openenv/pr_792/en/_app/immutable/nodes/4.2ffed17e.js

import{s as Ul,n as bl,o as Cl}from"../chunks/scheduler.2b22cead.js";import{S as vl,i as gl,e as i,s as n,c as r,h as $l,a as o,d as l,b as a,f as jl,g as M,j as d,k as Jl,l as Il,m as s,n as p,t as c,o as m,p as u}from"../chunks/index.1a0e8013.js";import{C as Al,H as y,E as _l}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.f6f2e20a.js";import{C as h}from"../chunks/CodeBlock.6eebe645.js";function El(Xe){let T,At,$t,_t,w,Et,f,Wt,j,Ye="AgentWorldModel-1K is a synthetic agentic environment suite containing <strong>1,000 tool-use environments</strong> with <strong>10,000 tasks</strong> for large-scale RL training. Each environment is a fully functional MCP server with tools, database state, and verification logic.",Bt,J,Nt,U,Ze='You can interact with the AWM environments at Huggingface Space : <a href="https://huggingface.co/spaces/ChilleD/agent_world_model_env" rel="nofollow">ChilleD/agent_world_model_env</a> 🤗.',kt,b,Lt,C,St,v,xt,g,Qt,$,Rt,I,Xt,A,Ve="AWM supports two action types:",Yt,_,qe="<thead><tr><th>Action</th> <th>Description</th></tr></thead> <tbody><tr><td><code>ListToolsAction()</code></td> <td>List all available MCP tools for the current scenario</td></tr> <tr><td><code>CallToolAction(tool_name, arguments)</code></td> <td>Call a specific tool with arguments</td></tr></tbody>",Zt,E,He="Special tool names:",Vt,W,ze="<li><code>&quot;verify&quot;</code> - Run verifier with <code>{verifier_mode: &quot;sql&quot;|&quot;code&quot;, final_answer: &quot;optional&quot;}</code> arguments</li> <li><code>&quot;done&quot;</code> - End the episode and destroy subprocess (does NOT run verifier)</li> <li><code>&quot;__list_scenarios__&quot;</code> - List all 1,000 available scenarios and their tasks</li>",qt,B,Ht,N,Ge="<thead><tr><th>Field</th> <th>Type</th> <th>Description</th></tr></thead> <tbody><tr><td><code>reward</code></td> <td>float</td> <td>Reward value based on reward_type and config</td></tr> <tr><td><code>reward_type</code></td> <td>str</td> <td>Outcome classification (see below)</td></tr> <tr><td><code>scenario</code></td> <td>str</td> <td>Current scenario name</td></tr> <tr><td><code>task</code></td> <td>str</td> <td>Task description in natural language</td></tr> <tr><td><code>task_idx</code></td> <td>int</td> <td>Task index (0-9)</td></tr> <tr><td><code>has_verifier</code></td> <td>dict/None</td> <td>Verifier support: <code>{sql: bool, code: bool}</code> or None</td></tr> <tr><td><code>num_tools</code></td> <td>int</td> <td>Number of tools available</td></tr> <tr><td><code>tool_name</code></td> <td>str</td> <td>Name of the tool called</td></tr> <tr><td><code>tool_result</code></td> <td>Any</td> <td>Result from the tool call</td></tr> <tr><td><code>error</code></td> <td>str</td> <td>Error message if any</td></tr> <tr><td><code>verify_result</code></td> <td>dict</td> <td>Verification output after calling verify</td></tr> <tr><td><code>trajectory_path</code></td> <td>str</td> <td>Path to saved trajectory JSON (after <code>done</code>)</td></tr> <tr><td><code>session_dir</code></td> <td>str</td> <td>Path to session directory (only if <code>keep_session=True</code>)</td></tr></tbody>",zt,k,Gt,L,Fe="Default reward configuration:",Ft,S,Pe="<thead><tr><th>Type</th> <th>Reward</th> <th>Description</th></tr></thead> <tbody><tr><td><code>complete</code></td> <td>1.0</td> <td>Task completed successfully (verifier passed)</td></tr> <tr><td><code>incomplete</code></td> <td>0.1</td> <td>Task not completed (verifier failed)</td></tr> <tr><td><code>format_error</code></td> <td>-1.0</td> <td>Format error (maps from tool_not_found, invalid_args)</td></tr> <tr><td><code>tool_not_found</code></td> <td>-1.0</td> <td>Tool name not recognized</td></tr> <tr><td><code>invalid_args</code></td> <td>-1.0</td> <td>Tool arguments invalid</td></tr> <tr><td>Other types</td> <td>0.0</td> <td>server_error, timeout, etc.