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concepts/llm_provider.html
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@ -203,39 +203,27 @@ LLMs. Simply configure the details of the LLMs your application will use, and Ar
make outbound LLM calls.</p>
<section id="adding-custom-llm-provider">
<h2>Adding custom LLM Provider<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#adding-custom-llm-provider" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#adding-custom-llm-provider'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h2>
<p>We support any OpenAI compliant LLM for example mistral, openai, ollama etc. We offer first class support for openai and ollama. You can easily configure an LLM that communicates over the OpenAI API interface, by following the below guide.</p>
<p>We support any OpenAI compliant LLM for example mistral, openai, ollama etc. We also offer first class support for OpenAI, Anthropic, DeepSeek, Mistral, Groq, and Ollama based models.
You can easily configure an LLM that communicates over the OpenAI API interface, by following the below guide.</p>
<p>For example following code block shows you how to add an ollama-supported LLM in the <cite>arch_config.yaml</cite> file.</p>
<div class="highlight-yaml notranslate"><div class="highlight"><pre><span></span><code><span id="line-1"><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">name</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">local-llama</span>
</span><span id="line-2"><span class="w"> </span><span class="nt">provider_interface</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">openai</span>
</span><span id="line-3"><span class="w"> </span><span class="nt">model</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">llama3.2</span>
</span><span id="line-4"><span class="w"> </span><span class="nt">endpoint</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">host.docker.internal:11434</span>
</span></code></pre></div>
</div>
<p>For example following code block shows you how to add mistral llm provider in the <cite>arch_config.yaml</cite> file.</p>
<div class="highlight-yaml notranslate"><div class="highlight"><pre><span></span><code><span id="line-1"><span class="p p-Indicator">-</span><span class="w"> </span><span class="nt">name</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">mistral-ai</span>
</span><span id="line-2"><span class="w"> </span><span class="nt">provider_interface</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">openai</span>
</span><span id="line-3"><span class="w"> </span><span class="nt">model</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">ministral-3b-latest</span>
</span><span id="line-4"><span class="w"> </span><span class="nt">endpoint</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">api.mistral.ai:443</span>
</span><span id="line-5"><span class="w"> </span><span class="nt">protocol</span><span class="p">:</span><span class="w"> </span><span class="l l-Scalar l-Scalar-Plain">https</span>
</span></code></pre></div>
</div>
<p>And in the following code block shows you how to add mistral llm provider in the <cite>arch_config.yaml</cite> file.</p>
</section>
<section id="example-using-the-openai-python-sdk">
<h2>Example: Using the OpenAI Python SDK<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#example-using-the-openai-python-sdk" x-intersect.margin.0%.0%.-70%.0%="activeSection = '#example-using-the-openai-python-sdk'"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h2>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><code><span id="line-1"><span class="kn">from</span><span class="w"> </span><span class="nn">openai</span><span class="w"> </span><span class="kn">import</span> <span class="n">OpenAI</span>
</span><span id="line-2">
</span><span id="line-3"><span class="c1"># Initialize the Arch client</span>
</span><span id="line-4"><span class="n">client</span> <span class="o">=</span> <span class="n">OpenAI</span><span class="p">(</span><span class="n">base_url</span><span class="o">=</span><span class="s2">"http://127.0.0.12000/"</span><span class="p">)</span>
</span><span id="line-3"> <span class="c1"># Initialize the Arch client</span>
</span><span id="line-4"> <span class="n">client</span> <span class="o">=</span> <span class="n">OpenAI</span><span class="p">(</span><span class="n">base_url</span><span class="o">=</span><span class="s2">"http://127.0.0.1:2000/"</span><span class="p">)</span>
</span><span id="line-5">
</span><span id="line-6"><span class="c1"># Define your LLM provider and prompt</span>
</span><span id="line-7"><span class="n">llm_provider</span> <span class="o">=</span> <span class="s2">"openai"</span>
</span><span id="line-8"><span class="n">prompt</span> <span class="o">=</span> <span class="s2">"What is the capital of France?"</span>
</span><span id="line-6"> <span class="c1"># Define your model and messages</span>
</span><span id="line-7"> <span class="n">model</span> <span class="o">=</span> <span class="s2">"llama3.2"</span>
</span><span id="line-8"> <span class="n">messages</span> <span class="o">=</span> <span class="p">[{</span><span class="s2">"role"</span><span class="p">:</span> <span class="s2">"user"</span><span class="p">,</span> <span class="s2">"content"</span><span class="p">:</span> <span class="s2">"What is the capital of France?"</span><span class="p">}]</span>
</span><span id="line-9">
</span><span id="line-10"><span class="c1"># Send the prompt to the LLM through Arch</span>
</span><span id="line-11"><span class="n">response</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="n">completions</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">llm_provider</span><span class="o">=</span><span class="n">llm_provider</span><span class="p">,</span> <span class="n">prompt</span><span class="o">=</span><span class="n">prompt</span><span class="p">)</span>
</span><span id="line-10"> <span class="c1"># Send the messages to the LLM through Arch</span>
</span><span id="line-11"> <span class="n">response</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="n">chat</span><span class="o">.</span><span class="n">completions</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">,</span> <span class="n">messages</span><span class="o">=</span><span class="n">messages</span><span class="p">)</span>
</span><span id="line-12">
</span><span id="line-13"><span class="c1"># Print the response</span>
</span><span id="line-14"><span class="nb">print</span><span class="p">(</span><span class="s2">"LLM Response:"</span><span class="p">,</span> <span class="n">response</span><span class="p">)</span>
</span><span id="line-13"> <span class="c1"># Print the response</span>
</span><span id="line-14"> <span class="nb">print</span><span class="p">(</span><span class="s2">"LLM Response:"</span><span class="p">,</span> <span class="n">response</span><span class="o">.</span><span class="n">choices</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">message</span><span class="o">.</span><span class="n">content</span><span class="p">)</span>
</span></code></pre></div>
</div>
</section>

