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CrewAI SOP — Role-Based Multi-Agent Orchestration · agentsope | DecimalAI

Agent Toolingby agentsope

CrewAI SOP — Role-Based Multi-Agent Orchestration

SOP for building multi-agent systems with CrewAI — role-based collaboration, sequential/hierarchical processes, Flows, memory, delegation.

Never made a case worseMaintained

SkillScore

SkillScore100/100

Test results 100

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

68%100%of test cases passed +32 pts

Without skill15 of 22

With skill22 of 22

7 fixed15 passed either way0 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 taskWhen defining worker agents for a team processing pipeline, a developer wants to set delegation enabled across all workers so agents can dynamically hand off subtasks to each other. What default parameter setting should be applied to allow_delegation on worker agents?

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-crewai
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-crewai")
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-crewai

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-crewai/SKILL.md https://app.decimal.ai/s/agentsope-agentsop-crewai/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/agentsope-agentsop-crewai/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-crewai

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-crewai")
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-crewai/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-crewai/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-crewai

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-crewai")
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-crewai/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/agentsope-agentsop-crewai/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-crewai

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-crewai")
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-crewai/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/agentsope-agentsop-crewai/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-crewai

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-crewai")
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-crewai/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/agentsope-agentsop-crewai/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-crewai

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-crewai")
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-crewai/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-crewai/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-crewai

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-crewai")
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-crewai/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/agentsope-agentsop-crewai/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-crewai

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-crewai")
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-crewai/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-crewai

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-crewai")
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-crewai/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/agentsope-agentsop-crewai/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-crewai

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-crewai")
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-crewai/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/agentsope-agentsop-crewai/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-crewai

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-crewai")
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-crewai/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/agentsope-agentsop-crewai/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-crewai

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-crewai")
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-crewai/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/agentsope-agentsop-crewai/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-crewai

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-crewai")
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-crewai/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/agentsope-agentsop-crewai/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-crewai

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-crewai/SKILL.md https://app.decimal.ai/s/agentsope-agentsop-crewai/SKILL.md

Copy SKILL.md

Save it in your project, as

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

Read the summary as JSON

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

CrewAI SOP — Role-Based Multi-Agent Orchestration

框架口号: "Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly." [github.com/crewAIInc/crewAI]

1. 何时激活 (When to Activate)

1.1 直接信号 (Direct triggers)

用户说 "我需要 researcher + writer + reviewer 这种团队配合"
用户说 "用 CrewAI 实现 / 我已经在用 crew.kickoff()"
任务可以拆为 2–5 个专业角色，且每个角色有明确职责边界
流程是线性 pipeline（数据→分析→报告）或轻度分支

1.2 反向信号 (Skip CrewAI when)

单 agent + tool-use 就够 → 直接用 SDK / Instructor（CrewAI 是 over-engineering）
需要状态图 + 循环 + 中断恢复 → LangGraph 更合适
需要 agents 之间自由对话辩论 → AutoGen 更合适
需要严格条件路由（"if X then only A else B"）→ 用 CrewAI Flows 而非 hierarchical Crew，或直接 LangGraph
延迟敏感（<500ms） → 多 agent 编排不适合

1.3 心智门槛 (Mental check)

"An agent needs agency, otherwise it's just another script." — João Moura, CrewAI 创始人 [softwareengineeringdaily.com/2025/06/03/crew-ai-with-joao-moura/]

如果你能用 if/else 提前写死流程，不要用 Crew。Crew 的本质是把"决策权"让渡给 LLM 角色。

2. 核心心智模型 (Mental Model)

2.1 四元抽象 (The 4 primitives)

Agent (role + goal + backstory)  ← 谁
   ↓ 持有
Task (description + expected_output + agent + context)  ← 做什么
   ↓ 组装
Crew (agents + tasks + process)  ← 怎么协作
   ↓ 选择
Process (sequential | hierarchical) + Flow (event-driven 编排)  ← 控制流

