLangGraphCrewAILlamaIndexAutoGenOpenAI AgentsGoogle ADKNeo4jAWS NeptuneApache AGEFalkorDBBlazegraphApache JenaEclipse RDF4JFAISSPineconeWeaviateQdrantMilvusPgVectorSnowflakeDatabricks

Native Plugin Bundles

Skills, agents, and hooks that work out of the box.

Claude Code
Cursor
Codex CLI

MCP Server + Plugin

Model Context Protocol with auto tool connection for AI IDEs.

Windsurf
Cline
Continue
VS Code
OpenClaw

MCP Server Only

Standalone server for desktop apps and custom integrations.

Claude Desktop

REST API

A full REST backend, ready for any HTTP client.

GitHub Copilot
Roo Code
Goose
Kilo Code
Aider
Amazon Q
Zed

Agentic Frameworks

Native integrations with leading AI agent platforms

View all
Agno Live
LangChain Soon
LangGraph Soon
CrewAI Soon
LlamaIndex Soon
AutoGen Soon
OpenAI Agents Soon
Google ADK Soon

Polyglot Graph Storage

Labeled property graphs and RDF triple stores, swappable without touching your code

Neo4jIndustry-standard graph DB, full Cypher support
Cypher
AWS NeptuneIAM authentication, full Gremlin support
Gremlin
Apache AGEPostgreSQL extension, openCypher queries
Cypher
FalkorDBNative support, high-performance graphs
Cypher
BlazegraphHigh-performance RDF triple store
SPARQL
Apache JenaFull-featured RDF framework
SPARQL
Eclipse RDF4JJava framework for RDF & SPARQL
SPARQL

Enterprise Data Platforms

First-class ingestion from the platforms enterprise data teams already run on

SnowflakeNative ingestion via Snowpark, warehouse-scale queries
Snowpark
DatabricksUnity Catalog + Delta Lake ingestion, lakehouse-native
Delta Lake

Vector Store Backends

Semantic and hybrid search with your preferred backend

FAISSMeta's similarity search
PineconeManaged vector DB
WeaviateAI-native search
QdrantHigh-performance vectors
MilvusScalable similarity
PgVectorPostgreSQL extension
Case Studies

Semantica in the
Real World

From marine natural capital finance to decade-long regulatory memory. Real deployments showing how Semantica turns complex, siloed knowledge into deterministic intelligence.

Marine Conservation & Climate Finance

Nereus · with Jayson A. Gutierrez Betancur

Turning 100 Million Biodiversity Records into Auditable Blue Finance Intelligence

The Challenge

The world's oceans generate an estimated $2.5 trillion in economic value annually: fisheries, coastal protection, tourism, and carbon sequestration. Yet this natural capital remains almost entirely invisible to the financial system. The problem is not a lack of data. OBIS holds over 100 million marine species occurrence records. Copernicus satellites pass over every reef and mangrove bed multiple times daily. Hundreds of peer-reviewed papers quantify ecosystem service values with confidence intervals. The problem is that none of this translates automatically into a financial number that capital can underwrite.

How Semantica Helped

Semantica is the provenance engine inside Nereus. It ingests peer-reviewed scientific literature, extracts causal claims with full DOI attribution, and assembles them into a deterministic financial inference graph. The core mechanism is the Bridge Axiom: a conditional financial primitive derived directly from a scientific paper and permanently linked to its source. An AI agent traverses the verifiable graph path from a raw satellite or sensor reading, through one or more validated axioms, to a calculated financial exposure metric. The output is a P5/P50/P95 exposure figure with a complete, reproducible audit trail from science to finance. The system also generates TNFD-compliant LEAP disclosure packages on demand, as a byproduct of the analysis rather than a separate workflow.

Semantica Modules

Provenance Tracing

Traces every extracted claim back to its source DOI, author, journal, and page number. No assertion in any output is unattributed. The audit trail is structural, not optional.

Causal Graph Assembly

Assembles a directed acyclic causal graph connecting sensor readings through Bridge Axioms to ecosystem service valuations and downstream financial outcomes.

Compliance & Disclosure

Evaluates the current portfolio against TNFD/LEAP compliance rules, identifies material dependencies and transition risks, and flags gaps before a disclosure window opens.

Decision Context Log

Records the complete decision context for every financial metric: which axioms fired, which inputs were used, who ran the query, and what the graph state was at execution time.

$1.62B

in natural capital quantified across the pilot portfolio

40

Bridge Axioms extracted from peer-reviewed literature

9

Indo-Pacific marine sites in the pilot

Provenance EngineScientific AttributionBridge AxiomsTNFD / LEAPGraphRAGBlue FinanceNatural Capital

Energy Regulation · Australia

Ausgrid · DNSP serving greater Sydney, NSW

A Decade of AER Regulatory History, Queryable in Under 3 Seconds

The Challenge

Each AER regulatory cycle produces thousands of pages across a structured document hierarchy: Regulatory Proposals, AER Issues Papers, AER Draft Determinations, Ausgrid Responses, Expert Witness Reports, Alternative Control Services determinations, and Final Determinations. Two complete cycles, 2019–24 and 2024–29, already exist in full. They contain every objection the AER has raised, every cost category Ausgrid has defended, every accepted and rejected justification, and every negotiated outcome. The AER references this history explicitly when assessing new proposals. Ausgrid's submission teams were not.

