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<!DOCTYPE html><html lang="en"> <head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><link rel="icon" href="/assets/favicon.ico"/><script defer="" data-domain="star-history.com" src="https://plausible.io/js/script.js"></script><meta name="next-head-count" content="4"/><link data-next-font="size-adjust" rel="preconnect" href="/" crossorigin="anonymous"/><link rel="preload" href="/_next/static/css/f94657194d4c857a.css" as="style" crossorigin=""/><link rel="stylesheet" href="/_next/static/css/f94657194d4c857a.css" crossorigin="" data-n-g=""/><noscript data-n-css=""></noscript><script defer="" crossorigin="" nomodule="" src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js"></script><script src="/_next/static/chunks/webpack-38cee4c0e358b1a3.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/framework-fda0a023b274c574.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/main-001c9e19b1894c7d.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/_app-915effad870aa62e.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/6c86d9ce-d8b7531786dd65a5.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/472-8057db644de3d496.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/590-d0a3c67c09cc0662.js" defer="" crossorigin=""></script><script src="/_next/static/chunks/pages/blog/%5Bslug%5D-7b378153203b51eb.js" defer="" crossorigin=""></script><script src="/_next/static/xKX4ZiOi_N7h3OBOEsSZu/_buildManifest.js" defer="" crossorigin=""></script><script src="/_next/static/xKX4ZiOi_N7h3OBOEsSZu/_ssgManifest.js" defer="" crossorigin=""></script></head><body><div id="__next"><div class="relative w-full h-auto min-h-screen overflow-auto flex flex-col"><title>Starlet #27 Langfuse: Open-source LLM Observability</title><meta name="description" content="Langfuse is a LLM engineering platform that helps understand LLM applications and offers ways to improve them."/><meta property="og:type" content="website"/><meta property="og:url" content="https://star-history.com/blog/langfuse"/><meta property="og:title" content="Starlet #27 Langfuse: Open-source LLM Observability"/><meta property="og:description" content="Langfuse is a LLM engineering platform that helps understand LLM applications and offers ways to improve them."/><meta property="og:image" content="https://star-history.com/assets/blog/langfuse/banner.webp"/><meta name="twitter:card" content="summary_large_image"/><meta name="twitter:url" content="https://star-history.com/blog/langfuse"/><meta name="twitter:title" content="Starlet #27 Langfuse: Open-source LLM Observability"/><meta name="twitter:description" content="Langfuse is a LLM engineering platform that helps understand LLM applications and offers ways to improve them."/><meta name="twitter:image" content="https://star-history.com/assets/blog/langfuse/banner.webp"/><nav><div class="flex justify-center items-center gap-x-6 bg-green-600 px-6 py-1 sm:px-3.5 "><p class="text-sm leading-6 text-white"><a href="/blog/list-your-open-source-project">Want to promote your open source project? 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hover:underline">Issue #28 - Trench</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/langfuse"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #27 - langfuse</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/thepipe"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #26 - thepi.pe</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/taipy"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #25 - Taipy</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/superlinked"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #24 - Superlinked</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/tea-tasting"><span class="inline -ml-2 text-sm 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text-sm text-blue-700 hover:underline">Issue #13 - Earthly</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/wasp"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #12 - Wasp</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/libsql"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #11 - libSQL</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/postgresml"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #10 - PostgresML</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/electricsql"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #9 - ElectricSQL</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/prompt-flow"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #8 - Prompt flow</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/clipboard"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #7 - Clipboard</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/hoppscotch"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #6 - Hoppscotch</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/metisfl"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #5 - MetisFL</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/chatgpt-js"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #4 - chatgpt.js</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/mockoon"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #3 - Mockoon</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/dlta-ai"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #2 - DLTA-AI</span></a></li><li class="mb-2 leading-3"><a class="cursor-pointer" rel="noopener noreferrer" href="/blog/sniffnet"><span class="inline -ml-2 text-sm text-blue-700 hover:underline">Issue #1 - Sniffnet</span></a></li></ul></div></div></div><div class="w-full flex flex-col justify-start items-center"><div class="w-full p-4 md:p-0 mt-6 md:w-5/6 lg:max-w-6xl h-full flex flex-col justify-start items-center self-center"><img class="hidden md:block w-auto max-w-full object-scale-down" src="/assets/blog/langfuse/banner.webp" alt=""/><div class="w-auto max-w-6xl mt-4 md:mt-12 prose prose-indigo prose-xl md:prose-2xl flex flex-col justify-center items-center"><h1 class="leading-16">Starlet #27 Langfuse: Open-source LLM Observability</h1></div><div class="w-full mt-8 mb-2 max-w-6xl px-2 flex flex-row items-center justify-center text-sm text-gray-900 font-semibold trackingwide uppercase"><div class="flex space-x-1 text-gray-500"><span class="text-gray-900">Clemens Rawert</span><span aria-hidden="true"> · </span><time dateTime="2024-11-01:00:00.000Z">Nov 1, 2024</time><span aria-hidden="true"> · </span><span> 4 min read </span></div></div><div class="mt-8 w-full max-w-5xl prose prose-indigo prose-xl md:prose-2xl"><p><em>This is the twenty-seventh issue of The Starlet List. If you want to prompt your open source project on star-history.com for free, please check out our <a href="/blog/list-your-open-source-project">announcement</a>.</em></p> <hr> <p><a href="https://github.com/langfuse/langfuse"><strong>Langfuse</strong></a> is the open-source LLM engineering platform. Langfuse helps engineers understand their LLM applications and offers ways to <a href="https://langfuse.com/faq/all/llm-analytics-101">improve</a> them. At its core lie LLM Observability, execution metrics, evaluations, prompt management and application testing with datasets.</p> <p>Langfuse is MIT-licensed and has > 5.9k stars <a href="https://github.com/langfuse/langfuse/">on GitHub</a>. It is incredibly easy to self-host (one docker container) and has a community of thousands of self-hosting deployments.</p> <p><img src="/assets/blog/langfuse/langfuse-llm-monitoring.webp" alt="Langfuse LLM Monitoring"></p> <h2>Core Features of Langfuse</h2> <p>At its core, Langfuse allows you to track any LLM call, no matter which model or framework you are using. Even beyond that, Langfuse is an application <a href="https://langfuse.com/docs/tracing">tracing</a> solution, meaning that you can log additional workloads in your application, such as API calls, embedding calls etc., to gain a holistic understanding of your LLM app.</p> <p><strong>Debugging & Iterating</strong><br>The most common use cases for Langfuse are inspecting and debugging executions of your LLM app in the Langfuse UI and testing different versions of your app.<br><a href="https://langfuse.com/docs/prompts/get-started">Langfuse Prompt Management</a> and its <a href="https://langfuse.com/docs/playground">Prompt Playground</a> help in trying out new things and tracking what works.</p> <p><strong>Monitoring Cost, Quality & Latency</strong><br>Langfuse records a large amount of data about your application. Using its <a href="https://langfuse.com/docs/analytics/overview">dashboards</a> and data exports, you can get fine-grained insights on the cost and latency of your application. This is useful for improving user experience while keeping an eye on your unit economics.</p> <p>Extensive evaluation capabilities allow you to get a handle on quality. You can run automated <a href="https://langfuse.com/docs/scores/model-based-evals">LLM-as-a-judge evaluations</a>, let your users <a href="https://langfuse.com/docs/scores/user-feedback">rate outputs</a>, or manually <a href="https://langfuse.com/docs/scores/annotation">label data</a> within Langfuse.</p> <p><strong>Structured testing & experimentation</strong><br>You can test your application in Langfuse by versioning it and by running tests of expected inputs and outputs via <a href="https://langfuse.com/docs/datasets/overview">curated datasets</a>. This allows you to build a pipeline of tests you can use to quantitatively understand if changes you make to your application (e.g. switching out a model, changing a prompt, etc.) improves your application.