resume-matcher vs vibe-trading
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.
resume-matcher
Tailors resumes and cover letters to job descriptions using local or hosted LLMs
vibe-trading
Finance research agent for backtests, market data and broker connectors
Verdict
resume-matcher ranks higher, 81 to 78. Of the 5 score parts we measure for both, resume-matcher leads on 1, vibe-trading on 2 and 2 are even.
- Adoptionvibe-trading leads by 8 points, 74 to 66.
- FreshnessEven at 100.
- Maintenancevibe-trading leads by 3 points, 99 to 96: close to even.
- Easy to runEven at 67.
- Agent-readyresume-matcher leads by 85 points, 85 to 0: a wide gap.
Pick resume-matcher if you are
Job seekers who want to self-host resume tailoring with local or API LLMs
- Works with Ollama locally and with OpenAI, Anthropic, Gemini, OpenRouter, DeepSeek and Groq
- SQLite by default, so no separate database is required
Pick vibe-trading if you are
Quant researchers and traders who want an LLM agent for backtesting
- Grounding gate checks each reported figure against session tool evidence and cuts failures
- Backtest run cards cite metrics only where saved CSV output agrees
| What we compare | resume-matcher | vibe-trading |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 66, popular | 74, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 96, healthy | 99, healthy |
| Easy to run | 67, easy | 67, easy |
| Agent-ready | 85, ready | 0, none |
| Facts from GitHub and the README | ||
| Stars | 28.8k | 36.2k |
| License | Apache-2.0 (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Python | Python |
| Docker | Yes | Yes |
| GPU | Optional | Not needed |
| arm64 or Apple Silicon | Mentioned | Mentioned |
resume-matcher
Resume Matcher takes a master resume (PDF or DOCX) and a pasted job description, then generates a tailored resume, cover letter and optional interview prep. It scores the match, highlights keywords, and exports PDFs from 10 templates through headless Chromium. The backend is FastAPI with LiteLLM, the frontend is Next.js, and storage is SQLite by default with optional PostgreSQL.
Who it is for: Job seekers who want to self-host resume tailoring with local or API LLMs
Strengths
- Works with Ollama locally and with OpenAI, Anthropic, Gemini, OpenRouter, DeepSeek and Groq
- SQLite by default, so no separate database is required
- Tailoring queue processes several job descriptions in the background and resumes after restart
- Multi-arch Docker images (amd64, arm64) serve app and API on port 3000
Weaknesses
- Manual setup needs Python 3.13+, Node.js 24+ and uv
- Claude CLI and ChatGPT sign-in providers do not work in the default Docker setup
- Subscription-based providers depend on unofficial third-party access and may stop working
- RAM and GPU requirements are not stated
- GPU optional
- Docker + Compose
- Needs Python 3.13+, Node.js 24+, uv, Playwright Chromium, SQLite or PostgreSQL
- Models: Ollama, OpenAI, Anthropic, Google Gemini, OpenRouter
- port 3000
vibe-trading
Vibe-Trading is a Python agent (FastAPI backend, React 19 web UI, CLI, MCP) for trading research. It pulls market data across many exchanges, runs backtests and factor analysis, and checks figures in generated reports against tool evidence. It also connects to 18 brokers and sends scheduled reports through channels such as Email, Slack, Discord and Feishu.
Who it is for: Quant researchers and traders who want an LLM agent for backtesting
Strengths
- Grounding gate checks each reported figure against session tool evidence and cuts failures
- Backtest run cards cite metrics only where saved CSV output agrees
- 18 broker connectors, several read-only, with paper/live permissions documented
- Active: v0.1.16 shipped 116 merged PRs, with near-daily fixes afterward
Weaknesses
- Narrow domain: finance research only, not a general agent builder
- Some broker features are unavailable, e.g. Robinhood options execution
- Data quality depends on public sources such as Stooq, which can block requests
- Frequent backtest-correctness fixes in the changelog; verify results independently
- no GPU
- Docker + Compose
- Needs LLM provider (OpenRouter is the default), Market data sources, Broker APIs (optional)
- Models: OpenRouter, Codex, Copilot, custom model endpoints
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