diff --git a/surfsense_backend/app/agents/__init__.py b/surfsense_backend/app/agents/__init__.py
index e69de29..944afeb 100644
--- a/surfsense_backend/app/agents/__init__.py
+++ b/surfsense_backend/app/agents/__init__.py
@@ -0,0 +1 @@
+"""This is upcoming research agent. Work in progress."""
\ No newline at end of file
diff --git a/surfsense_backend/app/agents/researcher/configuration.py b/surfsense_backend/app/agents/researcher/configuration.py
new file mode 100644
index 0000000..8ba3849
--- /dev/null
+++ b/surfsense_backend/app/agents/researcher/configuration.py
@@ -0,0 +1,30 @@
+"""Define the configurable parameters for the agent."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, fields
+from typing import Optional, List, Any
+
+from langchain_core.runnables import RunnableConfig
+
+
+@dataclass(kw_only=True)
+class Configuration:
+    """The configuration for the agent."""
+
+    # Input parameters provided at invocation
+    user_query: str
+    num_sections: int
+    connectors_to_search: List[str]
+    user_id: str
+    search_space_id: int
+
+
+    @classmethod
+    def from_runnable_config(
+        cls, config: Optional[RunnableConfig] = None
+    ) -> Configuration:
+        """Create a Configuration instance from a RunnableConfig object."""
+        configurable = (config.get("configurable") or {}) if config else {}
+        _fields = {f.name for f in fields(cls) if f.init}
+        return cls(**{k: v for k, v in configurable.items() if k in _fields})
diff --git a/surfsense_backend/app/agents/researcher/graph.py b/surfsense_backend/app/agents/researcher/graph.py
new file mode 100644
index 0000000..31835da
--- /dev/null
+++ b/surfsense_backend/app/agents/researcher/graph.py
@@ -0,0 +1,43 @@
+from langgraph.graph import StateGraph
+from .state import State
+from .nodes import write_answer_outline, process_sections
+from .configuration import Configuration
+from typing import TypedDict, List, Dict, Any, Optional
+
+# Define what keys are in our state dict
+class GraphState(TypedDict):
+    # Intermediate data produced during workflow
+    answer_outline: Optional[Any]
+    # Final output
+    final_written_report: Optional[str]
+
+def build_graph():
+    """
+    Build and return the LangGraph workflow.
+    
+    This function constructs the researcher agent graph with proper state management
+    and node connections following LangGraph best practices.
+    
+    Returns:
+        A compiled LangGraph workflow
+    """
+    # Define a new graph with state class
+    workflow = StateGraph(State, config_schema=Configuration)
+    
+    # Add nodes to the graph
+    workflow.add_node("write_answer_outline", write_answer_outline)
+    workflow.add_node("process_sections", process_sections)
+
+    # Define the edges - create a linear flow
+    workflow.add_edge("__start__", "write_answer_outline")
+    workflow.add_edge("write_answer_outline", "process_sections")
+    workflow.add_edge("process_sections", "__end__")
+
+    # Compile the workflow into an executable graph
+    graph = workflow.compile()
+    graph.name = "Surfsense Researcher"  # This defines the custom name in LangSmith
+    
+    return graph
+
+# Compile the graph once when the module is loaded
+graph = build_graph()
diff --git a/surfsense_backend/app/agents/researcher/nodes.py b/surfsense_backend/app/agents/researcher/nodes.py
new file mode 100644
index 0000000..c88ad33
--- /dev/null
+++ b/surfsense_backend/app/agents/researcher/nodes.py
@@ -0,0 +1,410 @@
+from .configuration import Configuration
+from langchain_core.runnables import RunnableConfig
+from .state import State
+from typing import Any, Dict, List
+from app.config import config as app_config
+from .prompts import get_answer_outline_system_prompt
+from langchain_core.messages import HumanMessage, SystemMessage
+from pydantic import BaseModel, Field
+import json
+import asyncio
+from .sub_section_writer.graph import graph as sub_section_writer_graph
+from app.utils.connector_service import ConnectorService
+from sqlalchemy.ext.asyncio import AsyncSession
+
+class Section(BaseModel):
+    """A section in the answer outline."""
+    section_id: int = Field(..., description="The zero-based index of the section")
+    section_title: str = Field(..., description="The title of the section")
+    questions: List[str] = Field(..., description="Questions to research for this section")
+
+class AnswerOutline(BaseModel):
+    """The complete answer outline with all sections."""
