OpenRouter Prompt Runner

A comprehensive tool for executing JSON prompt files using the OpenRouter API. Available as both a command-line interface (CLI) and a Flask web application. Choose the interface that best fits your workflow!

⚡ New User? Start Here!

👋 First time using this tool? → QUICK START GUIDE - Get running in 3 minutes!

🆕 Recent Updates

🌍 Global Installation: New ./install-global.sh script enables CLI tools to run from any directory without virtual environments.

🔧 Web Interface Fixed: Resolved "No prompts configured" issue - prompts now display correctly in the web interface.

📦 Installation Improved: Fixed pyproject.toml and added proper setup.py for better pip compatibility.

🔗 Entry Points Working: CLI commands (openrouter-runner, openrouter-chain, openrouter-web) now work as intended.

📚 Enhanced Documentation: Added comprehensive troubleshooting guide and updated all setup instructions.

📖 Quick Navigation

🚀 Get Started: Quick Start (QUICK-START.md) | Setup Guide (README_setup.md) | Linux Setup (SETUP_LINUX.md)
⛓️ Advanced: Prompt Chains (prompt_chain_readme.md) | Multi-File Processing
🌐 Web Interface: Flask Setup (setup_guide.md) | Templates (README_temple_setup.md)
🔧 Development: Claude Code Integration (CLAUDE.md) | Architecture Guide
🆘 Help: Troubleshooting Guide (TROUBLESHOOTING.md) | Common Issues & Fixes

🚀 Interfaces Available

📟 Command Line Interface (CLI)

Perfect for automation, batch processing, and CI/CD pipelines:

Batch mode: Process specific files directly
Interactive mode: Browse and select files interactively
Configuration management: YAML config files and command-line overrides
Logging: File and console logging with multiple levels

🌐 Web Application

Ideal for interactive use and team collaboration:

Web interface: Modern, responsive design
Session management: Track history and download results
Configuration UI: Web-based settings management
File uploads: Drag-and-drop file processing

⛓️ Prompt Chain Runner

Advanced automation for multi-step AI workflows:

Sequential Processing: Execute 1-99 prompts in sequence
Multi-File Support: Process multiple files through the same prompt chain
Per-Prompt Configuration: Use different LLMs for each step (Claude, GPT-4, DeepSeek, etc.)
Advanced File Management: Organized temp directories with full traceability
Flexible Output: Pattern-based naming for batch processing

📚 Documentation

This project includes comprehensive documentation for all components and use cases:

🚀 Setup & Installation

README_setup.md - Complete setup instructions for all platforms
SETUP_LINUX.md - Linux-specific automated setup script documentation
setup_guide.md - Step-by-step Flask web application setup guide
README_temple_setup.md - HTML template setup for Flask interface

⛓️ Advanced Features

prompt_chain_readme.md - Complete guide to Prompt Chain Runner for multi-step AI workflows
CLAUDE.md - Developer guidance for Claude Code integration and project architecture

📖 Reference Materials

compliance_analysis.md - Analysis framework and quality standards
style_reference.md - Writing style guidelines and formatting standards
analysis_framework.md - Document analysis methodology

🛠️ Setup and Installation

Quick Setup (Recommended)

For complete setup instructions, see the Setup Guide (README_setup.md).

Linux/Unix Systems

Comprehensive Linux setup (recommended):

# Download project files, then run:
chmod +x setup_linux.sh
./setup_linux.sh                    # Install to ~/.local/bin (user)
./setup_linux.sh --system          # Install to /usr/local/bin (system-wide, requires sudo)

Legacy setup script:

chmod +x setup_prompt_runner.sh
./setup_prompt_runner.sh

The Linux setup script will:

✅ Verify Python 3.7+ installation
✅ Install all required packages (requests, pyyaml, flask)
✅ Make all entry point scripts executable
✅ Copy all modules to ~/.local/bin or /usr/local/bin
✅ Copy shell scripts and make them executable
✅ Verify installation and provide next steps

Manual Setup

If you prefer manual installation, see the detailed instructions in README_setup.md.

