Semantica: AI Infrastructure for Accountable Agents
/semantica
Semantica is a Python framework that gives AI agents a deterministic knowledge graph and decision-record layer instead of a vector index alone. Every decision carries causal links and a W3C PROV-O export path, and none of the graph work or reasoning needs an LLM. Reach for it when agents make decisions that must survive a regulator's 'why' months later. Skip it for plain semantic search over documents — that's still a vector database's job.
The Challenge of Accountable AI
Semantica's own README opens with a specific gap: most AI agents store embeddings, not meaning, so their context can't be explained and their decisions can't be audited after the fact. In a regulated flow like loan underwriting, an agent's approval has to survive a regulator asking 'why' months later, and a vector index has no way to answer that. Standard RAG and LLM-memory setups also silently overwrite conflicting facts instead of flagging them — the exact failure mode Semantica's conflict detection is built to close.
What Semantica Is
Semantica is a Python library that functions as an underlying component for your LLM, vector store, and agent framework, offering deterministic infrastructure for context and decisions. It builds a Context Graph — a queryable knowledge graph of everything an agent ingests, extracts, decides, and reasons about — backed by ontology governance, rule-based reasoning (forward chaining, Rete, Datalog, SPARQL), and W3C PROV-O provenance, none of it requiring an LLM.
Core Capabilities
- ✓Decision Intelligence turns every agent decision into a first-class graph node: record_decision() stores category, reasoning, outcome, and confidence, and add_causal_relationship() links decisions with CAUSED, INFLUENCED, or PRECEDENT_FOR edges you can later trace with trace_decision_chain().
- ✓Context Graphs replace flat embedding search with graph traversal: entities, relationships, decisions, and facts are typed nodes and edges, so get_neighbors() can find a person three hops from a contract that a similarity search would miss.
- ✓Deterministic reasoning — forward chaining, a Rete network, Datalog, and SPARQL — produces explainable inference paths instead of a black-box answer, and none of it depends on calling an LLM.
- ✓Full auditability via W3C PROV-O: every fact carries provenance through a ProvenanceManager, and audit trails export to JSON, CSV, or RDF (Turtle in the README's own recipe) for regulator submission.
- ✓Native Databricks (Unity Catalog, Delta Lake) and Snowflake connectors pull tables already in your lakehouse or warehouse directly into the graph with lineage intact, instead of exporting to a third-party SaaS first.
- ✓Polyglot graph storage — RDF stores (embedded Oxigraph, Blazegraph, Apache Jena, Eclipse RDF4J) or Labeled Property Graphs (Neo4j, FalkorDB, Apache AGE, AWS Neptune) — swappable without touching your code.
- ✓Conflict detection and semantic deduplication run before facts merge into the graph, so contradictory or duplicate data gets flagged instead of silently overwriting what's already there.
- ✓The same graph is reachable through a Python API, a REST API, an MCP server, or the semantica CLI, plus a browser-based workbench for visualizing the graph, ontology, or a decision timeline.
Who Benefits from Semantica
Semantica targets AI/ML platform teams shipping agents whose decisions have consequences — lending, healthcare, compliance — and need structured, queryable context instead of just a vector index. It's also aimed at data platform teams already on Databricks or Snowflake who want a governed, lineage-tracked knowledge graph without exporting to a third-party SaaS, plus compliance, risk, and audit teams and regulated enterprises in finance, healthcare, legal, government, or defense that can't ship a black box. If you're a platform engineer who wants the knowledge graph, reasoning, and provenance stack self-hosted and swappable rather than tied to one vendor, or a data engineer wrangling messy multi-source data into a graph, the README positions Semantica for you directly. Skip it if you just want a fast vector-search memory layer for a low-stakes chatbot — the README's own comparison table treats that as the job of a plain vector DB, not something Semantica is meant to replace outright.
Getting Started with Installation
Install is one command: pip install semantica. There's no separate server to stand up for the core library — the README's quick-start example uses a FAISS-backed VectorStore out of the box, and graph storage is pluggable if you want RDF or a Labeled Property Graph backend instead. To verify the install, run semantica doctor: the README's example transcript shows it checking your Python version, the installed semantica version (0.6.0 in that example), the vector store backend, and the config file at ~/.semantica/config.yaml, each reported pass or fail. Wiring up the optional pieces — an external graph database, or the Databricks/Snowflake connectors — isn't walked through step by step in what's documented here, so treat that as separate setup beyond the pip install.
