Arango AutoGraph

What it does

AutoGraph takes enterprise documents such as PDF, Office, text and Markdown files and produces a Context Graph. It clusters documents into topic domains (the Corpus Graph), builds a Knowledge Graph of entities and embeddings, and assigns a retrieval strategy to each domain. Its RAG Strategizer gives complex domains a full graph with extracted entities (FullGraphRAG, labeled GraphRAG in the interface) and simpler domains a lighter vector partition (VectorRAG).

People use it in two stages, both driven from AutoGraph Studio in the web interface. First they build the Context Graph, then AutoRAG deploys retrievers that answer questions from it so agents and applications can query the data. Its distinguishing feature is automatic domain discovery rather than manual taxonomy design, and Incremental Graph Updates let users insert, delete or replace single documents without rebuilding the corpus. It flags partitions that may need reclustering but leaves that decision to the user.

It suits teams building grounded question-answering or agent systems over large document collections, and developers who want programmatic control through the HTTP API or Python SDK. The page does not state pricing, a free tier or sign-up requirements, and it does not list supported platforms beyond the web interface and API. Users should also expect to work through setup documentation before the Context Graph is usable.

What it can do

• Imports PDF, Office, text and Markdown documents and generates embeddings
• Clusters documents into domain partitions using graph algorithms to build a Corpus Graph
• Builds a Knowledge Graph of entities and relationships for complex domains
• Generates domain-specific ontologies for FullGraphRAG partitions
• Deploys AutoRAG retrievers that answer questions from the Context Graph
• Supports incremental insert, delete and replace of individual documents without full rebuilds

Before you choose

• Pricing and plan availability are not stated on the supplied page; confirm before adoption.
• Reclustering of drifted partitions is manual and carries its own processing cost, according to the page.
• Output quality depends on the retrieval strategy assigned per domain, which the RAG Strategizer determines automatically.

Listed under

• Data
• Ask your data

Get started

The page links to a Quick Start for turning a document set into a knowledge base. No sign-up or install route is stated on this page.

1. Review the Concepts page on knowledge graphs, LLMs and the GraphRAG approach.
2. Follow the Quick Start to turn a set of documents into a knowledge base.
3. Configure AutoGraph using either the web interface or the HTTP API, then build the Context Graph and deploy AutoRAG retrievers from AutoGraph Studio.

No pricing information was found on the provider’s documentation. Pricing was not confirmed.

We couldn’t find published prices for Arango AutoGraph.

Sources for this overview

Product pages, documentation, and pricing checked 30 Oct 2026. Each source supports part of the overview; availability and prices can change.

• AutoGraph • Arango Contextual Data Platform ↗docs.arango.ai

Company website

docs.arango.ai: product, pricing and policy pages

Social accounts

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Human review

Checked by a person

knowledge_base

manual_review

Last checked 30 Oct 2026. Every field keeps its source and date; unknown means we couldn’t confirm it.