+ requirements update

.gitignore

@@ -2,4 +2,6 @@ env*
 .env*
 review.csv
 *.csv
-*.cpython
+*.cpython*
+*.pyc
+*__pycache__*

agents/lead_agent.py

@@ -78,14 +78,80 @@ def lead_agent(state: Dict[str, Any]) -> Command[Literal["research", "code", "fo
     # Check for termination conditions first
     if loop_counter >= max_iterations:
         print("🔄 Maximum iterations reached, proceeding to formatter")
-        return Command(
-            goto="formatter",
-            update={
-                "loop_counter": loop_counter + 1,
-                "next": "formatter"
-            }
-        )
-    
+        
+        # Create draft answer even when max iterations reached
+        research_notes = state.get("research_notes", "")
+        code_outputs = state.get("code_outputs", "")
+        messages = state.get("messages", [])
+        user_query = ""
+        for msg in messages:
+            if isinstance(msg, HumanMessage):
+                user_query = msg.content
+                break
+        
+        # Create a comprehensive draft answer from gathered information
+        draft_prompt = f"""
+Create a comprehensive answer based on all gathered information:
+
+Original Question: {user_query}
+
+Research Information:
+{research_notes}
+
+Code Results:
+{code_outputs}
+
+Instructions:
+1. Synthesize all available information to answer the question
+2. If computational results are available, include them
+3. If research provides context, incorporate it
+4. Provide a clear, direct answer to the user's question
+5. Focus on accuracy and completeness
+
+What is your answer to the user's question?
+"""
+        
+        try:
+            # Initialize LLM for draft creation
+            llm = ChatGroq(
+                model="llama-3.3-70b-versatile",
+                temperature=0.1,
+                max_tokens=1024
+            )
+            
+            system_prompt = load_system_prompt()
+            draft_messages = [
+                SystemMessage(content=system_prompt),
+                HumanMessage(content=draft_prompt)
+            ]
+            
+            draft_response = llm.invoke(draft_messages)
+            draft_content = draft_response.content if hasattr(draft_response, 'content') else str(draft_response)
+            print(f"📝 Lead Agent: Created draft answer at max iterations ({len(draft_content)} characters)")
+            
+            return Command(
+                goto="formatter",
+                update={
+                    "loop_counter": loop_counter + 1,
+                    "next": "formatter",
+                    "draft_answer": draft_content
+                }
+            )
+            
+        except Exception as e:
+            print(f"⚠️  Error creating draft answer at max iterations: {e}")
+            # Fallback - create a simple answer from available data
+            fallback_answer = f"Based on the available information:\n\nResearch: {research_notes}\nCalculations: {code_outputs}"
+            
+            return Command(
+                goto="formatter",
+                update={
+                    "loop_counter": loop_counter + 1,
+                    "next": "formatter",
+                    "draft_answer": fallback_answer
+                }
+            )
+            
     try:
         # Get the system prompt
         system_prompt = load_system_prompt()

pyproject.toml

@@ -38,4 +38,6 @@ dependencies = [
     "arxiv>=2.2.0",
     "langchain-tavily>=0.2.4",
     "python-dotenv>=1.1.0",
+    "langchain-core>=0.3.65",
+    "pydantic>=2.11.5",
 ]

