Fix guardrail init, use alias1 as default

Signed-off-by: Víctor Mayoral Vilches <v.mayoralv@gmail.com>

src/cai/agents/__init__.py

@@ -61,7 +61,7 @@ from cai.sdk.agents.handoffs import handoff
 __path__ = pkgutil.extend_path(__path__, __name__)
 
 # Get model from environment or use default
-model = os.environ.get("CAI_MODEL", "alias0")
+model = os.environ.get("CAI_MODEL", "alias1")
 
 
 PATTERNS = ["hierarchical", "swarm", "chain_of_thought", "auction_based", "recursive"]

src/cai/agents/android_sast_agent.py

@@ -32,7 +32,7 @@ tools = [
 
 
 load_dotenv()
-model_name = os.getenv("CAI_MODEL", "alias0")
+model_name = os.getenv("CAI_MODEL", "alias1")
 
 app_logic_mapper = Agent(
     name="AppLogicMapper",

src/cai/agents/blue_teamer.py

@@ -43,7 +43,7 @@ blueteam_agent = Agent(
     description="""Agent that specializes in system defense and security monitoring.
                    Expert in cybersecurity protection and incident response.""",
     model=OpenAIChatCompletionsModel(
-        model=os.getenv('CAI_MODEL', "alias0"),
+        model=os.getenv('CAI_MODEL', "alias1"),
         openai_client=AsyncOpenAI(),
     ),
     tools=tools,

src/cai/agents/bug_bounter.py

@@ -27,6 +27,9 @@ from cai.tools.reconnaissance.shodan import (  # pylint: disable=import-error #
 from cai.agents.guardrails import get_security_guardrails
 
 load_dotenv()
+
+# Determine API key
+api_key = os.getenv("ALIAS_API_KEY", os.getenv("OPENAI_API_KEY", "sk-alias-1234567890"))
 # Prompts
 bug_bounter_system_prompt = load_prompt_template("prompts/system_bug_bounter.md")
 # Define tools list based on available API keys
@@ -52,8 +55,8 @@ bug_bounter_agent = Agent(
     input_guardrails=input_guardrails,
     output_guardrails=output_guardrails,
     model=OpenAIChatCompletionsModel(
-        model=os.getenv('CAI_MODEL', "alias0"),
-        openai_client=AsyncOpenAI(),
+        model=os.getenv('CAI_MODEL', "alias1"),
+        openai_client=AsyncOpenAI(api_key=api_key),
     )
    
 )

src/cai/agents/codeagent.py

@@ -184,7 +184,7 @@ class CodeAgent(Agent):
     def __init__(  # pylint: disable=too-many-arguments,too-many-locals # noqa: E501
         self,
         name: str = "CodeAgent",
-        model: str = "alias0",
+        model: str = "alias1",
         instructions: Union[str, Callable[[], str]] = None,
         tools: List[Callable] = None,
         additional_authorized_imports: Optional[List[str]] = None,

src/cai/agents/dfir.py

@@ -62,7 +62,7 @@ dfir_agent = Agent(
     description="""Agent that specializes in Digital Forensics and Incident Response.
                    Expert in investigation and analysis of digital evidence.""",
     model=OpenAIChatCompletionsModel(
-        model=os.getenv('CAI_MODEL', "alias0"),
+        model=os.getenv('CAI_MODEL', "alias1"),
         openai_client=AsyncOpenAI(),
     ),
     tools=tools,

src/cai/agents/factory.py

@@ -43,7 +43,7 @@ def create_generic_agent_factory(
         
         if not model_name:
             # Third priority: global CAI_MODEL
-            model_name = os.environ.get("CAI_MODEL", "alias0")
+            model_name = os.environ.get("CAI_MODEL", "alias1")
             
             
         api_key = os.getenv("OPENAI_API_KEY", "sk-placeholder-key-for-local-models")

src/cai/agents/flag_discriminator.py

@@ -6,7 +6,7 @@ from cai.sdk.agents import Agent, OpenAIChatCompletionsModel, handoff
 from openai import AsyncOpenAI
 from cai.agents.one_tool import one_tool_agent
 
-model = os.getenv('CAI_MODEL', "alias0")
+model = os.getenv('CAI_MODEL', "alias1")
 
