diff --git a/agent_system.py b/agent_system.py
index 3bcde05..67b56f0 100644
--- a/agent_system.py
+++ b/agent_system.py
@@ -13,10 +13,10 @@
 import ast  # Added for script validation
 from typing import Dict, List, Any, Optional, Tuple
 from pathlib import Path
-from google import genai
-from google.genai import types  # Added import for GenerateContentConfig
 import numpy as np
 
+from clients import LLMClient
+
 from prompts.data_analyzer import get_dataset_analysis_prompt
 from prompts.batch_size_optimizer import get_batch_size_optimization_prompt
 from prompts.batch_learnings import get_batch_learnings_prompt
@@ -63,7 +63,7 @@ class AgentSystem:
     Now supports custom dataset loaders.
     """
 
-    def __init__(self, dataset_loader=None):
+    def __init__(self, client: LLMClient, dataset_loader=None):
         """
         Initialize the agent system with a dataset loader
 
@@ -97,15 +97,8 @@ def __init__(self, dataset_loader=None):
         self.system_prompt = self._load_system_prompt()
         print(f"System prompt loaded: {len(self.system_prompt)} characters")
 
-        # Initialize Gemini API client
-        try:
-            self.client = genai.Client(
-                api_key=os.environ.get("GEMINI_API_KEY"))
-            print("Gemini API client initialized successfully")
-        except Exception as e:
-            print(f"Error initializing Gemini API client: {e}")
-            print("Make sure to set the GEMINI_API_KEY environment variable")
-            raise
+        # Initialize LLM API client
+        self.client = client 
 
         # Initialize learnings mechanism
         print("Initializing learnings mechanism...")
@@ -230,7 +223,7 @@ def analyze_dataset_with_llm(self):
         # Call LLM to analyze the dataset
         try:
             prompt, system_instruction = get_dataset_analysis_prompt(formatted_examples)
-            dataset_analysis = self.call_llm(prompt, system_instruction=system_instruction)
+            dataset_analysis = self.client.call_llm(prompt, system_instruction=system_instruction)
             print("Dataset analysis complete, adding to learnings.txt")
 
             # Format the analysis for learnings.txt
@@ -363,21 +356,6 @@ def _load_previous_state(self):
                 self.current_iteration = 0
                 print("No previous state found. Starting from iteration 0.")
     
-    def call_llm(self, prompt: str, system_instruction: str = None) -> str:
-        """Call the Gemini LLM with a prompt and return the response"""
-        try:
-            # Use provided system instruction or default to the loaded system prompt
-            sys_instruction = system_instruction if system_instruction is not None else ""
-
-            response = self.client.models.generate_content(
-                model="gemini-2.0-flash",
-                config=types.GenerateContentConfig(
-                    system_instruction=sys_instruction),
-                contents=prompt)
-            return response.text
-        except Exception as e:
-            print(f"Error calling Gemini API: {e}")
-            return f"Error: {str(e)}"
 
     def load_dataset(self) -> Dict:
         """Load the entire dataset from the specified file"""
@@ -552,7 +530,7 @@ def adjust_batch_size_with_llm(self, performance: Dict) -> Tuple[int, str]:
                 total_examples_seen=len(self.seen_examples),
                 performance_history=performance_history
             )
-            response = self.call_llm(
+            response = self.client.call_llm(
                 prompt, system_instruction=system_instruction)
 
             # Extract JSON from response
@@ -675,7 +653,7 @@ def generate_batch_learnings(self, iteration_data: Dict) -> str:
                                                                     error_examples, 
                                                                     capability_insights)
             
