Vibe-Research: Personal AI Investment Research Dashboard
An open personal AI investment research agent for China A-shares (plus US/HK stocks) — aggregates market data and plugs into your own AI model for analysis.
Evidence shows: README explicitly states API keys are stored only in local browser and not uploaded; holdings/reports are stored only in local user directory; there is a VR_API_KEY auth middleware (tested in test_fixes.py). However, no user confirmation mechanism (e.g., before deletion/overwrite) is found; data flow transparency (e.g., what data is sent to AI) is not detailed. Dependency security: no vulnerability scanning or lock files (requirements.txt exists but not reviewed). External effects (e.g., network requests) are not explicitly restricted. Rollback: data migration exists but no version rollback mechanism. Source attribution: data sources are clearly labeled (Eastmoney, Tencent, etc.), but no data licensing evidence.
Evidence shows: tests cover tool layer, debate, reflection, API validation, and test comments indicate fixes for multiple issues (e.g., dirty data, timeouts). Dependency availability: tiered dependency design (akshare/mootdx lazy import) and degradation handling (test_fixes.py) show service does not crash when dependencies missing. Failure messages: tests show errors return error field or 400/404 status codes, and CLI timeout has clear message.
Evidence shows: README clearly defines target users (individual investors) and scenarios (A-share/US/HK investment research), capability boundaries (no stock recommendations, no predictions), trigger precision (e.g., code format validation) tested, environment fit (local deployment, CLI/API/MCP integration) detailed.
Evidence shows: README structure clear, install steps explicit, naming stable (version number single source), examples (quick start, AI integration), known limitations (e.g., CLI does not support multi-turn tool calls) stated, MIT license, CHANGELOG present, maintenance responsibility (author Simon) clear.
Evidence shows: output usability (dashboard, reports, debate results) well designed, marginal value (multi-agent debate, reflection audit) distinctive, cost-benefit (debate cost table) explained.
Evidence shows: README claims correspond to test code (e.g., debate flow, data gap handling), but cross-source corroboration insufficient (no evidence of multiple independent data sources cross-validated), fact-inference separation (dossier first, no speculation) clearly designed.
- No user confirmation mechanism found; delete/overwrite operations may be without prompt.
- Dependency security lacks vulnerability scanning or lock files; recommend reviewing requirements.txt and pinning versions.
- Data flow transparency insufficient; what data is sent to AI models is not detailed.
- External network requests not explicitly restricted; may access third-party data sources.
- Rollback mechanism missing; may not be able to revert after version upgrade.
What does this agent do, and when should you use it?
Vibe-Research is an open-source 'personal AI investment research dashboard' focused on China A-shares while supporting US and HK stocks. It combines market quotes, research reports, valuations, financials, announcements, capital flows, and news from public sources into a clean interface, and offers multiple ways to connect your own AI model (subscription CLI, API key, MCP) for tasks like daily review, Q&A, and bull/bear debates. The project consists of a FastAPI backend (port 8900) and a React frontend (port 5899), with bundled data engines: a-stock-data (v3.6.0), global-stock-data (v2.0.3), and investment-news. It runs out-of-the-box after cloning. The core design emphasizes objective data presentation without stock recommendations or predictions; all analytical conclusions are generated by your own AI.
Vibe-Research runs a FastAPI backend (backend/, port 8900) and a React frontend (frontend/, port 5899). The backend uses astock.py, gstock.py, newsradar.py to provide A-share, US/HK stock data, and news aggregation, with a tools layer (tools.py) exposing 23 data tools. Users can connect AI in three ways: 1) Subscription CLI (cli_runtime.py) calls locally logged-in CLIs like Claude Code or Codex using the user's subscription; 2) API integration (chat.py) supports OpenAI-compatible endpoints with function-calling to auto-invoke data tools; 3) MCP server (mcp_server.py) for agents like Claude Code. Key features include daily review (global indices, short-term sentiment, capital flows), news radar (108 RSS sources), stock detail (valuation, financials, capital flows), and bull/bear debate (debate.py): first fetch 13 objective data points as a fact base, then bull and bear researchers argue based on the same data, with a neutral moderator summarizing consensus and divergences — deliberately no buy/sell conclusion. Reflection audit (reflection.py) reviews existing analyses. Portfolio, watchlist, and report uploads are stored locally in ~/.vibe-research/ and never uploaded.
