import{s as Se,n as xe,o as Ye}from"../chunks/scheduler.00b97157.js";import{S as He,i as Fe,e as p,s as n,c as o,h as Le,a as M,d as t,b as a,f as Re,g as i,j as m,k as ze,l as Pe,m as s,n as y,t as c,o as b,p as r}from"../chunks/index.a7c1a7a1.js";import{C as qe,H as ge,E as De}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.4292aef9.js";import{C as x}from"../chunks/CodeBlock.afb4ac0c.js";import{D as Ke}from"../chunks/DocNotebookDropdown.eca57a73.js";function Oe(fe){let w,F,Y,L,J,P,T,q,j,D,U,Ie="归根结底，Agent可以定义为由几个简单组件构成：它拥有工具、提示词。最重要的是，它具备对过往步骤的记忆，能够追溯完整的规划、执行和错误历史。",K,u,O,h,Ze="我们提供了多项功能来审查Agent的过往运行记录。",ee,d,Be='您可以通过插装（instrumentation）在可视化界面中查看Agent的运行过程，该界面支持对特定步骤进行缩放操作，具体方法参见<a href="./inspect_runs">插装指南</a>。',le,C,ve="您也可以使用<code>agent.replay()</code>方法实现回放：",te,g,Ve="当Agent完成运行后：",se,f,ne,I,_e="若要回放最近一次运行，只需使用：",ae,Z,pe,B,Me,v,Ne="许多高级应用场景需要对Agent的记忆进行动态修改。",me,V,ke="您可以通过以下方式访问Agent的记忆：",oe,_,ie,N,We="使用<code>agent.memory.get_full_steps()</code>可获取完整步骤字典数据。",ye,k,$e="您还可以通过步骤回调（step callbacks）实现记忆的动态修改。",ce,W,Xe="步骤回调函数可通过参数直接访问<code>agent</code>对象，因此能够访问所有记忆步骤并根据需要进行修改。例如，假设您正在监控网页浏览Agent每个步骤的屏幕截图，希望保留最新截图同时删除旧步骤的图片以节省token消耗。",be,$,Ee=`可参考以下代码示例：
<em>注：此代码片段不完整，部分导入语句和对象定义已精简，完整代码请访问<a href="https://github.com/huggingface/smolagents/blob/main/src/smolagents/vision_web_browser.py" rel="nofollow">原始脚本</a></em>`,re,X,we,E,Ae="最后在初始化Agent时，将此函数传入<code>step_callbacks</code>参数：",Je,A,Te,G,Ge='请访问我们的 <a href="https://github.com/huggingface/smolagents/blob/main/src/smolagents/vision_web_browser.py" rel="nofollow">vision web browser code</a> 查看完整可运行示例。',je,Q,Ue,R,Qe="当您需要处理耗时数天的工具调用时，这种方式特别有用：您可以逐步执行Agents。这还允许您在每一步更新记忆。",ue,z,he,S,de,H,Ce;return J=new qe({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),T=new ge({props:{title:"📚 管理Agent的记忆",local:"-管理agent的记忆",headingTag:"h1"}}),j=new Ke({props:{classNames:"absolute z-10 right-0 top-0",options:[{label:"Mixed",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/smolagents_doc/zh/memory.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/smolagents_doc/zh/pytorch/memory.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/smolagents_doc/zh/tensorflow/memory.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/smolagents_doc/zh/memory.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/smolagents_doc/zh/pytorch/memory.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/smolagents_doc/zh/tensorflow/memory.ipynb"}]}}),u=new ge({props:{title:"回放Agent的记忆",local:"回放agent的记忆",headingTag:"h3"}}),f=new x({props:{code:"ZnJvbSUyMHNtb2xhZ2VudHMlMjBpbXBvcnQlMjBJbmZlcmVuY2VDbGllbnRNb2RlbCUyQyUyMENvZGVBZ2VudCUwQSUwQWFnZW50JTIwJTNEJTIwQ29kZUFnZW50KHRvb2xzJTNEJTVCJTVEJTJDJTIwbW9kZWwlM0RJbmZlcmVuY2VDbGllbnRNb2RlbCgpJTJDJTIwdmVyYm9zaXR5X2xldmVsJTNEMCklMEElMEFyZXN1bHQlMjAlM0QlMjBhZ2VudC5ydW4oJTIyV2hhdCdzJTIwdGhlJTIwMjB0aCUyMEZpYm9uYWNjaSUyMG51bWJlciUzRiUyMik=",highlighted:`<span class="hljs-keyword">from</span> smolagents <span class="hljs-keyword">import</span> InferenceClientModel, CodeAgent

agent = CodeAgent(tools=[], model=InferenceClientModel(), verbosity_level=<span class="hljs-number">0</span>)

result = agent.run(<span class="hljs-string">&quot;What&#x27;s the 20th Fibonacci number?&quot;</span>)`,wrap:!1}}),Z=new x({props:{code:"YWdlbnQucmVwbGF5KCk=",highlighted:"agent.replay()",wrap:!1}}),B=new ge({props:{title:"动态修改Agent的记忆",local:"动态修改agent的记忆",headingTag:"h3"}}),_=new x({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> smolagents <span class="hljs-keyword">import</span> ActionStep

system_prompt_step = agent.memory.system_prompt
<span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;The system prompt given to the agent was:&quot;</span>)
<span class="hljs-built_in">print</span>(system_prompt_step.system_prompt)

task_step = agent.memory.steps[<span class="hljs-number">0</span>]
<span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;\\n\\nThe first task step was:&quot;</span>)
<span class="hljs-built_in">print</span>(task_step.task)

