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Dify SOP — LLM Application Platform, Visual-First with Code Escape Hatches · agentsope | DecimalAI

AI/MLby agentsope

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

SOP for building LLM applications on Dify — visual workflow + chatflow + agent + RAG knowledge base + plugin marketplace + observability, self-hostable.

Never made a case worseMaintained

SkillScore

SkillScore95/100

Test results 95

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

86%95%of test cases passed +9 pts

Without skill19 of 22

With skill21 of 22

2 fixed19 passed either way1 still failing0 broke

Measured on gemini-3.6-flash8/24/2026v121 comparable

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskAn engineer setting up self-hosted Dify via Docker Compose needs to pick the default recommended vector database for general RAG storage. Which vector store is recommended as the default option in Dify self-hosted setups?

Without the skillMissed

With the skillPassed

Same model, same task. Answers are capped and redacted.

Use this skill

Your agent runs on

Adds every file the skill has, pinned so you can update it. No account needed.

pip install "decimalai>=0.15"
decimalai skills add agentsope-agentsop-dify
claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"

The same install from Python, for a notebook or a setup script.

Install

pip install "decimalai>=0.15"

Add the skill

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("agentsope-agentsop-dify")
if result.exit_code:
    raise SystemExit(result.problems)

print("Added. Start Claude Code with the command below.")

Start Claude Code with it

claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"

Type these in a Claude Code session. No account or API key. It writes the skill and any files it bundles, and asks first when its safety rating isn't Passed.

Add the DecimalAI plugin, once

/plugin marketplace add decimal-labs/decimalai-claude-plugins
/plugin install decimalai@decimalai

Install this skill

/decimalai:install agentsope-agentsop-dify

The plugin needs python3 and curl on your machine.

No Python or account. Claude Code picks it up in its next session.

Save it into your project

curl -fsSL --create-dirs -o .claude/skills/agentsope-agentsop-dify/SKILL.md https://app.decimal.ai/s/agentsope-agentsop-dify/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/agentsope-agentsop-dify/SKILL.md

This gets SKILL.md only. The skill's 5 other files are not included.

Other ways to add it

Claude Code docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"
decimalai skills add agentsope-agentsop-dify

Add it to your agent

import asyncio

from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

async def main():
    question = "Hello!"
    async with support.run(question) as run:  # one user message, one run
        async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
            print(message)

# In a notebook or async code: await main()
asyncio.run(main())

Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

The same install from Python, for a notebook or a setup script. agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("agentsope-agentsop-dify")
if result.exit_code:
    raise SystemExit(result.problems)

import asyncio

from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

async def main():
    question = "Hello!"
    async with support.run(question) as run:  # one user message, one run
        async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
            print(message)

# In a notebook or async code: await main()
asyncio.run(main())

Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/agentsope-agentsop-dify/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from claude_agent_sdk import ClaudeAgentOptions, query

skill = Path(".agents/skills/agentsope-agentsop-dify/SKILL.md").read_text()
options = ClaudeAgentOptions(system_prompt=f"You are a helpful agent.\n\n{skill}", max_turns=6)

async def main():
    async for message in query(prompt="Hello!", options=options):
        print(message)

# In a notebook or async code: await main()
asyncio.run(main())

This gets SKILL.md only. The skill's 5 other files are not included.

Other ways to add it

Claude Agent SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai-agents]"
decimalai skills add agentsope-agentsop-dify

Add it to your agent

from agents import Agent, Runner
from decimalai.openai_agents import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    name=support.name,
    instructions=support.instructions,  # your prompt, then the skill list
    tools=[*support.tools],             # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.

Install

pip install "decimalai[openai-agents]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("agentsope-agentsop-dify")
if result.exit_code:
    raise SystemExit(result.problems)

from agents import Agent, Runner
from decimalai.openai_agents import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    name=support.name,
    instructions=support.instructions,  # your prompt, then the skill list
    tools=[*support.tools],             # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/agentsope-agentsop-dify/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/agentsope-agentsop-dify/SKILL.md").read_text()
agent = Agent(name="support", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

This gets SKILL.md only. The skill's 5 other files are not included.

Other ways to add it

OpenAI Agents docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai]"
decimalai skills add agentsope-agentsop-dify

Add it to your agent

import json

from openai import OpenAI
from decimalai.openai import agent_config

client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
                   "required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    # run.system is your prompt, then the skill list.
    messages = [{"role": "system", "content": run.system}, run.message]
    while True:
        reply = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
            tools=[*TOOLS, *run.tools],  # your tools, then load_skill
        ).choices[0].message
        messages.append(reply)
        if not reply.tool_calls:
            break
        for call in reply.tool_calls:
            # load_skill is answered by the run; your own tools by FUNCTIONS.
            function = FUNCTIONS.get(call.function.name)
            messages.append(run.tool_result(call) or {
                "role": "tool", "tool_call_id": call.id,
                "content": function(**json.loads(call.function.arguments)) if function
                else f"There is no tool named {call.function.name!r}.",
            })
print(reply.content)

# The Responses API takes run.response_tools instead of run.tools.

Install

pip install "decimalai[openai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("agentsope-agentsop-dify")
if result.exit_code:
    raise SystemExit(result.problems)

import json

from openai import OpenAI
from decimalai.openai import agent_config

client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
                   "required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    # run.system is your prompt, then the skill list.
    messages = [{"role": "system", "content": run.system}, run.message]
    while True:
        reply = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
            tools=[*TOOLS, *run.tools],  # your tools, then load_skill
        ).choices[0].message
        messages.append(reply)
        if not reply.tool_calls:
            break
        for call in reply.tool_calls:
            # load_skill is answered by the run; your own tools by FUNCTIONS.
            function = FUNCTIONS.get(call.function.name)
            messages.append(run.tool_result(call) or {
                "role": "tool", "tool_call_id": call.id,
                "content": function(**json.loads(call.function.arguments)) if function
                else f"There is no tool named {call.function.name!r}.",
            })
print(reply.content)

# The Responses API takes run.response_tools instead of run.tools.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/agentsope-agentsop-dify/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/agentsope-agentsop-dify/SKILL.md").read_text()
reply = client.chat.completions.create(
    model="gpt-5.4-mini",
    messages=[
        {"role": "system", "content": f"You are a helpful agent.\n\n{skill}"},
        {"role": "user", "content": "Hello!"},
    ],
).choices[0].message

This gets SKILL.md only. The skill's 5 other files are not included.