 # Create OpenAI client with fallback API key to prevent initialization errors
 # The actual API key should be set in environment variables or .env file
@@ -22,7 +22,7 @@ flag_discriminator = Agent(
                 4. If you do not find a flag, call `ctf_agent` to continue investigating.
                 """,
     model=OpenAIChatCompletionsModel(
-        model="alias0" if os.getenv('CAI_MODEL') == "o3-mini" else model,
+        model="alias1" if os.getenv('CAI_MODEL') == "o3-mini" else model,
         openai_client=AsyncOpenAI(api_key=api_key),
     ),
     handoffs=[

src/cai/agents/guardrails.py

@@ -223,7 +223,7 @@ injection_detector_agent = Agent(
     Only flag content that contains EXPLICIT attempts to manipulate the system.""",
     output_type=PromptInjectionCheck,
     model=OpenAIChatCompletionsModel(
-        model=os.getenv('CAI_MODEL', 'alias0'),
+        model=os.getenv('CAI_MODEL', 'alias1'),
         openai_client=AsyncOpenAI(api_key=api_key),
     )
 )

src/cai/agents/mail.py

@@ -9,6 +9,9 @@ from cai.sdk.agents import Agent, OpenAIChatCompletionsModel
 from cai.tools.misc.cli_utils import execute_cli_command
 from cai.sdk.agents import function_tool
 
+# Determine API key
+api_key = os.getenv("ALIAS_API_KEY", os.getenv("OPENAI_API_KEY", "sk-alias-1234567890"))
+
 
 
 def get_txt_record(domain, record_type='TXT'):
@@ -115,7 +118,7 @@ dns_smtp_agent = Agent(
     ),
     tools=[check_mail_spoofing_vulnerability, execute_cli_command],
     model=OpenAIChatCompletionsModel(
-        model=os.getenv('CAI_MODEL', "alias0"),
-        openai_client=AsyncOpenAI(),
+        model=os.getenv('CAI_MODEL', "alias1"),
+        openai_client=AsyncOpenAI(api_key=api_key),
     )
 )

src/cai/agents/memory.py

@@ -86,7 +86,7 @@ from cai.sdk.agents import Agent, OpenAIChatCompletionsModel
 from cai.tools.misc.rag import add_to_memory_semantic, add_to_memory_episodic
 
 # Get model from environment or use default
-model = os.getenv('CAI_MODEL', "alias0")
+model = os.getenv('CAI_MODEL', "alias1")
 
 
 def get_previous_steps(query: str) -> str:

src/cai/agents/memory_analysis_agent.py

@@ -43,7 +43,7 @@ memory_analysis_agent = Agent(
                    for security assessment, vulnerability discovery, and runtime behavior analysis.""",
     tools=functions,
     model=OpenAIChatCompletionsModel(
-        model=os.getenv('CAI_MODEL', "alias0"),
+        model=os.getenv('CAI_MODEL', "alias1"),
         openai_client=AsyncOpenAI(),
     )
 )

src/cai/agents/network_traffic_analyzer.py

@@ -76,7 +76,7 @@ network_security_analyzer_agent = Agent(
     description="""Agent that specializes in network security analysis.
                    Expert in monitoring, capturing, and analyzing network communications for security threats.""",
         model=OpenAIChatCompletionsModel(
-        model=os.getenv('CAI_MODEL', "alias0"),
+        model=os.getenv('CAI_MODEL', "alias1"),
         openai_client=AsyncOpenAI(),
     ),
     tools=tools,

src/cai/agents/one_tool.py

@@ -9,7 +9,7 @@ from cai.util import create_system_prompt_renderer
 from cai.agents.guardrails import get_security_guardrails
 
 # Get model from environment or use default
-model_name = os.getenv('CAI_MODEL', "alias0")
+model_name = os.getenv('CAI_MODEL', "alias1")
 