requirements.txt

@@ -8,6 +8,7 @@ aiohttp==3.12.9
     # via
     #   fsspec
     #   langchain-community
+    #   langchain-tavily
     #   llama-index-core
     #   realtime
 aiosignal==1.3.2
@@ -26,7 +27,9 @@ anyio==4.9.0
     #   openai
     #   starlette
 arxiv==2.2.0
-    # via llama-index-readers-papers
+    # via
+    #   final-assignment-template (pyproject.toml)
+    #   llama-index-readers-papers
 asttokens==3.0.0
     # via stack-data
 async-timeout==4.0.3
@@ -266,16 +269,19 @@ langchain==0.3.25
     # via
     #   final-assignment-template (pyproject.toml)
     #   langchain-community
+    #   langchain-tavily
 langchain-community==0.3.25
     # via final-assignment-template (pyproject.toml)
 langchain-core==0.3.65
     # via
+    #   final-assignment-template (pyproject.toml)
     #   langchain
     #   langchain-community
     #   langchain-google-genai
     #   langchain-groq
     #   langchain-huggingface
     #   langchain-openai
+    #   langchain-tavily
     #   langchain-text-splitters
     #   langgraph
     #   langgraph-checkpoint
@@ -288,6 +294,8 @@ langchain-huggingface==0.3.0
     # via final-assignment-template (pyproject.toml)
 langchain-openai==0.3.24
     # via final-assignment-template (pyproject.toml)
+langchain-tavily==0.2.4
+    # via final-assignment-template (pyproject.toml)
 langchain-text-splitters==0.3.8
     # via langchain
 langfuse==3.0.0
@@ -405,8 +413,12 @@ multidict==6.4.4
     #   yarl
 multiprocess==0.70.16
     # via datasets
+mypy==1.16.1
+    # via langchain-tavily
 mypy-extensions==1.1.0
-    # via typing-inspect
+    # via
+    #   mypy
+    #   typing-inspect
 nest-asyncio==1.6.0
     # via
     #   ipykernel
@@ -496,6 +508,8 @@ pandas==2.2.3
     #   llama-index-readers-file
 parso==0.8.4
     # via jedi
+pathspec==0.12.1
+    # via mypy
 pillow==11.2.1
     # via
     #   gradio
@@ -544,6 +558,7 @@ pyasn1-modules==0.4.2
     # via google-auth
 pydantic==2.11.5
     # via
+    #   final-assignment-template (pyproject.toml)
     #   banks
     #   fastapi
     #   gotrue
@@ -588,6 +603,7 @@ python-dateutil==2.9.0.post0
     #   storage3
 python-dotenv==1.1.0
     # via
+    #   final-assignment-template (pyproject.toml)
     #   dotenv
     #   llama-cloud-services
     #   pydantic-settings
@@ -595,7 +611,6 @@ python-multipart==0.0.20
     # via gradio
 pytz==2025.2
     # via pandas
-# pywin32==310
     # via jupyter-core
 pyyaml==6.0.2
     # via
@@ -626,6 +641,7 @@ requests==2.32.3
     #   huggingface-hub
     #   langchain
     #   langchain-community
+    #   langchain-tavily
     #   langfuse
     #   langsmith
     #   llama-index-core
@@ -718,7 +734,9 @@ tokenizers==0.21.1
     #   langchain-huggingface
     #   transformers
 tomli==2.2.1
-    # via pytest
+    # via
+    #   mypy
+    #   pytest
 tomlkit==0.13.3
     # via gradio
 torch==2.7.1
@@ -764,6 +782,7 @@ typing-extensions==4.14.0
     #   langchain-core
     #   llama-index-core
     #   multidict
+    #   mypy
     #   openai
     #   opentelemetry-api
     #   opentelemetry-exporter-otlp-proto-grpc

uv.lock

@@ -538,6 +538,7 @@ dependencies = [
     { name = "ipywidgets" },
     { name = "langchain" },
     { name = "langchain-community" },
+    { name = "langchain-core" },
     { name = "langchain-google-genai" },
     { name = "langchain-groq" },
     { name = "langchain-huggingface" },
@@ -556,6 +557,7 @@ dependencies = [
     { name = "llama-index-tools-tavily-research" },
     { name = "opencv-python" },
     { name = "pandas" },
+    { name = "pydantic" },
     { name = "python-dotenv" },
     { name = "rich" },
     { name = "sentence-transformers" },
@@ -575,6 +577,7 @@ requires-dist = [
     { name = "ipywidgets", specifier = ">=8.1.7" },
     { name = "langchain", specifier = ">=0.3.25" },
     { name = "langchain-community", specifier = ">=0.3.25" },
+    { name = "langchain-core", specifier = ">=0.3.65" },
     { name = "langchain-google-genai", specifier = ">=2.1.5" },
     { name = "langchain-groq", specifier = ">=0.3.2" },
     { name = "langchain-huggingface", specifier = ">=0.3.0" },
@@ -593,6 +596,7 @@ requires-dist = [
     { name = "llama-index-tools-tavily-research", specifier = ">=0.3.0" },
     { name = "opencv-python", specifier = ">=4.11.0.86" },
     { name = "pandas", specifier = ">=2.2.3" },
+    { name = "pydantic", specifier = ">=2.11.5" },
     { name = "python-dotenv", specifier = ">=1.1.0" },
     { name = "rich", specifier = ">=14.0.0" },
     { name = "sentence-transformers", specifier = ">=4.1.0" },