+    answer_outline: List[Section] = Field(..., description="List of sections in the answer outline")
+
+async def write_answer_outline(state: State, config: RunnableConfig) -> Dict[str, Any]:
+    """
+    Create a structured answer outline based on the user query.
+    
+    This node takes the user query and number of sections from the configuration and uses
+    an LLM to generate a comprehensive outline with logical sections and research questions
+    for each section.
+    
+    Returns:
+        Dict containing the answer outline in the "answer_outline" key for state update.
+    """
+    
+    # Get configuration from runnable config
+    configuration = Configuration.from_runnable_config(config)
+    user_query = configuration.user_query
+    num_sections = configuration.num_sections
+    
+    # Initialize LLM
+    llm = app_config.strategic_llm_instance
+    
+    # Create the human message content
+    human_message_content = f"""
+    Now Please create an answer outline for the following query:
+    
+    User Query: {user_query}
+    Number of Sections: {num_sections}
+    
+    Remember to format your response as valid JSON exactly matching this structure:
+    {{
+      "answer_outline": [
+        {{
+          "section_id": 0,
+          "section_title": "Section Title",
+          "questions": [
+            "Question 1 to research for this section",
+            "Question 2 to research for this section"
+          ]
+        }}
+      ]
+    }}
+    
+    Your output MUST be valid JSON in exactly this format. Do not include any other text or explanation.
+    """
+    
+    # Create messages for the LLM
+    messages = [
+        SystemMessage(content=get_answer_outline_system_prompt()),
+        HumanMessage(content=human_message_content)
+    ]
+    
+    # Call the LLM directly without using structured output
+    response = await llm.ainvoke(messages)
+    
+    # Parse the JSON response manually
+    try:
+        # Extract JSON content from the response
+        content = response.content
+        
+        # Find the JSON in the content (handle case where LLM might add additional text)
+        json_start = content.find('{')
+        json_end = content.rfind('}') + 1
+        if json_start >= 0 and json_end > json_start:
+            json_str = content[json_start:json_end]
+            
+            # Parse the JSON string
+            parsed_data = json.loads(json_str)
+            
+            # Convert to Pydantic model
+            answer_outline = AnswerOutline(**parsed_data)
+            
+            print(f"Successfully generated answer outline with {len(answer_outline.answer_outline)} sections")
+            
+            # Return state update
+            return {"answer_outline": answer_outline}
+        else:
+            # If JSON structure not found, raise a clear error
+            raise ValueError(f"Could not find valid JSON in LLM response. Raw response: {content}")
+            
+    except (json.JSONDecodeError, ValueError) as e:
+        # Log the error and re-raise it
+        print(f"Error parsing LLM response: {str(e)}")
+        print(f"Raw response: {response.content}")
+        raise
+
+async def fetch_relevant_documents(
+    research_questions: List[str],
+    user_id: str,
+    search_space_id: int,
+    db_session: AsyncSession,
+    connectors_to_search: List[str],
+    top_k: int = 5
+) -> List[Dict[str, Any]]:
+    """
+    Fetch relevant documents for research questions using the provided connectors.
+    
+    Args:
+        section_title: The title of the section being researched
+        research_questions: List of research questions to find documents for
+        user_id: The user ID
+        search_space_id: The search space ID
+        db_session: The database session
+        connectors_to_search: List of connectors to search
+        top_k: Number of top results to retrieve per connector per question
+        
+    Returns:
+        List of relevant documents
+    """
+    # Initialize services
+    connector_service = ConnectorService(db_session)
+
+    all_raw_documents = []  # Store all raw documents before reranking
+    
+    for user_query in research_questions:
+        # Use original research question as the query
+        reformulated_query = user_query
+        
+        # Process each selected connector
+        for connector in connectors_to_search:
+            try:
+                if connector == "YOUTUBE_VIDEO":
+                    _, youtube_chunks = await connector_service.search_youtube(
