Langflow is an open-source visual framework for building multi-agent and RAG applications. Its intuitive drag-and-drop interface allows developers to create complex AI workflows without writing extensive code. While Langflow provides powerful visual AI development capabilities, Portkey adds essential enterprise controls for production deployments:

Unified AI Gateway - Single interface for 1600+ LLMs with API key management (not just OpenAI)
Centralized AI observability: Real-time usage tracking for 40+ key metrics and logs for every request
Governance - Real-time spend tracking, set budget limits and RBAC in your Langflow workflows
Security Guardrails - PII detection, content filtering, and compliance controls

This guide will walk you through integrating Portkey with Langflow and setting up essential enterprise features including usage tracking, access controls, and budget management.

If you are an enterprise looking to use Langflow in your organisation, check out this section.

1. Setting up Portkey

Portkey allows you to use 1600+ LLMs with your Langflow setup, with minimal configuration required. Let’s set up the core components in Portkey that you’ll need for integration.

Create an Integration

Navigate to the Integrations section on Portkey’s Sidebar. This is where you’ll connect your LLM providers.

Find your preferred provider (e.g., OpenAI, Anthropic, etc.)
Click Connect on the provider card
In the “Create New Integration” window:
- Enter a Name for reference
- Enter a Slug for the integration
- Enter your API Key and other provider specific details for the provider
Click Next Step

In your next step you’ll see workspace provisioning options. You can select the default “Shared Team Workspace” if this is your first time OR chose your current one.

Configure Models

On the model provisioning page:

Leave all models selected (or customize)
Toggle Automatically enable new models if desired

Click Create Integration to complete the integration

Copy the Provider Slug

Once your Integration is created:

Go to Model Catalog → Models tab
Find and click on you your model button (if your model is not visible, you need to edit your integration from the last step)
Copy the slug (e.g., @openai-dev/gpt-4o)

We recommend clicking the Run Test Request button on this step to verify your integration. If you see the error: You do not have enough permissions to execute this request, you’ll need to create a User API Key for this step to work properly.You can create one here. You should be able to see simple chat request output on this step.

This is your unique identifier - you’ll need it for the next step. This slug is basically @your-provider-slug/your-model-name

Create Default Config

Portkey’s config is a JSON object used to define routing rules for requests to your gateway. You can create these configs in the Portkey app and reference them in requests via the config ID. For this setup, we’ll create a simple config using your provider (OpenAI) and model (gpt-4o).

Go to Configs in Portkey dashboard

Create new config with:

{
    "override_params": {
      "model": "@YOUR_SLUG-FROM-LAST_STEP" // example: @openai-test/gpt-4o-mini
    }
}

Save and note the Config ID & Name for the next step

Configure Portkey API Key

Finally, create a Portkey API key:

Go to API Keys in Portkey
Create new API key
Select the config that you create from previous step
Generate and save your API key

Save your API key securely - you’ll need it for Langflow integration.

🎉 Voila, Setup complete! You now have everything needed to integrate Portkey with your application.

2. Integrate Portkey with Langflow

Now that you have your Portkey components set up, let’s connect them to Langflow. Since Portkey provides OpenAI API compatibility, integration is straightforward and requires just a few configuration steps in your Langflow interface.

You need your Portkey API Key from Step 1 before going further.

Install Langflow

First, ensure you have Langflow installed. You can install it via:

Docker
pip
Desktop app

Follow the official Langflow installation guide for detailed instructions.

Create or Open a Flow

Launch Langflow and create a new flow or open an existing one that uses an OpenAI model component.

Configure the OpenAI Model Component

Find the OpenAI model component in your flow
Click on the component to select it
Click on Controls in the component settings

Add Portkey Configuration

In the OpenAI model component settings, configure the following:

Base URL: https://api.portkey.ai/v1
API Key: Your Portkey API key from the setup

Make sure your virtual key has sufficient budget and rate limits for your expected usage. Also use the complete model name given by the provider.

That’s it! Your Langflow workflows are now powered by Portkey. You can monitor your requests and usage in the Portkey Dashboard.