</p> <p>For a detailed list of features, have a look at <a href="https://langfuse.com/docs#overview-core-platform-features">Langfuse Features</a>.</p> <h2>Why use Langfuse Tracing to gain Observability in an LLM Application?</h2> <ul> <li>Capture the full context of the execution, including API calls, context, prompts, parallelism, and more </li> <li>Track model usage and cost </li> <li>Collect user feedback </li> <li>Identify low-quality outputs </li> <li>Build fine-tuning and testing datasets</li> </ul> <h2>Quick-Start: Tracing OpenAI Generations with Langfuse (Python)</h2> <p>Not using OpenAI? Langfuse can be used with any model or framework. It natively integrates with popular frameworks such as <a href="https://langfuse.com/docs/integrations/langchain/tracing">Langchain</a>, <a href="https://langfuse.com/docs/integrations/llama-index/get-started">LlamaIndex</a>, <a href="https://langfuse.com/docs/integrations/litellm/tracing">LiteLLM</a> and <a href="https://langfuse.com/docs/integrations/overview">more</a>.</p> <h3>Step 1: Create a New Project in Langfuse</h3> <ol> <li><a href="https://cloud.langfuse.com/auth/sign-up">Create a Langfuse account</a> or <a href="https://langfuse.com/docs/deployment/self-host">self-host</a>. </li> <li>Create a new project within Langfuse. </li> <li>Generate API credentials via the project settings.</li> </ol> <h3>Step 2: Log Your First LLM Call to Langfuse</h3> <p>The <a href="https://langfuse.com/docs/sdk/python/decorators">@observe() decorator</a> makes it easy to trace any Python LLM application. In this quickstart, we also use the Langfuse <a href="https://langfuse.com/docs/integrations/openai">OpenAI integration</a> to automatically capture all model parameters.</p> <pre><code class="language-bash">pip install langfuse openai </code></pre> <pre><code class="language-py"># Get keys for your project from the project settings page https://cloud.langfuse.com os.environ["LANGFUSE_SECRET_KEY"] = "" os.environ["LANGFUSE_PUBLIC_KEY"] = "" os.environ["LANGFUSE_HOST"] = "https://cloud.langfuse.com" # 🇪🇺 EU region # os.environ["LANGFUSE_HOST"] = "https://us.cloud.langfuse.com" # 🇺🇸 US region </code></pre> <pre><code class="language-py">from langfuse.decorators import observe from langfuse.openai import openai # OpenAI integration @observe() def story(): return openai.chat.completions.create( model="gpt-3.5-turbo", max_tokens=100, messages=[ {"role": "system", "content": "You are a great storyteller."}, {"role": "user", "content": "Once upon a time in a galaxy far, far away..."} ], ).choices[0].message.content @observe() def main(): return story() main() </code></pre> <h3>Step 3: See your Traces in Langfuse</h3> <p>Once the script runs, visit the <a href="https://cloud.langfuse.com/">Langfuse UI</a> to view the trace created.</p> <p><img src="/assets/blog/langfuse/example-trace-in-langfuse.webp" alt="Example trace in the Langfuse UI"></p> <p><em>Example trace in the Langfuse UI:</em> <a href="https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/fac231bc-90ee-490a-aa32-78c4269474e3">https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/fac231bc-90ee-490a-aa32-78c4269474e3</a> </p> <p>You can now take it further by managing your prompts through Langfuse or by starting to test or evaluate your LLM executions.</p> <h2>Learn more about Langfuse</h2> <p><img src="/assets/blog/langfuse/langfuse-star-history-graph.webp" alt="Langfuse star-history graph"></p> <p>Langfuse has a community of thousands of active users ranging from hobbyists to enterprises. We are most active on <a href="https://github.com/orgs/langfuse/discussions">GitHub discussions</a> and you can also join <a href="https://langfuse.com/discord">our Discord</a> for a chat. </p> <p>You can find more information in <a href="https://langfuse.com/docs">our documentation</a> and on our <a href="https://github.com/langfuse/langfuse/">GitHub repo</a>.</p> </div></div><div class="mt-12"><iframe src="https://embeds.beehiiv.com/2803dbaa-d8dd-4486-8880-4b843f3a7da6?slim=true" data-test-id="beehiiv-embed" height="52" frameBorder="0" scrolling="no" style="margin:0;border-radius:0px !important;background-color:transparent"></iframe></div></div><div class="w-full hidden lg:block"></div></div><footer class="relative w-full shrink-0 h-auto mt-6 flex flex-col justify-end items-center"><div class="w-full py-2 px-3 md:w-5/6 lg:max-w-7xl flex flex-row flex-wrap justify-between items-center text-neutral-700 border-t"><div class="text-sm leading-8 flex flex-row flex-wrap justify-start 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Rawert","featured":true,"featureImage":"/assets/blog/langfuse/banner.webp","publishedDate":"2024-11-01:00:00.000Z","excerpt":"Langfuse