import{s as Wl,o as Fl,n as Sa}from"../chunks/scheduler.f9cad8c1.js";import{S as ql,i as zl,e as s,s as n,c as i,h as Hl,a as r,d as a,b as o,f as _,g as m,j as h,k as f,l as t,m as u,n as g,t as d,o as c,p}from"../chunks/index.6e06ef99.js";import{T as Nl,D as x,E as Vl}from"../chunks/ExampleCodeBlock.484d42f2.js";import{C as Rl,H as V,E as Gl}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.c4302a62.js";import{C as Ul}from"../chunks/CodeBlock.72594161.js";function jl(z){let b,E="Smolagents는 실험적인 API로 언제든지 변경될 수 있습니다. API나 사용되는 모델이 변경될 수 있기 때문에 에이전트가 반환하는 결과도 달라질 수 있습니다.";return{c(){b=s("p"),b.textContent=E},l(C){b=r(C,"P",{"data-svelte-h":!0}),h(b)!=="svelte-1wtysip"&&(b.textContent=E)},m(C,P){u(C,b,P)},p:Sa,d(C){C&&a(b)}}}function Bl(z){let b,E=`Loading a tool from the Hub means that you’ll download the tool and execute it locally.
ALWAYS inspect the tool you’re downloading before loading it within your runtime, as you would do when
installing a package using pip/npm/apt.`;return{c(){b=s("p"),b.textContent=E},l(C){b=r(C,"P",{"data-svelte-h":!0}),h(b)!=="svelte-fpk7e5"&&(b.textContent=E)},m(C,P){u(C,b,P)},p:Sa,d(C){C&&a(b)}}}function Zl(z){let b,E="Example:",C,P,A;return P=new Ul({props:{code:"ZnJvbSUyMHNtb2xhZ2VudHMlMjBpbXBvcnQlMjBDb2RlQWdlbnQlMEFhZ2VudCUyMCUzRCUyMENvZGVBZ2VudCh0b29scyUzRCU1QiU1RCklMEFhZ2VudC5ydW4oJTIyV2hhdCUyMGlzJTIwdGhlJTIwcmVzdWx0JTIwb2YlMjAyJTIwcG93ZXIlMjAzLjczODQlM0YlMjIp",highlighted:`<span class="hljs-keyword">from</span> smolagents <span class="hljs-keyword">import</span> CodeAgent
agent = CodeAgent(tools=[])
agent.run(<span class="hljs-string">&quot;What is the result of 2 power 3.7384?&quot;</span>)`,wrap:!1}}),{c(){b=s("p"),b.textContent=E,C=n(),i(P.$$.fragment)},l(y){b=r(y,"P",{"data-svelte-h":!0}),h(b)!=="svelte-11lpom8"&&(b.textContent=E),C=o(y),m(P.$$.fragment,y)},m(y,L){u(y,b,L),u(y,C,L),g(P,y,L),A=!0},p:Sa,i(y){A||(d(P.$$.fragment,y),A=!0)},o(y){c(P.$$.fragment,y),A=!1},d(y){y&&(a(b),a(C)),p(P,y)}}}function Jl(z){let b,E="Example:",C,P,A;return P=new Ul({props:{code:"ZnJvbSUyMHNtb2xhZ2VudHMlMjBpbXBvcnQlMjBDb2RlQWdlbnQlMkMlMjBHcmFkaW9VSSUyQyUyMEluZmVyZW5jZUNsaWVudE1vZGVsJTBBJTBBbW9kZWwlMjAlM0QlMjBJbmZlcmVuY2VDbGllbnRNb2RlbChtb2RlbF9pZCUzRCUyMm1ldGEtbGxhbWElMkZNZXRhLUxsYW1hLTMuMS04Qi1JbnN0cnVjdCUyMiklMEFhZ2VudCUyMCUzRCUyMENvZGVBZ2VudCh0b29scyUzRCU1QiU1RCUyQyUyMG1vZGVsJTNEbW9kZWwpJTBBZ3JhZGlvX3VpJTIwJTNEJTIwR3JhZGlvVUkoYWdlbnQlMkMlMjBmaWxlX3VwbG9hZF9mb2xkZXIlM0QlMjJ1cGxvYWRzJTIyJTJDJTIwcmVzZXRfYWdlbnRfbWVtb3J5JTNEVHJ1ZSklMEFncmFkaW9fdWkubGF1bmNoKCk=",highlighted:`<span class="hljs-keyword">from</span> smolagents <span class="hljs-keyword">import</span> CodeAgent, GradioUI, InferenceClientModel

model = InferenceClientModel(model_id=<span class="hljs-string">&quot;meta-llama/Meta-Llama-3.1-8B-Instruct&quot;</span>)
agent = CodeAgent(tools=[], model=model)
gradio_ui = GradioUI(agent, file_upload_folder=<span class="hljs-string">&quot;uploads&quot;</span>, reset_agent_memory=<span class="hljs-literal">True</span>)
gradio_ui.launch()`,wrap:!1}}),{c(){b=s("p"),b.textContent=E,C=n(),i(P.$$.fragment)},l(y){b=r(y,"P",{"data-svelte-h":!0}),h(b)!=="svelte-11lpom8"&&(b.textContent=E),C=o(y),m(P.$$.fragment,y)},m(y,L){u(y,b,L),u(y,C,L),g(P,y,L),A=!0},p:Sa,i(y){A||(d(P.$$.fragment,y),A=!0)},o(y){c(P.$$.fragment,y),A=!1},d(y){y&&(a(b),a(C)),p(P,y)}}}function Ql(z){let b,E,C,P,A,y,L,_o,X,xo,Ve,Cr='에이전트와 도구에 대해 더 자세히 알아보려면 <a href="../index">소개 가이드</a>를 꼭 읽어보세요. 이 페이지에는 기본 클래스에 대한 API 문서가 포함되어 있습니다.',bo,Ue,$o,We,wr='저희 에이전트는 <a href="/docs/smolagents/main/ko/reference/agents#smolagents.MultiStepAgent">MultiStepAgent</a>를 상속받으며, 이는 하나의 생각과 하나의 도구 호출 및 실행으로 구성된 여러 단계를 수행할 수 있음을 의미합니다. 이 <a href="../conceptual_guides/react">개념 가이드</a>에서 더 자세히 알아보세요.',vo,Fe,Pr="저희는 메인 <code>Agent</code> 클래스를 기반으로 두 가지 유형의 에이전트를 제공합니다.",yo,qe,kr='<li><a href="/docs/smolagents/main/ko/reference/agents#smolagents.CodeAgent">CodeAgent</a>는 Python 코드로 도구 호출을 작성합니다.(이것이 기본값입니다.)</li> <li><a href="/docs/smolagents/main/ko/reference/agents#smolagents.ToolCallingAgent">ToolCallingAgent</a>는 JSON 형식으로 도구 호출을 작성합니다.</li>',Co,ze,Mr="두 경우 모두 초기화 시 <code>model</code>과 도구 목록인 <code>tools</code>를 인수로 요구합니다.",wo,He,Po,$,Re,Na,nn,Ar=`Agent class that solves the given task step by step, using the ReAct framework:
