🚀 LangGraph Agent System for Job Search

Intelligent job search system that uses LangGraph agents to search for jobs across multiple sources, extract contact information, and generate an interactive HTML report.

🆕 NEW LLM-ENHANCED FEATURES (v2.0)

The system now includes LLM-enhanced capabilities that make searches significantly smarter:

🎯 Adaptive Keywords: LLM generates optimized keywords for each source and region based on your profile (+30-50% better relevance)
🧠 Semantic Matching: Deep relevance analysis beyond simple keywords (+40-60% better precision)
🔄 Hybrid Approach: Combines fast heuristic matching with intelligent semantic analysis
🌍 Regional Adaptation: Keywords and analysis specific to Hispanic vs English-speaking regions

⚡ QUICK START ⚡

# 1. Install dependencies
pip install -r requirements.txt

# 2. Install Playwright
playwright install chromium

# 3. Configure .env (copy from env.example)

# 4. 🚀 START THE SYSTEM
python main.py

✨ Ready! The system will start searching for jobs automatically

⏱️ EXECUTION TIME ⏱️

⚠️ IMPORTANT: Estimated Process Time

The complete process normally takes between 30 minutes and 1.5 hours, depending on:

💻 Your machine specifications (processor, RAM, disk speed)
🌐 Your internet connection speed
📊 Number of jobs found in each source
🔍 Number of enabled sources in the configuration

💡 Recommendation: Let the system run and don't close the terminal. The system will show real-time progress and generate the HTML report when finished.

🎯 Features

🔍 Supported Job Sources

🔍 Multi-Source Search: Searches LinkedIn, RemoteOK, We Work Remotely, Stack Overflow Jobs, GitHub Jobs, Findjobit
🤖 Specialized Agents: Each source has its own optimized agent with advanced anti-bot techniques
📧 Intelligent Email Extraction: Uses LLMs to extract contact emails from job descriptions
🎯 Intelligent Matching: Calculates match score between jobs and your profile using heuristic matching + deep semantic analysis with LLM (hybrid)
🤖 Adaptive Keywords: Dynamically generates optimized keywords by source and region using LLM (NEW)
🧠 Semantic Analysis: Intelligent semantic matching that understands synonyms and real context (NEW)
📊 Interactive HTML Report: Generates an HTML report with filters, statistics, and visualizations
🔄 LangGraph Architecture: Coordinated workflow using LangGraph StateGraph for agent orchestration
🛡️ Anti-Bot Protection: Advanced system with User-Agent rotation, circuit breakers, adaptive rate limiting, and more
⚙️ Flexible Configuration: Environment variables to fully customize system behavior
✅ Comprehensive Testing: Full test suite with pytest for unit testing and validation
🏗️ Clean Architecture: Modular design with shared utilities, base classes, and proper error handling
📊 Configuration Validation: Pydantic models for type-safe configuration validation
🎯 Agent Skills System: LLM prompts organized as reusable skills following the agent-skills standard, enabling easy prompt management, versioning, and experimentation

🛠️ Tech Stack

Frameworks & Libraries

LLM Providers

Data & Config

Web Technologies

📋 Requirements

Python 3.9+
OpenAI or Anthropic API Key (for LLMs)
Playwright (for web scraping)
Resume file in Markdown format (or configure the path in environment variables)

🚀 Quick Installation

1️⃣ Clone or navigate to the project directory

cd job_search_agents

2️⃣ Install dependencies

pip install -r requirements.txt

3️⃣ Install Playwright browsers

playwright install chromium

4️⃣ Configure environment variables

Copy the example file and customize it:

# Windows
copy env.example .env

# Linux/Mac
cp env.example .env

Then edit the .env file with your values. See Environment Variables section for more details.

5️⃣ Configure user profile

Copy the profile example file:

# Windows
copy data\profile.json.example data\profile.json

# Linux/Mac
cp data/profile.json.example data/profile.json

Edit data/profile.json with your personal and professional information.