Other ways to add it

OpenAI SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[anthropic]"
decimalai skills add agentsope-agentsop-dify

Add it to your agent

import anthropic
from decimalai.anthropic import agent_config

client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

question = "Hello!"
with support.run(question) as run:  # one user message, one run
    answer = client.beta.messages.tool_runner(
        model="claude-haiku-5-5",
        max_tokens=4096,
        system=run.system,     # your prompt, then the skill list
        tools=[*run.tools],    # your tools, then load_skill
        messages=[{"role": "user", "content": question}],
    ).until_done()

Install

pip install "decimalai[anthropic]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("agentsope-agentsop-dify")
if result.exit_code:
    raise SystemExit(result.problems)

import anthropic
from decimalai.anthropic import agent_config

client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

question = "Hello!"
with support.run(question) as run:  # one user message, one run
    answer = client.beta.messages.tool_runner(
        model="claude-haiku-5-5",
        max_tokens=4096,
        system=run.system,     # your prompt, then the skill list
        tools=[*run.tools],    # your tools, then load_skill
        messages=[{"role": "user", "content": question}],
    ).until_done()

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/agentsope-agentsop-dify/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/agentsope-agentsop-dify/SKILL.md").read_text()
reply = client.messages.create(
    model="claude-haiku-5-5",
    max_tokens=4096,
    system=f"You are a helpful agent.\n\n{skill}",
    messages=[{"role": "user", "content": "Hello!"}],
)

This gets SKILL.md only. The skill's 5 other files are not included.

Other ways to add it

Anthropic docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[google-genai]"
decimalai skills add agentsope-agentsop-dify

Add it to your agent

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                               "required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    config = types.GenerateContentConfig(
        system_instruction=run.system,     # your prompt, then the skill list
        tools=[*TOOLS, *run.tool_params],  # your functions, then load_skill
    )
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(
            model="gemini-3.5-flash", contents=contents, config=config,
        )
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        parts = []
        for call in response.function_calls:
            # load_skill is answered by the run; your own functions by FUNCTIONS.
            # A loop of your own rather than automatic function calling, which
            # raises KeyError when the model asks for a function you did not pass.
            function = FUNCTIONS.get(call.name)
            parts.append(run.tool_result(call) or {"function_response": {
                "name": call.name, "id": call.id,
                "response": {"result": function(**(call.args or {})) if function
                             else f"There is no function named {call.name!r}."},
            }})
        contents.append({"role": "user", "parts": parts})
print(response.text)

Install

pip install "decimalai[google-genai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("agentsope-agentsop-dify")
if result.exit_code:
    raise SystemExit(result.problems)

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                               "required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    config = types.GenerateContentConfig(
        system_instruction=run.system,     # your prompt, then the skill list
        tools=[*TOOLS, *run.tool_params],  # your functions, then load_skill
    )
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(
            model="gemini-3.5-flash", contents=contents, config=config,
        )
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        parts = []
        for call in response.function_calls:
            # load_skill is answered by the run; your own functions by FUNCTIONS.
            # A loop of your own rather than automatic function calling, which
            # raises KeyError when the model asks for a function you did not pass.
            function = FUNCTIONS.get(call.name)
            parts.append(run.tool_result(call) or {"function_response": {
                "name": call.name, "id": call.id,
                "response": {"result": function(**(call.args or {})) if function
                             else f"There is no function named {call.name!r}."},
            }})
        contents.append({"role": "user", "parts": parts})
print(response.text)

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/agentsope-agentsop-dify/SKILL.md

Give it to your agent

from pathlib import Path
from google import genai
from google.genai import types

client = genai.Client()
skill = Path(".agents/skills/agentsope-agentsop-dify/SKILL.md").read_text()
response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Hello!",
    config=types.GenerateContentConfig(system_instruction=f"You are a helpful agent.\n\n{skill}"),
)

This gets SKILL.md only. The skill's 5 other files are not included.

Other ways to add it

Gemini API docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[langchain]" langchain-openai
decimalai skills add agentsope-agentsop-dify

Add it to your agent

from langchain.agents import create_agent
from decimalai.langchain import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = create_agent(
    "openai:gpt-5.4-mini",
    tools=[],                         # your tools; the middleware adds load_skill
    middleware=[support.middleware],  # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

Install

pip install "decimalai[langchain]" langchain-openai

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("agentsope-agentsop-dify")
if result.exit_code:
    raise SystemExit(result.problems)

from langchain.agents import create_agent
from decimalai.langchain import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = create_agent(
    "openai:gpt-5.4-mini",
    tools=[],                         # your tools; the middleware adds load_skill
    middleware=[support.middleware],  # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/agentsope-agentsop-dify/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/agentsope-agentsop-dify/SKILL.md").read_text()
agent = create_agent("openai:gpt-5.4-mini", tools=[], system_prompt=f"You are a helpful agent.\n\n{skill}")
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 5 other files are not included.

