import{s as Ma,o as Aa,n as In}from"../chunks/scheduler.f6319a2a.js";import{S as Ta,i as ka,g as s,s as n,r as g,A as Pa,h as r,f as a,c as o,j as x,u as d,x as m,k as $,y as t,a as i,v as p,d as c,t as u,w as h}from"../chunks/index.85aa684c.js";import{T as ya,D as y,E as wa}from"../chunks/ExampleCodeBlock.7120e9d6.js";import{C as Ca}from"../chunks/CodeBlock.3400fc53.js";import{H as Gt,E as Ia}from"../chunks/getInferenceSnippets.0233181f.js";function La(S){let f,T="Smolagents 是一个实验性的 API，可能会随时发生变化。由于 API 或底层模型可能发生变化，代理返回的结果也可能有所不同。";return{c(){f=s("p"),f.textContent=T},l(w){f=r(w,"P",{"data-svelte-h":!0}),m(f)!=="svelte-p58lhb"&&(f.textContent=T)},m(w,C){i(w,f,C)},p:In,d(w){w&&a(f)}}}function Sa(S){let f,T=`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(){f=s("p"),f.textContent=T},l(w){f=r(w,"P",{"data-svelte-h":!0}),m(f)!=="svelte-fpk7e5"&&(f.textContent=T)},m(w,C){i(w,f,C)},p:In,d(w){w&&a(f)}}}function Na(S){let f,T="Example:",w,C,M;return C=new Ca({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(){f=s("p"),f.textContent=T,w=n(),g(C.$$.fragment)},l(v){f=r(v,"P",{"data-svelte-h":!0}),m(f)!=="svelte-11lpom8"&&(f.textContent=T),w=o(v),d(C.$$.fragment,v)},m(v,k){i(v,f,k),i(v,w,k),p(C,v,k),M=!0},p:In,i(v){M||(c(C.$$.fragment,v),M=!0)},o(v){u(C.$$.fragment,v),M=!1},d(v){v&&(a(f),a(w)),h(C,v)}}}function Da(S){let f,T="Example:",w,C,M;return C=new Ca({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(){f=s("p"),f.textContent=T,w=n(),g(C.$$.fragment)},l(v){f=r(v,"P",{"data-svelte-h":!0}),m(f)!=="svelte-11lpom8"&&(f.textContent=T),w=o(v),d(C.$$.fragment,v)},m(v,k){i(v,f,k),i(v,w,k),p(C,v,k),M=!0},p:In,i(v){M||(c(C.$$.fragment,v),M=!0)},o(v){u(C.$$.fragment,v),M=!1},d(v){v&&(a(f),a(w)),h(C,v)}}}function Ea(S){let f,T,w,C,M,v,k,qt,de,Uo='要了解有关智能体和工具的更多信息，请务必阅读<a href="../index">入门指南</a>。本页面包含基础类的 API 文档。',jt,pe,Wt,ce,Vo='我们的智能体继承自 <a href="/docs/smolagents/main/zh/reference/agents#smolagents.MultiStepAgent">MultiStepAgent</a>，这意味着它们可以执行多步操作，每一步包含一个思考（thought），然后是一个工具调用和执行。请阅读<a href="../conceptual_guides/react">概念指南</a>以了解更多信息。',Rt,ue,zo="我们提供两种类型的代理，它们基于主要的 <code>Agent</code> 类：",Zt,he,Fo='<li><a href="/docs/smolagents/main/zh/reference/agents#smolagents.CodeAgent">CodeAgent</a> 是默认代理，它以 Python 代码编写工具调用。</li> <li><a href="/docs/smolagents/main/zh/reference/agents#smolagents.ToolCallingAgent">ToolCallingAgent</a> 以 JSON 编写工具调用。</li>',Jt,fe,Go="两者在初始化时都需要提供参数 <code>model</code> 和工具列表 <code>tools</code>。",Bt,_e,Qt,_,be,Ln,tt,Ho=`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).`,Sn,j,ve,Nn,nt,qo="Parse action from the LLM output",Dn,W,$e,En,ot,jo="Create agent from a dictionary representation.",Un,R,xe,Vn,at,Wo="Loads an agent from a local folder.",zn,N,ye,Fn,st,Ro="Loads an agent defined on the Hub.",Gn,Z,Hn,J,we,qn,rt,Zo="To be implemented in child classes",jn,B,Ce,Wn,lt,Jo="Interrupts the agent execution.",Rn,Q,Me,Zn,it,Bo="Provide the final answer to the task, based on the logs of the agent’s interactions.",Jn,O,Ae,Bn,mt,Qo="Upload the agent to the Hub.",Qn,Y,Te,On,gt,Oo="Prints a pretty replay of the agent’s steps.",Yn,D,ke,Xn,dt,Yo="Run the agent for the given task.",Kn,X,eo,E,Pe,to,pt,Xo="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:",no,ct,Ko=`<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>`,oo,K,Ie,ao,ut,ea=`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.`,so,ee,Le,ro,ht,ta="Convert the agent to a dictionary representation.",lo,te,Se,io,ft,na="Creates a rich tree visualization of the agent’s structure.",mo,ne,Ne,go,_t,oa=`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.