-            response = self.call_llm(prompt, system_instruction=system_instruction)
+            response = self.client.call_llm(prompt, system_instruction=system_instruction)
             return f"--- LEARNINGS FROM ITERATION {iteration_data.get('iteration')} ---\n{response.strip()}\n\n"
         except Exception as e:
             error_message = f"Error generating batch learnings: {str(e)}"
@@ -726,7 +704,7 @@ def synthesize_learnings(self, current_learnings: str, new_batch_learnings: str)
 
         try:
             print(f"Calling LLM to {'condense and synthesize' if approaching_limit else 'synthesize'} learnings...")
-            response = self.call_llm(prompt, system_instruction=system_instruction)
+            response = self.client.call_llm(prompt, system_instruction=system_instruction)
 
             response_length = len(response.strip())
             print(f"Received synthesized learnings: {response_length} characters")
@@ -786,31 +764,20 @@ def generate_baseline_script(self) -> str:
 from google import genai
 from google.genai import types
 
-def call_llm(prompt, system_instruction=None):
-    """Call the Gemini LLM with a prompt and return the response"""
-    try:
-        # Initialize the Gemini client
-        client = genai.Client(api_key=os.environ.get("GEMINI_API_KEY"))
-
-        # Call the API with system instruction if provided
-        if system_instruction:
-            response = client.models.generate_content(
-                model="gemini-2.0-flash", 
-                config=types.GenerateContentConfig(
-                    system_instruction=system_instruction
-                ),
-                contents=prompt
-            )
-        else:
-            response = client.models.generate_content(
-                model="gemini-2.0-flash",
-                contents=prompt
-            )
+from clients import GeminiClient, OpenAIClient
+
+provider_type = "{provider}"
+model_name = "{model_name}"
 
-        return response.text
-    except Exception as e:
-        print(f"Error calling Gemini API: {str(e)}")
-        return f"Error: {str(e)}"
+if provider_type == "gemini":
+    client = GeminiClient(model_name)
+elif provider_type == "openai":
+    client = OpenAIClient(model_name)
+else:
+    raise ValueError(f"Unknown provider: {provider}")
+
+def call_llm(prompt, system_instruction=""):
+    return client.call_llm(prompt, system_instruction)
 
 def main(question):
     """
@@ -985,7 +952,7 @@ def adjust_strategy_with_llm(self) -> Tuple[int, int, int]:
                                                                           performance_history, 
                                                                           capability_context=None)
             
-            response = self.call_llm(prompt, system_instruction=system_instruction)
+            response = self.client.call_llm(prompt, system_instruction=system_instruction)
 
             # Extract JSON from response
             response = response.strip()
@@ -1318,9 +1285,17 @@ def generate_script_with_llm(self, strategy_mode) -> str:
         # Set specific system instruction for script generation
         script_generator_system_instruction = f"{self.system_prompt}\n\nYou are now acting as a Script Generator for an {strategy_mode} task. Your goal is to create a Python script that uses LLM-driven agentic approaches with chain-of-thought reasoning, agentic LLM patterns, and python to solve the problem examples provided."
 
+        # Add a provider-agnostic LLM API example generator
+        def get_llm_api_example(provider, model_name):
+            if provider == "gemini":
+                return f'''from clients import GeminiClient\n\nmodel_name = "{model_name}"\nclient = GeminiClient(model_name)\n\ndef call_llm(prompt, system_instruction=""):\n    return client.call_llm(prompt, system_instruction)\n'''
+            elif provider == "openai":
+                return f'''from clients import OpenAIClient\n\nmodel_name = "{model_name}"\nclient = OpenAIClient(model_name)\n\ndef call_llm(prompt, system_instruction=""):\n    return client.call_llm(prompt, system_instruction)\n'''
+            else:
+                return f"# LLM provider '{provider}' not supported in example"
 
-        gemini_api_example = 'def call_llm(prompt, system_instruction=None):\n    """Call the Gemini LLM with a prompt and return the response. DO NOT deviate from this example template or invent configuration options. This is how you call the LLM."""\n    try:\n        from google import genai\n        from google.genai import types\n\n        # Initialize the Gemini client\n        client = genai.Client(api_key=os.environ.get("GEMINI_API_KEY"))\n\n        # Call the API with system instruction if provided\n        if system_instruction:\n            response = client.models.generate_content(\n                model="gemini-2.0-flash", \n                config=types.GenerateContentConfig(\n                    system_instruction=system_instruction\n                ),\n                contents=prompt\n            )\n        else:\n            response = client.models.generate_content(\n                model="gemini-2.0-flash",\n                contents=prompt\n            )\n\n        return response.text\n    except Exception as e:\n        print(f"Error calling Gemini API: {str(e)}")\n        return f"Error: {str(e)}"'
-
+        # Use provider-agnostic LLM API example in prompt construction
+        llm_api_example = self.get_llm_api_example(self.client.provider, self.client.model_name)
 