While the objective is not reached, the agent will perform a cycle of action (given by the LLM) and observation (obtained from the environment).`,Va,K,Ge,Ua,on,Tr="Parse action from the LLM output",Wa,ee,je,Fa,an,Er="Create agent from a dictionary representation.",qa,te,Be,za,sn,Lr="Loads an agent from a local folder.",Ha,H,Ze,Ra,rn,Ir="Loads an agent defined on the Hub.",Ga,ne,ja,oe,Je,Ba,ln,Dr="To be implemented in child classes",Za,ae,Qe,Ja,mn,Sr="Interrupts the agent execution.",Qa,se,Oe,Oa,gn,Nr="Provide the final answer to the task, based on the logs of the agent’s interactions.",Ya,re,Ye,Xa,dn,Vr="Upload the agent to the Hub.",Ka,le,Xe,es,cn,Ur="Prints a pretty replay of the agent’s steps.",ts,R,Ke,ns,pn,Wr="Run the agent for the given task.",os,ie,as,G,et,ss,un,Fr="Saves the relevant code files for your agent. This will copy the code of your agent in <code>output_dir</code> as well as autogenerate:",rs,hn,qr=`<li>a <code>tools</code> folder containing the logic for each of the tools under <code>tools/{tool_name}.py</code>.</li> <li>a <code>managed_agents</code> folder containing the logic for each of the managed agents.</li> <li>an <code>agent.json</code> file containing a dictionary representing your agent.</li> <li>a <code>prompt.yaml</code> file containing the prompt templates used by your agent.</li> <li>an <code>app.py</code> file providing a UI for your agent when it is exported to a Space with <code>agent.push_to_hub()</code></li> <li>a <code>requirements.txt</code> containing the names of the modules used by your tool (as detected when inspecting its
code)</li>`,ls,me,tt,is,fn,zr=`Perform one step in the ReAct framework: the agent thinks, acts, and observes the result.
Returns either None if the step is not final, or the final answer.`,ms,ge,nt,gs,_n,Hr="Convert the agent to a dictionary representation.",ds,de,ot,cs,xn,Rr="Creates a rich tree visualization of the agent’s structure.",ps,ce,at,us,bn,Gr=`Reads past llm_outputs, actions, and observations or errors from the memory into a series of messages
that can be used as input to the LLM. Adds a number of keywords (such as PLAN, error, etc) to help
the LLM.`,ko,D,st,hs,$n,jr="In this agent, the tool calls will be formulated by the LLM in code format, then parsed and executed.",fs,pe,rt,_s,vn,Br="Clean up resources used by the agent, such as the remote Python executor.",xs,ue,lt,bs,yn,Zr="Create CodeAgent from a dictionary representation.",Mo,S,it,$s,Cn,Jr="This agent uses JSON-like tool calls, using method <code>model.get_tool_call</code> to leverage the LLM engine’s tool calling capabilities.",vs,j,mt,ys,wn,Qr="Execute a tool or managed agent with the provided arguments.",Cs,Pn,Or="The arguments are replaced with the actual values from the state if they refer to state variables.",ws,he,gt,Ps,kn,Yr="Process tool calls from the model output and update agent memory.",Ao,dt,To,Z,ct,ks,Mn,Xr="Runs an agent with the given task and streams the messages from the agent as gradio ChatMessages.",Eo,pt,Lo,fe,Kr="<p>UI를 사용하려면 <code>gradio</code>가 설치되어 있어야 합니다. 설치되어 있지 않다면 <code>pip install &#39;smolagents[gradio]&#39;</code>를 실행해주세요.</p>",Io,T,ut,Ms,An,el='Gradio interface for interacting with a <a href="/docs/smolagents/main/ko/reference/agents#smolagents.MultiStepAgent">MultiStepAgent</a>.',As,Tn,tl=`This class provides a web interface to interact with the agent in real-time, allowing users to submit prompts, upload files, and receive responses in a chat-like format.
