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</li></ul></nav></div></div></aside><main class="docMainContainer_TBSr"><div class="container padding-top--md padding-bottom--lg"><div class="row"><div class="col docItemCol_VOVn"><div class="docItemContainer_Djhp"><article><nav class="theme-doc-breadcrumbs breadcrumbsContainer_Z_bl" aria-label="Breadcrumbs"><ul class="breadcrumbs" itemscope="" itemtype="https://schema.org/BreadcrumbList"><li class="breadcrumbs__item"><a aria-label="Home page" class="breadcrumbs__link" href="/"><svg viewBox="0 0 24 24" class="breadcrumbHomeIcon_YNFT"><path d="M10 19v-5h4v5c0 .55.45 1 1 1h3c.55 0 1-.45 1-1v-7h1.7c.46 0 .68-.57.33-.87L12.67 3.6c-.38-.34-.96-.34-1.34 0l-8.36 7.53c-.34.3-.13.87.33.87H5v7c0 .55.45 1 1 1h3c.55 0 1-.45 1-1z" fill="currentColor"></path></svg></a></li><li class="breadcrumbs__item"><span class="breadcrumbs__link">Integrations</span><meta itemprop="position" content="1"></li><li itemscope="" itemprop="itemListElement" itemtype="https://schema.org/ListItem" class="breadcrumbs__item breadcrumbs__item--active"><span class="breadcrumbs__link" itemprop="name">Cleanlab</span><meta itemprop="position" content="2"></li></ul></nav><div class="tocCollapsible_ETCw theme-doc-toc-mobile tocMobile_ITEo"><button type="button" class="clean-btn tocCollapsibleButton_TO0P">On this page</button></div><div class="theme-doc-markdown markdown"><header><h1>Integrate Cleanlab Evaluations with Langflow</h1></header><p>Unlock trustworthy Agentic, RAG, and LLM pipelines with Cleanlab's evaluation and remediation suite.</p>
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<p><a href="https://www.cleanlab.ai/" target="_blank" rel="noopener noreferrer">Cleanlab</a> adds automation and trust to every data point going in and every prediction coming out of AI and RAG solutions.</p>
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<p>This Langflow integration provides 3 modular components that assess and improve the <strong>trustworthiness</strong> of any LLM or RAG pipeline output, enabling critical oversight for safety-sensitive, enterprise, and production GenAI applications.</p>
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<p>Use this bundle to:</p>
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<ul>
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<li>Quantify trustworthiness of ANY LLM response with a <strong>0-1 score</strong></li>
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<li>Explain why a response may be good or bad</li>
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<li>Evaluate <strong>context sufficiency</strong>, <strong>groundedness</strong>, <strong>helpfulness</strong>, and <strong>query clarity</strong> with quantitative scores (for RAG/Agentic pipelines with context)</li>
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<li>Remediate low-trust responses with warnings or fallback answers</li>
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</ul>
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<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="prerequisites">Prerequisites<a href="#prerequisites" class="hash-link" aria-label="Direct link to Prerequisites" title="Direct link to Prerequisites"></a></h2>
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<p>Before using these components, you'll need:</p>
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<ul>
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<li>A <a href="https://tlm.cleanlab.ai/" target="_blank" rel="noopener noreferrer">Cleanlab API key</a></li>
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</ul>
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<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="components">Components<a href="#components" class="hash-link" aria-label="Direct link to Components" title="Direct link to Components"></a></h2>
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<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="cleanlabevaluator"><code>CleanlabEvaluator</code><a href="#cleanlabevaluator" class="hash-link" aria-label="Direct link to cleanlabevaluator" title="Direct link to cleanlabevaluator"></a></h3>
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<p><strong>Purpose:</strong> Evaluate and explain the trustworthiness of a prompt + response pair using Cleanlab. More details on how the score works <a href="https://help.cleanlab.ai/tlm/" target="_blank" rel="noopener noreferrer">here</a>.</p>
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<h4 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="inputs">Inputs<a href="#inputs" class="hash-link" aria-label="Direct link to Inputs" title="Direct link to Inputs"></a></h4>