⚙️ Detailed Initial Configuration

📝 Required Environment Variables

The system requires the following variables to function:

Variable	Description	Example
`USER_EMAIL`	Your contact email	`your_email@example.com`
`USER_PHONE`	Your phone (with country code)	`+1234567890`
`ANTHROPIC_API_KEY`	Anthropic API key (if `LLM_PROVIDER=anthropic`)	`sk-ant-api03-...`
`OPENAI_API_KEY`	OpenAI API key (if `LLM_PROVIDER=openai`)	`sk-...`

⚠️ Important: The system will validate these variables at startup and fail with a clear message if they are missing.

🔑 API Key Configuration

To use Anthropic (Claude):

LLM_PROVIDER=anthropic
ANTHROPIC_API_KEY=your_api_key_here
LLM_MODEL=claude-3-5-sonnet-20241022

Get your API key at: https://console.anthropic.com/

To use OpenAI:

LLM_PROVIDER=openai
OPENAI_API_KEY=your_api_key_here
LLM_MODEL=gpt-4o-mini

Get your API key at: https://platform.openai.com/api-keys

📄 Resume Configuration

The system looks for your resume at the configured path. You can specify it in two ways:

Option 1: Absolute path (recommended for Windows)

CV_PATH=C:\Users\YourUser\Mi hoja de vida\CVs_Principales\CV_Dev_Senior_AI_Improvement.md

Option 2: Relative path (portable)

CV_PATH=../CVs_Principales/CV_Dev_Senior_AI_Improvement.md

Option 3: Default value

If you don't specify CV_PATH, the system will use:

CVs_Principales/CV_Dev_Senior_AI_Improvement.md

(relative to the project base directory)

📚 Complete Environment Variables

The env.example file contains all available variables with complete documentation. Here's a summary by category:

🔐 API Keys and LLM

Variable	Required	Description
`ANTHROPIC_API_KEY`	If `LLM_PROVIDER=anthropic`	Anthropic API key
`OPENAI_API_KEY`	If `LLM_PROVIDER=openai`	OpenAI API key
`LLM_PROVIDER`	No (default: `openai`)	`openai` or `anthropic`
`LLM_MODEL`	No (default: `gpt-4o-mini`)	Model to use

👤 User Profile

Variable	Required	Description
`USER_EMAIL`	✅ Yes	Contact email
`USER_PHONE`	✅ Yes	Phone with country code

🔍 Search Configuration

Variable	Default	Description
`MAX_JOBS_PER_SOURCE`	`50`	Maximum jobs per source
`MIN_MATCH_SCORE`	`60`	Minimum score to consider relevant (0-100)
`SEARCH_TIMEOUT`	`30`	Timeout in seconds
`FAST_MODE`	`false`	Enable fast mode (reduced delays, disabled human simulation)
`EMAIL_EXTRACTION_CONCURRENCY`	`10`	Number of parallel email extractions
`EMAIL_BATCH_SIZE`	`5`	Batch size for email extraction

🕷️ Scraping Configuration

Variable	Default	Description
`SCRAPING_DELAY`	`2.0`	Delay between requests (seconds)
`MAX_RETRIES`	`3`	Maximum number of retries
`HEADLESS_BROWSER`	`true`	Run browser without interface
`PAGE_LOAD_TIMEOUT`	`30000`	Page load timeout in milliseconds
`SELECTOR_TIMEOUT`	`10000`	Selector timeout in milliseconds
`REQUEST_TIMEOUT`	`30`	HTTP request timeout in seconds
`DESCRIPTION_MAX_LENGTH`	`2000`	Maximum length for job descriptions
`TITLE_DISPLAY_LENGTH`	`50`	Maximum display length for job titles

🛡️ Basic Anti-Bot Configuration

Variable	Default	Description
`USE_USER_AGENT_ROTATION`	`true`	Rotate User-Agent automatically
`RANDOM_DELAY_ENABLED`	`true`	Random delays between requests
`MIN_DELAY`	`1.5`	Minimum delay (seconds)
`MAX_DELAY`	`4.0`	Maximum delay (seconds)
`ENABLE_BROWSER_STEALTH`	`true`	Browser stealth mode
`SIMULATE_HUMAN_BEHAVIOR`	`true`	Simulate human behavior