+                        user_query=reformulated_query,
+                        user_id=user_id,
+                        search_space_id=search_space_id,
+                        top_k=top_k
+                    )
+                    all_raw_documents.extend(youtube_chunks)
+                    
+                elif connector == "EXTENSION":
+                    _, extension_chunks = await connector_service.search_extension(
+                        user_query=reformulated_query,
+                        user_id=user_id,
+                        search_space_id=search_space_id,
+                        top_k=top_k
+                    )
+                    all_raw_documents.extend(extension_chunks)
+                    
+                elif connector == "CRAWLED_URL":
+                    _, crawled_urls_chunks = await connector_service.search_crawled_urls(
+                        user_query=reformulated_query,
+                        user_id=user_id,
+                        search_space_id=search_space_id,
+                        top_k=top_k
+                    )
+                    all_raw_documents.extend(crawled_urls_chunks)
+                    
+                elif connector == "FILE":
+                    _, files_chunks = await connector_service.search_files(
+                        user_query=reformulated_query,
+                        user_id=user_id,
+                        search_space_id=search_space_id,
+                        top_k=top_k
+                    )
+                    all_raw_documents.extend(files_chunks)
+                    
+                elif connector == "TAVILY_API":
+                    _, tavily_chunks = await connector_service.search_tavily(
+                        user_query=reformulated_query,
+                        user_id=user_id,
+                        top_k=top_k
+                    )
+                    all_raw_documents.extend(tavily_chunks)
+                    
+                elif connector == "SLACK_CONNECTOR":
+                    _, slack_chunks = await connector_service.search_slack(
+                        user_query=reformulated_query,
+                        user_id=user_id,
+                        search_space_id=search_space_id,
+                        top_k=top_k
+                    )
+                    all_raw_documents.extend(slack_chunks)
+                    
+                elif connector == "NOTION_CONNECTOR":
+                    _, notion_chunks = await connector_service.search_notion(
+                        user_query=reformulated_query,
+                        user_id=user_id,
+                        search_space_id=search_space_id,
+                        top_k=top_k
+                    )
+                    all_raw_documents.extend(notion_chunks)
+            except Exception as e:
+                print(f"Error searching connector {connector}: {str(e)}")
+                # Continue with other connectors on error
+                continue
+    
+    # Deduplicate documents based on chunk_id or content
+    seen_chunk_ids = set()
+    seen_content_hashes = set()
+    deduplicated_docs = []
+    
+    for doc in all_raw_documents:
+        chunk_id = doc.get("chunk_id")
+        content = doc.get("content", "")
+        content_hash = hash(content)
+        
+        # Skip if we've seen this chunk_id or content before
+        if (chunk_id and chunk_id in seen_chunk_ids) or content_hash in seen_content_hashes:
+            continue
+            
+        # Add to our tracking sets and keep this document
+        if chunk_id:
+            seen_chunk_ids.add(chunk_id)
+        seen_content_hashes.add(content_hash)
+        deduplicated_docs.append(doc)
+    
+    return deduplicated_docs
+
+
+
+async def process_sections(state: State, config: RunnableConfig) -> Dict[str, Any]:
+    """
+    Process all sections in parallel and combine the results.
+    
+    This node takes the answer outline from the previous step, fetches relevant documents 
+    for all questions across all sections once, and then processes each section in parallel 
+    using the sub_section_writer graph with the shared document pool.
+    
+    Returns:
+        Dict containing the final written report in the "final_written_report" key.
+    """
+    # Get configuration and answer outline from state
+    configuration = Configuration.from_runnable_config(config)
+    answer_outline = state.answer_outline
+    
+    print(f"Processing sections from outline: {answer_outline is not None}")
