
Free / Open-sourceBuild, deploy, and govern production-ready agentic AI workflows
Free / Open-sourceBuild, deploy, and govern production-ready agentic AI workflows

Build your own GenAI copilot with low code

This is our editors' assessment out of 5, not an average of user ratings. The viewpoints on this page are written by AI from what we know about the product. They are not customer reviews.
According to OpenAITools.app telemetry verified on 2026-11-11, RAGGENIE (raggenie.com) is an AI platform indexed under AI App Builder generating 166 monthly visits with a month-over-month traffic delta of +25 (+17.7%). Its primary geographic market is United Kingdom (100% traffic share), and it operates on a Free pricing model. Top competing alternatives by monthly volume include Dify (819.69K visits/mo), Base44 (14.04M visits/mo), Promptly AI (6.92K visits/mo).
Setting up custom RAG bots with my own docs took minutes!
Clean UI and great open-source codebase.
Chatting with my SQL database in plain English is awesome.
Documentation could use more deployment examples.
AI-synthesized perspectives grounded in live site inspection, pricing structure, and category benchmarks — presented as editorial viewpoints rather than individual customer testimonials.
Create a conversational copilot from the data you already have, without advanced technical skills.
Connect your tools and teams through out-of-the-box connectors available in the platform's directory.
Query and chat with your database in natural language to get answers without writing SQL.
Distribute the copilot to others, use it personally, or integrate it into your own application.
RAGGENIE is open source with a GitHub community and Slack channel, so you can run it locally and contribute.
RAGGENIE is a low-code platform for building your own GenAI copilot from data you already hold. Rather than requiring advanced technical skills, it lets you connect data sources through ready-made connectors and assemble a conversational AI tool that you can use personally, share with others, or embed into an existing app. A headline use case is chatting with your database in plain language, so non-engineers can ask questions of structured data without writing SQL.
The project is open source and community-oriented. The team maintains a GitHub community and a Slack channel, publishes tutorial videos, and offers a guide for running RAGGENIE locally — appealing to teams who want to self-host or extend the platform. The website also highlights an early-adopter programme with limited seats, where users can test and build tools on top of the platform with support.
Because RAGGENIE leans on the open-source and early-access model, the site does not present standard paid tiers; instead it invites visitors to book a demo and join the community. That makes it a good fit for technically comfortable builders and organisations who want control over their RAG stack.
Ask questions of a production database in plain language instead of writing complex SQL queries.
Add a conversational assistant powered by your documentation and data without hiring a large ML team.
Self-host RAGGENIE and extend it as an open-source project to build an internal knowledge assistant.
Side-by-side telemetry comparison of competing AI tools in the same primary category
| Rank | AI Tool | Monthly Visits | MoM Growth | Direct traffic share | Pricing | Head-to-Head |
|---|---|---|---|---|---|---|
| ● | RAGGENIE | 166 | +17.7% | 41% direct | Free | Current |
| 1 | Dify | 819.69K | -15% | 57% direct | Freemium | vs Dify |
| 2 | Base44 | 14.04M | +0.5% | 56% direct | Freemium, Paid, Contact for Pricing | vs Base44 |
| 3 | Promptly AI | 6.92K | -23% | 40% direct | Freemium | vs Promptly AI |
| 4 | MindStudio | 1.79M | +14.2% | 12% direct | Contact for Pricing | vs MindStudio |
| 5 | Softr | 587.27K | +3.2% | 53% direct | Freemium | vs Softr |
| 6 | Baserow | 391.71K | -5.9% | 63% direct | Freemium, Contact for Pricing | vs Baserow |

Free / Open-sourceBuild, deploy, and govern production-ready agentic AI workflows
Free / Open-sourceBuild, deploy, and govern production-ready agentic AI workflows

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