How Semantica Helped

Semantica ingested the complete public corpus of AER and Ausgrid regulatory documents across both cycles, parsing PDFs with structure-aware chunking that preserves the regulatory document hierarchy rather than breaking at arbitrary character limits. The output is a temporal knowledge graph where every extracted entity is linked to its source document, part, section heading, and page number, and where cross-cycle evolution is tracked through typed relationships. A hybrid retrieval layer combining semantic search and graph traversal answers plain-English questions with exact source citations in under 3 seconds. A multi-stage reasoning pipeline surfaces patterns that would otherwise require days of manual review.

Semantica Modules

Structure-Aware Parsing

Splits regulatory PDFs at section boundaries rather than character limits, preserving the document hierarchy that gives AER determinations their meaning. A chunk never spans two sections.

Entity Extraction

Identifies 12 regulatory entity types across every document in the corpus: Revenue Allowances, Capex Programs, Opex Categories, RAB values, Community Outcomes, AER Objections, AG Responses, Expert Witness positions, and more.

Temporal Linking

Links matched entities across regulatory cycles, enabling cross-cycle traversal queries such as how AER's position on a specific cost category changed between the two determinations.

Provenance Tracing

Links every extracted entity to its source document, part number, section heading, and page. Every answer is citable. No claim is generated without a verifiable reference.

Temporal Graph Assembly

Assembles the temporal knowledge graph with full cross-cycle relationship traversal. Both the 2019–24 and 2024–29 cycles exist as connected layers in the same graph.

Compliance & Disclosure

Evaluates the current regulatory proposal draft against AER compliance rules and prior accepted positions, flagging deviations before they reach the AER's formal review stage.

10 yrs

of AER regulatory history indexed with full provenance

12

regulatory entity types extracted and cross-linked

~20%

estimated reduction in submission preparation effort

Temporal Knowledge GraphHybrid RAGPolicy EngineProvenanceRegulatory AI

Working on a similar problem? Talk to us

Pricing

Start Free, Scale When Ready

Our open source core is free forever. Move to Hosting or a Custom Enterprise Deployment when you need team features or dedicated support — always on your own infrastructure, never ours.

Free Forever

MIT Licensed

Open Source

Free

MIT-licensed context graph engine for developers, startups, and researchers building agentic AI systems.

11.6k+

Stars

1.3k+

Forks

  • Full context graph engine
  • Decision tracking & causal chains
  • All reasoning engines
  • Semantic extraction pipeline
  • Knowledge Explorer UI
  • Vector store integrations
  • SPARQL & Cypher queries
  • Temporal intelligence
  • Community support
  • MIT License
Coming Soon

Runs on your infrastructure

Hosting

Early Access

Production-ready deployment inside your own VPC, with collaboration features, analytics, and priority support — your data never leaves your infrastructure.

  • Everything in Open Source
  • Runs entirely in your VPC
  • Team collaboration
  • Analytics dashboard
  • Custom ontology templates
  • SSO / SAML authentication
  • Audit logs
  • Priority support
  • No vendor lock-in
The Hosting version is currently under development and coming soon. Join the waitlist to get early access and be notified at launch.
Contact Us

Custom AI Systems

Custom AI Systems

Custom Enterprise Deployment

Custom

End-to-end domain-specific AI systems for organizations requiring dedicated infrastructure, advanced customization, and enterprise support — deployed on-premise or in your own cloud, never ours.

On-Prem / VPC

Deployment

  • Everything in Hosting
  • Dedicated infrastructure
  • On-premise deployment
  • Domain-specific customization
  • Custom integrations & APIs
  • Compliance & governance infrastructure
  • Dedicated success manager
  • 24/7 priority support
  • Custom SLA agreements
  • White-glove onboarding
We provide the infrastructure and tooling for compliance and governance. Obtaining formal compliance certifications (e.g. SOC 2, ISO 27001, HIPAA) remains your organization's responsibility.

Need a custom domain-specific solution?

Whether it's Open Source customization or a full Custom Enterprise Deployment, let's talk.

kaif@getsemantica.ai
Email Us
Frequently Asked Questions

Got Questions?
We Have Answers.

Everything you need to know about Semantica. Can't find what you're looking for? Ask on Discord.

Get Started Today

Ready to Build
Trustworthy AI?

Transform your AI systems with context graphs, decision intelligence, and full provenance tracking. Start with our open source core. It's free forever with MIT license.

pip install semantica