Resume Matcher tackles a common pain point for job seekers: tailoring resumes precisely to job descriptions with AI assistance. What sets it apart is its provider-agnostic architecture that integrates six different large language model (LLM) providers behind a unified abstraction layer called LiteLLM. This lets users run the system locally using Ollama or via popular cloud LLM APIs with the same prompt pipeline — a practical example of how to build multi-provider AI platforms.
what Resume Matcher does and its architecture
Resume Matcher is an open-source platform that uses AI to match and tailor resumes against job descriptions. The user workflow centers on creating a “master resume” which is then customized for specific job applications using AI-generated suggestions. Key features include AI-driven content improvement, keyword matching with match scoring, cover letter generation, and multi-template PDF export.
Under the hood, it uses a FastAPI backend written in Python, paired with LiteLLM — a multi-provider LLM abstraction layer that supports Ollama (local hosting), OpenAI, Anthropic, Gemini, OpenRouter, and DeepSeek. This abstraction means the backend can switch between LLM providers transparently, depending on availability, user preference, or deployment scenario.
The frontend is built with Next.js 16 and React 19, offering a modern web UI. Data storage is handled by TinyDB, a lightweight JSON file-based database, which suits the local-first approach without requiring heavyweight database installations.
For PDF export, the system uses Playwright with headless Chromium, allowing professional multi-template resume output. The whole platform supports local-first deployment with Ollama or cloud deployment via official Docker images for amd64 and arm64 architectures.
the strength in multi-provider LLM abstraction and tradeoffs
The standout technical feature is LiteLLM, a provider-agnostic LLM integration layer. By abstracting across six LLM providers, the system avoids vendor lock-in and offers flexibility in deployment and cost management. This is a practical demonstration of how to unify local and cloud LLMs in a single application.
The tradeoff here involves maintaining a consistent prompt pipeline and output handling across models that have different capabilities, rate limits, and cost structures. The codebase handles this by implementing a unified interface in LiteLLM, which simplifies the backend logic but requires careful prompt engineering and error handling.
The code quality for the backend is surprisingly clean for a multi-provider AI platform, with clear separation between LLM abstraction, resume processing logic, and API endpoints. The use of TinyDB for JSON storage is an opinionated choice favoring simplicity and ease of setup over scalability — suitable for individual or small team usage but likely a bottleneck for large-scale scenarios.
The frontend leverages modern React 19 and Next.js 16 features but does not impose heavy frontend state management libraries, keeping the DX straightforward. The PDF export via Playwright is an effective solution but adds a dependency on headless Chromium, which may increase resource usage during export.
quick start
Resume Matcher works by creating a master resume that you can use to tailor for each job application. Installation instructions here: How to Install
How It Works
- Upload your master resume (PDF or DOCX)
- Paste a job description you’re targeting
- Review AI-generated improvements and tailored content
- Cover Letter generator for the job application
- Customize the layout and sections to fit your style
- Export as a professional PDF with your preferred template
Stay Connected
Join our Discord for discussions, feature requests, and community support.
Follow us on LinkedIn for updates.
Star the repo to support development and get notified of new releases.
how to install
For detailed setup instructions, see SETUP.md (English) or: Español, 简体中文, 日本語.
Prerequisites
| Tool | Version | Installation |
|---|---|---|
| Python | 3.13+ | python.org |
| Node.js | 22+ | nodejs.org |
| uv | Latest | astral.sh/uv |
Quick Start
Fastest for MacOS, WSL and Ubuntu users:
### Docker Deployment
Official Docker images are published for `linux/amd64` and `linux/arm64` on:
- `ghcr.io/srbhr/resume-matcher`
- `srbhr/resume-matcher`
Run on a single public port (`3000`) with API available at `/api`:
```bash
docker run --name resume-matcher \
-p 3000:3000 \
-v resume-data:/app/backend/data \
ghcr.io/srbhr/resume-matcher:latest
Prefer pinning a version in production, for example ghcr.io/srbhr/resume-matcher:1.2.0 or
ghcr.io/srbhr/resume-matcher:1.2.
Using Ollama with Docker? Use
http://host.docker.internal:11434as the Ollama URL instead oflocalhost.
verdict
Resume Matcher is a solid example of how to build a multi-provider AI platform with a unified abstraction layer. The LiteLLM integration is the core technical strength, letting you switch between local and cloud LLMs without changing your prompts or backend logic.
It’s well suited for individual developers or small teams who want a local-first or hybrid deployment for AI-powered resume tailoring. The use of TinyDB and headless Chromium means the tool favors simplicity and ease of setup over large-scale performance or distributed deployment.
The tradeoff is clear: if you want to run this in a heavy production environment with many users, you’ll need to adapt the storage layer and possibly optimize PDF export. But for what it does—helping job seekers tailor resumes with AI across multiple LLM providers—it hits the mark with a clean and practical architecture.
If you’re intrigued by multi-LLM abstraction or need a flexible, open-source resume tailoring tool, Resume Matcher is worth a close look.
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→ GitHub Repo: srbhr/Resume-Matcher ⭐ 26,901 · TypeScript