AI Skill Hub 推荐使用：开源n8n工作流 是一款优质的n8n工作流。AI 综合评分 7.5 分，在同类工具中表现稳健。如果你正在寻找可靠的n8n工作流解决方案，这是一个值得深入了解的选择。
The largest high-quality open-source library of +3400 n8n AI workflows – ready，提供高质量的n8n AI工作流，适合开发者和企业使用。
开源n8n工作流 是一款基于 Python 开发的开源工具，专注于 n8n、python 等核心功能。作为 GitHub 开源项目，它拥有活跃的社区支持和持续的版本迭代，代码完全透明可审计，支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流，都能提供稳定可靠的解决方案。
The largest high-quality open-source library of +3400 n8n AI workflows – ready，提供高质量的n8n AI工作流，适合开发者和企业使用。
开源n8n工作流 是一款基于 Python 开发的开源工具，专注于 n8n、python 等核心功能。作为 GitHub 开源项目，它拥有活跃的社区支持和持续的版本迭代，代码完全透明可审计，支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流，都能提供稳定可靠的解决方案。
# 方式一：pip 安装（推荐）
pip install open-workflow-library
# 方式二：虚拟环境安装（推荐生产环境）
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install open-workflow-library
# 方式三：从源码安装（获取最新功能）
git clone https://github.com/oxbshw/Open-Workflow-Library
cd Open-Workflow-Library
pip install -e .
# 验证安装
python -c "import open_workflow_library; print('安装成功')"
# 命令行使用
open-workflow-library --help
# 基本用法
open-workflow-library input_file -o output_file
# Python 代码中调用
import open_workflow_library
# 示例
result = open_workflow_library.process("input")
print(result)
// n8n 工作流配置步骤 // 1. 在 n8n 中点击 "Import Workflow" // 2. 粘贴 JSON 文件内容或上传文件 // 3. 配置必要的 Credentials： // - Settings → Credentials → New // - 选择对应服务类型填写 API Key // 4. 激活工作流 (Toggle ON) // 5. 通过 Webhook 或定时触发器运行
python tools/build_unified_catalog.py
Every tool is standard-library Python 3.12+. Nothing is installed globally, nothing is published, nothing requires n8n on the local machine.
```bash
Open Workflow Library is an open workflow intelligence project for collecting, indexing, validating, repairing, and eventually generating automation workflows across frameworks. It starts with a large n8n workflow collection and is being expanded into a universal workflow knowledge base and tooling layer.
This repository is the working space for that project. It contains the existing n8n workflow collection that seeded the effort, the schemas that define the shared data model, the audit tooling that catalogues the collection, and the early structure of the LLM-usable wiki that the rest of the system will be built on.
---
python tools/audit_workflows.py
python tools/prompt_to_n8n.py "Create a workflow that receives website leads, scores them, saves them to CRM, and alerts Slack."
workflows/generated/open-workflow-library-v0/ contains a deterministic expansion pack of 420 template workflows across 21 categories. Each template ships as an n8n workflow.json, a Universal IR workflow.ir.json, and a README. The pack is indexed separately in catalog/generated-workflows.index.json.
These templates are starting points, not production-tested workflows. They contain placeholder credentials and placeholder URLs only — no real keys, tokens, phone numbers, emails, or private endpoints. Validate with tools/validate_generated_pack.py and review each template before importing.
Details: docs/generated-workflows.md.
Workflow Runtime Proof V1 is a deterministic, local proof of the full intelligence loop:
prompt
-> Universal Workflow IR
-> n8n workflow.json
-> static n8n compatibility validation
-> repair proposal (if needed)
-> learning event (if useful)
-> human-reviewable queueIt is static validation only. No workflow is imported into n8n, no node is executed, and no external service is called.
| Tool | Role |
|---|---|
tools/export_ir_to_n8n.py | IR JSON → n8n workflow JSON (conservative node whitelist, placeholders only) |
tools/validate_n8n_workflow.py | Static n8n compatibility validator (no runtime) |
tools/prompt_to_n8n.py | Orchestrator: prompt → IR → n8n → validation → proof README |
tools/propose_runtime_repair.py | Emits repair proposals from validation output (no auto-apply) |
tools/create_learning_event.py | Captures learning events from validation/repair (no promotion) |
tools/build_review_queue.py | Aggregates everything needing human review |
Run the proof loop locally:
python tools/prompt_to_n8n.py "Create a workflow that receives website leads, scores them, saves qualified leads to CRM, and alerts Slack."
python tools/validate_n8n_workflow.py reports/runtime-proof/<slug>/workflow.n8n.json
python tools/build_review_queue.pyHonest limitations:
- Static validation only. No runtime execution. - No external API calls. No real credentials. - No autonomous self-improvement. Learning events are evidence; promotion into the curated wiki or repair rules requires human review. - Multi-framework export remains planned. Only n8n is implemented.
Details: docs/runtime-proof.md.
The generator pipeline is documented in docs/prompt-to-workflow.md. It is not implemented in this pass. The schemas, catalog, and wiki here are the inputs the pipeline will consume.
We will not claim prompt-to-workflow works until it is implemented and validated end-to-end against the catalog.
该项目提供了大量的n8n AI工作流，适合开发者和企业使用，值得关注。
该工具使用 NOASSERTION 协议，商用场景请仔细阅读协议条款，必要时咨询法律意见。
AI Skill Hub 为第三方内容聚合平台，本页面信息基于公开数据整理，不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后，再部署至生产环境，并做好必要的安全评估。
📄 NOASSERTION — 请查阅原始协议条款了解具体使用限制。
总体来看，开源n8n工作流 是一款质量良好的n8n工作流，在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态，建议收藏备用，结合自身场景选择合适时机引入使用。
| 原始名称 | Open-Workflow-Library |
| Topics | n8npython |
| GitHub | https://github.com/oxbshw/Open-Workflow-Library |
| License | NOASSERTION |
| 语言 | Python |
收录时间：2026-05-24 · 更新时间：2026-05-24 · License：NOASSERTION · AI Skill Hub 不对第三方内容的准确性作法律背书。
选择 Agent 类型，复制安装指令后粘贴到对应客户端