AutoGPT vs daily_stock_analysis
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.
AutoGPT
Role-based AI agents coordinated by one lead agent, plus a visual workflow builder
daily_stock_analysis
LLM-driven daily analysis of watchlist stocks across six markets, pushed to chat apps
Verdict
AutoGPT ranks higher, 94 to 93. Of the 4 score parts we measure for both, AutoGPT leads on 1, daily_stock_analysis on 1 and 2 are even.
- AdoptionAutoGPT leads by 12 points, 100 to 88.
- FreshnessEven at 100.
- Maintenancedaily_stock_analysis leads by 9 points, 95 to 86.
- Agent-readyEven at 85.
Pick AutoGPT if you are
Teams delegating content, sales and recurring work to AI agents
- Single-container Docker image for self-hosting, plus an installer for Linux, macOS and Windows
- Visual builder with branching logic and a marketplace of reusable workflows
Pick daily_stock_analysis if you are
Retail investors and self-hosters wanting automated LLM stock reports
- Runs free on GitHub Actions with a fork and secrets; no server needed
- Works with Gemini, Claude, OpenAI-compatible APIs, DeepSeek, Qwen or local Ollama
| What we compare | AutoGPT | daily_stock_analysis |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 100, widely used | 88, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 86, healthy | 95, healthy |
| Agent-ready | 85, ready | 85, ready |
| Facts from GitHub and the README | ||
| Stars | 187.9k | 67.4k |
| License | custom license (read the license) | MIT (permissive) |
| Last commit | Nov 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Python | Python |
| Docker | Yes | Yes |
| GPU | Not stated | Not needed |
| arm64 or Apple Silicon | Mentioned | Mentioned |
AutoGPT
AutoGPT is a platform where a coordinator agent (Otto) hands work to configurable specialist agents called Experts, each with its own instructions, skills and connected apps. It also has a block-based visual workflow builder and a workflow marketplace. It runs as a paid hosted service or self-hosted from a single Docker image with your own model credentials.
Who it is for: Teams delegating content, sales and recurring work to AI agents
Strengths
- Single-container Docker image for self-hosting, plus an installer for Linux, macOS and Windows
- Visual builder with branching logic and a marketplace of reusable workflows
- Per-Expert account access and spending controls
- Self-hosting accepts your own model credentials or a local model
Weaknesses
- Platform code is Polyform Shield, not OSI open source; only classic/ is MIT
- Hosted Platform is paid; built-in model access is not included when self-hosting
- README gives no RAM or GPU requirements; they are in the linked guide
- Original standalone agent moved to classic/; main README centers on the platform
- Needs Docker, model provider credentials or a local model
- Models: bring-your-own model credentials, local models
- port 3000
daily_stock_analysis
Python app that pulls quotes, K-lines, news and fundamentals for A-share, HK, US, Japan, Korea and Taiwan tickers, then asks an LLM to produce a decision-dashboard report with score, trend, buy/sell levels and risks. It runs on a GitHub Actions schedule (weekdays 18:00 Beijing time), locally, or in Docker, and pushes results to WeCom, Feishu, Telegram, Discord, Slack or email. A web UI at port 8000 adds history, backtesting, portfolio and multi-turn strategy Q&A.
Who it is for: Retail investors and self-hosters wanting automated LLM stock reports
Strengths
- Runs free on GitHub Actions with a fork and secrets; no server needed
- Works with Gemini, Claude, OpenAI-compatible APIs, DeepSeek, Qwen or local Ollama
- Free data sources (AkShare, Baostock, YFinance) allow zero-config start
- Pushes to six channels: WeCom, Feishu, Telegram, Discord, Slack, email
Weaknesses
- Free market data sources are rate-limited and stability is not guaranteed
- Report quality depends on separately configured news search API keys
- README and config docs are mainly in Chinese; English is a separate file
- Not investment advice per its own disclaimer; outputs are LLM-generated
- no GPU
- Docker + Compose
- Needs LLM API key (or Ollama), Notification channel webhook/token, Optional: Tushare, Longbridge, SerpAPI, Tavily, Anspire, SearXNG
- Models: Gemini, Claude, OpenAI-compatible, DeepSeek, Qwen
- port 8000
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