</td></tr></tbody>",Pt,x,De="You can customize rewards at reset:",Dt,Q,Ot,R,Kt,X,Oe="When calling <code>done(keep_session=True)</code>, the session directory is preserved with:",te,Y,Ke="<thead><tr><th>File</th> <th>Description</th></tr></thead> <tbody><tr><td><code>trajectory.json</code></td> <td>Full episode trajectory (scenario, task, steps, each action/result)</td></tr> <tr><td><code>{scenario}.db</code></td> <td>SQLite database after agent interaction (final state)</td></tr> <tr><td><code>{scenario}_initial.db</code></td> <td>SQLite database snapshot before agent interaction</td></tr> <tr><td><code>server.py</code></td> <td>Patched Python code for the launched environment</td></tr> <tr><td><code>server.log</code></td> <td>Launched environment uvicorn logs (startup + HTTP requests)</td></tr></tbody>",ee,Z,tl="When <code>keep_session=False</code> (default), all files are cleaned up after the episode.",le,V,se,q,el="AWM supports two verification modes, selected when calling the <code>verify</code> tool:",ne,H,ae,z,ie,G,ll="Executes a Python verifier function that compares initial and final database states. Deterministic and does not require LLM.",oe,F,de,P,sl="This mode is recommended for judge performance. You need to set the LLM credentials via environment variables before using this mode.",re,D,Me,O,nl="Runs SQL queries to extract state changes, then uses an LLM judge to determine success.",pe,K,ce,tt,me,et,ue,lt,al="The server exposes a <code>/stats</code> endpoint for monitoring active sessions:",ye,st,Te,nt,il="Returns: <code>total_sessions</code>, <code>max_idle_time_config</code>, <code>cleanup_interval_config</code>, <code>scenarios</code> breakdown, and <code>max_idle_s</code>.",he,at,ol="A background cleanup daemon automatically kills sessions idle longer than <code>MAX_IDLE_TIME</code> (default 600s) when total sessions exceed <code>ALLOWED_IDLE_SESSIONS</code> (default 3000).",we,it,fe,ot,dl='See <a href="../../examples/agent_world_model/example_usage.py"><code>examples/agent_world_model/example_usage.py</code></a> for a complete example of an LLM-powered agent that:',je,dt,rl="<li>Discovers available tools via <code>list_tools</code></li> <li>Iteratively calls tools to accomplish the task</li> <li>Runs verification via <code>verify</code> tool (can use “sql” or “code” mode)</li> <li>Ends episode via <code>done</code> action with <code>keep_session=True</code> to inspect artifacts</li>",Je,rt,Ml="The example supports both a local server and the public Hugging Face Space, set <code>AWM_BASE_URL=https://chilled-agent-world-model-env.hf.space</code> (may be slow) to try without local setup.",Ue,Mt,be,pt,pl="AWM is designed for large-scale agentic RL. A single server supports thousands of concurrent WebSocket sessions, each with its own isolated environment subprocess.",Ce,ct,ve,mt,cl="A stress test simulating large-scale RL is included:",ge,ut,$e,yt,ml="This launches 1024 parallel episodes, each with 3-20 multi-turn tool interactions and 3-30s simulated LLM rollout time per turn.",Ie,Tt,Ae,ht,ul="Server configuration is in <code>server/config.py</code>, overridable via environment variables:",_e,wt,yl="<thead><tr><th>Config</th> <th>Default</th> <th>Env Var</th> <th>Description</th></tr></thead> <tbody><tr><td><code>MAX_CONCURRENT_ENVS</code></td> <td>10000</td> <td>—</td> <td>Max WebSocket sessions</td></tr> <tr><td><code>READY_TIMEOUT</code></td> <td>180s</td> <td><code>OPENENV_AWM_READY_TIMEOUT</code></td> <td>Subprocess startup timeout</td></tr> <tr><td><code>MAX_PORT_RETRIES</code></td> <td>5</td> <td><code>OPENENV_AWM_MAX_PORT_RETRIES</code></td> <td>Port-retry attempts on startup failure</td></tr> <tr><td><code>RETRY_READY_TIMEOUT</code></td> <td>30s</td> <td><code>OPENENV_AWM_RETRY_READY_TIMEOUT</code></td> <td>Shorter timeout for retry attempts</td></tr> <tr><td><code>READY_POLL_INTERVAL</code></td> <td>0.5s</td> <td>—</td> <td>Polling interval during startup check</td></tr> <tr><td><code>MAX_IDLE_TIME</code></td> <td>600s</td> <td><code>OPENENV_AWM_MAX_IDLE_TIME</code></td> <td>Idle session cleanup threshold</td></tr> <tr><td><code>ALLOWED_IDLE_SESSIONS</code></td> <td>3000</td> <td><code>OPENENV_AWM_ALLOWED_IDLE_SESSIONS</code></td> <td>Session count before idle cleanup triggers</td></tr> <tr><td><code>CLEANUP_INTERVAL</code></td> <td>5s</td> <td><code>OPENENV_AWM_CLEANUP_INTERVAL</code></td> <td>Cleanup daemon