OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
Workflow Agents

Firered Openstoryline

Firered Openstoryline is an open-source Python AI agent for directing video edits through natural-language conversations, LLM-based planning and tool orchestration. It supports script and clip refinement, media organization, and reusable editing Skills that can carry a style across projects.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Firered Openstoryline — OpenViST project profile in Workflow Agents.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want an agent-led, conversational editing workflow and can work with a Python setup, project resources and an API key. It is also relevant when you want to reuse editing styles across projects.

Who it is for

  • Video creators who want to request edits conversationally and keep a human involved in review.
  • Developers interested in building or adapting AI-assisted video-editing workflows.

Documented uses

  • Turn a video theme or creative instruction into planned editing work.
  • Find and organize relevant media, segment clips and develop a storyline or narration.
  • Refine clip order, scripts and visual details through natural-language requests.
  • Save an editing workflow as a Skill and reuse its style with other media.
Before you choose

Requirements & limits

The repository is public and identifies the project as open source under the Apache-2.0 license. Its documented local setup requires an API key.

  • Local setup requires Python 3.11 or later, downloaded resources, dependencies and API-key configuration.
  • The repository provides CLI and web conversation interfaces after starting the MCP server.
  • Operating-system setup differs: an automatic environment script is listed for Linux and macOS, with manual instructions for Windows.
  • AI-generated transitions depend on third-party video-generation services; the project notes relatively high costs and unpredictable results.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up the local conversation interface

Prepare the project environment and start its MCP server and conversation interface.

Before you start

  • Git and Conda, or a manually prepared Python environment; Python 3.11 or later is recommended.
  • Project resources and dependencies installed using the repository instructions.
  • An API key configured in config.toml.

Steps

  1. Clone the repository and enter its project directory.

  2. Create and activate a Conda environment with Python 3.11.

  3. Install resources and dependencies using the instructions for your operating system.

  4. Configure the API key in config.toml, then start the MCP server using the documented command for your operating system.

  5. Start either the CLI with python cli.py or the web interface with the documented Uvicorn command.

Expected outcome

The documented setup leads to a running MCP server and a CLI or web conversation interface.

Guide limitations

  • The repository gives different resource-installation paths for Linux/macOS and Windows; the automatic environment script is listed for Linux and macOS only.
  • The exact API-key setup details are in the linked configuration guide.
  • Local ASR use requires torchaudio in the same environment.
Documentation 1 ↗Documentation 2 ↗
COMPARE OPTIONS

Compare options for Agent orchestration

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Firered OpenstorylineTurn a video theme or creative instruction into planned editing work.The repository is public and identifies the project as open source under the Apache-2.0 license. Its documented local setup requires an API key.
Agentic AI Helpdesk PlatformClassify tickets by issue domain and category, and estimate urgency and complexity.Cloud, Docker, Web
TgoConfigure multiple AI agents for different customer-service scenarios and connect supported LLM providers.Docker, Self-hosted, ServerThe repository describes Tgo as open source and provides self-hosting instructions. Its README lists minimum server requirements of four CPU cores and 8 GiB of RAM; local development setup lists macOS, Linux, and WSL2.
Evidence and transparency

Sources & review

Official repository ↗Guide documentation ↗
Source record details
Source availability
Active
Sources checked
9 October 2026
Original website title
FireRedTeam/FireRed-OpenStoryline · Official homepage title · retained in its source language
How we review sourcesSuggest a correction