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Langflow integration with Openlayer Langflow is a visual framework for building multi-agent and RAG applications. Openlayer’s native integration with Langflow enables automatic tracing and monitoring of your Langflow workflows, providing real-time observability, performance tracking, and quality evaluation. The integration captures comprehensive trace data including LLM calls, tool executions, agent reasoning steps, retrieval operations, and full execution hierarchies.

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Benefits

The Openlayer integration for Langflow provides:
  • Automatic tracing - No code changes required; traces are captured automatically when you run your flows
  • Real-time monitoring - Track performance, latency, and costs across different flows
  • Quality evaluation - Run tests on production traces to ensure your flows meet quality standards
  • Debugging - Detailed trace hierarchies help identify issues in complex multi-step workflows
  • Multi-flow support - Configure different inference pipelines for different flows or environments

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Prerequisites

Before you begin, you need:
  1. Openlayer account - Sign up at openlayer.com
  2. Openlayer project - Create a project in your Openlayer dashboard
  3. Openlayer API key - Get your API key from Settings → API Keys
  4. Inference pipeline(s) - Create one or more inference pipelines in your Openlayer project for LLM monitoring
  5. Openlayer Python SDK - Install with pip install openlayer

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Quick start

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Step 1: Set environment variables

At minimum, you need to set your Openlayer API key and a default inference pipeline ID: 
export OPENLAYER_API_KEY="your_api_key_here"
export OPENLAYER_INFERENCE_PIPELINE_ID="your_pipeline_id_here"

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Step 2: Run Langflow

langflow run

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Step 3: Execute your flow

When you run any flow in Langflow, traces will automatically be sent to Openlayer.

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Step 4: View traces in Openlayer

  1. Navigate to your Openlayer project → Inference Pipeline
  2. See your traces appear in real-time with detailed execution information

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Configuration

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Environment variables

The integration uses environment variables for configuration:
VariableRequiredDescription
OPENLAYER_API_KEYYesYour Openlayer API key
OPENLAYER_INFERENCE_PIPELINE_IDYes*Default pipeline ID for all flows
OPENLAYER_PIPELINE_<FLOW_NAME>NoPipeline ID for a specific flow
OPENLAYER_LANGFLOW_MAPPINGNoJSON mapping of flows to pipeline IDs
*Required unless using flow-specific configuration

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Single flow configuration

If you have one flow or want all flows to use the same pipeline: 
export OPENLAYER_API_KEY="your_api_key"
export OPENLAYER_INFERENCE_PIPELINE_ID="pipeline_default_123"

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Multiple flows with flow-specific variables

For multiple flows with explicit control over each one: 
export OPENLAYER_API_KEY="your_api_key"

# For a flow named "Customer Support"
export OPENLAYER_PIPELINE_CUSTOMER_SUPPORT="pipeline_support_456"

# For a flow named "Sales Assistant"
export OPENLAYER_PIPELINE_SALES_ASSISTANT="pipeline_sales_789"

# Fallback for any other flows
export OPENLAYER_INFERENCE_PIPELINE_ID="pipeline_default_123"

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Flow name mapping

Flow names in Langflow are automatically converted to environment variable names:
  1. Convert to uppercase
  2. Replace spaces and special characters with underscores
  3. Add OPENLAYER_PIPELINE_ prefix
Examples:
Flow Name in LangflowEnvironment Variable
Customer SupportOPENLAYER_PIPELINE_CUSTOMER_SUPPORT
My-Production-BotOPENLAYER_PIPELINE_MY_PRODUCTION_BOT
Sales v2.0OPENLAYER_PIPELINE_SALES_V2_0

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Multiple flows with JSON mapping

For centralized configuration of many flows: 
export OPENLAYER_API_KEY="your_api_key"

export OPENLAYER_LANGFLOW_MAPPING='{
  "Customer Support": "pipeline_support_456",
  "Sales Assistant": "pipeline_sales_789",
  "Data Analysis": "pipeline_analytics_012"
}'

# Fallback for unmapped flows
export OPENLAYER_INFERENCE_PIPELINE_ID="pipeline_default_123"
Flow names in the JSON mapping must match exactly (case-sensitive, including spaces) with your flow names in Langflow.

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Configuration priority

When a flow runs, the system checks for pipeline IDs in this order:
  1. OPENLAYER_PIPELINE_<FLOW_NAME> (highest priority)
  2. OPENLAYER_LANGFLOW_MAPPING
  3. OPENLAYER_INFERENCE_PIPELINE_ID (fallback)