scan interval</td></tr></tbody>",Ee,ft,We,jt,Tl='AWM treats verifier code and scenario code from the curated <a href="https://huggingface.co/datasets/Snowflake/AgentWorldModel-1K" rel="nofollow">AgentWorldModel-1K</a> dataset as <strong>trusted</strong>. Verifier code (<code>server/_verifier_runner.py</code>) is run in a subprocess sandbox (rlimits, restricted builtins, import allowlist); scenario subprocesses run without per-process sandboxing and rely on the container as the outer isolation boundary. The codes are synthetically generated and carefully curated, however, there is no guarantee of absolute safety. We recommend only academic research use.',Be,Jt,Ne,Ut,hl="More details can be found at:",ke,bt,wl='<thead><tr><th>Resource</th> <th>Link</th></tr></thead> <tbody><tr><td>Hugging Face Space</td> <td><a href="https://huggingface.co/spaces/ChilleD/agent_world_model_env" rel="nofollow">ChilleD/agent_world_model_env</a></td></tr> <tr><td>Paper</td> <td><a href="https://arxiv.org/abs/2602.10090" rel="nofollow">arxiv.org/abs/2602.10090</a></td></tr> <tr><td>Synthesis Pipeline Code</td> <td><a href="https://github.com/Snowflake-Labs/agent-world-model" rel="nofollow">Snowflake-Labs/agent-world-model</a></td></tr> <tr><td>AgentWorldModel-1K</td> <td><a href="https://huggingface.co/datasets/Snowflake/AgentWorldModel-1K" rel="nofollow">Snowflake/AgentWorldModel-1K</a></td></tr> <tr><td>Arctic-AWM-4B</td> <td><a href="https://huggingface.co/Snowflake/Arctic-AWM-4B" rel="nofollow">Snowflake/Arctic-AWM-4B</a></td></tr> <tr><td>Arctic-AWM-8B</td> <td><a href="https://huggingface.co/Snowflake/Arctic-AWM-8B" rel="nofollow">Snowflake/Arctic-AWM-8B</a></td></tr> <tr><td>Arctic-AWM-14B</td> <td><a href="https://huggingface.co/Snowflake/Arctic-AWM-14B" rel="nofollow">Snowflake/Arctic-AWM-14B</a></td></tr></tbody>',Le,Ct,fl="If you find this work useful, please kindly cite:",Se,vt,xe,gt,Qe,It,Re;return w=new Al({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),f=new y({props:{title:"Agent World Model",local:"agent-world-model",headingTag:"h1"}}),J=new y({props:{title:"Quick Start",local:"quick-start",headingTag:"h2"}}),b=new y({props:{title:"1. Start the Server",local:"1-start-the-server",headingTag:"h3"}}),C=new h({props:{code:"JTIzJTIwRnJvbSUyMHRoZSUyME9wZW5FbnYlMjByb290JTIwZGlyZWN0b3J5JTBBUFlUSE9OUEFUSCUzRHNyYyUzQWVudnMlMjB1diUyMHJ1biUyMHV2aWNvcm4lMjBlbnZzLmFnZW50X3dvcmxkX21vZGVsX2Vudi5zZXJ2ZXIuYXBwJTNBYXBwJTIwLS1ob3N0JTIwMC4wLjAuMCUyMC0tcG9ydCUyMDg4OTk=",highlighted:`<span class="hljs-comment"># From the OpenEnv root directory</span>
PYTHONPATH=src:envs uv run uvicorn envs.agent_world_model_env.server.app:app --host 0.0.0.0 --port 8899`,lang:"bash",wrap:!1}}),v=new y({props:{title:"2. Connect with the Client",local:"2-connect-with-the-client",headingTag:"h3"}}),g=new h({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> asyncio
<span class="hljs-keyword">from</span> agent_world_model_env <span class="hljs-keyword">import</span> AWMEnv
<span class="hljs-keyword">from</span> openenv.core.env_server.mcp_types <span class="hljs-keyword">import</span> CallToolAction, ListToolsAction

<span class="hljs-keyword">async</span> <span class="hljs-keyword">def</span> <span class="hljs-title function_">main</span>():
    <span class="hljs-keyword">async</span> <span class="hljs-keyword">with</span> AWMEnv(base_url=<span class="hljs-string">&quot;http://localhost:8899&quot;</span>) <span class="hljs-keyword">as</span> env:
        <span class="hljs-comment"># Reset to a scenario with a specific task</span>
        result = <span class="hljs-keyword">await</span> env.reset(scenario=<span class="hljs-string">&quot;e_commerce_33&quot;</span>, task_idx=<span class="hljs-number">0</span>)
        <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Task: <span class="hljs-subst">{result.observation.task}</span>&quot;</span>)
        <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Tools available: <span class="hljs-subst">{result.observation.num_tools}</span>&quot;</span>)
        <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Verifier support: <span class="hljs-subst">{result.observation.has_verifier}</span>&quot;</span>)  <span class="hljs-comment"># {sql: True, code: True}</span>

