applyagent 0.3.3

AI-powered end-to-end job application pipeline

ApplyAgent

Applied to 1,000 jobs in 2 days. Fully autonomous. Open source.

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What It Does

ApplyAgent is a 6-stage autonomous job application pipeline. It discovers jobs across 5+ boards, scores them against your resume with AI, tailors your resume per job, writes cover letters, and submits applications for you. It navigates forms, uploads documents, answers screening questions, all hands-free.

Three commands. That's it.

pip install applyagent
pip install --no-deps python-jobspy && pip install pydantic tls-client requests markdownify regex
applyagent init          # one-time setup: resume, profile, preferences, API keys
applyagent doctor        # verify your setup — shows what's installed and what's missing
applyagent run           # discover > enrich > score > tailor > cover letters
applyagent run -w 4      # same but parallel (4 threads for discovery/enrichment)
applyagent apply         # autonomous browser-driven submission
applyagent apply -w 3    # parallel apply (3 Chrome instances)
applyagent apply --dry-run  # fill forms without submitting

Why two install commands? python-jobspy pins an exact numpy version in its metadata that conflicts with pip's resolver, but works fine at runtime with any modern numpy. The --no-deps flag bypasses the resolver; the second command installs jobspy's actual runtime dependencies. Everything except python-jobspy installs normally.

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Three Paths

Full Pipeline — Claude Code (recommended for best results)

Requires: Python 3.11+, Node.js (for npx), Gemini API key (free), Claude Code CLI, Chrome

Runs all 6 stages, from job discovery to autonomous application submission. This is the full power of ApplyAgent.

Full Pipeline — Local Model (completely free)

Requires: Python 3.11+, Chrome, local LLM server (Ollama/llama.cpp)

Same 6 stages, but auto-apply uses a local model instead of Claude Code. No API keys, no subscriptions, no Node.js. Just your hardware.

# Start Ollama with a capable model
ollama serve
ollama pull qwen2.5:32b    # or llama3.1, mistral, etc.

# Configure ApplyAgent for local
# In ~/.applyagent/.env:
#   LLM_URL=http://localhost:11434/v1
#   LLM_MODEL=qwen2.5:32b

applyagent apply --local   # uses local LLM + Python Playwright

You can also split models — use a small/free model for scoring and a larger one for the agent:

# In ~/.applyagent/.env:
GEMINI_API_KEY=xxx                          # free Gemini for scoring/tailoring
APPLY_LLM_URL=http://localhost:11434/v1     # local model for browser agent
APPLY_LLM_MODEL=qwen2.5:32b                # bigger model handles form-filling

Discovery + Tailoring Only

Requires: Python 3.11+, Gemini API key (free)

Runs stages 1-5: discovers jobs, scores them, tailors your resume, generates cover letters. You submit applications manually with the AI-prepared materials.

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The Pipeline

Stage	What Happens
1. Discover	Scrapes 5 job boards (Indeed, LinkedIn, Glassdoor, ZipRecruiter, Google Jobs) + 48 Workday employer portals + 30 direct career sites
2. Enrich	Fetches full job descriptions via JSON-LD, CSS selectors, or AI-powered extraction
3. Score	AI rates every job 1-10 based on your resume and preferences. Only high-fit jobs proceed
4. Tailor	AI rewrites your resume per job: reorganizes, emphasizes relevant experience, adds keywords. Never fabricates
5. Cover Letter	AI generates a targeted cover letter per job
6. Auto-Apply	Claude Code navigates application forms, fills fields, uploads documents, answers questions, and submits

Each stage is independent. Run them all or pick what you need.

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ApplyAgent vs The Alternatives

Feature	ApplyAgent	AIHawk	Manual
Job discovery	5 boards + Workday + direct sites	LinkedIn only	One board at a time
AI scoring	1-10 fit score per job	Basic filtering	Your gut feeling
Resume tailoring	Per-job AI rewrite	Template-based	Hours per application
Auto-apply	Full form navigation + submission	LinkedIn Easy Apply only	Click, type, repeat
Supported sites	Indeed, LinkedIn, Glassdoor, ZipRecruiter, Google Jobs, 46 Workday portals, 28 direct sites	LinkedIn	Whatever you open
License	AGPL-3.0	MIT	N/A

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Requirements

Component	Required For	Details
Python 3.11+	Everything	Core runtime
Node.js 18+	Auto-apply (Claude Code mode)	Needed for npx to run Playwright MCP server
Gemini API key	Scoring, tailoring, cover letters	Free tier (15 RPM / 1M tokens/day) is enough
Chrome/Chromium	Auto-apply	Auto-detected on most systems
Claude Code CLI	Auto-apply (Claude Code mode)	Install from claude.ai/code
Local LLM server	Auto-apply (local mode)	Ollama, llama.cpp, vLLM, or any OpenAI-compatible server

Gemini API key is free. Get one at aistudio.google.com. OpenAI and local models (Ollama/llama.cpp) are also supported.

