deer-workflow
An open-source graph engineering runtime that keeps orchestration in TypeScript and delegates semantic work to replaceable Agent runtimes.
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From the repo.
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deer-workflow
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
- Getting Started — learn the execution model and build a Workflow step by step.
- API Reference — inspect exact functions, types, events, and runtime behavior.
- Workflow Creator Skill — see the instructions used to generate Workflow modules.
- 简体中文文档
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.
Collected info
- ★ 547 stars
- ⎇ 56 forks
- Language: TypeScript
- Source updated: 9/21/2026
Config for your environment
Replace {MCP_ENDPOINT_URL} with this MCP’s endpoint URL (from its repo or docs above). No API key — you connect directly.
Tool
OS
Config file: ~/.cursor/mcp.json
{
"mcpServers": {
"mcp-server": {
"url": "{MCP_ENDPOINT_URL}"
}
}
}Paste into mcpServers in the config file. Restart Cursor after saving.
If this MCP is also published on mcpchannel.ai, you can subscribe from Browse and use the gateway config there instead.