        <span class="hljs-comment"># List available tools</span>
        tools = <span class="hljs-keyword">await</span> env.list_tools()
        <span class="hljs-keyword">for</span> tool <span class="hljs-keyword">in</span> tools[:<span class="hljs-number">3</span>]:
            <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;  - <span class="hljs-subst">{tool.name}</span>: <span class="hljs-subst">{tool.description}</span>&quot;</span>)

        <span class="hljs-comment"># Call a tool</span>
        obs = <span class="hljs-keyword">await</span> env.call_tool(<span class="hljs-string">&quot;search_products&quot;</span>, query=<span class="hljs-string">&quot;headphones&quot;</span>)
        <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Result: <span class="hljs-subst">{obs.tool_result}</span>&quot;</span>)

        <span class="hljs-comment"># Run verification (can be called multiple times with different modes)</span>
        result = <span class="hljs-keyword">await</span> env.step(CallToolAction(
            tool_name=<span class="hljs-string">&quot;verify&quot;</span>,
            arguments={<span class="hljs-string">&quot;verifier_mode&quot;</span>: <span class="hljs-string">&quot;code&quot;</span>, <span class="hljs-string">&quot;final_answer&quot;</span>: <span class="hljs-string">&quot;optional answer&quot;</span>}
        ))
        <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Reward type: <span class="hljs-subst">{result.observation.reward_type}</span>&quot;</span>)
        <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Reward: <span class="hljs-subst">{result.reward}</span>&quot;</span>)
        <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Verify result: <span class="hljs-subst">{result.observation.verify_result}</span>&quot;</span>)

        <span class="hljs-comment"># End episode (destroys subprocess; set keep_session=True to preserve files)</span>
        result = <span class="hljs-keyword">await</span> env.step(CallToolAction(tool_name=<span class="hljs-string">&quot;done&quot;</span>, arguments={<span class="hljs-string">&quot;keep_session&quot;</span>: <span class="hljs-literal">False</span>}))
        <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Episode done: <span class="hljs-subst">{result.done}</span>&quot;</span>)

asyncio.run(main())`,lang:"python",wrap:!1}}),$=new y({props:{title:"Environment Details",local:"environment-details",headingTag:"h2"}}),I=new y({props:{title:"Actions",local:"actions",headingTag:"h3"}}),B=new y({props:{title:"Observation Fields",local:"observation-fields",headingTag:"h3"}}),k=new y({props:{title:"Reward Types and Values",local:"reward-types-and-values",headingTag:"h3"}}),Q=new h({props:{code:"cmVzdWx0JTIwJTNEJTIwYXdhaXQlMjBlbnYucmVzZXQoJTBBJTIwJTIwJTIwJTIwc2NlbmFyaW8lM0QlMjJlX2NvbW1lcmNlXzMzJTIyJTJDJTBBJTIwJTIwJTIwJTIwdGFza19pZHglM0QwJTJDJTBBJTIwJTIwJTIwJTIwcmV3YXJkX2NvbmZpZyUzRCU3QiUyMmNvbXBsZXRlJTIyJTNBJTIwMS4wJTJDJTIwJTIyaW5jb21wbGV0ZSUyMiUzQSUyMDAuMCUyQyUyMCUyMmZvcm1hdF9lcnJvciUyMiUzQSUyMDAuMCU3RCUwQSk=",highlighted:`result = <span class="hljs-keyword">await</span> env.reset(
    scenario=<span class="hljs-string">&quot;e_commerce_33&quot;</span>,
    task_idx=<span class="hljs-number">0</span>,
    reward_config={<span class="hljs-string">&quot;complete&quot;</span>: <span class="hljs-number">1.0</span>, <span class="hljs-string">&quot;incomplete&quot;</span>: <span class="hljs-number">0.0</span>, <span class="hljs-string">&quot;format_error&quot;</span>: <span class="hljs-number">0.0</span>}
)`,lang:"python",wrap:!1}}),R=new y({props:{title:"Session Artifacts",local:"session-artifacts",headingTag:"h2"}}),V=new y({props:{title:"Verifier Modes",local:"verifier-modes",headingTag:"h2"}}),H=new y({props:{title:"Code Mode (Default, no LLM needed)",local:"code-mode-default-no-llm-needed",headingTag:"h3"}}),z=new h({props:{code:"cmVzdWx0JTIwJTNEJTIwYXdhaXQlMjBlbnYuc3RlcChDYWxsVG9vbEFjdGlvbiglMEElMjAlMjAlMjAlMjB0b29sX25hbWUlM0QlMjJ2ZXJpZnklMjIlMkMlMEElMjAlMjAlMjAlMjBhcmd1bWVudHMlM0QlN0IlMjJ2ZXJpZmllcl9tb2RlJTIyJTNBJTIwJTIyY29kZSUyMiUyQyUyMCUyMmZpbmFsX2Fuc3dlciUyMiUzQSUyMCUyMm9wdGlvbmFsJTIwYW5zd2VyJTIyJTdEJTBBKSk=",highlighted:`result = <span class="hljs-keyword">await</span> env.step(CallToolAction(