📟 Command Line Interface (CLI)

🛠️ CLI Setup

1. Required Files

Ensure you have all necessary Python modules:

project/
├── prompt_runner.py                    # Main CLI application
├── config_manager.py                   # Configuration handling
├── logging_manager.py                  # Logging setup
├── prompt_scanner.py                   # JSON prompt discovery
├── prompt_handler.py                   # Prompt loading and processing
├── input_handler.py                    # Input file handling
├── prompt_runner_api_client.py         # OpenRouter API client
├── response_handler.py                 # Output handling
├── file_handler.py                     # File operations
├── *.json                             # Your JSON prompt files
└── openrouter_editor.yaml             # Optional config file

2. Install Dependencies

pip install requests pyyaml

3. Set API Key

# Set your OpenRouter API key as environment variable
export OPENROUTER_API_KEY="your_api_key_here"

# Or add it to your config file (not recommended for security)
echo "api_key: your_api_key_here" >> openrouter_editor.yaml

🔧 CLI Configuration

Command Line Options

python prompt_runner.py [OPTIONS]

Mode Selection:
  -p, --prompt PROMPT_FILE     JSON prompt file (enables batch mode)
  -i, --input INPUT_FILE       Input file to process (requires --prompt)

Configuration Options:
  -c, --config CONFIG_FILE     Configuration file (YAML format)

Output Options:
  -o, --output-file OUTPUT     Output file for responses (markdown)

Logging Options:
  -l, --log-file LOG_FILE      Log file path (enables file logging)
  -v, --verbose                Enable verbose logging (DEBUG level)
  -q, --quiet                  Suppress output except errors
  --temp-dir TEMP_DIR          Temporary directory for logs and payload files

Help:
  -h, --help                   Show help message and examples

Configuration File Format

Create openrouter_editor.yaml:

# OpenRouter API Settings
model: anthropic/claude-4-sonnet-20250522
api_base_url: https://openrouter.ai/api/v1
temperature: 0.8
max_tokens: 25000

# Application Settings
log_level: INFO
log_to_file: false                      # Set true to enable file logging
log_file: prompt_runner.log             # Automatically enables file logging
payload_file: prompt_runner.payload.json

# Input/Output Settings
input_file: input.md
output_file: output.md
action_file: action.json

📝 CLI Usage Examples

Interactive Mode (Default)

# Basic interactive mode - scan directory and select files
python prompt_runner.py

# Interactive with output file
python prompt_runner.py -o responses.md

# Interactive with custom config
python prompt_runner.py -c my_config.yaml -o responses.md

# Interactive with verbose logging to file
python prompt_runner.py -l debug.log -v -o responses.md

Batch Mode

# Basic batch processing
python prompt_runner.py -p analysis.json -i document.md

# Batch with output file
python prompt_runner.py -p review.json -i code.py -o results.md

# Batch with custom config
python prompt_runner.py -p analysis.json -i data.txt -c config.yaml -o analysis.md

# Batch with logging
python prompt_runner.py -p review.json -i code.py -l batch.log -o results.md

Batch Processing Multiple Files

# Process multiple files with same prompt - perfect for automation
python prompt_runner.py -p review.json -i file1.md -o results.md -l batch.log
python prompt_runner.py -p review.json -i file2.md -o results.md -l batch.log
python prompt_runner.py -p review.json -i file3.md -o results.md -l batch.log

# Or use a loop for many files
for file in *.md; do
    python prompt_runner.py -p review.json -i "$file" -o results.md -l batch.log
done

Configuration Combinations

# Command line log file overrides config
python prompt_runner.py -c config.yaml -l custom.log -p analysis.json -i doc.md

# Mix command line and config file settings
python prompt_runner.py -c base_config.yaml -p custom_prompt.json -i input.txt -l debug.log -v

# Use custom temp directory for organized file management
python prompt_runner.py -p analysis.json -i document.md --temp-dir temp/analysis_run

🔍 CLI Workflow Examples

Development and Testing

# Debug a specific prompt with verbose logging
python prompt_runner.py -p debug_prompt.json -i test_input.md -l debug.log -v

