Problem: Migrating a mid-size ROS1 package (~5K–10K LoC) takes a senior engineer 2–4 weeks of manual refactoring. With ROS1 Noetic EOL (May 2025), thousands of packages face abandonment.

Solution: One command to reforge your legacy robotics packages. ROSForge uses LLMs to understand code semantics — not just regex-replace — and delivers end-to-end migration with automatic build verification and fix loops.

What makes it unique:

• BYOM (Bring Your Own Model) — freely switch between Claude, Gemini, and OpenAI
• Full-spectrum migration — C++ (roscpp→rclcpp), Python (rospy→rclpy), Launch files (XML→Python), CMakeLists.txt, package.xml, msg/srv definitions
• Validate-Fix loop — auto colcon build + AI-driven error repair
• Interactive mode — pause at critical steps for human review
• Workspace-level batch migration with cross-package dependency resolution
• Custom rules via .rosforge/rules.yaml

Stack

Python 3.14 | Click CLI
Pipeline Architecture
├─ Ingest (Parsers)
├─ Analyze (Deps)
├─ Transform (AI)
├─ Validate (Build)
└─ Report (Diff)

Pipeline

rosforge migrate ./pkg
  ↓ Parse AST/IR
  ↓ Resolve dependencies
  ↓ AI transform (BYOM)
  ↓ colcon build
  ↓ Auto-fix if failed
  ↓ Generate report

What it does: A full-featured AI assistant that runs Gemini agents in isolated containers, delivered via Telegram with a 9-module real-time web dashboard.

Key differentiators vs NanoClaw (Claude-based):

Highlights:

• 7-package monorepo — each package reusable independently
• Plugin system — custom tools, hooks, routes, background services
• Fast Path — Direct Gemini API streaming with context caching (75-90% token cost reduction)
• Knowledge Base — per-group FTS5 full-text search
• Scheduling — natural language → cron ("every morning at 8am")
• Multi-modal — voice transcription, image generation (Imagen 3), document parsing
• Web Dashboard — 9-module real-time SPA with Socket.IO

Monorepo

nanogemclaw/
├── packages/
│   ├── core/
│   ├── db/
│   ├── gemini/
│   ├── telegram/
│   ├── server/
│   ├── plugin-api/
│   └── dashboard/
├── app/
├── container/
└── docs/

Dashboard Modules

Overview    | Logs
Memory      | Group Detail
Tasks       | Analytics
Knowledge   | Calendar
Settings

What it does: Input a stock ticker, get fair value estimates from 7 independent models — weighted by industry classification, validated by backtesting, and auto-adjusted through feedback loops.

7 Valuation Models:

System features:

• 3 interfaces — CLI (colored terminal), HTTP API (Express), n8n automation workflow
• LLM smart classification — auto-categorize stocks by industry with guardrails
• Backtest engine — 90d/180d accuracy validation with hit rate, MAE metrics
• Adaptive weights — feedback loop adjusts model weights based on backtest accuracy per category
• Portfolio management — sector allocation, risk metrics, performance tracking
• Alert system — price/valuation/classification change triggers → Telegram/Email push

Architecture

src/
├── api/         # FinMind API
├── models/      # 7 valuation models
│   ├── dcf.js
│   ├── dividend.js
│   ├── per.js
│   ├── pbr.js
│   ├── capex.js
│   ├── ev-ebitda.js
│   └── psr.js
├── llm/         # LLM guardrails
├── backtest/    # Accuracy engine
├── feedback/    # Adaptive weights
├── portfolio/   # Analytics
└── report/      # Synthesis

Data Pipeline

FinMind API (8 datasets)
  ↓ Parallel fetch
7 Models (parallel calc)
  ↓ Smart weighting
LLM Classification
  ↓ Backtest feedback
Final Recommendation
  ↓ n8n automation
Telegram / Email