Graph-Native Infrastructure for Context and Accountable AI Systems
The Open Source Palantir for AI Agents
A structured, queryable graph of everything your agent knows, decides, and reasons about
Every decision is a first-class object: traceable, searchable by precedent, and causally linked
SHACL constraints, conflict detection, compliance rules, OWL generation, and SKOS vocabulary management
W3C PROV-O provenance on every fact, with audit trails exportable to JSON, CSV, or RDF
Forward chaining, Rete network, Datalog, and SPARQL with fully explainable paths, not black boxes
Multi-source ingestion, entity-aware chunking, NER/relation extraction, and knowledge graph construction
pip install semanticafrom 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 gateNative connectors for Databricks and Snowflake, so tables already living in your lakehouse or warehouse become graph nodes with provenance
RDF triple stores (Oxigraph, Blazegraph, Apache Jena, Eclipse RDF4J) and Labeled Property Graphs (Neo4j, FalkorDB, Apache AGE, AWS Neptune)
Explore any graph, ontology, or timeline in an interactive browser workbench with force-directed layouts and temporal scrubbers
Loan underwriting audit trails, fraud detection, AML compliance, regulatory risk knowledge graphs
Clinical decision support, drug interaction graphs, and patient safety audit trails
Evidence-backed research, contract analysis, case law reasoning, and privilege tracking
Policy decision records, classified information governance, and regulatory reporting
Threat attribution, incident response timelines, and IOC provenance tracking
Decision logs, safety validation, and explainable AI for certification
Semantica is a graph-native infrastructure for context and accountable AI systems. It provides a structured, queryable graph of everything your AI agent knows, decides, and reasons about, with full decision provenance, deterministic reasoning, and audit trails.
While RAG relies on embeddings and similarity search, Semantica uses context graphs with structured knowledge, decision intelligence, and deterministic reasoning. Every fact is traceable to its source, decisions are auditable, and conflicts are detected and resolved.
Yes, Semantica is open source under the MIT License. You can find the source code on GitHub and contribute to the project.
Semantica is designed for high-stakes, regulated domains such as finance, healthcare, legal, government, and defense, where AI outputs must be explainable, auditable, and defensible.
Yes, Semantica has native connectors for Databricks and Snowflake, allowing you to ingest tables directly from your data lakehouse or warehouse without exporting them first.
Install Semantica via pip and use the quick start examples to build your first context graph. Check the documentation for detailed guides and tutorials.
Semantica is licensed under the MIT License, which is permissive and allows for commercial use, modification, and distribution.
Yes, Semantica provides multi-agent support through its Agno integration, enabling shared context graphs across multiple AI agents.
Semantica is designed for self-hosting with zero vendor lock-in. It supports encrypted storage, secure authentication, and fine-grained access controls to protect sensitive data.
If Semantica solves a real problem for you, a star helps others find it.
⭐ Star on GitHub