Self-hosted, AI-powered resume builder for software developers.
One sentence in → polished, ATS-optimised resume out — running entirely on your own machine.

StackResume.mp4

1. What it does
2. Why self-host?
3. Quick start (Docker)
4. Build from source
5. Features
6. Tech stack
7. Architecture & pipeline
8. Configuration
9. Running without Docker
10. Testing
11. Contributing
12. License

StackResume turns minimal input into a polished, interview-ready software-developer resume. The multi-agent pipeline:

• Parses your message into structured data, with optional persistent profile memory injected automatically.
• Generates a complete resume JSON with STAR-format bullets and quantified metrics.
• Reviews & enhances in a loop — a Reviewer agent scores ATS compatibility, writing quality, impact, and completeness (0–100 each), then an Enhancer rewrites weak bullets and injects missing keywords. Loops until the score crosses MIN_QUALITY_SCORE (default 82) or hits MAX_REVIEW_ITERATIONS (default 3).
• Tailors — paste a job description to unlock a cover letter and a library of outreach email templates alongside the resume.
• Exports to PDF, Word (.docx), or OpenDocument (.odt) across five visual templates.

Everything is wrapped in a chat-style web UI with persistent sessions, search, favourites, an application tracker, and a live LLM activity log.

StackResume is built to live on your infrastructure — a laptop, a homelab box, a private VPS, a corporate VM. Nothing is rented, nothing phones home.

• Your data stays put. Sessions, profile, master resumes, and uploads sit in a single SQLite file under the bind-mounted ./data directory. Back it up, encrypt it, move it between machines — it's a flat file.
• No SaaS, no telemetry, no tracking. The container talks to one place: whichever LLM provider you configure. Nothing else leaves the box.
• Bring your own key — or skip keys entirely. Plug in OpenAI / Anthropic / Google Gemini, or point at a local Ollama install for fully offline, zero-cost inference. No vendor lock-in.
• Open source under GPL-3.0. Read it, audit it, fork it, patch it. The whole pipeline — agents, prompts, document renderers — is in this repo.
• One container, anywhere Docker runs. Multi-arch image (amd64, arm64) — runs on a Raspberry Pi just as happily as on a bare-metal server.
• Optional single-user auth. Flip one env var to put it behind SHA-512 + session-token login if you expose it on a LAN or behind a tunnel.

No clone needed — pull the pre-built multi-arch image (amd64 / arm64) straight from Docker Hub.

Minimal:

docker run -d -p 8000:8000 -v ./data:/data sathvikrao/stackresume:latest

API keys and provider settings can be configured from the in-app ⚙️ Settings UI after the container is running.

services:
  stackresume:
    image: sathvikrao/stackresume:latest
    container_name: stackresume
    ports:
      - "8000:8000"
    volumes:
      - ./data:/data
    environment:
      # ── LLM Provider ────────────────────────────────────────────────────────
      - LLM_PROVIDER=google           # openai | anthropic | google | ollama | custom
      - LLM_MODEL=gemini-2.5-flash
      - LLM_TEMPERATURE=0.7           # 0 = deterministic, 1 = creative

      # ── API Keys (set the one matching LLM_PROVIDER) ────────────────────────
      - OPENAI_API_KEY=
      - ANTHROPIC_API_KEY=
      - GOOGLE_API_KEY=
      - OPENAI_BASE_URL=              # custom OpenAI-compatible endpoint only

      # ── Ollama (local inference, no key needed) ──────────────────────────────
      - OLLAMA_BASE_URL=http://host.docker.internal:11434

      # ── Pipeline tuning ──────────────────────────────────────────────────────
      - MAX_REVIEW_ITERATIONS=3       # max Reviewer→Enhancer loops
      - MIN_QUALITY_SCORE=82.0        # stop early once score crosses this (0–100)

      # ── Auth (off by default) ────────────────────────────────────────────────
      - AUTH_ENABLED=false
      - AUTH_USERNAME=admin
      - AUTH_PASSWORD=                # must be set when AUTH_ENABLED=true

