Merge pull request #406 from hud-evals/codex/fix-public-doc-imports

Fix public docs SDK imports

Author: jdchawla29

docs/platform/subagent.mdx

@@ -25,23 +25,21 @@ The workflow is:
 Scenarios define what your agent does. In your environment code:
 
 ```python
-from hud import Env
+from hud import Environment
 
-env = Env("my-assistant")
+env = Environment("my-assistant")
 
 @env.scenario()
 async def answer_question(query: str):
     """Answer a user question using available tools."""
 
-    yield env.setup(f"Answer this question: {query}")
-    
-    result = yield  # Agent runs here
+    result = yield f"Answer this question: {query}"
 
-    # Evaluate the result
+    # Evaluate the result.
     if "helpful" in result.lower():
-        yield env.reward(1.0)
+        yield 1.0
     else:
-        yield env.reward(0.0)
+        yield 0.0
 ```
 
 Deploy your environment through the platform:
@@ -74,7 +72,7 @@ Once your scenario exists, call it via the REST API:
 ### cURL
 
 ```bash
-curl -X POST https://api.hud.so/v1/agent/run \
+curl -X POST https://api.hud.ai/agent/run \
   -H "Content-Type: application/json" \
   -H "Authorization: Bearer $HUD_API_KEY" \
   -d '{
@@ -161,7 +159,7 @@ Trigger agent runs from external events:
 @app.post("/webhook")
 async def handle_webhook(event: dict):
     response = requests.post(
-        "https://api.hud.so/v1/agent/run",
+        "https://api.hud.ai/agent/run",
         headers={"Authorization": f"Bearer {HUD_API_KEY}"},
         json={
             "env_name": "support-agent",
@@ -179,7 +177,7 @@ Run agents on a schedule with cron or similar:
 
 ```bash
 # Run daily cleanup agent
-0 0 * * * curl -X POST https://api.hud.so/v1/agent/run \
+0 0 * * * curl -X POST https://api.hud.ai/agent/run \
   -H "Authorization: Bearer $HUD_API_KEY" \
   -d '{"env_name": "ops", "scenario_name": "daily_cleanup", "scenario_args": {}, "model": "claude-sonnet-4-5"}'
 ```
@@ -190,7 +188,7 @@ Power a chat UI with agent capabilities:
 
 ```javascript
 async function sendMessage(message) {
-  const response = await fetch("https://api.hud.so/v1/agent/run", {
+  const response = await fetch("https://api.hud.ai/agent/run", {
     method: "POST",
     headers: {
       "Content-Type": "application/json",

docs/reference/agents.mdx

@@ -11,7 +11,7 @@ The HUD SDK provides a base `MCPAgent` class and several pre-built agent impleme
 Use the `create()` factory method to instantiate agents with typed parameters:
 
 ```python
-from hud.agents import ClaudeAgent
+from hud.agents.claude import ClaudeAgent
 
 agent = ClaudeAgent.create(
     model="claude-sonnet-4-5",
@@ -78,7 +78,7 @@ def get_available_tools() -> list[types.Tool]
 ### ClaudeAgent
 
 ```python
-from hud.agents import ClaudeAgent
+from hud.agents.claude import ClaudeAgent
 ```
 
 Claude-specific implementation using Anthropic's API.
@@ -97,7 +97,7 @@ Claude-specific implementation using Anthropic's API.
 
 ```python
 from hud import Environment
-from hud.agents import ClaudeAgent
+from hud.agents.claude import ClaudeAgent
 
 env = Environment("browser").connect_hub("hud-evals/browser")
 
@@ -162,7 +162,7 @@ Inherits all `OpenAIAgent` parameters.
 ### GeminiAgent
 
 ```python
-from hud.agents import GeminiAgent
+from hud.agents.gemini import GeminiAgent
 ```
 
 Google Gemini agent for standard tool-calling tasks.
@@ -192,7 +192,7 @@ agent = GeminiAgent.create(
 ### GeminiCUAAgent
 
 ```python
-from hud.agents import GeminiCUAAgent
+from hud.agents.gemini_cua import GeminiCUAAgent
 ```
 
 Google Gemini Computer Use Agent with native computer-use capabilities. Extends `GeminiAgent` with support for Gemini's predefined computer actions (click, type, scroll, etc.).
@@ -231,7 +231,7 @@ GeminiCUAAgent supports these native Gemini computer actions:
 
 ```python
 from hud import Environment
-from hud.agents import GeminiCUAAgent
+from hud.agents.gemini_cua import GeminiCUAAgent
 
 env = Environment("browser").connect_hub("hud-evals/browser")
 
@@ -290,7 +290,7 @@ agent = OpenAIChatAgent.create(
 
 ```python
 from hud import Environment
-from hud.agents import ClaudeAgent
+from hud.agents.claude import ClaudeAgent
 
 # Define environment with scenario
 env = Environment("browser").connect_hub("hud-evals/browser")

docs/reference/mcpserver.mdx

@@ -492,19 +492,19 @@ hud analyze my-env:latest
 
 # Python testing
 async def test():
-    from hud.clients import MCPClient
+    from hud import Environment
 
-    client = MCPClient({
+    env = Environment("test").connect_mcp_config({
         "env": {"command": "docker", "args": ["run", "-i", "my-env"]}
     })
 
-    async with client:
-        tools = await client.list_tools()
-        result = await client.call_tool("setup", {"value": 0})
+    async with env:
+        tools = await env.list_tools()
+        result = await env.call_tool("setup", value=0)
 ```
 
 ## See Also
 
 - [Environments](/reference/environments) - Environment class (client-side)
 - [Tools](/reference/tools) - Tool implementation reference
-- [Evals](/reference/evals) - Running evaluations
+- [Evals](/reference/evals) - Running evaluations