fireredteam/firered-openstoryline

FireRed-OpenStoryline is an AI video editing agent that transforms manual editing into intention-driven directing through natural language interaction,...

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Summary Information

Updated 3 minutes ago
Added to GitGenius on September 20th, 2026
Created on February 7th, 2026
Open Issues & Pull Requests: 30 (+0)
GitHub issues: Enabled
Number of forks: 401
Total Stargazers: 3,428 (+0)
Total Subscribers: 15 (+0)

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Issue Activity (beta)

Open issues: 21
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 144 days
Stale 30+ days: 19
Stale 90+ days: 16

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

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Detailed Description

FireRed-OpenStoryline is an AI video editing agent that transforms manual editing workflows into intention-driven directing through natural language commands.

The tool addresses the complexity of video editing by letting users describe their creative intent in plain language rather than manually manipulating timelines and effects. An LLM-powered planning layer interprets these descriptions and orchestrates precise editing operations. The system maintains human control throughout the process, presenting editing decisions for approval before execution. It supports reusable Style Skills that encode consistent visual and narrative patterns, enabling users to apply professional-grade storytelling conventions across projects without rebuilding them each time.

Developers building video creation tools or content platforms should consider this agent if they want to reduce the technical barrier to video editing. It suits projects where users lack traditional editing expertise but have clear creative vision. The tool is designed for accessibility while maintaining enterprise-grade reliability, making it appropriate for both individual creators and production workflows that need consistent output quality.

The project maintains active development with regular updates to its core agent and skill system. The codebase demonstrates ongoing refinement of the LLM planning and tool orchestration mechanisms that drive editing decisions. Community engagement appears sustained through multiple demonstration platforms and documentation channels. The project shows commitment to making the agent's decision-making process transparent and controllable rather than treating editing as a black-box operation.