318 lines
8.1 KiB
Markdown
318 lines
8.1 KiB
Markdown
# ❓ Frequently Asked Questions
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**The questions everyone asks (so you don't have to)**
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---
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## General
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**Q: Is this free?**
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A: The software is free and open source. You pay for:
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- LLM API usage (~$0.10-0.20 per analysis with Claude/GPT-4)
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- Free options exist (Google Gemini, Alpaca paper trading)
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**Q: Is this actually AI-powered or just buzzwords?**
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A: Actually AI-powered. Multiple LLM agents (using Claude/GPT-4) debate and analyze stocks. It's like having a team of analysts arguing about your trades.
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**Q: Will this make me rich?**
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A: No. This is a tool, not a crystal ball. Use it to inform decisions, not make them for you.
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**Q: Can I use this for real trading?**
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A: Yes, but start with paper trading! The Alpaca integration supports both.
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---
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## Setup
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**Q: Which LLM provider should I use?**
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A:
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- **Claude (Anthropic)**: Best reasoning, great for complex analysis
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- **GPT-4 (OpenAI)**: Faster, well-tested, slightly cheaper
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- **Gemini (Google)**: Free tier available, good for experimentation
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**Q: Do I need to know Python?**
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A: Not for basic use! The web interface is point-and-click. Python knowledge helps for customization.
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**Q: Docker or local install?**
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A: Docker is easier (one command). Local install gives you more control.
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**Q: What's in the .env file?**
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A: Your API credentials. These are secrets - never commit to git. Use `.env.example` as a template and fill in your actual keys.
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---
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## Features
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**Q: What's multi-agent analysis?**
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A: Instead of one AI opinion, you get multiple specialized agents:
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- Market Analyst (trends, technicals)
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- Fundamentals Expert (financials, ratios)
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- News Analyst (sentiment, events)
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- Trader (synthesizes everything into a decision)
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They literally debate the trade before giving you a signal.
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**Q: How accurate are the predictions?**
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A: We don't make predictions - we provide analysis. Accuracy depends on market conditions, which LLM you use, and what data is available. Backtest your strategies first!
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**Q: Can I customize the analysis?**
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A: Yes! Edit the agent prompts, add new analysts, change the debate process. It's all Python code.
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**Q: What stocks can I analyze?**
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A: Any US stock. Just provide the ticker symbol (NVDA, AAPL, TSLA, etc.). International stocks coming soon!
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---
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## Paper Trading
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**Q: What is paper trading?**
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A: Simulated trading with fake money but REAL market prices. Practice without risk.
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**Q: Does Alpaca paper trading cost money?**
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A: No! Completely free. You get $100,000 virtual dollars to play with.
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**Q: Can I test my strategy without paper trading?**
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A: Yes - use the backtesting framework. Simulate months of trading in seconds.
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**Q: How do I switch from paper to live trading?**
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A: Set `ALPACA_PAPER_TRADING=false` in your .env file. But seriously - practice more first!
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---
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## Technical
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**Q: What's the difference between the brokers?**
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A:
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- **Alpaca**: Free paper trading, easy API, US stocks only
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- **Interactive Brokers** (coming soon): Professional platform, global markets
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**Q: How do I add a new LLM provider?**
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A: Check `tradingagents/llm_factory.py` - add your provider following the existing pattern. PRs welcome!
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**Q: Can I run this on a server?**
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A: Yes! Docker makes it easy. Check [DOCKER.md](DOCKER.md) for deployment guides.
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**Q: How much does it cost to run?**
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A: Mostly LLM API costs. One analysis:
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- Claude: ~$0.15
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- GPT-4: ~$0.10
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- Gemini: Free (with limits)
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Running 24/7 with frequent analyses: Budget $50-200/month.
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**Q: Does this support real-time data?**
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A: Currently batch processing. Real-time streaming is on the roadmap!
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**Q: Can I integrate with other brokers?**
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A: Currently Alpaca and Interactive Brokers. Want to add another? Submit a PR or open an issue!
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---
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## Troubleshooting
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**Q: "API quota exceeded" - what do I do?**
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A: You hit your LLM provider's limit. Wait for reset or upgrade your plan.
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**Q: Analysis takes forever**
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A: Normal! Deep analysis with multiple agents takes 60-90 seconds. Grab coffee. It's worth the wait.
