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Running Benchmarks
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Attack & Defense CTFs
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Cyber Ranges
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Knowledge Benchmarks
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Running Agents
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Running Agents
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Streaming
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Continue Mode
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Working with Results
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Streaming
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Context Management
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Guardrails & Security
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Environment Variables
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<h1 id="running-agents">Running agents</h1>
<p>You can run agents via the <a class="autorefs autorefs-internal" title="Runner" href="../ref/run/#cai.sdk.agents.run.Runner"><code>Runner</code></a> class. You have 3 options:</p>
<ol>
<li><a class="autorefs autorefs-internal" title="run
async
classmethod
" href="../ref/run/#cai.sdk.agents.run.Runner.run"><code>Runner.run()</code></a>, which runs async and returns a <a class="autorefs autorefs-internal" title="RunResult
dataclass
" href="../ref/result/#cai.sdk.agents.result.RunResult"><code>RunResult</code></a>.</li>
<li><a class="autorefs autorefs-internal" title="run_sync
classmethod
" href="../ref/run/#cai.sdk.agents.run.Runner.run_sync"><code>Runner.run_sync()</code></a>, which is a sync method and just runs <code>.run()</code> under the hood.</li>
<li><a class="autorefs autorefs-internal" title="run_streamed
classmethod
" href="../ref/run/#cai.sdk.agents.run.Runner.run_streamed"><code>Runner.run_streamed()</code></a>, which runs async and returns a <a class="autorefs autorefs-internal" title="RunResultStreaming
dataclass
" href="../ref/result/#cai.sdk.agents.result.RunResultStreaming"><code>RunResultStreaming</code></a>. It calls the LLM in streaming mode, and streams those events to you as they are received.</li>
</ol>
<div class="language-python highlight"><pre><span></span><code><span id="__span-0-1"><span class="kn">from</span><span class="w"> </span><span class="nn">cai.sdk.agents</span><span class="w"> </span><span class="kn">import</span> <span class="n">Agent</span><span class="p">,</span> <span class="n">Runner</span>
</span><span id="__span-0-2">
</span><span id="__span-0-3"><span class="k">async</span> <span class="k">def</span><span class="w"> </span><span class="nf">main</span><span class="p">():</span>
</span><span id="__span-0-4"> <span class="n">agent</span> <span class="o">=</span> <span class="n">Agent</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;Assistant&quot;</span><span class="p">,</span> <span class="n">instructions</span><span class="o">=</span><span class="s2">&quot;You are a helpful assistant&quot;</span><span class="p">)</span>
</span><span id="__span-0-5">
</span><span id="__span-0-6"> <span class="n">result</span> <span class="o">=</span> <span class="k">await</span> <span class="n">Runner</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">agent</span><span class="p">,</span> <span class="s2">&quot;Write a haiku about recursion in programming.&quot;</span><span class="p">)</span>
</span><span id="__span-0-7"> <span class="nb">print</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">final_output</span><span class="p">)</span>
</span><span id="__span-0-8"> <span class="c1"># Code within the code,</span>
</span><span id="__span-0-9"> <span class="c1"># Functions calling themselves,</span>
</span><span id="__span-0-10"> <span class="c1"># Infinite loop&#39;s dance.</span>
</span></code></pre></div>
<p>Read more in the <a href="../results/">results guide</a>.</p>
<h2 id="the-agent-loop">The agent loop</h2>
<p>When you use the run method in <code>Runner</code>, you pass in a starting agent and input. The input can either be a string (which is considered a user message), or a list of input items, which are the items in the OpenAI Responses API.</p>
<p>The runner then runs a loop:</p>
<ol>
<li>We call the LLM for the current agent, with the current input.</li>
<li>The LLM produces its output.<ol>
<li>If the LLM returns a <code>final_output</code>, the loop ends and we return the result.</li>
<li>If the LLM does a handoff, we update the current agent and input, and re-run the loop.</li>
<li>If the LLM produces tool calls, we run those tool calls, append the results, and re-run the loop.</li>
</ol>
</li>
<li>If we exceed the <code>max_turns</code> passed, we raise a <a class="autorefs autorefs-internal" title="MaxTurnsExceeded" href="../ref/exceptions/#cai.sdk.agents.exceptions.MaxTurnsExceeded"><code>MaxTurnsExceeded</code></a> exception.</li>