      # ── LangSmith tracing (optional) ─────────────────────────────────────────
      - LANGSMITH_API_KEY=
      - LANGSMITH_PROJECT=stackresume
      - LANGSMITH_TRACING=false

      # ── Misc ─────────────────────────────────────────────────────────────────
      - CORS_ORIGINS=*                # collapses to same-origin when AUTH_ENABLED=true
      - DATABASE_URL=sqlite+aiosqlite:////data/resume_builder.db
      - DEBUG=false
    extra_hosts:
      - "host.docker.internal:host-gateway"   # needed for Ollama on the host
    restart: unless-stopped
    healthcheck:
      test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8000/api/health')"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 20s

docker compose up -d

Open http://localhost:8000 once the container is running.

Prefer building the image yourself? Clone the repo, drop in a provider key, and let docker compose do the rest.

git clone https://github.com/Sathvik-Rao/StackResume.git
cd StackResume
cp .env.example .env

# Google Gemini (default — fast and cheap)
LLM_PROVIDER=google
LLM_MODEL=gemini-2.5-flash
GOOGLE_API_KEY=AIza...

# OpenAI
# LLM_PROVIDER=openai
# LLM_MODEL=gpt-4o
# OPENAI_API_KEY=sk-...

# Anthropic Claude
# LLM_PROVIDER=anthropic
# LLM_MODEL=claude-sonnet-4-6
# ANTHROPIC_API_KEY=sk-ant-...

# Local Ollama — no API key needed
# LLM_PROVIDER=ollama
# LLM_MODEL=llama3.1

docker compose up --build

Open http://localhost:8000. The backend serves both the API and the frontend from the same port.

Nine specialised agents orchestrated with LangGraph — each resume goes through intent guarding, input parsing, optional JD analysis, generation, a Reviewer→Enhancer quality loop, and finalisation. If a job description is attached, a Cover Letter Writer and Outreach Writer run after finalisation.

Click 🎯 Tailor to JD, paste any JD, and drag the intensity slider (0–100):

Click 🧠 My Profile to store your identity, work history, projects, education, and target roles across five tabs. Profile data is injected into every pipeline run while the 🧠 Use memory toggle is on — a global preference that persists across chats, sessions, and reloads (stored server-side). Each generated reply also notes whether your master resume and/or profile memory were used, so it's clear later what fed the result.

Import / Export — download your profile as a portable JSON file and load it back on any install. Invalid files are rejected with a clear error.

Save any generated resume as a named master resume and mark one as the default — great for keeping a polished base and forking variants per role. Three ways to put them to work:

• ⭐ Use Master (input bar) attaches a master as the starting point for your next message, so you can tailor it to a JD or refine it through the pipeline.
• ⭐ New from Master (sidebar) opens a fresh chat with the master loaded verbatim — no AI changes. Click the main button for your default, or the caret to pick any saved master. Ideal for forking a polished base, or just attaching the application tracker to an untouched copy.
• Compare to Master on any resume version diffs it against a chosen master, so you can see exactly what tailoring changed relative to your base.

Every resume has an ✏ Edit tab for inline editing of every section — summary, bullets, skills, education, etc. 💾 Save edits stamps metadata.manually_edited=true so subsequent agent runs preserve your changes. 🎯 Save & Re-score reruns only the Quality Reviewer — fresh scores in seconds without touching any content.

Five templates: Classic ATS (max ATS compatibility), Modern Clean (blue accents), Executive (dark slate), Dark Theme (deep navy), and LaTeX (serif, academic — Computer Modern with Font Awesome icons). Font size (9 / 10 / 11pt) and page cap (auto / 1 / 2) configurable per export. The Export modal includes a live side-by-side PDF preview that updates as you change options.

Each session carries an optional tracker — status, apply URL, account email, masked password, and notes. Status pills appear in the sidebar so you can scan your whole pipeline at a glance.

Switch provider and model at any time from ⚙️ AI Model Settings. Supported: OpenAI, Anthropic, Google Gemini, Ollama (local), and any OpenAI-compatible custom endpoint.

