LLM Orchestration

AI RAG

RAG system template for AI developers

Status
Generally available
Last checked
Checked · Oct 15, 2026
License
Open source
Pricing
Free tier · from $0
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What it is

DCYFR AI RAG starter provides a framework for building retrieval-augmented generation systems, enabling developers to integrate AI-driven content retrieval and generation into their applications.

DCYFR AI RAG starter offers a streamlined, open-source approach for developers looking to integrate retrieval-augmented generation into their applications without the complexity of building from scratch.

When it fits — and when it doesn’t

A good fit for

  • Teams looking to quickly prototype and deploy RAG systems using JavaScript

  • Individual developers who need a flexible starting point for AI projects without the overhead of setting up from scratch

Look elsewhere if

  • Projects requiring real-time data processing capabilities beyond what this template provides

  • Developers seeking a fully managed service or platform as opposed to a self-hosted library

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

  1. 01

    Limited language support beyond JavaScript and Python

    Primary focus on JavaScript with a secondary community-maintained TypeScript SDK, no official support for other languages

  2. 02

    Small community and limited third-party integrations

    GitHub repository has low activity and few contributors; limited plugins or extensions from the community

What it does well

  • Template-based approach for RAG systems

  • Modular design for easy customization and extension

  • MIT-licensed open-source project

Pricing

Free tier
YesOpen source — free to use
Paid from
$0
Model
Flat rate
Enterprise plan
No

Pricing not independently confirmed — check the vendor’s page.

Performance benchmarks

How Fast Is It?

Set up AI RAG

Setup steps, common gotchas and what to check after 30 days.

Open the setup guide →
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