         # Create appropriate prompt based on strategy
         if strategy_mode == "explore":
@@ -1331,7 +1306,7 @@ def generate_script_with_llm(self, strategy_mode) -> str:
                 last_scripts_context=last_scripts_context,
                 learning_context=learning_context,
                 capability_context=capability_context,
-                llm_api_example=gemini_api_example
+                llm_api_example=llm_api_example
             )
         elif strategy_mode == "exploit":
             # Exploitation prompt - combine strengths from multiple top scripts
@@ -1357,7 +1332,7 @@ def generate_script_with_llm(self, strategy_mode) -> str:
                 top_scripts_analysis=top_scripts_analysis,
                 learning_context=learning_context,
                 capability_context=capability_context,
-                llm_api_example=gemini_api_example
+                llm_api_example=llm_api_example
             )
 
         elif strategy_mode == "refine":
@@ -1411,7 +1386,7 @@ def generate_script_with_llm(self, strategy_mode) -> str:
                                              best_script_errors=best_script_errors,
                                              learning_context=learning_context, 
                                              capability_context=capability_context, 
-                                             llm_api_example=gemini_api_example)
+                                             llm_api_example=llm_api_example)
 
 
         
@@ -1429,7 +1404,7 @@ def generate_script_with_llm(self, strategy_mode) -> str:
             attempts += 1
 
             # Call LLM to generate script with the specific system instruction
-            response = self.call_llm(
+            response = self.client.call_llm(
                 prompt, system_instruction=script_generator_system_instruction)
 
             # Extract code block from response
@@ -1591,7 +1566,7 @@ def repair_script_with_llm(self, script: str, question: str, error_output: str)
         """
         
         try:
-            response = self.call_llm(prompt, system_instruction=repairer_system_instruction)
+            response = self.client.call_llm(prompt, system_instruction=repairer_system_instruction)
 
             # Extract code block from response
             if "```python" in response:
@@ -1644,7 +1619,7 @@ def check_output_for_errors(self, output: str, answer: str) -> tuple:
         """
 
         try:
-            response = self.call_llm(prompt, system_instruction=error_checker_system_instruction)
+            response = self.client.call_llm(prompt, system_instruction=error_checker_system_instruction)
 
             # Check if the response indicates an error
             response = response.strip()
@@ -1792,6 +1767,7 @@ def execute_script(self, script: str, sample: Dict) -> Dict:
 
         # Set up trace file in the archive directory
         trace_file = self.archive_dir / f"trace_iteration_{self.current_iteration}.jsonl"
+        
 
         # Load the test script template
         template_path = Path("test_script_template.py")
@@ -1822,7 +1798,9 @@ def execute_script(self, script: str, sample: Dict) -> Dict:
                 current_iteration=self.current_iteration,
                 sample_id=sample_id,
                 question_repr=repr(question),
-                script_path=script_path
+                script_path=script_path,
+                provider_type=self.client.provider,  # e.g., "gemini" or "openai"
+                model_name=self.client.model_name
             )
         except Exception as e:
             return {
@@ -2168,7 +2146,7 @@ def evaluate_with_llm(self, samples: List[Dict], results: List[Dict]) -> Dict:
     
             # Call LLM for detailed error analysis as text
             try:
-                error_analysis_text = self.call_llm(prompt, system_instruction=error_analyzer_system_instruction)
+                error_analysis_text = self.client.call_llm(prompt, system_instruction=error_analyzer_system_instruction)
                 print("Generated error analysis text report")
     
