Update agent.py

agent.py

@@ -22,7 +22,7 @@ langsmith_tracing = os.getenv("LANGSMITH_TRACING")
 llm = ChatOpenAI(
     base_url="https://openrouter.ai/api/v1",
     api_key=os.getenv("OPENROUTER_API_KEY"),
-    model="moonshotai/kimi-k2:free", # Model must support function calling in OpenRouter
+    model="qwen/qwen3-coder:free", # Model must support function calling in OpenRouter
     temperature=1
 )
 
@@ -37,19 +37,19 @@ community_tools = [search_tool, python_tool]
 custom_tools = calculator_basic + datetime_tools + [transcribe_audio, transcribe_youtube, query_image, webpage_content, read_excel]
 
 tools = community_tools + custom_tools
-llm_with_tools = llm.bind_tools(tools)
 
 # Prepare tools by name
 tools_by_name = {tool.name: tool for tool in tools}
 
 class MessagesState(TypedDict):  # creates the state (is like the agent's memory at any moment)
     messages: Annotated[list[AnyMessage], add_messages]
+    llm_with_tools: object
 
 # LLM node
 def llm_call(state: MessagesState):
     return {
         "messages": [
-            llm_with_tools.invoke(
+            state["llm_with_tools"].invoke(
                 [SystemMessage(content=system_prompt)] + state["messages"]
             )
         ]
@@ -103,10 +103,27 @@ gaia_agent = builder.compile()  # converts my builder into a runnable agent by u
 # Wrapper class to initialize and call the LangGraph agent with a user question
 class LangGraphAgent:
     def __init__(self):
+        self.api_keys = [
+            os.getenv("OPENROUTER_API_KEY"),
+            os.getenv("OPENROUTER_API_KEY_1"),
+            os.getenv("OPENROUTER_API_KEY_2"),
+        ]
+        self.key_index = 0
         print("LangGraphAgent initialized.")
 
     def __call__(self, question: str) -> str:
-        input_state = {"messages": [HumanMessage(content=question)]}  # prepare the initial user message
+        api_key = self.api_keys[self.key_index]
+        self.key_index = (self.key_index + 1) % len(self.api_keys)
+
+        llm = ChatOpenAI(
+            base_url="https://openrouter.ai/api/v1",
+            api_key=api_key,
+            model="qwen/qwen3-coder:free",
+            temperature=1
+        )
+        llm_with_tools = llm.bind_tools(tools)
+
+        input_state = {"messages": [HumanMessage(content=question)], "llm_with_tools": llm_with_tools}  # prepare the initial user message
         print(f"Running LangGraphAgent with input: {question[:150]}...")
         
         # tracing configuration for LangSmith