</ol>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The rule for whether the LLM output is considered as a "final output" is that it produces text output with the desired type, and there are no tool calls.</p>
</div>
<h2 id="streaming">Streaming</h2>
<p>Streaming allows you to additionally receive streaming events as the LLM runs. Once the stream is done, the <a class="autorefs autorefs-internal" title="RunResultStreaming
dataclass
" href="../ref/result/#cai.sdk.agents.result.RunResultStreaming"><code>RunResultStreaming</code></a> will contain the complete information about the run, including all the new outputs produces. You can call <code>.stream_events()</code> for the streaming events. Read more in the <a href="../streaming/">streaming guide</a>.</p>
<h2 id="run-config">Run config</h2>
<p>The <code>run_config</code> parameter lets you configure some global settings for the agent run:</p>
<ul>
<li><a class="autorefs autorefs-internal" title="model
class-attribute
instance-attribute
" href="../ref/run/#cai.sdk.agents.run.RunConfig.model"><code>model</code></a>: Allows setting a global LLM model to use, irrespective of what <code>model</code> each Agent has.</li>
<li><a class="autorefs autorefs-internal" title="model_provider
class-attribute
instance-attribute
" href="../ref/run/#cai.sdk.agents.run.RunConfig.model_provider"><code>model_provider</code></a>: A model provider for looking up model names, which defaults to OpenAI.</li>
<li><a class="autorefs autorefs-internal" title="model_settings
class-attribute
instance-attribute
" href="../ref/run/#cai.sdk.agents.run.RunConfig.model_settings"><code>model_settings</code></a>: Overrides agent-specific settings. For example, you can set a global <code>temperature</code> or <code>top_p</code>.</li>
<li><a class="autorefs autorefs-internal" title="input_guardrails
class-attribute
instance-attribute
" href="../ref/run/#cai.sdk.agents.run.RunConfig.input_guardrails"><code>input_guardrails</code></a>, <a class="autorefs autorefs-internal" title="output_guardrails
class-attribute
instance-attribute
" href="../ref/run/#cai.sdk.agents.run.RunConfig.output_guardrails"><code>output_guardrails</code></a>: A list of input or output guardrails to include on all runs.</li>
<li><a class="autorefs autorefs-internal" title="handoff_input_filter
class-attribute
instance-attribute
" href="../ref/run/#cai.sdk.agents.run.RunConfig.handoff_input_filter"><code>handoff_input_filter</code></a>: A global input filter to apply to all handoffs, if the handoff doesn't already have one. The input filter allows you to edit the inputs that are sent to the new agent. See the documentation in <a class="autorefs autorefs-internal" title="input_filter
class-attribute
instance-attribute
" href="../ref/handoffs/#cai.sdk.agents.handoffs.Handoff.input_filter"><code>Handoff.input_filter</code></a> for more details.</li>
<li><a class="autorefs autorefs-internal" title="tracing_disabled
class-attribute
instance-attribute
" href="../ref/run/#cai.sdk.agents.run.RunConfig.tracing_disabled"><code>tracing_disabled</code></a>: Allows you to disable <a href="../tracing/">tracing</a> for the entire run.</li>
<li><a class="autorefs autorefs-internal" title="trace_include_sensitive_data
class-attribute
instance-attribute
" href="../ref/run/#cai.sdk.agents.run.RunConfig.trace_include_sensitive_data"><code>trace_include_sensitive_data</code></a>: Configures whether traces will include potentially sensitive data, such as LLM and tool call inputs/outputs.</li>
<li><a class="autorefs autorefs-internal" title="workflow_name
class-attribute
instance-attribute
" href="../ref/run/#cai.sdk.agents.run.RunConfig.workflow_name"><code>workflow_name</code></a>, <a class="autorefs autorefs-internal" title="trace_id
class-attribute
instance-attribute
" href="../ref/run/#cai.sdk.agents.run.RunConfig.trace_id"><code>trace_id</code></a>, <a class="autorefs autorefs-internal" title="group_id
class-attribute
instance-attribute
" href="../ref/run/#cai.sdk.agents.run.RunConfig.group_id"><code>group_id</code></a>: Sets the tracing workflow name, trace ID and trace group ID for the run. We recommend at least setting <code>workflow_name</code>. The session ID is an optional field that lets you link traces across multiple runs.</li>
<li><a class="autorefs autorefs-internal" title="trace_metadata
class-attribute
instance-attribute
" href="../ref/run/#cai.sdk.agents.run.RunConfig.trace_metadata"><code>trace_metadata</code></a>: Metadata to include on all traces.</li>
</ul>
<h2 id="conversationschat-threads">Conversations/chat threads</h2>
<p>Calling any of the run methods can result in one or more agents running (and hence one or more LLM calls), but it represents a single logical turn in a chat conversation. For example:</p>
<ol>
<li>User turn: user enter text</li>
<li>Runner run: first agent calls LLM, runs tools, does a handoff to a second agent, second agent runs more tools, and then produces an output.</li>
</ol>