Building Accountable AI with Semantica
Usage centers on ContextGraph: instantiate it with advanced_analytics=True, then call record_decision() with a category, scenario, reasoning, outcome, and confidence score for each agent decision. add_causal_relationship() links decisions together as CAUSED, INFLUENCED, or PRECEDENT_FOR, which lets trace_decision_chain() walk the full causal ancestry back to a root cause and find_similar_decisions() search past decisions as precedent. For raw context beyond decisions, add_node() and add_edge() build typed entities and relationships directly, get_neighbors() traverses the graph by hop count, and state_at() returns a point-in-time snapshot. AgentContext wraps a VectorStore and a ContextGraph together for simple store()/retrieve() calls in agent-memory workflows. Provenance runs through a separate ProvenanceManager, and RDFExporter turns the graph into RDF — Turtle, in the README's own audit-trail recipe.
Semantica vs. Other AI Context Approaches
Frequently Asked Questions
Semantica is released under the MIT license, per its GitHub repository, so you can self-host, modify, and embed it commercially without a copyleft obligation.
Semantica can be self-hosted — the README lists Open Source, Self-Hostable, Auditable, Governed, and Zero Vendor Lock-In among its core design points, with every storage backend swappable without touching your code.
Semantica does not require an LLM for its core graph work — the README states the reasoning engines, knowledge graph construction, and provenance layer are fully deterministic and need no LLM to run.
Semantica supports RDF triple stores (embedded Oxigraph, Blazegraph, Apache Jena, Eclipse RDF4J via SPARQL) and Labeled Property Graphs (Neo4j, FalkorDB, Apache AGE, AWS Neptune via Cypher), all swappable without code changes.
Semantica ships native connectors for Databricks (Unity Catalog and Delta Lake, with PAT or OAuth M2M auth and catalog/schema/table/lineage introspection) and Snowflake (warehouse/database/schema access with key-pair or OAuth auth).
Semantica is built specifically for high-stakes, regulated domains — the README names finance, healthcare, legal, government, and defense, and centers its audit-trail recipe on exporting W3C PROV-O for regulator submission.
Strengths
- ✓Decisions are first-class, queryable graph objects with causal links and a documented export path to W3C PROV-O — a concrete answer to 'why did the AI do that' rather than a log line.
- ✓Deterministic reasoning and knowledge graph construction don't require an LLM at all, which matters for cost, latency, and reproducibility in a regulated pipeline.
- ✓Native Databricks and Snowflake connectors with lineage introspection mean data teams already on a lakehouse or warehouse don't need a separate export/import step to get a governed graph.
- ✓Storage is genuinely polyglot — four RDF backends and four Labeled Property Graph backends, plus vector stores, swappable without touching your code, per the README's architecture section.
- ✓MIT-licensed, self-hostable, and explicitly positioned around zero vendor lock-in, with access through a Python API, REST API, MCP server, and CLI rather than one integration path.
Limitations & risks
- △The README's own doctor-command example shows semantica at version 0.6.0 — pre-1.0 software, so treat the API surface (module names, method signatures) as more likely to shift than a mature library's.
- △The core reasoning, graph, and provenance stack needs no LLM, but the README doesn't document what the extraction stages (NER, relation, event extraction) run on internally — that isn't stated in the facts available here.
- △Getting from pip install semantica to a production setup — an external graph database, Databricks/Snowflake auth, a chosen vector store — isn't walked through step by step in what's documented; budget time with the module reference.
- △The ontology and reasoning layer (SHACL, OWL, Rete, Datalog, SPARQL) assumes some familiarity with semantic-web and rules-engine concepts, real ground to cover for a team used to plain vector search.
- △This is a smaller community than the mainstream RAG frameworks it implicitly gets compared against, so expect thinner third-party tutorials and Stack Overflow coverage.
Best use cases
- •Building a regulator-ready audit trail for a lending, healthcare, or compliance decision: record a causally-linked decision chain, attach provenance to each entity, and export W3C PROV-O for submission, per the README's own recipe.
- •Tables that reside in Databricks Unity Catalog or a Snowflake warehouse can be transformed into a governed, lineage-tracked knowledge graph, and this can be achieved without initially sending that data to a third-party SaaS.
- •Giving a multi-agent team a single shared Context Graph instead of separate, siloed memory per agent.
- •Running GraphRAG-style retrieval where entity-aware chunking and graph traversal answer 'what's connected and why,' not just 'what's similar.'
- •Detecting and resolving conflicting or duplicate facts pulled in from multiple sources before they corrupt a knowledge base.
Related repositories
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