🔧 Advanced Anti-Bot Configuration

Variable	Default	Description
`USE_CIRCUIT_BREAKER`	`true`	Enable circuit breaker
`CIRCUIT_BREAKER_THRESHOLD`	`5`	Errors before activating
`CIRCUIT_BREAKER_TIMEOUT`	`300`	Circuit breaker timeout (seconds)
`USE_SESSION_PERSISTENCE`	`true`	Maintain persistent sessions
`USE_ADAPTIVE_RATE_LIMITING`	`true`	Adaptive rate limiting
`USE_REFERER_HEADERS`	`true`	Use Referer headers
`USE_SESSION_WARMUP`	`true`	Session warm-up before scraping
`USE_QUERY_VARIATIONS`	`true`	Generate query variations with LLM

🤖 LLM-Enhanced Configuration (NEW)

Variable	Default	Description
`USE_ADAPTIVE_KEYWORDS`	`true`	Generate adaptive keywords by source/region with LLM
`USE_SEMANTIC_MATCHING`	`true`	Deep semantic relevance analysis with LLM
`SEMANTIC_MATCHING_THRESHOLD`	`50`	Minimum heuristic score for semantic analysis (0-100)
`SEMANTIC_MAX_JOBS`	`100`	Maximum jobs to analyze semantically
`SEMANTIC_WEIGHT`	`0.6`	Weight of semantic score in final score (0-1)
`HEURISTIC_WEIGHT`	`0.4`	Weight of heuristic score in final score (0-1, must sum 1.0 with SEMANTIC_WEIGHT)

📁 Path Configuration

Variable	Default	Description
`CV_PATH`	`CVs_Principales/CV_Dev_Senior_AI_Improvement.md`	Path to resume file
`OUTPUT_DIR`	`job_search_agents/results`	Output directory
`DATA_DIR`	`job_search_agents/data`	Data directory

📝 Logging Configuration

Variable	Default	Description
`LOG_LEVEL`	`INFO`	Level: `DEBUG`, `INFO`, `WARNING`, `ERROR`, `CRITICAL`
`LOG_FILE`	(optional)	Log file (if not specified, console only)

💾 Cache Configuration

Variable	Default	Description
`USE_CACHE`	`true`	Enable cache
`CACHE_EXPIRY_HOURS`	`24`	Cache expiration (hours)

🔗 Job Board API Keys (Optional)

Variable	Description
`LINKEDIN_API_KEY`	Official LinkedIn API key
`REMOTEOK_API_KEY`	RemoteOK API key (premium)

📖 Usage

🚀 Start the System

⚡ MAIN COMMAND ⚡

python main.py

🎯 Run this command to start the job search

The system will execute the following flow:

✅ Validate configuration: Verify that all required variables are configured
📄 Parse your resume: Extract information from your resume from the configured path
🔍 Search for jobs: Query all enabled sources in parallel
📧 Extract emails: Use LLMs to find contact emails in job descriptions
🎯 Calculate matches: Compare each job with your profile and assign a score
📊 Generate report: Create an interactive HTML file with results

Example Output

Example of the final output showing the HTML report generation, top 5 recommended jobs, search statistics, and total execution time (approximately 1 hour and 4 minutes in this example).

