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Unoplat-CodeConfluence - The Universal Code Context Engine

🎯 Vision

Unoplat-CodeConfluence aims to be the definitive solution for extracting, understanding, and providing precise code context across repositories and domains. By combining deterministic code grammar with state-of-the-art LLM pipelines, we achieve human-like understanding of codebases in minutes rather than months. Our graph-based architecture ensures relationships and context are preserved at every level.

🌟 Why Unoplat-CodeConfluence?

  • Precision First: Built for deterministic and accurate code context extraction using Antlr and tree-sitter grammars
  • AI-Powered Understanding: Advanced LLM pipelines that comprehend code relationships and semantics just like humans based on inhouse graph based structures and parsing algorithms.
  • Graph Intelligence: Uses graph databases for both ingestion and querying, enabling deep contextual understanding
  • Enterprise-Grade Reliability: Powered by workflow orchestration for scalable and reliable processing
  • Cross-Repository Intelligence: Understand relationships between codebases across your domain
  • Comprehensive Metadata: Parse and analyze package dependencies and project configurations
  • Integration Ready: Designed to enhance existing AI coding tools and knowledge bases
  • Open Source Foundation: Built transparently with and for the developer community

🤝 For Knowledge Engine and AI Coding Assistants' Integration Partners

Unoplat-CodeConfluence provides:

  • High-precision code context API powered by graph-based retrieval
  • Cross-repository semantic understanding through LLM pipelines
  • Reduced operational complexity for context extraction
  • Ready-to-use integration with popular tools like OpenDevin, Devon, Danswer, and Continue Dev

🔍 OSS Atlas Initiative

Our UnoplatOssAtlas project is designed to dramatically accelerate contributor onboarding and productivity in open-source projects. By providing deep, contextual understanding of popular repositories, we help developers:

  • Accelerate Onboarding: Understand complex codebases in minutes instead of months
  • Boost Contribution Velocity: Make meaningful contributions faster with deep contextual insights
  • Navigate Complex Systems: Easily understand dependencies, patterns, and architectural decisions
  • Learn Best Practices: Study and adopt patterns from well-established open-source projects

This initiative demonstrates our commitment to:

  • Empowering the open-source ecosystem by reducing barriers to contribution
  • Showcasing practical applications of our context extraction capabilities
  • Supporting sustainable open-source development through better understanding and reducing time to value for oss projects.

🚀 Getting Started

Ready to enhance your development workflow? Check out our Quick Start Guide We ran our code graph capability on Onyx for its python backend and here is the result available on result

Project Status

ALPHA

Programming Language Support

Language In-POC Alpha Beta Stable
Python ✓ ✓ ✓
Java ✓
TypeScript
Go

Tasks Status

Task Research POC Released
Code Grammar ✓ ✓ ✓
Integration With Workflow Orchestrator ✓ ✓
Data Modelling for Code Summarisation ✓ ✓
SOTA LLM Pipelines For Code Grammar Summarisation/Reports ✓ ✓
Automatic Documentation ✓
Data Modelling for Graph ✓ ✓
Insertion into Graph Database ✓ ✓
GraphRag based Query Module
SDK for integration

Code Grammar

Python

Feature Beta Stable Limitations
Package Parsing ✓
Package Metadata Parsing - Poetry and PIP ✓
Inheritance ✓
Function Parsing ✓
Class Parsing ✓
Procedural Code Parsing ✓
Global Variable Parsing ✓
Function Call Parsing ✓ Certain function calls are not detected.
Class Variables/Instance Variables Parsing ✓ Not available per class/procedure. But available per function across functions.
Function Local Variable Parsing ✓
Function Return Type Parsing ✓ Return type is not captured properly.
Figuring out dependent internal classes ✓
Import Segregation ✓
Sorting functions within a class/procedure based on dependency ✓ Circular dependencies/recursion will not work as topological sort is used.

Note: For detailed limitations and resolutions that we are working towards refer to Limitations_Resolutions.md

Credits/heroes/supporters/Inspirations

These are the people because of which this work has been possible. Unoplat code confluence would not exist without them.

  1. Phodal from Chapi and ArcGuard
  2. [Ishaan & Krrish from Litellm]([email protected] / [email protected])
  3. Omar Khattab
  4. Joao Moura from crewai
  5. Vipin Shreyas Kumar
  6. Danswer
  7. Continue
  8. OpenDevin
  9. Devon
  10. Apeksha
  11. Argilla
  12. Jeremy Howard

Maintainers

  1. Jay Ghiya
  2. Vipin Shreyas Kumar

Book a call with us - Cal Link

Community Channel

UnoplatCodeConfluence Discord Channel

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