# Quick test without saving output
python prompt_runner.py -p test.json -i sample.md -q

# Test with different models using config
python prompt_runner.py -c gpt4_config.yaml -p analysis.json -i document.md

Production Batch Processing

# Process a batch of documents with consistent logging
mkdir -p logs outputs
for doc in inputs/*.md; do
    filename=$(basename "$doc" .md)
    python prompt_runner.py \
        -p production_review.json \
        -i "$doc" \
        -o "outputs/${filename}_review.md" \
        -l "logs/batch_$(date +%Y%m%d).log" \
        -c production_config.yaml
done

CI/CD Integration

# Automated analysis in CI pipeline
python prompt_runner.py \
    -p code_review.json \
    -i src/main.py \
    -o reports/code_analysis.md \
    -l logs/ci_analysis.log \
    -c ci_config.yaml || exit 1

# Exit code 0 = success, 1 = failure (perfect for scripts)

📊 CLI Output and Logging

Console Output

# Normal operation shows progress
$ python prompt_runner.py -p analysis.json -i document.md
2025-01-26 10:30:15 - INFO - Initializing OpenRouter Prompt Runner
2025-01-26 10:30:15 - INFO - ✓ Files validated successfully
2025-01-26 10:30:15 - INFO - ✓ Prompt loaded successfully
2025-01-26 10:30:15 - INFO - ✓ Input content loaded
2025-01-26 10:30:16 - INFO - ✓ API call successful
✓ Successfully processed document.md with analysis.json

Log File Contents

When using -l or config file logging:

================================================================================
NEW SESSION STARTED
================================================================================
2025-01-26 10:30:15 - INFO - Initializing OpenRouter Prompt Runner
2025-01-26 10:30:15 - INFO - Command line log file specified: batch.log
2025-01-26 10:30:15 - INFO - ✓ Logging to file: batch.log
2025-01-26 10:30:15 - INFO - Model: anthropic/claude-4-sonnet-20250522
2025-01-26 10:30:15 - INFO - API call completed in 1.23 seconds
2025-01-26 10:30:15 - INFO - ✓ Batch processing completed successfully

Output File Format

When using -o output.md:

## Prompt Response - 2025-01-26 10:30:16

**Prompt File:** `analysis.json`  
**Input File:** `document.md`  
**Timestamp:** 2025-01-26 10:30:16

---

[AI Response Content Here]

---

⚙️ CLI Advanced Features

File Validation

# CLI validates files at startup
$ python prompt_runner.py -p missing.json -i document.md
ERROR: Prompt file not found: missing.json

$ python prompt_runner.py -p analysis.json -i missing.md
ERROR: Input file not found: missing.md

Configuration Priority

Command line options (highest priority)
Config file settings (-c config.yaml)
Default configuration

Auto-Detection Features

File logging: Automatically enabled when log file specified
Batch mode: Triggered when both -p and -i provided
Directory creation: Log and output directories created automatically

File Management Features

Unique payload files: Each execution creates timestamped payload files (prompt_runner_20250131_143052_12345.payload.json)
Temporary directory support: Use --temp-dir to organize all files in one location
Shared temp directories: When used with prompt chains, all files stored in the same temp directory
File preservation: Payload files preserved after execution for debugging and review

🚨 CLI Troubleshooting

Common Issues and Solutions

1. API Key Not Found

$ python prompt_runner.py -p test.json -i input.md
ERROR: API key not found

# Solution:
export OPENROUTER_API_KEY="your_key_here"

2. Missing Dependencies

$ python prompt_runner.py
ModuleNotFoundError: No module named 'yaml'

# Solution:
pip install pyyaml requests

3. File Permission Errors

$ python prompt_runner.py -p test.json -i input.md -l /restricted/log.log
ERROR: Failed to save payload

# Solution:
python prompt_runner.py -p test.json -i input.md -l ./log.log

4. Invalid JSON Prompts

$ python prompt_runner.py -p broken.json -i input.md
ERROR: Invalid JSON in prompt file broken.json

# Solution: Validate JSON format
python -m json.tool broken.json

🌐 Web Application Interface

🛠️ Web Setup

1. Quick Start with Shell Script

The easiest way to run the Flask web application:

# Make the launcher script executable
chmod +x prompt_runner_flask.sh

# Run initial setup (creates templates and config)
./prompt_runner_flask.sh --setup

# Start the web application
./prompt_runner_flask.sh

# Or start in production mode
./prompt_runner_flask.sh --production

2. Flask Launcher Script Options

The prompt_runner_flask.sh script provides convenient management:

# Basic usage
./prompt_runner_flask.sh                      # Start with defaults (127.0.0.1:5000)
./prompt_runner_flask.sh -d                   # Debug mode
./prompt_runner_flask.sh -H 0.0.0.0 -p 8080   # Custom host and port

# Management commands
./prompt_runner_flask.sh --setup              # Create templates and config
./prompt_runner_flask.sh --check              # Verify dependencies
./prompt_runner_flask.sh --background         # Run as daemon
./prompt_runner_flask.sh --kill               # Stop background instances
./prompt_runner_flask.sh --logs               # View application logs

# Production deployment
./prompt_runner_flask.sh --production         # Production settings (0.0.0.0, no debug)
./prompt_runner_flask.sh --public             # Public access (0.0.0.0)

3. Script Features

Dependency checking: Validates Python, Flask, and required modules
Automatic setup: Creates templates and configuration files
Process management: Start, stop, and monitor background processes
Logging: File and console logging with real-time viewing
Error handling: Clear error messages and recovery suggestions

4. Manual Flask Setup

If you prefer to run Flask directly:

4. Manual Flask Setup

If you prefer to run Flask directly:

Additional Required Files for Web Interface

project/
├── prompt_runner_flask.py              # Flask web application
├── create_templates.py                 # Template setup script
├── flask_config.yaml                   # Web app configuration
├── prompts_registry.yaml               # Prompt registry
├── templates/                          # HTML templates directory
│   ├── base.html                      # Base template
│   ├── index.html                     # Main page
│   ├── config.html                    # Configuration page
│   ├── prompts_registry.html          # Registry management
│   ├── prompt_form.html              # Prompt execution form
│   └── history.html                  # Session history
└── [CLI files from above]             # All CLI files also needed

Manual Web Application Setup

# Install additional web dependencies
pip install flask

# Create templates and configuration
python create_templates.py

# Set API key
export OPENROUTER_API_KEY="your_api_key_here"

# Start web application
python prompt_runner_flask.py

Access Web Interface

Navigate to: http://localhost:5000

⛓️ Prompt Chain Runner

The Prompt Chain Runner (prompt_chain_runner.py) enables advanced automation workflows by executing multiple prompts in sequence. Perfect for complex document processing pipelines that require multiple AI processing steps.

🚀 New Enhanced Features

Multi-File Processing

Process multiple files through the same prompt chain:

# Process 3 files through the same 3-step prompt chain
python prompt_chain_runner.py -c multi_file_config.yaml

Configuration Example:

input_files:
  - "document1.md"
  - "document2.md" 
  - "document3.txt"
output_pattern: "processed_{input_name}_final{input_ext}"
prompts:
  prompt 1: "analysis.json"
  prompt 2: "refinement.json"
  prompt 3: "polish.json"

Per-Prompt Configuration (Different LLMs)

Use different AI models optimized for each step:

# Use Claude for creative tasks, GPT-4 for technical analysis
python prompt_chain_runner.py -c multi_llm_config.yaml

Configuration Example:

input_file: "document.md"
output_file: "processed_document.md"
prompts:
  prompt 1:
    prompt_file: "creative_brainstorm.json"
    config_file: "claude_config.yaml"      # Claude for creativity
  prompt 2:
    prompt_file: "technical_analysis.json"
    config_file: "gpt4_config.yaml"       # GPT-4 for technical work
  prompt 3:
    prompt_file: "final_polish.json"
    config_file: "deepseek_config.yaml"   # DeepSeek for final review

📊 Execution Workflows

Single File Processing:

input.md → Analysis → Refinement → Polish → output.md

Multi-File Processing:

doc1.md → Analysis → Refinement → Polish → processed_doc1_final.md
doc2.md → Analysis → Refinement → Polish → processed_doc2_final.md  
doc3.txt → Analysis → Refinement → Polish → processed_doc3_final.txt