Other ways to add it

LangChain docs

CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"
decimalai skills add agentsope-agentsop-dify

Add it to your agent

import decimalai.crewai
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                               # your own CrewAI LLM
    skills=decimalai.crewai.skills(),      # the installed folder
    tools=decimalai.crewai.skill_tools(),  # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

The same install from Python, for a notebook or a setup script. CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("agentsope-agentsop-dify")
if result.exit_code:
    raise SystemExit(result.problems)

import decimalai.crewai
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                               # your own CrewAI LLM
    skills=decimalai.crewai.skills(),      # the installed folder
    tools=decimalai.crewai.skill_tools(),  # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

No DecimalAI package. CrewAI's own loader reads the folder, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/agentsope-agentsop-dify/SKILL.md

Give it to your agent

from pathlib import Path
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                         # your own CrewAI LLM
    skills=[Path(".agents/skills")],
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

This gets SKILL.md only. The skill's 5 other files are not included.

Other ways to add it

CrewAI docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[adk]"
decimalai skills add agentsope-agentsop-dify

Add it to your agent

from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = LlmAgent(
    name="support",          # ADK needs a Python identifier
    model="gemini-3.5-flash",
    instruction=support.instruction,  # your prompt, then the skill list
    tools=[*support.tools],           # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])

Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

Install

pip install "decimalai[adk]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("agentsope-agentsop-dify")
if result.exit_code:
    raise SystemExit(result.problems)

from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = LlmAgent(
    name="support",          # ADK needs a Python identifier
    model="gemini-3.5-flash",
    instruction=support.instruction,  # your prompt, then the skill list
    tools=[*support.tools],           # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])

Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/agentsope-agentsop-dify/SKILL.md

Give it to your agent

from pathlib import Path
from google.adk.agents import LlmAgent

skill = Path(".agents/skills/agentsope-agentsop-dify/SKILL.md").read_text()
agent = LlmAgent(
    name="support",
    model="gemini-3.5-flash",
    instruction=lambda context: f"You are a helpful agent.\n\n{skill}",
)

This gets SKILL.md only. The skill's 5 other files are not included.

Other ways to add it

Google ADK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[pydantic-ai]"
decimalai skills add agentsope-agentsop-dify

Add it to your agent

from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    "openai:gpt-4o-mini",
    name=support.name,
    tools=[],                 # your tools; the capability adds load_skill
    capabilities=[support],   # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.

Install

pip install "decimalai[pydantic-ai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("agentsope-agentsop-dify")
if result.exit_code:
    raise SystemExit(result.problems)

from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    "openai:gpt-4o-mini",
    name=support.name,
    tools=[],                 # your tools; the capability adds load_skill
    capabilities=[support],   # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/agentsope-agentsop-dify/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/agentsope-agentsop-dify/SKILL.md").read_text()
agent = Agent("openai:gpt-4o-mini", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

This gets SKILL.md only. The skill's 5 other files are not included.

Other ways to add it

Pydantic AI docs

Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents
decimalai skills add agentsope-agentsop-dify

Add it to your agent

import decimalai.deepagents
from deepagents import create_deep_agent

agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).

The same install from Python, for a notebook or a setup script. Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("agentsope-agentsop-dify")
if result.exit_code:
    raise SystemExit(result.problems)

import decimalai.deepagents
from deepagents import create_deep_agent

agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/agentsope-agentsop-dify/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/agentsope-agentsop-dify/SKILL.md").read_text()
agent = create_deep_agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 5 other files are not included.

Other ways to add it

Deep Agents docs

Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework
decimalai skills add agentsope-agentsop-dify

Add it to your agent

import asyncio

from agent_framework import Agent
from decimalai.agent_framework import skills

agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))

It caches skills by default, so restart the process after decimalai skills update.

The same install from Python, for a notebook or a setup script. Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("agentsope-agentsop-dify")
if result.exit_code:
    raise SystemExit(result.problems)

import asyncio

from agent_framework import Agent
from decimalai.agent_framework import skills

agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))

It caches skills by default, so restart the process after decimalai skills update.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/agentsope-agentsop-dify/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/agentsope-agentsop-dify/SKILL.md").read_text()
agent = Agent(client=client, instructions=f"You are a helpful agent.\n\n{skill}")
asyncio.run(agent.run("Hello!"))

This gets SKILL.md only. The skill's 5 other files are not included.

Other ways to add it

Microsoft Agent Framework docs

Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents
decimalai skills add agentsope-agentsop-dify

Add it to your agent

from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell

agent = Agent(
    model=model,                # always pass one: the default is Amazon Bedrock
    system_prompt="You are a helpful agent.",
    tools=[file_editor, shell],
    plugins=[skills()],
)
agent("Hello!")

Strands loads an agent's skills once, so build the agent again after decimalai skills update.

The same install from Python, for a notebook or a setup script. Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("agentsope-agentsop-dify")
if result.exit_code:
    raise SystemExit(result.problems)

from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell

agent = Agent(
    model=model,                # always pass one: the default is Amazon Bedrock
    system_prompt="You are a helpful agent.",
    tools=[file_editor, shell],
    plugins=[skills()],
)
agent("Hello!")

Strands loads an agent's skills once, so build the agent again after decimalai skills update.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/agentsope-agentsop-dify/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/agentsope-agentsop-dify/SKILL.md").read_text()
agent = Agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent("Hello!")

This gets SKILL.md only. The skill's 5 other files are not included.

Other ways to add it

Strands docs

Adds every file the skill has, pinned so you can update it. Cursor, Copilot, Cline and Warp read this folder by themselves.

pip install "decimalai>=0.15"
decimalai skills add agentsope-agentsop-dify

No Python or account. Cursor, Copilot, Cline and Warp read this folder by themselves.

Save it into your project

curl -fsSL --create-dirs -o .agents/skills/agentsope-agentsop-dify/SKILL.md https://app.decimal.ai/s/agentsope-agentsop-dify/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/agentsope-agentsop-dify/SKILL.md

This gets SKILL.md only. The skill's 5 other files are not included.

Other ways to add it

Cursor and other editors docs

Fetch the file and give its text to your model as instructions. No account or package needed.