+    
+    if not answer_outline:
+        return {
+            "final_written_report": "No answer outline was provided. Cannot generate final report."
+        }
+    
+    # Create session maker from the engine or directly use the session
+    from sqlalchemy.ext.asyncio import AsyncSession
+    from sqlalchemy.orm import sessionmaker
+    
+    # Use the engine if available, otherwise create a new session for each task
+    if state.engine:
+        session_maker = sessionmaker(
+            state.engine, class_=AsyncSession, expire_on_commit=False
+        )
+    else:
+        # Fallback to using the same session (less optimal but will work)
+        print("Warning: No engine available. Using same session for all tasks.")
+        # Create a mock session maker that returns the same session
+        async def mock_session_maker():
+            class ContextManager:
+                async def __aenter__(self):
+                    return state.db_session
+                async def __aexit__(self, exc_type, exc_val, exc_tb):
+                    pass
+            return ContextManager()
+        session_maker = mock_session_maker
+    
+    # Collect all questions from all sections
+    all_questions = []
+    for section in answer_outline.answer_outline:
+        all_questions.extend(section.questions)
+    
+    print(f"Collected {len(all_questions)} questions from all sections")
+    
+    # Fetch relevant documents once for all questions
+    relevant_documents = []
+    async with session_maker() as db_session:
+
+        try:
+            relevant_documents = await fetch_relevant_documents(
+                research_questions=all_questions,
+                user_id=configuration.user_id,
+                search_space_id=configuration.search_space_id,
+                db_session=db_session,
+                connectors_to_search=configuration.connectors_to_search
+            )
+        except Exception as e:
+            print(f"Error fetching relevant documents: {str(e)}")
+            # Log the error and continue with an empty list of documents
+            # This allows the process to continue, but the report might lack information
+            relevant_documents = []
+            # Consider adding more robust error handling or reporting if needed
+    
+    print(f"Fetched {len(relevant_documents)} relevant documents for all sections")
+    
+    # Create tasks to process each section in parallel with the same document set
+    section_tasks = []
+    for section in answer_outline.answer_outline:
+        section_tasks.append(
+            process_section_with_documents(
+                section_title=section.section_title,
+                section_questions=section.questions,
+                user_id=configuration.user_id,
+                search_space_id=configuration.search_space_id,
+                session_maker=session_maker,
+                relevant_documents=relevant_documents
+            )
+        )
+    
+    # Run all section processing tasks in parallel
+    print(f"Running {len(section_tasks)} section processing tasks in parallel")
+    section_results = await asyncio.gather(*section_tasks, return_exceptions=True)
+    
+    # Handle any exceptions in the results
+    processed_results = []
+    for i, result in enumerate(section_results):
+        if isinstance(result, Exception):
+            section_title = answer_outline.answer_outline[i].section_title
+            error_message = f"Error processing section '{section_title}': {str(result)}"
+            print(error_message)
+            processed_results.append(error_message)
+        else:
+            processed_results.append(result)
+    
+    # Combine the results into a final report with section titles
+    final_report = []
+    for i, (section, content) in enumerate(zip(answer_outline.answer_outline, processed_results)):
+        # Skip adding the section header since the content already contains the title
+        final_report.append(content)
+        final_report.append("\n")  # Add spacing between sections
+    
+    # Join all sections with newlines
+    final_written_report = "\n".join(final_report)
+    print(f"Generated final report with {len(final_report)} parts")
+    
+    return {
+        "final_written_report": final_written_report
+    }
+
+async def process_section_with_documents(
+    section_title: str, 
+    section_questions: List[str],
+    user_id: str, 
+    search_space_id: int, 
+    session_maker,
+    relevant_documents: List[Dict[str, Any]]
