在前面的文章里《一文探秘LLM应用开发(26)-Prompt(架构模式之Agent框架AutoGPT、AutoGen等) 》，笔者曾介绍了一些诸如AutoGen、ChatDev这样的多Agent框架。最近行业内又出现一款不错的框架——CrewAI，可谓是站在AutoGen这样的框架肩膀上，以实际投产为目标，兼具AutoGen对话代理的灵活性和ChatDev的领域流程化的优点，规避了AutoGen缺乏框架层面的流程支持和ChatDev领域流程过于狭窄不够泛化的问题，支持动态的泛场景的流程设计，能够无缝适应开发和生产工作流程。也就是说，在CrewAI中可以 像AutoGen一样结合场景定义自己的角色，又能像ChatDev那样约定一定的流程执行，让这些Agents能够更好完成特定的复杂目标。该项目现已收获3.9K星，并获得ProductHunt排名第二的好位置。
https://github.com/joaomdmoura/crewAI
CrewAI具备以下 一些关键feature：
- 基于角色的Agent设计：定制具有特定角色、目标和工具的Agent。
- Agent间自主委托：Agent 可自主委派任务，并在Agent 之间进行询问，从而提高解决问题的效率。
- 灵活的任务管理： 使用 可定制的工具定义任务，并将其动态分配给Agent。
- 流程驱动（最大的亮点）： 目前仅支持顺序（ sequential ） 任务执行 ， 但在规划中的有更高阶的流程定义，如共识和分层流程。
创建一个流程大体如下：
import os
from crewai import Agent, Task, Crew, Process
os.environ["OPENAI_API_KEY"] = "YOUR KEY"
# You can choose to use a local model through Ollama for example.
#
# from langchain.llms import Ollama
# ollama_llm = Ollama(model="openhermes")
# Install duckduckgo-search for this example:
# !pip install -U duckduckgo-search
from langchain.tools import DuckDuckGoSearchRun
search_tool = DuckDuckGoSearchRun()
# Define your agents with roles and goals
researcher = Agent(
role='Senior Research Analyst',
goal='Uncover cutting-edge developments in AI and data science',
backstory="""You work at a leading tech think tank.
Your expertise lies in identifying emerging trends.
You have a knack for dissecting complex data and presenting
actionable insights.""",
verbose=True,
allow_delegation=False,
tools=[search_tool]
# You can pass an optional llm attribute specifying what mode you wanna use.
# It can be a local model through Ollama / LM Studio or a remote
# model like OpenAI, Mistral, Antrophic of others (https://python.langchain.com/docs/integrations/llms/)
#
# Examples:
# llm=ollama_llm # was defined above in the file
# llm=ChatOpenAI(model_name="gpt-3.5", temperature=0.7)
)
writer = Agent(
role='Tech Content Strategist',
goal='Craft compelling content on tech advancements',
backstory="""You are a renowned Content Strategist, known for
your insightful and engaging articles.
You transform complex concepts into compelling narratives.""",
verbose=True,
allow_delegation=True,
# (optional) llm=ollama_llm
)
# Create tasks for your agents
task1 = Task(
description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.
Identify key trends, breakthrough technologies, and potential industry impacts.
Your final answer MUST be a full analysis report""",
agent=researcher
)
task2 = Task(
description="""Using the insights provided, develop an engaging blog
post that highlights the most significant AI advancements.
Your post should be informative yet accessible, catering to a tech-savvy audience.
Make it sound cool, avoid complex words so it doesn't sound like AI.
Your final answer MUST be the full blog post of at least 4 paragraphs.""",
agent=writer
)
# Instantiate your crew with a sequential process
crew = Crew(
agents=[researcher, writer],
tasks=[task1, task2],
verbose=2,# You can set it to 1 or 2 to different logging levels
process=Process.sequential
)
# Get your crew to work!
result = crew.kickoff()
print("######################")
print(result)
如上，定义了两个Agent，researcher负责调研收集信息，writer基于这些信息写作，然后根据任务顺序执行的流程进行执行。 对于 任务本身也可以进行自定义，比如 指定特定的信源收集信息，如下是在reddit的 LocalLLaMA版 上获取信息 。
# pip install praw
from langchain.tools import tool
class BrowserTool():
@tool("Scrape reddit content")
def scrape_reddit(max_comments_per_post=5):
"""Useful to scrape a reddit content"""
reddit = praw.Reddit(
client_id="your-client-id",
client_secret="your-client-secret",
user_agent="your-user-agent",
)
subreddit = reddit.subreddit("LocalLLaMA")
scraped_data = []
for post in subreddit.hot(limit=10):
post_data = {"title": post.title, "url": post.url, "comments": []}
try:
post.comments.replace_more(limit=0) # Load top-level comments only
comments = post.comments.list()
if max_comments_per_post is not None:
comments = comments[:5]
for comment in comments:
post_data["comments"].append(comment.body)
scraped_data.append(post_data)
except praw.exceptions.APIException as e:
print(f"API Exception: {e}")
time.sleep(60) # Sleep for 1 minute before retrying
return scraped_data
而使用它只需要将search_tool更换为BrowserTool().scrape_reddit。同时，它也支持将human作为工具编织在Agent中。对于这样做的好处可以参考文章《人充当LLM Agent的工具（Human-In-The-Loop ），提升复杂问题解决成功率》。
# Define your agents with roles and goals
researcher = Agent(
role='Senior Research Analyst',
goal='Uncover cutting-edge developments in AI and data science in',
backstory="""You are a Senior Research Analyst at a leading tech think tank.
Your expertise lies in identifying emerging trends and technologies in AI and
data science. You have a knack for dissecting complex data and presenting
actionable insights.""",
verbose=True,
allow_delegation=False,
# Passing human tools to the agent
tools=[search_tool]+human_tools
)
另外，为了节省token费用以及隐私性，还可以对接本地LLM平台Ollama，有关Ollama介绍参看《 一文探秘LLM应用开发(17)-模型部署与推理(框架工具-ggml、mlc-llm、ollama) 》，安装，配置完成后可通过以下方式集成，Ollama上有很多本地模型，推荐使用当红的mistral模型。
from langchain.llms import Ollama
ollama_openhermes = Ollama(model="openhermes")
# Pass Ollama Model to Agents: When creating your agents within the CrewAI framework, you can pass the Ollama model as an argument to the Agent constructor. For instance:
local_expert = Agent(
role='Local Expert at this city',
goal='Provide the BEST insights about the selected city',
backstory="""A knowledgeable local guide with extensive information
about the city, it's attractions and customs""",
tools=[
SearchTools.search_internet,
BrowserTools.scrape_and_summarize_website,
],
llm=ollama_openhermes, # Ollama model passed here
verbose=True
)
下面是一个完整基于Ollama的CrewAI的例子视频：