🤖 CrewAI + DeepSeek/Qwen: Build Cost-Effective AI Teams

Last updated: July 29, 2026 · 7 min read

Why CrewAI + DeepSeek?

CrewAI lets you build teams of AI agents that collaborate on complex tasks. But running multiple agents on GPT-4o or Claude gets expensive fast. With DeepSeek V4:

• Research agent + Writer agent + Editor agent = 3x the agents, same budget as 1x GPT-4o
• 128K context for processing long documents
• Excellent at following structured instructions — perfect for CrewAI's role-based prompts
• OpenAI-compatible — zero code changes to your CrewAI setup

Setup Guide

pip install crewai crewai-tools

from crewai import LLM

# Configure DeepSeek as your LLM provider
deepseek = LLM(
    model="deepseek-v4",
    base_url="https://eaf9553505eeb8f5-115-190-107-107.serveousercontent.com/v1",
    api_key="your-api-key-here",
    temperature=0.7,
)

# For budget tasks, use flash models
deepseek_fast = LLM(
    model="deepseek-v4-flash",
    base_url="https://eaf9553505eeb8f5-115-190-107-107.serveousercontent.com/v1",
    api_key="your-api-key-here",
    temperature=0.3,
)

from crewai import Agent, Task, Crew, Process

# Research Agent — uses full model for deep analysis
researcher = Agent(
    role="Senior Research Analyst",
    goal="Uncover cutting-edge developments in AI",
    backstory="""You are a veteran analyst at a leading AI research firm.
    You excel at finding patterns in complex data and identifying trends.""",
    llm=deepseek,
    verbose=True,
)

# Writer Agent — uses full model for quality content
writer = Agent(
    role="Tech Content Writer",
    goal="Craft compelling articles about AI discoveries",
    backstory="""You are a renowned tech writer known for making complex
    topics accessible. Your articles get millions of reads.""",
    llm=deepseek,
    verbose=True,
)

# Quick summarizer — uses flash model to save costs
summarizer = Agent(
    role="Summary Specialist",
    goal="Create concise executive summaries",
    backstory="You distill complex reports into actionable bullet points.",
    llm=deepseek_fast,  # Flash model for simple tasks
    verbose=True,
)

# Define tasks
research_task = Task(
    description="Research the latest developments in AI agents and multi-agent systems.",
    expected_output="A detailed report with key findings and sources.",
    agent=researcher,
)

write_task = Task(
    description="Write an engaging article based on the research findings.",
    expected_output="A 1000-word article suitable for publication.",
    agent=writer,
)

summary_task = Task(
    description="Create a brief executive summary of the article.",
    expected_output="3-5 bullet point summary.",
    agent=summarizer,
)

# Assemble the crew
crew = Crew(
    agents=[researcher, writer, summarizer],
    tasks=[research_task, write_task, summary_task],
    process=Process.sequential,
    verbose=True,
)

# Run it!
result = crew.kickoff()
print(result)

Cost Comparison

Using Different Models for Different Agents

# Strategy: Right-size each agent
agents_config = {
    "researcher": "deepseek-r1",      # Best reasoning for research
    "analyst": "deepseek-v4",         # Strong general purpose
    "writer": "deepseek-v4",          # Quality output needed
    "summarizer": "qwen3.6-flash",    # Simple task, cheap model
    "translator": "deepseek-v4-flash", # Fast and good at languages
    "code_reviewer": "deepseek-v4",   # Needs strong coding ability
}

def get_llm(model_name):
    return LLM(
        model=model_name,
        base_url="https://eaf9553505eeb8f5-115-190-107-107.serveousercontent.com/v1",
        api_key="your-api-key-here",
    )

Troubleshooting