is a LLM engineering platform that helps understand LLM applications and offers ways to improve them.","readingTime":4},"parsedBlogHTML":"\u003cp\u003e\u003cem\u003eThis is the twenty-seventh issue of The Starlet List. If you want to prompt your open source project on star-history.com for free, please check out our \u003ca href=\"/blog/list-your-open-source-project\"\u003eannouncement\u003c/a\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003e\u003ca href=\"https://github.com/langfuse/langfuse\"\u003e\u003cstrong\u003eLangfuse\u003c/strong\u003e\u003c/a\u003e is the open-source LLM engineering platform. Langfuse helps engineers understand their LLM applications and offers ways to \u003ca href=\"https://langfuse.com/faq/all/llm-analytics-101\"\u003eimprove\u003c/a\u003e them. At its core lie LLM Observability, execution metrics, evaluations, prompt management and application testing with datasets.\u003c/p\u003e\n\u003cp\u003eLangfuse is MIT-licensed and has \u0026gt; 5.9k stars \u003ca href=\"https://github.com/langfuse/langfuse/\"\u003eon GitHub\u003c/a\u003e. It is incredibly easy to self-host (one docker container) and has a community of thousands of self-hosting deployments.\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"/assets/blog/langfuse/langfuse-llm-monitoring.webp\" alt=\"Langfuse LLM Monitoring\"\u003e\u003c/p\u003e\n\u003ch2\u003eCore Features of Langfuse\u003c/h2\u003e\n\u003cp\u003eAt its core, Langfuse allows you to track any LLM call, no matter which model or framework you are using. Even beyond that, Langfuse is an application \u003ca href=\"https://langfuse.com/docs/tracing\"\u003etracing\u003c/a\u003e solution, meaning that you can log additional workloads in your application, such as API calls, embedding calls etc., to gain a holistic understanding of your LLM app.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDebugging \u0026amp; Iterating\u003c/strong\u003e\u003cbr\u003eThe most common use cases for Langfuse are inspecting and debugging executions of your LLM app in the Langfuse UI and testing different versions of your app.\u003cbr\u003e\u003ca href=\"https://langfuse.com/docs/prompts/get-started\"\u003eLangfuse Prompt Management\u003c/a\u003e and its \u003ca href=\"https://langfuse.com/docs/playground\"\u003ePrompt Playground\u003c/a\u003e help in trying out new things and tracking what works.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMonitoring Cost, Quality \u0026amp; Latency\u003c/strong\u003e\u003cbr\u003eLangfuse records a large amount of data about your application. Using its \u003ca href=\"https://langfuse.com/docs/analytics/overview\"\u003edashboards\u003c/a\u003e and data exports, you can get fine-grained insights on the cost and latency of your application. This is useful for improving user experience while keeping an eye on your unit economics.\u003c/p\u003e\n\u003cp\u003eExtensive evaluation capabilities allow you to get a handle on quality. You can run automated \u003ca href=\"https://langfuse.com/docs/scores/model-based-evals\"\u003eLLM-as-a-judge evaluations\u003c/a\u003e, let your users \u003ca href=\"https://langfuse.com/docs/scores/user-feedback\"\u003erate outputs\u003c/a\u003e, or manually \u003ca href=\"https://langfuse.com/docs/scores/annotation\"\u003elabel data\u003c/a\u003e within Langfuse.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStructured testing \u0026amp; experimentation\u003c/strong\u003e\u003cbr\u003eYou can test your application in Langfuse by versioning it and by running tests of expected inputs and outputs via \u003ca href=\"https://langfuse.com/docs/datasets/overview\"\u003ecurated datasets\u003c/a\u003e. This allows you to build a pipeline of tests you can use to quantitatively understand if changes you make to your application (e.g. switching out a model, changing a prompt, etc.) improves your application.\u003c/p\u003e\n\u003cp\u003eFor a detailed list of features, have a look at \u003ca href=\"https://langfuse.com/docs#overview-core-platform-features\"\u003eLangfuse Features\u003c/a\u003e.\u003c/p\u003e\n\u003ch2\u003eWhy use Langfuse Tracing to gain Observability in an LLM Application?