feat(cli): allow custom OpenRouter model IDs

cli/utils.py

@@ -1,5 +1,6 @@
+from typing import Callable, List, Optional, Tuple, Dict
+
 import questionary
-from typing import List, Optional, Tuple, Dict
 
 from rich.console import Console
 
@@ -7,6 +8,8 @@ from cli.models import AnalystType
 
 console = Console()
 
+CUSTOM_OPENROUTER_MODEL = "__custom_openrouter_model__"
+
 ANALYST_ORDER = [
     ("Market Analyst", AnalystType.MARKET),
     ("Social Media Analyst", AnalystType.SOCIAL),
@@ -68,6 +71,43 @@ def get_analysis_date() -> str:
     return date.strip()
 
 
+def prompt_custom_openrouter_model(model_role: str) -> Optional[str]:
+    """Prompt for an OpenRouter model id when the built-in list is insufficient."""
+    return questionary.text(
+        f"Enter the OpenRouter model ID for the {model_role} model (e.g. minimax/minimax-m2.1):",
+        validate=lambda x: len(x.strip()) > 0
+        or "Please enter a valid OpenRouter model ID.",
+        style=questionary.Style(
+            [
+                ("text", "fg:magenta"),
+                ("highlighted", "noinherit"),
+            ]
+        ),
+    ).ask()
+
+
+def resolve_model_choice(
+    provider: str,
+    choice: Optional[str],
+    model_role: str,
+    prompt_fn: Optional[Callable[[str], Optional[str]]] = None,
+) -> str:
+    """Resolve built-in and custom model selections into a concrete model id."""
+    if choice is None:
+        console.print(f"\n[red]No {model_role.lower()} llm engine selected. Exiting...[/red]")
+        exit(1)
+
+    if provider.lower() == "openrouter" and choice == CUSTOM_OPENROUTER_MODEL:
+        prompt_fn = prompt_fn or prompt_custom_openrouter_model
+        custom_model = prompt_fn(model_role)
+        if not custom_model or not custom_model.strip():
+            console.print("\n[red]No OpenRouter model ID provided. Exiting...[/red]")
+            exit(1)
+        return custom_model.strip()
+
+    return choice
+
+
 def select_analysts() -> List[AnalystType]:
     """Select analysts using an interactive checkbox."""
     choices = questionary.checkbox(
@@ -158,6 +198,7 @@ def select_shallow_thinking_agent(provider) -> str:
         "openrouter": [
             ("NVIDIA Nemotron 3 Nano 30B (free)", "nvidia/nemotron-3-nano-30b-a3b:free"),
             ("Z.AI GLM 4.5 Air (free)", "z-ai/glm-4.5-air:free"),
+            ("Custom OpenRouter model ID", CUSTOM_OPENROUTER_MODEL),
         ],
         "ollama": [
             ("Qwen3:latest (8B, local)", "qwen3:latest"),
@@ -182,13 +223,7 @@ def select_shallow_thinking_agent(provider) -> str:
         ),
     ).ask()
 
-    if choice is None:
-        console.print(
-            "\n[red]No shallow thinking llm engine selected. Exiting...[/red]"
-        )
-        exit(1)
-
-    return choice
+    return resolve_model_choice(provider, choice, "Quick-Thinking", prompt_custom_openrouter_model)
 
 
 def select_deep_thinking_agent(provider) -> str:
@@ -225,6 +260,7 @@ def select_deep_thinking_agent(provider) -> str:
         "openrouter": [
             ("Z.AI GLM 4.5 Air (free)", "z-ai/glm-4.5-air:free"),
             ("NVIDIA Nemotron 3 Nano 30B (free)", "nvidia/nemotron-3-nano-30b-a3b:free"),
+            ("Custom OpenRouter model ID", CUSTOM_OPENROUTER_MODEL),
         ],
         "ollama": [
             ("GLM-4.7-Flash:latest (30B, local)", "glm-4.7-flash:latest"),
@@ -249,11 +285,7 @@ def select_deep_thinking_agent(provider) -> str:
         ),
     ).ask()
 
-    if choice is None:
-        console.print("\n[red]No deep thinking llm engine selected. Exiting...[/red]")
-        exit(1)
-
-    return choice
+    return resolve_model_choice(provider, choice, "Deep-Thinking", prompt_custom_openrouter_model)
 
 def select_llm_provider() -> tuple[str, str]:
     """Select the OpenAI api url using interactive selection."""

tests/test_openrouter_model_selection.py

@@ -0,0 +1,24 @@
+import unittest
+
+from cli.utils import CUSTOM_OPENROUTER_MODEL, resolve_model_choice
+
+
+class OpenRouterModelSelectionTests(unittest.TestCase):
+    def test_builtin_model_is_returned_unchanged(self):
+        self.assertEqual(
+            resolve_model_choice("openrouter", "z-ai/glm-4.5-air:free", "Quick-Thinking"),
+            "z-ai/glm-4.5-air:free",
+        )
+
+    def test_custom_model_prompt_value_is_trimmed(self):
+        chosen = resolve_model_choice(
+            "openrouter",
+            CUSTOM_OPENROUTER_MODEL,
+            "Deep-Thinking",
+            prompt_fn=lambda _: " minimax/minimax-m2.1 ",
+        )
+        self.assertEqual(chosen, "minimax/minimax-m2.1")
+
+
+if __name__ == "__main__":
+    unittest.main()