 # NOTE: This is needed when using LiteLLM Proxy Server
 #

src/cai/agents/patterns/configs/agents.yml.example

@@ -15,7 +15,7 @@ parallel_agents:
     unified_context: false
     
   - name: bug_bounter_agent
-    model: alias0
+    model: alias1
     prompt: "Search for bugs and create detailed reports"
     unified_context: false
 

src/cai/agents/red_teamer.py

@@ -21,7 +21,10 @@ from cai.util import load_prompt_template, create_system_prompt_renderer
 from cai.agents.guardrails import get_security_guardrails
 
 load_dotenv()
-model_name = os.getenv("CAI_MODEL", "alias0")
+model_name = os.getenv("CAI_MODEL", "alias1")
+
+# Determine API key
+api_key = os.getenv("ALIAS_API_KEY", os.getenv("OPENAI_API_KEY", "sk-alias-1234567890"))
 # Prompts
 redteam_agent_system_prompt = load_prompt_template("prompts/system_red_team_agent.md")
 # Define tools list based on available API keys
@@ -48,7 +51,7 @@ redteam_agent = Agent(
     output_guardrails=output_guardrails,
     model=OpenAIChatCompletionsModel(
         model=model_name,
-        openai_client=AsyncOpenAI(),
+        openai_client=AsyncOpenAI(api_key=api_key),
     ),
 )
 

src/cai/agents/replay_attack_agent.py

@@ -68,7 +68,7 @@ replay_attack_agent = Agent(
     description="""Agent that specializes in network replay attacks and counteroffensive techniques.
                    Expert in packet manipulation, traffic replay, and protocol exploitation.""",
     model=OpenAIChatCompletionsModel(
-        model=os.getenv('CAI_MODEL', "alias0"),
+        model=os.getenv('CAI_MODEL', "alias1"),
         openai_client=AsyncOpenAI(),
     ),
     tools=tools,

src/cai/agents/reporter.py

@@ -31,7 +31,7 @@ reporting_agent = Agent(
     description="""Agent that generates reports in html.""",
     tools=functions,
     model=OpenAIChatCompletionsModel(
-        model=os.getenv('CAI_MODEL', "alias0"),
+        model=os.getenv('CAI_MODEL', "alias1"),
         openai_client=AsyncOpenAI(),
     )
 )

src/cai/agents/retester.py

@@ -17,6 +17,9 @@ from cai.tools.reconnaissance.exec_code import (  # pylint: disable=import-error
 
 load_dotenv()
 
+# Determine API key
+api_key = os.getenv("ALIAS_API_KEY", os.getenv("OPENAI_API_KEY", "sk-alias-1234567890"))
+
 # Load the triage agent system prompt
 retester_system_prompt = load_prompt_template("prompts/system_triage_agent.md")
 
@@ -36,8 +39,8 @@ retester_agent = Agent(
                    eliminating false positives.""",
     tools=tools,
     model=OpenAIChatCompletionsModel(
-        model=os.getenv('CAI_MODEL', "alias0"),
-        openai_client=AsyncOpenAI(),
+        model=os.getenv('CAI_MODEL', "alias1"),
+        openai_client=AsyncOpenAI(api_key=api_key),
     )
 )
 

src/cai/agents/reverse_engineering_agent.py

@@ -44,7 +44,7 @@ reverse_engineering_agent = Agent(
                    like Ghidra, Binwalk, and various binary analysis utilities.""",
     tools=functions,
     model=OpenAIChatCompletionsModel(
-        model=os.getenv('CAI_MODEL', "alias0"),
+        model=os.getenv('CAI_MODEL', "alias1"),
         openai_client=AsyncOpenAI(),
     )
 )

src/cai/agents/subghz_sdr_agent.py

@@ -43,7 +43,7 @@ subghz_sdr_agent = Agent(
                    automotive, industrial, and wireless security applications.""",
     tools=functions,
     model=OpenAIChatCompletionsModel(
-        model=os.getenv('CAI_MODEL', "alias0"),
+        model=os.getenv('CAI_MODEL', "alias1"),
         openai_client=AsyncOpenAI(),
     )
 )

src/cai/agents/thought.py

@@ -11,14 +11,17 @@ from openai import AsyncOpenAI
 from cai.util import load_prompt_template, create_system_prompt_renderer
 import os
 
+# Determine API key
+api_key = os.getenv("ALIAS_API_KEY", os.getenv("OPENAI_API_KEY", "sk-alias-1234567890"))
+
 thought_agent_system_prompt = load_prompt_template("prompts/system_thought_router.md")
 
