CrewAI
Use Portkey with CrewAI to take your AI Agents to production
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.
Portkey 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
Generate API Key
Create a Portkey API key with optional budget/rate limits from the Portkey dashboard. You can also attach configurations for reliability, caching, and more to this key. More on this later.
Configure CrewAI with Portkey
The integration is simple - you just need to update the LLM configuration in your CrewAI setup:
What are Virtual Keys? Virtual keys in Portkey securely store your LLM provider API keys (OpenAI, Anthropic, etc.) in an encrypted vault. They allow for easier key rotation and budget management. Learn more about virtual keys here.
Production Features
1. Enhanced Observability
Portkey provides comprehensive observability for your CrewAI agents, helping you understand exactly what’s happening during each execution.
Traces provide a hierarchical view of your crew’s execution, showing the sequence of LLM calls, tool invocations, and state transitions.
Traces provide a hierarchical view of your crew’s execution, showing the sequence of LLM calls, tool invocations, and state transitions.
Portkey logs every interaction with LLMs, including:
- Complete request and response payloads
- Latency and token usage metrics
- Cost calculations
- Tool calls and function executions
All logs can be filtered by metadata, trace IDs, models, and more, making it easy to debug specific crew runs.
Portkey provides built-in dashboards that help you:
- Track cost and token usage across all crew runs
- Analyze performance metrics like latency and success rates
- Identify bottlenecks in your agent workflows
- Compare different crew configurations and LLMs
You can filter and segment all metrics by custom metadata to analyze specific crew types, user groups, or use cases.
Add custom metadata to your CrewAI LLM configuration to enable powerful filtering and segmentation:
This metadata can be used to filter logs, traces, and metrics on the Portkey dashboard, 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. Portkey’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 a Portkey 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, Portkey 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
Portkey’s Prompt Engineering Studio helps you create, manage, and optimize the prompts used in your CrewAI agents. Instead of hardcoding prompts or instructions, use Portkey’s prompt rendering API to dynamically fetch and apply your versioned prompts.
Manage prompts in Portkey's Prompt Library
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.
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.
The Prompt Render API retrieves your prompt templates with all parameters configured:
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:
Portkey prompts use Mustache-style templating for easy variable substitution:
When rendering, simply pass the variables:
Prompt Engineering Studio
Learn more about Portkey’s prompt management features
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
Portkey’s guardrails add protections for both inputs and outputs.
Implementing Guardrails
Portkey’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 Portkey’s guardrail features to enhance agent safety
5. User Tracking with Metadata
Track individual users through your CrewAI agents using Portkey’s metadata system.
What is Metadata in Portkey?
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:
Filter analytics by user
This enables:
- Per-user cost tracking and budgeting
- Personalized user analytics
- Team or organization-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 performs exact matches on input prompts, caching identical requests to avoid redundant model executions.
Simple caching performs exact matches on input prompts, caching identical requests to avoid redundant model executions.
Semantic caching considers the contextual similarity between input requests, caching responses for semantically similar inputs.
7. Model Interoperability
CrewAI supports multiple LLM providers, and Portkey 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:
Portkey 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 Portkey
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
Portkey adds a comprehensive governance layer to address these enterprise needs. Let’s implement these controls step by step.
Create Virtual Key
Virtual Keys are Portkey’s secure way to manage your LLM provider API keys. They provide essential controls like:
- Budget limits for API usage
- Rate limiting capabilities
- Secure API key storage
To create a virtual key: Go to Virtual Keys in the Portkey App. Save and copy the virtual key ID
Save your virtual key ID - you’ll need it for the next step.
Create Default Config
Configs in Portkey define how your requests are routed, with features like advanced routing, fallbacks, and retries.
To create your config:
- Go to Configs in Portkey dashboard
- Create new config with:
- Save and note the Config name for the next step
Configure Portkey API Key
Now create a Portkey API key and attach the config you created in Step 2:
- Go to API Keys in Portkey and Create new API key
- Select your config from
Step 2
- Generate and save your API key
Connect to CrewAI
After setting up your Portkey API key with the attached config, connect it to your CrewAI agents:
Enterprise Features Now Available
Your CrewAI integration now has:
- Departmental budget controls
- Model access governance
- Usage tracking & attribution
- Security guardrails
- Reliability features