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140 lines
4.7 KiB
Plaintext
140 lines
4.7 KiB
Plaintext
---
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title: Openlayer
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slug: /integrations-openlayer
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---
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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[Openlayer](https://www.openlayer.com/) is a testing and evaluation platform for LLM applications. It provides comprehensive observability, testing, and monitoring capabilities to help you ship high-quality AI systems with confidence.
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You can configure Langflow to collect tracing data about your flow executions and automatically send the data to Openlayer for analysis, monitoring, and evaluation.
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## Prerequisites
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- An [Openlayer account](https://www.openlayer.com/)
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- A [running Langflow server](/get-started-installation) with a [flow](/concepts-flows) that you want to trace
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- An Openlayer inference pipeline
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:::tip
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If you need a flow to test the Openlayer integration, see the [Langflow quickstart](/get-started-quickstart).
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:::
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## Set Openlayer credentials as environment variables
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1. Get your [Openlayer API key](https://app.openlayer.com/settings/api-keys) from your Openlayer account.
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2. Create an inference pipeline in Openlayer and copy the pipeline ID.
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3. Set your Openlayer credentials as environment variables in the same environment where you run Langflow.
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In the following examples, replace `YOUR_API_KEY` and `YOUR_PIPELINE_ID` with your actual Openlayer credentials.
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<Tabs>
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<TabItem value="linux-macos" label="Linux or macOS" default>
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These commands set the environment variables in a Linux or macOS terminal session:
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```bash
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export OPENLAYER_API_KEY="YOUR_API_KEY"
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export OPENLAYER_INFERENCE_PIPELINE_ID="YOUR_PIPELINE_ID"
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```
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</TabItem>
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<TabItem value="windows" label="Windows">
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These commands set the environment variables in a Windows command prompt session:
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```cmd
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set OPENLAYER_API_KEY=YOUR_API_KEY
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set OPENLAYER_INFERENCE_PIPELINE_ID=YOUR_PIPELINE_ID
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```
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</TabItem>
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</Tabs>
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## Start Langflow and view traces in Openlayer
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1. Start Langflow in the same environment where you set the Openlayer environment variables:
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```bash
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uv run langflow run
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```
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2. Run a flow in Langflow.
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Langflow automatically collects and sends tracing data about the flow execution to Openlayer, including:
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- Component inputs and outputs
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- Execution timing and latency
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- LLM calls and nested operations
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- User and session context
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3. View the collected data in your [Openlayer dashboard](https://app.openlayer.com/).
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Each flow execution appears as a trace with a hierarchical view of all components and their nested operations.
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## Advanced configuration
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### Flow-specific pipelines
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You can configure different Openlayer inference pipelines for different flows using flow-specific environment variables:
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```bash
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export OPENLAYER_PIPELINE_MY_FLOW_NAME="pipeline-id-1"
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export OPENLAYER_PIPELINE_ANOTHER_FLOW="pipeline-id-2"
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```
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The flow name is converted to uppercase and non-alphanumeric characters are replaced with underscores. For example, "My Flow-Name" becomes `OPENLAYER_PIPELINE_MY_FLOW_NAME`.
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### JSON mapping
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Alternatively, you can use a JSON mapping to configure multiple flows at once:
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<Tabs>
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<TabItem value="linux-macos" label="Linux or macOS" default>
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```bash
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export OPENLAYER_LANGFLOW_MAPPING='{"Flow Name 1":"pipeline-id-1","Flow Name 2":"pipeline-id-2"}'
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```
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</TabItem>
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<TabItem value="windows" label="Windows">
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```cmd
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set OPENLAYER_LANGFLOW_MAPPING={"Flow Name 1":"pipeline-id-1","Flow Name 2":"pipeline-id-2"}
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```
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</TabItem>
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</Tabs>
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### Configuration priority
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Openlayer configuration is resolved in the following order (highest priority first):
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1. Flow-specific environment variable: `OPENLAYER_PIPELINE_<FLOW_NAME>`
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2. JSON mapping: `OPENLAYER_LANGFLOW_MAPPING`
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3. Default environment variable: `OPENLAYER_INFERENCE_PIPELINE_ID`
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This allows you to set a default pipeline for all flows and override it for specific flows as needed.
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## Disable Openlayer tracing
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To disable the Openlayer integration, remove the `OPENLAYER_API_KEY` environment variable, and then restart Langflow.
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## Features
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The Openlayer integration automatically captures:
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- **Component hierarchy**: All flow components with parent-child relationships
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- **LangChain callbacks**: Nested LLM calls and tool executions appear within their parent components
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- **Timing metrics**: Start time, end time, and latency for each component
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- **Inputs and outputs**: Component inputs and outputs with automatic type conversion
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- **User context**: User ID and session ID propagation for better analytics
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- **Error tracking**: Errors and logs captured in component metadata
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## See also
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* [Openlayer documentation](https://docs.openlayer.com/)
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* [Openlayer GitHub repository](https://github.com/openlayer-ai/openlayer-python)
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