FireRed-OpenStoryline

FireRed-OpenStoryline is a Python-based MCP server that powers advanced video creation workflows through natural language prompts. It integrates smart media search and organization, intelligent script generation, music/voiceover/font recommendations, and conversational editing to streamline end-to-end video production. With this server running, you can describe your desired scene, tone, and pacing in plain language, and the system will fetch appropriate media, assemble a storyline, suggest or apply voiceovers and fonts, and iteratively refine edits via prompts. The platform emphasizes accessibility and enterprise-grade reliability, enabling users to generate complex video narratives with minimal manual editing while preserving control over styling and timing. Tools include automated media retrieval, script construction with tone transfer, mood-aware music and font recommendations, and rapid, natural-language-driven edits to clips and sequences.

Prerequisites:

• OS: Linux, macOS, or Windows (Git Bash or WSL for Windows)
• Python 3.11 or newer
• Conda (Miniforge recommended) for environment management
• Git

Installation steps:

1. Clone the repository

git clone https://github.com/FireRedTeam/FireRed-OpenStoryline.git
cd FireRed-OpenStoryline

2. Create and activate a Python environment

# Recommended (via Conda)
conda create -n storyline python=3.11
conda activate storyline

3. Install dependencies or prepare environment

# If there is a requirements.txt or setup, install as appropriate
# Example (if provided):
pip install -r requirements.txt

4. Prepare and run the server

# Start the MCP server (example command)
# Replace with the actual startup invocation if different
python -m firered_openstoryline

5. Optional: run the automatic setup script if available

sh build_env.sh

Notes:

• If you prefer manual setup, follow the repository's guidance for creating a development environment and installing dependencies.
• The project suggests using Conda Miniforge for automatic environment variable configuration during installation.

Tips and common considerations:

• Ensure Python 3.11 is active in the environment before starting the server.
• If you encounter module not found errors, verify that the package name used in the -m flag matches the actual Python package entry point in the repository.
• You can override defaults with OPENSTORYLINE_CONFIG to point to a YAML/JSON configuration file if the project supports it.
• The installation instructions mention both automatic (build_env.sh) and manual installation paths; use the automatic path on supported Linux/macOS systems for convenience.
• For performance tuning, consider allocating more CPU/RAM to the environment and enabling any available media preprocessing caches as described in the docs.
• If running in production, containerization (Docker) or a managed Python environment is recommended to ensure reproducibility.