feat: migrate from Anthropic API to OpenRouter

Replace direct Anthropic API integration with OpenRouter to enable
more flexible LLM provider access while maintaining Claude 3.5 Sonnet.

Changes:
- Replace anthropic package with openai in requirements.txt
- Update config to use OPENROUTER_API_KEY instead of ANTHROPIC_API_KEY
- Migrate LLMAnalyzer from Anthropic client to OpenAI client with
  OpenRouter base URL (https://openrouter.ai/api/v1)
- Update model identifier to OpenRouter format: anthropic/claude-3.5-sonnet
- Convert API calls from messages.create() to chat.completions.create()
- Update response parsing to match OpenAI format
- Rename API key parameter in CompanyAnalyzer from anthropic_api_key
  to openrouter_api_key
- Update all tests to mock OpenAI client instead of Anthropic
- Fix client initialization to accept direct API key parameter

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

SPARC/analyzer.py

@@ -13,13 +13,13 @@ from typing import List
 class CompanyAnalyzer:
     """Orchestrates end-to-end company performance analysis via patents."""
 
-    def __init__(self, anthropic_api_key: str | None = None):
+    def __init__(self, openrouter_api_key: str | None = None):
         """Initialize the company analyzer.
 
         Args:
-          anthropic_api_key: Optional Anthropic API key. If None, loads from config.
+          openrouter_api_key: Optional OpenRouter API key. If None, loads from config.
         """
-        self.llm_analyzer = LLMAnalyzer(api_key=anthropic_api_key)
+        self.llm_analyzer = LLMAnalyzer(api_key=openrouter_api_key)
 
     def analyze_company(self, company_name: str) -> str:
         """Analyze a company's performance based on their patent portfolio.

SPARC/config.py

@@ -10,5 +10,5 @@ load_dotenv()
 # SerpAPI key for patent search
 api_key = os.getenv("API_KEY")
 
-# Anthropic API key for LLM analysis
-anthropic_api_key = os.getenv("ANTHROPIC_API_KEY")
+# OpenRouter API key for LLM analysis
+openrouter_api_key = os.getenv("OPENROUTER_API_KEY")

SPARC/llm.py

@@ -1,6 +1,6 @@
-"""LLM integration for patent analysis using Anthropic's Claude."""
+"""LLM integration for patent analysis using OpenRouter."""
 
-from anthropic import Anthropic
+from openai import OpenAI
 from SPARC import config
 from typing import Dict
 
@@ -12,14 +12,17 @@ class LLMAnalyzer:
         """Initialize the LLM analyzer.
 
         Args:
-          api_key: Anthropic API key. If None, will attempt to load from config.
+          api_key: OpenRouter API key. If None, will attempt to load from config.
           test_mode: If True, print prompts instead of making API calls
         """
         self.test_mode = test_mode
 
-        if config.anthropic_api_key and not test_mode:
-            self.client = Anthropic(api_key=api_key or config.anthropic_api_key)
-            self.model = "claude-3-5-sonnet-20241022"
+        if (api_key or config.openrouter_api_key) and not test_mode:
+            self.client = OpenAI(
+                api_key=api_key or config.openrouter_api_key,
+                base_url="https://openrouter.ai/api/v1"
+            )
+            self.model = "anthropic/claude-3.5-sonnet"
         else:
             self.client = None
 
@@ -55,12 +58,12 @@ Provide a concise analysis (2-3 paragraphs) focusing on what this patent reveals
             return "[TEST MODE - No API call made]"
 
         if self.client:
-            message = self.client.messages.create(
+            response = self.client.chat.completions.create(
                 model=self.model,
                 max_tokens=1024,
                 messages=[{"role": "user", "content": prompt}],
             )
-            return message.content[0].text
+            return response.choices[0].message.content
        
     def analyze_patent_portfolio(
         self, patents_data: list[Dict[str, str]], company_name: str
@@ -103,13 +106,13 @@ Provide a comprehensive analysis (4-5 paragraphs) with a final verdict on the co
             return "[TEST MODE]"
 
         try:
-            message = self.client.messages.create(
+            response = self.client.chat.completions.create(
                 model=self.model,
                 max_tokens=2048,
                 messages=[{"role": "user", "content": prompt}],
             )
 
-            return message.content[0].text
+            return response.choices[0].message.content
         except AttributeError:
             return prompt