                 # Extract useful information for the dictionary return
@@ -2292,7 +2270,7 @@ def evaluate_with_llm(self, samples: List[Dict], results: List[Dict]) -> Dict:
             Pay particular attention to any patterns in the execution outputs that might reveal issues with the implementation.
             """
     
-            capability_report_text = self.call_llm(capability_prompt, system_instruction=capability_reporter_system_instruction)
+            capability_report_text = self.client.call_llm(capability_prompt, system_instruction=capability_reporter_system_instruction)
             print("Generated capability report text successfully")
     
             # Extract the improvement focus for the dictionary return
@@ -2425,7 +2403,7 @@ def evaluate_answer_with_llm(self, system_answer: str, golden_answer: str) -> Di
         print ("SYSTEM ANSWER: ....", system_answer)
         print ("GOLDEN ANSWER: ", golden_answer)
         try:
-            response = self.call_llm(
+            response = self.client.call_llm(
                 prompt, system_instruction=evaluator_system_instruction)
 
             # Extract JSON from response
@@ -2488,7 +2466,7 @@ def generate_approach_summary(self, script: str) -> str:
         """
 
         try:
-            summary = self.call_llm(
+            summary = self.client.call_llm(
                 prompt, system_instruction=summarizer_system_instruction)
             return summary.strip()
         except Exception as e:
@@ -2591,7 +2569,7 @@ def get_best_script_info(self) -> Dict:
             {{"best_iteration": <integer>, "rationale": "<brief explanation>"}}
             """
 
-            response = self.call_llm(
+            response = self.client.call_llm(
                 prompt, system_instruction=script_evaluator_system_instruction)
 
             # Extract JSON from response
@@ -2872,7 +2850,8 @@ def run_iteration(self) -> Dict:
         # Handle baseline generation for iteration 0
         if self.current_iteration == 0:
             print("Generating baseline script to calibrate performance expectations...")
-            script = self.generate_baseline_script()
+            base_script = self.generate_baseline_script()
+            script = base_script.format(provider=self.client.provider, model_name=self.client.model_name)
             strategy_mode = "baseline"
             approach_summary = "Simple baseline script: Direct LLM call without sophisticated techniques"
 
@@ -3243,6 +3222,28 @@ def run_iteration(self) -> Dict:
 