- Daily market review for individual investors: use the 'Daily Review' page to see indices, global markets, short-term sentiment, capital flows, and generate an AI summary.
- Fundamental research on individual stocks: enter a ticker (A-share, AAPL, 00700) in 'Stock Detail' to view valuation, financials, capital flows, reports, and ask AI for five-dimension analysis.
- News intelligence gathering: use the 'News Radar' page to extract today's key points from 108 public RSS sources, filtered by your watchlist.
- Bull/bear debate: initiate a debate on a stock to get structured arguments from both sides based on the same objective fact base, highlighting agreements, disagreements, and verification checklists.
- Portfolio management: log holdings and closed positions in 'My Portfolio' to track real-time P&L; upload your own research reports (PDF/Word) auto-categorized by industry, stored locally.
- Reflection and audit: send previous AI analyses to the 'Reflection Audit' to detect unsupported claims and identify weak points in reasoning.
What are this agent's strengths and limitations?
- Comprehensive data out-of-the-box: bundled A-share, US/HK stock, and news data sources work immediately without extra setup; snapshots ensure reliability.
- Flexible AI integration: supports subscription CLI (claude-code, codex), API key (DeepSeek, OpenAI, etc.), and MCP, giving users control over model choice and cost.
- Innovative multi-agent debate: the bull/bear debate uses an objective fact base and deliberately avoids buy/sell conclusions, helping users see divergences and verification checklists instead of making decisions for them.
- Strong local privacy: portfolio, watchlist, reports, and API keys are stored locally only, never uploaded, and survive redeployment.
- Clear compliance stance: no stock recommendations, no predictions, only objective data presentation — suitable for compliance-conscious users.
- Bull/bear debate is resource-heavy: one round takes ~100 seconds and 3 model calls, double for two rounds, adding latency and token costs.
- Requires users to bring their own AI model: without a subscription CLI or API key, core AI analysis is unavailable, requiring additional setup.
- Data sources are rate-limited: built-in throttling may still hit upstream limits during heavy use.
- Frontend uses React 19 and modern tooling; older Node.js environments may face compatibility issues.
How do you install or deploy this agent?
- Clone the repo: git clone https://github.com/simonlin1212/Vibe-Research
- Backend: cd backend && python3 -m venv .venv && .venv/bin/pip install -r requirements.txt
- Frontend: cd frontend && npm install
- Start backend: cd backend && .venv/bin/python -m uvicorn app:app --host 127.0.0.1 --port 8900
- Start frontend: cd frontend && npm run dev
- Open http://localhost:5899
No API key needed for data features; AI features require configuration in the 'AI Setup' page (CLI or API key).
How do you use this agent?
- After starting both servers, open the frontend.
- Configure AI in the 'AI Setup' page: choose Subscription CLI (requires a locally logged-in CLI) or API integration (paste key, baseURL auto-filled).
- On the 'Daily Review' page, view market data and click 'AI Daily Review' for a generated summary.
- On the 'Stock Detail' page, enter a ticker (e.g., 600519, AAPL, 00700) to view data and click 'Ask AI' for analysis.
- On the 'Watchlist' page, paste codes in bulk to add, toggle real-time quotes for auto-refresh.
- On the 'Bull/Bear Debate' page, select a stock and start a debate (~100 seconds), then review the consensus and disagreements.
- Log holdings in 'My Portfolio' and upload reports in 'My Reports' — all stored locally.
How does this agent compare with similar options?
Compared to open-source multi-agent financial frameworks like TradingAgents or ai-hedge-fund, Vibe-Research deliberately omits a trader/portfolio-manager role that outputs buy/sell/position-size decisions. Instead its multi-agent endpoint is divergence and verification checklists, positioning it as a research tool rather than an auto-trader.