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<table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>system_prompt</td><td>Message</td><td>(Optional) System message prepended to the prompt</td></tr><tr><td>prompt</td><td>Message</td><td>The user-facing input to the LLM</td></tr><tr><td>response</td><td>Message</td><td>OpenAI's, Claude, etc. model's response to evaluate</td></tr><tr><td>cleanlab_api_key</td><td>Secret</td><td>Your Cleanlab API key</td></tr><tr><td>cleanlab_evaluation_model</td><td>Dropdown</td><td>Evaluation model used by Cleanlab (GPT-4, Claude, etc.) This does not need to be the same model that generated the response.</td></tr><tr><td>quality_preset</td><td>Dropdown</td><td>Tradeoff between evaluation speed and accuracy</td></tr></tbody></table>
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<h4 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="outputs">Outputs<a href="#outputs" class="hash-link" aria-label="Direct link to Outputs" title="Direct link to Outputs"></a></h4>
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<table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>score</td><td>number</td><td>Trust score between 0–1</td></tr><tr><td>explanation</td><td>Message</td><td>Explanation of the trust score</td></tr><tr><td>response</td><td>Message</td><td>Returns the original response for easy chaining to <code>CleanlabRemediator</code> component</td></tr></tbody></table>
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<hr>
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<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="cleanlabremediator"><code>CleanlabRemediator</code><a href="#cleanlabremediator" class="hash-link" aria-label="Direct link to cleanlabremediator" title="Direct link to cleanlabremediator"></a></h3>
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<p><strong>Purpose:</strong> Use the trust score from the <code>CleanlabEvaluator</code> component to determine whether to show, warn about, or replace an LLM response. This component has configurables for the score threshold, warning text, and fallback message which you can customize as needed.</p>
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<h4 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="inputs-1">Inputs<a href="#inputs-1" class="hash-link" aria-label="Direct link to Inputs" title="Direct link to Inputs"></a></h4>
|
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<table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>response</td><td>Message</td><td>The response to potentially remediate</td></tr><tr><td>score</td><td>number</td><td>Trust score from <code>CleanlabEvaluator</code></td></tr><tr><td>explanation</td><td>Message</td><td>(Optional) Explanation to append if warning is shown</td></tr><tr><td>threshold</td><td>float</td><td>Minimum trust score to pass response unchanged</td></tr><tr><td>show_untrustworthy_response</td><td>bool</td><td>Show original response with warning if untrustworthy</td></tr><tr><td>untrustworthy_warning_text</td><td>Prompt</td><td>Warning text for untrustworthy responses</td></tr><tr><td>fallback_text</td><td>Prompt</td><td>Fallback message if response is hidden</td></tr></tbody></table>
|
||
<h4 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="output">Output<a href="#output" class="hash-link" aria-label="Direct link to Output" title="Direct link to Output"></a></h4>
|
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<table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>remediated_response</td><td>Message</td><td>Final message shown to user after remediation logic</td></tr></tbody></table>
|
||
<p>See example outputs below!</p>
|
||
<hr>
|
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<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="cleanlabragevaluator"><code>CleanlabRAGEvaluator</code><a href="#cleanlabragevaluator" class="hash-link" aria-label="Direct link to cleanlabragevaluator" title="Direct link to cleanlabragevaluator"></a></h3>
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<p><strong>Purpose:</strong> Comprehensively evaluate RAG and LLM pipeline outputs by analyzing the context, query, and response quality using Cleanlab. This component assesses trustworthiness, context sufficiency, response groundedness, helpfulness, and query ease. Learn more about Cleanlab's evaluation metrics <a href="https://help.cleanlab.ai/tlm/use-cases/tlm_rag/" target="_blank" rel="noopener noreferrer">here</a>. You can also use the <code>CleanlabRemediator</code> component with this one to remediate low-trust responses coming from the RAG pipeline.</p>
