// the find
semantica-agi/semantica
Graph-Native Infrastructure for Context and Accountable AI Systems
Semantica is a Python library for building auditable AI agent infrastructure: it sits underneath your LLM and vector store, adding a knowledge graph, causal decision records, W3C PROV-O provenance, and deterministic reasoning engines. The target is regulated industries (finance, healthcare, government) where 'the AI decided' is not an acceptable answer to a compliance question. Think structured memory with a full audit trail, not just embeddings.
The decision record model is the genuinely interesting part — every AI choice becomes a graph node with causal ancestry, confidence scores, and exportable PROV-O, which is exactly what a bank's model risk team wants. The polyglot storage layer (swap Neo4j for FalkorDB, or Blazegraph for Jena, without touching application code) is a real architectural win for shops that can't commit to one graph vendor. The conflict detection module — flagging contradictory facts before they silently corrupt the KG — solves a problem that most RAG pipelines just ignore. The MCP server and Agno integration mean it plugs into the current agent tooling ecosystem without forking anything.
The README itself calls out that the Rete engine's condition matcher is 'intentionally simple' and warns you to validate before wiring it into a production compliance gate — that's a significant caveat for a library whose main pitch is regulatory-grade reasoning. The `RDFExporter` API shape mismatch documented inline (you have to manually remap `ContextGraph.to_dict()`'s `nodes`/`edges` to `entities`/`relationships`) is the kind of sharp edge that shouldn't exist in a 0.6.x release. The library's surface area is enormous — 20+ modules, dozens of backends, Databricks/Snowflake connectors, a React frontend — and it's maintained by a small team; that combination historically produces modules that are documented but undertested at depth. No mention of performance characteristics at scale: a knowledge graph with 10M nodes and a bi-temporal fact layer hits very different query latencies than the toy examples in the README.