<p>At the end of the agent run, you can choose what to show to the user. For example, you might show the user every new item generated by the agents, or just the final output. Either way, the user might then ask a followup question, in which case you can call the run method again.</p>
<p>You can use the base <a class="autorefs autorefs-internal" title="to_input_list" href="../ref/result/#cai.sdk.agents.result.RunResultBase.to_input_list"><code>RunResultBase.to_input_list()</code></a> method to get the inputs for the next turn.</p>
<div class="language-python highlight"><pre><span></span><code><span id="__span-1-1"><span class="k">async</span> <span class="k">def</span><span class="w"> </span><span class="nf">main</span><span class="p">():</span>
</span><span id="__span-1-2"> <span class="n">agent</span> <span class="o">=</span> <span class="n">Agent</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;Assistant&quot;</span><span class="p">,</span> <span class="n">instructions</span><span class="o">=</span><span class="s2">&quot;Reply very concisely.&quot;</span><span class="p">)</span>
</span><span id="__span-1-3">
</span><span id="__span-1-4"> <span class="k">with</span> <span class="n">trace</span><span class="p">(</span><span class="n">workflow_name</span><span class="o">=</span><span class="s2">&quot;Conversation&quot;</span><span class="p">,</span> <span class="n">group_id</span><span class="o">=</span><span class="n">thread_id</span><span class="p">):</span>
</span><span id="__span-1-5"> <span class="c1"># First turn</span>
</span><span id="__span-1-6"> <span class="n">result</span> <span class="o">=</span> <span class="k">await</span> <span class="n">Runner</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">agent</span><span class="p">,</span> <span class="s2">&quot;What is phishing?&quot;</span><span class="p">)</span>
</span><span id="__span-1-7"> <span class="nb">print</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">final_output</span><span class="p">)</span>
</span><span id="__span-1-8"> <span class="c1"># Expected: A type of cyberattack where users are tricked into giving sensitive info.</span>
</span><span id="__span-1-9">
</span><span id="__span-1-10"> <span class="c1"># Second turn</span>
</span><span id="__span-1-11"> <span class="n">new_input</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">to_input_list</span><span class="p">()</span> <span class="o">+</span> <span class="p">[{</span><span class="s2">&quot;role&quot;</span><span class="p">:</span> <span class="s2">&quot;user&quot;</span><span class="p">,</span> <span class="s2">&quot;content&quot;</span><span class="p">:</span> <span class="s2">&quot;How can I protect myself from it?&quot;</span><span class="p">}]</span>
</span><span id="__span-1-12"> <span class="n">result</span> <span class="o">=</span> <span class="k">await</span> <span class="n">Runner</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">agent</span><span class="p">,</span> <span class="n">new_input</span><span class="p">)</span>
</span><span id="__span-1-13"> <span class="nb">print</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">final_output</span><span class="p">)</span>
</span><span id="__span-1-14"> <span class="c1"># Expected: Use email filters, don&#39;t click unknown links, and enable 2FA.</span>
</span></code></pre></div>
<h2 id="exceptions">Exceptions</h2>
<p>The SDK raises exceptions in certain cases. The full list is in <a class="autorefs autorefs-internal" href="../ref/exceptions/#cai.sdk.agents.exceptions"><code>cai.sdk.agents.exceptions</code></a>. As an overview:</p>
<ul>
<li><a class="autorefs autorefs-internal" title="AgentsException" href="../ref/exceptions/#cai.sdk.agents.exceptions.AgentsException"><code>AgentsException</code></a> is the base class for all exceptions raised.</li>
<li><a class="autorefs autorefs-internal" title="MaxTurnsExceeded" href="../ref/exceptions/#cai.sdk.agents.exceptions.MaxTurnsExceeded"><code>MaxTurnsExceeded</code></a> is raised when the run exceeds the <code>max_turns</code> passed to the run methods.</li>
<li><a class="autorefs autorefs-internal" title="ModelBehaviorError" href="../ref/exceptions/#cai.sdk.agents.exceptions.ModelBehaviorError"><code>ModelBehaviorError</code></a> is raised when the model produces invalid outputs, e.g. malformed JSON or using non-existent tools.</li>
<li><a class="autorefs autorefs-internal" title="UserError" href="../ref/exceptions/#cai.sdk.agents.exceptions.UserError"><code>UserError</code></a> is raised when you (the person writing code using CAI) make an error using it .</li>
<li><a class="autorefs autorefs-internal" title="InputGuardrailTripwireTriggered" href="../ref/exceptions/#cai.sdk.agents.exceptions.InputGuardrailTripwireTriggered"><code>InputGuardrailTripwireTriggered</code></a>, <a class="autorefs autorefs-internal" title="OutputGuardrailTripwireTriggered" href="../ref/exceptions/#cai.sdk.agents.exceptions.OutputGuardrailTripwireTriggered"><code>OutputGuardrailTripwireTriggered</code></a> is raised when a <a href="../guardrails/">guardrail</a> is tripped.</li>
</ul>
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