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<title data-rh="true">Agents powered by Langflow | OpenRAG</title><meta data-rh="true" name="viewport" content="width=device-width,initial-scale=1"><meta data-rh="true" name="twitter:card" content="summary_large_image"><meta data-rh="true" property="og:url" content="https://langflow-ai.github.io/openrag/agents"><meta data-rh="true" property="og:locale" content="en"><meta data-rh="true" name="docusaurus_locale" content="en"><meta data-rh="true" name="docsearch:language" content="en"><meta data-rh="true" name="docusaurus_version" content="current"><meta data-rh="true" name="docusaurus_tag" content="docs-default-current"><meta data-rh="true" name="docsearch:version" content="current"><meta data-rh="true" name="docsearch:docusaurus_tag" content="docs-default-current"><meta data-rh="true" property="og:title" content="Agents powered by Langflow | OpenRAG"><meta data-rh="true" name="description" content="OpenRAG leverages Langflow's Agent component to power the OpenRAG OpenSearch Agent flow."><meta data-rh="true" property="og:description" content="OpenRAG leverages Langflow's Agent component to power the OpenRAG OpenSearch Agent flow."><link data-rh="true" rel="icon" href="/openrag/img/favicon.ico"><link data-rh="true" rel="canonical" href="https://langflow-ai.github.io/openrag/agents"><link data-rh="true" rel="alternate" href="https://langflow-ai.github.io/openrag/agents" hreflang="en"><link data-rh="true" rel="alternate" href="https://langflow-ai.github.io/openrag/agents" hreflang="x-default"><script data-rh="true" type="application/ld+json">{"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Langflow Agents","item":"https://langflow-ai.github.io/openrag/agents"}]}</script><link rel="stylesheet" href="/openrag/assets/css/styles.4376ff55.css">
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href="/openrag/agents">Langflow Agents</a></li><li class="theme-doc-sidebar-item-link theme-doc-sidebar-item-link-level-2 menu__list-item"><a class="menu__link" tabindex="0" href="/openrag/knowledge">OpenSearch Knowledge</a></li><li class="theme-doc-sidebar-item-link theme-doc-sidebar-item-link-level-2 menu__list-item"><a class="menu__link" tabindex="0" href="/openrag/ingestion">Docling Ingestion</a></li></ul></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist menu__link--sublist-caret" role="button" aria-expanded="false" href="/openrag/configure/configuration">Configuration</a></div></li><li class="theme-doc-sidebar-item-category theme-doc-sidebar-item-category-level-1 menu__list-item menu__list-item--collapsed"><div class="menu__list-item-collapsible"><a class="menu__link menu__link--sublist menu__link--sublist-caret" role="button" aria-expanded="false" href="/openrag/support/troubleshoot">Support</a></div></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="/openrag/"><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">Core components</span></li><li class="breadcrumbs__item breadcrumbs__item--active"><span class="breadcrumbs__link">Langflow Agents</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>Agents powered by Langflow</h1></header><p>OpenRAG leverages Langflow's Agent component to power the OpenRAG OpenSearch Agent flow.</p>
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<p>This flow intelligently chats with your knowledge by embedding your query, comparing it the vector database embeddings, and generating a response with the LLM.</p>
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<p>The Agent component shines here in its ability to make decisions on not only what query should be sent, but when a query is necessary to solve the problem at hand.</p>
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<details class="details_lb9f alert alert--info details_b_Ee" data-collapsed="true"><summary>How do agents work?</summary><div><div class="collapsibleContent_i85q"><p>Agents extend Large Language Models (LLMs) by integrating tools, which are functions that provide additional context and enable autonomous task execution. These integrations make agents more specialized and powerful than standalone LLMs.</p><p>Whereas an LLM might generate acceptable, inert responses to general queries and tasks, an agent can leverage the integrated context and tools to provide more relevant responses and even take action. For example, you might create an agent that can access your company's documentation, repositories, and other resources to help your team with tasks that require knowledge of your specific products, customers, and code.</p><p>Agents use LLMs as a reasoning engine to process input, determine which actions to take to address the query, and then generate a response. The response could be a typical text-based LLM response, or it could involve an action, like editing a file, running a script, or calling an external API.</p><p>In an agentic context, tools are functions that the agent can run to perform tasks or access external resources. A function is wrapped as a Tool object with a common interface that the agent understands. Agents become aware of tools through tool registration, which is when the agent is provided a list of available tools typically at agent initialization. The Tool object's description tells the agent what the tool can do so that it can decide whether the tool is appropriate for a given request.</p></div></div></details>
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<h2 class="anchor anchorWithStickyNavbar_LWe7" id="use-the-openrag-opensearch-agent-flow">Use the OpenRAG OpenSearch Agent flow<a href="#use-the-openrag-opensearch-agent-flow" class="hash-link" aria-label="Direct link to Use the OpenRAG OpenSearch Agent flow" title="Direct link to Use the OpenRAG OpenSearch Agent flow"></a></h2>
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<p>If you've chatted with your knowledge in OpenRAG, you've already experienced the OpenRAG OpenSearch Agent chat flow.
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To view the flow, click <svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-settings2 lucide-settings-2" aria-hidden="true"><path d="M14 17H5"></path><path d="M19 7h-9"></path><circle cx="17" cy="17" r="3"></circle><circle cx="7" cy="7" r="3"></circle></svg> <strong>Settings</strong>, and then click <strong>Edit in Langflow</strong>.
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This flow contains seven components:</p>
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<ul>
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<li>The Agent component orchestrates the entire flow by deciding when to search the knowledge base, how to formulate search queries, and how to combine retrieved information with the user's question to generate a comprehensive response.
