CrewAI - Portkey Docs

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. The AI Gateway 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 3,000+ 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

Generate API Key

Create an AI Gateway API key with optional budget/rate limits from the Strata Cloud Manager. You can also attach configurations for reliability, caching, and more to this key. More on this later.

Configure CrewAI with the AI Gateway

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

Production Features

1. Enhanced Observability

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

Traces
Logs
Metrics & Dashboards
Metadata Filtering

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

# Add trace_id to enable hierarchical tracing in the AI Gateway
portkey_llm = LLM(
    model="@YOUR_OPENAI_PROVIDER/gpt-4o",
    base_url="https://aigw.portkey.ai/v1",
    api_key="YOUR_PORTKEY_API_KEY",
    extra_headers={
        "x-portkey-trace-id": "unique-session-id",  # Add unique trace ID
    }
)

Add custom metadata to your CrewAI LLM configuration to enable powerful filtering and segmentation:

import json
portkey_llm = LLM(
    model="@YOUR_OPENAI_PROVIDER/gpt-4o",
    base_url="https://aigw.portkey.ai/v1",
    api_key="YOUR_PORTKEY_API_KEY",
    extra_headers={
        "x-portkey-metadata": json.dumps({
            "crew_type": "research_crew",
            "environment": "production",
            "_user": "user_123",   # Special _user field for user analytics
            "request_source": "mobile_app"
        }),
    }
)

This metadata can be used to filter logs, traces, and metrics on Strata Cloud Manager, allowing you to analyze specific crew runs, users, or environments.

2. Reliability - Keep Your Crews Running Smoothly

When running crews in production, things can go wrong - API rate limits, network issues, or provider outages. The AI Gateway’s reliability features ensure your agents keep running smoothly even when problems occur. It’s simple to enable fallback in your CrewAI setup by using an AI Gateway Config: This configuration will automatically try Claude if the GPT-4o request fails, ensuring your crew can continue operating.

Automatic Retries

Handles temporary failures automatically. If an LLM call fails, the AI Gateway will retry the same request for the specified number of times - perfect for rate limits or network blips.

Request Timeouts

Prevent your agents from hanging. Set timeouts to ensure you get responses (or can fail gracefully) within your required timeframes.

Conditional Routing

Send different requests to different providers. Route complex reasoning to GPT-4, creative tasks to Claude, and quick responses to Gemini based on your needs.

Fallbacks

Keep running even if your primary provider fails. Automatically switch to backup providers to maintain availability.

Load Balancing

Spread requests across multiple API keys or providers. Great for high-volume crew operations and staying within rate limits.

3. Prompting in CrewAI

The AI Gateway’s Prompt Engineering Studio helps you create, manage, and optimize the prompts used in your CrewAI agents. Instead of hardcoding prompts or instructions, use the AI Gateway’s prompt rendering API to dynamically fetch and apply your versioned prompts.

Prompt Playground
Using Prompt Templates
Prompt Versioning
Mustache Templating for variables

Prompt Playground is a place to compare, test and deploy perfect prompts for your AI application. It’s where you experiment with different models, test variables, compare outputs, and refine your prompt engineering strategy before deploying to production. It allows you to:

Iteratively develop prompts before using them in your agents
Test prompts with different variables and models
Compare outputs between different prompt versions
Collaborate with team members on prompt development

This visual environment makes it easier to craft effective prompts for each step in your CrewAI agents’ workflow.

You can:

Create multiple versions of the same prompt
Compare performance between versions
Roll back to previous versions if needed
Specify which version to use in your code:

# Use a specific prompt version
prompt_data = portkey_admin.prompts.render(
    prompt_id="YOUR_PROMPT_ID@version_number",
    variables={
        "agent_role": "Senior Research Scientist",
        "agent_goal": "Discover groundbreaking insights"
    }
)

AI Gateway prompts use Mustache-style templating for easy variable substitution:

You are a {{agent_role}} with expertise in {{domain}}.

Your mission is to {{agent_goal}} by leveraging your knowledge
and experience in the field.