         return iteration_data
 
+    def get_llm_api_example(self, provider, model_name):
+        if provider == "gemini":
+            return f'''from clients import GeminiClient
+
+model_name = "{model_name}"
+client = GeminiClient(model_name)
+
+def call_llm(prompt, system_instruction=""):
+    return client.call_llm(prompt, system_instruction)
+'''
+        elif provider == "openai":
+            return f'''from clients import OpenAIClient
+
+model_name = "{model_name}"
+client = OpenAIClient(model_name)
+
+def call_llm(prompt, system_instruction=""):
+    return client.call_llm(prompt, system_instruction)
+'''
+        else:
+            return f"# LLM provider '{provider}' not supported in example"
+
 class CapabilityTracker:
     """
     Tracks and generates qualitative assessments of system capabilities across iterations.
diff --git a/clients.py b/clients.py
new file mode 100644
index 0000000..3ab2da5
--- /dev/null
+++ b/clients.py
@@ -0,0 +1,73 @@
+import os
+from abc import ABC, abstractmethod
+
+from google import genai
+from google.genai import types
+from openai import OpenAI
+
+
+class LLMClient(ABC):
+
+    @property
+    def provider(self):
+        raise NotImplementedError
+
+    @abstractmethod
+    def call_llm(self, prompt: str, system_instruction: str = "") -> str:
+        pass
+
+class GeminiClient(LLMClient):
+    def __init__(self, model_name: str):
+        self.model_name = model_name
+        try:
+            self.client = genai.Client(
+                api_key=os.environ.get("GEMINI_API_KEY"))
+            print("Gemini API client initialized successfully")
+        except Exception as e:
+            print(f"Error initializing Gemini API client: {e}")
+            print("Make sure to set the GEMINI_API_KEY environment variable")
+            raise
+
+    @property
+    def provider(self):
+        return "gemini"
+
+    def call_llm(self, prompt: str, system_instruction: str = "") -> str:
+        """Call the Gemini LLM with a prompt and return the response"""
+        try:
+            # Use provided system instruction or default to the loaded system prompt
+            sys_instruction = system_instruction if system_instruction is not None else ""
+
+            response = self.client.models.generate_content(
+                model=self.model_name,
+                config=types.GenerateContentConfig(
+                    system_instruction=sys_instruction),
+                contents=prompt)
+            return response.text
+        except Exception as e:
+            print(f"Error calling Gemini API: {e}")
+            return f"Error: {str(e)}"
+
+
+
+class OpenAIClient(LLMClient):
+    def __init__(self, model_name: str):
+        self.model_name = model_name
+        api_key = os.environ.get("OPENAI_API_KEY")
+        org_id = os.environ.get("OPENAI_ORG_ID")
+        self.client = OpenAI(api_key=api_key, organization=org_id) if org_id else OpenAI(api_key=api_key)
+    
+    @property
+    def provider(self):
+        return "openai"
+
+    def call_llm(self, prompt: str, system_instruction: str = "") -> str:
+        try:
+            response = self.client.chat.completions.create(
+                model=self.model_name,
+                messages=[{"role": "system", "content": system_instruction}, {"role": "user", "content": prompt}]
+            )
+            return response.choices[0].message.content
+        except Exception as e:
+            print(f"Error calling OpenAI API: {e}")
+            return f"Error: {str(e)}"
\ No newline at end of file
diff --git a/prompts/script_generation/strategies.py b/prompts/script_generation/strategies.py
index a322ffd..70dea68 100644
--- a/prompts/script_generation/strategies.py
+++ b/prompts/script_generation/strategies.py
@@ -11,7 +11,7 @@ def get_explore_instructions(example_problems, historical_context, last_scripts_
         last_scripts_context: Context about the last 5 scripts tried
         learning_context: Accumulated learnings from previous iterations
         capability_context: Capability assessment and improvement guidance
-        gemini_api_example: Standard API usage example
+        llm_api_example: Standard API usage example
 
     Returns:
         str: Complete exploration prompt
@@ -71,7 +71,7 @@ def get_explore_instructions(example_problems, historical_context, last_scripts_
    - If it is unknown how successful a processing state or part of the pipeline is, include verification steps to different parts of the pipeline in order to help deduce which parts are successful and where the system is breaking
    - Answer checkers to validate the final answer against the problem statement. If the answer is incorrect, the checker can send the answer back to an earlier part of the system for for refinement with feedback
 
-Here's how to call the Gemini API. Use this example without modification and don't invent configuration options:
+Here's how to call the LLM API. Use this example without modification and don't invent configuration options:
 {llm_api_example}
 
 Since this is an EXPLORATION phase:
@@ -122,7 +122,7 @@ def get_exploit_instructions(example_problems, historical_context, top_scripts_a
    top_scripts_analysis: Analysis of top performing scripts to combine
    learning_context: Accumulated learnings from previous iterations
    capability_context: Capability assessment and improvement guidance
-   gemini_api_example: Standard API usage example
+   llm_api_example: Standard API usage example
    
    Returns:
    str: Complete exploitation prompt
@@ -185,7 +185,7 @@ def get_exploit_instructions(example_problems, historical_context, top_scripts_a
    4. The hybrid should be more robust than any individual approach
    5. Address the weaknesses identified in the capability assessment through synthesis
    
-   Here's how to call the Gemini API. Use this example without modification:
+   Here's how to call the LLM API. Use this example without modification:
    {llm_api_example}
    
    SYNTHESIS IMPLEMENTATION:
@@ -223,7 +223,7 @@ def get_refine_instructions(example_problems, historical_context, best_script_to
    error_samples: List of failed examples from the best script
    learning_context: Accumulated learnings from previous iterations
    capability_context: Capability assessment and improvement guidance
-   gemini_api_example: Standard API usage example
+   llm_api_example: Standard API usage example
    
