CrewAI

CrewAI is a framework for orchestrating role-playing AI agents. Connect it to TensorX using the OpenAI-compatible configuration.

Prerequisites

• Python 3.10+
• CrewAI installed
• TensorX API key from app.tensorx.ai

Installation

pip install crewai
# or with OpenAI support
pip install "crewai[openai]"

Configuration

Environment Variables

Set environment variables in your .env file:

OPENAI_API_KEY=your-tensorx-api-key
OPENAI_BASE_URL=https://api.tensorx.ai/v1
MODEL=openai/claude-sonnet-4-20250514

Direct Code Configuration

from crewai import LLM

llm = LLM(
    model="openai/claude-sonnet-4-20250514",
    api_key="your-tensorx-api-key",
    base_url="https://api.tensorx.ai/v1",
    temperature=0.7,
    max_tokens=4000
)

Basic Usage

Creating Agents

from crewai import Agent, LLM

# Configure TensorX LLM
llm = LLM(
    model="openai/claude-sonnet-4-20250514",
    api_key="your-tensorx-api-key",
    base_url="https://api.tensorx.ai/v1"
)

# Create an agent
researcher = Agent(
    role="Research Analyst",
    goal="Find and summarize the latest AI trends",
    backstory="You are an expert research analyst specializing in AI technology",
    llm=llm,
    verbose=True
)

Creating Tasks

from crewai import Task

research_task = Task(
    description="Research the latest developments in large language models and summarize key findings",
    expected_output="A comprehensive summary of LLM developments in 2024",
    agent=researcher
)

Running a Crew

from crewai import Crew, Agent, Task, LLM

# Configure LLM
llm = LLM(
    model="openai/claude-sonnet-4-20250514",
    api_key="your-tensorx-api-key",
    base_url="https://api.tensorx.ai/v1"
)

# Create agents
researcher = Agent(
    role="Research Analyst",
    goal="Research and analyze information",
    backstory="Expert analyst with deep research skills",
    llm=llm
)

writer = Agent(
    role="Content Writer",
    goal="Write clear and engaging content",
    backstory="Professional writer with technical expertise",
    llm=llm
)

# Create tasks
research_task = Task(
    description="Research the benefits of AI in healthcare",
    expected_output="Key findings about AI healthcare applications",
    agent=researcher
)

writing_task = Task(
    description="Write a blog post based on the research findings",
    expected_output="A 500-word blog post about AI in healthcare",
    agent=writer
)

# Create and run crew
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, writing_task],
    verbose=True
)

result = crew.kickoff()
print(result)

YAML Configuration

For larger projects, use YAML configuration:

agents.yaml

researcher:
  role: Research Analyst
  goal: Conduct comprehensive research and analysis
  backstory: A dedicated research professional with years of experience
  verbose: true
  llm: openai/claude-sonnet-4-20250514

writer:
  role: Content Writer
  goal: Create engaging and informative content
  backstory: Expert writer with technical communication skills
  verbose: true
  llm: openai/gpt-4o

tasks.yaml

research_task:
  description: Research the specified topic thoroughly
  expected_output: Detailed research findings with citations
  agent: researcher

writing_task:
  description: Write content based on research findings
  expected_output: Well-structured article or report
  agent: writer

Advanced Configuration

Custom Parameters

from crewai import LLM

llm = LLM(
    model="openai/claude-sonnet-4-20250514",
    api_key="your-tensorx-api-key",
    base_url="https://api.tensorx.ai/v1",
    temperature=0.7,
    max_tokens=4000,
    top_p=0.9,
    frequency_penalty=0.1,
    presence_penalty=0.1,
    timeout=120,
    max_retries=3,
    stream=True
)

Multiple Models per Crew

from crewai import Agent, LLM

# High-capability model for complex tasks
claude_llm = LLM(
    model="openai/claude-sonnet-4-20250514",
    api_key="your-tensorx-api-key",
    base_url="https://api.tensorx.ai/v1"
)

# Fast model for simple tasks
gpt_mini_llm = LLM(
    model="openai/gpt-4o-mini",
    api_key="your-tensorx-api-key",
    base_url="https://api.tensorx.ai/v1"
)

# Complex reasoning agent
analyst = Agent(
    role="Senior Analyst",
    goal="Perform deep analysis",
    backstory="Expert analyst",
    llm=claude_llm
)

# Quick response agent
assistant = Agent(
    role="Assistant",
    goal="Handle routine tasks",
    backstory="Efficient assistant",
    llm=gpt_mini_llm
)

Available Models

See TensorX Models for all available models.

Model	Best For
openai/claude-sonnet-4-20250514	Complex reasoning, agents
openai/claude-3-5-sonnet-20241022	General tasks
openai/gpt-4o	Multi-modal, tool use
openai/gpt-4o-mini	Fast, cost-effective

Note: Prefix model names with openai/ when using with CrewAI.

Troubleshooting

Connection errors

Ensure environment variables are set:

export OPENAI_API_KEY=your-tensorx-api-key
export OPENAI_BASE_URL=https://api.tensorx.ai/v1

Model not found

Use the openai/ prefix for model names:

llm = LLM(model="openai/claude-sonnet-4-20250514")  # Correct
llm = LLM(model="claude-sonnet-4-20250514")         # May not work

Timeout issues

Increase timeout for complex tasks:

llm = LLM(
    model="openai/claude-sonnet-4-20250514",
    timeout=300  # 5 minutes
)

Resources

• CrewAI Documentation
• CrewAI GitHub
• TensorX API Reference