CrewAI

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Introduction

CrewAI is a framework for orchestrating role-playing, autonomous AI agents designed to solve complex, open-ended tasks through collaboration. It provides a robust structure for agents to work together, leverage tools, and exchange insights to accomplish sophisticated objectives.

Obiguard enhances CrewAI with production-readiness features, turning your experimental agent crews into robust systems by providing:

• Complete observability of every agent step, tool use, and interaction
• Built-in reliability with fallbacks, retries, and load balancing
• Cost tracking and optimization to manage your AI spend
• Access to 200+ LLMs through a single integration
• Guardrails to keep agent behavior safe and compliant
• Version-controlled prompts for consistent agent performance

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Installation & Setup

pip install -U crewai obiguard

from crewai import LLM
from obiguard import OBIGUARD_GATEWAY_URL, createHeaders

# Create an LLM instance with Obiguard integration
gpt_llm = LLM(
  model="gpt-4o",
  base_url=OBIGUARD_GATEWAY_URL,
  api_key="dummy", # We are using a Virtual key, so this is a placeholder
  extra_headers=obiguard.copy_headers()
)

#Use them in your Crew Agents like this:

@agent
def lead_market_analyst(self) -> Agent:
  return Agent(
    config=self.agents_config['lead_market_analyst'],
    verbose=True,
    memory=False,
    llm=gpt_llm
  )

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Production Features

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1. Enhanced Observability

Obiguard provides comprehensive observability for your CrewAI agents, helping you understand exactly what’s happening during each execution.

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2. Model Interoperability

CrewAI supports multiple LLM providers, and Obiguard extends this capability by providing access to over 200 LLMs through a unified interface. You can easily switch between different models without changing your core agent logic:

from crewai import Agent, LLM
from obiguard import OBIGUARD_GATEWAY_URL, createHeaders

# Set up LLMs with different providers
openai_llm = LLM(
    model="gpt-4o",
    base_url=OBIGUARD_GATEWAY_URL,
    api_key="dummy",
    extra_headers=createHeaders(
        obiguard_api_key="vk-obg***",  # Your Obiguard virtual key
    )
)

anthropic_llm = LLM(
    model="claude-3-5-sonnet-latest",
    max_tokens=1000,
    base_url=OBIGUARD_GATEWAY_URL,
    api_key="dummy",
    extra_headers=createHeaders(
        obiguard_api_key="vk-obg***",  # Your Obiguard virtual key
    )
)

# Choose which LLM to use for each agent based on your needs
researcher = Agent(
    role="Senior Research Scientist",
    goal="Discover groundbreaking insights about the assigned topic",
    backstory="You are an expert researcher with deep domain knowledge.",
    verbose=True,
    llm=openai_llm  # Use anthropic_llm for Anthropic
)

Obiguard provides access to LLMs from providers including:

• OpenAI (GPT-4o, GPT-4 Turbo, etc.)
• Anthropic (Claude 3.5 Sonnet, Claude 3 Opus, etc.)
• Mistral AI (Mistral Large, Mistral Medium, etc.)
• Google Vertex AI (Gemini 1.5 Pro, etc.)
• Cohere (Command, Command-R, etc.)
• AWS Bedrock (Claude, Titan, etc.)
• Local/Private Models

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Set Up Enterprise Governance for CrewAI

Why Enterprise Governance?

If you are using CrewAI inside your organization, you need to consider several governance aspects:

• Cost Management: Controlling and tracking AI spending across teams
• Access Control: Managing which teams can use specific models
• Usage Analytics: Understanding how AI is being used across the organization
• Security & Compliance: Maintaining enterprise security standards
• Reliability: Ensuring consistent service across all users

Obiguard adds a comprehensive governance layer to address these enterprise needs. Let’s implement these controls step by step.

from crewai import Agent, LLM
from obiguard import OBIGUARD_GATEWAY_URL, createHeaders

# Configure LLM with your API key
obiguard_llm = LLM(
  model="gpt-4o",
  base_url=OBIGUARD_GATEWAY_URL,
  api_key="YOUR_Obiguard_API_KEY"
)

# Create agent with Obiguard-enabled LLM
researcher = Agent(
  role="Senior Research Scientist",
  goal="Discover groundbreaking insights about the assigned topic",
  backstory="You are an expert researcher with deep domain knowledge.",
  verbose=True,
  llm=obiguard_llm
)

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Frequently Asked Questions

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Resources