`,Ot,P,De,po,bt,aa="In this agent, the tool calls will be formulated by the LLM in code format, then parsed and executed.",co,oe,Ee,uo,vt,sa="Clean up resources used by the agent, such as the remote Python executor.",ho,ae,Ue,fo,$t,ra="Create CodeAgent from a dictionary representation.",Yt,I,Ve,_o,xt,la="This agent uses JSON-like tool calls, using method <code>model.get_tool_call</code> to leverage the LLM engine’s tool calling capabilities.",bo,U,ze,vo,yt,ia="Execute a tool or managed agent with the provided arguments.",$o,wt,ma="The arguments are replaced with the actual values from the state if they refer to state variables.",xo,se,Fe,yo,Ct,ga="Process tool calls from the model output and update agent memory.",Xt,Ge,Kt,z,He,wo,Mt,da="Runs an agent with the given task and streams the messages from the agent as gradio ChatMessages.",en,qe,tn,re,pa="<p>您必须安装 <code>gradio</code> 才能使用 UI。如果尚未安装，请运行 <code>pip install &#39;smolagents[gradio]&#39;</code>。</p>",nn,A,je,Co,At,ca='Gradio interface for interacting with a <a href="/docs/smolagents/main/zh/reference/agents#smolagents.MultiStepAgent">MultiStepAgent</a>.',Mo,Tt,ua=`This class provides a web interface to interact with the agent in real-time, allowing users to submit prompts, upload files, and receive responses in a chat-like format.
It  can reset the agent’s memory at the start of each interaction if desired.
It supports file uploads, which are saved to a specified folder.
It uses the <code>gradio.Chatbot</code> component to display the conversation history.
This class requires the <code>gradio</code> extra to be installed: <code>pip install &#39;smolagents[gradio]&#39;</code>.`,Ao,le,To,ie,We,ko,kt,ha="Launch the Gradio app with the agent interface.",Po,V,Re,Io,Pt,fa="Upload a file and add it to the list of uploaded files in the session state.",Lo,It,_a=`The file is saved to the <code>self.file_upload_folder</code> folder.
If the file type is not allowed, it returns a message indicating the disallowed file type.`,on,Ze,an,F,Je,So,Lt,ba="Prompt templates for the agent.",sn,G,Be,No,St,va="Prompt templates for the planning step.",rn,H,Qe,Do,Nt,$a="Prompt templates for the managed agent.",ln,q,Oe,Eo,Dt,xa="Prompt templates for the final answer.",mn,Ye,gn,Ht,dn;return M=new Gt({props:{title:"Agents（智能体）",local:"agents智能体",headingTag:"h1"}}),k=new ya({props:{warning:!0,$$slots:{default:[La]},$$scope:{ctx:S}}}),pe=new Gt({props:{title:"智能体（Agents）",local:"智能体agents",headingTag:"h2"}}),_e=new Gt({props:{title:"智能体类",local:"smolagents.MultiStepAgent",headingTag:"h3"}}),be=new y({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; <a href="/docs/smolagents/main/zh/reference/tools#smolagents.Tool">Tool</a>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/zh/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 and the agent&#x2019;s memory 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"}}),ve=new y({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"}}),$e=new y({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>
`}}),xe=new y({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"}}),ye=new y({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"}}),Z=new ya({props:{warning:!0,$$slots:{default:[Sa]},$$scope:{ctx:S}}}),we=new y({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"}}),Ce=new y({props:{name:"interrupt",anchor:"smolagents.MultiStepAgent.interrupt",parameters:[],source:"https://github.com/huggingface/smolagents/blob/main/src/smolagents/agents.py#L748"}}),Me=new y({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>
`}}),Ae=new y({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"}}),Te=new y({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 y({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>).
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<p>Dictionary representation of the agent.</p>
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<p><code>dict</code></p>
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<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>
						
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<p>Instance of the CodeAgent class.</p>
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<p><code>CodeAgent</code></p>
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<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>
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