    tool_name=<span class="hljs-string">&quot;verify&quot;</span>,
    arguments={<span class="hljs-string">&quot;verifier_mode&quot;</span>: <span class="hljs-string">&quot;code&quot;</span>, <span class="hljs-string">&quot;final_answer&quot;</span>: <span class="hljs-string">&quot;optional answer&quot;</span>}
))`,lang:"python",wrap:!1}}),F=new y({props:{title:"SQL Mode (code-augmented LLM-as-a-Judge)",local:"sql-mode-code-augmented-llm-as-a-judge",headingTag:"h3"}}),D=new h({props:{code:"JTIzJTIwU2V0JTIwTExNJTIwY3JlZGVudGlhbHMlMjB2aWElMjBlbnZpcm9ubWVudCUyMHZhcmlhYmxlcyUwQSUyMyUyME9QRU5FTlZfQVdNX0xMTV9CQVNFX1VSTCUyQyUyME9QRU5FTlZfQVdNX0xMTV9BUElfS0VZJTJDJTIwT1BFTkVOVl9BV01fTExNX01PREVMJTBBJTBBcmVzdWx0JTIwJTNEJTIwYXdhaXQlMjBlbnYuc3RlcChDYWxsVG9vbEFjdGlvbiglMEElMjAlMjAlMjAlMjB0b29sX25hbWUlM0QlMjJ2ZXJpZnklMjIlMkMlMEElMjAlMjAlMjAlMjBhcmd1bWVudHMlM0QlN0IlMjJ2ZXJpZmllcl9tb2RlJTIyJTNBJTIwJTIyc3FsJTIyJTdEJTBBKSk=",highlighted:`<span class="hljs-comment"># Set LLM credentials via environment variables</span>
<span class="hljs-comment"># OPENENV_AWM_LLM_BASE_URL, OPENENV_AWM_LLM_API_KEY, OPENENV_AWM_LLM_MODEL</span>

result = <span class="hljs-keyword">await</span> env.step(CallToolAction(
    tool_name=<span class="hljs-string">&quot;verify&quot;</span>,
    arguments={<span class="hljs-string">&quot;verifier_mode&quot;</span>: <span class="hljs-string">&quot;sql&quot;</span>}
))`,lang:"python",wrap:!1}}),K=new y({props:{title:"Listing Scenarios & Tasks",local:"listing-scenarios--tasks",headingTag:"h2"}}),tt=new h({props:{code:"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",highlighted:`<span class="hljs-keyword">async</span> <span class="hljs-keyword">with</span> AWMEnv(base_url=<span class="hljs-string">&quot;http://localhost:8899&quot;</span>) <span class="hljs-keyword">as</span> env:
    <span class="hljs-comment"># List all 1,000 scenarios</span>
    result = <span class="hljs-keyword">await</span> env.step(CallToolAction(tool_name=<span class="hljs-string">&quot;__list_scenarios__&quot;</span>, arguments={}))

    <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Total scenarios: <span class="hljs-subst">{result.observation.total}</span>&quot;</span>)
    <span class="hljs-keyword">for</span> scenario <span class="hljs-keyword">in</span> result.observation.scenarios[:<span class="hljs-number">5</span>]:
        <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;  - <span class="hljs-subst">{scenario[<span class="hljs-string">&#x27;name&#x27;</span>]}</span>: <span class="hljs-subst">{scenario[<span class="hljs-string">&#x27;num_tasks&#x27;</span>]}</span> tasks&quot;</span>)
        <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;    Sample task: <span class="hljs-subst">{scenario[<span class="hljs-string">&#x27;tasks&#x27;</span>][<span class="hljs-number">0</span>][:<span class="hljs-number">80</span>]}</span>...&quot;</span>)`,lang:"python",wrap:!1}}),et=new y({props:{title:"Server Monitoring",local:"server-monitoring",headingTag:"h2"}}),st=new h({props:{code:"Y3VybCUyMGh0dHAlM0ElMkYlMkZsb2NhbGhvc3QlM0E4ODk5JTJGc3RhdHM=",highlighted:"curl http://localhost:8899/stats",lang:"bash",wrap:!1}}),it=new y({props:{title:"Full Agent Interaction Example",local:"full-agent-interaction-example",headingTag:"h2"}}),Mt=new y({props:{title:"Large-Scale RL Training",local:"large-scale-rl-training",headingTag:"h2"}}),ct=new y({props:{title:"Simulated Stress Test",local:"simulated-stress-test",headingTag:"h3"}}),ut=new h({props:{code:"JTIzJTIwYWZ0ZXIlMjBzZXJ2ZXIlMjBzdGFydGVkJTJDJTIwdGhlbiUyMGluJTIwYW5vdGhlciUyMHRlcm1pbmFsJTNBJTBBUFlUSE9OUEFUSCUzRHNyYyUzQWVudnMlMjB1diUyMHJ1biUyMHB5dGhvbiUyMGV4YW1wbGVzJTJGYWdlbnRfd29ybGRfbW9kZWwlMkZleGFtcGxlX3N0cmVzc190ZXN0LnB5JTIwJTVDJTBBJTIwJTIwJTIwJTIwLS1zY2FsZSUyMDEwMjQlMjAtLWNvbmN1cnJlbmN5JTIwNjQlMjAtLW1pbi10dXJucyUyMDMlMjAtLW1heC10dXJucyUyMDIwJTIwJTVDJTBBJTIwJTIwJTIwJTIwLS10aGluay1taW4lMjAzLjAlMjAtLXRoaW5rLW1heCUyMDMwLjA=",highlighted:`<span class="hljs-comment"># after server started, then in another terminal:</span>