It  can reset the agent’s memory at the start of each interaction if desired.
It supports file uploads, which are saved to a specified folder.
It uses the <code>gradio.Chatbot</code> component to display the conversation history.
This class requires the <code>gradio</code> extra to be installed: <code>pip install &#39;smolagents[gradio]&#39;</code>.`,Ts,_e,Es,xe,ht,Ls,En,nl="Launch the Gradio app with the agent interface.",Is,B,ft,Ds,Ln,ol="Upload a file and add it to the list of uploaded files in the session state.",Ss,In,al=`The file is saved to the <code>self.file_upload_folder</code> folder.
If the file type is not allowed, it returns a message indicating the disallowed file type.`,Do,_t,So,J,xt,Ns,Dn,sl="Prompt templates for the agent.",No,Q,bt,Vs,Sn,rl="Prompt templates for the planning step.",Vo,O,$t,Us,Nn,ll="Prompt templates for the managed agent.",Uo,Y,vt,Ws,Vn,il="Prompt templates for the final answer.",Wo,yt,Fo,Ct,ml="Smolagents는 여러 단계에 걸쳐 정보를 저장하기 위해 메모리를 사용합니다.",qo,w,wt,Fs,Un,gl="Memory for the agent, containing the system prompt and all steps taken by the agent.",qs,Wn,dl=`This class is used to store the agent’s steps, including tasks, actions, and planning steps.
It allows for resetting the memory, retrieving succinct or full step information, and replaying the agent’s steps.`,zs,Fn,cl="<strong>Attributes</strong>:",Hs,qn,pl="<li><strong>system_prompt</strong> (<code>SystemPromptStep</code>) — System prompt step for the agent.</li> <li><strong>steps</strong> (<code>list[TaskStep | ActionStep | PlanningStep]</code>) — List of steps taken by the agent, which can include tasks, actions, and planning steps.</li>",Rs,be,Pt,Gs,zn,ul="Return a full representation of the agent’s steps, including model input messages.",js,$e,kt,Bs,Hn,hl="Return a succinct representation of the agent’s steps, excluding model input messages.",Zs,ve,Mt,Js,Rn,fl="Prints a pretty replay of the agent’s steps.",Qs,ye,At,Os,Gn,_l="Reset the agent’s memory, clearing all steps and keeping the system prompt.",Ys,Ce,Tt,Xs,jn,xl="Returns all code actions from the agent’s steps, concatenated as a single script.",zo,Et,Ho,Lt,It,Ro,Dt,Go,W,St,Ks,Bn,bl="Executor of Python code in a local environment.",er,Zn,$l=`This executor evaluates Python code with restricted access to imports and built-in functions,
making it suitable for running untrusted code. It maintains state between executions,
allows for custom tools and functions to be made available to the code, and captures
print outputs separately from return values.`,jo,Nt,Bo,F,Vt,tr,we,Ut,nr,Jn,vl=`Execute code, return the result and output, also determining if
the result is the final answer.`,or,Pe,Wt,ar,Qn,yl="Send variables to the kernel namespace using pickle.",Zo,Ft,Jo,q,qt,sr,On,Cl="Executes Python code using E2B.",rr,ke,zt,lr,Yn,wl="Clean up the E2B sandbox and resources.",Qo,Ht,Oo,N,Rt,ir,Xn,Pl="Executes Python code using Jupyter Kernel Gateway in a Docker container.",mr,Me,Gt,gr,Kn,kl="Clean up the Docker container and resources.",dr,Ae,jt,cr,eo,Ml="Ensure cleanup on deletion.",Yo,Bt,Xo,M,Zt,pr,to,Al="Remote Python code executor in a sandboxed WebAssembly environment powered by Pyodide and Deno.",ur,no,Tl=`This executor combines Deno’s secure runtime with Pyodide’s WebAssembly‑compiled Python interpreter to deliver s
trong isolation guarantees while enabling full Python execution.`,hr,Te,Jt,fr,oo,El="Clean up resources used by the executor.",_r,Ee,Qt,xr,ao,Ll="Ensure cleanup on deletion.",br,Le,Ot,$r,so,Il="Install additional Python packages in the Pyodide environment.",vr,Ie,Yt,yr,ro,Dl="Execute Python code in the Pyodide environment and return the result.",Ko,Xt,ea,fo,ta;return A=new Rl({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),L=new V({props:{title:"에이전트",local:"agents",headingTag:"h1"}}),X=new Nl({props:{warning:!0,$$slots:{default:[jl]},$$scope:{ctx:z}}}),Ue=new V({props:{title:"에이전트",local:"agents",headingTag:"h2"}}),He=new V({props:{title:"에이전트 클래스",local:"smolagents.MultiStepAgent ][ smolagents.MultiStepAgent",headingTag:"h3"}}),Re=new x({props:{name:"class smolagents.MultiStepAgent",anchor:"smolagents.MultiStepAgent",parameters:[{name:"tools",val:": list"},{name:"model",val:": Model"},{name:"prompt_templates",val:": smolagents.agents.PromptTemplates | None = None"},{name:"instructions",val:": str | None = None"},{name:"max_steps",val:": int = 20"},{name:"add_base_tools",val:": bool = False"},{name:"verbosity_level",val:": LogLevel = <LogLevel.INFO: 1>"},{name:"managed_agents",val:": list | None = None"},{name:"step_callbacks",val:": list[collections.abc.Callable] | dict[typing.Type[smolagents.memory.MemoryStep], collections.abc.Callable | list[collections.abc.Callable]] | None = None"},{name:"planning_interval",val:": int | None = None"},{name:"name",val:": str | None = None"},{name:"description",val:": str | None = None"},{name:"provide_run_summary",val:": bool = False"},{name:"final_answer_checks",val:": list[collections.abc.Callable] | None = None"},{name:"return_full_result",val:": bool = False"},{name:"logger",val:": smolagents.monitoring.AgentLogger | None = None"}],parametersDescription:[{anchor:"smolagents.MultiStepAgent.tools",description:"<strong>tools</strong> (<code>list[Tool]</code>) &#x2014; <code>Tool</code>s that the agent can use.",name:"tools"},{anchor:"smolagents.MultiStepAgent.model",description:"<strong>model</strong> (<code>Callable[[list[dict[str, str]]], ChatMessage]</code>) &#x2014; Model that will generate the agent&#x2019;s actions.",name:"model"},{anchor:"smolagents.MultiStepAgent.prompt_templates",description:'<strong>prompt_templates</strong> (<a href="/docs/smolagents/main/ko/reference/agents#smolagents.PromptTemplates">PromptTemplates</a>, <em>optional</em>) &#x2014; Prompt templates.',name:"prompt_templates"},{anchor:"smolagents.MultiStepAgent.instructions",description:"<strong>instructions</strong> (<code>str</code>, <em>optional</em>) &#x2014; Custom instructions for the agent, will be inserted in the system prompt.",name:"instructions"},{anchor:"smolagents.MultiStepAgent.max_steps",description:"<strong>max_steps</strong> (<code>int</code>, default <code>20</code>) &#x2014; Maximum number of steps the agent can take to solve the task.",name:"max_steps"},{anchor:"smolagents.MultiStepAgent.add_base_tools",description:"<strong>add_base_tools</strong> (<code>bool</code>, default <code>False</code>) &#x2014; Whether to add the base tools to the agent&#x2019;s tools.",name:"add_base_tools"},{anchor:"smolagents.MultiStepAgent.verbosity_level",description:"<strong>verbosity_level</strong> (<code>LogLevel</code>, default <code>LogLevel.INFO</code>) &#x2014; Level of verbosity of the agent&#x2019;s logs.",name:"verbosity_level"},{anchor:"smolagents.MultiStepAgent.managed_agents",description:"<strong>managed_agents</strong> (<code>list</code>, <em>optional</em>) &#x2014; Managed agents that the agent can call.",name:"managed_agents"},{anchor:"smolagents.MultiStepAgent.step_callbacks",description:"<strong>step_callbacks</strong> (<code>list[Callable]</code> | <code>dict[Type[MemoryStep], Callable | list[Callable]]</code>, <em>optional</em>) &#x2014; Callbacks that will be called at each step.",name:"step_callbacks"},{anchor:"smolagents.MultiStepAgent.planning_interval",description:"<strong>planning_interval</strong> (<code>int</code>, <em>optional</em>) &#x2014; Interval at which the agent will run a planning step.",name:"planning_interval"},{anchor:"smolagents.MultiStepAgent.name",description:"<strong>name</strong> (<code>str</code>, <em>optional</em>) &#x2014; Necessary for a managed agent only - the name by which this agent can be called.",name:"name"},{anchor:"smolagents.MultiStepAgent.description",description:"<strong>description</strong> (<code>str</code>, <em>optional</em>) &#x2014; Necessary for a managed agent only - the description of this agent.",name:"description"},{anchor:"smolagents.MultiStepAgent.provide_run_summary",description:"<strong>provide_run_summary</strong> (<code>bool</code>, <em>optional</em>) &#x2014; Whether to provide a run summary when called as a managed agent.",name:"provide_run_summary"},{anchor:"smolagents.MultiStepAgent.final_answer_checks",description:`<strong>final_answer_checks</strong> (<code>list[Callable]</code>, <em>optional</em>) &#x2014; List of validation functions to run before accepting a final answer.
