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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"><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></li><li class="breadcrumbs__item breadcrumbs__item--active"><span class="breadcrumbs__link">Cleanlab</span></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 three Langflow components that assess and improve the trustworthiness of any LLM or RAG pipeline output.</p>
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<p>Use the components in this bundle to quantify the trustworthiness of any LLM response with a score between <code>0</code> and <code>1</code>, and explain why a response may be good or bad. For RAG/Agentic pipelines with context, you can evaluate context sufficiency, groundedness, helpfulness, and query clarity with quantitative scores. Additionally, you can remediate low-trust responses with warnings or fallback answers.</p>
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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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<ul>
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<li><a href="https://tlm.cleanlab.ai/" target="_blank" rel="noopener noreferrer">A Cleanlab API key</a></li>
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</ul>
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<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="cleanlabevaluator">CleanlabEvaluator<a href="#cleanlabevaluator" class="hash-link" aria-label="Direct link to CleanlabEvaluator" title="Direct link to CleanlabEvaluator"></a></h2>
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<p>This component evaluates and explains the trustworthiness of a prompt and response pair using Cleanlab. For more information on how the score works, see the <a href="https://help.cleanlab.ai/tlm/" target="_blank" rel="noopener noreferrer">Cleanlab documentation</a>.</p>
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<details class="details_lb9f alert alert--info details_b_Ee" data-collapsed="true"><summary>Parameters</summary><div><div class="collapsibleContent_i85q"><p><strong>Inputs</strong></p><table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>system_prompt</td><td>Message</td><td>The system message prepended to the prompt. Optional.</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>The 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, such as GPT-4 or Claude. 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><p><strong>Outputs</strong></p><table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>score</td><td>number</td><td>Displays the trust score between <code>0–1</code>.</td></tr><tr><td>explanation</td><td>Message</td><td>Provides an explanation of the trust score.</td></tr><tr><td>response</td><td>Message</td><td>Returns the original response for easy chaining to the <code>CleanlabRemediator</code> component.</td></tr></tbody></table></div></div></details>
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<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="cleanlabremediator">CleanlabRemediator<a href="#cleanlabremediator" class="hash-link" aria-label="Direct link to CleanlabRemediator" title="Direct link to CleanlabRemediator"></a></h2>
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<p>This component uses the trust score from the <a href="#cleanlabevaluator">CleanlabEvaluator</a> 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 that you can customize as needed.</p>
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<details class="details_lb9f alert alert--info details_b_Ee" data-collapsed="true"><summary>Parameters</summary><div><div class="collapsibleContent_i85q"><p><strong>Inputs</strong></p><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>The trust score from <code>CleanlabEvaluator</code>.</td></tr><tr><td>explanation</td><td>Message</td><td>The explanation to append if a warning is shown. Optional.</td></tr><tr><td>threshold</td><td>float</td><td>The minimum trust score to pass a response unchanged.</td></tr><tr><td>show_untrustworthy_response</td><td>bool</td><td>Whether to display or hide the original response with a warning if a response is deemed untrustworthy.</td></tr><tr><td>untrustworthy_warning_text</td><td>Prompt</td><td>The warning text for untrustworthy responses.</td></tr><tr><td>fallback_text</td><td>Prompt</td><td>The fallback message if the response is hidden.</td></tr></tbody></table><p><strong>Outputs</strong></p><table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>remediated_response</td><td>Message</td><td>The final message shown to user after remediation logic.</td></tr></tbody></table></div></div></details>
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<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="cleanlabragevaluator">CleanlabRAGEvaluator<a href="#cleanlabragevaluator" class="hash-link" aria-label="Direct link to CleanlabRAGEvaluator" title="Direct link to CleanlabRAGEvaluator"></a></h2>
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<p>This component evaluates RAG and LLM pipeline outputs for 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>.</p>
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<p>Additionally, use the <a href="#cleanlabremediator">CleanlabRemediator</a> component with this component to remediate low-trust responses coming from the RAG pipeline.</p>
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<details class="details_lb9f alert alert--info details_b_Ee" data-collapsed="true"><summary>Parameters</summary><div><div class="collapsibleContent_i85q"><p><strong>Inputs</strong></p><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>Thevaluation model used by Cleanlab, such as GPT-4, or Claude. This does not need to be the same model that generated the response.</td></tr><tr><td>quality_preset</td><td>Dropdown</td><td>The tradeoff between evaluation speed and accuracy.</td></tr><tr><td>context</td><td>Message</td><td>The 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>The 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><p><strong>Outputs</strong></p><table><thead><tr><th>Name</th><th>Type</th><th>Description</th></tr></thead><tbody><tr><td>trust_score</td><td>number</td><td>The overall trust score.</td></tr><tr><td>trust_explanation</td><td>Message</td><td>The explanation for the trust score.</td></tr><tr><td>other_scores</td><td>dict</td><td>A dictionary of optional enabled RAG evaluation metrics.</td></tr><tr><td>evaluation_summary</td><td>Message</td><td>A Markdown summary of query, context, response, and evaluation results.</td></tr></tbody></table></div></div></details>