Get the file

curl -fsSL https://app.decimal.ai/s/agentsope-agentsop-dify/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/agentsope-agentsop-dify.json

This gets SKILL.md only. The skill's 5 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/24/2026
Source: agentsope/skillalchemy ↗
License: MIT

Manage it in DecimalAI

Saved prompts and skills, pinned versions, and traces of this skill in your runs. DecimalAI does not run your agent.

Claude Code reads files from your project, so DecimalAI config does not apply there.

On Claude Agent SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI Agents: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Anthropic: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Gemini API: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On LangChain: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On CrewAI: set DECIMAL_API_KEY and add decimalai.init(crewai=True) to record runs and skill use. The skill version stays in your repo.

On Google ADK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Pydantic AI: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Deep Agents: set DECIMAL_API_KEY and add decimalai.init(langchain=True) to record runs and skill use. The skill version stays in your repo.

On Microsoft Agent Framework: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

On Strands: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

Editors read files from your project, so DecimalAI config does not apply there.

Anywhere else: record a use with decimalai.log_skill_activation() inside a trace.

Use this skill

SKILL.md

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]

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

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

参考: [docs.dify.ai/en/introduction], [dify.ai/blog/dify-plugin-system-design-and-implementation]

2.2 五种 App 类型 — 选哪个？

App Type	触发模型	记忆	编排方式	典型场景
Chatbot (legacy)	多轮对话	内置	单 prompt + tools	简单客服、FAQ bot
Agent (legacy)	多轮对话	内置	ReAct / FC 自主决策	自治工具使用、多步推理
Text Generator (legacy)	单次调用	无	单 prompt	文案生成、翻译
Workflow	单次调用 / 批处理	无	可视化 DAG	API 后端、批量任务、ETL
Chatflow	每轮对话触发整图	有 (conversation vars)	可视化 DAG + 对话状态	复杂对话流、guided dialogue

核心决策树:

有对话上下文需求？
├─ 是 ─→ Chatflow (复杂逻辑) 或 Chatbot/Agent (简单)
└─ 否 ─→ Workflow (复杂逻辑) 或 Text Generator (单 prompt)

需要 LLM 自主选择工具 / 多步推理？
├─ 是 ─→ Agent app  或  Workflow + Agent Node (推荐, 1.9+)
└─ 否 ─→ Workflow / Chatflow + 显式节点编排

参考: [docs.dify.ai/en/use-dify/getting-started/key-concepts], [hellodify.com/en/docs/workflow/workflow-chatflow-difference], [zediot.com/blog/dify-difference-between-agent-and-workflow]

"Workflow behaves more like a script… each Workflow run is a completely fresh start. Chatflow… is a robot that can interact with users in a loop." [hellodify.com/en/docs/workflow/workflow-chatflow-difference]

2.3 Graph Engine = "graphon"

自研 DAG 执行引擎，所有 app 类型底层一套
v1.9+ 重写为 queue-based scheduling: 任务统一入队、调度器管依赖与并行 [github.com/langgenius/dify/discussions/26138]
节点有标准签名：输入变量 → 处理 → 输出变量
支持：partial run、step debugging、stream-stitch across nodes、pause/terminate commands

2.4 节点目录 (Node taxonomy)

类别	节点	用途
基础	Start, End, Answer	入口 / 出口
LLM	LLM, Question Classifier, Parameter Extractor	调模型
RAG	Knowledge Retrieval	查知识库
逻辑	IF/ELSE, Iteration, Loop, Variable Assigner, Variable Aggregator	控制流
代码	Code (Python/Node.js), Template (Jinja2)	自定义逻辑
外部	HTTP Request, Tool, Agent Node	调外部 / 子 agent
数据	List Operator, Document Extractor	处理结构化数据

"Dify's canvas includes LLM calls, knowledge retrieval, conditionals, HTTP requests, code nodes (Python/Node), Jinja transforms, iterators/loops, and aggregators." [legacy-docs.dify.ai/guides/workflow/node]

2.5 Plugin 系统 (五元类型)

v1.0+ 引入插件系统，从 monolith → marketplace。 [dify.ai/blog/introducing-dify-plugins]

类型	用途	何时选它
Models	接入新模型 provider	内置不支持的模型
Tools	给 agent / workflow 加能力	需要复用的领域工具 (Slack, Notion, 自家 API)
Agent Strategies	自定义 reasoning loop	想用 ToT / GoT / 自研推理
Extensions	轻量 HTTP webhook	简单 API 接入，不需打包
Bundles	多插件打包	整套行业解决方案

决策: 一次性 = HTTP Request 节点；可复用 = Tool plugin；逻辑深 = Agent Strategy plugin。

2.6 Knowledge Base (RAG) 心智

RAG 流程: Ingest → Extract → Clean → Chunk → Embed → Index → Retrieve → Rerank → Augment [docs.dify.ai/en/guides/knowledge-base/readme]

三种构建方式:

Quick create — 上传即用，自动处理
Knowledge pipelines (v1.9+) — 可视化定义 ingest 流程，类似 workflow but for data
External integration — 接外部知识库 API (Pinecone / Weaviate / 自有 RAG 服务)

向量库支持: 13+ (Qdrant 默认推荐, Weaviate, Milvus, Pinecone, PGVector, TiDB, Chroma…) [deepwiki.com/langgenius/dify-docs/8.3-vector-database-configuration]

检索策略:

N-to-1 Recall (需 reasoning model 选库)
Multi-way Recall (并行检索 + Rerank model 合并)

3. SOP 工作流 (Standard Operating Procedure)

Phase 0: 选定 App 类型 (5 分钟决策)

问：对话还是一次性任务？ → Chatflow 系 vs Workflow 系
问：逻辑能写死，还是要 LLM 自主决策？ → 显式 workflow vs Agent (app or node)
问：会不会跟 PM/业务方共编？ → 是 → 优先 visual app；否 → 考虑直接代码
问：是 MVP 还是会长期演进？ → 长期 → 强烈建议 Workflow/Chatflow（legacy 三件套不推荐）