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<h4 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="inputs-2">Inputs<a href="#inputs-2" class="hash-link" aria-label="Direct link to Inputs" title="Direct link to Inputs"></a></h4>
|
||
<table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>cleanlab_api_key</td><td>Secret</td><td>Your Cleanlab API key</td></tr><tr><td>cleanlab_evaluation_model</td><td>Dropdown</td><td>Evaluation model used by Cleanlab (GPT-4, Claude, etc.) This does not need to be the same model that generated the response.</td></tr><tr><td>quality_preset</td><td>Dropdown</td><td>Tradeoff between evaluation speed and accuracy</td></tr><tr><td>context</td><td>Message</td><td>Retrieved context from your RAG system</td></tr><tr><td>query</td><td>Message</td><td>The original user query</td></tr><tr><td>response</td><td>Message</td><td>OpenAI's, Claude, etc. model's response based on the context and query</td></tr><tr><td>run_context_sufficiency</td><td>bool</td><td>Evaluate whether context supports answering the query</td></tr><tr><td>run_response_groundedness</td><td>bool</td><td>Evaluate whether the response is grounded in the context</td></tr><tr><td>run_response_helpfulness</td><td>bool</td><td>Evaluate how helpful the response is</td></tr><tr><td>run_query_ease</td><td>bool</td><td>Evaluate if the query is vague, complex, or adversarial</td></tr></tbody></table>
|
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<h4 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="outputs-1">Outputs<a href="#outputs-1" class="hash-link" aria-label="Direct link to Outputs" title="Direct link to Outputs"></a></h4>
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<table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>trust_score</td><td>number</td><td>Overall trust score</td></tr><tr><td>trust_explanation</td><td>Message</td><td>Explanation for trust score</td></tr><tr><td>other_scores</td><td>dict</td><td>Dictionary of optional enabled RAG evaluation metrics</td></tr><tr><td>evaluation_summary</td><td>Message</td><td>Markdown summary of query, context, response, and evaluation results</td></tr></tbody></table>
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||
<hr>
|
||
<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="example-flows">Example Flows<a href="#example-flows" class="hash-link" aria-label="Direct link to Example Flows" title="Direct link to Example Flows"></a></h2>
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||
<p>The following example flows show how to use the <code>CleanlabEvaluator</code> and <code>CleanlabRemediator</code> components to evaluate and remediate responses from any LLM, and how to use the <code>CleanlabRAGEvaluator</code> component to evaluate RAG pipeline outputs.</p>
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||
<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="evaluate-and-remediate-responses-from-any-llm">Evaluate and remediate responses from any LLM<a href="#evaluate-and-remediate-responses-from-any-llm" class="hash-link" aria-label="Direct link to Evaluate and remediate responses from any LLM" title="Direct link to Evaluate and remediate responses from any LLM"></a></h3>
|
||
<p><a href="/assets/files/eval_and_remediate_cleanlab-aac380677836d5237ad3db4405aae6f7.json" target="_blank">Download</a> the flow to follow along!</p>
|
||
<p>This flow evaluates and remediates the trustworthiness of a response from any LLM using the <code>CleanlabEvaluator</code> and <code>CleanlabRemediator</code> components.</p>
|
||
<p><img decoding="async" loading="lazy" alt="Evaluate response trustworthiness" src="/assets/images/eval_response-5947d6e0eb45b0feec6a36d4b489bf99.png" width="3092" height="1468" class="img_ev3q"></p>
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<p>Simply connect the <code>Message</code> output from any LLM component (like OpenAI, Anthropic, or Google) to the <code>response</code> input of the <code>CleanlabEvaluator</code> component, along with connecting your prompt to its <code>prompt</code> input.</p>
|
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<p>That's it! The <code>CleanlabEvaluator</code> component will return a trust score and explanation which you can use however you'd like.</p>
|
||
<p>The <code>CleanlabRemediator</code> component uses this trust score and user configurable settings to determine whether to output the original response, warn about it, or replace it with a fallback answer.</p>
