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 memory, 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.

What is a context graph?

A context graph is a structured, queryable knowledge graph that stores entities, relationships, and decisions made by your AI — with temporal validity and full provenance. Unlike a vector store, it understands causal relationships.

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.

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Semantica Blog

Engineering-first writing on production AI

Context graphs, decision intelligence, provenance, reasoning engines, and what it actually takes to build AI systems that can show their work.

5 articles published

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 — with a direct comparison of nine frameworks.

Kaif Ahmad

Founder, Semantica

Read article

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What we cover

Five content tracks. Engineering-first, no fluff.

Engineering

Architecture decisions, performance deep-dives, and lessons from building Semantica in production.

Concepts

The ideas behind context graphs, decision intelligence, and what trustworthy AI actually means.

Research

Summaries and commentary on papers shaping the future of AI memory and knowledge graphs.

Company

Product updates, release notes, and what the Semantica team is thinking about next.

Tutorials

Step-by-step guides for integrating Semantica with your AI stack, framework by framework.

More tracks coming

Case Studies Docs GitHub