 # Thought Process Agent for analysis and planning
 thought_agent = Agent(
     name="ThoughtAgent",
     model=OpenAIChatCompletionsModel(
-        model=os.getenv('CAI_MODEL', "alias0"),
-        openai_client=AsyncOpenAI(),
+        model=os.getenv('CAI_MODEL', "alias1"),
+        openai_client=AsyncOpenAI(api_key=api_key),
     ),
     description="""Agent focused on analyzing and planning the next steps
                    in a security assessment or CTF challenge.""",

src/cai/agents/usecase.py

@@ -7,7 +7,7 @@ from cai.tools.reconnaissance.generic_linux_command import null_tool
 from cai.util import load_prompt_template, create_system_prompt_renderer
 
 load_dotenv()
-model_name = os.getenv("CAI_MODEL", "alias0")
+model_name = os.getenv("CAI_MODEL", "alias1")
 
 # Load prompt
 use_case_agent_system_prompt = load_prompt_template("prompts/system_use_cases.md")

src/cai/agents/wifi_security_tester.py

@@ -42,7 +42,7 @@ wifi_security_agent = Agent(
                    Specializes in wireless attacks, password recovery, and communication disruption.""",
     tools=functions,
     model=OpenAIChatCompletionsModel(
-        model=os.getenv('CAI_MODEL', "alias0"),
+        model=os.getenv('CAI_MODEL', "alias1"),
         openai_client=AsyncOpenAI(),
     )
 )

src/cai/cli.py

@@ -20,7 +20,7 @@ Environment Variables
             within container (default: "true")
 
         CAI_MODEL: Model to use for agents
-            (default: "alias0")
+            (default: "alias1")
         CAI_DEBUG: Set debug output level (default: "1")
             - 0: Only tool outputs
             - 1: Verbose debug output
@@ -79,36 +79,36 @@ Usage Examples:
 
     # Run against a CTF
     CTF_NAME="kiddoctf" CTF_CHALLENGE="02 linux ii" \
-        CAI_AGENT_TYPE="one_tool_agent" CAI_MODEL="alias0" \
+        CAI_AGENT_TYPE="one_tool_agent" CAI_MODEL="alias1" \
         CAI_TRACING="false" cai
 
     # Run a harder CTF
     CTF_NAME="hackableii" CAI_AGENT_TYPE="redteam_agent" \
-        CTF_INSIDE="False" CAI_MODEL="alias0" \
+        CTF_INSIDE="False" CAI_MODEL="alias1" \
         CAI_TRACING="false" cai
 
     # Run without a target in human-in-the-loop mode, generating a report
-    CAI_TRACING=False CAI_REPORT=pentesting CAI_MODEL="alias0" \
+    CAI_TRACING=False CAI_REPORT=pentesting CAI_MODEL="alias1" \
         cai
 
     # Run with online episodic memory
     #   registers memory every 5 turns:
     #   limits the cost to 5 dollars
     CTF_NAME="hackableII" CAI_MEMORY="episodic" \
-        CAI_MODEL="alias0" CAI_MEMORY_ONLINE="True" \
+        CAI_MODEL="alias1" CAI_MEMORY_ONLINE="True" \
         CTF_INSIDE="False" CTF_HINTS="False"  \
         CAI_PRICE_LIMIT="5" cai
 
     # Run with custom long_term_memory interval
     # Executes memory long_term_memory every 3 turns:
     CTF_NAME="hackableII" CAI_MEMORY="episodic" \
-        CAI_MODEL="alias0" CAI_MEMORY_ONLINE_INTERVAL="3" \
+        CAI_MODEL="alias1" CAI_MEMORY_ONLINE_INTERVAL="3" \
         CAI_MEMORY_ONLINE="False" CTF_INSIDE="False" \
         CTF_HINTS="False" cai
         
     # Run with parallel agents (3 instances)
     CTF_NAME="hackableII" CAI_AGENT_TYPE="redteam_agent" \
-        CAI_MODEL="alias0" CAI_PARALLEL="3" cai
+        CAI_MODEL="alias1" CAI_PARALLEL="3" cai
 """
 
 # Load environment variables from .env file FIRST, before any imports
@@ -441,7 +441,7 @@ def run_cai_cli(
     turn_count = 0
     idle_time = 0
     console = Console()
-    last_model = os.getenv("CAI_MODEL", "alias0")
+    last_model = os.getenv("CAI_MODEL", "alias1")
     last_agent_type = os.getenv("CAI_AGENT_TYPE", "one_tool_agent")
     parallel_count = int(os.getenv("CAI_PARALLEL", "1"))
     use_initial_prompt = initial_prompt is not None
@@ -549,7 +549,7 @@ def run_cai_cli(
             idle_start_time = time.time()
 