|
||
<p>The example below shows a response that was determined to be untrustworthy (score of .09) and flagged with a warning by the <code>CleanlabRemediator</code> component.</p>
|
||
<p><img decoding="async" loading="lazy" alt="CleanlabRemediator Example" src="/assets/images/cleanlab_remediator_example-5d7e2d1782b3fe5e7ef8abd73d46e628.png" width="1890" height="1322" class="img_ev3q"></p>
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<p>If you don't want to show untrustworthy responses, you can also configure the <code>CleanlabRemediator</code> to replace the response with a fallback message.</p>
|
||
<p><img decoding="async" loading="lazy" alt="CleanlabRemediator Example" src="/assets/images/cleanlab_remediator_example_fallback-7c585c112d908654873c27eb21de1702.png" width="1654" height="416" class="img_ev3q"></p>
|
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<h3 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="evaluate-rag-pipeline">Evaluate RAG pipeline<a href="#evaluate-rag-pipeline" class="hash-link" aria-label="Direct link to Evaluate RAG pipeline" title="Direct link to Evaluate RAG pipeline"></a></h3>
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<p>The below flow is the <code>Vector Store RAG</code> example template, with the <code>CleanlabRAGEvaluator</code> component added to evaluate the context, query, and response. You can use the <code>CleanlabRAGEvaluator</code> with any flow that has a context, query, and response. Simply connect the <code>context</code>, <code>query</code>, and <code>response</code> outputs from any RAG pipeline to the <code>CleanlabRAGEvaluator</code> component.</p>
|
||
<p><img decoding="async" loading="lazy" alt="Evaluate RAG pipeline" src="/assets/images/eval_rag-a8bfd2d0d1ac01168ca183f856c14545.png" width="3058" height="1274" class="img_ev3q"></p>
|
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<p>Here is an example of the <code>Evaluation Summary</code> output from the <code>CleanlabRAGEvaluator</code> component.</p>
|
||
<p><img decoding="async" loading="lazy" alt="Evaluate RAG pipeline" src="/assets/images/eval_summary_rag-a48e121abf7d5f2761fc1022c61be17d.png" width="1666" height="1568" class="img_ev3q"></p>
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<p>Notice how the <code>Evaluation Summary</code> includes the query, context, response, and all the evaluation results! In this example, the <code>Context Sufficiency</code> and <code>Response Groundedness</code> scores are low (0.002) because the context doesn't contain information about the query and the response is not grounded in the context.</p></div></article><nav class="pagination-nav docusaurus-mt-lg" aria-label="Docs pages"><a class="pagination-nav__link pagination-nav__link--prev" href="/integrations-composio"><div class="pagination-nav__sublabel">Previous</div><div class="pagination-nav__label">Composio</div></a><a class="pagination-nav__link pagination-nav__link--next" href="/integrations-setup-google-oauth-langflow"><div class="pagination-nav__sublabel">Next</div><div class="pagination-nav__label">Integrate Google OAuth with Langflow</div></a></nav></div></div><div class="col col--3"><div class="tableOfContents_bqdL thin-scrollbar theme-doc-toc-desktop"><ul class="table-of-contents table-of-contents__left-border"><li><a href="#prerequisites" class="table-of-contents__link toc-highlight">Prerequisites</a></li><li><a href="#components" class="table-of-contents__link toc-highlight">Components</a><ul><li><a href="#cleanlabevaluator" class="table-of-contents__link toc-highlight"><code>CleanlabEvaluator</code></a></li><li><a href="#cleanlabremediator" class="table-of-contents__link toc-highlight"><code>CleanlabRemediator</code></a></li><li><a href="#cleanlabragevaluator" class="table-of-contents__link toc-highlight"><code>CleanlabRAGEvaluator</code></a></li></ul></li><li><a href="#example-flows" class="table-of-contents__link toc-highlight">Example Flows</a><ul><li><a href="#evaluate-and-remediate-responses-from-any-llm" class="table-of-contents__link toc-highlight">Evaluate and remediate responses from any LLM</a></li><li><a href="#evaluate-rag-pipeline" class="table-of-contents__link toc-highlight">Evaluate RAG pipeline</a></li></ul></li></ul></div></div></div></div></main></div></div></div><footer class="footer"><div class="container container-fluid"><div class="row footer__links"><div class="col footer__col"><div class="footer__title"></div><ul class="footer__items clean-list"><li class="footer__item"><div class="footer-links">
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