Close local release-quality gaps and reconcile workplan status

Assistant: codex
Assistant-Model: gpt-6-astra
Assistant-Session: 01a0e332-3365-77c0-8491-084e9ea33ac1

llm_connect/models.py

@@ -7,7 +7,7 @@ markitect.prompts.execution.models for backward compatibility.
 
 import threading
 from dataclasses import dataclass, field
-from typing import Dict, Any, Optional
+from typing import Any, Optional
 
 from llm_connect.exceptions import LLMBudgetExceededError
 
@@ -70,13 +70,13 @@ class RunConfig:
     model_name: str = "gpt-4"
     temperature: float = 0.7
     max_tokens: int = 2000
-    model_params: Dict[str, Any] = field(default_factory=dict)
+    model_params: dict[str, Any] = field(default_factory=dict)
     max_depth: int = 3
     skip_if_exists: bool = True
     timeout_seconds: int = 300
     budget_tracker: Optional["BudgetTracker"] = field(default=None, repr=False)
 
-    def to_dict(self) -> Dict[str, Any]:
+    def to_dict(self) -> dict[str, Any]:
         """Convert to dictionary. ``budget_tracker`` is excluded (runtime object)."""
         return {
             "model_name": self.model_name,
@@ -89,7 +89,7 @@ class RunConfig:
         }
 
     @classmethod
-    def from_dict(cls, data: Dict[str, Any]) -> "RunConfig":
+    def from_dict(cls, data: dict[str, Any]) -> "RunConfig":
         """Create from dictionary."""
         return cls(
             model_name=data.get("model_name", "gpt-4"),
@@ -116,11 +116,11 @@ class LLMResponse:
     """
     content: str
     model: str
-    usage: Dict[str, int] = field(default_factory=dict)
+    usage: dict[str, int] = field(default_factory=dict)
     finish_reason: str = "stop"
-    metadata: Dict[str, Any] = field(default_factory=dict)
+    metadata: dict[str, Any] = field(default_factory=dict)
 
-    def to_dict(self) -> Dict[str, Any]:
+    def to_dict(self) -> dict[str, Any]:
         """Convert to dictionary."""
         return {
             "content": self.content,