🗂️ Advanced File Organization

All execution artifacts are preserved in organized temp directories:

temp/input_document_20250131_143052_12345/
├── input_document_20250131_143052_12345.log           # Execution log
├── original_input_document.md                         # Original input
├── file01_step01_analysis.tmp                         # Intermediate files
├── file01_step02_refinement.tmp
├── prompt_runner_20250131_143053_12346.payload.json   # API payloads
├── prompt_runner_20250131_143054_12347.payload.json
└── processed_document_final.md                        # Final output copy

🛠️ Quick Start

1. Create Configuration:

# Generate sample config
python prompt_chain_runner.py --create-sample

2. Run Single File Chain:

python prompt_chain_runner.py -c my_chain.yaml

3. Run Multi-File Processing:

python prompt_chain_runner.py -c multi_file_config.yaml

4. Debug with Verbose Logging:

python prompt_chain_runner.py -c config.yaml --debug

📖 Full Documentation

For complete details, see prompt_chain_readme.md

🤖 Supported AI Models

This application works with 400+ AI models through OpenRouter.ai, including the most popular LLMs:

Top 12 Most Used Models:

OpenAI
- GPT-4o: openai/gpt-4o-2024-11-20
- GPT-4.5: openai/gpt-4.5-preview
- o3-mini: openai/o3-mini
Claude (Anthropic)
- Claude 4 Sonnet: anthropic/claude-4-sonnet-20250522
- Claude 3.7 Sonnet: anthropic/claude-3.7-sonnet
- Claude 3.5 Sonnet: anthropic/claude-3.5-sonnet:beta
Gemini (Google)
- Gemini 2.5 Pro: google/gemini-2.5-pro-exp-03-25
- Gemini 2.0 Flash: google/gemini-2.0-flash-experimental
- Gemini Pro: google/gemini-pro-1.5-latest
DeepSeek
- DeepSeek R1: deepseek/deepseek-r1
- DeepSeek V3: deepseek/deepseek-chat-v3-0324
- DeepSeek Coder: deepseek/deepseek-coder
Llama (Meta)
- Llama 4 Maverick: meta-llama/llama-4-maverick
- Llama 3.3 70B: meta-llama/llama-3.3-70b-instruct
- Llama 3.1 Nemotron: nvidia/llama-3.1-nemotron-70b-instruct
Grok (xAI)
- Grok Beta: x-ai/grok-beta
- Grok 2: x-ai/grok-2-1212
Qwen (Alibaba)
- Qwen 3: qwen/qwen-3-turbo
- Qwen 2.5 Coder: qwen/qwen-2.5-coder-32b-instruct
- QwQ: qwen/qwq-32b-preview
Mistral
- Mistral Large: mistralai/mistral-large-2411
- Mistral Small: mistralai/mistral-small-3.1-24b-instruct
- Pixtral: mistralai/pixtral-12b-2409
Cohere
- Command R+: cohere/command-r-plus
- Command R7B: cohere/command-r7b-12-2024
- Command R: cohere/command-r
Amazon Nova
- Nova Pro: amazon/nova-pro-v1
- Nova Lite: amazon/nova-lite-v1
- Nova Micro: amazon/nova-micro-v1
Dolphin (Cognitive Computations)
- Dolphin Mixtral 8x7B: cognitivecomputations/dolphin-mixtral-8x7b
- Dolphin Mixtral 8x22B: cognitivecomputations/dolphin-mixtral-8x22b
- Dolphin 2.6 Mixtral: cognitivecomputations/dolphin-2.6-mixtral-8x7b
Venice.ai Models (via direct API or OpenRouter)
- Venice Large (Qwen3): Use Venice.ai API directly
- Venice Medium: Use Venice.ai API directly
- Venice Small: Use Venice.ai API directly
- Note: Venice.ai has its own API separate from OpenRouter
- Website: https://venice.ai
- API URL: https://api.venice.ai/api/v1

Free Models Available:

Many models offer free tiers with rate limits. Add :free suffix to model names:

deepseek/deepseek-chat:free
meta-llama/llama-4-maverick:free
google/gemini-2.5-pro-exp-03-25:free
mistralai/mistral-small-3.1-24b-instruct:free
cognitivecomputations/dolphin-mixtral-8x7b:free

Using Venice.ai Models:

Venice.ai provides private, uncensored AI models through their own API. To use Venice models:

Direct Venice API: Set up a Venice.ai account and use their API directly
Model Configuration: Update api_base_url to https://api.venice.ai/api/v1
Available Models: llama-3.3-70b, deepseek-r1-llama-70b, qwen32b, dolphin-2.9.2-qwen2

Note: Venice.ai requires separate API credentials and is not part of OpenRouter

Model Selection:

Configure your preferred model in the configuration file or web interface:

CLI Configuration (openrouter_editor.yaml):

model: anthropic/claude-4-sonnet-20250522  # Default model
temperature: 0.8
max_tokens: 25000

Web Configuration (flask_config.yaml):

model: anthropic/claude-4-sonnet-20250522  # Default model
temperature: 0.8
max_tokens: 10000

🚀 Features

CLI Features

Batch Processing: Automated file processing for CI/CD pipelines
Interactive Mode: Browse and select files with guided prompts
Configuration Management: YAML config files with command-line overrides
Flexible Logging: Console and file logging with multiple levels
File Validation: Comprehensive input validation and error handling
Session Persistence: Append logging for batch processing workflows

Web Features

Modern Interface: Clean, responsive design with mobile support
Session History: Track all responses with download capability
Configuration UI: Web-based settings management
File Uploads: Support for text input and file uploads
Live Updates: Real-time processing with AJAX
Prompts Registry: Web-based prompt management

🎯 Choosing the Right Interface

Use Flask Launcher When:

Quick setup: Need to get started fast with minimal configuration
Development: Testing and debugging with built-in process management
Production deployment: Running on servers with monitoring needs
Process management: Need to start/stop/monitor the application
Logging: Want centralized log management and viewing

Use Direct Flask When:

Automation: Integrating with scripts, CI/CD, or batch processing
Performance: Processing many files efficiently
Scripting: Building automated workflows
Headless: Running on servers without GUI
Version Control: Configuration files can be tracked in git

Use Web Interface When:

Interactive Use: Exploring prompts and experimenting
Team Collaboration: Sharing access with multiple users
File Uploads: Processing files through web interface
Visual Feedback: Need immediate visual feedback
Session Management: Tracking history within sessions

🔒 Security Considerations

API Key Protection: Store your OpenRouter API key as an environment variable
File Upload Limits: Configure appropriate upload size limits (web interface)
Input Validation: All inputs are validated before processing
File Permissions: Ensure proper read/write permissions for config and log files
Session Management: Web sessions are memory-based and cleared on restart

🛠️ Development

Custom JSON Prompts

Create custom prompts in JSON format:

{
  "instruction": "Analyze the following text for sentiment",
  "type": "analysis",
  "requirements": [
    "Identify overall sentiment (positive/negative/neutral)",
    "Highlight key emotional indicators",
    "Provide confidence score"
  ],
  "output_format": "structured analysis with clear sections"
}

Adding New Features

CLI Features: Modify prompt_runner.py and related modules
Web Features: Add routes to prompt_runner_flask.py and create templates
Configuration: Add new settings to config management system
API Integration: Extend API client for new functionality

Dependencies

Core Requirements:

requests - HTTP client for API calls
pyyaml - YAML configuration handling
pathlib - File path handling (built-in)
json - JSON processing (built-in)
logging - Logging system (built-in)

Web Interface Additional:

flask - Web framework for GUI

📈 Monitoring and Logging

Log Levels

DEBUG: Detailed debugging information
INFO: General operational messages
WARNING: Warning messages for potential issues
ERROR: Error messages for failed operations

CLI Logging Options

# Console logging (default)
python prompt_runner.py -p test.json -i input.md

# File logging
python prompt_runner.py -p test.json -i input.md -l debug.log

# Verbose console logging
python prompt_runner.py -p test.json -i input.md -v

# Quiet mode (errors only)
python prompt_runner.py -p test.json -i input.md -q

🚨 Troubleshooting

CLI Issues

Missing Files: Ensure all required Python modules are present
API Key: Set OPENROUTER_API_KEY environment variable
Dependencies: Install required packages with pip install requests pyyaml
Permissions: Check file read/write permissions
JSON Format: Validate prompt files with python -m json.tool file.json

Web Interface Issues

Templates Missing: Run python create_templates.py
Config Missing: Check flask_config.yaml exists
Port Conflicts: Flask runs on port 5000 by default
File Uploads: Check upload size limits in configuration