📂 Project Structure

job_search_agents/
├── agents/                      # 🤖 Specialized agents
│   ├── orchestrator.py          # Main orchestrator (LangGraph)
│   ├── linkedin_agent.py        # LinkedIn agent
│   ├── indeed_agent.py          # Indeed agent (disabled)
│   ├── remote_jobs_agent.py     # Remote jobs agent
│   ├── tech_jobs_agent.py       # Tech jobs agent
│   ├── findjobit_agent.py       # Findjobit agent (LATAM)
│   ├── email_extractor_agent.py # Email extraction
│   └── matcher_agent.py         # Profile matching
│   ├── cv_parser.py             # Resume parser
│   ├── html_generator.py         # HTML generator
│   ├── user_agent_rotator.py    # User-Agent rotation
│   ├── circuit_breaker.py       # Circuit breaker pattern
│   ├── adaptive_rate_limiter.py # Adaptive rate limiting
│   ├── url_utils.py             # URL utilities
│   ├── http_helpers.py          # HTTP helper functions
│   ├── job_enricher.py          # Job enrichment utilities
│   ├── exceptions.py            # Custom exceptions
│   └── ...                      # More anti-bot utilities
├── config/                      # ⚙️ Configuration
│   ├── settings.py               # System configuration
│   ├── validators.py            # Pydantic validators
│   ├── config_loader.py         # YAML config loader
│   └── job_sources.yaml         # Job sources and keywords
├── tools/                       # 🛠️ Support tools
│   ├── web_scraper.py           # Advanced web scraping
│   ├── base_api_client.py       # Base API client class
│   ├── api_clients.py            # API clients
│   ├── email_validator.py        # Email validation
│   └── http_client_strategy.py   # HTTP strategies
├── tests/                       # 🧪 Test suite
│   ├── conftest.py              # Pytest fixtures
│   ├── test_agents/             # Agent tests
│   ├── test_utils/              # Utility tests
│   └── test_config/             # Configuration tests
├── templates/                   # 📄 HTML templates
│   └── results_template.html
├── skills/                      # 🎯 Agent Skills (LLM prompts)
│   ├── email-extractor/         # Skill: email extraction
│   ├── query-variator/          # Skill: query variations
│   ├── keyword-generator/       # Skill: adaptive keywords (NEW)
│   └── semantic-matcher/        # Skill: semantic matching (NEW)
│   ├── email-extractor/         # Email extraction skill
│   │   └── SKILL.md
│   ├── job-matcher/             # Job matching skill
│   │   └── SKILL.md
│   ├── query-variator/           # Query variation skill
│   │   └── SKILL.md
│   └── README.md                # Skills documentation
├── data/                        # 💾 Data
│   ├── profile.json.example     # Profile example
│   └── profile.json              # Your profile (not uploaded to repo)
├── results/                      # 📊 HTML results
├── main.py                      # 🚀 Entry point
├── env.example                  # 📋 Environment variables example
├── .env                         # 🔐 Your variables (not uploaded to repo)
├── .gitignore                   # Files excluded from repo
└── requirements.txt             # 📦 Dependencies

⚙️ Advanced Configuration

🎯 Customize Search Keywords

Option 1: Static Keywords (Traditional)

Edit config/job_sources.yaml to change base keywords:

keywords:
  - "AI Engineer"
  - "LLMOps Engineer"
  - "Python Senior Developer"
  - "Machine Learning Engineer"
  # Add more keywords according to your profile

Option 2: Adaptive Keywords with LLM (Recommended - NEW)

If USE_ADAPTIVE_KEYWORDS=true (default), the system will automatically generate optimized keywords for each source and region based on your profile. Keywords in job_sources.yaml are used as a base and the LLM adapts them dynamically.

💡 Advantage: Adaptive keywords improve relevance by 30-50% compared to static keywords.

🔄 Filter Job Sources

You can enable/disable sources in config/job_sources.yaml:

job_sources:
  linkedin:
    enabled: true
    max_results: 50
  indeed:
    enabled: false  # Disabled: difficult access
    max_results: 50
  remoteok:
    enabled: false  # Disable this source

🎚️ Adjust Minimum Score

Control which jobs are shown in the report:

MIN_MATCH_SCORE=70  # Only jobs with score >= 70

🛡️ Anti-Bot Configuration to Avoid Blocks

If you experience frequent blocks, adjust these variables:

# Increase delays
SCRAPING_DELAY=3.0
MIN_DELAY=2.0
MAX_DELAY=5.0

# Enable all protections
USE_USER_AGENT_ROTATION=true
RANDOM_DELAY_ENABLED=true
ENABLE_BROWSER_STEALTH=true
SIMULATE_HUMAN_BEHAVIOR=true
USE_CIRCUIT_BREAKER=true

📝 Logging Configuration

For debugging, change the log level:

LOG_LEVEL=DEBUG
LOG_FILE=debug.log

📁 Customize Directories

# Custom path for results
OUTPUT_DIR=/custom/path/results

# Custom path for data
DATA_DIR=/custom/path/data

📊 Output

The system generates the following files:

1. 📄 Interactive HTML Report

File generated at results/job_search_results_YYYYMMDD_HHMMSS.html with:

✅ Executive summary: General statistics
📋 Jobs table: Sorted by match score
🔍 Interactive filters: By source, score, keywords
📧 Consolidated email list: All emails found
📈 Statistics by source: Job distribution
🎯 Top recommended jobs: Best matches

2. 📝 Profile JSON File

data/profile.json with profile extracted from resume (generated automatically).

