The best Onyx alternative

The open-source platform that understands how your data connects, not just where the keywords match.

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Knowledge-graph-native. Open source.

Onyx is a capable open-source RAG platform. It indexes your tools and retrieves isolated chunks of text by vector proximity, ranked by relevance, leaving you to connect the dots.

Pipeshub is the knowledge-graph-native alternative, built to understand how your data connects. It maps the relationships between your files, threads, issues, and owners, traces every answer to the exact sentence, row, or slide with a confidence level, and turns that context into agentic workflows across structured and unstructured data. Both are open source, both self-host in your VPC, on-prem, or air-gapped, and both give you total model freedom. The difference is the engine underneath.

Onyx made open-source RAG real. Pipeshub goes a layer deeper: a knowledge graph that turns isolated results into connected context, with visual citations, structured-data support, and agentic workflows you can build, cite, and trust, on data that never leaves your perimeter.

Features

Open-source & self-hostable

Apache 2.0

MIT

Data sovereignty (on-prem, VPC, air-gapped)

Core architecture

Knowledge Graph + Vector + Agentic RAG

Vector indexing + Agentic RAG¹

Data understanding

Maps cross-app relationships across files, threads, issues, and owners

Retrieves isolated text chunks by vector proximity

Citation precision

Paragraph, row, and page level with confidence levels

File-name level

Structured & unstructured data

Both

Unstructured only

Visual citations

Every document type, with confidence level

Matching text only

Model flexibility

Extensibility

Builder-centric SDK and API for custom agentic workflows

APIs and MCP server for embedding Onyx in other apps

Pricing

Free to self-host

Per-seat SaaS, enterprise minimums

¹Onyx describes an LLM-derived knowledge-graph layer used to improve search relevance on top of vector retrieval. Pipeshub is graph-native: the knowledge graph is the primary structure for retrieval and reasoning, modeling relationships across applications, not a layer added on top of vector search.

SECURE BY DESIGN.

Every document keeps its original permissions, with SOC 2 Type II compliance in place

Permission-Aware

Role-based access control with granular agent permissions, enforced end-to-end across all connectors.

Compliance Ready

SOC 2 Type II, ISO 27001, VAPT certified. Full audit trail for every query and agent action.

Deployment Flexibility

Cloud, VPC, on-premise, or air-gapped. You choose the security boundary.

Which Is Right

For You?

Both are open-source and self-hostable with no vendor access to your data.

The differences that matter are above the sovereignty line: how data is understood, how answers are verified, and what you can build on top.

Both platforms promise to unify your workplace data and make it searchable.

The difference is how much control, visibility, and extensibility you get once you look under the hood.

CROSS-APP CONTEXT

Teams whose questions span multiple apps, where the answer lives in the relationships between a Slack thread, a Jira ticket, and a runbook, not in any single document.

VERIFIABLE ANSWERS

Anyone who needs every answer backed by a visual citation and a confidence level, across every document type.

DEVELOPER-CENTRIC PLATFORM

Developers building proprietary, agentic workflows on a knowledge-graph backbone they can own and extend.

CONNECTED WORKFLOWS

Operations and engineering workflows over large, interconnected document sets.

STRUCTURED + UNSTRUCTURED DATA

Use cases that combine structured data (SQL, warehouses, APIs) with unstructured content.

CROSS-APP CONTEXT

Teams whose questions span multiple apps, where the answer lives in the relationships between a Slack thread, a Jira ticket, and a runbook, not in any single document.

CONNECTED WORKFLOWS

Operations and engineering workflows over large, interconnected document sets.

VERIFIABLE ANSWERS

Anyone who needs every answer backed by a visual citation and a confidence level, across every document type.

STRUCTURED + UNSTRUCTURED DATA

Use cases that combine structured data (SQL, warehouses, APIs) with unstructured content.

DEVELOPER-CENTRIC PLATFORM

Developers building proprietary, agentic workflows on a knowledge-graph backbone they can own and extend.

ENTERPRISE SEARCH

Teams whose primary need is reliable enterprise search across many tools.

SELF-SERVE PRICING

Buyers who want a transparent self-serve price and a free trial to start quickly.

MATURE & WIDELY ADOPTED

Organizations that want an established open-source project with a large community and a managed cloud option.

ENTERPRISE SEARCH

Teams whose primary need is reliable enterprise search across many tools.

MATURE & WIDELY ADOPTED

Organizations that want an established open-source project with a large community and a managed cloud option.

SELF-SERVE PRICING

Buyers who want a transparent self-serve price and a free trial to start quickly.

Frequently
Asked

Questions

Answers to the questions teams ask most when evaluating an open-source Onyx alternative.

If both are open source and both do agentic RAG, how is Pipeshub different from Onyx?

The difference is the retrieval engine. Onyx uses vector retrieval, which fetches isolated chunks of text by similarity. Pipeshub adds a knowledge graph on top of vector and agentic RAG, so it understands the relationships between pieces of content, across applications. In practice, Onyx finds the documents that mention a topic; Pipeshub connects the Slack alert, the Jira issue, and the Confluence runbook into a single contextual answer.