GLM 5.2 + CrewAI：低成本编排 AI 智能体团队

pip install crewai crewai-tools

export OPENAI_API_BASE="https://open.bigmodel.cn/api/paas/v4/"
export OPENAI_API_KEY="your-zhipuai-api-key"

import os
from crewai import Agent, Task, Crew, Process

# Environment variables should already be set:
# OPENAI_API_BASE=https://open.bigmodel.cn/api/paas/v4/
# OPENAI_API_KEY=your-zhipuai-api-key

GLM_MODEL = "openai/glm-4-plus"

# ── Agents ──────────────────────────────────────────────────────────────────

researcher = Agent(
    role="Research Specialist",
    goal=(
        "Gather comprehensive, accurate information on the given topic. "
        "Identify key facts, statistics, expert opinions, and current developments."
    ),
    backstory=(
        "You are a meticulous research analyst with a background in technology journalism. "
        "You prioritize accuracy, cite sources clearly, and surface non-obvious insights."
    ),
    llm=GLM_MODEL,
    verbose=True,
)

writer = Agent(
    role="Content Writer",
    goal=(
        "Transform research notes into a clear, engaging, well-structured article "
        "aimed at technically literate readers."
    ),
    backstory=(
        "You are a senior technical writer who specializes in making complex topics "
        "accessible without sacrificing depth. You follow a logical structure: "
        "problem, context, solution, implications."
    ),
    llm=GLM_MODEL,
    verbose=True,
)

editor = Agent(
    role="Senior Editor",
    goal=(
        "Review the draft article for factual accuracy, logical flow, grammar, "
        "and readability. Return a polished final version with tracked changes explained."
    ),
    backstory=(
        "You are a senior editor at a technology publication. "
        "You cut filler, fix ambiguities, and ensure every claim is supported."
    ),
    llm=GLM_MODEL,
    verbose=True,
)

# ── Tasks ────────────────────────────────────────────────────────────────────

TOPIC = "The current state of mixture-of-experts (MoE) LLM architectures in 2025"

research_task = Task(
    description=(
        f"Research the following topic thoroughly: {TOPIC}. "
        "Cover: key technical concepts, leading models and their specs, "
        "trade-offs vs dense models, and recent developments. "
        "Output structured notes with clear sections."
    ),
    expected_output=(
        "A detailed research brief (800–1200 words) organized into sections: "
        "Overview, Key Players, Technical Trade-offs, Recent Developments, Sources."
    ),
    agent=researcher,
)

writing_task = Task(
    description=(
        "Using the research brief provided, write a complete article on the topic. "
        "Structure it with an introduction, 3–4 body sections with subheadings, "
        "and a conclusion. Target audience: senior software engineers."
    ),
    expected_output=(
        "A complete article draft of 900–1300 words with clear subheadings, "
        "no unverified claims, and a logical narrative arc."
    ),
    agent=writer,
)

editing_task = Task(
    description=(
        "Edit the article draft for clarity, accuracy, conciseness, and flow. "
        "Fix grammar issues, cut redundant sentences, and ensure all technical "
        "claims are clearly explained. Return the final polished article."
    ),
    expected_output=(
        "A publication-ready article with all edits applied. "
        "Include a brief editor's note listing the main changes made."
    ),
    agent=editor,
)

# ── Crew ─────────────────────────────────────────────────────────────────────

research_crew = Crew(
    agents=[researcher, writer, editor],
    tasks=[research_task, writing_task, editing_task],
    process=Process.sequential,  # tasks run in order; each gets the previous output
    verbose=True,
)

# ── Run ───────────────────────────────────────────────────────────────────────

if __name__ == "__main__":
    result = research_crew.kickoff()
    print("\n" + "=" * 60)
    print("FINAL OUTPUT")
    print("=" * 60)
    print(result)

    # Optional: write to file
    with open("output_article.md", "w") as f:
        f.write(str(result))
    print("\nArticle saved to output_article.md")

python crew_research.py

from crewai import Crew, Process

complex_crew = Crew(
    agents=[researcher, writer, editor],
    tasks=[research_task, writing_task, editing_task],
    process=Process.hierarchical,
    manager_llm=GLM_MODEL,  # the manager agent uses the same model
    verbose=True,
)

from crewai_tools import SerperDevTool, FileReadTool

search_tool = SerperDevTool()  # requires SERPER_API_KEY
file_tool = FileReadTool()

researcher_with_tools = Agent(
    role="Research Specialist",
    goal="Gather accurate, current information using web search.",
    backstory="You are a research analyst who verifies claims with live sources.",
    llm=GLM_MODEL,
    tools=[search_tool, file_tool],
    verbose=True,
)