semantica

semantica

https://docs.getsemantica.ai/

https://github.com/semantica-agi/semantica/tree/main

https://github.com/meiyoutou/semantica-explorer-demo/tree/main

 

DEMO

https://github.com/fanqingsong/accountable-lending

 

Welcome to Semantica

The Context and Semantic Layer for AI in High-Stakes Domains: Context Graphs · Decision Intelligence · Full Provenance

 

pip install semantica

 

Most AI agents run on embeddings, not meaning. A similarity score has no structure, no relationships, and no way to explain why a result came back.Semantica is the semantic and context layer underneath your LLM, vector store, and agent framework: deterministic infrastructure, not a model. Graph construction, reasoning, and provenance all run without an LLM in the loop. It turns fragmented enterprise data into a structured, queryable context graph and knowledge graph, governed by ontologies, taxonomies, and controlled vocabularies (OWL, SHACL, SKOS), so your data’s meaning is explicit rather than approximated by an embedding.Provenance and audit trails aren’t a bolt-on. They fall out naturally once your data has that structure, so the same graph that powers retrieval and reasoning also gives you a straight answer when a regulator asks why.

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What you get

• Context graphs: a persistent, queryable graph of everything your agent knows, decides, and reasons about
• Decision intelligence: record_decision() captures the full lifecycle and causal chain of every decision
• Full provenance: every fact links back to its source, W3C PROV-O compliant and audit-ready for HIPAA, SOX, and GDPR
• Explainable reasoning: forward chaining, Datalog, and SPARQL, each with a derivation path you can inspect
• Temporal intelligence: Allen interval algebra and point-in-time snapshots, so the graph knows not just what but when

 

https://docs.getsemantica.ai/guides/decision-intelligence/

Decision Intelligence

How Semantica records, stores, traces, and queries AI agent decisions as first-class knowledge graph objects — with causal chains, precedent search, policy enforcement, and full explainability.

AgentContext.record_decision() stores every AI decision as a node in the knowledge graph, linked by causal edges to the decisions that preceded it and the outcomes that followed. Use it to build an auditable reasoning trail — one that lets you reconstruct, six months later, exactly which classification caused which escalation, and which policy was checked before it was recorded.

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What Is Decision Intelligence?

Decision Intelligence records and analyzes an agent’s own decisions as structured data that can be queried, analyzed, and reused. Instead of decisions disappearing after execution, they become persistent graph nodes with searchable metadata, reasoning chains, and causal relationships.Decision Intelligence records decisions by capturing the scenario, reasoning, outcome, confidence, and decision maker for each choice the agent makes. These decisions become queryable nodes in your knowledge graph.Decisions become graph nodes that can be linked causally (Decision A caused Decision B), searched by similarity (find decisions like this scenario), and analyzed statistically (confidence trends, common outcomes).The goal is auditability, explainability, precedent search, and causal tracing. You can trace why decisions were made, find similar past decisions for consistency, and understand the full causal chain from initial detection to final action.Decision Intelligence vs. Agent Memory: Agent Memory stores external knowledge (documents, facts, observations). Decision Intelligence stores internal decisions (classifications, approvals, actions the agent itself made).Decision Intelligence vs. Reasoning: Reasoning derives new facts from existing data using logical rules. Decision Intelligence records the choices and judgments the agent made during problem-solving.Decision Intelligence vs. Graph Analytics: Graph Analytics analyzes the structural properties of your knowledge graph. Decision Intelligence focuses specifically on the decision-making process and its audit trail.

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Why Use Decision Intelligence?

Auditable AI actions. Every decision is recorded with reasoning, confidence, and timestamp, creating a complete audit trail for AI behavior in production systems.Explainability. When stakeholders ask “why did the system do X?”, you can trace the exact decision chain that led to that action, including intermediate reasoning steps.Precedent reuse. Before making new decisions, agents can search for similar past scenarios and their outcomes, promoting consistency and learning from previous experience.Causal analysis. Understand how early decisions cascade into later outcomes by following causal relationships between linked decision nodes.Governance and compliance. Policy engines can gate decisions against compliance rules, and all policy applications are recorded for regulatory audit.

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When To Use / When Not To Use

Use Decision Intelligence when:

• Building autonomous agents that make consequential choices
• Implementing decision workflows requiring audit trails
• Operating under compliance requirements (financial services, healthcare, defense)
• Building approval systems with multiple decision points
• Working in risk-sensitive environments where decisions must be explainable

Do not use when:

• Building stateless chatbots that only retrieve information
• Implementing simple RAG systems without decision-making
• Creating read-only information retrieval applications
• Building applications that never make actionable decisions requiring audit trails