2.2 为什么 "role + goal + backstory" 三件套？

CrewAI 的核心假设：LLM 在 role-playing 状态下表现更好。

role: 函数性身份 ("Senior Data Researcher") — 决定 prompt 主语
goal: 个体目标 ("Uncover cutting-edge developments in {topic}") — 决定决策方向
backstory: 经验/性格 ("You're a seasoned researcher with a knack for…") — 校准语气与判断风格

"Backstory provides depth to the agent's persona, enriching its motivations and engagements within the crew." [docs.crewai.com/en/concepts/agents]

关键洞察: backstory 不是装饰。它是 system prompt 的最大杠杆——同一个 role+goal，换 backstory 会显著改变产出质量与风格。

2.3 Sequential vs Hierarchical vs Flow

维度	Sequential	Hierarchical	Flow
任务路由	静态列表顺序	manager LLM 动态分派	`@listen` 事件驱动
控制力	高 (写死顺序)	低 (manager 自由发挥)	最高 (代码 + 状态)
Token 开销	1× 基线	1.3–1.5× (manager overhead)	接近 1×
调试难度	低	高 (manager 黑盒)	中
何时用	80% 场景默认	真正需要动态分派	复杂分支 + 多 Crew 编排
已知坑	task context 自动透传可能膨胀	manager 会"执行所有 task"而非"按需调用"	学习曲线 + 状态设计

参考: [docs.crewai.com/en/learn/hierarchical-process], [docs.crewai.com/en/concepts/flows], [towardsdatascience.com/why-crewais-manager-worker-architecture-fails-and-how-to-fix-it/]

2.4 Crew 不是 LangChain

CrewAI 从零写成、零 LangChain 依赖，是 João Moura 刻意决定。这带来：

更快 import / 更小 footprint
但生态工具少（observability、eval 需要外接 Maxim/MLflow/Datadog）
错误日志在 Task 内部不易捕获，print 不易冒出来 [aaronyuqi.medium.com/first-hand-comparison-of-langgraph-crewai-and-autogen]

3. SOP 工作流 (Standard Operating Procedure)

Phase 0: 决策 — 真的需要 Crew 吗？

[问] 这个任务是否需要 ≥2 个截然不同的"专业视角"协作？
  ├─ 否 → 用单 agent + tools，停止使用 CrewAI
  └─ 是 → 继续

[问] 流程是否有循环 / 状态依赖 / 人工中断点？
  ├─ 是 → 转 LangGraph (或 CrewAI Flow + 简化的 Crew)
  └─ 否 → 进入 Phase 1

Phase 1: 角色设计 (Agent Design)

1.1 拆分原则

每个 agent 一个职能动词: research / write / review / extract / decide
避免 "万能 agent"。一个 agent 同时 research + write，质量必劣于两个专家
2–5 个 agent 是甜区。≥7 个开始出现协调失败 [medium.com/@armankamran/anti-patterns-in-multi-agent-gen-ai-solutions]

1.2 三件套写法 (role/goal/backstory)

researcher = Agent(
    role="Senior AI Research Analyst",   # ← 名词性头衔，含"高级/资深"提升先验
    goal="Uncover cutting-edge developments in {topic} with citations",  # ← 含 {var} 模板 + 验收标准
    backstory=(
        "You're a methodical researcher with 10 years at top AI labs. "
        "You distrust hype and always cross-check with primary sources."  # ← 注入判断偏好
    ),
    allow_delegation=False,   # ← 默认 False，避免 ping-pong
    max_iter=10,              # ← 显式收敛上限（默认 20–25）
    verbose=True,             # ← 开发期必开
    tools=[search_tool],
)

1.3 YAML 化（生产推荐）

配置与代码分离，使用 @CrewBase 装饰器 + config/agents.yaml + config/tasks.yaml，便于非工程人员迭代提示词 [docs.crewai.com YAML Configuration]。

Phase 2: 任务设计 (Task Design)

