Semantica Maps AI Decisions to a Graph So You Can Finally Ask 'Why'
Agent frameworks are shipping decisions into production, but most leave behind only a log line. Semantica makes the decision chain a queryable graph with provenance and deterministic rules, giving compliance and platform teams an auditable alternative to black-box reasoning.
Semantica sits beneath LLMs and agent frameworks, ingesting data from files, databases, and enterprise platforms to build a knowledge graph. It treats AI decisions as first-class graph nodes, linking them to context, causal relationships, and W3C PROV-O provenance records. The system provides deterministic reasoning through Rete, Datalog, and SPARQL, and explicitly does not require an LLM for graph construction or inference.
Version 0.6.0 added Databricks and SQLite connectors, fixed seven correctness bugs in provenance tracking and rule reasoning, and patched a silent write failure for remote Fuseki stores. The release also brought named graph and CONSTRUCT query parity across Blazegraph, RDF4J, and Jena backends, signaling that cross-backend consistency is an active engineering concern.
The project targets regulated teams that cannot ship data to third-party SaaS and need an open-source, self-hosted way to answer why an agent made a specific decision. The trade-off is real: a broad surface area spanning ingestion, conflict detection, deduplication, four reasoning engines, and multiple storage backends, all still pre-1.0 with no published benchmarks.
The spike to 967 stars in one day suggests a market hungry for AI auditability, but the project's pre-1.0 version number and broad scope mean early adopters are signing up for integration work, not a finished product.
Decoupling graph construction and reasoning from the LLM is a deliberate architectural choice that avoids embedding model drift inside the audit trail, but it also means the graph's quality depends entirely on the deterministic extraction and resolution pipelines.
Cross-backend consistency is not a feature flag; v0.6.0's named graph and CONSTRUCT parity work shows that 'replaceable backends' is a promise that requires continuous, test-heavy engineering to keep.
Fixing a silent write failure in a provenance system is a self-referential warning: if the tool that records evidence can lose writes without error, the evidence chain itself is only as reliable as the regression suite.