PYTHONPATH=src:envs uv run python examples/agent_world_model/example_stress_test.py \\
    --scale 1024 --concurrency 64 --min-turns 3 --max-turns 20 \\
    --think-min 3.0 --think-max 30.0`,lang:"bash",wrap:!1}}),Tt=new y({props:{title:"AWM Server Configuration",local:"awm-server-configuration",headingTag:"h2"}}),ft=new y({props:{title:"Warning",local:"warning",headingTag:"h2"}}),Jt=new y({props:{title:"Citation",local:"citation",headingTag:"h2"}}),vt=new h({props:{code:"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",highlighted:`<span class="hljs-comment">@article{wang2026agentworldmodelinfinity,</span>
      title={Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning},
      author={Zhaoyang Wang <span class="hljs-keyword">and</span> Canwen Xu <span class="hljs-keyword">and</span> Boyi Liu <span class="hljs-keyword">and</span> Yite Wang <span class="hljs-keyword">and</span> Siwei Han <span class="hljs-keyword">and</span> Zhewei Yao <span class="hljs-keyword">and</span> Huaxiu Yao <span class="hljs-keyword">and</span> Yuxiong He},
      year={<span class="hljs-number">2026</span>},
      eprint={<span class="hljs-number">2602</span>.<span class="hljs-number">10090</span>},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https:<span class="hljs-comment">//arxiv.org/abs/2602.10090},</span>
}`,lang:"bibtex",wrap:!1}}),gt=new _l({props:{source:"https://github.com/huggingface/openenv/blob/main/docs/source/environments/agent_world_model.md"}}),{c(){T=i("meta"),At=n(),$t=i("p"),_t=n(),r(w.$$.fragment),Et=n(),r(f.$$.fragment),Wt=n(),j=i("p"),j.innerHTML=Ye,Bt=n(),r(J.$$.fragment),Nt=n(),U=i("p"),U.innerHTML=Ze,kt=n(),r(b.$$.fragment),Lt=n(),r(C.$$.fragment),St=n(),r(v.$$.fragment),xt=n(),r(g.$$.fragment),Qt=n(),r($.$$.fragment),Rt=n(),r(I.$$.fragment),Xt=n(),A=i("p"),A.textContent=Ve,Yt=n(),_=i("table"),_.innerHTML=qe,Zt=n(),E=i("p"),E.textContent=He,Vt=n(),W=i("ul"),W.innerHTML=ze,qt=n(),r(B.$$.fragment),Ht=n(),N=i("table"),N.innerHTML=Ge,zt=n(),r(k.$$.fragment),Gt=n(),L=i("p"),L.textContent=Fe,Ft=n(),S=i("table"),S.innerHTML=Pe,Pt=n(),x=i("p"),x.textContent=De,Dt=n(),r(Q.$$.fragment),Ot=n(),r(R.$$.fragment),Kt=n(),X=i("p"),X.innerHTML=Oe,te=n(),Y=i("table"),Y.innerHTML=Ke,ee=n(),Z=i("p"),Z.innerHTML=tl,le=n(),r(V.$$.fragment),se=n(),q=i("p"),q.innerHTML=el,ne=n(),r(H.$$.fragment),ae=n(),r(z.$$.fragment),ie=n(),G=i("p"),G.textContent=ll,oe=n(),r(F.$$.fragment),de=n(),P=i("p"),P.textContent=sl,re=n(),r(D.$$.fragment),Me=n(),O=i("p"),O.textContent=nl,pe=n(),r(K.$$.fragment),ce=n(),r(tt.$$.fragment),me=n(),r(et.$$.fragment),ue=n(),lt=i("p"),lt.innerHTML=al,ye=n(),r(st.$$.fragment),Te=n(),nt=i("p"),nt.innerHTML=il,he=n(),at=i("p"),at.innerHTML=ol,we=n(),r(it.$$.fragment),fe=n(),ot=i("p"),ot.innerHTML=dl,je=n(),dt=i("ol"),dt.innerHTML=rl,Je=n(),rt=i("p"),rt.innerHTML=Ml,Ue=n(),r(Mt.$$.fragment),be=n(),pt=i("p"),pt.textContent=pl,Ce=n(),r(ct.$$.fragment),ve=n(),mt=i("p"),mt.textContent=cl,ge=n(),r(ut.$$.fragment),$e=n(),yt=i("p"),yt.textContent=ml,Ie=n(),r(Tt.$$.fragment),Ae=n(),ht=i("p"),ht.innerHTML=ul,_e=n(),wt=i("table"),wt.innerHTML=yl,Ee=n(),r(ft.$$.fragment),We=n(),jt=i("p"),jt.innerHTML=Tl,Be=n(),r(Jt.$$.fragment),Ne=n(),Ut=i("p"),Ut.textContent=hl,ke=n(),bt=i("table"),bt.innerHTML=wl,Le=n(),Ct=i("p"),Ct.textContent=fl,Se=n(),r(vt.$$.fragment),xe=n(),r(gt.$$.fragment),Qe=n(),It=i("p"),this.h()},l(t){const e=$l("svelte-u9bgzb",document.head);T=o(e,"META",{name:!0,content:!0}),e.forEach(l),At=a(t),$t=o(t,"P",{}),jl($t).forEach(l),_t=a(t),M(w.$$.fragment,t),Et=a(t),M(f.$$.fragment,t),Wt=a(t),j=o(t,"P",{"data-svelte-h":!0}),d(j)!=="svelte-17lxrjs"&&(j.innerHTML=Ye),Bt=a(t),M(J.$$.fragment,t),Nt=a(t),U=o(t,"P",{"data-svelte-h":!0}),d(U)!=="svelte-1p6r28c"&&(U.innerHTML=Ze),kt=a(t),M(b.$$.fragment,t),Lt=a(t),M(C.$$.fragment,t),St=a(t),M(v.$$.fragment,t),xt=a(t),M(g.$$.fragment,t),Qt=a(t),M($.$$.fragment,t),Rt=a(t),M(I.$$.fragment,t),Xt=a(t),A=o(t,"P",{"data-svelte-h":!0}),d(A)!=="svelte-1fxezv8"&&(A.textContent=Ve),Yt=a(t),_=o(t,"TABLE",{"data-svelte-h":!0}),d(_)!=="svelte-1396cg1"&&(_.innerHTML=qe),Zt=a(t),E=o(t,"P",{"data-svelte-h":!0}),d(E)!