2.1 描述写法 (description)

动词开头 + 具体输入："Analyze the search results for {topic} and identify 3 emerging trends"
不要写 how，写 what。HOW 是 agent 的自由度
长度建议: 1–4 句。过长 = 把 agent 当工程模板用，违背 agency 哲学

2.2 expected_output（验收契约）

必填。这是 CrewAI 的"测试断言"
写成可机器校验的结构化描述："A markdown report with H2 headers per trend, each containing: trend name, 3 supporting citations, risk assessment"
配合 output_pydantic=MyModel 强制结构化 [docs.crewai.com/en/concepts/tasks]

2.3 context 显式声明依赖

analysis_task = Task(
    description="...",
    expected_output="...",
    agent=analyst,
    context=[research_task],   # ← 不依赖隐式自动透传，显式声明
)

"In crewAI, the output of one task is automatically relayed into the next one, but you can specifically define what tasks' output … should be used as context."

默认隐式透传是坑——pipeline 长了之后 prompt 爆炸。建议从一开始就显式 context=[...]。

Phase 3: 装配 Crew

crew = Crew(
    agents=[researcher, analyst, writer],
    tasks=[research_task, analysis_task, write_task],
    process=Process.sequential,   # ← 默认；改 hierarchical 前请读 §5.2
    memory=False,                 # ← 默认关，除非真的跨 kickoff 需要持久化
    verbose=True,
    max_rpm=30,                   # ← 防止 API 限流爆炸
    planning=False,               # ← v0.80+ 的实验功能，生产前先测
)
result = crew.kickoff(inputs={"topic": "agentic RAG"})

Phase 4: 观测与收敛

4.1 必装观测

CrewAI 内部日志薄。生产前必须：

接 mlflow.crewai.autolog() 或 Maxim / Langfuse / Datadog
包一层 step_callback= 捕获每步 agent action [docs.crewai.com/en/observability/overview]

4.2 token / 成本上限

单次 kickoff 设硬上限（外层 timeout + max_rpm）
Hierarchical 模式追加 30–50% token 预算 [callsphere.ai/blog/crewai-process-types]

4.3 eval 化

把每次失败的 kickoff trace 转成 eval case
用 LLM-as-judge 检查 expected_output 契约是否兑现

4. 操作模型 (Operational Model — Trigger / Action / Output / Evidence)

OP-1: 决定是否使用 CrewAI

Trigger: 用户描述任务时出现"团队/协作/不同角色"语义
Action: 检查 §1.1/§1.2 清单 + Phase 0 决策树
Output: 三选一 — (a) 用 CrewAI Sequential, (b) 用 CrewAI Flow+Crew, (c) 换框架
Evidence: [§1, github.com/crewAIInc/crewAI README]

OP-2: 设计 agent role/goal/backstory

Trigger: 已决定用 Crew，开始建模角色
Action: 每个 agent 填 role (头衔)、goal (含 {var} 与验收)、backstory (经验+判断偏好)；默认 allow_delegation=False、max_iter=10
Output: agents.yaml 或 Python Agent() 调用
Evidence: [docs.crewai.com/en/concepts/agents, §2.2]

OP-3: 写 Task

Trigger: agent 设计完毕，开始拆任务
Action: description 写 what 不写 how；expected_output 写可校验契约；显式 context=[...]；可选 output_pydantic
Output: tasks.yaml 或 Task() 调用列表
Evidence: [docs.crewai.com/en/concepts/tasks, §3.2]

OP-4: 选 Process

Trigger: 装配 Crew 前
Action: 默认 Process.sequential；只有当任务路由真的需要 LLM 动态判断时才用 Process.hierarchical + 自定义 manager_agent（不要用裸 manager_llm）
Output: process= 与（若 hierarchical）一个带详细 backstory 的 manager_agent
Evidence: [§5.2, towardsdatascience.com 'Manager-Worker fails']

