Semantica is an open-source Python framework for building context graphs and decision intelligence layers on top of AI agents and LLM applications. It provides structured context, causal reasoning, and full decision provenance, making AI explainable and traceable.

Yes. The core library is 100% free and open source under the MIT license. No feature gating, no usage limits, no hidden tiers.

How is Semantica different from LangChain or LlamaIndex?

LangChain and LlamaIndex focus on orchestration and retrieval. Semantica adds a structured knowledge layer — context graphs, causal decision tracking, and W3C PROV-O provenance — that integrates on top of any framework.

Which AI frameworks does Semantica support?

Semantica integrates with LangGraph, CrewAI, LlamaIndex, Claude Code, AutoGen, and any Python-based agent framework, along with 100+ LLM models via LiteLLM.

Does Semantica require a vector database?

No. Semantica works standalone. When you want hybrid graph + semantic retrieval it integrates natively with FAISS, Pinecone, Weaviate, Qdrant, Milvus, and pgvector.

What is decision provenance?

Decision provenance means every action your AI takes is stored as a first-class object with its inputs, reasoning chain, and outcome — compliant with W3C PROV-O. You can always audit why the AI did what it did.

How do I get started?

Run pip install semantica, then follow the quickstart in the docs at docs.getsemantica.ai. The core graph is up and running in under 10 lines of Python.

Semantica — AI Context Graphs & Decision Intelligence

Featured

ConceptsJune 18, 202614 min read

Why Semantica

Every AI framework today solves retrieval. None of them solve verifiability. Here is why that gap exists, what it costs in regulated environments, and what a system looks like when it is actually designed to close it, illustrated with a direct comparison of nine frameworks.

Kaif Ahmad

Founder, Semantica

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All Articles

Concepts8 min

What Is a Context Graph? The Missing Layer in AI Agent Memory

Most AI agents forget everything between turns. Context graphs give them structured, queryable, persistent memory, and the reasoning to use it.

KAMay 28, 2026

Engineering10 min

Explainable AI in Production: Beyond the Black Box

Accuracy matters, but so does trust. Here's how Semantica's decision intelligence layer makes AI agent choices auditable, reproducible, and defensible.

STMay 14, 2026

Research12 min

Beyond RAG: Why Knowledge Graphs Are the Next Frontier for AI Memory

Retrieval-Augmented Generation is powerful but fundamentally limited. Knowledge graphs offer structure, reasoning, and temporal awareness that vectors alone cannot provide.

KAApril 22, 2026

Company5 min

Semantica v0.5.0: Ontology Management, SPARQL, and the Road Ahead

Our biggest release yet adds OWL ontology auto-generation, SHACL validation, SPARQL reasoning, and a new Temporal GraphRAG API. Here's what's in it and what comes next.

KAApril 8, 2026

Engineering-first writing on production AI

Why Semantica

What Is a Context Graph? The Missing Layer in AI Agent Memory

Explainable AI in Production: Beyond the Black Box

Beyond RAG: Why Knowledge Graphs Are the Next Frontier for AI Memory

Semantica v0.5.0: Ontology Management, SPARQL, and the Road Ahead

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