3. 📋 Logs

Console: Real-time progress
File (if LOG_FILE is configured): Complete log for analysis

🔧 Troubleshooting

❌ Error: "USER_EMAIL environment variable is required"

Cause: Missing required variables configuration.

Solution:

Make sure you have a .env file in the job_search_agents directory
Copy env.example to .env if it doesn't exist

Configure USER_EMAIL and USER_PHONE in your .env:

USER_EMAIL=your_email@example.com
USER_PHONE=+1234567890

❌ Error: "ANTHROPIC_API_KEY environment variable is required"

Cause: Missing API key for the configured provider.

Solution:

If LLM_PROVIDER=anthropic, configure ANTHROPIC_API_KEY
If LLM_PROVIDER=openai, configure OPENAI_API_KEY
Verify that the variable is in your .env file

❌ Error: "No module named 'langgraph'"

Cause: Missing dependencies.

Solution:

pip install -r requirements.txt

❌ Error: "Playwright browser not found"

Cause: Playwright browser not installed.

Solution:

playwright install chromium

❌ Error: "CV file not found"

Cause: The resume file doesn't exist at the configured path.

Solution:

Verify that the file exists
Configure CV_PATH in your .env with the correct path:
```
CV_PATH=C:\full\path\to\your\resume.md
```
Or place your resume at the default path: CVs_Principales/CV_Dev_Senior_AI_Improvement.md

⚠️ LinkedIn blocking requests

Note: Indeed is disabled by default due to frequent blocks.

Cause: Too many requests or bot detection.

Solution:

Increase delays:

SCRAPING_DELAY=5.0
MIN_DELAY=3.0
MAX_DELAY=8.0

Enable all anti-bot protections
Consider using official APIs if available
Reduce MAX_JOBS_PER_SOURCE to make fewer requests

⚠️ Search timeout

Cause: Timeout too short or slow connection.

Solution:

SEARCH_TIMEOUT=60  # Increase timeout to 60 seconds

⚠️ Empty results

Cause: Keywords too specific or disabled sources.

Solution:

Review config/job_sources.yaml and verify that sources are enabled
Adjust keywords to be more general
Reduce MIN_MATCH_SCORE to see more results

🧪 Testing

The project includes a comprehensive test suite using pytest. Run tests to verify functionality:

Run All Tests

pytest

Run Tests with Coverage

pytest --cov=. --cov-report=html

Run Specific Test Categories

# Test utilities
pytest tests/test_utils/

# Test agents
pytest tests/test_agents/

# Test configuration
pytest tests/test_config/

Test Structure

tests/conftest.py: Shared fixtures and test configuration
tests/test_utils/: Tests for utility functions (URL utils, HTTP helpers, job enricher, etc.)
tests/test_agents/: Tests for agent functionality (email extractor, matcher, etc.)
tests/test_config/: Tests for configuration validation

🎯 Agent Skills System

The project uses an Agent Skills system inspired by the agent-skills standard to organize and manage LLM prompts. This approach separates business logic from LLM instructions, making prompts easier to maintain, version, and experiment with.

What are Skills?

Skills are reusable instruction sets stored in SKILL.md files with YAML frontmatter. Each skill contains:

Metadata: Name, description, version, tags
System Message: Instructions for the LLM's role
Human Message Template: User input template with variables
Documentation: Usage examples, input/output specifications, and best practices

Available Skills

The system includes the following skills:

email-extractor: Extracts contact emails from job descriptions using intelligent LLM analysis. Used by EmailExtractorAgent.
query-variator: Generates natural variations of search queries to appear more human-like. Used by QueryVariator utility.
keyword-generator: Generates search keywords dynamically adapted to profile, source, and region. Used by KeywordGeneratorAgent. (NEW)
semantic-matcher: Semantically analyzes relevance between jobs and candidate profile. Used by SemanticMatcherAgent. (NEW)

Benefits of the Skills System

Separation of Concerns: Business logic is separated from LLM instructions
Easy Maintenance: Update prompts without modifying Python code
Better Documentation: Each skill documents its purpose, usage, and examples
Experimentation: Test different prompt variations easily
Versioning: Skills can be versioned independently
Reusability: Skills can be shared between agents