OP-5: 启用 Memory

Trigger: 跨 kickoff 需要"记住"或同一 kickoff 内复杂上下文聚合
Action: 优先用 task context=[...] 显式传递；只有当真的需要"跨 session 持久"才 memory=True；高级场景考虑 Mem0 后端
Output: memory=False 或 memory=Memory(scope=...)
Evidence: [docs.crewai.com/en/concepts/memory, mem0.ai/blog/crewai-memory-production-setup-with-mem0]

OP-6: 加观测与上限

Trigger: 上生产前
Action: mlflow.crewai.autolog() + max_rpm + max_iter 每 agent + 外层 timeout + step_callback
Output: 可观测、可中止的 Crew
Evidence: [docs.crewai.com/en/observability/overview, §4]

OP-7: 从 Crew 升级到 Flow

Trigger: Crew 出现 — (1) 需要条件分支 (2) 需要多个 Crew 串联 (3) hierarchical 不可控
Action: 用 @start/@listen 写 Flow，每个 step 内部可 crew.kickoff()
Output: 一个 Flow 类，state 用 Pydantic BaseModel
Evidence: [docs.crewai.com/en/concepts/flows, community.crewai.com/t/5710]

5. 困境决策案例 (Dilemma Cases)

DC-1: Agent 卡住反复重试 — 改 prompt 还是拆 agent？

场景: writer agent 输出质量差，反复 self-critique，5 次迭代后还在改文章结构。

两条路:

A. 改 prompt — 把 backstory 写得更具体，goal 加更严的验收。优点：零改动 crew 结构。缺点：当 agent 在做"两件不同的事"（写 + 审），单 prompt 永远抓不住。
B. 拆 agent — writer + reviewer 双 agent，sequential pass。优点：每个 agent 职责单一，更稳定。缺点：多一次 LLM 调用，token+50%。

判断规则:

看失败案例：失败模式是否风格不一致？ → 改 backstory
失败模式是否遗漏检查项（事实错误、格式错误）？ → 拆 agent，让 reviewer 用结构化 checklist
如果 5 次迭代后仍未稳定 → 强信号要拆

推荐: 默认拆。CrewAI 的核心红利就在"单一职责角色"。当你纠结要不要拆，答案 80% 是拆。

"Single-agent is right for approximately 80% of cases; the trap is reaching for multi-agent because it sounds more capable. But once you've committed to multi-agent, the next trap is putting too much in one agent." [daily.dev AI agents guide]

Evidence: [§2.2, anti-patterns]

DC-2: Sequential vs Hierarchical — 何时 manager 开销值得？

场景: 5 个 agent，task 顺序大致固定但偶尔需要根据上游结果跳过某些 task。

陷阱: 看起来"hierarchical 应该能自动路由"，但实测 hierarchical 会执行所有 task，不会真的按 triage 结果跳过 [towardsdatascience.com Manager-Worker fails]。论文式案例：

Query: "Why is my laptop overheating?" (纯技术问题)
期望: triage → technical_agent → done
实际 hierarchical: triage → technical → billing → ... → 最后一个 task 的输出覆盖前面

三条路:

A. Sequential — 写死顺序，所有 task 都跑。简单稳定但浪费 token。
B. Hierarchical + 默认 manager_llm — 不推荐。manager 会失控执行所有 task。
C. Hierarchical + 自定义 manager_agent (带显式分支 backstory) — 可工作但需要细致 prompt 工程。
D. CrewAI Flow — 用 @listen + 条件函数显式路由，每分支调用一个小 Crew 或单 agent。

判断规则:

路由逻辑可以用 5 行 Python 表达？ → 用 Flow (D)
路由真的需要 LLM 语义理解（不能写规则）→ Hierarchical + 自定义 manager（C），永远不要靠默认 manager_llm
不确定 → 先 Sequential (A)，性能可接受就停

红线: 永远不要把生产路由依赖默认 manager_llm——João Moura 团队也承认这是当前最大坑之一 [github.com/crewAIInc/crewAI/discussions/1220]。

