import{s as st,n as pt,o as mt}from"../chunks/scheduler.f6319a2a.js";import{S as ot,i as rt,g as s,s as i,r as o,A as Tt,h as p,f as l,c as a,j as it,u as r,x as m,k as at,y as Jt,a as n,v as T,d as J,t as u,w as c}from"../chunks/index.85aa684c.js";import{C as Le}from"../chunks/CodeBlock.3400fc53.js";import{H as f,E as ut}from"../chunks/getInferenceSnippets.0233181f.js";function ct(We){let E,O,q,ee,h,te,N,Ze=`This page demonstrates advanced usage of the smolagents library, with a special focus on <strong>Human-in-the-Loop (HITL)</strong> approaches for interactive plan creation, user-driven plan modification, and memory preservation in agentic workflows.
The example is based on the code in <code>examples/plan_customization/plan_customization.py</code>.`,le,w,ne,C,Pe="This example teaches you how to implement Human-in-the-Loop strategies to:",ie,$,Qe="<li>Interrupt agent execution after a plan is created (using step callbacks)</li> <li>Allow users to review and modify the agent’s plan before execution (Human-in-the-Loop)</li> <li>Resume execution while preserving the agent’s memory</li> <li>Dynamically update plans based on user feedback, keeping the human in control</li>",ae,d,se,M,pe,U,Ve="The agent is configured to pause after creating a plan. This is achieved by registering a step callback for the <code>PlanningStep</code>:",me,g,oe,v,re,y,Ae="When the agent creates a plan, the callback displays it and prompts the human user to:",Te,R,Se="<li>Approve the plan</li> <li>Modify the plan</li> <li>Cancel execution</li>",Je,b,Ge="Example interaction:",ue,z,ce,x,Ye="This Human-in-the-Loop step enables a human to intervene and review or modify the plan before execution continues, and ensures that the agent’s actions align with human intent.",fe,I,Fe="If the user chooses to modify, they can edit the plan directly. The updated plan is then used for subsequent execution steps.",Ee,j,he,H,Xe="By running the agent with <code>reset=False</code>, all previous steps and memory are preserved. This allows you to resume execution after an interruption or plan modification:",Ne,B,we,_,Ce,k,De="You can inspect the agent’s memory to see all steps taken so far:",$e,L,de,W,Me,Z,qe="<li>Agent starts with a complex task</li> <li>Planning step is created and execution pauses for human review</li> <li>Human reviews and optionally modifies the plan (Human-in-the-Loop)</li> <li>Execution resumes with the approved/modified plan</li> <li>All steps are preserved for future runs, maintaining transparency and control</li>",Ue,P,ge,Q,Ke="The example includes error handling for:",ve,V,Oe="<li>User cancellation</li> <li>Plan modification errors</li> <li>Resume execution failures</li>",ye,A,Re,S,et="<li>smolagents library</li> <li>DuckDuckGoSearchTool (included with smolagents)</li> <li>InferenceClientModel (requires HuggingFace API token)</li>",be,G,ze,Y,tt="This example demonstrates:",xe,F,lt="<li>Step callback implementation for custom agent behavior</li> <li>Memory management in multi-step agents</li> <li>User interaction patterns in agentic systems</li> <li>Plan modification techniques for dynamic agent control</li> <li>Error handling in interactive agent systems</li>",Ie,je,He,X,nt='For the full code, see <a href="https://github.com/huggingface/smolagents/tree/main/examples/plan_customization" rel="nofollow"><code>examples/plan_customization</code></a>.',Be,D,_e,K,ke;return h=new f({props:{title:"Human-in-the-Loop: Customize Agent Plan Interactively",local:"human-in-the-loop-customize-agent-plan-interactively",headingTag:"h1"}}),w=new