    Returns:
    str: Complete refinement prompt
@@ -296,7 +296,7 @@ def get_refine_instructions(example_problems, historical_context, best_script_to
    5. EVERY LLM PROMPT must include embedded examples
    6. Test your hypothesis with additional verification
    
-   Here's how to call the Gemini API. Use this example without modification:
+   Here's how to call the LLM API. Use this example without modification:
    {llm_api_example}
    
    REFINEMENT IMPLEMENTATION:
diff --git a/providers.py b/providers.py
new file mode 100644
index 0000000..94a6529
--- /dev/null
+++ b/providers.py
@@ -0,0 +1,40 @@
+from abc import ABC, abstractmethod
+
+from clients import GeminiClient, LLMClient, OpenAIClient
+
+
+class LLMProvider(ABC):
+
+    @abstractmethod
+    def build_client(self, model_name: str) -> LLMClient:
+        return GeminiClient(model_name)
+
+
+
+class GeminiProvider(LLMProvider):
+    def build_client(self, model_name: str) -> GeminiClient:
+        return GeminiClient(model_name)
+
+class OpenAIProvider(LLMProvider):
+    def build_client(self, model_name: str) -> OpenAIClient:
+        return OpenAIClient(model_name)
+
+
+
+class ProviderFactory:
+    @staticmethod
+    def get_provider(provider_type: str) -> LLMProvider:
+        if provider_type == "gemini":
+            return GeminiProvider()
+        elif provider_type == "openai":
+            return OpenAIProvider()
+        elif provider_type == "huggingface":
+            # TODO(jam): add in separate PR
+            raise NotImplementedError("HF not yet supported")
+        else:
+            raise ValueError(f"Invalid provider type: {provider_type}")
+
+    @staticmethod
+    def get_client(provider_type: str, model_name: str) -> LLMClient:
+        provider = ProviderFactory.get_provider(provider_type)
+        return provider.build_client(model_name)
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index 85047e4..5081b56 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,6 +4,7 @@ version = "0.1.0"
 description = ""
 authors = ["Nick Ryan <nickcdryan@gmail.com>"]
 requires-python = ">=3.11"
+
 dependencies = [
     "openai>=1.72.0",
     "sift-stack-py>=0.5.0",
@@ -12,4 +13,10 @@ dependencies = [
     "matplotlib>=3.10.0",
     "numpy>=2.2.0",
     "requests>=2.32.0",
+    "ruff"
 ]
+
+
+[tool.ruff]
+line-length = 120
+lint.select = ["E", "F", "I"]  # enable error, pyflakes, and import codes
diff --git a/run_script.py b/run_script.py
index ca15ac2..f392547 100644
--- a/run_script.py
+++ b/run_script.py
@@ -16,6 +16,7 @@
 
 from agent_system import AgentSystem
 from dataset_loader import create_dataset_loader
+from providers import ProviderFactory
 
 # Fixed random seed for reproducibility (if shuffling is enabled)
 RANDOM_SEED = 42
@@ -31,11 +32,15 @@ def run_agent(iterations: int, loader_config: Dict) -> None:
     # Create the appropriate dataset loader
     try:
         loader_type = loader_config.pop("loader_type")
+        provider = loader_config.pop("provider")
+        model = loader_config.pop("model")
+        print(f"Loading {provider} {model}")
         dataset_loader = create_dataset_loader(loader_type, **loader_config)
+        client = ProviderFactory.get_client(provider, model)
         print(f"Created {loader_type} dataset loader with {dataset_loader.get_total_count()} examples")
 
         # Initialize the agent system with the dataset loader
-        agent = AgentSystem(dataset_loader=dataset_loader)
+        agent = AgentSystem(client=client, dataset_loader=dataset_loader)
     except Exception as e:
         print(f"Error initializing system: {e}")
         sys.exit(1)
@@ -171,6 +176,17 @@ def parse_arguments():
     parser = argparse.ArgumentParser(
         description="Run the Agentic Learning System with custom dataset loaders")
 