<span class="hljs-keyword">for</span> step <span class="hljs-keyword">in</span> agent.memory.steps:
    <span class="hljs-keyword">if</span> <span class="hljs-built_in">isinstance</span>(step, ActionStep):
        <span class="hljs-keyword">if</span> step.error <span class="hljs-keyword">is</span> <span class="hljs-keyword">not</span> <span class="hljs-literal">None</span>:
            <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;\\nStep <span class="hljs-subst">{step.step_number}</span> got this error:\\n<span class="hljs-subst">{step.error}</span>\\n&quot;</span>)
        <span class="hljs-keyword">else</span>:
            <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;\\nStep <span class="hljs-subst">{step.step_number}</span> got these observations:\\n<span class="hljs-subst">{step.observations}</span>\\n&quot;</span>)`,wrap:!1}}),X=new x({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> helium
<span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-keyword">from</span> io <span class="hljs-keyword">import</span> BytesIO
<span class="hljs-keyword">from</span> time <span class="hljs-keyword">import</span> sleep

<span class="hljs-keyword">def</span> <span class="hljs-title function_">update_screenshot</span>(<span class="hljs-params">memory_step: ActionStep, agent: CodeAgent</span>) -&gt; <span class="hljs-literal">None</span>:
    sleep(<span class="hljs-number">1.0</span>)  <span class="hljs-comment"># Let JavaScript animations happen before taking the screenshot</span>
    driver = helium.get_driver()
    latest_step = memory_step.step_number
    <span class="hljs-keyword">for</span> previous_memory_step <span class="hljs-keyword">in</span> agent.memory.steps:  <span class="hljs-comment"># Remove previous screenshots from logs for lean processing</span>
        <span class="hljs-keyword">if</span> <span class="hljs-built_in">isinstance</span>(previous_memory_step, ActionStep) <span class="hljs-keyword">and</span> previous_memory_step.step_number &lt;= latest_step - <span class="hljs-number">2</span>:
            previous_memory_step.observations_images = <span class="hljs-literal">None</span>
    png_bytes = driver.get_screenshot_as_png()
    image = Image.<span class="hljs-built_in">open</span>(BytesIO(png_bytes))
    memory_step.observations_images = [image.copy()]`,wrap:!1}}),A=new x({props:{code:"Q29kZUFnZW50KCUwQSUyMCUyMCUyMCUyMHRvb2xzJTNEJTVCV2ViU2VhcmNoVG9vbCgpJTJDJTIwZ29fYmFjayUyQyUyMGNsb3NlX3BvcHVwcyUyQyUyMHNlYXJjaF9pdGVtX2N0cmxfZiU1RCUyQyUwQSUyMCUyMCUyMCUyMG1vZGVsJTNEbW9kZWwlMkMlMEElMjAlMjAlMjAlMjBhZGRpdGlvbmFsX2F1dGhvcml6ZWRfaW1wb3J0cyUzRCU1QiUyMmhlbGl1bSUyMiU1RCUyQyUwQSUyMCUyMCUyMCUyMHN0ZXBfY2FsbGJhY2tzJTNEJTVCdXBkYXRlX3NjcmVlbnNob3QlNUQlMkMlMEElMjAlMjAlMjAlMjBtYXhfc3RlcHMlM0QyMCUyQyUwQSUyMCUyMCUyMCUyMHZlcmJvc2l0eV9sZXZlbCUzRDIlMkMlMEEp",highlighted:`CodeAgent(
    tools=[WebSearchTool(), go_back, close_popups, search_item_ctrl_f],
    model=model,
    additional_authorized_imports=[<span class="hljs-string">&quot;helium&quot;</span>],
    step_callbacks=[update_screenshot],
    max_steps=<span class="hljs-number">20</span>,
    verbosity_level=<span class="hljs-number">2</span>,
)`,wrap:!1}}),Q=new ge({props:{title:"分步运行 Agents",local:"分步运行-agents",headingTag:"h3"}}),z=new x({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> smolagents <span class="hljs-keyword">import</span> InferenceClientModel, CodeAgent, ActionStep, TaskStep

agent = CodeAgent(tools=[], model=InferenceClientModel(), verbosity_level=<span class="hljs-number">1</span>)
<span class="hljs-built_in">print</span>(agent.memory.system_prompt)

task = <span class="hljs-string">&quot;What is the 20th Fibonacci number?&quot;</span>

<span class="hljs-comment"># You could modify the memory as needed here by inputting the memory of another agent.</span>
<span class="hljs-comment"># agent.memory.steps = previous_agent.memory.steps</span>

<span class="hljs-comment"># Let&#x27;s start a new task!</span>
agent.memory.steps.append(TaskStep(task=task, task_images=[]))

final_answer = <span class="hljs-literal">None</span>
step_number = <span class="hljs-number">1</span>
<span class="hljs-keyword">while</span> final_answer <span class="hljs-keyword">is</span> <span class="hljs-literal">None</span> <span class="hljs-keyword">and</span> step_number &lt;= <span class="hljs-number">10</span>:
    memory_step = ActionStep(
        step_number=step_number,
        observations_images=[],
    )
    <span class="hljs-comment"># Run one step.</span>
    final_answer = agent.step(memory_step)
    agent.memory.steps.append(memory_step)
    step_number += <span class="hljs-number">1</span>

    <span class="hljs-comment"># Change the memory as you please!</span>
    <span class="hljs-comment"># For instance to update the latest step:</span>
    <span class="hljs-comment"># agent.memory.steps[-1] = ...</span>

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