CrewAI

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

CrewAI Official Documentation

Learn more about CrewAI’s core concepts and features

Installation & Setup

Install the required packages

pip install -U crewai obiguard

Create an access key

Create an access key from the Obiguard dashboard. Your provider credentials stay stored in Obiguard and are never shared with your application.

Configure CrewAI with Obiguard

The integration is simple - you just need to update the LLM configuration in your CrewAI setup:

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", # Placeholder; provider credential is stored in Obiguard
  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
  )

Where are my provider keys? Your LLM provider credentials (OpenAI, Anthropic, etc.) are stored encrypted in Obiguard and never leave it. Your application only uses an access key.

Production Features

1. Enhanced Observability

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

Traces
Logs
Metrics & Dashboards

Traces provide a hierarchical view of your crew’s execution, showing the sequence of LLM calls, tool invocations, and state transitions.

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="ak***",  # Your Obiguard access 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="ak***",  # Your Obiguard access 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

Supported Providers

See the full list of LLM providers supported by Obiguard.

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.

Create guardrail policy

You can choose to create a guardrail policy to protect your data and ensure compliance with organizational policies. Add guardrail validators on your LLM inputs and output to govern your LLM usage.

Create an access key

Callers authenticate to the Obiguard gateway with an access key. Your provider credentials stay stored in Obiguard and are never shared with your application.In the Obiguard dashboard, create an access key for the agent or project that should use CrewAI, and copy it.

Save your access key securely - you’ll need it for the next step.

Connect to CrewAI

After creating your access key, connect it to your CrewAI agents:

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
)

Enterprise Features Now Available

Your CrewAI integration now has:

Model access governance
Usage tracking & attribution
Security guardrails
Reliability features

Frequently Asked Questions

How does Obiguard enhance CrewAI?

Obiguard adds production-readiness to CrewAI through comprehensive observability (traces, logs, metrics), reliability features (fallbacks, retries, caching), and access to 200+ LLMs through a unified interface. This makes it easier to debug, optimize, and scale your agent applications.

Can I use Obiguard with existing CrewAI applications?

Yes! Obiguard integrates seamlessly with existing CrewAI applications. You just need to update your LLM configuration code with the Obiguard-enabled version. The rest of your agent and crew code remains unchanged.

Does Obiguard work with all CrewAI features?

Obiguard supports all CrewAI features, including agents, tools, human-in-the-loop workflows, and all task process types (sequential, hierarchical, etc.). It adds observability and reliability without limiting any of the framework’s functionality.

Can I use my own API keys with Obiguard?

Yes! Obiguard uses your own API keys for the various LLM providers. It securely stores them as provider credentials, allowing you to easily manage and rotate keys without changing your code.

Resources

CrewAI Docs

Official CrewAI documentation

Ecosystem

AI Apps

Agents

LLMs

Vector Databases

Introduction

CrewAI Official Documentation

Installation & Setup

Production Features

1. Enhanced Observability

2. Model Interoperability

Supported Providers

Set Up Enterprise Governance for CrewAI

Enterprise Features Now Available

Frequently Asked Questions

Resources

CrewAI Docs

​Introduction

CrewAI Official Documentation

​Installation & Setup

​Production Features

​1. Enhanced Observability

​2. Model Interoperability

Supported Providers

​Set Up Enterprise Governance for CrewAI

​Enterprise Features Now Available

​Frequently Asked Questions

​Resources

CrewAI Docs

Introduction

Installation & Setup

Production Features

1. Enhanced Observability

2. Model Interoperability

Set Up Enterprise Governance for CrewAI

Enterprise Features Now Available

Frequently Asked Questions

Resources