3. Set Up Enterprise Governance for Langflow

Why Enterprise Governance? If you are using Langflow inside your orgnaization, you need to consider several governance aspects:

Cost Management: Controlling and tracking AI spending across teams
Access Control: Managing team access and workspaces
Usage Analytics: Understanding how AI is being used across the organization
Security & Compliance: Maintaining enterprise security standards
Reliability: Ensuring consistent service across all users
Model Management: Managing what models are being used in your setup

Portkey adds a comprehensive governance layer to address these enterprise Enterprise Implementation Guide

Step 1: Implement Budget Controls & Rate Limits

Model Catalog enables you to have 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 Model Catalog in Portkey dashboard
Create new Provider for each engineering team 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. You can simply manage AI models in your org by provisioning model at the top integration level.

Step 4: Set Routing Configuration

Portkey allows you to control your routing logic very simply with it’s Configs feature. Portkey Configs provide this control layer with things like:

Data Protection: Implement guardrails for sensitive code and data
Reliability Controls: Add fallbacks, load-balance, retry and smart conditional routing logic
Caching: Implement Simple and Semantic Caching. and more…

Example Configuration:

Here’s a basic configuration to load-balance requests to OpenAI and Anthropic:

{
	"strategy": {
		"mode": "load-balance"
	},
	"targets": [
		{
			"override_params": {
				"model": "@YOUR_OPENAI_PROVIDER_SLUG/gpt-model"
			}
		},
		{
			"override_params": {
				"model": "@YOUR_ANTHROPIC_PROVIDER/claude-sonnet-model"
			}
		}
	]
}

Create your config on the Configs page in your Portkey dashboard. You’ll need the config ID for connecting to Langflow’s setup.

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

Step 4: Implement Access Controls

Step 3: Implement Access Controls

Create User-specific API keys that automatically:

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

Create API keys through:

Example using Python SDK:

from portkey_ai import Portkey

portkey = Portkey(api_key="YOUR_ADMIN_API_KEY")

api_key = portkey.api_keys.create(
    name="frontend-engineering",
    type="organisation",
    workspace_id="YOUR_WORKSPACE_ID",
    defaults={
        "config_id": "your-config-id",
        "metadata": {
            "environment": "development",
            "department": "engineering",
            "team": "frontend"
        }
    },
    scopes=["logs.view", "configs.read"]
)

For detailed key management instructions, see our API Keys documentation.

Step 5: Deploy & Monitor

Step 4: Deploy & Monitor

After distributing API keys to your engineering teams, your enterprise-ready Langflow setup is ready to go. Each developer can now use their designated API keys with appropriate access levels and budget controls. Apply your governance setup using the integration steps from earlier sections Monitor usage in Portkey dashboard:

Cost tracking by engineering team
Model usage patterns for AI agent tasks
Request volumes
Error rates and debugging logs

Enterprise Features Now Available

Langflow now has:

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

Portkey Features

Now that you have enterprise-grade Langflow setup, let’s explore the comprehensive features Portkey provides to ensure secure, efficient, and cost-effective AI operations.

1. Comprehensive Metrics

Using Portkey you can track 40+ key metrics including cost, token usage, response time, and performance across all your LLM providers in real time. You can also filter these metrics based on custom metadata that you can set in your configs. Learn more about metadata here.

2. Advanced Logs

Portkey’s logging dashboard provides detailed logs for every request made to your LLMs. These logs include:

Complete request and response tracking
Metadata tags for filtering
Cost attribution and much more…

3. Unified Access to 1600+ LLMs

You can easily switch between 1600+ LLMs. Call various LLMs such as Anthropic, Gemini, Mistral, Azure OpenAI, Google Vertex AI, AWS Bedrock, and many more by simply changing the virtual key in your default config object.

4. Advanced Metadata Tracking

Using Portkey, you can add custom metadata to your LLM requests for detailed tracking and analytics. Use metadata tags to filter logs, track usage, and attribute costs across departments and teams.

Custom Metadata

5. Enterprise Access Management

Budget Controls

Set and manage spending limits across teams and departments. Control costs with granular budget limits and usage tracking.

