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app/models.py

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+from pydantic import BaseModel, Field
+from typing import List, Optional, Any
+
+
+from typing import List, Optional, Any, Dict, Literal, Union
+
+# --- MCP Tool Configuration Models ---
+
+class StdioConfig(BaseModel):
+    """Configuration for Stdio-based MCP server."""
+    command: str = Field(..., description="Executable command (e.g., 'npx', 'python')")
+    args: List[str] = Field(default_factory=list, description="Command arguments")
+    env: Optional[Dict[str, str]] = Field(None, description="Environment variables")
+
+class SSEConfig(BaseModel):
+    """Configuration for SSE-based MCP server."""
+    url: str = Field(..., description="Server URL (e.g., 'http://localhost:8000/sse')")
+
+class MCPServerConfig(BaseModel):
+    """Configuration for an MCP Server connection."""
+    name: str = Field(..., description="Unique name for the toolset")
+    type: Literal["stdio", "sse"] = Field("stdio", description="Connection type")
+    stdio_config: Optional[StdioConfig] = Field(None, description="Stdio configuration")
+    sse_config: Optional[SSEConfig] = Field(None, description="SSE configuration")
+    tool_filter: Optional[List[str]] = Field(None, description="Optional whitelist of tools to enable")
+
+# --- LLM Configuration Models ---
+
+class LLMConfig(BaseModel):
+    """Custom LLM API configuration for per-request override."""
+    api_key: Optional[str] = Field(None, description="API key for the LLM service")
+    api_base: Optional[str] = Field(None, description="Base URL for the LLM API")
+    model: Optional[str] = Field(None, description="Model name (e.g., 'openai/gpt-4o')")
+
+
+class GenerationConfig(BaseModel):
+    """LLM generation parameters for fine-tuning responses."""
+    temperature: Optional[float] = Field(None, ge=0.0, le=2.0, description="Randomness (0.0-2.0)")
+    max_output_tokens: Optional[int] = Field(None, gt=0, description="Max response length")
+    top_p: Optional[float] = Field(None, ge=0.0, le=1.0, description="Nucleus sampling")
+    top_k: Optional[int] = Field(None, gt=0, description="Top-k sampling")
+
+
+# --- Request Models ---
+
+class CreateSessionRequest(BaseModel):
+    """Request to create a new chat session."""
+    user_id: str
+    app_name: Optional[str] = None
+
+
+
+class ThinkingConfigModel(BaseModel):
+    """Configuration for BuiltInPlanner thinking features."""
+    include_thoughts: bool = Field(True, description="Whether to include internal thoughts in the response")
+    thinking_budget: int = Field(1024, gt=0, description="Token budget for thinking")
+
+
+class AgentConfig(BaseModel):
+    """Comprehensive configuration for creating an ADK Agent."""
+    name: str = Field(..., description="Unique name for the agent")
+    description: Optional[str] = Field(None, description="Description of agent capabilities")
+    model: str = Field(..., description="Model identifier (e.g., 'gemini-2.5-flash')")
+    instruction: str = Field(..., description="System instruction / persona for the agent")
+    tools: List[str] = Field(default_factory=list, description="List of tool names to enable")
+    enable_code_execution: bool = Field(False, description="Enable built-in code executor")
+    
+    thinking_config: Optional[ThinkingConfigModel] = Field(None, description="Configuration for reasoning/planning")
+    
+    # Nested configurations
+    llm_config: Optional[LLMConfig] = Field(None, description="Custom LLM API connection details")
+    generation_config: Optional[GenerationConfig] = Field(None, description="Generation parameters")
+
+
+
+class AgentDefinition(AgentConfig):
+    """Persisted Agent definition with unique ID."""
+    id: str = Field(..., description="Unique identifier for the agent")
+    created_at: float = Field(default_factory=lambda: __import__("time").time(), description="Creation timestamp")
+    updated_at: float = Field(default_factory=lambda: __import__("time").time(), description="Last update timestamp")
+
+
+class ChatTurnRequest(BaseModel):
+    """Request for a single chat turn with a specific agent."""
+    agent_id: str = Field(..., description="ID of the agent to use for this turn")
+    message: str = Field(..., description="User message content")
+    # Optional overrides for this specific turn
+    streaming_mode: Optional[str] = Field("sse", description="Streaming mode: 'sse', 'none'")
+
+
+class ChatRequest(BaseModel):
+    """
+    LEGACY: Request for chat completion. 
+    Used by the old /chat/stream endpoint.
+    """
+    session_id: str
+    user_id: str
+    message: str
+    app_name: Optional[str] = None
+    
+    agent_config: Optional[AgentConfig] = Field(None, description="Ephemeral agent config")
+    max_llm_calls: Optional[int] = Field(500, gt=0)
+    streaming_mode: Optional[str] = "sse"
+
+
+class SessionResponse(BaseModel):
+    """Response for session operations."""
+    id: str
+    app_name: str
+    user_id: str
+    updated_at: Any = None
+
+
+class HistoryEvent(BaseModel):
+    """A single event in chat history."""
+    type: str
+    role: str
+    content: str
+    agent_name: Optional[str] = None
+    timestamp: Optional[Any] = None
+    invocation_id: Optional[str] = None
+
+
+class SessionDetailResponse(SessionResponse):
+    """Session details including chat history."""
+    history: List[HistoryEvent] = []
+    events_count: int = 0
+    created_at: Any = None