semantica is an open-source, developer-first knowledge infrastructure library that turns fragmented enterprise data into a structured, queryable Context Graph and knowledge graph carrying business context. It targets the gap where AI agents run on embeddings and similarity scores with no structure, relationships, or explainable results.
Project overview
Its reasoning, knowledge graph construction, and provenance are fully deterministic with no LLM required, and every AI choice becomes a permanent, auditable, queryable decision record — a distinguishing approach in the enterprise RAG space.
Developers and AI engineers comfortable writing Python who want open-source knowledge infrastructure as an alternative to expensive enterprise platforms.
Enterprise teams that need structure, relationships, and explainable results from their data instead of opaque similarity scores.
Key capabilities
Every AI choice becomes a permanent, auditable, queryable record: decisions are first-class graph nodes with a full lifecycle.
The project ships a REST API and CLI, plus the semantica doctor command for install verification.
Native plugin bundles are available for Claude Code, Cursor, Codex, Windsurf, Cline, Continue, VS Code, OpenClaw, and pi.
Limitations and risks
semantica provides system-level explainability only; it does not expose or reconstruct what happens inside the LLM.
Ingestors require credentials such as tokens, passwords, and keys; these must not be hardcoded in production. Use environment variables or secrets managers instead.
Getting started
Install the core package with pip install semantica; optional extras exist for heavier features, and semantica doctor verifies the installation. Using the library API requires Python coding, so setup difficulty is moderate.
Evidence and sources
Release: v0.7.0
README: Most AI agents run on embeddings, not meaning: similarity scores with no structure, no relationships, and no way to explain why a result came back.
README: pip install semantica
README: Drop-in Integrations: Agno, CrewAI, and LangChain support, a full MCP server, a CLI, a REST API, and plugins across major editors
README: A Context Graph is the structured memory layer that traditional RAG is missing.