docs: replace all placeholder content with real documentation

MCP docs:
- quickstart.md - Installation, basic usage, transport modes
- configuration.md - CLI options, env vars, config files
- vscode.md - VS Code, Cursor, Windsurf integration

Agent SDK docs:
- agents.md - Creating and configuring agents
- running_agents.md - Runner, RunConfig, async/sync
- tools.md - Function tools, context, custom tools
- guardrails.md - Input/output validation
- handoffs.md - Agent delegation patterns
- streaming.md - Stream events and responses
- tracing.md - Observability and debugging
- context.md - Mutable run context
- config.md - RunConfig and ModelSettings
- results.md - RunResult, items, usage
- models.md - Providers and model selection
- multi_agent.md - Complex multi-agent patterns

docs/agent/models.md

@@ -1,2 +1,203 @@
-# models
-Documentation coming soon.
+# Models
+
+Configure and use different LLM providers.
+
+## Supported Models
+
+### OpenAI
+
+```python
+from agents import Agent
+
+# GPT-4o (recommended)
+agent = Agent(name="gpt4", model="gpt-4o")
+
+# GPT-4o mini (faster, cheaper)
+agent = Agent(name="mini", model="gpt-4o-mini")
+
+# GPT-4 Turbo
+agent = Agent(name="turbo", model="gpt-4-turbo")
+```
+
+### Anthropic (via Hanzo)
+
+```python
+agent = Agent(name="claude", model="claude-3-5-sonnet-20241022")
+agent = Agent(name="opus", model="claude-3-opus-20240229")
+```
+
+### Other Providers
+
+```python
+# Gemini
+agent = Agent(name="gemini", model="gemini-pro")
+
+# Mistral
+agent = Agent(name="mistral", model="mistral-large")
+```
+
+## Model Providers
+
+### OpenAI Provider (default)
+
+```python
+from agents import OpenAIProvider, RunConfig
+
+provider = OpenAIProvider(
+    api_key="sk-...",  # Or use OPENAI_API_KEY env var
+)
+
+config = RunConfig(model_provider=provider)
+```
+
+### Hanzo Node Provider
+
+```python
+from agents import create_hanzo_node_provider, RunConfig
+
+provider = create_hanzo_node_provider(
+    api_key="your-key",  # Or use HANZO_API_KEY env var
+)
+
+config = RunConfig(model_provider=provider)
+result = await Runner.run(agent, "Hello!", run_config=config)
+```
+
+### Custom Base URL
+
+```python
+from agents import OpenAIProvider
+
+# Use Azure OpenAI
+provider = OpenAIProvider(
+    api_key="azure-key",
+    base_url="https://your-resource.openai.azure.com/",
+)
+
+# Use local model (Ollama, vLLM, etc.)
+provider = OpenAIProvider(
+    api_key="not-needed",
+    base_url="http://localhost:11434/v1",
+)
+```
+
+## Custom Model Implementation
+
+```python
+from agents import Model, ModelProvider
+
+class MyModel(Model):
+    async def complete(
+        self,
+        messages: list[dict],
+        tools: list[dict] | None = None,
+        **kwargs,
+    ) -> dict:
+        # Your implementation
+        response = await my_api_call(messages, tools)
+        return {
+            "content": response.text,
+            "tool_calls": response.tool_calls,
+        }
+
+class MyProvider(ModelProvider):
+    def get_model(self, model_name: str) -> Model:
+        return MyModel(model_name)
+```
+
+## Model Settings
+
+```python
+from agents import Agent, ModelSettings
+
+agent = Agent(
+    name="creative",
+    model="gpt-4o",
+    model_settings=ModelSettings(
+        temperature=0.9,      # More creative
+        top_p=0.95,
+        max_tokens=2000,
+        presence_penalty=0.1,
+        frequency_penalty=0.1,
+    ),
+)
+
+agent = Agent(
+    name="precise",
+    model="gpt-4o",
+    model_settings=ModelSettings(
+        temperature=0.1,      # More deterministic
+        max_tokens=500,
+    ),
+)
+```
+
+## Model Tracing
+
+Enable detailed model tracing:
+
+```python
+from agents import ModelTracing
+
+class TracedModel(Model):
+    tracing: ModelTracing = ModelTracing.ENABLED
+    
+    async def complete(self, messages, tools=None, **kwargs):
+        # Automatically traced
+        ...
+```
+
+## Response Models
+
+### Chat Completions
+
+Standard OpenAI-compatible response:
+
+```python
+from agents import OpenAIChatCompletionsModel
+
+model = OpenAIChatCompletionsModel("gpt-4o")
+```
+
+### Responses API
+
+For models supporting the newer responses format:
+
+```python
+from agents import OpenAIResponsesModel
+
+model = OpenAIResponsesModel("gpt-4o")
+```
+
+## Model Selection Strategy
+
+```python
+from agents import Agent, RunConfig
+
+# Development: faster, cheaper
+dev_config = RunConfig(model="gpt-4o-mini")
+
+# Production: best quality
+prod_config = RunConfig(model="gpt-4o")
+
+# Use based on environment
+import os
+config = prod_config if os.environ.get("ENV") == "prod" else dev_config
+
+result = await Runner.run(agent, "Hello!", run_config=config)
+```
+
+## Environment Variables
+
+| Variable | Description |
+|----------|-------------|
+| `OPENAI_API_KEY` | OpenAI API key |
+| `OPENAI_BASE_URL` | Custom OpenAI-compatible endpoint |
+| `ANTHROPIC_API_KEY` | Anthropic API key |
+| `HANZO_API_KEY` | Hanzo API key |
+
+## See Also
+
+- [Configuration](config.md) - Model settings
+- [Agents](agents.md) - Using models with agents
+- [Running Agents](running_agents.md) - Runtime model selection