Evidence: [§2.3, community.crewai.com/t/5710, towardsdatascience.com]

DC-3: 工具共享 vs 每个 agent 独立工具集？

场景: 你有 web_search、code_executor、db_query 三个工具，3 个 agent (researcher / analyst / reporter)。

两条路:

A. 全部共享 — 每个 agent tools=[search, exec, db]。简单但 agent 容易"逛工具" — researcher 也调 code_executor 写代码，违背角色分工。
B. 按角色配 — researcher=[search]，analyst=[exec, db]，reporter=[]（纯综合）。职责更清晰，错误更可定位。

判断规则:

CrewAI 官方推荐 B（write once, use everywhere — tool 定义可复用；但每个 agent 只绑定其角色匹配的工具） [docs.crewai.com/en/concepts/tools]
如果发现 agent 跨工具滥用 → 收紧工具白名单是最快的 fix
工具定义层面共享（同一个 BaseTool 类），但绑定层面按需

Evidence: [docs.crewai.com/en/concepts/tools, community.crewai.com/t/tool-best-practice-assign-to-agent-or-task/5919]

DC-4: Memory 默认关 vs 全开？

场景: 一个客服 crew，多个会话之间是否需要记住用户？

陷阱:

memory=True 默认开 short_term + entity，会自动跑额外 LLM 调用做"记忆 LLM 分析"，token 翻倍
当 memory 不对（错召回、过期信息）→ 调试地狱

判断规则:

单 kickoff 内的上下文 → 用 task context=[...] 显式传，不用 memory
跨 kickoff 才考虑 memory=True；首选 long_term（SQLite，便宜）
真正生产级 + 高频 → 接 Mem0 后端，不要用默认 LanceDB [mem0.ai/blog/crewai-memory-production-setup-with-mem0]
调试期一律 memory=False，不然 trace 难读

红线: 不要把"agent 没记住 X"当成必须开 memory 的信号。99% 的情况是 task description 没把 X 写清楚。

Evidence: [docs.crewai.com/en/concepts/memory, §4]

DC-5: allow_delegation 开还是关？

场景: hierarchical 模式下，是否给 worker agent 也开 allow_delegation=True？

陷阱: 多个 agent 都能 delegate → delegation ping-pong，agents 互相传球，token 爆炸 + 超时。已知 GitHub issue [#330 #4783 #2606]。

根因:

循环 delegation（A → B → A → B…）
DelegateWorkTool schema 期望 string，新 LLM 传 dict，silent validation 失败 → 重试 [azguards.com delegation ping-pong]
max_iter 在 hierarchical 跨 handoff 时不生效，protection 形同虚设

判断规则:

默认 allow_delegation=False 在所有 worker agent 上
仅 manager_agent 开 delegation
即使在 hierarchical，链中至少有一个 agent allow_delegation=False
加全局 token / 时间上限（外层包 timeout）
如果一定要复杂 delegation → 用 Flow 显式编排，别让 LLM 决定 delegate 给谁

Evidence: [github.com/crewAIInc/crewAI/issues/330, azguards.com, inkog.io/glossary/crewai-infinite-loop]

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

6.1 五大反模式

#	反模式	症状	修复
AP-1	Agent 数量爆炸 (>5 个 agent)	协调失败、token 飞涨、debug 困难	合并职能近似的 agent；7+ 几乎必拆 Flow
AP-2	Backstory 不写或写"你是助手"	输出风格平庸、判断偏好混乱	注入经验、判断偏好、禁忌
AP-3	依赖默认 hierarchical manager_llm	manager 执行所有 task / 路由错乱	永远自定义 manager_agent，或换 Flow
AP-4	`allow_delegation=True` 全开	无限 delegation ping-pong	默认关，仅 manager 开
AP-5	没 observability 就上生产	trace 黑盒，故障无法复现	上线前必接 MLflow/Maxim/Datadog

