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<p>Build a <strong>Simple Agent</strong> flow for an agentic application using the Tool-calling agent.</p>
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<p>An <strong>agent</strong> uses an LLM as its "brain" to select among the connected tools and complete its tasks.</p>
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<p>In this flow, the <strong>Tool-calling agent</strong> reasons using an <strong>Open AI</strong> LLM to solve math problems. It will select the <strong>Calculator</strong> tool for simpler math, and the <strong>Python REPL</strong> tool (with the Python <code>math</code> library) for more complex problems.</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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<p>To use this flow, you need an OpenAI API key.</p>
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<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="open-langflow-and-start-a-new-flow">Open Langflow and start a new flow<a href="#open-langflow-and-start-a-new-flow" class="hash-link" aria-label="Direct link to Open Langflow and start a new flow" title="Direct link to Open Langflow and start a new flow"></a></h2>
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<p>Click <strong>New Flow</strong>, and then select the <strong>Simple Agent</strong> flow.</p>
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<p>This opens a starter flow with the necessary components to run an agentic application using the Tool-calling agent.</p>
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<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="simple-agent-flow">Simple Agent flow<a href="#simple-agent-flow" class="hash-link" aria-label="Direct link to Simple Agent flow" title="Direct link to Simple Agent flow"></a></h2>
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<p><img decoding="async" loading="lazy" src="/assets/images/starter-flow-simple-agent-6c6df85ce6d01bfafebe3e4247cefc83.png" width="2104" height="1746" class="img_ev3q"></p>
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<p>The <strong>Simple Agent</strong> flow consists of these components:</p>
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<ul>
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<li>The <strong>Tool calling agent</strong> component uses the connected LLM to reason through the user's input and select among the connected tools to complete its task.</li>
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<li>The <strong>Python REPL tool</strong> component executes Python code in a REPL (Read-Evaluate-Print Loop) interpreter.</li>
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<li>The <strong>Calculator</strong> component performs basic arithmetic operations.</li>
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<li>The <strong>Chat Input</strong> component accepts user input to the chat.</li>
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<li>The <strong>Prompt</strong> component combines the user input with a user-defined prompt.</li>
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<li>The <strong>Chat Output</strong> component prints the flow's output to the chat.</li>
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<li>The <strong>OpenAI</strong> model component sends the user input and prompt to the OpenAI API and receives a response.</li>
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</ul>
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<h2 class="anchor anchorWithHideOnScrollNavbar_WYt5" id="run-the-simple-agent-flow">Run the Simple Agent flow<a href="#run-the-simple-agent-flow" class="hash-link" aria-label="Direct link to Run the Simple Agent flow" title="Direct link to Run the Simple Agent flow"></a></h2>
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<ol>
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<li>Add your credentials to the Open AI component.</li>
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<li>In the <strong>Chat output</strong> component, click ▶️ Play to start the end-to-end application flow.
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A <strong>Chat output built successfully</strong> message and a ✅ Check on all components indicate that the flow ran successfully.</li>
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<li>Click <strong>Playground</strong> to start a chat session.</li>
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||
<li>Enter a simple math problem, like <code>2 + 2</code>, and then make sure the bot responds with the correct answer.</li>
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||
<li>To confirm the REPL interpreter is working, prompt the <code>math</code> library directly with <code>math.sqrt(4)</code> and see if the bot responds with <code>4</code>.</li>
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||
<li>The agent will also reason through more complex word problems. For example, prompt the agent with the following math problem:</li>
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||
</ol>
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||
<div class="ch-codeblock not-prose" data-ch-theme="github-dark"><div class="ch-code-wrapper ch-code" data-ch-measured="false"><code class="ch-code-scroll-parent"><br><div><span class="ch-code-line-number">_<!-- -->10</span><div style="display:inline-block;margin-left:16px"><span>The equation 24x2+25x−47ax−2=−8x−3−53ax−2 is true for all values of x≠2a, where a is a constant.</span></div></div><div><span class="ch-code-line-number">_<!-- -->10</span><div style="display:inline-block;margin-left:16px"><span>What is the value of a?</span></div></div><div><span class="ch-code-line-number">_<!-- -->10</span><div style="display:inline-block;margin-left:16px"><span>A) -16</span></div></div><div><span class="ch-code-line-number">_<!-- -->10</span><div style="display:inline-block;margin-left:16px"><span>B) -3</span></div></div><div><span class="ch-code-line-number">_<!-- -->10</span><div style="display:inline-block;margin-left:16px"><span>C) 3</span></div></div><div><span class="ch-code-line-number">_<!-- -->10</span><div style="display:inline-block;margin-left:16px"><span>D) 16</span></div></div><br></code></div></div>
|
||
<p>The agent should respond with <code>B</code>.</p>
|
||
<p>Now that your query has completed the journey from <strong>Chat input</strong> to <strong>Chat output</strong>, you have completed the <strong>Simple Agent</strong> flow.</p></div></article><nav class="pagination-nav docusaurus-mt-lg" aria-label="Docs pages"><a class="pagination-nav__link pagination-nav__link--prev" href="/starter-projects-vector-store-rag"><div class="pagination-nav__sublabel">Previous</div><div class="pagination-nav__label">Vector Store RAG</div></a><a class="pagination-nav__link pagination-nav__link--next" href="/tutorials-blog-writer"><div class="pagination-nav__sublabel">Next</div><div class="pagination-nav__label">Blog Writer</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="#open-langflow-and-start-a-new-flow" class="table-of-contents__link toc-highlight">Open Langflow and start a new flow</a></li><li><a href="#simple-agent-flow" class="table-of-contents__link toc-highlight">Simple Agent flow</a></li><li><a href="#run-the-simple-agent-flow" class="table-of-contents__link toc-highlight">Run the Simple Agent flow</a></li></ul></div></div></div></div></main></div></div></div><div id="ms-floating-button" class="ms-fixed ms-bottom-4 ms-right-8 ms-z-[100] ms-flex ms-flex-col ms-items-end hover:ms-cursor-pointer"><div class="ms-relative ms-mb-4"><div style="background-color:#f6f6f6" class="ms-absolute -ms-bottom-1 ms-right-6 -ms-z-10 ms-h-4 ms-w-4 ms-rotate-45"></div><div style="background-color:#f6f6f6" class="ms-z-10 ms-flex ms-items-center ms-justify-center ms-rounded-lg ms-px-3 ms-py-2"><p style="color:#000000;margin:0" class="ms-text-sm">Hi, how can I help you?</p></div></div><div style="background-color:#f6f6f6" class="ms-flex ms-h-16 ms-w-16 ms-flex-col ms-items-center ms-justify-center ms-self-end ms-rounded-full ms-p-2 ms-transition"><div class="ms-rotate-0 ms-flex ms-h-full ms-w-full ms-transform ms-cursor-pointer ms-items-center ms-justify-center ms-rounded-full ms-transition-all ms-delay-75 ms-duration-100 ms-ease-in-out"><div style="color:#000000;font-size:22px;width:48px;height:48px;margin:0px;padding:0px;display:flex;align-items:center;justify-content:center;text-align:center"><img src="/img/langflow-icon-black-transparent.svg" srcdark="" style="width:40px"></div></div></div><div></div></div></div>
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