LLM Orchestration

MongoDB RAG

RAG library for MongoDB Vector Search

Status
UnmaintainedGitHub repository has been archived.
Last checked
Checked · Oct 16, 2026
License
Open source
Pricing
Free tier · from $0
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What it is

A Retrieval Augmented Generation library that leverages MongoDB's vector search capabilities to enhance data retrieval and generation processes.

MongoDB RAG stands out as a specialized library that integrates Retrieval Augmented Generation directly into MongoDB's vector search capabilities, offering developers a unique way to enhance their data retrieval processes.

When it fits — and when it doesn’t

A good fit for

  • Developers looking to integrate RAG workflows into their MongoDB applications

  • Projects requiring efficient vector-based search capabilities within a NoSQL database environment

  • Teams that need to enhance AI models with context-aware data retrieval from MongoDB

Look elsewhere if

  • Applications needing real-time streaming or batch-only architectures

  • Projects where the use of JavaScript is not feasible or preferred

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 Python and JavaScript

    Primary SDKs are in Python and JavaScript, other languages require community support

  2. 02

    Performance issues with large datasets

    Vector search performance degrades significantly with dataset sizes over 1 million documents

What it does well

  • Integration with MongoDB Vector Search for efficient data retrieval

  • Supports Retrieval Augmented Generation (RAG) workflows

  • Open-source and MIT licensed

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 MongoDB RAG

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

Open the setup guide →
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