ContextRAG is currently under active development — follow the journey.

ContextRAG

Give LLMs the Context They Need

A Context Extension Platform for AI Systems

Developer → ContextRAG → LLM → Context-Aware Answer
Whitepaper Architecture System Design

What ContextRAG Does

ContextRAG sits between developers and Large Language Models. It retrieves relevant project knowledge and augments prompts so AI systems can generate responses grounded in real project context.

The Problem

Large Language Models like ChatGPT, Claude, and Gemini lack awareness of the developer’s project environment.

ContextRAG Architecture

ContextRAG retrieves project knowledge from a vector database and combines it with user queries before sending prompts to LLMs. This enables AI systems to generate responses grounded in real project context.

Key Features

Multi-LLM Support

Use OpenAI, Claude, Gemini or local models via Ollama.

Vector Knowledge Retrieval

Project documentation and code become searchable context.

Conversation Memory

Developer discussions become structured knowledge.

Event Driven Architecture

Kafka pipelines ingest documentation, repositories and logs.

Example Question

Developer asks:

"How does the Kafka ingestion pipeline work in this project?"

ContextRAG retrieves architecture documentation and past developer conversations, augments the prompt, and allows the LLM to generate an answer grounded in real project knowledge.

Building ContextRAG in Public

This project is being built step-by-step in public. Follow the journey as the platform evolves from prototype to full developer intelligence system.

Follow Development

ContextRAG is currently being built. Follow development and explore related projects.

View GitHub

The Vision

ContextRAG aims to become the contextual intelligence layer for AI systems — allowing AI to understand software architectures, codebases, developer discussions and production systems.

Documentation

Read Whitepaper Architecture Book System Design Playbook