Common Solutions

# Reinstall dependencies
pip install --upgrade requests pyyaml flask

# Check file permissions
ls -la *.py *.yaml *.json

# Validate JSON prompts
for file in *.json; do python -m json.tool "$file" >/dev/null && echo "$file: OK" || echo "$file: ERROR"; done

# Test API key
echo $OPENROUTER_API_KEY

🤝 Contributing

Fork the repository
Create a feature branch
Make your changes with appropriate tests
Update documentation for new features
Test both CLI and web interfaces
Submit a pull request

📄 License

This project is provided as-is for educational and development purposes. Please ensure compliance with OpenRouter's terms of service when using their API.

🆘 Support

For issues and questions:

Check the troubleshooting section above
Review configuration files and logs
Verify API key and network connectivity
Test with simple prompts first
Check file permissions and dependencies

🔧 Additional Utilities

This project includes several specialized utilities for different use cases:

📚 Book Generation Tools

BookGen Utilities - AI-powered book chapter generation and editing
- bookGen.py - Main book generation application
- bookFileManager.py - File management for book content
- Smart text chunking, action-based prompts, comprehensive logging

🎯 Specialized Applications

generateProse.py - Prose generation utility
openrouter_editor.py - Original text editor interface
callAPI.py - Basic API testing utility

📖 Complete Documentation Index

Setup & Installation:

Quick Start Guide - Get running in 3 minutes
Complete Setup Guide - Detailed installation instructions
Linux Automated Setup - Linux-specific setup script
Flask Web Setup - Web interface setup guide
Template Setup - HTML template configuration

Advanced Features:

Prompt Chain Runner - Multi-step AI workflows
BookGen Utilities - Book generation tools
Claude Code Integration - Developer architecture guide

Reference Materials:

Compliance Analysis - Quality standards framework
Style Reference - Writing style guidelines
Analysis Framework - Document analysis methodology

Configuration Examples:

config.yaml - Main application configuration
flask_config.yaml - Web interface settings
bookGen.yaml - Book generation configuration
openrouter_editor.yaml - Legacy editor settings

Happy Prompting with CLI and Web! 🎉

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OpenRouter Prompt Runner

⚡ New User? Start Here!

🆕 Recent Updates

📖 Quick Navigation

🚀 Interfaces Available

📟 Command Line Interface (CLI)

🌐 Web Application

⛓️ Prompt Chain Runner

📚 Documentation

🚀 Setup & Installation

⛓️ Advanced Features

📖 Reference Materials

🛠️ Setup and Installation

Quick Setup (Recommended)

Linux/Unix Systems

Manual Setup

📟 Command Line Interface (CLI)

🛠️ CLI Setup

1. Required Files

2. Install Dependencies

3. Set API Key

🔧 CLI Configuration

Command Line Options

Configuration File Format

📝 CLI Usage Examples

Interactive Mode (Default)

Batch Mode

Batch Processing Multiple Files

Configuration Combinations

🔍 CLI Workflow Examples

Development and Testing

Production Batch Processing

CI/CD Integration

📊 CLI Output and Logging

Console Output

Log File Contents

Output File Format

⚙️ CLI Advanced Features

File Validation

Configuration Priority

Auto-Detection Features

File Management Features

🚨 CLI Troubleshooting

Common Issues and Solutions

🌐 Web Application Interface

🛠️ Web Setup

1. Quick Start with Shell Script

2. Flask Launcher Script Options

3. Script Features

4. Manual Flask Setup

4. Manual Flask Setup

Additional Required Files for Web Interface

Manual Web Application Setup

Access Web Interface

⛓️ Prompt Chain Runner

🚀 New Enhanced Features

Multi-File Processing

Per-Prompt Configuration (Different LLMs)

📊 Execution Workflows

🗂️ Advanced File Organization

🛠️ Quick Start

📖 Full Documentation

🤖 Supported AI Models

Top 12 Most Used Models:

Free Models Available:

Using Venice.ai Models:

Model Selection:

🚀 Features

CLI Features

Web Features

🎯 Choosing the Right Interface

Use Flask Launcher When:

Use Direct Flask When:

Use Web Interface When: