head to head · open source
Agent-Reach vs daily_stock_analysis
Agent-Reach has 82,763 GitHub stars, 7,236 forks, 137 open issues and last shipped 3 days ago. daily_stock_analysis has 65,204 stars, 54,570 forks, 44 open issues and last shipped 5 days ago. Agent-Reach leads on adoption by 27% (82,763 vs 65,204 stars). Agent-Reach is written in Python under MIT; daily_stock_analysis is written in Python under MIT. Agent-Reach has attracted 9% as many forks as stars, daily_stock_analysis 84%. Agent-Reach was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 1 topic tag (ai-agent), so they are genuine substitutes rather than adjacent tools.
Two open source projects, one decision. Both are free and self-hostable — the differences are community size, license terms, language stack and release pace.
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Side by side
| Agent-Reach | daily_stock_analysis | |
|---|---|---|
| GitHub stars | ★ 83K | ★ 65K |
| License | MIT | MIT |
| Written in | Python | Python |
| Last push | 2026-09-15 | 2026-09-13 |
| Forks | ⑂ 7.2K | ⑂ 55K |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick Agent-Reach if
- You weight community size — 83K stars and counting
- You want the MIT license terms
- Your stack matches Python
- You value the larger contributor base for long-term maintenance
pick daily_stock_analysis if
- You want the daily_stock_analysis feature set and don't need the biggest community
- You prefer the MIT license terms
- Your stack matches Python
- You evaluated both and daily_stock_analysis fits your workflow better
About Agent-Reach
Agent Reach is an open source Python command line tool that gives AI agents the ability to read and search the wider internet — Twitter/X, Reddit, YouTube, GitHub, Bilibili, and XiaoHongShu among others — for developers and agent builders who want that reach without paying for platform APIs.
read the full Agent-Reach overview →
About daily_stock_analysis
daily stock analysis is an MIT licensed Python system that uses large language models to analyse self selected stocks across the A share, Hong Kong, US, Japanese, Korean and Taiwanese markets and push a daily decision dashboard to chat and email channels, aimed at individual investors and quant minded developers who want scheduled AI analysis without operating a server.
read the full daily_stock_analysis overview →
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Frequently asked questions
Is Agent-Reach or daily_stock_analysis more popular?
Agent-Reach has 82,763 GitHub stars and daily_stock_analysis has 65,204. Agent-Reach has the larger community by that measure.
Are Agent-Reach and daily_stock_analysis free?
Both are open source. Agent-Reach is licensed under MIT and daily_stock_analysis under MIT. Neither carries a licence fee.
What is the difference between Agent-Reach and daily_stock_analysis?
Agent-Reach is written in Python and daily_stock_analysis in Python. The practical differences are community size, licence terms, language stack and release cadence — all compared in the table above.
Which should I choose, Agent-Reach or daily_stock_analysis?
Choose Agent-Reach if you want the larger community (82,763 stars) or its MIT licence terms. Choose daily_stock_analysis if its feature set, stack or MIT licence fits better. Both are self-hostable.