> ## Documentation Index
> Fetch the complete documentation index at: https://docs.keywordsai.co/llms.txt
> Use this file to discover all available pages before exploring further.

# Haystack

> Monitor and trace your Haystack pipelines with Keywords AI for complete workflow visibility

<Note> This integration is for **workflow tracing**. For gateway features, see [Haystack Gateway](/integration/development-frameworks/llm_framework/haystack). </Note>

## Overview

Haystack pipelines can have multiple components (retrievers, prompt builders, LLMs). The Keywords AI tracing integration captures your entire workflow execution, showing you exactly how data flows through each component.

<Frame className="rounded-md">
  <img src="https://keywordsai-static.s3.us-east-1.amazonaws.com/docs/Integrations/haystack/haystack_tracing.png" alt="Haystack tracing visualization" />
</Frame>

## Installation

```bash theme={"system"}
pip install keywordsai-exporter-haystack
```

## Quickstart

<Steps>
  <Step title="Set Environment Variables">
    ```bash theme={"system"}
    export KEYWORDSAI_API_KEY="your-keywords-ai-key"
    export OPENAI_API_KEY="your-openai-key"
    export HAYSTACK_CONTENT_TRACING_ENABLED="true"
    ```

    The `HAYSTACK_CONTENT_TRACING_ENABLED` variable activates Haystack's tracing system.
  </Step>

  <Step title="Add KeywordsAIConnector to Your Pipeline">
    ```python theme={"system"}
    import os
    from haystack import Pipeline
    from haystack.components.builders import PromptBuilder
    from haystack.components.generators import OpenAIGenerator
    from keywordsai_exporter_haystack import KeywordsAIConnector

    os.environ["HAYSTACK_CONTENT_TRACING_ENABLED"] = "true"

    # Create pipeline with tracing
    pipeline = Pipeline()
    pipeline.add_component("tracer", KeywordsAIConnector("My Workflow"))
    pipeline.add_component("prompt", PromptBuilder(template="Tell me about {{topic}}."))
    pipeline.add_component("llm", OpenAIGenerator(model="gpt-4o-mini"))
    pipeline.connect("prompt", "llm")

    # Run
    result = pipeline.run({"prompt": {"topic": "artificial intelligence"}})
    print(result["llm"]["replies"][0])
    print(f"\nTrace URL: {result['tracer']['trace_url']}")
    ```
  </Step>

  <Step title="View Your Trace">
    After running, you'll get a trace URL. Visit it to see:

    * Pipeline execution timeline
    * Each component's input/output
    * Timing per component
    * Token usage and costs

    Dashboard: [platform.keywordsai.co/platform/traces](https://platform.keywordsai.co/platform/traces)
  </Step>
</Steps>

## Gateway Integration

For production workflows, combine tracing with gateway features like automatic logging, fallbacks, and cost optimization.

<Card title="Haystack Gateway Integration" href="/integration/development-frameworks/llm_framework/haystack" icon="link">
  Learn how to route LLM calls through Keywords AI gateway
</Card>