ppt-master vs tradingagents
Two of the top projects in Agent frameworks and SDKs, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
ppt-master
Agent skill that turns documents into natively editable PowerPoint decks
tradingagents
LangGraph multi-agent framework simulating a trading firm to analyze tickers
Verdict
ppt-master ranks higher, 90 to 86. Of the 4 score parts we measure for both, ppt-master leads on 2, tradingagents on 1 and 1 is even.
- Adoptiontradingagents leads by 13 points, 92 to 79.
- FreshnessEven at 100.
- Maintenanceppt-master leads by 3 points, 99 to 96: close to even.
- Agent-readyppt-master leads by 70 points, 70 to 0: a wide gap.
Pick ppt-master if you are
People who want editable PowerPoint decks generated from documents by an agent
- Exports native PowerPoint objects (shapes, charts, tables), not flattened images
- Runs on your machine; only model API calls leave it
Pick tradingagents if you are
Researchers and developers experimenting with LLM agents for market analysis
- Many LLM providers, plus Ollama and any OpenAI-compatible endpoint such as vLLM or llama.cpp
- Quick and deep model tiers, each configurable with its own provider
| What we compare | ppt-master | tradingagents |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 79, popular | 92, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 99, healthy | 96, healthy |
| Easy to run | Not scored for this kind | Not scored for this kind |
| Agent-ready | 70, partly | 0, none |
| Facts from GitHub and the README | ||
| Stars | 62.1k | 111.2k |
| License | MIT (permissive) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Python | Python |
| Docker | No | Yes |
| GPU | Not needed | Optional |
| arm64 or Apple Silicon | Not stated | Not stated |
ppt-master
PPT Master is a workflow ("skill") that runs inside an agent-capable AI tool such as Claude Code, Cursor or Codex CLI. It reads source material like PDFs, DOCX and web pages, plans the argument, and exports a .pptx with native shapes, data-backed charts and tables, and slide masters via the template route. It can also fill existing decks, extract templates, and add transitions, animations and narration.
Who it is for: People who want editable PowerPoint decks generated from documents by an agent
Strengths
- Exports native PowerPoint objects (shapes, charts, tables), not flattened images
- Runs on your machine; only model API calls leave it
- Works with any agent-capable tool and model: Claude, GPT, Gemini, Kimi
- Also fills existing .pptx files and distills reusable templates
Weaknesses
- Output quality depends on the driving model; README recommends Kimi K3 or Claude with ~1M context
- Image-heavy decks need a separate AI image generation API such as gpt-image-2
- Needs an agent tool installed plus Python 3.10+; no standalone UI
- SmartArt is deliberately unsupported; one-shot decks still need manual polish
- no GPU
- Needs Python 3.10+, An agent-capable AI tool (Claude Code, Cursor, Codex CLI, etc.), pandoc (only for legacy formats like .doc, .odt, .rtf)
- Models: Kimi K3, Claude, GPT, Gemini, gpt-image-2
tradingagents
TradingAgents runs a team of LLM agents (fundamentals, sentiment, news and technical analysts, bull and bear researchers, trader, risk team, portfolio manager) that debate and produce a trading decision for a ticker and date. It is built on LangGraph, with a CLI and a Python API, and works with many hosted providers plus Ollama and OpenAI-compatible servers. The README states it is for research, not financial advice.
Who it is for: Researchers and developers experimenting with LLM agents for market analysis
Strengths
- Many LLM providers, plus Ollama and any OpenAI-compatible endpoint such as vLLM or llama.cpp
- Quick and deep model tiers, each configurable with its own provider
- Point-in-time data for backtests, with SEC EDGAR statements as originally filed
- Interactive CLI, flag-driven non-interactive runs, and a Python API
Weaknesses
- Approved orders go only to a simulated exchange; no live broker execution described
- Results vary with model, temperature and data quality; README says research use only
- Past-dated runs withhold Yahoo fundamentals and insider trades for non-SEC companies
- Needs a provider API key; import paths changed in v0.5.1
- GPU optional
- Docker + Compose
- Needs Python 3.11+, LangGraph, LLM provider API key or Ollama, Yahoo Finance, SEC EDGAR, Alpha Vantage (optional), FRED (optional)
- Models: OpenAI, Anthropic, Google Gemini, xAI, DeepSeek
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