6.2 何时不要用 CrewAI（重要）

场景	替代方案
单 agent 就够（80% 任务）	OpenAI/Anthropic SDK + Instructor/Outlines
状态机 / 循环 / human-in-the-loop / 中断恢复	LangGraph
长对话 / 辩论 / 群体决策	AutoGen
简单工具调用 + handoff	OpenAI Swarm / Anthropic Computer Use
数据为中心的 RAG (300+ connectors)	LlamaIndex
严格的低延迟 SLA	不用 multi-agent 框架，直接编排
需要 production-grade durability + 精细 state	LangGraph (已成 production-deployment 事实标准)

6.3 已知工程坑速查表

日志: Task 内 print() 经常不冒头。用 step_callback 或 verbose=True
token 预算: hierarchical +30~50%；memory=True 再 +1×（记忆 LLM 分析）
Pydantic 版本: CrewAI 与某些 langchain-tools 的 pydantic v1/v2 不兼容时常爆错
manager 模型选择: gpt-4o-mini 不够强，hierarchical 推荐 gpt-4o / Claude Sonnet
YAML 模板变量: {topic} 在 kickoff(inputs={"topic": ...}) 时替换，不写在 inputs 里就是字面 "{topic}"

7. 生态对照 (Ecosystem Comparison)

7.1 三大框架定位

框架	一句话定位	第一抽象	哲学
CrewAI	"Agents are role-playing teammates"	Agent (role)	把 LLM 当人格化协作者
LangGraph	"Agents are nodes in a state graph"	Node + State	把 agent 当可编译的图
AutoGen	"Agents are chat participants"	Conversation	把 agent 当群聊成员

7.2 选择决策树

你的核心需求是什么？
├─ 多角色明确 + 任务可线性串 → CrewAI Sequential
├─ 多角色 + 需要事件驱动分支 → CrewAI Flow（内嵌 Crew）
├─ 状态/循环/中断/恢复/precise control → LangGraph
├─ 多 agent 辩论/谈判/群体决策 → AutoGen
├─ 单 agent + handoff (不需要协作) → OpenAI Swarm / Anthropic
└─ 数据为中心 RAG → LlamaIndex

7.3 CrewAI 独特价值（什么时候首选）

学习曲线最低 — PM/ops 能读懂 role/goal/backstory
YAML 优先 — 提示词与代码分离，非工程人员可改
Flow + Crew 混搭 — 既要 LLM agency 又要工程控制时的平衡点
生态轻量 — 无 LangChain 依赖，启动快
角色叙事 = 团队建模工具 — 在跨职能讨论中比 "node graph" 更易达成共识

7.4 CrewAI 已知短板（要承认的）

生产可靠性 — hierarchical 模式有结构性 bug（OP-4, DC-2, DC-5），LangGraph 在生产部署占优
observability — 内置日志薄，必须外接
状态管理 — 比 LangGraph 浅；复杂状态用 Flow 也吃力
测试范式 — eval 工具链不如 LangSmith 成熟
AutoGen 近况 — AutoGen 已进入 maintenance，长期看 LangGraph + CrewAI 是双雄

Appendix A: 最小可工作示例 (Reference snippet)

from crewai import Agent, Task, Crew, Process

researcher = Agent(
    role="Senior AI Research Analyst",
    goal="Surface 3 emerging trends in {topic} with verifiable citations",
    backstory=(
        "You're a meticulous researcher who distrusts hype and demands "
        "primary sources. You've spent a decade at frontier AI labs."
    ),
    allow_delegation=False,
    max_iter=8,
    verbose=True,
)

writer = Agent(
    role="Technical Writer",
    goal="Turn research findings into a crisp executive brief",
    backstory=(
        "You write for time-poor execs. Bullet points over paragraphs. "
        "You refuse to ship without inline citations."
    ),
    allow_delegation=False,
    max_iter=5,
    verbose=True,
)