f({props:{title:"Overview",local:"overview",headingTag:"h2"}}),d=new f({props:{title:"Key Concepts",local:"key-concepts",headingTag:"h2"}}),M=new f({props:{title:"Step Callbacks for Plan Interruption",local:"step-callbacks-for-plan-interruption",headingTag:"h3"}}),g=new Le({props:{code:"YWdlbnQlMjAlM0QlMjBDb2RlQWdlbnQoJTBBJTIwJTIwJTIwJTIwbW9kZWwlM0RJbmZlcmVuY2VDbGllbnRNb2RlbCgpJTJDJTBBJTIwJTIwJTIwJTIwdG9vbHMlM0QlNUJEdWNrRHVja0dvU2VhcmNoVG9vbCgpJTVEJTJDJTBBJTIwJTIwJTIwJTIwcGxhbm5pbmdfaW50ZXJ2YWwlM0Q1JTJDJTIwJTIwJTIzJTIwUGxhbiUyMGV2ZXJ5JTIwNSUyMHN0ZXBzJTBBJTIwJTIwJTIwJTIwc3RlcF9jYWxsYmFja3MlM0QlN0JQbGFubmluZ1N0ZXAlM0ElMjBpbnRlcnJ1cHRfYWZ0ZXJfcGxhbiU3RCUyQyUwQSUyMCUyMCUyMCUyMG1heF9zdGVwcyUzRDEwJTJDJTBBJTIwJTIwJTIwJTIwdmVyYm9zaXR5X2xldmVsJTNEMSUwQSk=",highlighted:`agent = CodeAgent(
    model=InferenceClientModel(),
    tools=[DuckDuckGoSearchTool()],
    planning_interval=<span class="hljs-number">5</span>,  <span class="hljs-comment"># Plan every 5 steps</span>
    step_callbacks={PlanningStep: interrupt_after_plan},
    max_steps=<span class="hljs-number">10</span>,
    verbosity_level=<span class="hljs-number">1</span>
)`,wrap:!1}}),v=new f({props:{title:"Human-in-the-Loop: Interactive Plan Review and Modification",local:"human-in-the-loop-interactive-plan-review-and-modification",headingTag:"h3"}}),z=new Le({props:{code:"JTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTBBJUYwJTlGJUE0JTk2JTIwQUdFTlQlMjBQTEFOJTIwQ1JFQVRFRCUwQSUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUzRCUwQTEuJTIwU2VhcmNoJTIwZm9yJTIwcmVjZW50JTIwQUklMjBkZXZlbG9wbWVudHMlMEEyLiUyMEFuYWx5emUlMjB0aGUlMjB0b3AlMjByZXN1bHRzJTBBMy4lMjBTdW1tYXJpemUlMjB0aGUlMjAzJTIwbW9zdCUyMHNpZ25pZmljYW50JTIwYnJlYWt0aHJvdWdocyUwQTQuJTIwSW5jbHVkZSUyMHNvdXJjZXMlMjBmb3IlMjBlYWNoJTIwYnJlYWt0aHJvdWdoJTBBJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTNEJTBBJTBBQ2hvb3NlJTIwYW4lMjBvcHRpb24lM0ElMEExLiUyMEFwcHJvdmUlMjBwbGFuJTBBMi4lMjBNb2RpZnklMjBwbGFuJTBBMy4lMjBDYW5jZWwlMEFZb3VyJTIwY2hvaWNlJTIwKDEtMyklM0E=",highlighted:`============================================================
🤖 AGENT PLAN CREATED
============================================================
1. Search for recent AI developments
2. Analyze the top results
3. Summarize the 3 most significant breakthroughs
<span class="hljs-section">4. Include sources for each breakthrough
============================================================</span>

Choose an option:
1. Approve plan
2. Modify plan
3. Cancel
Your choice (1-3):`,wrap:!1}}),j=new f({props:{title:"Memory Preservation and Resuming Execution",local:"memory-preservation-and-resuming-execution",headingTag:"h3"}}),B=new Le({props:{code:"JTIzJTIwRmlyc3QlMjBydW4lMjAobWF5JTIwYmUlMjBpbnRlcnJ1cHRlZCklMEFhZ2VudC5ydW4odGFzayUyQyUyMHJlc2V0JTNEVHJ1ZSklMEElMEElMjMlMjBSZXN1bWUlMjB3aXRoJTIwcHJlc2VydmVkJTIwbWVtb3J5JTBBYWdlbnQucnVuKHRhc2slMkMlMjByZXNldCUzREZhbHNlKQ==",highlighted:`<span class="hljs-comment"># First run (may be interrupted)</span>
agent.run(task, reset=<span class="hljs-literal">True</span>)

<span class="hljs-comment"># Resume with preserved memory</span>
agent.run(task, reset=<span class="hljs-literal">False</span>)`,wrap:!1}}),_=new f({props:{title:"Inspecting Agent Memory",local:"inspecting-agent-memory",headingTag:"h3"}}),L=new Le({props:{code:"cHJpbnQoZiUyMkN1cnJlbnQlMjBtZW1vcnklMjBjb250YWlucyUyMCU3QmxlbihhZ2VudC5tZW1vcnkuc3RlcHMpJTdEJTIwc3RlcHMlM0ElMjIpJTBBZm9yJTIwaSUyQyUyMHN0ZXAlMjBpbiUyMGVudW1lcmF0ZShhZ2VudC5tZW1vcnkuc3RlcHMpJTNBJTBBJTIwJTIwJTIwJTIwc3RlcF90eXBlJTIwJTNEJTIwdHlwZShzdGVwKS5fX25hbWVfXyUwQSUyMCUyMCUyMCUyMHByaW50KGYlMjIlMjAlMjAlN0JpJTJCMSU3RC4lMjAlN0JzdGVwX3R5cGUlN0QlMjIp",highlighted:`<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Current memory contains <span class="hljs-subst">{<span class="hljs-built_in">len</span>(agent.memory.steps)}</span> steps:&quot;</span>)