Always maintain a {{tone}} tone and focus on providing {{focus_area}}.

When rendering, simply pass the variables:

prompt_data = portkey_admin.prompts.render(
    prompt_id="YOUR_PROMPT_ID",
    variables={
        "agent_role": "Senior Research Scientist",
        "domain": "artificial intelligence",
        "agent_goal": "discover groundbreaking insights",
        "tone": "professional",
        "focus_area": "practical applications"
    }
)

4. Guardrails for Safe Crews

Guardrails ensure your CrewAI agents operate safely and respond appropriately in all situations. Why Use Guardrails? CrewAI agents can experience various failure modes:

Generating harmful or inappropriate content
Leaking sensitive information like PII
Hallucinating incorrect information
Generating outputs in incorrect formats

The AI Gateway’s guardrails add protections for both inputs and outputs. Implementing Guardrails The AI Gateway’s guardrails can:

Detect and redact PII in both inputs and outputs
Filter harmful or inappropriate content
Validate response formats against schemas
Check for hallucinations against ground truth
Apply custom business logic and rules

Learn More About Guardrails

Explore the AI Gateway’s guardrail features to enhance agent safety

5. User Tracking with Metadata

Track individual users through your CrewAI agents using the AI Gateway’s metadata system. What is Metadata in the AI Gateway? Metadata allows you to associate custom data with each request, enabling filtering, segmentation, and analytics. The special _user field is specifically designed for user tracking. Filter Analytics by User With metadata in place, you can filter analytics by user and analyze performance metrics on a per-user basis: This enables:

Per-user cost tracking and budgeting
Personalized user analytics
Team or organisation-level metrics
Environment-specific monitoring (staging vs. production)

Learn More About Metadata

Explore how to use custom metadata to enhance your analytics

6. Caching for Efficient Crews

Implement caching to make your CrewAI agents more efficient and cost-effective:

Simple Caching
Semantic Caching

Simple caching performs exact matches on input prompts, caching identical requests to avoid redundant model executions.

7. Model Interoperability

CrewAI supports multiple LLM providers, and the AI Gateway 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: The AI Gateway 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 the AI Gateway

Set Up Enterprise Governance for CrewAI

Why Enterprise Governance? If you are using CrewAI inside your organisation, 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 organisation
Security & Compliance: Maintaining enterprise security standards
Reliability: Ensuring consistent service across all users

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

Create LLM Integrations

Go to Integrations in Strata Cloud Manager, choose your LLM and connect to it. Save and copy the generated provider ID.

Create Default Config

Configs in the AI Gateway define how your requests are routed, with features like advanced routing, fallbacks, and retries.To create your config:

Go to Configs in Strata Cloud Manager

Create new config with:

{
    "provider":"@YOUR_PROVIDER_FROM_STEP1",
   	"override_params": {
      "model": "gpt-4o" // Your preferred model name
    }
}

Save and note the Config name for the next step

Configure AI Gateway API Key

Now create an AI Gateway API key and attach the config you created in Step 2:

Go to API Keys in the AI Gateway and Create new API key
Select your config from Step 2
Generate and save your API key

Connect to CrewAI

After setting up your AI Gateway API key with the attached config, connect it to your CrewAI agents:

from crewai import Agent, LLM

# Configure LLM with your API key
portkey_llm = LLM(
    model="gpt-4o",
    base_url="https://aigw.portkey.ai/v1",
    api_key="YOUR_PORTKEY_API_KEY"
)

# Create agent with Portkey-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=portkey_llm
)

Step 1: Implement Budget Controls & Rate Limits

Enable granular control over LLM access at the team/department level. This helps you:

Set up budget limits
Prevent unexpected usage spikes using Rate limits
Track departmental spending

Setting Up Department-Specific Controls:

Navigate to Integrations in Strata Cloud Manager and create a new LLM integration
Provision this integration for each department with budget limits and rate limits
Configure department-specific limits

Step 2: Define Model Access Rules

As your AI usage scales, controlling which teams can access specific models becomes crucial. AI Gateway Configs provide this control layer with features like:

Access Control Features:

Model Restrictions: Limit access to specific models
Data Protection: Implement guardrails for sensitive data
Reliability Controls: Add fallbacks and retry logic

Example Configuration:

Here’s a basic configuration to route requests to OpenAI, specifically using GPT-4o:

{
	"strategy": {
		"mode": "single"
	},
	"targets": [
		{
			"provider":"@YOUR_OPENAI_PROVIDER",
			"override_params": {
				"model": "gpt-4o"
			}
		}
	]
}

Create your config on the Configs page in Strata Cloud Manager.