Each function should:
<ul>
<li>Take the final answer, the agent&#x2019;s memory, and the agent itself as arguments.</li>
<li>Return a boolean indicating whether the final answer is valid.</li>
</ul>`,name:"final_answer_checks"},{anchor:"smolagents.MultiStepAgent.return_full_result",description:"<strong>return_full_result</strong> (<code>bool</code>, default <code>False</code>) &#x2014; Whether to return the full <code>RunResult</code> object or just the final answer output from the agent run.",name:"return_full_result"}],source:"https://github.com/huggingface/smolagents/blob/main/src/smolagents/agents.py#L266"}}),Ge=new x({props:{name:"extract_action",anchor:"smolagents.MultiStepAgent.extract_action",parameters:[{name:"model_output",val:": str"},{name:"split_token",val:": str"}],parametersDescription:[{anchor:"smolagents.MultiStepAgent.extract_action.model_output",description:"<strong>model_output</strong> (<code>str</code>) &#x2014; Output of the LLM",name:"model_output"},{anchor:"smolagents.MultiStepAgent.extract_action.split_token",description:"<strong>split_token</strong> (<code>str</code>) &#x2014; Separator for the action. Should match the example in the system prompt.",name:"split_token"}],source:"https://github.com/huggingface/smolagents/blob/main/src/smolagents/agents.py#L783"}}),je=new x({props:{name:"from_dict",anchor:"smolagents.MultiStepAgent.from_dict",parameters:[{name:"agent_dict",val:": dict"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"smolagents.MultiStepAgent.from_dict.agent_dict",description:"<strong>agent_dict</strong> (<code>dict[str, Any]</code>) &#x2014; Dictionary representation of the agent.",name:"agent_dict"},{anchor:"smolagents.MultiStepAgent.from_dict.*kwargs",description:"*<strong>*kwargs</strong> &#x2014; Additional keyword arguments that will override agent_dict values.",name:"*kwargs"}],source:"https://github.com/huggingface/smolagents/blob/main/src/smolagents/agents.py#L1004",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Instance of the agent class.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>MultiStepAgent</code></p>
`}}),Be=new x({props:{name:"from_folder",anchor:"smolagents.MultiStepAgent.from_folder",parameters:[{name:"folder",val:": str | pathlib.Path"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"smolagents.MultiStepAgent.from_folder.folder",description:"<strong>folder</strong> (<code>str</code> or <code>Path</code>) &#x2014; The folder where the agent is saved.",name:"folder"},{anchor:"smolagents.MultiStepAgent.from_folder.*kwargs",description:"*<strong>*kwargs</strong> &#x2014; Additional keyword arguments that will be passed to the agent&#x2019;s init.",name:"*kwargs"}],source:"https://github.com/huggingface/smolagents/blob/main/src/smolagents/agents.py#L1102"}}),Ze=new x({props:{name:"from_hub",anchor:"smolagents.MultiStepAgent.from_hub",parameters:[{name:"repo_id",val:": str"},{name:"token",val:": str | None = None"},{name:"trust_remote_code",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"smolagents.MultiStepAgent.from_hub.repo_id",description:`<strong>repo_id</strong> (<code>str</code>) &#x2014;
The name of the repo on the Hub where your tool is defined.`,name:"repo_id"},{anchor:"smolagents.MultiStepAgent.from_hub.token",description:`<strong>token</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The token to identify you on hf.co. If unset, will use the token generated when running
<code>huggingface-cli login</code> (stored in <code>~/.huggingface</code>).`,name:"token"},{anchor:"smolagents.MultiStepAgent.from_hub.trust_remote_code(bool,",description:`<strong>trust_remote_code(<code>bool</code>,</strong> <em>optional</em>, defaults to False) &#x2014;
This flags marks that you understand the risk of running remote code and that you trust this tool.
If not setting this to True, loading the tool from Hub will fail.`,name:"trust_remote_code(bool,"},{anchor:"smolagents.MultiStepAgent.from_hub.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Additional keyword arguments that will be split in two: all arguments relevant to the Hub (such as
<code>cache_dir</code>, <code>revision</code>, <code>subfolder</code>) will be used when downloading the files for your agent, and the
others will be passed along to its init.`,name:"kwargs"}],source:"https://github.com/huggingface/smolagents/blob/main/src/smolagents/agents.py#L1048"}}),ne=new Nl({props:{warning:!0,$$slots:{default:[Bl]},$$scope:{ctx:z}}}),Je=new x({props:{name:"initialize_system_prompt",anchor:"smolagents.MultiStepAgent.initialize_system_prompt",parameters:[],source:"https://github.com/huggingface/smolagents/blob/main/src/smolagents/agents.py#L743"}}),Qe=new x({props:{name:"interrupt",anchor:"smolagents.MultiStepAgent.interrupt",parameters:[],source:"https://github.com/huggingface/smolagents/blob/main/src/smolagents/agents.py#L748"}}),Oe=new x({props:{name:"provide_final_answer",anchor:"smolagents.MultiStepAgent.provide_final_answer",parameters:[{name:"task",val:": str"}],parametersDescription:[{anchor:"smolagents.MultiStepAgent.provide_final_answer.task",description:"<strong>task</strong> (<code>str</code>) &#x2014; Task to perform.",name:"task"},{anchor:"smolagents.MultiStepAgent.provide_final_answer.images",description:"<strong>images</strong> (<code>list[PIL.Image.Image]</code>, <em>optional</em>) &#x2014; Image(s) objects.",name:"images"}],source:"https://github.com/huggingface/smolagents/blob/main/src/smolagents/agents.py#L804",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Final answer to the task.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>str</code></p>
`}}),Ye=new x({props:{name:"push_to_hub",anchor:"smolagents.MultiStepAgent.push_to_hub",parameters:[{name:"repo_id",val:": str"},{name:"commit_message",val:": str = 'Upload agent'"},{name:"private",val:": bool | None = None"},{name:"token",val:": bool | str | None = None"},{name:"create_pr",val:": bool = False"}],parametersDescription:[{anchor:"smolagents.MultiStepAgent.push_to_hub.repo_id",description:`<strong>repo_id</strong> (<code>str</code>) &#x2014;
The name of the repository you want to push to. It should contain your organization name when
pushing to a given organization.`,name:"repo_id"},{anchor:"smolagents.MultiStepAgent.push_to_hub.commit_message",description:`<strong>commit_message</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;Upload agent&quot;</code>) &#x2014;
Message to commit while pushing.`,name:"commit_message"},{anchor:"smolagents.MultiStepAgent.push_to_hub.private",description:`<strong>private</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
Whether to make the repo private. If <code>None</code>, the repo will be public unless the organization&#x2019;s default is private. This value is ignored if the repo already exists.`,name:"private"},{anchor:"smolagents.MultiStepAgent.push_to_hub.token",description:`<strong>token</strong> (<code>bool</code> or <code>str</code>, <em>optional</em>) &#x2014;
The token to use as HTTP bearer authorization for remote files. If unset, will use the token generated
when running <code>huggingface-cli login</code> (stored in <code>~/.huggingface</code>).`,name:"token"},{anchor:"smolagents.MultiStepAgent.push_to_hub.create_pr",description:`<strong>create_pr</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to create a PR with the uploaded files or directly commit.`,name:"create_pr"}],source:"https://github.com/huggingface/smolagents/blob/main/src/smolagents/agents.py#L1134"}}),Xe=new x({props:{name:"replay",anchor:"smolagents.MultiStepAgent.replay",parameters:[{name:"detailed",val:": bool = False"}],parametersDescription:[{anchor:"smolagents.MultiStepAgent.replay.detailed",description:`<strong>detailed</strong> (bool, optional) &#x2014; If True, also displays the memory at each step. Defaults to False.