Set AUTH_ENABLED=true to require login. Passwords are SHA-512 hashed client-side; the server compares with secrets.compare_digest and issues a UUID session token valid for 24 h.

Key generation parameters are configurable via .env or the in-app settings UI:

Set LANGSMITH_API_KEY + LANGSMITH_TRACING=true to stream every LLM call to your LangSmith project. A Test trace button in Settings verifies the connection end-to-end.

                   ┌──────────────┐
                   │ Intent Guard │ ── off-topic ──▶ polite redirect ──▶ END
                   └──────┬───────┘
                          │ resume-related
                          ▼
                   ┌──────────────┐
                   │ Input Parser │
                   └──────┬───────┘
                          │ (jd_text present?)
              ┌───── yes ─┴─ no ─────┐
              ▼                      ▼
       ┌──────────────┐         ┌──────────────────┐
       │ JD Analyzer  │ ───────▶│ Resume Generator │
       └──────────────┘         └────────┬─────────┘
                                          ▼
                                ┌──────────────────┐
                                │ Quality Reviewer │◀─────────┐
                                └────────┬─────────┘          │
                                         │                    │
                          score ≥ MIN_QUALITY_SCORE           │
                          or iter ≥ MAX_REVIEW_ITERATIONS    no
                                         │                    │
                                       yes                    │
                                         ▼                    │
                                  ┌──────────────┐    ┌───────┴──────────┐
                                  │  Finalizer   │    │ Resume Enhancer  │
                                  └──────┬───────┘    └──────────────────┘
                                         │ (jd_analysis present?)
                          ┌──── yes ─────┴───── no ────┐
                          ▼                            ▼
                ┌──────────────────┐                  END
                │ Cover Letter     │
                └──────┬───────────┘
                       ▼
                ┌──────────────────┐
                │ Outreach Writer  │
                └──────┬───────────┘
                       ▼
                      END

The graph is compiled once at startup (RESUME_GRAPH) and reused across requests. Background execution runs in a thread via loop.run_in_executor so LangChain's synchronous streaming doesn't block the FastAPI event loop. Cancellation goes through an in-process threading.Event registry keyed by message id.

Settings flow through two layers:

1. Baseline — .env (or shell env) loaded at startup into the settings object.
2. DB overlay — the app_settings table stores live edits from the 🔑 API Keys / ⚙️ AI Models modals. Non-empty values here override the baseline; clearing a field falls back to baseline.

runtime_settings.py also writes keys like OPENAI_API_KEY into os.environ so LangChain instrumentation and LangSmith pick up new values without a restart.

cd backend
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

export LLM_PROVIDER=google
export LLM_MODEL=gemini-2.5-flash
export GOOGLE_API_KEY=AIza...
export DATABASE_URL="sqlite+aiosqlite:///./stackresume.db"

uvicorn app.main:app --reload --port 8000

Open http://localhost:8000. For the frontend, either rely on FastAPI's static mount or open frontend/index.html directly with a local static server behind the same origin.

The pytest suite covers API routes, LangGraph agent nodes, document generators, and pure helpers. Every LLM call is swapped for a deterministic FakeLLM — the full suite runs offline with no provider keys.

cd backend
pip install -r requirements-dev.txt

pytest                              # everything (~30s)
pytest tests/unit                   # pure functions
pytest tests/api                    # HTTP-level via httpx.AsyncClient
pytest tests/agents                 # LangGraph node + pipeline
pytest tests/documents              # PDF / DOCX / ODT smoke tests
pytest --cov=app --cov-report=term-missing

In Docker:

docker compose --profile test up --build --abort-on-container-exit

See backend/tests/README.md for fixture reference and contribution conventions.

See CONTRIBUTING.md for branch / test / PR conventions. Short version: open a PR against main, pytest must be green, and the CI bot will suggest which tests to add based on the changed files.

StackResume is free, open-source software released under the GNU General Public License v3.0 — see LICENSE for the full text.

You're free to run it, study it, modify it, and redistribute it. Derived works must remain GPL-3.0.