PageIndex vs Semantica
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
Lets an AI find answers in long documents by reading a table-of-contents-style tree, not vector search.
Open source · MIT
Python toolkit that builds knowledge graphs and decision audit trails to give AI systems structured, explainab
The short answer
Choose PageIndex if you need…
- Developers building question-answering over long PDFs such as annual reports or manuals
- Teams who want retrieved answers to be traceable to explicit sections of a document
- Projects that already have access to an LLM API key and can afford per-query model calls
- Users with a Claude subscription who want to use Claude Code models through the SDK
Choose Semantica if you need…
- AI/ML platform teams building agents that need structured context beyond vector search
- Data engineers building a knowledge graph from multi-source data with conflict detection
- Teams in regulated domains who want self-hosted, auditable decision trails
- Developers who want an open-source alternative to enterprise knowledge platforms
Side-by-side
| PageIndex | Semantica | |
|---|---|---|
| Type | Library | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 40k | 14.4k |
| Setup difficulty | Intermediate | Advanced |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | Python | Python |
| Last code update | 8 days ago | 4 days ago |
What each one is
PageIndex
PageIndex is a Python library that builds a hierarchical tree index of a document, similar to a table of contents, and then uses an LLM to reason through that tree to find the relevant sections. It is aimed at long, complex professional documents such as financial reports, legal filings, and manuals, where the README says vector similarity search often returns the wrong passages. It does not use a vector database or split documents into chunks.
Semantica
Semantica is a Python library for turning scattered data into a structured knowledge graph, which it calls a Context Graph, that AI agents can query. It records relationships, ontologies, and decision provenance so you can trace what context fed into an AI decision. The README says it is meant to sit under an LLM, vector store, or agent framework rather than replace them.
Best for
PageIndex
- Developers building question-answering over long PDFs such as annual reports or manuals
- Teams who want retrieved answers to be traceable to explicit sections of a document
- Projects that already have access to an LLM API key and can afford per-query model calls
- Users with a Claude subscription who want to use Claude Code models through the SDK
Semantica
- AI/ML platform teams building agents that need structured context beyond vector search
- Data engineers building a knowledge graph from multi-source data with conflict detection
- Teams in regulated domains who want self-hosted, auditable decision trails
- Developers who want an open-source alternative to enterprise knowledge platforms
Strengths
PageIndex
- The README describes retrieval with explicit, traceable references rather than opaque similarity scores
- No vector database or chunking step is required, according to the README
- Released under the MIT license with a published PyPI package
Semantica
- Deterministic graph construction and reasoning; the README says no LLM is required for these steps
- Open source under MIT with self-hostable, swappable storage backends
- Supports standard formats such as RDF, OWL, SHACL, and SKOS
Not ideal for
PageIndex
- People who need a fast, very cheap search over huge collections, since the README says each query relies on LLM reasoning over the tree and cost depends on the chosen model
- Users who want a no-code tool; the README centers on a Python SDK and API
- Short or simple documents, where the README's positioning suggests ordinary retrieval is enough
Semantica
- People who only need simple retrieval over documents; a vector database or basic RAG setup is likely enough
- Anyone wanting explainability of the LLM's internal reasoning, since the README says Semantica explains only what is outside the model
- Non-technical users looking for a ready-made end-user app, since the README centers on a Python install
Trade-offs
PageIndex
- Each query calls an LLM, so answer cost and speed depend on the model you choose; the README reports benchmark figures but your results will vary
- The benchmarks and cost numbers are published by the project itself and rely on its own test setup
- The README is truncated and does not fully document limits or edge cases
Semantica
- The README is truncated and long, so the full scope and maturity of each module is hard to verify from it alone
- Project is marketed heavily as enterprise-grade; claims in the README are self-reported and not independently verified here
- Requires familiarity with knowledge graphs and ontologies to get real value
Getting started
Frequently asked questions
What is the difference between PageIndex and Semantica?
PageIndex: Lets an AI find answers in long documents by reading a table-of-contents-style tree, not vector search. Semantica: Python toolkit that builds knowledge graphs and decision audit trails to give AI systems structured, explainab. PageIndex suits developers building question-answering over long pdfs such as annual reports or manuals, while Semantica suits ai/ml platform teams building agents that need structured context beyond vector search.
Are PageIndex and Semantica free?
PageIndex: Free and open source. Semantica: Free and open source.
Which is easier to set up, PageIndex or Semantica?
PageIndex is easier to get started with (rated intermediate); Semantica is rated advanced and usually needs more setup.
Is PageIndex or Semantica more popular?
PageIndex has more GitHub stars (40k) than Semantica (14.4k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to PageIndex and Semantica?
Other options people compare them with include LlamaIndex, LangChain, LEANN, BGE, LightRAG.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.