agentsop-dify

name	agentsop-dify
version	1.0.0
description	SOP for building LLM applications on Dify — visual workflow + chatflow + agent + RAG knowledge base + plugin marketplace + observability, self-hostable. Use when shipping LLM apps fast with a "no-code to pro-code" gradient, especially when non-engineers need to co-author the flow.
domain	llm-application-platform
framework	Dify
framework_version	>=1.0, current 1.14.x (May 2026)
trigger_keywords	["Dify workflow","Dify chatflow","Dify agent","Dify knowledge base","visual LLM workflow","self-host LLM platform","low-code AI app","Dify plugin","Dify DSL","LLMOps platform"]
when_to_use	["shipping an internal LLM app (Q&A bot, doc-grounded copilot, content pipeline) in days not weeks","team has a mix of PMs / ops / engineers and needs a shared visual artifact (chatflow / workflow canvas)","need RAG + tools + monitoring + auth + web UI + API in one box (vs. assembling from libraries)","enterprise asks for self-hostable, on-prem, air-gapped LLM platform with multi-tenant workspaces","prototype-to-production gradient: start visual, drop into Code/Python nodes when needed"]
when_not_to_use	["high-throughput production (>10 QPS per pod) — Dify hits a known per-node DB-query bottleneck [memo.d.foundation/breakdown/dify]","sub-second latency / real-time streaming pipelines — workflow engine overhead dominates","human-in-the-loop with pause-and-wait-for-user semantics — not supported as of v1.14 [github.com/langgenius/dify/issues/21455]","model training / fine-tuning workflows — Dify is inference + orchestration only","your team is 100% engineers already shipping LangChain/LangGraph in code — Dify's visual layer is overhead, not leverage","deeply custom retrieval (graph RAG, late interaction, hand-tuned chunking) — RAGFlow or a raw stack wins [sider.ai/blog/ai-tools/dify-vs-ragflow]"]

Dify SOP — LLM Application Platform, Visual-First with Code Escape Hatches

框架定位: "An open-source platform for building agentic workflows" — visual workflow + RAG + agent + monitoring + deploy in one box, self-hostable. [docs.dify.ai/en/introduction], [github.com/langgenius/dify]

"Dify is the only tool that gives you data ingestion, RAG, an API, and a polished, shareable web UI in one click." [learnwithparam.com/blog/batteries-included-rag-platforms-dify-ragflow-onyx]

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1. 何时激活 (When to Activate)

1.1 直接信号 (Direct triggers)

• 用户说 "我需要个 LLM 应用 / 客服 bot / 知识库问答 / 文档处理流"，且希望几天内交付
• 用户说 "我们在用 Dify / 自己部署了 Dify / 想自己 host LLM 平台"
• 团队有非工程师参与编排逻辑（PM / 运营 / 业务方画 chatflow）
• 需要一站式：模型管理 + RAG + 工具调用 + 用户访问 + API + 监控 + 评估
• 需要多租户 / workspace 权限（典型 SaaS / 企业内多团队场景） [blog.elest.io/dify-vs-langflow-vs-flowise]

1.2 反向信号 (Skip Dify when)

• 高吞吐: 单 pod ~10 QPS 上限，每个 workflow 节点单独打 DB [memo.d.foundation/breakdown/dify]
• 实时低延迟: workflow 引擎开销 + 同步 DB 调用，sub-second 场景不适用
• 需要 pause-and-wait-for-user: 审批流 / 用户多步交互 → Dify workflow 不支持，issue #21455 已关 "not planned"
• 纯工程团队 + 已有 LangChain/LangGraph 投入: Dify 的可视化层成为负担而非杠杆
• 训练 / 微调 workflow: Dify 是 inference + orchestration，不碰训练
• 极致 RAG: 需要 KG-RAG、deep parsing、混合检索调优 → RAGFlow 更专 [sider.ai/blog/ai-tools/dify-vs-ragflow]
• AI 代码已经能写: 业内已有声音 "既然在 Dify 里也要写 Python，何不直接 Python？" [zhuanlan.zhihu.com/p/1947389040702781389]

1.3 心智门槛 (Mental check)

Dify 的核心价值是 "把 LLM 应用工程的脚手架打平"——auth、API、UI、向量库、模型 provider、日志、版本——而不是替代 LLM 编排框架本身的表达力。

判断公式:

• 如果你的瓶颈是 "组装周边设施" → 用 Dify
• 如果你的瓶颈是 "逻辑表达力 / 状态管理 / 极致性能" → 用 LangGraph / 直接代码

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2. 核心心智模型 (Mental Model)

2.1 五层架构 (The 5-layer stack)

[Studio]        ← Visual canvas (workflow / chatflow / agent / chatbot / text-gen)
   ↓ 编排
[Apps]          ← 5 种 app 类型，全部跑在统一 Graph Engine 上
   ↓ 依赖
[Knowledge]     ← RAG pipeline (ingest → chunk → embed → index → retrieve → rerank)
   ↓ + 调用
[Tools/Plugins] ← Marketplace: Models / Tools / Agent Strategies / Extensions / Bundles
   ↓ 观察
[Monitoring]    ← 内建 logs + 外接 LangSmith / Langfuse / Arize Phoenix / Opik