Single Sign-On (SSO)

Enterprise-grade SSO integration with support for SAML 2.0, Okta, Azure AD, and custom providers for secure authentication.

Organization Management

Hierarchical organization structure with workspaces, teams, and role-based access control for enterprise-scale deployments.

Access Rules & Audit Logs

Comprehensive access control rules and detailed audit logging for security compliance and usage tracking.

6. Reliability Features

Fallbacks

Automatically switch to backup targets if the primary target fails.

Conditional Routing

Route requests to different targets based on specified conditions.

Load Balancing

Distribute requests across multiple targets based on defined weights.

Caching

Enable caching of responses to improve performance and reduce costs.

Smart Retries

Automatic retry handling with exponential backoff for failed requests

Budget Limits

Set and manage budget limits across teams and departments. Control costs with granular budget limits and usage tracking.

7. Advanced Guardrails

Protect your Langflow workflows and enhance reliability with real-time checks on LLM inputs and outputs. Leverage guardrails to:

Prevent sensitive data leaks
Enforce compliance with organizational policies
PII detection and masking
Content filtering
Custom security rules
Data compliance checks

Guardrails

Implement real-time protection for your LLM interactions with automatic detection and filtering of sensitive content, PII, and custom security rules. Enable comprehensive data protection while maintaining compliance with organizational policies.

FAQs

How do I update my Virtual Key limits after creation?

You can update your Virtual Key limits at any time from the Portkey dashboard:

Go to Virtual Keys section
Click on the Virtual Key you want to modify
Update the budget or rate limits
Save your changes

Can I use multiple LLM providers with the same API key?

Yes! You can create multiple Virtual Keys (one for each provider) and attach them to a single config. This config can then be connected to your API key, allowing you to use multiple providers through a single API key.

How do I track costs for different teams?

Portkey provides several ways to track team costs:

Create separate Virtual Keys for each team
Use metadata tags in your configs
Set up team-specific API keys
Monitor usage in the analytics dashboard

What happens if a team exceeds their budget limit?

When a team reaches their budget limit:

Further requests will be blocked
Team admins receive notifications
Usage statistics remain available in dashboard
Limits can be adjusted if needed

Can I use Portkey with other Langflow model components?

Yes! While this guide focuses on the OpenAI component, Portkey can work with any model component that supports custom base URLs. Simply configure the base URL to https://api.portkey.ai/v1 and use your Portkey API key.

Next Steps

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For enterprise support and custom features, contact our enterprise team.

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Documentation Index

​1. Setting up Portkey

​2. Integrate Portkey with Langflow

​3. Set Up Enterprise Governance for Langflow

​Step 1: Implement Budget Controls & Rate Limits

​Setting Up Department-Specific Controls:

​Step 2: Define Model Access Rules

​Example Configuration:

​Step 3: Implement Access Controls

​Step 4: Deploy & Monitor

​Enterprise Features Now Available

​Portkey Features

​1. Comprehensive Metrics

​2. Advanced Logs

​3. Unified Access to 1600+ LLMs

​4. Advanced Metadata Tracking

Custom Metadata

​5. Enterprise Access Management

Budget Controls

Single Sign-On (SSO)

Organization Management

Access Rules & Audit Logs

​6. Reliability Features

Fallbacks

Conditional Routing

Load Balancing

Caching

Smart Retries

Budget Limits

​7. Advanced Guardrails

Guardrails

​FAQs

​Next Steps

1. Setting up Portkey

2. Integrate Portkey with Langflow

3. Set Up Enterprise Governance for Langflow

Step 1: Implement Budget Controls & Rate Limits

Setting Up Department-Specific Controls:

Step 2: Define Model Access Rules

Example Configuration:

Step 3: Implement Access Controls

Step 4: Deploy & Monitor

Enterprise Features Now Available

Portkey Features

1. Comprehensive Metrics

2. Advanced Logs

3. Unified Access to 1600+ LLMs

4. Advanced Metadata Tracking

5. Enterprise Access Management

6. Reliability Features

7. Advanced Guardrails

FAQs

Next Steps