=="svelte-1d6sepr"&&(E.textContent=He),Vt=a(t),W=o(t,"UL",{"data-svelte-h":!0}),d(W)!=="svelte-1htijkg"&&(W.innerHTML=ze),qt=a(t),M(B.$$.fragment,t),Ht=a(t),N=o(t,"TABLE",{"data-svelte-h":!0}),d(N)!=="svelte-181esnc"&&(N.innerHTML=Ge),zt=a(t),M(k.$$.fragment,t),Gt=a(t),L=o(t,"P",{"data-svelte-h":!0}),d(L)!=="svelte-bnrvhc"&&(L.textContent=Fe),Ft=a(t),S=o(t,"TABLE",{"data-svelte-h":!0}),d(S)!=="svelte-1l3h4e8"&&(S.innerHTML=Pe),Pt=a(t),x=o(t,"P",{"data-svelte-h":!0}),d(x)!=="svelte-1q2lxpm"&&(x.textContent=De),Dt=a(t),M(Q.$$.fragment,t),Ot=a(t),M(R.$$.fragment,t),Kt=a(t),X=o(t,"P",{"data-svelte-h":!0}),d(X)!=="svelte-u97iah"&&(X.innerHTML=Oe),te=a(t),Y=o(t,"TABLE",{"data-svelte-h":!0}),d(Y)!=="svelte-qnnb5q"&&(Y.innerHTML=Ke),ee=a(t),Z=o(t,"P",{"data-svelte-h":!0}),d(Z)!=="svelte-lpdbik"&&(Z.innerHTML=tl),le=a(t),M(V.$$.fragment,t),se=a(t),q=o(t,"P",{"data-svelte-h":!0}),d(q)!=="svelte-1vtucz4"&&(q.innerHTML=el),ne=a(t),M(H.$$.fragment,t),ae=a(t),M(z.$$.fragment,t),ie=a(t),G=o(t,"P",{"data-svelte-h":!0}),d(G)!=="svelte-100eohf"&&(G.textContent=ll),oe=a(t),M(F.$$.fragment,t),de=a(t),P=o(t,"P",{"data-svelte-h":!0}),d(P)!=="svelte-2ss3jc"&&(P.textContent=sl),re=a(t),M(D.$$.fragment,t),Me=a(t),O=o(t,"P",{"data-svelte-h":!0}),d(O)!=="svelte-vxswv9"&&(O.textContent=nl),pe=a(t),M(K.$$.fragment,t),ce=a(t),M(tt.$$.fragment,t),me=a(t),M(et.$$.fragment,t),ue=a(t),lt=o(t,"P",{"data-svelte-h":!0}),d(lt)!=="svelte-d2ypcg"&&(lt.innerHTML=al),ye=a(t),M(st.$$.fragment,t),Te=a(t),nt=o(t,"P",{"data-svelte-h":!0}),d(nt)!=="svelte-gqamzt"&&(nt.innerHTML=il),he=a(t),at=o(t,"P",{"data-svelte-h":!0}),d(at)!=="svelte-1tgdpf3"&&(at.innerHTML=ol),we=a(t),M(it.$$.fragment,t),fe=a(t),ot=o(t,"P",{"data-svelte-h":!0}),d(ot)!=="svelte-1hhlrce"&&(ot.innerHTML=dl),je=a(t),dt=o(t,"OL",{"data-svelte-h":!0}),d(dt)!=="svelte-sahtzr"&&(dt.innerHTML=rl),Je=a(t),rt=o(t,"P",{"data-svelte-h":!0}),d(rt)!=="svelte-1viw3ys"&&(rt.innerHTML=Ml),Ue=a(t),M(Mt.$$.fragment,t),be=a(t),pt=o(t,"P",{"data-svelte-h":!0}),d(pt)!=="svelte-dyf6v"&&(pt.textContent=pl),Ce=a(t),M(ct.$$.fragment,t),ve=a(t),mt=o(t,"P",{"data-svelte-h":!0}),d(mt)!=="svelte-1p5ov40"&&(mt.textContent=cl),ge=a(t),M(ut.$$.fragment,t),$e=a(t),yt=o(t,"P",{"data-svelte-h":!0}),d(yt)!=="svelte-1r1irno"&&(yt.textContent=ml),Ie=a(t),M(Tt.$$.fragment,t),Ae=a(t),ht=o(t,"P",{"data-svelte-h":!0}),d(ht)!=="svelte-iykpvl"&&(ht.innerHTML=ul),_e=a(t),wt=o(t,"TABLE",{"data-svelte-h":!0}),d(wt)!=="svelte-hc75ky"&&(wt.innerHTML=yl),Ee=a(t),M(ft.$$.fragment,t),We=a(t),jt=o(t,"P",{"data-svelte-h":!0}),d(jt)!=="svelte-ul7lbf"&&(jt.innerHTML=Tl),Be=a(t),M(Jt.$$.fragment,t),Ne=a(t),Ut=o(t,"P",{"data-svelte-h":!0}),d(Ut)!=="svelte-1q56mmn"&&(Ut.textContent=hl),ke=a(t),bt=o(t,"TABLE",{"data-svelte-h":!0}),d(bt)!=="svelte-15yibf0"&&(bt.innerHTML=wl),Le=a(t),Ct=o(t,"P",{"data-svelte-h":!0}),d(Ct)!=="svelte-cvqvus"&&(Ct.textContent=fl),Se=a(t),M(vt.$$.fragment,t),xe=a(t),M(gt.$$.fragment,t),Qe=a(t),It=o(t,"P",{}),jl(It).forEach(l),this.h()},h(){Jl(T,"name","hf:doc:metadata"),Jl(T,"content",Wl)},m(t,e){Il(document.head,T),s(t,At,e),s(t,$t,e),s(t,_t,e),p(w,t,e),s(t,Et,e),p(f,t,e),s(t,Wt,e),s(t,j,e),s(t,Bt,e),p(J,t,e),s(t,Nt,e),s(t,U,e),s(t,kt,e),p(b,t,e),s(t,Lt,e),p(C,t,e),s(t,St,e),p(v,t,e),s(t,xt,e),p(g,t,e),s(t,Qt,e),p($,t,e),s(t,Rt,e),p(I,t,e),s(t,Xt,e),s(t,A,e),s(t,Yt,e),s(t,_,e),s(t,Zt,e),s(t,E,e),s(t,Vt,e),s(t,W,e),s(t,qt,e),p(B,t,e),s(t,Ht,e),s(t,N,e),s(t,zt,e),p(k,t,e),s(t,Gt,e),s(t,L,e),s(t,Ft,e),s(t,S,e),s(t,Pt,e),s(t,x,e),s(t,Dt,e),p(Q,t,e),s(t,Ot,e),p(R,t,e),s(t,Kt,e),s(t,X,e),s(t,te,e),s(t,Y,e),s(t,ee,e),s(t,Z,e),s(t,le,e),p(V,t,e),s(t,se,e),s(t,q,e),s(t,ne,e),p(H,t,e),s(t,ae,e),p(z,t,e),s(t,ie,e),s(t,G,e),s(t,oe,e),p(F,t,e),s(t,de,e),s(t,P,e),s(t,re,e),p(D,t,e),s(t,Me,e),s(t,O,e),s(t,pe,e),p(K,t,e),s(t,ce,e),p(tt,t,e),s(t,me,e),p(et,t,e),s(t,ue,e),s(t,lt,e),s(t,ye,e),p(st,t,e),s(t,Te,e),s(t,nt,e),s(t,he,e),s(t,at,e),s(t,we,e),p(it,t,e),s(t,fe,e),s(t,ot,e),s(t,je,e),s(t,dt,e),s(t,Je,e),s(t,rt,e),s(t,Ue,e),p(Mt,t,e),s(t,be,e),s(t,pt,e),s(t,Ce,e),p(ct,t,e),s(t,ve,e),s(t,mt,e),s(t,ge,e),p(ut,t,e),s(t,$e,e),s(t,yt,e),s(t,Ie,e),p(Tt,t,e),s(t,Ae,e),s(t,ht,e),s(t,_e,e),s(t,wt,e),s(t,Ee,e),p(ft,t,e),s(t,We,e),s(t,jt,e),s(t,Be,e),p(Jt,t,e),s(t,Ne,e),s(t,Ut,e),s(t,ke,e),s(t,bt,e),s(t,Le,e),s(t,Ct,e),s(t,Se,e),p(vt,t,e),s(t,xe,e),p(gt,t,e),s(t,Qe,e),s(t,It,e),Re=!0},p:bl,i(t){Re||(c(w.