             # Check if model has changed and update if needed
-            current_model = os.getenv("CAI_MODEL", "alias0")
+            current_model = os.getenv("CAI_MODEL", "alias1")
             # Check for agent-specific model override
             agent_specific_model = os.getenv(f"CAI_{last_agent_type.upper()}_MODEL")
             if agent_specific_model:
@@ -1047,7 +1047,7 @@ def run_cai_cli(
                             custom_name = f"{agent_display_name} #{idx}"
                             
                             # Determine model
-                            model_to_use = config.model or os.getenv("CAI_MODEL", "alias0")
+                            model_to_use = config.model or os.getenv("CAI_MODEL", "alias1")
                             
                             # Create and store the instance
                             # No shared_message_history - each agent gets its own isolated copy
@@ -1106,7 +1106,7 @@ def run_cai_cli(
                                 custom_name = agent_display_name
                             
                             # Determine which model to use
-                            model_to_use = config.model or os.getenv("CAI_MODEL", "alias0")
+                            model_to_use = config.model or os.getenv("CAI_MODEL", "alias1")
                             
                             # Create agent instance with the determined model
                             # Each agent gets its own isolated history from PARALLEL_ISOLATION
@@ -1125,7 +1125,7 @@ def run_cai_cli(
                         AGENT_MANAGER.set_parallel_agent(agent_id, instance_agent, agent_display_name)
 
                         # Ensure the model is properly set for the agent and all handoff agents
-                        model_to_use = config.model or os.getenv("CAI_MODEL", "alias0")
+                        model_to_use = config.model or os.getenv("CAI_MODEL", "alias1")
                         if model_to_use:
                             update_agent_models_recursively(instance_agent, model_to_use)
 
@@ -1871,7 +1871,7 @@ def main():
             agent.model.suppress_final_output = False  # Changed to False to show all agent messages
 
     # Ensure the agent and all its handoff agents use the current model
-    current_model = os.getenv("CAI_MODEL", "alias0")
+    current_model = os.getenv("CAI_MODEL", "alias1")
     update_agent_models_recursively(agent, current_model)
 
     # Run the CLI with the selected agent and optional initial prompt

src/cai/repl/commands/compact.py

@@ -521,7 +521,7 @@ class CompactCommand(Command):
             # Pass the compact model if set
             if self.compact_model:
                 # Temporarily override the model for this operation
-                original_model = os.environ.get("CAI_MODEL", "alias0")
+                original_model = os.environ.get("CAI_MODEL", "alias1")
                 os.environ["CAI_MODEL"] = self.compact_model
                 try:
                     result = MEMORY_COMMAND_INSTANCE.handle_save([memory_name], preserve_history=False)

src/cai/repl/commands/config.py

@@ -46,7 +46,7 @@ ENV_VARS = {
     6: {
         "name": "CAI_MODEL",
         "description": "Model to use for agents",
-        "default": "alias0"
+        "default": "alias1"
     },
     7: {
         "name": "CAI_DEBUG",

src/cai/repl/commands/memory.py

@@ -1144,7 +1144,7 @@ Model: {get_compact_model() or os.environ.get("CAI_MODEL", "gpt-4")}
         from cai.repl.commands.compact import get_compact_model, get_custom_prompt
         
         # Create summary agent
-        model_name = get_compact_model() or os.environ.get("CAI_MODEL", "alias0")
+        model_name = get_compact_model() or os.environ.get("CAI_MODEL", "alias1")
         
         # Use custom prompt if set, otherwise use default
         custom_prompt = get_custom_prompt()

src/cai/repl/commands/model.py

@@ -36,15 +36,15 @@ def get_predefined_model_categories() -> Dict[str, List[Dict[str, str]]]:
     return {
         "Alias": [
             {
-                "name": "alias0",
+                "name": "alias1",
                 "description": (
                     "Best model for Cybersecurity AI tasks"
                 )
             },
             {
-                "name": "alias0-fast",
+                "name": "alias1-fast",
                 "description": (
-                    "Fast version of alias0 for quick tasks"
+                    "Fast version of alias1 for quick tasks"
                 )
             }
         ],

src/cai/repl/commands/parallel.py

@@ -649,7 +649,7 @@ class ParallelCommand(Command):
             console.print("[yellow]No parallel configurations to override[/yellow]")
             return False
 