官方建议: "Workflow/Chatflow are recommended for most use cases. Use the legacy basic types (Chatbot/Agent/Text Generator) only if preferring simplified interfaces over advanced features." [docs.dify.ai/en/use-dify/getting-started/key-concepts]

Phase 1: 部署 (Deploy) — 30 分钟

Cloud (推荐起步):

Sandbox (Free): 200 credits / 10 apps / 5MB storage
Professional ($59/mo): 5k credits / 50 apps / 5GB
Team ($159/mo): 10k credits / 20GB / SSO
Enterprise: 自定义 [architjn.com/blog/dify-cloud-pricing-plans-free-tier-when-to-self-host]

Self-host (Docker Compose) — 最常用:

git clone https://github.com/langgenius/dify
cd dify/docker
cp .env.example .env
docker compose up -d

最低: 2 vCPU / 4GB RAM
生产建议: 2 vCPU / 4GB / 40GB SSD + 反向代理 (Caddy/Nginx) + HTTPS
切勿直接暴露内部端口到公网 [docs.dify.ai/en/self-host/quick-start/docker-compose]

关键环境变量:

VECTOR_STORE — 选 Qdrant / Weaviate / Milvus / Pinecone…
PLUGIN_DAEMON_TIMEOUT=300 — 插件超时
WORKFLOW_MAX_EXECUTION_TIME / HTTP_REQUEST_MAX_READ_TIMEOUT — 长链路必调

Phase 2: Prompt + 单节点 (Prompt Studio) — 1 小时

先在 Prompt IDE 调通单个 LLM 调用——别上来就堆 workflow
在 Prompt IDE 多模型对比 (GPT-4 / Claude / 自有模型)
把变量 {{var}} 抽出来——后续会成为 workflow 输入

Phase 3: 接知识库 (Knowledge) — 半天

上传文档 → 选 High-Quality (embed + vector) 或 Economical (BM25, 省钱)
调 chunk size / overlap (默认 500/50 起手，长文档调到 1000+)
跑 Retrieval Testing——用真实问题模拟，看召回的 chunks
调 retrieval mode: Semantic / Full-text / Hybrid (推荐)
接 Rerank 模型（jina-rerank, cohere-rerank, BGE-rerank）
文档量大 / 多类型时 → 用 Knowledge Pipeline 自定义 ingest

"Dify provides a comprehensive RAG pipeline handling the full lifecycle from raw file upload through extraction, cleaning, segmentation, embedding, indexing, retrieval, and reranking." [pyshine.com/2026/04/20/Dify-Open-Source-LLM-App-Development-Platform]

Phase 4: 接工具 (Tools) — 半天

内置 50+ 工具: Google Search, DALL·E, WolframAlpha, Stable Diffusion… [github.com/langgenius/dify]
自家 API → 优先用 HTTP Request 节点 (零代码)
复用 / 团队共享 → 写成 Tool plugin
复杂业务逻辑 → Code 节点 (Python/Node.js, sandboxed)

Phase 5: 编 workflow (Build) — 1-3 天

构建顺序 (反直觉但重要):

先线性跑通——LLM → Knowledge → LLM → End
加 IF/ELSE 分支——只在真正需要分流时
加 Iteration——批量处理列表
加 Agent Node (1.9+)——当确实需要 LLM 自主选工具时
再加 Code 节点——填补无法表达的逻辑

反模式: 一上来就画 30+ 节点的复杂图。先线性跑通，再分支。

Phase 6: 测试 + 发布 (Test & Publish) — 1 天

Step debugging——每个节点单步执行
Test run——从任意节点开始跑 (1.9+ 支持)
Annotation Reply (chat apps)——把好回答标注下来，下次直接返回 (省 token + 提质) [dify.ai/blog/boosting-chatbot-quality-cutting-costs-with-dify-annotation-replies]
Version control——发布版本带 release notes，可回滚 [legacy-docs.dify.ai/guides/management/version-control]
导出 DSL (YAML)——纳入 git 版本管理 [github.com/langgenius/dify-docs/blob/main/en/guides/workflow/export_import.md]
发布为 Web App / API endpoint / MCP Server

Phase 7: 接观测 (Monitor) — 半天

内建 logs：每次调用的 input/output/token/latency
外接 LLMOps:
- LangSmith — LangChain 全家桶用户 [docs.dify.ai/guides/monitoring/integrate-external-ops-tools]
- Langfuse — 开源自托管首选 [langfuse.com/integrations/no-code/dify]
- Arize Phoenix — 评测优先 [dify.ai/blog/dify-arize-how-to-evaluate-monitor-and-improve-agents]
- Opik — 性能最快 [comet.com Opik]
跟踪 7 类 trace: Workflows / Messages / Moderation / Suggested Questions / Dataset Retrieval / Tools / Generated Names

Phase 8: 迭代 (Iterate) — 持续

看监控里的 bad cases → 标注 → annotation reply 或返工 prompt
收集 traces → 在 Phoenix/Langfuse 建 eval dataset → 对比 prompt 变体
复杂 workflow 出现性能问题 → 拆 sub-workflow / 改 queue model / 接外部服务

4. 操作模型 (Operation Model)

4.1 工作流构件清单

# 一个典型 Dify workflow 的 building blocks
inputs:           # Start 节点变量 (text / number / file / select)
nodes:
  - llm:                    # 调模型
      model: gpt-4-turbo
      prompt: "..."
      memory: false         # workflow 永远是 false
  - knowledge_retrieval:    # 查知识库
      datasets: [kb_id_1, kb_id_2]
      mode: hybrid
      rerank: bge-rerank
  - if_else:                # 分支
      conditions: [...]
  - iteration:              # 批处理列表
      iterator: {{node.output}}
      sub_workflow: [...]
  - code:                   # Python/Node
      code: "def main(x): return ..."
  - http_request:           # 调外部
      method: POST
      url: "..."
  - agent:                  # 自主决策子节点 (1.9+)
      strategy: react / function_calling / custom
      tools: [...]
      max_iterations: 5
outputs:          # End / Answer 节点