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**Q: My trades aren't executing**
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A: Check:
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1. Market is open (9:30 AM - 4 PM ET, Mon-Fri)
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2. Broker is connected (`connect` command)
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3. You have buying power
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4. Ticker symbol is valid
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**Q: Docker container keeps restarting**
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A: Check logs: `docker-compose logs`. Usually a missing .env or invalid API key.
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**Q: "Connection refused" on localhost:8000**
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A: Port 8000 is already in use. Try:
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```bash
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lsof -i :8000 # Find what's using it
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docker-compose down && docker-compose up # Restart containers
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```
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**Q: I see "ModuleNotFoundError"**
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A: Dependencies missing. Run:
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```bash
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pip install -r requirements.txt
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```
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**Q: Web UI is slow or freezing**
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A: Likely waiting for AI analysis. Check browser console for errors. Restart if needed: `docker-compose restart`
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---
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## Safety & Security
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**Q: Is my API key safe?**
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A: Yes - stored in .env which is gitignored. Never committed to repos. Good practice: rotate keys periodically.
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**Q: Can someone hack my trading account?**
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A: Use paper trading first! For live trading, use Alpaca's security features (2FA, IP whitelist).
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**Q: What data do you collect?**
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A: We don't collect anything. All analysis happens locally or via your API keys. Read our privacy policy for details.
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**Q: Is the code audited?**
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A: It's open source - you can audit it yourself! We encourage security reviews. Found a vulnerability? Report it responsibly.
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---
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## Contributing
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**Q: Can I contribute?**
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A: Please do! We need:
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- New broker integrations
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- Better UI/UX
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- Strategy templates
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- Documentation improvements
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**Q: I found a bug - where do I report it?**
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A: GitHub Issues: https://github.com/TauricResearch/TradingAgents/issues
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**Q: Can I fork this for my own use?**
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A: Absolutely! It's open source. Just follow the license terms.
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**Q: How do I run tests?**
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A: Check the contributing guide. Generally:
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```bash
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pytest tests/
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```
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---
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## Advanced
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**Q: Can I backtest strategies?**
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A: Yes! Check `examples/backtest_example.py` for details.
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**Q: How do I add custom indicators?**
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A: Add them to `tradingagents/indicators/` and reference in your agents.
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**Q: Can I trade crypto?**
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A: Not yet. Stocks only for now. Crypto support is on the roadmap.
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**Q: Mobile app?**
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A: On the roadmap! Web app works great on mobile for now.
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**Q: Can I use this in production?**
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A: It's production-ready for personal use. For commercial use, consult your legal team.
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**Q: How do I scale this?**
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A: Docker deployment handles most scaling. For enterprise needs, check [DOCKER.md](DOCKER.md).
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---
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## Mistakes & Learning
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**Q: I made a bad trade with paper money - does it matter?**
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A: Nope! That's the whole point of paper trading. Make mistakes, learn, improve. Zero consequences.
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**Q: The AI recommended something stupid - should I blame it?**
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A: Nah. AI is a tool, not infallible. It's trained on data with limitations. Always do your own research.
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**Q: Can I see what the AI is thinking?**
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A: Yes! The analysis output shows each agent's reasoning. You're not flying blind.
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**Q: How do I get better at this?**
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A:
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1. Start with paper trading
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2. Analyze real trades with the AI
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3. Compare AI analysis to your own
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4. Backtest strategies
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5. Read the code and understand the logic
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6. Iterate and improve
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---
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## Performance & Optimization
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**Q: How fast is the analysis?**
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A: Typically 60-90 seconds for multi-agent analysis. Depends on LLM provider and market data availability.
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**Q: Can I speed it up?**
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A: Yes:
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- Use GPT-4 (faster than Claude for some queries)
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- Reduce the number of agents
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- Cache historical data
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- Use paper trading vs live (no latency)
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**Q: Does it work offline?**
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A: No - requires API access to LLMs and market data. But you could cache results for offline review.
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---
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## Getting Help
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**Didn't find your answer?**
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- Check the docs: [FEATURES.md](FEATURES.md), [DOCKER.md](DOCKER.md)
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- Ask on GitHub Discussions
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- Read the code (it's well-commented!)
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- Check the examples in `examples/`
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**Still stuck?**
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- Open a GitHub issue with:
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- What you tried
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- Error message (if any)
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- Your setup (Docker/local, Python version, OS)
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- Relevant logs
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We're here to help! 🤝
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---
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**Last Updated:** November 2025
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**Have a question not listed?** Open an issue and we'll add it here!
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