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The Agent behaves according to the prompt in the <strong>Agent Instructions</strong> field.</li>
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<li>The Chat Input component is connected to the Agent component's Input port. This allows to flow to be triggered by an incoming prompt from a user or application.</li>
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<li>The OpenSearch component is connected to the Agent component's Tools port. The agent may not use this database for every request; the agent only uses this connection if it decides the knowledge can help respond to the prompt.</li>
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<li>The Language Model component is connected to the Agent component's Language Model port. The agent uses the connected LLM to reason through the request sent through Chat Input.</li>
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<li>The Embedding Model component is connected to the OpenSearch component's Embedding port. This component converts text queries into vector representations that are compared with document embeddings stored in OpenSearch for semantic similarity matching. This gives your Agent's queries context.</li>
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<li>The Text Input component is populated with the global variable <code>OPENRAG-QUERY-FILTER</code>.
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This filter is the Knowledge filter, and filters which knowledge sources to search through.</li>
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<li>The Agent component's Output port is connected to the Chat Output component, which returns the final response to the user or application.</li>
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</ul>
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<p>All flows included with OpenRAG are designed to be modular, performant, and provider-agnostic.
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To modify a flow, click <svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-settings2 lucide-settings-2" aria-hidden="true"><path d="M14 17H5"></path><path d="M19 7h-9"></path><circle cx="17" cy="17" r="3"></circle><circle cx="7" cy="7" r="3"></circle></svg> <strong>Settings</strong>, and click <strong>Edit in Langflow</strong>.
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Flows are edited in the same way as in the <a href="https://docs.langflow.org/concepts-overview" target="_blank" rel="noopener noreferrer">Langflow visual editor</a>.</p>
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<p>For an example of changing out the agent's LLM in OpenRAG, see the <a href="/openrag/quickstart#change-components">Quickstart</a>.</p>
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<p>To restore the flow to its initial state, in OpenRAG, click <svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-settings" aria-hidden="true"><path d="M9.671 4.136a2.34 2.34 0 0 1 4.659 0 2.34 2.34 0 0 0 3.319 1.915 2.34 2.34 0 0 1 2.33 4.033 2.34 2.34 0 0 0 0 3.831 2.34 2.34 0 0 1-2.33 4.033 2.34 2.34 0 0 0-3.319 1.915 2.34 2.34 0 0 1-4.659 0 2.34 2.34 0 0 0-3.32-1.915 2.34 2.34 0 0 1-2.33-4.033 2.34 2.34 0 0 0 0-3.831A2.34 2.34 0 0 1 6.35 6.051a2.34 2.34 0 0 0 3.319-1.915"></path><circle cx="12" cy="12" r="3"></circle></svg> <strong>Settings</strong>, and then click <strong>Restore Flow</strong>.
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OpenRAG warns you that this discards all custom settings. Click <strong>Restore</strong> to restore the flow.</p></div><footer class="theme-doc-footer docusaurus-mt-lg"><div class="row margin-top--sm theme-doc-footer-edit-meta-row"><div class="col"><a href="https://github.com/openrag/openrag/tree/main/docs/docs/core-components/agents.mdx" target="_blank" rel="noopener noreferrer" class="theme-edit-this-page"><svg fill="currentColor" height="20" width="20" viewBox="0 0 40 40" class="iconEdit_Z9Sw" aria-hidden="true"><g><path d="m34.5 11.7l-3 3.1-6.3-6.3 3.1-3q0.5-0.5 1.2-0.5t1.1 0.5l3.9 3.9q0.5 0.4 0.5 1.1t-0.5 1.2z m-29.5 17.1l18.4-18.5 6.3 6.3-18.4 18.4h-6.3v-6.2z"></path></g></svg>Edit this page</a></div><div class="col lastUpdated_JAkA"></div></div></footer></article><nav class="docusaurus-mt-lg pagination-nav" aria-label="Docs pages"><a class="pagination-nav__link pagination-nav__link--prev" href="/openrag/get-started/tui"><div class="pagination-nav__sublabel">Previous</div><div class="pagination-nav__label">Terminal Interface (TUI)</div></a><a class="pagination-nav__link pagination-nav__link--next" href="/openrag/knowledge"><div class="pagination-nav__sublabel">Next</div><div class="pagination-nav__label">OpenSearch Knowledge</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="#use-the-openrag-opensearch-agent-flow" class="table-of-contents__link toc-highlight">Use the OpenRAG OpenSearch Agent flow</a></li></ul></div></div></div></div></main></div></div></div><footer class="theme-layout-footer footer footer--dark"><div class="container container-fluid"><div class="row footer__links"><div class="theme-layout-footer-column col footer__col"><div class="footer__title">Documentation</div><ul class="footer__items clean-list"><li class="footer__item"><a class="footer__link-item" href="/openrag/">Getting Started</a></li></ul></div><div class="theme-layout-footer-column col footer__col"><div class="footer__title">Community</div><ul class="footer__items clean-list"><li class="footer__item"><a href="https://github.com/openrag/openrag" target="_blank" rel="noopener noreferrer" class="footer__link-item">GitHub<svg width="13.5" height="13.5" aria-hidden="true" class="iconExternalLink_nPIU"><use href="#theme-svg-external-link"></use></svg></a></li><li class="footer__item"><a href="https://discord.gg/openrag" target="_blank" rel="noopener noreferrer" class="footer__link-item">Discord<svg width="13.5" height="13.5" aria-hidden="true" class="iconExternalLink_nPIU"><use href="#theme-svg-external-link"></use></svg></a></li></ul></div></div><div class="footer__bottom text--center"><div class="footer__copyright">Copyright © 2025 OpenRAG. Built with Docusaurus.</div></div></div></footer></div>
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