Connecting langchain agent to questions, trying to output the final

app.py

@@ -7,8 +7,11 @@ import json
 import re
 from phoenix.otel import register
 from openinference.instrumentation.smolagents import SmolagentsInstrumentor
+from openinference.instrumentation.langchain import LangChainInstrumentor
 from llm_only_agent import LLMOnlyAgent
 from multi_agent import MultiAgent
+from langchain_agent import LangChainAgent
+
 
 # (Keep Constants as is)
 # --- Constants ---
@@ -50,7 +53,7 @@ def run_and_submit_all(profile: gr.OAuthProfile | None, limit: int | None):
 
     # 1. Instantiate Agent ( modify this part to create your agent)
     try:
-        agent = LLMOnlyAgent()
+        agent = LangChainAgent()
     except Exception as e:
         print(f"Error instantiating agent: {e}")
         return f"Error initializing agent: {e}", None
@@ -113,7 +116,7 @@ def run_and_submit_all(profile: gr.OAuthProfile | None, limit: int | None):
     results_log = []
     answers_payload = []
     # Limit and skip youtube for test purposes
-    limit = None
+    limit = 1
     skip_youtube = False
     if limit is not None:
         questions_data = questions_data[:limit]
@@ -253,6 +256,7 @@ if __name__ == "__main__":
     # Telemetry
     tracer_provider = register(project_name="final_assignment_template")
     SmolagentsInstrumentor().instrument(tracer_provider=tracer_provider)
+    LangChainInstrumentor().instrument(tracer_provider=tracer_provider)
 
     # Check for SPACE_HOST and SPACE_ID at startup for information
     space_host_startup = os.getenv("SPACE_HOST")

langchain_agent.py

@@ -1,34 +1,44 @@
 import os
 from dotenv import load_dotenv
-from gradio.external import load_blocks_from_huggingface
 from langchain.agents import create_agent
-from langchain_community.tools.ddg_search.tool import DuckDuckGoSearchResults
 from langgraph.checkpoint.memory import InMemorySaver
 from langchain_community.tools import DuckDuckGoSearchRun
-from google.ai.generativelanguage_v1beta.types import Tool as GenAITool
-from langchain.messages import HumanMessage, AIMessage, SystemMessage
-from numpy import load
+from langchain.agents.middleware import (
+    ModelCallLimitMiddleware,
+    ToolCallLimitMiddleware,
+)
+from langchain.messages import HumanMessage, AIMessage
 
 load_dotenv()
-os.environ["GOOGLE_API_KEY"] = os.getenv("GEMINI_API_KEY")
 
-system_prompt = """finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER].
+
+class LangChainAgent:
+    def __init__(self):
+        os.environ["GOOGLE_API_KEY"] = os.getenv("GEMINI_API_KEY")
+
+        system_prompt = """finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER].
 YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings.
 If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise.
 If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise.
 If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string."""
 
+        self.agent = create_agent(
+            model="google_genai:gemini-2.5-flash",
+            tools=[DuckDuckGoSearchRun()],
+            system_prompt=system_prompt,
+            checkpointer=InMemorySaver(),
+            middleware=[
+                ModelCallLimitMiddleware(run_limit=10, exit_behavior="end"),
+                ToolCallLimitMiddleware(run_limit=20, exit_behavior="end"),
+            ],
+        )
 
-agent = create_agent(
-    model="google_genai:gemini-2.5-flash",
-    tools=[DuckDuckGoSearchRun()],
-    system_prompt=system_prompt,
-    checkpointer=InMemorySaver(),
-)
-
-response = agent.invoke(
-    {"messages": [{"role": "user", "content": "what is the weather in sf"}]},
-    {"configurable": {"thread_id": "1"}},
-)
-
-print(response)
+    def __call__(self, question: str) -> str:
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        response = self.agent.invoke(
+            {"messages": [HumanMessage(content=question)]},
+            {"configurable": {"thread_id": "1"}, "recursion_limit": 50},
+        )
+        answer = response["messages"][-1].text()
+        print(f"Agent returning answer: {answer}")
+        return answer

requirements.txt

@@ -17,3 +17,4 @@ langchain-google-genai
 duckduckgo-search
 langchain-community
 python-dotenv
+openinference-instrumentation-langchain