4.2 变量系统

User Input — Start 节点定义
Node Output — {{node_id.var}} 跨节点引用
Environment Variables — secrets, API keys
Conversation Variables (Chatflow only) — 跨轮持久化 [docs.dify.ai/en/use-dify/getting-started/key-concepts]
System Variables — sys.user_id, sys.conversation_id 等

4.3 发布形态

Web App — 一键得 hosted chat UI
API — REST endpoint + API key
Embed — iframe / web SDK
MCP Server (新) — 暴露给外部 agent 调用
Tool — 作为另一个 Dify app 的工具

4.4 多租户 / Workspace

同一 Dify 实例支持多 workspace
每 workspace 独立的 models / API keys / members / apps
RBAC: Owner / Admin / Editor / Normal [blog.elest.io/dify-vs-langflow-vs-flowise]
⚠️ 已知限制: model provider key 是 workspace 级，无法 per-app 区分 [github.com/langgenius/dify/issues/32167]

4.5 DSL (YAML) 工作流

导出: Studio → 应用菜单 → Export DSL
导入: 跨实例迁移、git 版本管理、CI/CD
限制: Knowledge base 数据本身不打包到 DSL (v1.8.1 时仍未支持) [github.com/langgenius/dify/issues/25999]

5. 困境决策案例 (Dilemma Cases)

Case 1: "Workflow 变得不可维护——拆子工作流还是改写代码？"

症状:

Workflow 画到 30+ 节点，canvas 拖动卡顿 (issue #28245) [github.com/langgenius/dify/issues/28245]
分支嵌套深，看不清数据流
改一处怕碰别处

决策树:

节点数 < 20 且分支扁平
   → 留在 Dify, 优化命名 + 加 Note
节点数 20-40, 有可复用子流程
   → 拆 sub-workflow (打包成 Tool 给主流程调用)
节点数 > 40 或 大量 Code 节点
   → 信号: 业务逻辑已超出可视化优势
   → 把核心逻辑改写为 Python 服务, Dify 只做编排前端 + RAG

实操:

Workflow app 可以被打包为另一个 workflow/chatflow 的 Tool — 这是 Dify 提供的模块化路径 [hellodify.com/en/docs/workflow/workflow-chatflow-difference]
复杂业务逻辑 → 用 HTTP Request 节点调外部微服务，Dify 退化为 orchestrator

反模式: 在 Code 节点里写 200 行 Python——这是 "Dify 已不合适" 的信号。

Case 2: "可视化已到天花板——继续 Dify 还是切 LangGraph / 直接代码？"

症状:

需要 pause-and-wait-for-user（审批、用户多步选择）— Dify 不支持，issue #21455 closed "not planned"
需要复杂状态机 / 循环 / 中断恢复 / time-travel
需要 sub-second 延迟
节点级 DB 查询拖累吞吐 [memo.d.foundation/breakdown/dify]

判断公式:

信号	留 Dify	切代码
团队非 100% 工程师	✅	❌
需要快速 UI / API / 认证	✅	❌ (得自己搭)
pause-and-wait	❌	✅ (LangGraph 原生)
QPS > 10 / pod	❌	✅
复杂状态 + 回滚	❌	✅
RAG + 简单编排	✅	—

混合方案 (推荐): Dify 做前端 / RAG / 用户管理 / 监控；核心 agent 逻辑跑在 LangGraph 服务后端，Dify HTTP Request 节点调用。

"Dify wins when you're building an LLM-powered SaaS, an internal product with multiple teams… Langflow is for teams that need power now and will need more later." [blog.elest.io/dify-vs-langflow-vs-flowise]

Case 3: "Self-host 还是 Cloud——什么时候值得自己运维？"

症状:

团队在评估 Dify Cloud Pro ($59) vs 自部署 Docker

决策矩阵 [architjn.com/blog/dify-cloud-pricing-plans-free-tier-when-to-self-host]:

条件	选 Cloud	选 Self-host
团队 ≤ 3 人, 用量小	✅ ($59-159/mo 比养 DevOps 便宜)	❌
合规 / 数据驻留 (金融、医疗)	❌	✅
用量大 (millions of API calls)	❌ (订阅成本超过 infra)	✅
Air-gapped / 内网部署	❌	✅
自定义 vector store / 模型	部分支持	✅ 完全控制
不想管 backup / scaling	✅	❌

实操要点:

自部署不是"docker compose up"完事——需要反向代理 + HTTPS + 备份 + 监控 + 升级策略
生产 self-host 用 Kubernetes + Helm + 外部 PostgreSQL/Redis/Vector DB（不要用容器内置的）
⚠️ Community / Premium / Enterprise 用同一 Docker image，差异在环境变量 [github.com/langgenius/dify/discussions/32254]

Case 4: "Dify 知识库 vs 独立向量库 (Pinecone / Weaviate 自管)——边界在哪？"

症状:

团队有现存 Pinecone 索引 / 多个产品共享 RAG / 想精细控制 chunking 和 embedding pipeline

决策:

场景	用 Dify 内建 Knowledge	用 External Knowledge
单个 app 单个数据集	✅ 一键搞定	❌ over-kill
多 app 共享同一份 KB	✅ Dify KB 可被多 app 引用	—
极致 chunking / 实验性 retrieval	❌ Dify 抽象有限	✅
已有自建 RAG 服务	❌	✅ Dify HTTP/External KB 接入
数据量 > 100M chunks	❌ Dify 有 memory leak 风险 [memo.d.foundation/breakdown/dify]	✅
需要 KG-RAG / hybrid + late interaction	❌	✅ 或换 RAGFlow

