Continuum

Continuum

An autonomous AI showrunner for serialized vertical micro-drama. Give it a premise and a recurring protagonist; it writes the script, storyboards the shots, generates the video with Wan, and edits the cut together, producing episode after episode with the same character, world, and style. The moat is an agent-maintained Series Bible that locks the protagonist's canonical look and injects it into every episode, so continuity holds across episodes rather than drifting shot to shot.

!Mei across both episodes of the demo series, generated by Continuum

_The same protagonist across two episodes and four very different shots. Cross-episode identity, measured by a Qwen-VL critic, is 0.98 on this series._

Built for the Qwen Cloud Global AI Hackathon, Track 2 (AI Showrunner).

How it works

A small team of agents hands work down a pipeline, orchestrated by the Showrunner:

1. Showrunner owns the Series Bible, locks the protagonist's appearance, and runs the series.
2. Writer (Qwen3) writes each episode and its dialogue, with the locked look injected.
3. Storyboard breaks the script into shots.
4. Cinematographer renders each shot on Wan (9:16 vertical).
5. Critic scores continuity and can re-render a shot through an optimizer loop.
6. Editor concatenates the shots and burns in subtitles with ffmpeg.

The agents are also exposed as MCP tools (backend/mcp/) so a Qwen-Agent can drive the studio.

Stack

• Brain: Qwen3 (qwen3-max) for scripting and prompt optimization, via the DashScope

OpenAI-compatible endpoint.

• Video: Wan on Qwen Cloud (wan2.6-t2v; image/reference models wired for the next pass).
• Edit: ffmpeg (concat, subtitle burn-in, 9:16 vertical).
• Serving: Python + FastAPI with Server-Sent Events for the live-agent view.

Status

Working end to end. A real 2-episode series renders from a single premise, the protagonist stays visually consistent across episodes (a Qwen-VL critic measures the cross-episode identity match, 0.98 on the demo series), and the live control room streams each agent's work as it happens. Tests pass (tests/). Next: routing character-bearing shots to Wan reference-to-video (wan2.6-r2v / wan2.7-i2v), and packaging the Function Compute deployment.

Setup

python3 -m venv .venv
.venv/bin/pip install -r requirements.txt
cp .env.example .env        # then paste your DASHSCOPE_API_KEY

ffmpeg must be on PATH.

Run

Live control room (premise in, episodes out, streamed agent activity):

PYTHONPATH=. .venv/bin/uvicorn backend.server:app --port 8000
# open http://127.0.0.1:8000

Or from the CLI:

# one episode
PYTHONPATH=. .venv/bin/python -m backend.run_episode "<premise>"

# a multi-episode series with a recurring protagonist
PYTHONPATH=. .venv/bin/python -m backend.run_series "<premise>" "<name>" "<locked look>" 2

Tests

for m in test_series_bible test_consistency test_critic_loop; do
  PYTHONPATH=. .venv/bin/python -m tests.$m
done

License

MIT