ArcReel Video Creation Workspace
A self-hosted agent workflow that turns fiction into storyboards and short-form video.
Per-dimension scores and reasoning
Evidence shows: README mentions default admin password generation, AUTH_PASSWORD setting, warning about AUTH_ENABLED=false, and SECURITY.md notes about lack of multi-tenant isolation, indicating some consideration for permissions and sensitive data handling. However, no explicit least privilege design (e.g., minimal privilege roles), user confirmation mechanisms (e.g., confirmation before generation), data flow transparency (e.g., data flow diagrams), or details on encryption of sensitive data were found. For dependency security, CodeQL and Trivy scanning workflows exist, but no vulnerability remediation strategy or dependency locking details. External effects: third-party service dependencies are mentioned, but no explicit restrictions or audits. Rollback: README mentions version history and project archives, supporting rollback. Source attribution: LICENSE and NOTICE exist, but no explicit third-party code attribution. Deductions: lack of specific implementation details, mostly assertive descriptions.
Evidence shows: README and pyproject.toml specify dependency versions, test configuration (pytest markers) and CI workflow (test.yml) exist, indicating some self-consistency. However, no specific failure message examples or error handling strategies provided. Dependency availability: dependency list is complete, but no version compatibility or locking mechanism described. Deductions: static review cannot verify runtime behavior, and failure message details are missing.
Evidence shows: README clearly lists multiple workflows (novel adaptation, short videos, ads) and target users (creators, operators, contributors), capability boundaries (e.g., no multi-tenant support, partial Windows support) are described. Trigger precision: mentions Agent detects project stage and delegates to subagents, but no specific trigger conditions. Environment fit: supports Docker, SQLite/PostgreSQL, multi-platform images. Deductions: trigger precision description is vague, lacking specific rules.
Evidence shows: README provides detailed information architecture (documentation index, quick start, architecture diagram), clear installation instructions (Docker Compose, environment variables), stable naming (version managed by release-please), examples and FAQ exist, known limitations are explicit, license is AGPL-3.0, CHANGELOG and version management exist. Maintenance responsibility: CONTRIBUTING.md and community channels exist, but no explicit maintainer identity or responsibility statement. Deductions: maintenance responsibility description is unclear.
Evidence shows: README describes output usability (editable CapCut drafts, video export), marginal value (complete pipeline from content to final cut), cost-benefit (cost estimation and actual usage tracking). However, no actual user feedback or performance data. Deductions: static review cannot verify actual effectiveness, and quantitative metrics are lacking.
Evidence shows: README feature descriptions correspond to code structure (e.g., provider abstraction, queue), but no specific implementation links or test results provided. Cross-source corroboration: CI badges and Codecov links exist, but no specific data. Fact-inference separation: README distinguishes feature descriptions and limitations, but does not explicitly mark inferences. Deductions: lack of traceable specific evidence, mostly assertions.
- Static review cannot verify runtime behavior; all scores are inferred from file contents.
- No explicit user confirmation mechanism found; generation operations may execute automatically, configure with caution.
- Sensitive data handling details are insufficient; review code to confirm API key storage.
- Dependency security scanning exists, but no vulnerability remediation strategy; monitor dependency updates.
What does this agent do, and when should you use it?
ArcReel is a self-hosted web workspace for turning a novel or completed screenplay into character and clue assets, scripts, storyboard images, video clips, and finished video. Its React 19 interface is backed by FastAPI, while its assistant runs on the Claude Agent SDK with an orchestration Skill and focused Subagents. ImageBackend, VideoBackend, and TextBackend provide a common layer over multiple generation providers, with paths for custom OpenAI-compatible and Google-compatible APIs. GenerationQueue schedules image, video, and audio work in separate channels with RPM limiting, lease-based scheduling, and resume support; the UI exposes live progress through SSE. Docker Compose supports a default SQLite deployment and a PostgreSQL production deployment, while FFmpeg can assemble output video and episode assets can be exported as Jianying drafts.