\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003eCapture the full context of the execution, including API calls, context, prompts, parallelism, and more \u003c/li\u003e\n\u003cli\u003eTrack model usage and cost \u003c/li\u003e\n\u003cli\u003eCollect user feedback \u003c/li\u003e\n\u003cli\u003eIdentify low-quality outputs \u003c/li\u003e\n\u003cli\u003eBuild fine-tuning and testing datasets\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003eQuick-Start: Tracing OpenAI Generations with Langfuse (Python)\u003c/h2\u003e\n\u003cp\u003eNot using OpenAI? Langfuse can be used with any model or framework. It natively integrates with popular frameworks such as \u003ca href=\"https://langfuse.com/docs/integrations/langchain/tracing\"\u003eLangchain\u003c/a\u003e, \u003ca href=\"https://langfuse.com/docs/integrations/llama-index/get-started\"\u003eLlamaIndex\u003c/a\u003e, \u003ca href=\"https://langfuse.com/docs/integrations/litellm/tracing\"\u003eLiteLLM\u003c/a\u003e and \u003ca href=\"https://langfuse.com/docs/integrations/overview\"\u003emore\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003eStep 1: Create a New Project in Langfuse\u003c/h3\u003e\n\u003col\u003e\n\u003cli\u003e\u003ca href=\"https://cloud.langfuse.com/auth/sign-up\"\u003eCreate a Langfuse account\u003c/a\u003e or \u003ca href=\"https://langfuse.com/docs/deployment/self-host\"\u003eself-host\u003c/a\u003e. \u003c/li\u003e\n\u003cli\u003eCreate a new project within Langfuse. \u003c/li\u003e\n\u003cli\u003eGenerate API credentials via the project settings.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003eStep 2: Log Your First LLM Call to Langfuse\u003c/h3\u003e\n\u003cp\u003eThe \u003ca href=\"https://langfuse.com/docs/sdk/python/decorators\"\u003e@observe() decorator\u003c/a\u003e makes it easy to trace any Python LLM application. In this quickstart, we also use the Langfuse \u003ca href=\"https://langfuse.com/docs/integrations/openai\"\u003eOpenAI integration\u003c/a\u003e to automatically capture all model parameters.\u003c/p\u003e\n\u003cpre\u003e\u003ccode class=\"language-bash\"\u003epip install langfuse openai\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode class=\"language-py\"\u003e# Get keys for your project from the project settings page https://cloud.langfuse.com\nos.environ[\u0026quot;LANGFUSE_SECRET_KEY\u0026quot;] = \u0026quot;\u0026quot;\nos.environ[\u0026quot;LANGFUSE_PUBLIC_KEY\u0026quot;] = \u0026quot;\u0026quot;\nos.environ[\u0026quot;LANGFUSE_HOST\u0026quot;] = \u0026quot;https://cloud.langfuse.com\u0026quot; # 🇪🇺 EU region\n# os.environ[\u0026quot;LANGFUSE_HOST\u0026quot;] = \u0026quot;https://us.cloud.langfuse.com\u0026quot; # 🇺🇸 US region\n\u003c/code\u003e\u003c/pre\u003e\n\u003cpre\u003e\u003ccode class=\"language-py\"\u003efrom langfuse.decorators import observe\nfrom langfuse.openai import openai # OpenAI integration\n \n@observe()\ndef story():\n return openai.chat.completions.create(\n model=\u0026quot;gpt-3.5-turbo\u0026quot;,\n max_tokens=100,\n messages=[\n {\u0026quot;role\u0026quot;: \u0026quot;system\u0026quot;, \u0026quot;content\u0026quot;: \u0026quot;You are a great storyteller.\u0026quot;},\n {\u0026quot;role\u0026quot;: \u0026quot;user\u0026quot;, \u0026quot;content\u0026quot;: \u0026quot;Once upon a time in a galaxy far, far away...\u0026quot;}\n ],\n ).choices[0].message.content\n \n@observe()\ndef main():\n return story()\n \nmain()\n\u003c/code\u003e\u003c/pre\u003e\n\u003ch3\u003eStep 3: See your Traces in Langfuse\u003c/h3\u003e\n\u003cp\u003eOnce the script runs, visit the \u003ca href=\"https://cloud.langfuse.com/\"\u003eLangfuse UI\u003c/a\u003e to view the trace created.\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"/assets/blog/langfuse/example-trace-in-langfuse.webp\" alt=\"Example trace in the Langfuse UI\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eExample trace in the Langfuse UI:\u003c/em\u003e \u003ca href=\"https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/fac231bc-90ee-490a-aa32-78c4269474e3\"\u003ehttps://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/fac231bc-90ee-490a-aa32-78c4269474e3\u003c/a\u003e \u003c/p\u003e\n\u003cp\u003eYou can now take it further by managing your prompts through Langfuse or by starting to test or evaluate your LLM executions.\u003c/p\u003e\n\u003ch2\u003eLearn more about Langfuse\u003c/h2\u003e\n\u003cp\u003e\u003cimg src=\"/assets/blog/langfuse/langfuse-star-history-graph.webp\" alt=\"Langfuse star-history graph\"\u003e\u003c/p\u003e\n\u003cp\u003eLangfuse has a community of thousands of active users ranging from hobbyists to enterprises. We are most active on \u003ca href=\"https://github.com/orgs/langfuse/discussions\"\u003eGitHub discussions\u003c/a\u003e and you can also join \u003ca href=\"https://langfuse.com/discord\"\u003eour Discord\u003c/a\u003e for a chat. \u003c/p\u003e\n\u003cp\u003eYou can find more information in \u003ca href=\"https://langfuse.com/docs\"\u003eour documentation\u003c/a\u003e and on our \u003ca href=\"https://github.com/langfuse/langfuse/\"\u003eGitHub repo\u003c/a\u003e.\u003c/p\u003e\n"},"__N_SSG":true},"page":"/blog/[slug]","query":{"slug":"langfuse"},"buildId":"xKX4ZiOi_N7h3OBOEsSZu","isFallback":false,"gsp":true,"scriptLoader":[]}</script></body></html>