context-course/pr_6/en/_app/immutable/nodes/37.92ee32bf.js

import{s as Dt,n as Kt,o as te}from"../chunks/scheduler.4048030c.js";import{S as ee,i as se,e as i,s as l,c as r,h as ne,a as o,d as s,b as a,f as Vt,j as p,g as d,k as Pt,l as le,m as n,n as h,t as m,o as u,p as f}from"../chunks/index.5d0b9360.js";import{C as ae,H as c,E as ie}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.8dfdfd7d.js";import{C as Ut}from"../chunks/CodeBlock.da9970c4.js";function oe(Lt){let g,V,O,D,w,K,y,tt,T,Bt="Mainstream agent frameworks (Codex, Claude Code, OpenCode) hide the loop that makes them work, which is great for production use, but not for learning.",et,x,Zt="Nano Harness is a ~220-line Python agent built for reading, not production. It shows the system prompt, the step loop, tool execution, message history, error handling, and sandboxing in one file.",st,$,Nt="<p>nano_harness is a learning tool and not intended for production use. It’s just a fun way to understand how agents work under the hood. Do not use it for real work!</p>",nt,M,lt,b,Ft="Learning from scratch is a great way to understand how anything works under the hood.",at,v,zt="Reading a minimal harness makes the design choices legible: when to retry, how to parse output, how to handle errors, and where the security boundary sits. This unit uses <code>zai-org/GLM-5.1</code> via Hugging Face Inference Providers as the single model, so the only moving parts are the loop and the tools.",it,C,ot,j,qt="Nano Harness is a ~220-line Python agent framework that:",rt,k,Wt="<li><strong>Takes a task</strong> — “Inspect the workspace and provide a summary”</li> <li><strong>Calls an LLM</strong> — Uses OpenAI-compatible API (defaults to HF router)</li> <li><strong>Generates Python code</strong> — Model outputs executable Python code that will help complete the task</li> <li><strong>Executes safely</strong> — In a sandboxed environment with allowed tools</li> <li><strong>Observes results</strong> — stdout, stderr, exceptions, or a captured final answer</li> <li><strong>Updates context</strong> — Feeds observations back to the model</li> <li><strong>Repeats</strong> — Until the task is done or we hit step limit (we’re assuming 50 steps in our implementation)</li>",pt,J,dt,I,ht,_,Gt="Instead of returning JSON or text, the model outputs Python code:",mt,H,ut,P,Rt="The agent parses and executes it.",ft,U,ct,L,At="Four tools available:",gt,B,Et="<thead><tr><th>Tool</th> <th>What It Does</th> <th>Security</th></tr></thead> <tbody><tr><td><code>list_dir(path)</code></td> <td>List directory contents</td> <td>Path confinement</td></tr> <tr><td><code>read_file(path, max_chars)</code></td> <td>Read file</td> <td>Path confinement, size limit</td></tr> <tr><td><code>write_file(path, content)</code></td> <td>Create/modify file</td> <td>Path confinement, disabled by default</td></tr> <tr><td><code>exec_cmd(args)</code></td> <td>Run shell command</td> <td>Allowlist (only: ls, cat, pwd, echo, head, tail, wc, rg)</td></tr></tbody>",$t,Z,wt,N,Xt="<li>All file access confined to workspace</li> <li>Commands restricted to allowlist</li> <li>Output size limited to prevent context overflow</li> <li>Timeouts prevent hanging</li>",yt,F,Tt,z,St="The harness defaults to the Hugging Face router, which exposes an OpenAI-compatible <code>/v1</code> surface backed by Inference Providers:",xt,q,Mt,W,bt,G,Qt="Nano Harness implements the core loop:",vt,R,Ct,A,jt,E,Ot="This unit walks through the agent loop code piece by piece, explains how the tools are designed and