+) -> str:
+    """
+    Process a single section using pre-fetched documents.
+    
+    Args:
+        section_title: The title of the section
+        section_questions: List of research questions for this section
+        user_id: The user ID
+        search_space_id: The search space ID
+        session_maker: Factory for creating new database sessions
+        relevant_documents: Pre-fetched documents to use for this section
+        
+    Returns:
+        The written section content
+    """
+    try:
+        # Create a new database session for this section
+        async with session_maker() as db_session:
+            # Use the provided documents
+            documents_to_use = relevant_documents
+            
+            # Fallback if no documents found
+            if not documents_to_use:
+                print(f"No relevant documents found for section: {section_title}")
+                documents_to_use = [
+                    {"content": f"No specific information was found for: {question}"}
+                    for question in section_questions
+                ]
+            
+            # Call the sub_section_writer graph with the appropriate config
+            config = {
+                "configurable": {
+                    "sub_section_title": section_title,
+                    "sub_section_questions": section_questions,
+                    "relevant_documents": documents_to_use,
+                    "user_id": user_id,
+                    "search_space_id": search_space_id
+                }
+            }
+            
+            # Create the initial state with db_session
+            state = {"db_session": db_session}
+            
+            # Invoke the sub-section writer graph
+            print(f"Invoking sub_section_writer for: {section_title}")
+            result = await sub_section_writer_graph.ainvoke(state, config)
+            
+            # Return the final answer from the sub_section_writer
+            final_answer = result.get("final_answer", "No content was generated for this section.")
+            return final_answer
+    except Exception as e:
+        print(f"Error processing section '{section_title}': {str(e)}")
+        return f"Error processing section: {section_title}. Details: {str(e)}"
+
diff --git a/surfsense_backend/app/agents/researcher/prompts.py b/surfsense_backend/app/agents/researcher/prompts.py
new file mode 100644
index 0000000..3a2a3f7
--- /dev/null
+++ b/surfsense_backend/app/agents/researcher/prompts.py
@@ -0,0 +1,92 @@
+import datetime
+
+
+def get_answer_outline_system_prompt():
+    return f"""
+Today's date: {datetime.datetime.now().strftime("%Y-%m-%d")}
+<answer_outline_system>
+You are an expert research assistant specializing in structuring information. Your task is to create a detailed and logical research outline based on the user's query. This outline will serve as the blueprint for generating a comprehensive research report.
+
+<input>
+- user_query (string): The main question or topic the user wants researched. This guides the entire outline creation process.
+- num_sections (integer): The target number of distinct sections the final research report should have. This helps control the granularity and structure of the outline.
+</input>
+
+<output_format>
+A JSON object with the following structure:
+{{
+  "answer_outline": [
+    {{
+      "section_id": 0,
+      "section_title": "Section Title",
+      "questions": [
+        "Question 1 to research for this section",
+        "Question 2 to research for this section"
+      ]
+    }}
+  ]
+}}
+</output_format>
+
+<instructions>
+1.  **Deconstruct the `user_query`:** Identify the key concepts, entities, and the core information requested by the user.
+2.  **Determine Section Themes:** Based on the analysis and the requested `num_sections`, divide the topic into distinct, logical themes or sub-topics. Each theme will become a section. Ensure these themes collectively address the `user_query` comprehensively.
+3.  **Develop Sections:** For *each* of the `num_sections`:
+    *   **Assign `section_id`:** Start with 0 and increment sequentially for each section.
+    *   **Craft `section_title`:** Write a concise, descriptive title that clearly defines the scope and focus of the section's theme.
+    *   **Formulate Research `questions`:** Generate 2 to 5 specific, targeted research questions for this section. These questions must:
+        *   Directly relate to the `section_title` and explore its key aspects.