Configs can be updated anytime to adjust controls without affecting running applications.

Step 3: Implement Access Controls

Create User-specific API keys that automatically:

Track usage per user/team with metadata
Apply appropriate configs to route requests
Collect relevant metadata to filter logs
Enforce access permissions

Create API keys through:

Example using Python SDK:For detailed key management instructions, see our API Keys documentation.

Step 4: Deploy & Monitor

After distributing API keys to your team members, your enterprise-ready CrewAI setup is ready to go. Each team member can now use their designated API keys with appropriate access levels and budget controls.Monitor usage in Strata Cloud Manager:

Cost tracking by department
Model usage patterns
Request volumes
Error rates

Enterprise Features Now Available

Your CrewAI integration now has:

Departmental budget controls
Model access governance
Usage tracking & attribution
Security guardrails
Reliability features

Frequently Asked Questions

How does the AI Gateway enhance CrewAI?

The AI Gateway adds production-readiness to CrewAI through comprehensive observability (traces, logs, metrics), reliability features (fallbacks, retries, caching), and access to 3,000+ LLMs through a unified interface. This makes it easier to debug, optimize, and scale your agent applications.

Can I use the AI Gateway with existing CrewAI applications?

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

Does the AI Gateway work with all CrewAI features?

The AI Gateway 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 track usage across multiple agents in a crew?

Yes, the AI Gateway allows you to use a consistent trace_id across multiple agents in a crew to track the entire workflow. This is especially useful for complex crews where you want to understand the full execution path across multiple agents.

How do I filter logs and traces for specific crew runs?

The AI Gateway allows you to add custom metadata to your LLM configuration, which you can then use for filtering. Add fields like crew_name, crew_type, or session_id to easily find and analyze specific crew executions.

Can I use my own API keys with the AI Gateway?

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

Resources

CrewAI Docs

Official CrewAI documentation

​Introduction

CrewAI Official Documentation

​Installation & Setup

​Production Features

​1. Enhanced Observability

​2. Reliability - Keep Your Crews Running Smoothly

Automatic Retries

Request Timeouts

Conditional Routing

Fallbacks

Load Balancing

​3. Prompting in CrewAI

​4. Guardrails for Safe Crews

Learn More About Guardrails

​5. User Tracking with Metadata

Learn More About Metadata

​6. Caching for Efficient Crews

​7. Model Interoperability

Supported Providers

​Set Up Enterprise Governance for CrewAI

​Step 1: Implement Budget Controls & Rate Limits

​Setting Up Department-Specific Controls:

​Step 2: Define Model Access Rules

​Access Control Features:

​Example Configuration:

​Step 3: Implement Access Controls

​Step 4: Deploy & Monitor

​Enterprise Features Now Available

​Frequently Asked Questions

​Resources

CrewAI Docs

Introduction

Installation & Setup

Production Features

1. Enhanced Observability

2. Reliability - Keep Your Crews Running Smoothly

3. Prompting in CrewAI

4. Guardrails for Safe Crews

5. User Tracking with Metadata

6. Caching for Efficient Crews

7. Model Interoperability

Set Up Enterprise Governance for CrewAI

Step 1: Implement Budget Controls & Rate Limits

Setting Up Department-Specific Controls:

Step 2: Define Model Access Rules

Access Control Features:

Example Configuration:

Step 3: Implement Access Controls

Step 4: Deploy & Monitor

Enterprise Features Now Available

Frequently Asked Questions

Resources