-        global_model = os.getenv("CAI_MODEL", "alias0")
+        global_model = os.getenv("CAI_MODEL", "alias1")
         count = 0
 
         for config in PARALLEL_CONFIGS:

src/cai/repl/commands/quickstart.py

@@ -260,7 +260,7 @@ class QuickstartCommand(Command):
             console.print("  [yellow]1.[/yellow] Run: [bold green]/model-show[/bold green] to see all available models")
             console.print("  [yellow]2.[/yellow] Run: [bold green]/model-show supported[/bold green] to see only models with function calling support")
             console.print("  [yellow]3.[/yellow] Select a model: [bold green]/model <model-name>[/bold green]")
-            console.print("\n[dim]Note: The default model 'alias0' requires configuration. Please select a specific model.[/dim]")
+            console.print("\n[dim]Note: The default model 'alias1' requires configuration. Please select a specific model.[/dim]")
         else:
             console.print(
                 Panel(

src/cai/repl/ui/banner.py

@@ -348,7 +348,7 @@ def display_quick_guide(console: Console):
     )
     
     # Get current environment variable values
-    current_model = os.getenv('CAI_MODEL', "alias0")
+    current_model = os.getenv('CAI_MODEL', "alias1")
     current_agent_type = os.getenv('CAI_AGENT_TYPE', "one_tool_agent")
     
     config_text = Text.assemble(
@@ -413,19 +413,19 @@ def display_quick_guide(console: Console):
         Text.assemble(
             ("🔒 Security-Focused AI Framework\n\n", "bold white"),
             "For optimal cybersecurity AI performance, use\n", 
-            ("alias0", "bold green"), 
+            ("alias1", "bold green"), 
             " - specifically designed for cybersecurity\n"
             "tasks with superior domain knowledge.\n\n",
-            ("alias0", "bold green"), 
+            ("alias1", "bold green"), 
             " outperforms general-purpose models in:\n",
             "• Vulnerability assessment\n",
             "• Penetration testing and bug bounty\n",
             "• Security analysis\n",
             "• Threat detection\n\n",
             "Learn more about ", 
-            ("alias0", "bold green"), 
+            ("alias1", "bold green"), 
             " and its privacy-first approach:\n",
-            ("https://news.aliasrobotics.com/alias0-a-privacy-first-cybersecurity-ai/", "blue underline")
+            ("https://news.aliasrobotics.com/alias1-a-privacy-first-cybersecurity-ai/", "blue underline")
         ),
         title="[bold yellow]🛡️ Alias0 - best model for cybersecurity [/bold yellow]",
         border_style="yellow",

src/cai/sdk/agents/models/openai_chatcompletions.py

@@ -2699,7 +2699,7 @@ class OpenAIChatCompletionsModel(Model):
         # Determine provider based on model string
         model_str = str(kwargs["model"]).lower()
 
-        if "alias" in model_str and "alias0.5" not in model_str:  # NOTE: exclude alias0.5
+        if "alias" in model_str and "alias1.5" not in model_str:  # NOTE: exclude alias1.5
             kwargs["api_base"] = "https://api.aliasrobotics.com:666/"
             kwargs["custom_llm_provider"] = "openai"
             kwargs["api_key"] = os.getenv("ALIAS_API_KEY", "REDACTED_ALIAS_KEY")
@@ -2815,7 +2815,7 @@ class OpenAIChatCompletionsModel(Model):
             elif "gemini" in model_str:
                 kwargs.pop("parallel_tool_calls", None)
             elif "qwen" in model_str or ":" in model_str:
-                # Handle Ollama-served models with custom formats (e.g., alias0)
+                # Handle Ollama-served models with custom formats (e.g., alias1)
                 # These typically need the Ollama provider
                 litellm.drop_params = True
                 kwargs.pop("parallel_tool_calls", None)

src/cai/util.py

@@ -2211,7 +2211,7 @@ def update_agent_streaming_content(context, text_delta, token_stats=None):
                 footer_stats.append(f"${session_total_cost:.4f}", style="bold magenta")
 
                 # Add context usage indicator
-                model_name = context.get("model", os.environ.get("CAI_MODEL", "alias0"))
+                model_name = context.get("model", os.environ.get("CAI_MODEL", "alias1"))
                 context_pct = input_tokens / get_model_input_tokens(model_name) * 100
                 if context_pct < 50:
                     indicator = "🟩"