混合: Dify Knowledge 管常规文档；External Knowledge API 接你的自有 RAG 服务做长尾。

Case 5: "用 Agent app vs Workflow + Agent Node vs 显式 Workflow——分歧何在？"

症状: 同一个需求，三种实现方式纠结。

决策原则 [zediot.com/blog/dify-difference-between-agent-and-workflow], [dify.ai/blog/dify-agent-node-introduction-when-workflows-learn-autonomous-reasoning]:

任务路径可预测、调试性优先
  → 显式 Workflow (写死分支)

任务路径不可预测、需 LLM 选工具
  → Workflow + Agent Node (1.9+)
     [推荐!! 比纯 Agent app 更可控, 既享受自主性又有 workflow 外壳]

纯对话 + 工具调用、不需要复杂编排
  → Agent app (legacy)

混合: 主流程显式, 局部不确定路径
  → Workflow 主图 + 不确定段塞 Agent Node

"The best systems combine both — workflows as orchestrators that delegate reasoning tasks to agents. This hybrid model provides both predictability and intelligence." [zediot.com/blog/dify-difference-between-agent-and-workflow]

反模式:

Agent app 包打天下——失去 workflow 的可观察性
用 Workflow 强行表达完全不可预测的任务——逻辑膨胀

6. 反模式与边界 (Anti-patterns & Boundaries)

6.1 常见反模式

反模式	症状	修法
30+ 节点单图	卡顿 + 难调 [issue #28245]	拆 sub-workflow / Tool
Code 节点写业务核心	Code 节点 200+ 行 Python	抽到外部微服务, Dify 用 HTTP 调用
不上 Knowledge Pipeline	文档质量飘忽	用 1.9+ 的 pipeline 自定义 ingest
没接 LLMOps	上线后看不见 bad case	接 Langfuse / Phoenix
DSL 不入 git	改飞了无法回滚	导出 DSL → git, 用 dify-dsl-exporter 批量化 [github.com/linhai0872/dify-dsl-exporter]
legacy Chatbot/Agent app 长期演进	无法表达分支 / 后期被迫迁移	直接上 Chatflow / Workflow
workspace 共用 API key	不同 app 计费混乱	已知限制, 暂用多 workspace 规避
不做 retrieval testing	RAG 召回质量差还不自知	上传后立刻用真实 query 在 Retrieval Testing 里跑
stream 长任务 > 120s 不调超时	Agent node 突然断开 [issue #27053]	调 `WORKFLOW_MAX_EXECUTION_TIME` + 反代超时

6.2 性能边界 (硬约束)

~10 QPS / pod (1 CPU 2GB)——超过需水平扩 worker + queue depth 调优 [memo.d.foundation/breakdown/dify]
每节点单独 DB query——长 workflow 累积延迟
120s streaming——agent node 默认 timeout (可调但反代也要调) [github.com/langgenius/dify/discussions/27053]
plugin daemon 300s——PLUGIN_DAEMON_TIMEOUT
Cloud 配额硬上限——超额无法 reindex, 只能删数据 [discussions/32013]

6.3 不要用 Dify 做的事

模型训练 / 微调编排（用 Axolotl / LLaMA-Factory）
通用工作流自动化（n8n / Zapier 更全, 400+ 集成）
极致 RAG（RAGFlow / 自建栈）
pause-and-wait-for-user 审批流（LangGraph / Temporal）
高频实时推理路由（vLLM + 自己的网关层）
拿 Code 节点当微服务（迟早爆）

6.4 治理与升级

升级前必读 release notes——1.9 升级会摧毁 beta knowledge pipelines [discussions/26138]
API 兼容性：DSL 跨大版本可能需 migration
插件签名: 1.0+ 引入加密签名, marketplace 上传需通过审核
Apache 2.0-like license（"not really"）——商业重度依赖前确认条款 [memo.d.foundation/breakdown/dify]

7. 生态对照 (Ecosystem Comparison)

7.1 对位表 — 何时选谁

框架	抽象层	上手	天花板	选它的硬触发
Dify	平台 (UI+API+RAG+Auth)	几小时	中等 (DB-bound, 单图复杂度)	全栈 + 多角色协作 + 快速上线
Flowise	节点编辑器 (薄层)	几小时	低 (chatbot+RAG 为主)	1GB RAM 最小部署、纯 chatbot
LangFlow	LangChain 可视化包	半天	高 (可导出代码)	已用 LangChain，想加 UI
Coze (扣子)	字节托管平台	小时	中等 (国内生态)	国内市场 / 飞书集成 / 不要 self-host
RAGFlow	专精 RAG 引擎	半天	RAG 维度极高	文档解析 + KG-RAG 是核心
n8n	通用 workflow	半天	LLM 是 plugin	400+ 非 AI 集成 + AI 是辅助
LangGraph	代码框架	几天	极高	需要状态机 / 中断 / 时间旅行
LlamaIndex	RAG 框架	几天	极高 (数据侧)	RAG 是 P0 + 工程团队
CrewAI	多 agent 代码	半天	中 (角色编排)	2-5 个角色协作 pipeline

参考: [blog.elest.io/dify-vs-langflow-vs-flowise], [jimmysong.io/blog/open-source-ai-agent-workflow-comparison], [toolhalla.ai/blog/dify-vs-flowise-vs-langflow-2026]

7.2 关键对比 (一行话)