Local auto-apply is completely free. Use applyagent apply --local with any local model. No API keys needed for auto-apply — just Chrome + a local LLM server.

Optional

Component	What It Does
CapSolver API key	Solves CAPTCHAs during auto-apply (hCaptcha, reCAPTCHA, Turnstile, FunCaptcha). Without it, CAPTCHA-blocked applications just fail gracefully

Note: python-jobspy is installed separately with --no-deps because it pins an exact numpy version in its metadata that conflicts with pip's resolver. It works fine with modern numpy at runtime.

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Configuration

All generated by applyagent init:

profile.json

Your personal data in one structured file: contact info, work authorization, compensation, experience, skills, resume facts (preserved during tailoring), and EEO defaults. Powers scoring, tailoring, and form auto-fill.

searches.yaml

Job search queries, target titles, locations, boards. Run multiple searches with different parameters.

.env

API keys and runtime config: GEMINI_API_KEY, LLM_MODEL, CAPSOLVER_API_KEY (optional). For local auto-apply, APPLY_LLM_URL and APPLY_LLM_MODEL let you use a different model for the browser agent than for scoring.

Package configs (shipped with ApplyAgent)

• config/employers.yaml - Workday employer registry (48 preconfigured)
• config/sites.yaml - Direct career sites (30+), blocked sites, base URLs, manual ATS domains
• config/searches.example.yaml - Example search configuration

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How Stages Work

Discover

Queries Indeed, LinkedIn, Glassdoor, ZipRecruiter, Google Jobs via JobSpy. Scrapes 48 Workday employer portals (configurable in employers.yaml). Hits 30 direct career sites with custom extractors. Deduplicates by URL.

Enrich

Visits each job URL and extracts the full description. 3-tier cascade: JSON-LD structured data, then CSS selector patterns, then AI-powered extraction for unknown layouts.

Score

AI scores every job 1-10 against your profile. 9-10 = strong match, 7-8 = good, 5-6 = moderate, 1-4 = skip. Only jobs above your threshold proceed to tailoring.

Tailor

Generates a custom resume per job: reorders experience, emphasizes relevant skills, incorporates keywords from the job description. Your resume_facts (companies, projects, metrics) are preserved exactly. The AI reorganizes but never fabricates.

Cover Letter

Writes a targeted cover letter per job referencing the specific company, role, and how your experience maps to their requirements.

Auto-Apply

An AI agent launches a Chrome instance, navigates to each application page, detects the form type, fills personal information and work history, uploads the tailored resume and cover letter, answers screening questions with AI, and submits. A live dashboard shows progress in real-time.

Two agent backends are available:

• Claude Code (default): spawns the claude CLI with Playwright MCP. Best results but requires an Anthropic subscription + Node.js.
• Local model (--local): uses your local LLM (Ollama/llama.cpp) + Python Playwright directly. Completely free, no external dependencies beyond Chrome.

applyagent apply                       # Claude Code mode (default)
applyagent apply --local               # Local model mode (free)
applyagent apply --local --dry-run     # Local mode, fill forms without submitting
applyagent apply --local -w 2          # Local mode, 2 parallel workers

# Utility modes (no Chrome/agent needed)
applyagent apply --mark-applied URL    # manually mark a job as applied
applyagent apply --mark-failed URL     # manually mark a job as failed
applyagent apply --reset-failed        # reset all failed jobs for retry
applyagent apply --gen --url URL       # generate prompt file for manual debugging

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CLI Reference

applyagent init                         # First-time setup wizard
applyagent doctor                       # Verify setup, diagnose missing requirements
applyagent run [stages...]              # Run pipeline stages (or 'all')
applyagent run --workers 4              # Parallel discovery/enrichment
applyagent run --stream                 # Concurrent stages (streaming mode)
applyagent run --min-score 8            # Override score threshold
applyagent run --dry-run                # Preview without executing
applyagent run --validation lenient     # Relax validation (recommended for Gemini free tier)
applyagent run --validation strict      # Strictest validation (retries on any banned word)
applyagent apply                        # Launch auto-apply (Claude Code)
applyagent apply --local                # Launch auto-apply (local LLM, free)
applyagent apply --workers 3            # Parallel browser workers
applyagent apply --dry-run              # Fill forms without submitting
applyagent apply --continuous           # Run forever, polling for new jobs
applyagent apply --headless             # Headless browser mode
applyagent apply --url URL              # Apply to a specific job
applyagent status                       # Pipeline statistics
applyagent dashboard                    # Open HTML results dashboard