<span class="hljs-keyword">for</span> i, step <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(agent.memory.steps):
    step_type = <span class="hljs-built_in">type</span>(step).__name__
    <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;  <span class="hljs-subst">{i+<span class="hljs-number">1</span>}</span>. <span class="hljs-subst">{step_type}</span>&quot;</span>)`,wrap:!1}}),W=new f({props:{title:"Example Human-in-the-Loop Workflow",local:"example-human-in-the-loop-workflow",headingTag:"h2"}}),P=new f({props:{title:"Error Handling",local:"error-handling",headingTag:"h2"}}),A=new f({props:{title:"Requirements",local:"requirements",headingTag:"h2"}}),G=new f({props:{title:"Educational Value",local:"educational-value",headingTag:"h2"}}),D=new ut({props:{source:"https://github.com/huggingface/smolagents/blob/main/docs/source/en/examples/plan_customization.md"}}),{c(){E=s("meta"),O=i(),q=s("p"),ee=i(),o(h.$$.fragment),te=i(),N=s("p"),N.innerHTML=Ze,le=i(),o(w.$$.fragment),ne=i(),C=s("p"),C.textContent=Pe,ie=i(),$=s("ul"),$.innerHTML=Qe,ae=i(),o(d.$$.fragment),se=i(),o(M.$$.fragment),pe=i(),U=s("p"),U.innerHTML=Ve,me=i(),o(g.$$.fragment),oe=i(),o(v.$$.fragment),re=i(),y=s("p"),y.textContent=Ae,Te=i(),R=s("ol"),R.innerHTML=Se,Je=i(),b=s("p"),b.textContent=Ge,ue=i(),o(z.$$.fragment),ce=i(),x=s("p"),x.textContent=Ye,fe=i(),I=s("p"),I.textContent=Fe,Ee=i(),o(j.$$.fragment),he=i(),H=s("p"),H.innerHTML=Xe,Ne=i(),o(B.$$.fragment),we=i(),o(_.$$.fragment),Ce=i(),k=s("p"),k.textContent=De,$e=i(),o(L.$$.fragment),de=i(),o(W.$$.fragment),Me=i(),Z=s("ol"),Z.innerHTML=qe,Ue=i(),o(P.$$.fragment),ge=i(),Q=s("p"),Q.textContent=Ke,ve=i(),V=s("ul"),V.innerHTML=Oe,ye=i(),o(A.$$.fragment),Re=i(),S=s("ul"),S.innerHTML=et,be=i(),o(G.$$.fragment),ze=i(),Y=s("p"),Y.textContent=tt,xe=i(),F=s("ul"),F.innerHTML=lt,Ie=i(),je=s("hr"),He=i(),X=s("p"),X.innerHTML=nt,Be=i(),o(D.$$.fragment),_e=i(),K=s("p"),this.h()},l(e){const 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ft='{"title":"Human-in-the-Loop: Customize Agent Plan Interactively","local":"human-in-the-loop-customize-agent-plan-interactively","sections":[{"title":"Overview","local":"overview","sections":[],"depth":2},{"title":"Key Concepts","local":"key-concepts","sections":[{"title":"Step Callbacks for Plan Interruption","local":"step-callbacks-for-plan-interruption","sections":[],"depth":3},{"title":"Human-in-the-Loop: Interactive Plan Review and Modification","local":"human-in-the-loop-interactive-plan-review-and-modification","sections":[],"depth":3},{"title":"Memory Preservation and Resuming Execution","local":"memory-preservation-and-resuming-execution","sections":[],"depth":3},{"title":"Inspecting Agent Memory","local":"inspecting-agent-memory","sections":[],"depth":3}],"depth":2},{"title":"Example Human-in-the-Loop Workflow","local":"example-human-in-the-loop-workflow","sections":[],"depth":2},{"title":"Error Handling","local":"error-handling","sections":[],"depth":2},{"title":"Requirements","local":"requirements","sections":[],"depth":2},{"title":"Educational Value","local":"educational-value","sections":[],"depth":2}],"depth":1}';function Et(We){return mt(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class $t extends ot{constructor(E){super(),rt(this,E,Et,ct,st,{})}}export{$t as component};