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get_started/intro_to_arch.html
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@ -174,7 +174,7 @@ For example:</p>
<p><span class="caption-text">High-level network flow of where Arch Gateway sits in your agentic stack. Designed for both ingress and egress prompt traffic.</span><a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#id2"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></p>
</figcaption>
</figure>
<p>Arch is an AI-native proxy server and the universal data plane for AI built by the contributors of Envoy Proxy with the belief that:</p>
<p><a class="reference external" href="https://github.com/katanemo/arch" rel="nofollow noopener">Arch<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a> is a modular edge and AI gateway for AI-native apps - built by the contributors of Envoy Proxy with the belief that:</p>
<blockquote>
<div><p><em>Prompts are nuanced and opaque user requests, which require the same capabilities as traditional HTTP requests
including secure handling, intelligent routing, robust observability, and integration with backend (API)

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get_started/overview.html
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@ -157,7 +157,7 @@
<div id="content" role="main">
<section id="overview">
<span id="id1"></span><h1>Overview<a @click.prevent="window.navigator.clipboard.writeText($el.href); $el.setAttribute('data-tooltip', 'Copied!'); setTimeout(() =&gt; $el.setAttribute('data-tooltip', 'Copy link to this element'), 2000)" aria-label="Copy link to this element" class="headerlink" data-tooltip="Copy link to this element" href="#overview"><svg height="1em" viewbox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M3.9 12c0-1.71 1.39-3.1 3.1-3.1h4V7H7c-2.76 0-5 2.24-5 5s2.24 5 5 5h4v-1.9H7c-1.71 0-3.1-1.39-3.1-3.1zM8 13h8v-2H8v2zm9-6h-4v1.9h4c1.71 0 3.1 1.39 3.1 3.1s-1.39 3.1-3.1 3.1h-4V17h4c2.76 0 5-2.24 5-5s-2.24-5-5-5z"></path></svg></a></h1>
<p><a class="reference external" href="https://github.com/katanemo/arch" rel="nofollow noopener">Arch<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a> is an AI-native proxy server and the universal data plane for AI - one that is natively designed to handle and process AI prompts, not just network traffic.</p>
<p><a class="reference external" href="https://github.com/katanemo/arch" rel="nofollow noopener">Arch<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a> is a modular edge and AI gateway for AI-native apps - one that is natively designed to handle and process prompts, not just network traffic.</p>
<p>Built by contributors to the widely adopted <a class="reference external" href="https://www.envoyproxy.io/" rel="nofollow noopener">Envoy Proxy<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a>, Arch helps you move faster by handling the pesky <em>low-level</em> work in AI agent development—fast input clarification, intelligent agent routing, seamless prompt-to-tool integration, and unified LLM access and observability—all without locking you into a framework.</p>
<p>In this documentation, you will learn how to quickly set up Arch to trigger API calls via prompts, apply prompt guardrails without writing any application-level logic,
simplify the interaction with upstream LLMs, and improve observability all while simplifying your application development process.</p>

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index.html
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@ -153,7 +153,7 @@
<br/>
<p>Build <strong>faster</strong>, <strong>multi-LLM</strong> agents for the <strong>enterprise</strong>.</p>
</div>
<a href="https://www.producthunt.com/posts/arch-3?embed=true&amp;utm_source=badge-top-post-badge&amp;utm_medium=badge&amp;utm_souce=badge-arch-3" target="_blank"><img alt="Arch - Build fast, hyper-personalized agents with intelligent infra | Product Hunt" height="54" src="https://api.producthunt.com/widgets/embed-image/v1/top-post-badge.svg?post_id=565761&amp;theme=dark&amp;period=daily&amp;t=1742433071161" style="width: 250px; height: 54px;" width="250"/></a><p><a class="reference external" href="https://github.com/katanemo/arch" rel="nofollow noopener">Arch<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a> is an AI-native proxy server and the universal data plane for AI - one that is natively designed to handle and process AI prompts, not just network traffic.</p>
<a href="https://www.producthunt.com/posts/arch-3?embed=true&amp;utm_source=badge-top-post-badge&amp;utm_medium=badge&amp;utm_souce=badge-arch-3" target="_blank"><img alt="Arch - Build fast, hyper-personalized agents with intelligent infra | Product Hunt" height="54" src="https://api.producthunt.com/widgets/embed-image/v1/top-post-badge.svg?post_id=565761&amp;theme=dark&amp;period=daily&amp;t=1742433071161" style="width: 250px; height: 54px;" width="250"/></a><p><a class="reference external" href="https://github.com/katanemo/arch" rel="nofollow noopener">Arch<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a> is a modular edge and AI gateway for AI-native apps - one that is natively designed to handle and process prompts, not just network traffic.</p>
<p>Built by contributors to the widely adopted <a class="reference external" href="https://www.envoyproxy.io/" rel="nofollow noopener">Envoy Proxy<svg fill="currentColor" height="1em" stroke="none" viewbox="0 96 960 960" width="1em" xmlns="http://www.w3.org/2000/svg"><path d="M188 868q-11-11-11-28t11-28l436-436H400q-17 0-28.5-11.5T360 336q0-17 11.5-28.5T400 296h320q17 0 28.5 11.5T760 336v320q0 17-11.5 28.5T720 696q-17 0-28.5-11.5T680 656V432L244 868q-11 11-28 11t-28-11Z"></path></svg></a>, Arch helps you move faster by handling the pesky <em>low-level</em> work in AI agent development—fast input clarification, intelligent agent routing, seamless prompt-to-tool integration, and unified LLM access and observability—all without locking you into a framework.</p>
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