+    parser.add_argument("--provider",
+                        type=str,
+                        default="gemini",
+                        choices=["gemini", "openai"],
+                        help="Provider to use (default: gemini)")
+    
+    parser.add_argument("--model",
+                        type=str,
+                        default="gemini-2.0-flash",
+                        help="Model to use (default: gemini-2.0-flash)")
+
     parser.add_argument("--iterations",
                         "-i",
                         type=int,
@@ -248,17 +264,12 @@ def parse_arguments():
     # Parse command-line arguments
     args = parse_arguments()
 
-    # Check environment variables
-    if not os.environ.get("GEMINI_API_KEY"):
-        print("Error: GEMINI_API_KEY environment variable is not set.")
-        print(
-            "Please set this variable to your Gemini API key before running the script."
-        )
-        print("Example: export GEMINI_API_KEY=your_api_key_here")
-        sys.exit(1)
 
+    
     # Create loader configuration
     loader_config = {
+        "provider": args.provider,
+        "model": args.model,
         "loader_type": args.loader,
         "dataset_path": args.dataset,
         "shuffle": not args.no_shuffle,
diff --git a/test_script_template.py b/test_script_template.py
index 8a886ca..a3ddde9 100644
--- a/test_script_template.py
+++ b/test_script_template.py
@@ -7,6 +7,7 @@
 import functools
 import importlib.util
 
+
 # Add the scripts directory to the path
 sys.path.append("{scripts_dir}")
 
@@ -14,34 +15,25 @@
 trace_file = "{trace_file}"
 os.makedirs(os.path.dirname(trace_file), exist_ok=True)
 
-def call_llm(prompt, system_instruction=None):
-    try:
-        from google import genai
-        from google.genai import types
-        import os  # Import the os module
-
-        # Initialize the Gemini client
-        client = genai.Client(api_key=os.environ.get("GEMINI_API_KEY"))
-
-        # Call the API with system instruction if provided
-        if system_instruction:
-            response = client.models.generate_content(
-                model="gemini-2.0-flash", 
-                config=types.GenerateContentConfig(
-                    system_instruction=system_instruction
-                ),
-                contents=prompt
-            )
-        else:
-            response = client.models.generate_content(
-                model="gemini-2.0-flash",
-                contents=prompt
-            )
-
-        return response.text
-    except Exception as e:
-        print("Error calling Gemini API: " + str(e))
-        return "Error: " + str(e)
+import sys
+import os
+sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+from clients import GeminiClient, OpenAIClient
+
+provider_type = "{provider_type}"
+model_name = "{model_name}"
+
+if provider_type == "gemini":
+    client = GeminiClient(model_name)
+elif provider_type == "openai":
+    client = OpenAIClient(model_name)
+else:
+    raise ValueError(f"Unknown provider: {provider_type}")
+
+def call_llm(prompt, system_instruction=""):
+    return client.call_llm(prompt, system_instruction)
+
 
 def execute_code(code_str, timeout=10):
     """Execute Python code with automatic package installation and proper scoping"""
diff --git a/uv.lock b/uv.lock
index 3d777fe..11b345f 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,4 +1,5 @@
 version = 1
+revision = 2
 requires-python = ">=3.11"
 resolution-markers = [
     "python_full_version >= '3.12'",
@@ -9,9 +10,9 @@ resolution-markers = [
 name = "about-time"
 version = "4.2.1"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/1c/3f/ccb16bdc53ebb81c1bf837c1ee4b5b0b69584fd2e4a802a2a79936691c0a/about-time-4.2.1.tar.gz", hash = "sha256:6a538862d33ce67d997429d14998310e1dbfda6cb7d9bbfbf799c4709847fece", size = 15380 }
+sdist = { url = "https://files.pythonhosted.org/packages/1c/3f/ccb16bdc53ebb81c1bf837c1ee4b5b0b69584fd2e4a802a2a79936691c0a/about-time-4.2.1.tar.gz", hash = "sha256:6a538862d33ce67d997429d14998310e1dbfda6cb7d9bbfbf799c4709847fece", size = 15380, upload-time = "2022-12-21T04:15:54.991Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/fb/cd/7ee00d6aa023b1d0551da0da5fee3bc23c3eeea632fbfc5126d1fec52b7e/about_time-4.2.1-py3-none-any.whl", hash = "sha256:8bbf4c75fe13cbd3d72f49a03b02c5c7dca32169b6d49117c257e7eb3eaee341", size = 13295 },
+    { url = "https://files.pythonhosted.org/packages/fb/cd/7ee00d6aa023b1d0551da0da5fee3bc23c3eeea632fbfc5126d1fec52b7e/about_time-4.2.1-py3-none-any.whl", hash = "sha256:8bbf4c75fe13cbd3d72f49a03b02c5c7dca32169b6d49117c257e7eb3eaee341", size = 13295, upload-time = "2022-12-21T04:15:53.613Z" },
 ]
 