sandboxed, extends the harness with <code>web_fetch</code> and HF Hub search, and runs it against <code>zai-org/GLM-5.1</code> through Hugging Face Inference Providers.",kt,X,Jt,S,Yt="Basic Python, familiarity with HTTP APIs and sandboxing, and an HF token with access to Inference Providers.",It,Q,_t,Y,Ht;return w=new ae({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),y=new c({props:{title:"Introduction to Nano Harness",local:"introduction-to-nano-harness",headingTag:"h1"}}),M=new c({props:{title:"Why start from scratch?",local:"why-start-from-scratch",headingTag:"h2"}}),C=new c({props:{title:"What is Nano Harness?",local:"what-is-nano-harness",headingTag:"h2"}}),J=new c({props:{title:"Key Features",local:"key-features",headingTag:"h2"}}),I=new c({props:{title:"Code-First Agent",local:"code-first-agent",headingTag:"h3"}}),H=new Ut({props:{code:"JTIzJTIwTW9kZWwlMjBzYXlzJTNBJTBBZmlsZXMlMjAlM0QlMjBsaXN0X2RpciglMjIuJTIyKSUwQW1vZGVscyUyMCUzRCUyMHJlYWRfZmlsZSglMjJtb2RlbHMudHh0JTIyKSUwQWZpbmFsX2Fuc3dlciglMjJGaWxlcyUzQSU1Q24lMjIlMjAlMkIlMjAlMjIlNUNuJTIyLmpvaW4oZmlsZXMpJTIwJTJCJTIwJTIyJTVDbiU1Q25tb2RlbHMudHh0JTNBJTVDbiUyMiUyMCUyQiUyMG1vZGVscyk=",highlighted:`<span class="hljs-comment"># Model says:</span>
files = list_dir(<span class="hljs-string">&quot;.&quot;</span>)
models = read_file(<span class="hljs-string">&quot;models.txt&quot;</span>)
final_answer(<span class="hljs-string">&quot;Files:\\n&quot;</span> + <span class="hljs-string">&quot;\\n&quot;</span>.join(files) + <span class="hljs-string">&quot;\\n\\nmodels.txt:\\n&quot;</span> + models)`,wrap:!1}}),U=new c({props:{title:"Constrained Tools",local:"constrained-tools",headingTag:"h3"}}),Z=new c({props:{title:"Sandboxed Execution",local:"sandboxed-execution",headingTag:"h3"}}),F=new c({props:{title:"Model via Hugging Face Inference Providers",local:"model-via-hugging-face-inference-providers",headingTag:"h3"}}),q=new Ut({props:{code:"ZXhwb3J0JTIwTkFOT19NT0RFTCUzRCUyMnphaS1vcmclMkZHTE0tNS4xJTIyJTBBZXhwb3J0JTIwSEZfVE9LRU4lM0QlMjJoZl8uLi4lMjI=",highlighted:`<span class="hljs-built_in">export</span> NANO_MODEL=<span class="hljs-string">&quot;zai-org/GLM-5.1&quot;</span>
<span class="hljs-built_in">export</span> HF_TOKEN=<span class="hljs-string">&quot;hf_...&quot;</span>`,wrap:!1}}),W=new c({props:{title:"The Agentic Loop",local:"the-agentic-loop",headingTag:"h2"}}),R=new Ut({props:{code:"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",highlighted:`<span class="hljs-number">1.</span> <span class="hljs-keyword">Call</span> LLM <span class="hljs-keyword">with</span> task + message history
<span class="hljs-number">2.</span> Parse model output <span class="hljs-keyword">as</span> Python code
<span class="hljs-number">3.</span> <span class="hljs-keyword">Execute</span> Python (<span class="hljs-keyword">with</span> tools available)
<span class="hljs-number">4.</span> Collect stdout, stderr, exceptions
<span class="hljs-number">5.</span> Append observation <span class="hljs-keyword">to</span> message history
<span class="hljs-number">6.</span> Repeat (max <span class="hljs-number">50</span> steps)
<span class="hljs-number">7.</span> Done <span class="hljs-keyword">when</span>: final_answer() <span class="hljs-keyword">called</span> <span class="hljs-keyword">or</span> max steps reached`,wrap:!1}}),A=new c({props:{title:"What You’ll Learn",local:"what-youll-learn",headingTag:"h2"}}),X=new c({props:{title:"Prerequisites",local:"prerequisites",headingTag:"h2"}}),Q=new 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