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🤖 Benchmarking OpenClaw & Other AI Assistants

Dieses Repository enthält eine umfassende Testsuite und Benchmarking-Umgebung für verschiedene AI-Coding-Assistenten und OpenCode-Plugins. Das Ziel ist es, Metriken wie Ausführungsgeschwindigkeit, Token-Verbrauch und Code-Qualität in isolierten Docker-Umgebungen zu vergleichen.

📊 Benchmark-Ergebnisse & Ziel

Wir vergleichen die Leistung von AI Agents in standardisierten Szenarien.

Getestete Agents / Tools

Tool Verzeichnis Status Beschreibung
Ralphy /ralphy Ready PRD-Orchestrator mit striktem Workflow
OpenClaw /openclaw 🔧 Setup Autonomer Agent für komplexe Aufgaben
Oh My OpenCode /ohmyopencode Pending Plugin-Sammlung und Hilfsmittel
BMAD Method /bmadmethod Pending Experimentelle Methodik

🛠 Setup & Installation

Voraussetzungen

  • Docker & Docker Compose
  • OpenCode CLI (optional, für lokale Entwicklung)
  • Node.js 20+

Starten der Benchmarks

Jeder Agent befindet sich in seinem eigenen Verzeichnis mit einem passenden Dockerfile und run.sh Skript.

Beispiel: Starten von Ralphy

cd ralphy
./run.sh

Beispiel: Starten von OpenClaw

cd openclaw
./run.sh

Docker Naming-Convention

Das Projekt nutzt eine strikte Namenskonvention für Docker-Container, um Konflikte zu vermeiden und Filterung zu erleichtern.

  • Images: benchmark/<tool-name> (z.B. benchmark/ralphy)
  • Container: benchmark--<tool-name> (z.B. benchmark--ralphy)
  • Labels: project=benchmark, component=<tool-name>

Alle aktiven Benchmark-Container anzeigen:

docker ps --filter 'label=project=benchmark'

🧪 Standard-Tasks

Alle Agents werden gegen denselben Satz von Aufgaben getestet, um Vergleichbarkeit zu gewährleisten.

Detaillierte Aufgabenbeschreibung: 📄 benchmark/standard_tasks.md

  1. Setup: Initialisierung der Umgebung.
  2. Implementation: Umsetzung eines Features (z.B. Weather CLI).
  3. Refactoring: Code-Verbesserung.
  4. Testing: Schreiben und Ausführen von Tests.

📈 Analyse

Detaillierte Analysen und Gedanken zur Architektur finden sich im Ordner my_idea.


Erstellt von Enving