Who it is forA developer or analyst who wants a multi-step AI pipeline, such as parallel research plus a written report, but does not want to hand-write the orchestration code.
The problemMulti-step agent jobs are hard to repeat and review when the control flow lives inside a long chat with an AI agent.
The outcomeA workflow.ts file that holds the execution path in readable TypeScript, which you can run, inspect, and edit.
What you need first
- Bun installed (https://bun.sh)
- Codex CLI installed and signed in, since Codex is the default Agent runtime
- A terminal on macOS, Linux, or Windows with Bun support
- Git, if you plan to clone the repository for examples or development
Steps
- Install the released CLI with
bun install --global @deerwork-ai/deer-workflow. - Make sure you are signed in to Codex CLI, which is the default Agent runtime.
- Generate the module with
deer-workflow create "Create a Workflow that accepts a topics string array, researches each topic in parallel, and synthesizes a report" > workflow.ts. (inferred, check the README) - Run it with its example input:
deer-workflow run ./workflow.ts --input '{"topics":["Agent Skills","Dynamic Workflows"]}'. (inferred, check the README) - Open workflow.ts and review the phases, inputs, and failure handling before you change anything.
Prompt for your agent
Using the deer-workflow repository (deerwork-ai/deer-workflow), generate a runnable TypeScript Workflow for <target job description> with the deer-workflow create command, save it to workflow.ts, and run it with a sample input. Expected output: a workflow.ts file plus a run result. Verify that the run finishes and that the phases in the file match the steps described in the job.
Caveat. The README says Codex is the default runtime and Claude Code and Pi are built in, so the generation step depends on an installed and signed-in Agent CLI.
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