Careful: will increase log length exponentially. Use only for debugging.`,name:"detailed"}],source:"https://github.com/huggingface/smolagents/blob/main/src/smolagents/agents.py#L853"}}),Ke=new x({props:{name:"run",anchor:"smolagents.MultiStepAgent.run",parameters:[{name:"task",val:": str"},{name:"stream",val:": bool = False"},{name:"reset",val:": bool = True"},{name:"images",val:": list['PIL.Image.Image'] | None = None"},{name:"additional_args",val:": dict | None = None"},{name:"max_steps",val:": int | None = None"},{name:"return_full_result",val:": bool | None = None"}],parametersDescription:[{anchor:"smolagents.MultiStepAgent.run.task",description:"<strong>task</strong> (<code>str</code>) &#x2014; Task to perform.",name:"task"},{anchor:"smolagents.MultiStepAgent.run.stream",description:`<strong>stream</strong> (<code>bool</code>) &#x2014; Whether to run in streaming mode.
If <code>True</code>, returns a generator that yields each step as it is executed. You must iterate over this generator to process the individual steps (e.g., using a for loop or <code>next()</code>).
If <code>False</code>, executes all steps internally and returns only the final answer after completion.`,name:"stream"},{anchor:"smolagents.MultiStepAgent.run.reset",description:"<strong>reset</strong> (<code>bool</code>) &#x2014; Whether to reset the conversation or keep it going from previous run.",name:"reset"},{anchor:"smolagents.MultiStepAgent.run.images",description:"<strong>images</strong> (<code>list[PIL.Image.Image]</code>, <em>optional</em>) &#x2014; Image(s) objects.",name:"images"},{anchor:"smolagents.MultiStepAgent.run.additional_args",description:"<strong>additional_args</strong> (<code>dict</code>, <em>optional</em>) &#x2014; Any other variables that you want to pass to the agent run, for instance images or dataframes. Give them clear names!",name:"additional_args"},{anchor:"smolagents.MultiStepAgent.run.max_steps",description:"<strong>max_steps</strong> (<code>int</code>, <em>optional</em>) &#x2014; Maximum number of steps the agent can take to solve the task. if not provided, will use the agent&#x2019;s default value.",name:"max_steps"},{anchor:"smolagents.MultiStepAgent.run.return_full_result",description:`<strong>return_full_result</strong> (<code>bool</code>, <em>optional</em>) &#x2014; Whether to return the full <code>RunResult</code> object or just the final answer output.