Dify vs Flowise: Dify 是 "production SaaS 全家桶"; Flowise 是 "最小可用 chatbot 编辑器"。生产用 Dify, demo 用 Flowise。 [blog.elest.io/...]
Dify vs LangFlow: Dify 不绑定 LangChain, 更平台化; LangFlow 是 LangChain 可视化壳, 工程团队友好。
Dify vs Coze: Coze 是托管 only + 字节生态; Dify 开源 + 自部署。海外 / 自主可控选 Dify。
Dify vs RAGFlow: Dify 是 "平台 with RAG"; RAGFlow 是 "极致 RAG with thin UI"。文档解析需求重选 RAGFlow，应用层选 Dify。 [sider.ai/blog/ai-tools/dify-vs-ragflow]
Dify vs n8n: n8n 集成多 (400+) 但 AI 是补丁; Dify AI 原生但非 AI 集成弱。AI 为主选 Dify, 自动化为主选 n8n。 [zhuanlan.zhihu.com/p/1898775808660710158]
Dify vs LangGraph: Dify 是 visual platform; LangGraph 是 code framework。可视化协作选 Dify, 复杂状态机选 LangGraph。混合用最常见。
Dify vs LlamaIndex: 不同层。LlamaIndex 做数据/RAG 底层, Dify 可以接 LlamaIndex 的 retrieval 输出。

7.3 "为什么 50k+ stars"

打平了脚手架——auth、UI、API、向量库、模型 provider、日志、版本，开箱
多角色友好——PM/运营可以画 chatflow，工程师下钻 Code 节点
国际化——中文社区强 (江西 LangGenius 团队) + 英文文档完整 + 海外用户多
真在打磨"production"——1.0 引入插件，1.9 重写 graph engine, 持续高频迭代

7.4 "什么时候 Dify 已经不够"

单图节点数 > 50 (canvas 卡 + 难维护)
需要 pause-wait-resume 或复杂时间旅行
QPS > 10/pod 持续负载
极致 RAG 实验
业务核心是 agent 自主性（Agent 比 workflow 更核心）

此时: Dify 退到 "前端 + 监控 + RAG 仓库"，核心逻辑切到 LangGraph / 自研代码服务。

附录 A: 关键链接

主页 / Repo: https://github.com/langgenius/dify
文档: https://docs.dify.ai/
Marketplace: https://marketplace.dify.ai/
官方插件: https://github.com/langgenius/dify-official-plugins
Cloud: https://cloud.dify.ai/
中文文档镜像: https://hellodify.com/

附录 B: 最小可运行示例（Workflow YAML 心智）

# 一个 "知识库问答 + 工具调用" 的最小 chatflow 骨架
app:
  name: support-copilot
  mode: advanced-chat   # = chatflow
graph:
  nodes:
    - start: { variables: [{name: query, type: text}] }
    - knowledge_retrieval:
        dataset_ids: [kb_docs]
        retrieval_mode: hybrid
        top_k: 5
        rerank_model: bge-reranker-v2
    - llm:
        model: gpt-4-turbo
        prompt:
          - role: system
            content: "You are a support agent. Use the context to answer."
          - role: user
            content: "Context: {{knowledge_retrieval.result}}\n\nQ: {{start.query}}"
        memory: { enabled: true, window: 10 }  # chatflow 才有
    - answer: { stream: true, content: "{{llm.text}}" }
features:
  - annotation_reply: true
  - moderation: openai-moderation
  - conversation_variables: [user_tier]

When it activates

Automatically, when a task matches. Use when shipping LLM apps fast with a "no-code to pro-code" gradient, especially when non-engineers need to co-author the flow.

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IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

agentsope / skillalchemy

skills/agentsop-dify/SKILL.md

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License MITImported 8/3/2026Version v1Bundled 5 files

Illustrative Want to see what agentsope/agentsop-dify would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

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

1. 何时激活 (When to Activate)

1.1 直接信号 (Direct triggers)

1.2 反向信号 (Skip Dify when)

1.3 心智门槛 (Mental check)

2. 核心心智模型 (Mental Model)

2.1 五层架构 (The 5-layer stack)

2.2 五种 App 类型 — 选哪个？

2.3 Graph Engine = "graphon"

2.4 节点目录 (Node taxonomy)

2.5 Plugin 系统 (五元类型)

2.6 Knowledge Base (RAG) 心智

3. SOP 工作流 (Standard Operating Procedure)

Phase 0: 选定 App 类型 (5 分钟决策)

Phase 1: 部署 (Deploy) — 30 分钟

Phase 2: Prompt + 单节点 (Prompt Studio) — 1 小时

Phase 3: 接知识库 (Knowledge) — 半天

Phase 4: 接工具 (Tools) — 半天

Phase 5: 编 workflow (Build) — 1-3 天

Phase 6: 测试 + 发布 (Test & Publish) — 1 天

Phase 7: 接观测 (Monitor) — 半天

Phase 8: 迭代 (Iterate) — 持续

4. 操作模型 (Operation Model)

4.1 工作流构件清单

4.2 变量系统

4.3 发布形态

4.4 多租户 / Workspace

4.5 DSL (YAML) 工作流

5. 困境决策案例 (Dilemma Cases)

Case 1: "Workflow 变得不可维护——拆子工作流还是改写代码？"

Case 2: "可视化已到天花板——继续 Dify 还是切 LangGraph / 直接代码？"

Case 3: "Self-host 还是 Cloud——什么时候值得自己运维？"

Case 4: "Dify 知识库 vs 独立向量库 (Pinecone / Weaviate 自管)——边界在哪？"

Case 5: "用 Agent app vs Workflow + Agent Node vs 显式 Workflow——分歧何在？"

6. 反模式与边界 (Anti-patterns & Boundaries)

6.1 常见反模式

6.2 性能边界 (硬约束)

6.3 不要用 Dify 做的事

6.4 治理与升级

7. 生态对照 (Ecosystem Comparison)

7.1 对位表 — 何时选谁

7.2 关键对比 (一行话)

7.3 "为什么 50k+ stars"

7.4 "什么时候 Dify 已经不够"

附录 A: 关键链接

附录 B: 最小可运行示例（Workflow YAML 心智）

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