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<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="cleanlab-component-example-flows">Cleanlab component example flows<a href="#cleanlab-component-example-flows" class="hash-link" aria-label="Direct link to Cleanlab component example flows" title="Direct link to Cleanlab component example flows"></a></h2>
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<p>The following example flows show how to use the <strong>CleanlabEvaluator</strong> and <strong>CleanlabRemediator</strong> 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-an-llm">Evaluate and remediate responses from an LLM<a href="#evaluate-and-remediate-responses-from-an-llm" class="hash-link" aria-label="Direct link to Evaluate and remediate responses from an LLM" title="Direct link to Evaluate and remediate responses from an LLM"></a></h3>
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<div class="theme-admonition theme-admonition-tip admonition_xJq3 alert alert--success"><div class="admonitionHeading_Gvgb"><span class="admonitionIcon_Rf37"><svg viewBox="0 0 12 16"><path fill-rule="evenodd" d="M6.5 0C3.48 0 1 2.19 1 5c0 .92.55 2.25 1 3 1.34 2.25 1.78 2.78 2 4v1h5v-1c.22-1.22.66-1.75 2-4 .45-.75 1-2.08 1-3 0-2.81-2.48-5-5.5-5zm3.64 7.48c-.25.44-.47.8-.67 1.11-.86 1.41-1.25 2.06-1.45 3.23-.02.05-.02.11-.02.17H5c0-.06 0-.13-.02-.17-.2-1.17-.59-1.83-1.45-3.23-.2-.31-.42-.67-.67-1.11C2.44 6.78 2 5.65 2 5c0-2.2 2.02-4 4.5-4 1.22 0 2.36.42 3.22 1.19C10.55 2.94 11 3.94 11 5c0 .66-.44 1.78-.86 2.48zM4 14h5c-.23 1.14-1.3 2-2.5 2s-2.27-.86-2.5-2z"></path></svg></span>tip</div><div class="admonitionContent_BuS1"><p>Optionally, <a href="/assets/files/eval_and_remediate_cleanlab-aac380677836d5237ad3db4405aae6f7.json" target="_blank">Download</a> the Evaluate and Remediate flow and follow along.</p></div></div>
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<p>This flow evaluates and remediates the trustworthiness of a response from any LLM using the <strong>CleanlabEvaluator</strong> and <strong>CleanlabRemediator</strong> components.</p>
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<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>Connect the <code>Message</code> output from any LLM component to the <code>response</code> input of the <strong>CleanlabEvaluator</strong> component, and then connect the Prompt component to its <code>prompt</code> input.</p>
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<p>The <strong>CleanlabEvaluator</strong> component returns a trust score and explanation from the flow.</p>
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<p>The <strong>CleanlabRemediator</strong> component uses this trust score to determine whether to output the original response, warn about it, or replace it with a fallback answer.</p>
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<p>This example shows a response that was determined to be untrustworthy (a score of <code>.09</code>) and flagged with a warning by the <strong>CleanlabRemediator</strong> component.</p>
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<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>To hide untrustworthy responses, configure the <strong>CleanlabRemediator</strong> component to replace the response with a fallback message.</p>
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<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>This example flow includes the <a href="/vector-store-rag">Vector Store RAG</a> template with the <strong>CleanlabRAGEvaluator</strong> component added to evaluate the flow's context, query, and response.</p>
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<p>To use the <strong>CleanlabRAGEvaluator</strong> component in a flow, connect the <code>context</code>, <code>query</code>, and <code>response</code> outputs from any RAG pipeline to the <strong>CleanlabRAGEvaluator</strong> component.</p>
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<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 <strong>CleanlabRAGEvaluator</strong> component.</p>
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<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>The <code>Evaluation Summary</code> includes the query, context, response, and all evaluation results. In this example, the <code>Context Sufficiency</code> and <code>Response Groundedness</code> scores are low (a score of <code>0.002</code>) because the context doesn't contain information about the query, and the response is not grounded in the context.</p></div></article><nav class="docusaurus-mt-lg pagination-nav" aria-label="Docs pages"><a class="pagination-nav__link pagination-nav__link--prev" href="/mcp-component-astra"><div class="pagination-nav__sublabel">Previous</div><div class="pagination-nav__label">Astra DB MCP server</div></a><a class="pagination-nav__link pagination-nav__link--next" href="/integrations-composio"><div class="pagination-nav__sublabel">Next</div><div class="pagination-nav__label">Composio</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="#cleanlabevaluator" class="table-of-contents__link toc-highlight">CleanlabEvaluator</a></li><li><a href="#cleanlabremediator" class="table-of-contents__link toc-highlight">CleanlabRemediator</a></li><li><a href="#cleanlabragevaluator" class="table-of-contents__link toc-highlight">CleanlabRAGEvaluator</a></li><li><a href="#cleanlab-component-example-flows" class="table-of-contents__link toc-highlight">Cleanlab component example flows</a><ul><li><a href="#evaluate-and-remediate-responses-from-an-llm" class="table-of-contents__link toc-highlight">Evaluate and remediate responses from an 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="theme-layout-footer footer"><div class="container container-fluid"><div class="row footer__links"><div class="theme-layout-footer-column 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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