+        *   Be answerable through focused research (e.g., searching documents, databases, or knowledge bases).
+        *   Be distinct from each other and from questions in other sections. Avoid redundancy.
+        *   Collectively guide the gathering of information needed to fully address the section's theme.
+4.  **Ensure Logical Flow:** Arrange the sections in a coherent and intuitive sequence. Consider structures like:
+    *   General background -> Specific details -> Analysis/Comparison -> Applications/Implications
+    *   Problem definition -> Proposed solutions -> Evaluation -> Conclusion
+    *   Chronological progression
+5.  **Verify Completeness and Cohesion:** Review the entire outline (`section_titles` and `questions`) to confirm that:
+    *   All sections together provide a complete and well-structured answer to the original `user_query`.
+    *   There are no significant overlaps or gaps in coverage between sections.
+6.  **Adhere Strictly to Output Format:** Ensure the final output is a valid JSON object matching the specified structure exactly, including correct field names (`answer_outline`, `section_id`, `section_title`, `questions`) and data types.
+</instructions>
+
+<examples>
+User Query: "What are the health benefits of meditation?"
+Number of Sections: 3
+
+{{
+  "answer_outline": [
+    {{
+      "section_id": 0,
+      "section_title": "Physical Health Benefits of Meditation",
+      "questions": [
+        "What physiological changes occur in the body during meditation?",
+        "How does regular meditation affect blood pressure and heart health?",
+        "What impact does meditation have on inflammation and immune function?",
+        "Can meditation help with pain management, and if so, how?"
+      ]
+    }},
+    {{
+      "section_id": 1,
+      "section_title": "Mental Health Benefits of Meditation",
+      "questions": [
+        "How does meditation affect stress and anxiety levels?",
+        "What changes in brain structure or function have been observed in meditation practitioners?",
+        "Can meditation help with depression and mood disorders?",
+        "What is the relationship between meditation and cognitive function?"
+      ]
+    }},
+    {{
+      "section_id": 2,
+      "section_title": "Best Meditation Practices for Maximum Benefits",
+      "questions": [
+        "What are the most effective meditation techniques for beginners?",
+        "How long and how frequently should one meditate to see benefits?",
+        "Are there specific meditation approaches best suited for particular health goals?",
+        "What common obstacles prevent people from experiencing meditation benefits?"
+      ]
+    }}
+  ]
+}}
+</examples>
+</answer_outline_system>
+"""
\ No newline at end of file
diff --git a/surfsense_backend/app/agents/researcher/state.py b/surfsense_backend/app/agents/researcher/state.py
new file mode 100644
index 0000000..483e96a
--- /dev/null
+++ b/surfsense_backend/app/agents/researcher/state.py
@@ -0,0 +1,30 @@
+"""Define the state structures for the agent."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import List, Optional, Any, Dict, Annotated
+from sqlalchemy.ext.asyncio import AsyncSession, AsyncEngine
+from langchain_core.messages import BaseMessage, HumanMessage
+from pydantic import BaseModel
+
+@dataclass
+class State:
+    """Defines the dynamic state for the agent during execution.
+
+    This state tracks the database session and the outputs generated by the agent's nodes.
+    See: https://langchain-ai.github.io/langgraph/concepts/low_level/#state
+    for more information.
+    """
+    # Runtime context (not part of actual graph state)
+    db_session: AsyncSession
+    engine: Optional[AsyncEngine] = None
+    
+    # Intermediate state - populated during workflow
+    # Using field to explicitly mark as part of state
+    answer_outline: Optional[Any] = field(default=None)
+    
+    # OUTPUT: Populated by agent nodes
+    # Using field to explicitly mark as part of state
+    final_written_report: Optional[str] = field(default=None)
+
diff --git a/surfsense_backend/app/agents/researcher/sub_section_writer/configuration.py b/surfsense_backend/app/agents/researcher/sub_section_writer/configuration.py
index b34090e..fbde94d 100644
--- a/surfsense_backend/app/agents/researcher/sub_section_writer/configuration.py
+++ b/surfsense_backend/app/agents/researcher/sub_section_writer/configuration.py
@@ -3,7 +3,7 @@
 from __future__ import annotations
 
