AI Architecture & Components
Deep dive into RepoSage's AI system architecture and planned components for GSoC 2025
🧠 AI Architecture & Components
RepoSage is building a comprehensive AI-powered platform for open source contribution management. This document outlines our AI architecture and the components available as GSoC 2025 projects.
GSoC Focus Areas
All components listed here are available as GSoC 2025 project ideas. Check the Ideas Page for detailed descriptions, requirements, and mentor information.
🏗️ Architecture Overview
┌─────────────────────────────────────────────────────────────────┐
│ PRESENTATION LAYER │
│ Next.js 15 | React 19 | MUI | Tailwind CSS │
└─────────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────────┐
│ AI ORCHESTRATION │
│ Multi-Agent System | Workflow Engine | Task Delegation │
└─────────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────────┐
│ AI INFERENCE LAYER │
│ Offline AI (SLM) | Cloud AI (LLM) | Hybrid Routing │
└─────────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────────┐
│ SECURITY LAYER │
│ Guardrails | Prompt Injection Detection | Compliance │
└─────────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────────┐
│ KNOWLEDGE LAYER │
│ RAG System | Vector DB | Knowledge Graph | Memory │
└─────────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────────┐
│ DATA LAYER │
│ PostgreSQL | Supabase | GitHub API | Real-time Sync │
└─────────────────────────────────────────────────────────────────┘🤖 Core AI Components
1. Multi-Agent Orchestration System
Status: GSoC 2025 Project (Large - 350h)
An intelligent system for coordinating multiple specialized AI agents that work together on complex tasks.
Key Features:
- Agent registry with capability discovery
- Dynamic task decomposition and delegation
- Inter-agent communication protocol
- Conflict resolution and consensus
- Real-time monitoring dashboard
Technologies: LangChain, OpenAI API, WebSockets, Redis
2. Hybrid Offline AI Layer (SLM + LLM)
Status: GSoC 2025 Project (Large - 350h)
A privacy-first AI system that intelligently routes requests between local Small Language Models and cloud-based Large Language Models.
Key Features:
- Local SLM inference with Ollama/llama.cpp
- Intelligent routing based on query complexity
- PII detection and data classification
- Seamless cloud fallback
- Offline-first caching
Technologies: Ollama, llama.cpp, ONNX Runtime, FastAPI
3. Visual AI Workflow Engine
Status: GSoC 2025 Project (Large - 350h)
A no-code/low-code visual workflow builder for creating AI-powered automation pipelines.
Key Features:
- Drag-and-drop workflow editor
- AI node library (LLM, Vision, Audio)
- Workflow version control
- Real-time execution monitoring
- Conditional branching and error handling
Technologies: React Flow, Bull Queue, Node.js, PostgreSQL
4. Secure AI Reasoning Pipeline
Status: GSoC 2025 Project (Medium - 175h)
A comprehensive security layer for AI applications with guardrails, validation, and compliance logging.
Key Features:
- Prompt injection detection
- PII redaction with Presidio
- Output validation rules
- SOC2/GDPR compliance logging
- Bias detection metrics
Technologies: NeMo Guardrails, Presidio, Prometheus
5. Context-Aware RAG System
Status: GSoC 2025 Project (Medium - 175h)
Advanced Retrieval-Augmented Generation with hybrid search and intelligent chunking.
Key Features:
- Intelligent document chunking
- Hybrid search (semantic + keyword)
- Context-aware re-ranking
- Multi-modal embeddings
- Query expansion
Technologies: LangChain, Pinecone/Weaviate, Sentence Transformers
6. AI Memory & Knowledge Graph
Status: GSoC 2025 Project (Medium - 175h)
Persistent memory system for AI agents with long-term context retention and relationship tracking.
Key Features:
- Episodic and semantic memory
- Automatic entity extraction
- Knowledge graph construction
- Memory consolidation
- Context retrieval with scoring
Technologies: Neo4j, LangChain, Sentence Transformers
📁 Source Code Organization
The AI components will be organized under src/lib/ai/:
src/lib/ai/
├── agents/ # Multi-agent orchestration
│ ├── registry.ts # Agent capability registry
│ ├── orchestrator.ts # Task delegation engine
│ └── protocols/ # Communication protocols
├── inference/ # SLM + LLM hybrid layer
│ ├── router.ts # Intelligent request routing
│ ├── slm/ # Local model inference
│ └── llm/ # Cloud model connectors
├── workflows/ # Visual workflow engine
│ ├── engine.ts # Execution engine
│ ├── nodes/ # AI node library
│ └── editor/ # React Flow components
├── security/ # Guardrails and safety
│ ├── guardrails.ts # Input/output validation
│ ├── pii.ts # PII detection
│ └── compliance.ts # Audit logging
├── rag/ # Retrieval system
│ ├── chunker.ts # Document chunking
│ ├── embeddings.ts # Embedding generation
│ └── retriever.ts # Hybrid search
└── memory/ # Long-term memory
├── episodic.ts # Conversation memory
├── semantic.ts # Knowledge extraction
└── graph.ts # Neo4j integration🛠️ Technology Stack
| Layer | Technologies |
|---|---|
| Orchestration | LangChain, LangGraph, OpenAI Function Calling |
| Local Inference | Ollama, llama.cpp, ONNX Runtime |
| Cloud Inference | OpenAI API, Anthropic API |
| Vector Storage | Pinecone, Weaviate, pgvector |
| Graph Database | Neo4j |
| Security | NeMo Guardrails, Presidio |
| Monitoring | OpenTelemetry, Prometheus, Grafana |
🚀 Getting Started
- Explore the Ideas: Visit /ideas for detailed project descriptions
- Read the Contributor Guide: Setup at /contributors-guide
- Understand the Architecture: Explore /projects
- Start Contributing: Find good first issues on GitHub
Join GSoC 2025!
These AI components represent cutting-edge work in AI systems. Apply for GSoC 2025 to build the future of AI-powered developer tools!