Deer Workflow AI Agent

by deerwork-ai• 553•• Scanned•Claim this listing•Request removal
Productivity & Workflowagentaiai agentai agentsai coding

An open-source graph engineering runtime that keeps orchestration in TypeScript and delegates semantic work to replaceable Agent runtimes.

Deer Workflow is an open-source ai agent by deerwork-ai, written mainly in TypeScript, with 553 GitHub stars and the MIT license. Its code was last updated Aug 9, 2026.

English | 简体中文

deer-workflow

License: MIT npm Bun TypeScript Codex CLI DeerFlow Stars GitHub Stars

An open-source Dynamic Workflow runtime for building observable, reusable Agent graphs.

deer-workflow is a pilot project for DeerFlow 3.0, also known as DeerWork.

Index

Why Deer Workflow

Deer Workflow is a code-first implementation of Graph Engineering: TypeScript defines the valid execution paths, while Coding Agents perform the semantic work inside each node.

  • Code is the plan. Control flow, phases, inputs, and failure handling live in reviewable TypeScript rather than an opaque Agent conversation.
  • Agents are replaceable. Codex is the default runtime; Claude Code and Pi are built in; and the public Agent interface remains vendor-neutral.
  • Execution is observable. Interactive runs provide a phase-aware TUI; automation can consume a stable JSONL event stream.

How to use

Quick Start

Install Bun and sign in to Codex CLI, then install the released CLI:

bun install --global @deerwork-ai/deer-workflow

Describe the orchestration you want. Deer Workflow asks Codex to apply the bundled workflow-creator Skill and writes a runnable TypeScript module:

deer-workflow create \
  "Create a Workflow that accepts a topics string array, researches each topic in parallel, and synthesizes a report" \
  > workflow.ts

Use --agent claude or --agent pi to generate with another installed Harness. Codex remains the default.

Run the generated Workflow with its example input:

deer-workflow run ./workflow.ts \
  --input '{"topics":["Agent Skills","Dynamic Workflows"]}'

Interactive terminals show phases and Markdown logs in a live TUI. For servers, CI, and process pipelines, add --print or -p to stream one JSON event per stdout line.

Want to understand or edit the generated module? Continue with the Getting Started guide.

Examples

  • Deep Research discovers research angles, investigates them in parallel, verifies claims, and produces an interactive HTML report.
  • Blog Writer plans an article, drafts its sections through a pipeline, reviews them, and returns structured output.

These examples live in the repository. Clone or download it before running their documented commands.

Documentation

How to develop

Set up

Clone the repository and install local dependencies and Git hooks:

git clone https://github.com/deerwork-ai/deer-workflow.git
cd deer-workflow
bun install

Run the CLI directly from source:

bun run dev -- --help

Validate changes

Run the complete quality gate before submitting changes:

bun run check

Contribute

Codex CLI is the default Agent runtime, not an architectural dependency. ClaudeAgent and PiAgent ship as built-in Harnesses; integrations for other Coding Agents are welcome.

See the Getting Started guide for the full command reference.

License

This project is licensed under the MIT License.

Deer Workflow alternatives

View more →

Similar ai agents in Productivity & Workflow, ranked by GitHub stars. Compare licence, recent activity and scan status before you choose.

NameStarsLicenceLast updatedScan
Deer Workflow (this page)
deerwork-ai
553MITAug 9, 2026Scanned, no flagged issues
Paperclip
paperclipai
110kMITNov 1, 2026Scanned, no flagged issues
Ppt Master
hugohe3
72kMITOct 30, 2026Scanned, no flagged issues
Llama Index
run-llama
53kMITOct 26, 2026Scanned, see report
Nanobot
HKUDS
50kMITOct 31, 2026Scanned, no flagged issues
Sim
simstudioai
30kApache-2.0Nov 1, 2026Scanned, see report