After a user uploads a novel, manga-workflow reads project.json and the filesystem to determine the project stage, then dispatches Subagents such as analyze-characters-clues, split-narration-segments, normalize-drama-script, and create-episode-script as needed. It produces character and clue reference images, storyboard or grid images, then sends work through configured ImageBackend, VideoBackend, and TextBackend providers; video generation can be image-to-video, grid-to-video, or reference-to-video. GenerationQueue processes media jobs, ProjectManager handles filesystem and version history, and UsageTracker records provider-specific usage. Generated clips can be assembled with FFmpeg or exported as Jianying 5.x/6+ draft ZIP files. External agent platforms such as OpenClaw can authenticate with a Bearer API key, call POST /api/v1/agent/chat, and retrieve the Skill definition from /skill.md.
- An independent creator adapting a long novel episode by episode, with approval before each planned section is physically split and produced.
- An animation team that needs character, scene, and prop reference images to maintain visual continuity across shots.
- A commerce-video team uploading product photos to create an eight-part sales-video script and a Jianying draft with narration subtitles.
- A producer with a finished screenplay who wants to preserve dialogue and voice-over while generating assets and video afterward.
- A self-hosting team that needs to switch providers globally or per project among Gemini, Volcengine Ark, Grok, OpenAI, Vidu, DashScope, MiniMax, Kling, or compatible custom endpoints.
- A team using an external agent platform such as OpenClaw to create projects, generate scripts, and produce video through natural-language requests.
What are this agent's strengths and limitations?
- Defines a concrete production path from novel or screenplay input through reference assets, storyboards, clips, FFmpeg assembly, and Jianying-draft export.
- Uses ImageBackend, VideoBackend, and TextBackend to normalize multiple providers while also accepting custom OpenAI-compatible and Google-compatible APIs.
- Separates orchestration from focused Subagents, keeping large source text inside the Subagent context and returning summaries for user confirmation between stages.
- Its queue has distinct image, video, and audio channels with RPM limiting, lease-based scheduling, and resume support.
- Supports global and project-level provider selection, version rollback, and provider-specific cost tracking and estimates.
- Generation requires API credentials for at least one external provider, and supported modalities, parameters, and pricing differ by provider.
- Production deployment requires PostgreSQL; even the default deployment depends on Docker, Docker Compose, and network access.
- On native Windows, Bash sandboxing and bwrap-based POSIX isolation are automatically downgraded; WSL2 or Docker Desktop is recommended for production.
- The project is licensed under AGPL-3.0 with additional terms in NOTICE, which may be incompatible with some organizational commercial-use policies.
- Only OpenClaw is explicitly documented as an external agent-platform integration; equivalent integrations for other agent platforms are not described.
How do you install or deploy this agent?
Deploy on Linux, macOS, WSL2, or Docker. Run:
git clone https://github.com/ArcReel/ArcReel.git
cd ArcReel/deploy
cp .env.example .env
docker compose up -dOpen http://localhost:1241. For PostgreSQL production deployment, enter deploy/production, copy .env.example to .env, set POSTGRES_PASSWORD, and run docker compose up -d. The initial username is admin; set its password with AUTH_PASSWORD in .env, or allow first startup to generate and write one back to .env.
How do you use this agent?
Sign in and open /app/settings. Under ArcReel Agent, configure credentials, Base URL, and model for an official Anthropic or compatible provider. Then configure at least one image, video, or text provider API key, or add a custom OpenAI-compatible or Google-compatible provider. Create a project, upload a novel, finished screenplay, or product images, choose narration, drama, or ad/short-film mode, and confirm the assistant’s stage summaries as the workflow advances. When storyboard and video jobs complete, preview assets, follow SSE task status, inspect version history, then assemble the final output with FFmpeg or export a Jianying draft.
FAQ
Do I need to bring my own model credentials?
Can I use an endpoint that is not one of the built-in providers?
/v1/models to discover available models and infers image, video, or text media types from their names.