Using Skills in Code

Agents load skills using the SkillLoader utility:

from utils.skill_loader import SkillLoader

# Initialize loader
skill_loader = SkillLoader()

# Load a skill and get a ChatPromptTemplate
prompt_template = skill_loader.load_skill("email-extractor")

# Use with LangChain
chain = prompt_template | llm | output_parser
result = chain.invoke({
    "description": job_description,
    "format_instructions": parser.get_format_instructions()
})

Creating a New Skill

Create a directory in skills/ with the skill name (e.g., skills/my-skill/)
Create a SKILL.md file with YAML frontmatter:

---
name: my-skill
description: What this skill does
version: 1.0.0
agent: langgraph
tags:
  - tag1
  - tag2
---

# My Skill

## System Message

Your system instructions here...

## Human Message Template

Your template with {variables} here...

Use the skill in your agent:

from utils.skill_loader import SkillLoader

skill_loader = SkillLoader()
self.prompt_template = skill_loader.load_skill("my-skill")

Skill Loader API

The SkillLoader class provides several useful methods:

# Load a skill
prompt = skill_loader.load_skill("email-extractor")

# Get skill metadata
metadata = skill_loader.get_skill_metadata("email-extractor")

# List all available skills
skills = skill_loader.list_available_skills()

# Validate a skill
is_valid, error = skill_loader.validate_skill("email-extractor")

# Clear cache
skill_loader.clear_cache()

Skill File Structure

Each skill directory should contain:

SKILL.md: Main skill file with frontmatter and instructions
Optional: Additional documentation, examples, or reference files

Best Practices

Version your skills: Use semantic versioning in the frontmatter
Document thoroughly: Include usage examples and variable descriptions
Test prompts: Validate skills before deploying
Keep skills focused: One skill should handle one specific task
Use descriptive names: Skill names should clearly indicate their purpose

🎨 Customization

🎨 Modify HTML Template

Edit templates/results_template.html to customize the report design. The template uses HTML, CSS, and vanilla JavaScript.

➕ Add New Job Source

Create a new agent in agents/ (e.g., new_source_agent.py)

Create API client in tools/api_clients.py inheriting from BaseAPIClient if necessary:

from tools.base_api_client import BaseAPIClient

class NewSourceClient(BaseAPIClient):
    def __init__(self):
        super().__init__(base_url="https://new-source.com/api")
    
    def search_jobs(self, keywords: List[str], **kwargs) -> List[Dict]:
        # Implementation

Add the agent to the orchestrator in agents/orchestrator.py

Configure in config/job_sources.yaml:

new_source:
  enabled: true
  max_results: 50
  base_url: "https://new-source.com"

The configuration will be automatically validated using Pydantic models

🔧 Customize Profile

Edit data/profile.json to reflect your exact profile. This file is used for:

Job matching
Relevant information extraction
Score calculation

🎯 Customize LLM Prompts (Skills)

Instead of editing Python code to change LLM prompts, you can now edit the skill files directly:

Navigate to skills/ directory
Find the skill you want to modify (e.g., email-extractor/SKILL.md)
Edit the ## System Message or ## Human Message Template sections
Save the file - changes take effect immediately (no code changes needed!)

This makes it much easier to:

Experiment with different prompt strategies
Fine-tune LLM behavior without touching business logic
Version prompt changes independently
Document prompt improvements

Example: To improve email extraction, edit skills/email-extractor/SKILL.md and modify the system message instructions.

📝 Important Notes

⚡ The system respects rate limits and has delays between requests to avoid blocks
🔒 LinkedIn has strong anti-scraping protection; may require authentication or official APIs
🔄 Some sources may change their HTML structures, requiring code updates
🎯 Matching uses keywords and heuristics; for better accuracy, consider using vector embeddings
📁 The .env and data/profile.json files are in .gitignore and are not uploaded to the repository
🔐 Never share your .env file with sensitive information

🤝 Contributions

This is a personal project, but improvements are welcome:

🐛 Report bugs: Open an issue with problem details
💡 Suggest improvements: Ideas for new features
🔧 Scraping improvements: Optimizations and new anti-bot techniques
➕ New job sources: Add more job portals
🎯 Matching improvements: More accurate algorithms
⚡ Performance optimizations: Make the system faster

📄 License

Personal use.

