Let your assistant drive it

gflow-cli is built to be driven by an AI assistant. It ships an MCP server, so Claude, Cursor, or Copilot connects to it natively — no pasting docs, no copying commands back and forth. CLI-to-MCP parity is enforced in CI: anything the terminal can do, your assistant can do.

Start the server

gflow mcp run
#   serving over stdio:
#   • gflow_generate_image
#   • gflow_generate_video
#   ready — your assistant can drive Flow.

gflow mcp run speaks MCP over stdio; gflow serve exposes an HTTP/SSE endpoint if you'd rather connect over the network. gflow mcp setup helps wire it into a client.

Point your agent at it

The repo ships the files an agent reads to understand the tool:

File	Purpose	Use with
AGENTS.md	Universal agent onboarding	Cursor, Codex, Aider, Claude Code
llms.txt	LLM-readable command reference	Paste into ChatGPT / Claude / Gemini
CLAUDE.md	Claude Code memory hub	Claude Code specifically

Once connected, describe what you want in plain language:

"Make a 9:16 clip of a lighthouse in a storm, moody, slow push-in."

The agent picks the Veo model, aspect, and prompt tooling, runs the generation on your own Flow session, and hands you the files.

What the agent leans on

• --tool creative-director — expands a terse prompt with Google's 5-component formula before spending credits.
• gflow instructions — persistent, credit-free brief cards that steer look and tone across generations.
• Asset reuse by UUID — chain a still into a video by its id, no re-upload.
• --ui-mode — aborts up front when Flow's UI cohort isn't what the command needs, instead of burning a generation.

It runs on your account

Even when an agent drives it, gflow-cli still uses your own headed Flow session and bills your own credits. The same alpha/account-risk caveats apply.

Next: Known issues →.