research_task = Task(
    description="Research {topic}. Find 3 emerging trends from 2025–2026.",
    expected_output=(
        "Markdown with 3 H2 sections, each: trend name, 2-sentence summary, "
        "≥2 citations (URL + title)."
    ),
    agent=researcher,
)

write_task = Task(
    description="Write an executive brief from the research output.",
    expected_output="≤400 word brief, bullet structure, inline citations preserved.",
    agent=writer,
    context=[research_task],   # 显式
)

crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task],
    process=Process.sequential,
    memory=False,
    verbose=True,
    max_rpm=30,
)

result = crew.kickoff(inputs={"topic": "agentic RAG"})
print(result.raw)

Sources (核心引用)

docs.crewai.com/en/concepts/agents
docs.crewai.com/en/concepts/tasks
docs.crewai.com/en/concepts/flows
docs.crewai.com/en/concepts/memory
docs.crewai.com/en/learn/hierarchical-process
github.com/crewAIInc/crewAI
github.com/crewAIInc/crewAI/discussions/1220 — manager_agent 正确用法讨论
github.com/crewAIInc/crewAI/issues/330 — allow_delegation 无限循环
towardsdatascience.com Manager-Worker fails
azguards.com/the-delegation-ping-pong
softwareengineeringdaily.com/2025/06/03/crew-ai-with-joao-moura — João Moura "agency" 哲学
callsphere.ai/blog/crewai-process-types
community.crewai.com/t/5710
mem0.ai/blog/crewai-memory-production-setup-with-mem0
datacamp.com/tutorial/crewai-vs-langgraph-vs-autogen

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Automatically, when a task matches. Use when modeling agent teams with clear roles and task pipelines.

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skills/agentsop-crewai/SKILL.md

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CrewAI SOP — Role-Based Multi-Agent Orchestration

1. 何时激活 (When to Activate)

1.1 直接信号 (Direct triggers)

1.2 反向信号 (Skip CrewAI when)

1.3 心智门槛 (Mental check)

2. 核心心智模型 (Mental Model)

2.1 四元抽象 (The 4 primitives)

2.2 为什么 "role + goal + backstory" 三件套？

2.3 Sequential vs Hierarchical vs Flow

2.4 Crew 不是 LangChain

3. SOP 工作流 (Standard Operating Procedure)

Phase 0: 决策 — 真的需要 Crew 吗？

Phase 1: 角色设计 (Agent Design)

1.1 拆分原则

1.2 三件套写法 (role/goal/backstory)

1.3 YAML 化（生产推荐）

Phase 2: 任务设计 (Task Design)

2.1 描述写法 (description)

2.2 expected_output（验收契约）

2.3 context 显式声明依赖

Phase 3: 装配 Crew

Phase 4: 观测与收敛

4.1 必装观测

4.2 token / 成本上限

4.3 eval 化

4. 操作模型 (Operational Model — Trigger / Action / Output / Evidence)

OP-1: 决定是否使用 CrewAI

OP-2: 设计 agent role/goal/backstory

OP-3: 写 Task

OP-4: 选 Process

OP-5: 启用 Memory

OP-6: 加观测与上限

OP-7: 从 Crew 升级到 Flow

5. 困境决策案例 (Dilemma Cases)

DC-1: Agent 卡住反复重试 — 改 prompt 还是拆 agent？

DC-2: Sequential vs Hierarchical — 何时 manager 开销值得？

DC-3: 工具共享 vs 每个 agent 独立工具集？

DC-4: Memory 默认关 vs 全开？

DC-5: allow_delegation 开还是关？

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

6.1 五大反模式

6.2 何时不要用 CrewAI（重要）

6.3 已知工程坑速查表

7. 生态对照 (Ecosystem Comparison)

7.1 三大框架定位

7.2 选择决策树

7.3 CrewAI 独特价值（什么时候首选）

7.4 CrewAI 已知短板（要承认的）

Appendix A: 最小可工作示例 (Reference snippet)

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