$$.fragment,t),c(f.$$.fragment,t),c(J.$$.fragment,t),c(b.$$.fragment,t),c(C.$$.fragment,t),c(v.$$.fragment,t),c(g.$$.fragment,t),c($.$$.fragment,t),c(I.$$.fragment,t),c(B.$$.fragment,t),c(k.$$.fragment,t),c(Q.$$.fragment,t),c(R.$$.fragment,t),c(V.$$.fragment,t),c(H.$$.fragment,t),c(z.$$.fragment,t),c(F.$$.fragment,t),c(D.$$.fragment,t),c(K.$$.fragment,t),c(tt.$$.fragment,t),c(et.$$.fragment,t),c(st.$$.fragment,t),c(it.$$.fragment,t),c(Mt.$$.fragment,t),c(ct.$$.fragment,t),c(ut.$$.fragment,t),c(Tt.$$.fragment,t),c(ft.$$.fragment,t),c(Jt.$$.fragment,t),c(vt.$$.fragment,t),c(gt.$$.fragment,t),Re=!0)},o(t){m(w.$$.fragment,t),m(f.$$.fragment,t),m(J.$$.fragment,t),m(b.$$.fragment,t),m(C.$$.fragment,t),m(v.$$.fragment,t),m(g.$$.fragment,t),m($.$$.fragment,t),m(I.$$.fragment,t),m(B.$$.fragment,t),m(k.$$.fragment,t),m(Q.$$.fragment,t),m(R.$$.fragment,t),m(V.$$.fragment,t),m(H.$$.fragment,t),m(z.$$.fragment,t),m(F.$$.fragment,t),m(D.$$.fragment,t),m(K.$$.fragment,t),m(tt.$$.fragment,t),m(et.$$.fragment,t),m(st.$$.fragment,t),m(it.$$.fragment,t),m(Mt.$$.fragment,t),m(ct.$$.fragment,t),m(ut.$$.fragment,t),m(Tt.$$.fragment,t),m(ft.$$.fragment,t),m(Jt.$$.fragment,t),m(vt.$$.fragment,t),m(gt.$$.fragment,t),Re=!1},d(t){t&&(l(At),l($t),l(_t),l(Et),l(Wt),l(j),l(Bt),l(Nt),l(U),l(kt),l(Lt),l(St),l(xt),l(Qt),l(Rt),l(Xt),l(A),l(Yt),l(_),l(Zt),l(E),l(Vt),l(W),l(qt),l(Ht),l(N),l(zt),l(Gt),l(L),l(Ft),l(S),l(Pt),l(x),l(Dt),l(Ot),l(Kt),l(X),l(te),l(Y),l(ee),l(Z),l(le),l(se),l(q),l(ne),l(ae),l(ie),l(G),l(oe),l(de),l(P),l(re),l(Me),l(O),l(pe),l(ce),l(me),l(ue),l(lt),l(ye),l(Te),l(nt),l(he),l(at),l(we),l(fe),l(ot),l(je),l(dt),l(Je),l(rt),l(Ue),l(be),l(pt),l(Ce),l(ve),l(mt),l(ge),l($e),l(yt),l(Ie),l(Ae),l(ht),l(_e),l(wt),l(Ee),l(We),l(jt),l(Be),l(Ne),l(Ut),l(ke),l(bt),l(Le),l(Ct),l(Se),l(xe),l(Qe),l(It)),l(T),u(w,t),u(f,t),u(J,t),u(b,t),u(C,t),u(v,t),u(g,t),u($,t),u(I,t),u(B,t),u(k,t),u(Q,t),u(R,t),u(V,t),u(H,t),u(z,t),u(F,t),u(D,t),u(K,t),u(tt,t),u(et,t),u(st,t),u(it,t),u(Mt,t),u(ct,t),u(ut,t),u(Tt,t),u(ft,t),u(Jt,t),u(vt,t),u(gt,t)}}}const Wl='{"title":"Agent World Model","local":"agent-world-model","sections":[{"title":"Quick Start","local":"quick-start","sections":[{"title":"1. Start the Server","local":"1-start-the-server","sections":[],"depth":3},{"title":"2. Connect with the Client","local":"2-connect-with-the-client","sections":[],"depth":3}],"depth":2},{"title":"Environment Details","local":"environment-details","sections":[{"title":"Actions","local":"actions","sections":[],"depth":3},{"title":"Observation Fields","local":"observation-fields","sections":[],"depth":3},{"title":"Reward Types and Values","local":"reward-types-and-values","sections":[],"depth":3}],"depth":2},{"title":"Session Artifacts","local":"session-artifacts","sections":[],"depth":2},{"title":"Verifier Modes","local":"verifier-modes","sections":[{"title":"Code Mode (Default, no LLM needed)","local":"code-mode-default-no-llm-needed","sections":[],"depth":3},{"title":"SQL Mode (code-augmented LLM-as-a-Judge)","local":"sql-mode-code-augmented-llm-as-a-judge","sections":[],"depth":3}],"depth":2},{"title":"Listing Scenarios & Tasks","local":"listing-scenarios--tasks","sections":[],"depth":2},{"title":"Server Monitoring","local":"server-monitoring","sections":[],"depth":2},{"title":"Full Agent Interaction Example","local":"full-agent-interaction-example","sections":[],"depth":2},{"title":"Large-Scale RL Training","local":"large-scale-rl-training","sections":[{"title":"Simulated Stress Test","local":"simulated-stress-test","sections":[],"depth":3}],"depth":2},{"title":"AWM Server Configuration","local":"awm-server-configuration","sections":[],"depth":2},{"title":"Warning","local":"warning","sections":[],"depth":2},{"title":"Citation","local":"citation","sections":[],"depth":2}],"depth":1}';function Bl(Xe){return Cl(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class xl extends vl{constructor(T){super(),gl(this,T,Bl,El,Ul,{})}}export{xl as component};