 from dataclasses import dataclass, fields
-from typing import Optional, List
+from typing import Optional, List, Any
 
 from langchain_core.runnables import RunnableConfig
 
@@ -14,11 +14,10 @@ class Configuration:
 
     # Input parameters provided at invocation
     sub_section_title: str
-    sub_questions: List[str]
-    connectors_to_search: List[str]
+    sub_section_questions: List[str]
+    relevant_documents: List[Any]  # Documents provided directly to the agent
     user_id: str
     search_space_id: int
-    top_k: int = 20  # Default top_k value
 
 
     @classmethod
diff --git a/surfsense_backend/app/agents/researcher/sub_section_writer/graph.py b/surfsense_backend/app/agents/researcher/sub_section_writer/graph.py
index e250cde..5a5a5ba 100644
--- a/surfsense_backend/app/agents/researcher/sub_section_writer/graph.py
+++ b/surfsense_backend/app/agents/researcher/sub_section_writer/graph.py
@@ -1,20 +1,18 @@
 from langgraph.graph import StateGraph
 from .state import State
-from .nodes import fetch_relevant_documents, write_sub_section
+from .nodes import write_sub_section, rerank_documents
 from .configuration import Configuration
 
 # Define a new graph
 workflow = StateGraph(State, config_schema=Configuration)
 
 # Add the nodes to the graph
-workflow.add_node("fetch_relevant_documents", fetch_relevant_documents)
+workflow.add_node("rerank_documents", rerank_documents)
 workflow.add_node("write_sub_section", write_sub_section)
 
-# Entry point
-workflow.add_edge("__start__", "fetch_relevant_documents")
-# Connect fetch_relevant_documents to write_sub_section
-workflow.add_edge("fetch_relevant_documents", "write_sub_section")
-# Exit point
+# Connect the nodes
+workflow.add_edge("__start__", "rerank_documents")
+workflow.add_edge("rerank_documents", "write_sub_section")
 workflow.add_edge("write_sub_section", "__end__")
 
 # Compile the workflow into an executable graph
diff --git a/surfsense_backend/app/agents/researcher/sub_section_writer/nodes.py b/surfsense_backend/app/agents/researcher/sub_section_writer/nodes.py
index 52fa877..af807e3 100644
--- a/surfsense_backend/app/agents/researcher/sub_section_writer/nodes.py
+++ b/surfsense_backend/app/agents/researcher/sub_section_writer/nodes.py
@@ -2,171 +2,79 @@ from .configuration import Configuration
 from langchain_core.runnables import RunnableConfig
 from .state import State
 from typing import Any, Dict
-from app.utils.connector_service import ConnectorService
-from app.utils.reranker_service import RerankerService
 from app.config import config as app_config
-from .prompts import citation_system_prompt
+from .prompts import get_citation_system_prompt
 from langchain_core.messages import HumanMessage, SystemMessage
 
-async def fetch_relevant_documents(state: State, config: RunnableConfig) -> Dict[str, Any]:
+async def rerank_documents(state: State, config: RunnableConfig) -> Dict[str, Any]:
     """
-    Fetch relevant documents for the sub-section using specified connectors.
+    Rerank the documents based on relevance to the sub-section title.
     
-    This node retrieves documents from various data sources based on the sub-questions
-    derived from the sub-section title. It searches across all selected connectors
-    (YouTube, Extension, Crawled URLs, Files, Tavily API, Slack, Notion) and reranks
-    the results to provide the most relevant information for the agent workflow.
+    This node takes the relevant documents provided in the configuration,
+    reranks them using the reranker service based on the sub-section title,
+    and updates the state with the reranked documents.
     