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<p>Dictionary representation of the agent.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>dict</code></p>
`}}),ot=new x({props:{name:"visualize",anchor:"smolagents.MultiStepAgent.visualize",parameters:[],source:"https://github.com/huggingface/smolagents/blob/main/src/smolagents/agents.py#L849"}}),at=new x({props:{name:"write_memory_to_messages",anchor:"smolagents.MultiStepAgent.write_memory_to_messages",parameters:[{name:"summary_mode",val:": bool = False"}],source:"https://github.com/huggingface/smolagents/blob/main/src/smolagents/agents.py#L752"}}),st=new x({props:{name:"class smolagents.CodeAgent",anchor:"smolagents.CodeAgent",parameters:[{name:"tools",val:": list"},{name:"model",val:": Model"},{name:"prompt_templates",val:": smolagents.agents.PromptTemplates | None = None"},{name:"additional_authorized_imports",val:": list[str] | None = None"},{name:"planning_interval",val:": int | None = None"},{name:"executor",val:": PythonExecutor = None"},{name:"executor_type",val:": typing.Literal['local', 'e2b', 'modal', 'docker', 'wasm'] = 'local'"},{name:"executor_kwargs",val:": dict[str, typing.Any] | None = None"},{name:"max_print_outputs_length",val:": int | None = None"},{name:"stream_outputs",val:": bool = False"},{name:"use_structured_outputs_internally",val:": bool = False"},{name:"code_block_tags",val:": str | tuple[str, str] | None = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"smolagents.CodeAgent.tools",description:"<strong>tools</strong> (<code>list[Tool]</code>) &#x2014; <code>Tool</code>s that the agent can use.",name:"tools"},{anchor:"smolagents.CodeAgent.model",description:"<strong>model</strong> (<code>Model</code>) &#x2014; Model that will generate the agent&#x2019;s actions.",name:"model"},{anchor:"smolagents.CodeAgent.prompt_templates",description:'<strong>prompt_templates</strong> (<a href="/docs/smolagents/main/ko/reference/agents#smolagents.PromptTemplates">PromptTemplates</a>, <em>optional</em>) &#x2014; Prompt templates.',name:"prompt_templates"},{anchor:"smolagents.CodeAgent.additional_authorized_imports",description:"<strong>additional_authorized_imports</strong> (<code>list[str]</code>, <em>optional</em>) &#x2014; Additional authorized imports for the agent.",name:"additional_authorized_imports"},{anchor:"smolagents.CodeAgent.planning_interval",description:"<strong>planning_interval</strong> (<code>int</code>, <em>optional</em>) &#x2014; Interval at which the agent will run a planning step.",name:"planning_interval"},{anchor:"smolagents.CodeAgent.executor",description:'<strong>executor</strong> (<a href="/docs/smolagents/main/ko/reference/agents#smolagents.PythonExecutor">PythonExecutor</a>, <em>optional</em>) &#x2014; Custom Python code executor. If not provided, a default executor will be created based on <code>executor_type</code>.',name:"executor"},{anchor:"smolagents.CodeAgent.executor_type",description:"<strong>executor_type</strong> (<code>Literal[&quot;local&quot;, &quot;e2b&quot;, &quot;modal&quot;, &quot;docker&quot;, &quot;wasm&quot;]</code>, default <code>&quot;local&quot;</code>) &#x2014; Type of code executor.",name:"executor_type"},{anchor:"smolagents.CodeAgent.executor_kwargs",description:"<strong>executor_kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014; Additional arguments to pass to initialize the executor.",name:"executor_kwargs"},{anchor:"smolagents.CodeAgent.max_print_outputs_length",description:"<strong>max_print_outputs_length</strong> (<code>int</code>, <em>optional</em>) &#x2014; Maximum length of the print outputs.",name:"max_print_outputs_length"},{anchor:"smolagents.CodeAgent.stream_outputs",description:"<strong>stream_outputs</strong> (<code>bool</code>, <em>optional</em>, default <code>False</code>) &#x2014; Whether to stream outputs during execution.",name:"stream_outputs"},{anchor:"smolagents.CodeAgent.use_structured_outputs_internally",description:`<strong>use_structured_outputs_internally</strong> (<code>bool</code>, default <code>False</code>) &#x2014; Whether to use structured generation at each action step: improves performance for many models.</p>
<div class="course-tip  bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400">
						<p class="font-medium">Added in 1.17.0</p>
						
					</div>`,name:"use_structured_outputs_internally"},{anchor:"smolagents.CodeAgent.code_block_tags",description:"<strong>code_block_tags</strong> (<code>tuple[str, str]</code> | <code>Literal[&quot;markdown&quot;]</code>, <em>optional</em>) &#x2014; Opening and closing tags for code blocks (regex strings). Pass a custom tuple, or pass &#x2018;markdown&#x2019; to use (&#x201D;<code>(?:python|py)&quot;, &quot;\\n</code>&#x201D;), leave empty to use (&#x201D;<code>&#x201D;, &#x201D;</code>&#x201D;).",name:"code_block_tags"},{anchor:"smolagents.CodeAgent.*kwargs",description:"*<strong>*kwargs</strong> &#x2014; Additional keyword arguments.",name:"*kwargs"}],source:"https://github.com/huggingface/smolagents/blob/main/src/smolagents/agents.py#L1478"}}),rt=new x({props:{name:"cleanup",anchor:"smolagents.CodeAgent.cleanup",parameters:[],source:"https://github.com/huggingface/smolagents/blob/main/src/smolagents/agents.py#L1566"}}),lt=new x({props:{name:"from_dict",anchor:"smolagents.CodeAgent.from_dict",parameters:[{name:"agent_dict",val:": dict"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"smolagents.CodeAgent.from_dict.agent_dict",description:"<strong>agent_dict</strong> (<code>dict[str, Any]</code>) &#x2014; Dictionary representation of the agent.",name:"agent_dict"},{anchor:"smolagents.CodeAgent.from_dict.*kwargs",description:"*<strong>*kwargs</strong> &#x2014; Additional keyword arguments that will override agent_dict values.",name:"*kwargs"}],source:"https://github.com/huggingface/smolagents/blob/main/src/smolagents/agents.py#L1752",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Instance of the CodeAgent class.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>CodeAgent</code></p>
`}}),it=new x({props:{name:"class smolagents.ToolCallingAgent",anchor:"smolagents.ToolCallingAgent",parameters:[{name:"tools",val:": list"},{name:"model",val:": Model"},{name:"prompt_templates",val:": smolagents.agents.PromptTemplates | None = None"},{name:"planning_interval",val:": int | None = None"},{name:"stream_outputs",val:": bool = False"},{name:"max_tool_threads",val:": int | None = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"smolagents.ToolCallingAgent.tools",description:"<strong>tools</strong> (<code>list[Tool]</code>) &#x2014; <code>Tool</code>s that the agent can use.",name:"tools"},{anchor:"smolagents.ToolCallingAgent.model",description:"<strong>model</strong> (<code>Model</code>) &#x2014; Model that will generate the agent&#x2019;s actions.",name:"model"},{anchor:"smolagents.ToolCallingAgent.prompt_templates",description:'<strong>prompt_templates</strong> (<a href="/docs/smolagents/main/ko/reference/agents#smolagents.PromptTemplates">PromptTemplates</a>, <em>optional</em>) &#x2014; Prompt templates.',name:"prompt_templates"},{anchor:"smolagents.ToolCallingAgent.planning_interval",description:"<strong>planning_interval</strong> (<code>int</code>, <em>optional</em>) &#x2014; Interval at which the agent will run a planning step.",name:"planning_interval"},{anchor:"smolagents.ToolCallingAgent.stream_outputs",description:"<strong>stream_outputs</strong> (<code>bool</code>, <em>optional</em>, default <code>False</code>) &#x2014; Whether to stream outputs during execution.",name:"stream_outputs"},{anchor:"smolagents.ToolCallingAgent.max_tool_threads",description:`<strong>max_tool_threads</strong> (<code>int</code>, <em>optional</em>) &#x2014; Maximum number of threads for parallel tool calls.
