Your AI agent’s black box, now with a paper trail

What it does

Semantica is a Python library and CLI that bolts onto existing AI stacks to add structured memory and accountability. It stores agent knowledge as queryable context graphs, records every decision as a first-class object with causal links and impact analysis, and traces outputs back to their sources using W3C PROV-O compliant provenance. It also bundles reasoning engines—Rete, forward chaining, deductive, abductive, and SPARQL—alongside deduplication, conflict detection, and ontology management tools.

The interesting bit

Instead of treating reasoning as a side effect, Semantica treats decisions as native graph entities you can audit, compare, and replay. It also ships with an almost overwhelming surface area: a CLI covering ingestion to backups, a 109-endpoint REST API, an MCP server, an Explorer UI, and native plugins for Claude Code and Cursor.

Key highlights

• Records decisions as first-class objects with causal chains, precedent search, and impact analysis
• W3C PROV-O compliant provenance tracing every fact back to its source
• Multiple reasoning engines: forward chaining, Rete, deductive, abductive, and SPARQL
• Broad backend support for graph stores (Neo4j, FalkorDB, Apache AGE, Neptune) and vector stores (Qdrant, Pinecone, Faiss)
• Ships with a CLI, REST API, MCP server, Explorer UI, and native plugins for Claude Code and Cursor

Caveats

• LangChain, LangGraph, and CrewAI integrations are listed as “coming soon”; current third-party support is mainly Agno
• The v0.5.0 release patched 12 disclosed vulnerabilities, including SQL injection, XXE, and eval injection
• Core imports previously failed when optional packages were absent; v0.5.0 introduced lazy-loading to fix this

Verdict

Evaluate it if you’re building agents in regulated or high-stakes domains where “the LLM said so” isn’t a valid audit answer. Skip it if you just need a lightweight RAG wrapper and don’t want to maintain graph infrastructure.