Regular-RAG
Framework-agnostic RAG engine with built-in support for Knowledge Graphs and PostgreSQL/pgvector.
Pricing
Free tier
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
UnverifiedOverview
What is Regular-RAG?
Regular-RAG is a framework-agnostic Retrieval-Augmented Generation (RAG) engine that supports integration with Knowledge Graphs and PostgreSQL/pgvector, making it suitable for developers looking to enhance their applications with retrieval-augmented generation capabilities.
Key differentiator
“Regular-RAG stands out as a framework-agnostic RAG engine with built-in support for Knowledge Graphs, offering developers flexibility in integrating retrieval-augmented generation capabilities without vendor lock-in.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
API requires Python-specific patterns, TypeScript SDK is community-maintained
v0.1 to v0.2 migration required rewriting chain definitions
Documentation lacks detailed guides on advanced configurations with Knowledge Graphs and PostgreSQL/pgvector
Observed slow query response times and high memory usage with datasets exceeding 1 million records
Fit analysis
Who is it for?
✓ Best for
Teams building RAG apps who need seamless integration with PostgreSQL/pgvector and Knowledge Graphs
Developers looking for a framework-agnostic solution to integrate RAG capabilities into their applications
✕ Not a fit for
Projects requiring real-time streaming capabilities (batch-only architecture)
Budget-constrained projects where the cost of setting up and maintaining self-hosted infrastructure is prohibitive
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Alternatives
Works well with
Next step
Get Started with Regular-RAG
Step-by-step setup guide with code examples and common gotchas.