Semantica

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The Problem Every Production AI Team Hits

• No way to ask why a fact was recalled
• No link from a recalled fact back to its source document
• Context is a black box that resets on every run

• No history to hand to a regulator or auditor
• No way to replay or reproduce a past decision
• Debugging means re-running, not reviewing

• In healthcare, finance, and legal: this is a hard compliance blocker
• No lineage from inference back to the original document
• Impossible to demonstrate what the agent actually relied on

• Impossible to validate the reasoning path
• Impossible to contest a specific conclusion
• No basis for improving or correcting future behavior

• No detection when two sources disagree
• Outputs become inconsistent and unpredictable over time
• Silent failures compound as the knowledge base grows

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What Semantica Adds to Your Stack

• Persistent across agent runs: no context loss between sessions
• Queryable with SPARQL and full graph algorithms
• Temporal model with valid_from / valid_until on nodes and edges
• Point-in-time snapshots of the full knowledge state

• record_decision() captures full lifecycle and causal chain
• Hybrid precedent search over past decisions for consistency
• analyze_decision_impact() shows downstream consequences
• Causal chain visualization from trigger to outcome

• W3C PROV-O compliant lineage across all modules
• Full traceability from raw input to final inference
• recorded_at stamping with OWL-Time export
• Audit-ready for HIPAA, SOX, GDPR, FDA 21 CFR Part 11

• Forward chaining, Rete, deductive, abductive
• SPARQL query-based inference over RDF graphs
• Datalog with recursive Horn clause rules
• Every conclusion backed by a traceable derivation path

• Allen interval algebra: all 13 temporal relations
• Point-in-time queries over historical graph states
• Temporal provenance stamping on every fact
• OWL-Time export for standards-compliant archiving

• Visual editor for schema design and editing
• SHACL Studio for constraint authoring and validation
• Alignment authoring across multiple ontologies
• Health dashboard and version control built in

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See It In Action

pip install semantica

from semantica.context import AgentContext, ContextGraph
from semantica.vector_store import VectorStore
from semantica.llms import OpenAI

context = AgentContext(
    vector_store=VectorStore(backend="faiss", dimension=1536),
    knowledge_graph=ContextGraph(advanced_analytics=True),
    decision_tracking=True,
    llm=OpenAI(model="gpt-4o"),
)

context.store("GPT-4 outperforms GPT-3.5 on reasoning benchmarks by 40%")

decision_id = context.record_decision(
    category="model_selection",
    scenario="Choose LLM for production reasoning pipeline",
    reasoning="GPT-4 benchmark advantage justifies 3x cost increase",
    outcome="selected_gpt4",
    confidence=0.91,
)

precedents = context.find_precedents("model selection reasoning", limit=5)
influence  = context.analyze_decision_influence(decision_id)

from semantica.context import AgentContext, ContextGraph
from semantica.vector_store import VectorStore
from semantica.llms import LiteLLM
import os

context = AgentContext(
    vector_store=VectorStore(backend="faiss", dimension=1024),
    knowledge_graph=ContextGraph(advanced_analytics=True),
    decision_tracking=True,
    llm=LiteLLM(model="anthropic/claude-opus-4-7", api_key=os.getenv("ANTHROPIC_API_KEY")),
)

context.store("Claude excels at long-context reasoning and code generation")

decision_id = context.record_decision(
    category="model_selection",
    scenario="Choose LLM for document analysis pipeline",
    reasoning="Claude's 200k context window eliminates chunking overhead",
    outcome="selected_claude",
    confidence=0.94,
)

precedents = context.find_precedents("document analysis model", limit=5)

from semantica.context import AgentContext, ContextGraph
from semantica.vector_store import VectorStore
from semantica.llms import LiteLLM

context = AgentContext(
    vector_store=VectorStore(backend="faiss", dimension=768),
    knowledge_graph=ContextGraph(advanced_analytics=True),
    decision_tracking=True,
    llm=LiteLLM(model="ollama/llama3.2", base_url="http://localhost:11434"),
)

# Fully local: no data leaves your infrastructure
context.store("Local LLMs enable air-gapped compliance deployments")

decision_id = context.record_decision(
    category="deployment_model",
    scenario="Choose inference strategy for on-prem environment",
    reasoning="Air-gap requirement eliminates cloud API options",
    outcome="local_inference",
    confidence=0.99,
)

• Full Quickstart — Step-by-step pipeline walkthrough
• Cookbook — 40+ real-world Jupyter notebooks
• Join Discord — Community chat and support

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Built for Where Mistakes Have Consequences

• Clinical decision support with full audit trails
• Drug interaction and contraindication graphs
• Patient safety event tracking and root-cause analysis
• HIPAA-compliant provenance chains out of the box

• Fraud detection knowledge graphs
• Risk assessment trails built to survive an audit
• SOX, GDPR, and MiFID II compliance infrastructure
• Model decision lineage for regulatory reporting

• Evidence-backed research with every cited fact provenance-linked
• Contract analysis with traceable clause extraction
• Regulatory change tracking across jurisdictions
• Full reasoning paths ready for court-admissible documentation

• Threat attribution graphs linking actors, TTPs, and indicators
• Incident response timelines with full event provenance
• Security audit trails across the complete kill chain
• MITRE ATT&CK-aligned knowledge graph integration

• Policy decision trails from brief to outcome
• Classified information handling with provenance chains
• Chain-of-custody scrutiny for intelligence reporting
• Air-gapped deployment with local LLM support

• Power grid state tracking with temporal intelligence
• Transportation safety event graphs
• Emergency response coordination with decision audit trails
• Consequence modeling for high-stakes operational decisions

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Start Here

pip install semantica

• Installation — Get Semantica installed in under a minute
• Quickstart — Build a complete knowledge graph pipeline in 5 minutes
• Core Concepts — The mental model behind the API
• API Reference — Exact module, class, and method details
• Cookbook — Domain notebooks for real-world use cases
• Changelog — Release history

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Full Capabilities

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Module Reference

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Why Semantica?

• Full source available on GitHub
• Every line auditable by your security team
• Fork, extend, and self-host with no restrictions
• No telemetry, no usage reporting

• 1,000+ passing tests with full regression coverage
• PipelineValidator catches configuration errors at startup
• FailureHandler with exponential backoff and dead-letter queues
• 12 security vulnerabilities fixed in v0.5.0

• Use NERExtractor without a graph store
• Use ContextGraph without vector storage
• Every component independently swappable and testable
• No framework lock-in: works with any agent stack