📜 License

This project is licensed under the Hippocratic License 3.0, an ethical open source license that allows the use, modification, and distribution of the software with specific restrictions.

✅ What does this license allow?

✅ Commercial and non-commercial use
✅ Modification and creation of derivative works
✅ Distribution and sublicensing
✅ Private use

❌ What does this license prohibit?

The license prohibits the use of the software for:

❌ Aggressive military purposes or human rights violations
❌ Criminal activities or illegal acts
❌ Violations of fundamental rights (slavery, torture, discrimination, etc.)
❌ Illegal environmental damage
❌ Any use that violates the ethical standards defined in the Universal Declaration of Human Rights

📖 More information

For more details, see the LICENSE file or visit firstdonoharm.dev.

Note: This license is designed to promote ethical use of software while maintaining open source freedom for legitimate purposes.

Developed with ❤️ using LangGraph and LangChain for intelligent job search 🚀

Need help? Check the Troubleshooting section or consult the env.example file to see all available configuration options.

Name		Name	Last commit message	Last commit date
Latest commit History 11 Commits
agents		agents
config		config
data		data
examples		examples
image		image
results		results
skills		skills
templates		templates
tests		tests
tools		tools
utils		utils
.gitignore		.gitignore
LICENSE		LICENSE
QUICK_START.md		QUICK_START.md
README.md		README.md
README_es.md		README_es.md
__init__.py		__init__.py
env.example		env.example
main.py		main.py
pytest.ini		pytest.ini
requirements.txt		requirements.txt
validate_skills.py		validate_skills.py

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🚀 LangGraph Agent System for Job Search

🆕 NEW LLM-ENHANCED FEATURES (v2.0)

⚡ QUICK START ⚡

⏱️ EXECUTION TIME ⏱️

⚠️ IMPORTANT: Estimated Process Time

🎯 Features

🔍 Supported Job Sources

🛠️ Tech Stack

Frameworks & Libraries

LLM Providers

Data & Config

Web Technologies

📋 Requirements

🚀 Quick Installation

1️⃣ Clone or navigate to the project directory

2️⃣ Install dependencies

3️⃣ Install Playwright browsers

4️⃣ Configure environment variables

5️⃣ Configure user profile

⚙️ Detailed Initial Configuration

📝 Required Environment Variables

🔑 API Key Configuration

To use Anthropic (Claude):

To use OpenAI:

📄 Resume Configuration

Option 1: Absolute path (recommended for Windows)

Option 2: Relative path (portable)

Option 3: Default value

📚 Complete Environment Variables

🔐 API Keys and LLM

👤 User Profile

🔍 Search Configuration

🕷️ Scraping Configuration

🛡️ Basic Anti-Bot Configuration

🔧 Advanced Anti-Bot Configuration

🤖 LLM-Enhanced Configuration (NEW)

📁 Path Configuration

📝 Logging Configuration

💾 Cache Configuration

🔗 Job Board API Keys (Optional)

📖 Usage

🚀 Start the System

⚡ MAIN COMMAND ⚡

Example Output

📂 Project Structure

⚙️ Advanced Configuration

🎯 Customize Search Keywords

🔄 Filter Job Sources

🎚️ Adjust Minimum Score

🛡️ Anti-Bot Configuration to Avoid Blocks

📝 Logging Configuration

📁 Customize Directories

📊 Output

1. 📄 Interactive HTML Report

2. 📝 Profile JSON File

3. 📋 Logs

🔧 Troubleshooting

❌ Error: "USER_EMAIL environment variable is required"

❌ Error: "ANTHROPIC_API_KEY environment variable is required"

❌ Error: "No module named 'langgraph'"

❌ Error: "Playwright browser not found"

❌ Error: "CV file not found"

⚠️ LinkedIn blocking requests

⚠️ Search timeout

⚠️ Empty results

🧪 Testing

Run All Tests

Run Tests with Coverage

Run Specific Test Categories

Test Structure

🎯 Agent Skills System

What are Skills?

Available Skills

Benefits of the Skills System

Using Skills in Code

Creating a New Skill

Packages