Semantica — Open-Source Palantir Alternative for AI Agents

By Prahlad Menon Published 2026-08-09 3 min read

Most AI agents operate as black boxes. They store embeddings, not meaning. Make decisions that can’t be explained. Take actions that can’t be audited.

In regulated industries — finance, healthcare, government — that’s not an inconvenience. It’s a compliance exposure. An underwriting agent’s approval needs to survive a regulator’s “why?” months later.

Semantica is the open-source answer: a graph-native infrastructure layer that sits underneath your LLM, vector store, and agent framework. Context graphs. Explainable reasoning. W3C PROV-O audit trails. All deterministic — no LLM required for graph construction or provenance.

GitHub: semantica-agi/semantica
Install: pip install semantica

The Gap Semantica Fills

	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

Decision Intelligence: The Core Idea

In Semantica, a decision isn’t a log line. It’s a first-class graph node with a full lifecycle:

from semantica.context import ContextGraph

graph = ContextGraph(advanced_analytics=True)

# Record with full structured context
decision_id = graph.record_decision(
    category="credit_application",
    scenario="Personal loan, $85k income, 31% DTI",
    reasoning="Income meets threshold; employment stable; no adverse credit events",
    outcome="proceed_to_underwriting",
    confidence=0.88,
    metadata={"applicant_id": "A-7291"},
)

# Query the intelligence
chain = graph.trace_decision_chain(decision_id)  # full causal ancestry
similar = graph.find_similar_decisions("personal loan approval", max_results=5)  # precedents
impact = graph.analyze_decision_impact(decision_id)  # downstream influence
compliant = graph.check_decision_rules({"category": "credit_application"})  # policy gate

Every decision links to upstream causes and downstream effects. You can export the entire chain as W3C PROV-O — the format regulators actually accept.

Context Graphs vs. Embeddings

A Context Graph answers different questions than embeddings:

Embeddings: “What is similar?”
Context Graph: “What is connected, why, and how?”

Every entity, relationship, decision, and fact is a node. Entities link to source documents. Decisions link to evidence. Facts carry provenance. Conflicts are detected, not silently overwritten.

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 edges
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")

# Traverse
neighbors = graph.get_neighbors("acme_corp", hops=2)

# Time travel — the graph as it existed on any past date
snapshot = graph.state_at("2024-01-01")

The Full Pipeline

Semantica is an end-to-end system, not a single library:

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

Ingestion: Files, web, databases, Databricks (Unity Catalog), Snowflake, Kafka, Git, email, MCP servers

Extraction: NER, relations, events, triplets — with conflicts flagged before merge

Storage: Polyglot by design. RDF stores (Oxigraph, Blazegraph, Jena, RDF4J) and Labeled Property Graphs (Neo4j, FalkorDB, Apache AGE, Neptune)

Reasoning: Forward chaining, Rete network, Datalog, SPARQL — fully explainable, not black boxes

Who It’s For

AI/ML platform teams shipping agents that make consequential decisions
Data platform teams on Databricks/Snowflake who need governed knowledge graphs without exporting to third-party SaaS
Compliance, risk, and audit teams who need structured answers to “why did the AI do that?”
Regulated enterprises (finance, healthcare, legal, government) that can’t ship black boxes
Platform engineers who want KG + reasoning + provenance self-hosted and swappable

Audit Trail Recipe

The flagship pattern: record a causally-linked decision chain, attach provenance, export regulator-ready artifacts.

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 decision chain (healthcare example)
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",
    outcome="dose_reduced_30pct",
    confidence=0.87,
)
graph.add_causal_relationship(d1, d2, relationship_type="CAUSED")

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

# Export W3C PROV-O for regulator submission
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")

Enterprise Data Platform Integration

Pull directly from Databricks or Snowflake — no export/import hop:

from semantica.ingest import DatabricksIngestor

databricks = DatabricksIngestor(
    host="https://adb-xxx.azuredatabricks.net",
    token="dapi-xxxxxxxx",
    http_path="/sql/1.0/warehouses/xxxxxxxx",
    catalog="main",
)
customers = databricks.ingest_table("customers", limit=10_000)
lineage = databricks.get_table_lineage("customers", catalog="main", schema="default")

Tables become graph nodes with provenance automatically.

Why “Open-Source Palantir”?

Palantir’s Foundry/Ontology is powerful but proprietary. Semantica offers:

Same core primitives: Knowledge graphs, ontology management, decision intelligence, provenance
Open source (MIT): Self-host, fork, extend, no lock-in
Polyglot storage: Bring your own graph database
Modern stack: Python-native, designed for LLM-era agents
Deterministic reasoning: No LLM required for graph construction

You don’t need Palantir’s enterprise sales process to get enterprise-grade decision intelligence.

Quick Start

pip install semantica
semantica doctor  # verify install

from semantica.context import ContextGraph

graph = ContextGraph(advanced_analytics=True)
decision = graph.record_decision(
    category="vendor_selection",
    scenario="Choose cloud provider for HIPAA workload",
    reasoning="AWS offers BAA, mature HIPAA tooling, existing team expertise",
    outcome="selected_aws",
    confidence=0.93,
)