Higher values increase concurrency but resource usage as well.
Defaults to <code>ThreadPoolExecutor</code>&#x2019;s default.`,name:"max_tool_threads"},{anchor:"smolagents.ToolCallingAgent.*kwargs",description:"*<strong>*kwargs</strong> &#x2014; Additional keyword arguments.",name:"*kwargs"}],source:"https://github.com/huggingface/smolagents/blob/main/src/smolagents/agents.py#L1189"}}),mt=new x({props:{name:"execute_tool_call",anchor:"smolagents.ToolCallingAgent.execute_tool_call",parameters:[{name:"tool_name",val:": str"},{name:"arguments",val:": dict[str, str] | str"}],parametersDescription:[{anchor:"smolagents.ToolCallingAgent.execute_tool_call.tool_name",description:"<strong>tool_name</strong> (<code>str</code>) &#x2014; Name of the tool or managed agent to execute.",name:"tool_name"},{anchor:"smolagents.ToolCallingAgent.execute_tool_call.arguments",description:"<strong>arguments</strong> (dict[str, str] | str) &#x2014; Arguments passed to the tool call.",name:"arguments"}],source:"https://github.com/huggingface/smolagents/blob/main/src/smolagents/agents.py#L1426"}}),gt=new x({props:{name:"process_tool_calls",anchor:"smolagents.ToolCallingAgent.process_tool_calls",parameters:[{name:"chat_message",val:": ChatMessage"},{name:"memory_step",val:": ActionStep"}],parametersDescription:[{anchor:"smolagents.ToolCallingAgent.process_tool_calls.chat_message",description:"<strong>chat_message</strong> (<code>ChatMessage</code>) &#x2014; Chat message containing tool calls from the model.",name:"chat_message"},{anchor:"smolagents.ToolCallingAgent.process_tool_calls.memory_step",description:"<strong>memory_step</strong> (<code>ActionStep)</code> &#x2014; Memory ActionStep to update with results.",name:"memory_step"}],source:"https://github.com/huggingface/smolagents/blob/main/src/smolagents/agents.py#L1335",returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>ToolCall | ToolOutput</code></p>
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<ul>
<li><code>ModuleNotFoundError</code> — If the <code>gradio</code> extra is not installed.</li>
</ul>
`,raiseType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>ModuleNotFoundError</code></p>
`}}),_e=new Vl({props:{anchor:"smolagents.GradioUI.example",$$slots:{default:[Jl]},$$scope:{ctx:z}}}),ht=new x({props:{name:"launch",anchor:"smolagents.GradioUI.launch",parameters:[{name:"share",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"smolagents.GradioUI.launch.share",description:"<strong>share</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014; Whether to share the app publicly.",name:"share"},{anchor:"smolagents.GradioUI.launch.*kwargs",description:"*<strong>*kwargs</strong> &#x2014; Additional keyword arguments to pass to the Gradio launch method.",name:"*kwargs"}],source:"https://github.com/huggingface/smolagents/blob/main/src/smolagents/gradio_ui.py#L406"}}),ft=new x({props:{name:"upload_file",anchor:"smolagents.GradioUI.upload_file",parameters:[{name:"file",val:""},{name:"file_uploads_log",val:""},{name:"allowed_file_types",val:" = None"}],parametersDescription:[{anchor:"smolagents.GradioUI.upload_file.file",description:"<strong>file</strong> (<code>gradio.File</code>) &#x2014; The uploaded file.",name:"file"},{anchor:"smolagents.GradioUI.upload_file.file_uploads_log",description:"<strong>file_uploads_log</strong> (<code>list</code>) &#x2014; A list to log uploaded files.",name:"file_uploads_log"},{anchor:"smolagents.GradioUI.upload_file.allowed_file_types",description:"<strong>allowed_file_types</strong> (<code>list</code>, <em>optional</em>) &#x2014; List of allowed file extensions. 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