 [[package]]
@@ -22,18 +23,18 @@ dependencies = [
     { name = "about-time" },
     { name = "grapheme" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/28/66/c2c1e6674b3b7202ce529cf7d9971c93031e843b8e0c86a85f693e6185b8/alive-progress-3.2.0.tar.gz", hash = "sha256:ede29d046ff454fe56b941f686f89dd9389430c4a5b7658e445cb0b80e0e4deb", size = 113231 }
+sdist = { url = "https://files.pythonhosted.org/packages/28/66/c2c1e6674b3b7202ce529cf7d9971c93031e843b8e0c86a85f693e6185b8/alive-progress-3.2.0.tar.gz", hash = "sha256:ede29d046ff454fe56b941f686f89dd9389430c4a5b7658e445cb0b80e0e4deb", size = 113231, upload-time = "2024-10-26T04:22:31.4Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/57/39/cade3a5a97fffa3ae84f298208237b3a9f7112d6b0ed57e8ff4b755e44b4/alive_progress-3.2.0-py3-none-any.whl", hash = "sha256:0677929f8d3202572e9d142f08170b34dbbe256cc6d2afbf75ef187c7da964a8", size = 77106 },
+    { url = "https://files.pythonhosted.org/packages/57/39/cade3a5a97fffa3ae84f298208237b3a9f7112d6b0ed57e8ff4b755e44b4/alive_progress-3.2.0-py3-none-any.whl", hash = "sha256:0677929f8d3202572e9d142f08170b34dbbe256cc6d2afbf75ef187c7da964a8", size = 77106, upload-time = "2024-10-26T04:22:29.103Z" },
 ]
 
 [[package]]
 name = "annotated-types"
 version = "0.7.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081 }
+sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081, upload-time = "2024-05-20T21:33:25.928Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" },
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 [[package]]
@@ -45,137 +46,275 @@ dependencies = [
     { name = "sniffio" },
     { name = "typing-extensions", marker = "python_full_version < '3.13'" },
 ]
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+sdist = { url = "https://files.pythonhosted.org/packages/95/7d/4c1bd541d4dffa1b52bd83fb8527089e097a106fc90b467a7313b105f840/anyio-4.9.0.tar.gz", hash = "sha256:673c0c244e15788651a4ff38710fea9675823028a6f08a5eda409e0c9840a028", size = 190949, upload-time = "2025-03-17T00:02:54.77Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/a1/ee/48ca1a7c89ffec8b6a0c5d02b89c305671d5ffd8d3c94acf8b8c408575bb/anyio-4.9.0-py3-none-any.whl", hash = "sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c", size = 100916, upload-time = "2025-03-17T00:02:52.713Z" },
+]
+
+[[package]]
+name = "cachetools"
+version = "5.5.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/6c/81/3747dad6b14fa2cf53fcf10548cf5aea6913e96fab41a3c198676f8948a5/cachetools-5.5.2.tar.gz", hash = "sha256:1a661caa9175d26759571b2e19580f9d6393969e5dfca11fdb1f947a23e640d4", size = 28380, upload-time = "2025-02-20T21:01:19.524Z" }
+wheels = [
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 [[package]]
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 version = "2025.1.31"
 source = { registry = "https://pypi.org/simple" }
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