daily_stock_analysis vs mindshub
Two of the top projects in Agent platforms, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
daily_stock_analysis
Daily LLM stock analysis for A-share, HK, US and other markets, pushed to chat apps
mindshub
Agent workspace for knowledge work, with swappable open agent harnesses
Verdict
daily_stock_analysis ranks higher, 80 to 78. Of the 3 score parts we measure for both, daily_stock_analysis leads on 2, mindshub on none and 1 is even.
- Adoptiondaily_stock_analysis leads by 12 points, 81 to 69.
- FreshnessEven at 100.
- MaintenanceWe do not score mindshub on this; daily_stock_analysis has 96.
- Easy to runWe do not score mindshub on this; daily_stock_analysis has 50.
- Agent-readydaily_stock_analysis leads by 45 points, 85 to 40: a wide gap.
Pick daily_stock_analysis if you are
Retail investors and self-hosters wanting scheduled LLM watchlist reports
- Runs on a GitHub Actions fork with no server; default schedule is weekdays 18:00 Beijing time
- Free data sources (AkShare, Baostock, YFinance) work with zero configuration
Pick mindshub if you are
Teams wanting a self-hostable agent workspace for research and automation
- Swappable open-source agent harnesses (Anton, Hermes) selectable from a dropdown
- Connectors for BigQuery, Postgres, Gmail, Drive, HubSpot, Notion and Linear with per-connection credentials
| What we compare | daily_stock_analysis | mindshub |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 81, widely used | 69, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 96, healthy | Not scored for this kind |
| Easy to run | 50, easy | Not scored for this kind |
| Agent-ready | 85, ready | 40, minimal |
| Facts from GitHub and the README | ||
| Stars | 66.4k | 39.8k |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Sep 2026 |
| Last release | Sep 2026 | Apr 2026 |
| Language | Python | Makefile |
| Docker | Yes | Yes |
| GPU | Not needed | Not stated |
| arm64 or Apple Silicon | Mentioned | Not stated |
daily_stock_analysis
Python app that pulls quotes, K-lines, news and fundamentals for a watchlist across A-share, HK, US, Japan, Korea and Taiwan markets. It has an LLM write a decision report with score, trend, buy/sell levels and risks, then pushes it to WeCom, Feishu, Telegram, Discord, Slack or email. It runs on GitHub Actions, Docker or locally, with a FastAPI web workbench for history, backtests and strategy chat.
Who it is for: Retail investors and self-hosters wanting scheduled LLM watchlist reports
Strengths
- Runs on a GitHub Actions fork with no server; default schedule is weekdays 18:00 Beijing time
- Free data sources (AkShare, Baostock, YFinance) work with zero configuration
- Supports Gemini, Claude, OpenAI-compatible APIs, DeepSeek, Qwen and local Ollama models
- Six push channels plus a web UI with history, backtest, portfolio and 15 built-in chat strategies
Weaknesses
- README is mainly Chinese; English docs are in a separate file
- Free data sources are subject to rate limits and upstream changes; stability not guaranteed
- Report quality depends on a paid news-search API key; a search service is recommended
- Output is for study only; the disclaimer excludes investment advice and liability
- no GPU
- Docker + Compose
- Needs LLM API key or Ollama, News search API (Anspire, SerpAPI, Tavily, Bocha, Brave, MiniMax or SearXNG), Market data sources (AkShare, Baostock, YFinance; optional TickFlow, Tushare, Longbridge), Notification channel
- Models: Gemini, OpenAI-compatible, DeepSeek, Qwen, Claude
- port 8000
mindshub
Superproject that builds the MindsHub desktop (Electron) and web app, agent backend and data vault from git submodules. Agents handle research, reports, scheduled tasks and app or document artifacts, using the Anton (default) or Hermes harness and a model router across Claude, GPT, Gemini, DeepSeek, Qwen and Kimi. Also offered as a hosted web app and macOS/Windows installers.
Who it is for: Teams wanting a self-hostable agent workspace for research and automation
Strengths
- Swappable open-source agent harnesses (Anton, Hermes) selectable from a dropdown
- Connectors for BigQuery, Postgres, Gmail, Drive, HubSpot, Notion and Linear with per-connection credentials
- Cross-session memory, skill library and scheduled tasks included
- MIT license; deployable locally, in a VPC, on-prem or air-gapped
Weaknesses
- Linux has no installer; requires building from source with make and submodules
- Pro tier needed for all frontier models and private artifacts
- README gives no RAM, GPU or port requirements
- Bundled submodules carry their own licenses
- Compose
- Needs make, uv, Python 3.10-3.13, Electron
- Models: Claude, GPT, Gemini, DeepSeek, Qwen
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