KI-Konzil/docs/index.md

CouncilOS Documentation Index

Type: Multi-Part Full-Stack Web Application Primary Languages: Python (Backend), TypeScript (Frontend) Architecture: LangGraph Cyclic Multi-Agent Pipeline + React Flow Visual Builder Last Updated: 2026-03-13

Project Overview

CouncilOS ("KI-Rat Baukasten") ist eine visuelle No-Code-Plattform zum Erstellen und Ausführen von Multi-Agenten-KI-Pipelines. Nutzer bauen per Drag & Drop einen "KI-Rat" aus spezialisierten Agenten, die in zyklischen Schleifen iterativ zusammenarbeiten, bis die gewünschte Ergebnisqualität erreicht ist.

Project Structure

Dieses Projekt besteht aus 2 Hauptteilen:

Backend (api)

• Type: FastAPI REST/WebSocket API + LangGraph AI Engine
• Location: backend/
• Tech Stack: Python 3.11+, FastAPI, LangGraph, SQLAlchemy (async), PostgreSQL, ChromaDB
• Entry Point: backend/main.py

Frontend (ui)

• Type: Next.js Single-Page Application mit React Flow Canvas
• Location: frontend/
• Tech Stack: Next.js 16, React, React Flow (@xyflow/react), Zustand, TypeScript
• Entry Point: frontend/app/page.tsx

Cross-Part Integration

• Frontend kommuniziert mit Backend via REST API (/api/councils/*, /api/runs/*) und WebSocket (/ws/council/{run_id})
• Blueprint-JSON ist das kanonische Austauschformat zwischen Frontend und Backend
• WebSocket-Events steuern die Echtzeit-Visualisierung des aktiven Agent-Nodes im Frontend

Quick Reference

Backend Quick Ref

• Stack: FastAPI, LangGraph, SQLAlchemy, PostgreSQL, ChromaDB
• Entry: backend/main.py
• Pattern: Service Layer → Agent Nodes → LangGraph StateGraph

Frontend Quick Ref

• Stack: Next.js, React Flow, Zustand, TypeScript
• Entry: frontend/app/page.tsx
• Pattern: React Flow Canvas → Blueprint Parser → API Client

Generated Documentation

Core Documentation

• Project Overview — Executive Summary und High-Level-Architektur
• Source Tree Analysis — Annotierte Verzeichnisstruktur

Existing Documentation

• Test Coverage Analysis — Testabdeckung und QA-Analyse

BMAD Planning Artifacts

• Product Brief — Produkt-Vision und Scope
• PRD — Product Requirements Document
• Architecture — Technische Architektur
• UX Design — UX-Spezifikation
• Epics & Stories — Epic- und Story-Breakdown
• Implementation Readiness — Implementierungs-Assessment
• PRD Validation Report — PRD-Qualitätsprüfung
• Project Context — AI-Kontext-Regeln

BMAD Implementation Artifacts

• Sprint Status — Aktueller Sprint-Stand
• Epic 1 Retrospective — Projekt-Setup & Infrastruktur
• Epic 2 Retrospective — LangGraph Engine Backend
• Epic 3 Retrospective — Visueller Baukasten Frontend
• Epic 4 Retrospective — Frontend-Backend-Integration
• Epic 5 Retrospective — Tools & God Mode
• QA E2E Tests — End-to-End-Testplan

Getting Started

Backend Setup

Prerequisites: Python 3.11+, PostgreSQL 16 (oder Docker)

cd backend
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
uvicorn main:app --reload

Frontend Setup

Prerequisites: Node.js 18+

cd frontend
npm install
npm run dev

Docker Compose (empfohlen)

Prerequisites: Docker, Docker Compose

cp .env.example .env
# API-Keys in .env eintragen
docker compose up -d

Tests ausführen

# Backend (pytest)
cd backend && pytest tests/ -v

# Frontend (vitest)
cd frontend && npm test

For AI-Assisted Development

This documentation was generated specifically to enable AI agents to understand and extend this codebase.

When Planning New Features:

UI-only features: → Reference: _bmad-output/planning-artifacts/architecture.md, frontend/app/components/

API/Backend features: → Reference: _bmad-output/planning-artifacts/architecture.md, backend/api/, backend/services/

Full-stack features: → Reference: All architecture docs + CLAUDE.md for conventions

New Agent Tools: → Reference: backend/tools/, Factory-Pattern in backend/services/dynamic_graph_builder.py

Deployment changes: → Reference: docker-compose.yml, backend/Dockerfile, frontend/Dockerfile

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Documentation generated by BMAD Method document-project workflow