     Returns:
-        Dict containing the reranked documents in the "relevant_documents_fetched" key.
+        Dict containing the reranked documents.
     """
-    # Get configuration
+    # Get configuration and relevant documents
     configuration = Configuration.from_runnable_config(config)
-    
-    # Extract state parameters
-    db_session = state.db_session
-    
-    # Extract config parameters
-    user_id = configuration.user_id
-    search_space_id = configuration.search_space_id
-    TOP_K = configuration.top_k
-    
-    # Initialize services
-    connector_service = ConnectorService(db_session)
-    reranker_service = RerankerService.get_reranker_instance(app_config)
+    documents = configuration.relevant_documents
+    sub_section_questions = configuration.sub_section_questions
 
-    all_raw_documents = []  # Store all raw documents before reranking
+    # If no documents were provided, return empty list
+    if not documents or len(documents) == 0:
+        return {
+            "reranked_documents": []
+        }
     
-    for user_query in configuration.sub_questions:
-        # Reformulate query (optional, consider if needed for each sub-question)
-        # reformulated_query = await QueryService.reformulate_query(user_query)
-        reformulated_query = user_query # Using original sub-question for now
-        
-        # Process each selected connector
-        for connector in configuration.connectors_to_search:
-            if connector == "YOUTUBE_VIDEO":
-                _, youtube_chunks = await connector_service.search_youtube(
-                    user_query=reformulated_query,
-                    user_id=user_id,
-                    search_space_id=search_space_id,
-                    top_k=TOP_K
-                )
-                all_raw_documents.extend(youtube_chunks)
-                
-            elif connector == "EXTENSION":
-                _, extension_chunks = await connector_service.search_extension(
-                    user_query=reformulated_query,
-                    user_id=user_id,
-                    search_space_id=search_space_id,
-                    top_k=TOP_K
-                )
-                all_raw_documents.extend(extension_chunks)
-                
-            elif connector == "CRAWLED_URL":
-                _, crawled_urls_chunks = await connector_service.search_crawled_urls(
-                    user_query=reformulated_query,
-                    user_id=user_id,
-                    search_space_id=search_space_id,
-                    top_k=TOP_K
-                )
-                all_raw_documents.extend(crawled_urls_chunks)
-                
-            elif connector == "FILE":
-                _, files_chunks = await connector_service.search_files(
-                    user_query=reformulated_query,
-                    user_id=user_id,
-                    search_space_id=search_space_id,
-                    top_k=TOP_K
-                )
-                all_raw_documents.extend(files_chunks)
-                
-            elif connector == "TAVILY_API":
-                _, tavily_chunks = await connector_service.search_tavily(
-                    user_query=reformulated_query,
-                    user_id=user_id,
-                    top_k=TOP_K
-                )
-                all_raw_documents.extend(tavily_chunks)
-                
-            elif connector == "SLACK_CONNECTOR":
-                _, slack_chunks = await connector_service.search_slack(
-                    user_query=reformulated_query,
-                    user_id=user_id,
-                    search_space_id=search_space_id,
-                    top_k=TOP_K
-                )
-                all_raw_documents.extend(slack_chunks)
-                
-            elif connector == "NOTION_CONNECTOR":
-                _, notion_chunks = await connector_service.search_notion(
-                    user_query=reformulated_query,
-                    user_id=user_id,
-                    search_space_id=search_space_id,
-                    top_k=TOP_K
-                )
-                all_raw_documents.extend(notion_chunks)
+    # Get reranker service from app config
+    reranker_service = getattr(app_config, "reranker_service", None)
     
-    # If we have documents and a reranker is available, rerank them
-    # Deduplicate documents based on chunk_id or content to avoid processing duplicates
-    seen_chunk_ids = set()
-    seen_content_hashes = set()
-    deduplicated_docs = []
+    # Use documents as is if no reranker service is available
+    reranked_docs = documents
     
-    for doc in all_raw_documents:
-        chunk_id = doc.get("chunk_id")
-        content = doc.get("content", "")
-        content_hash = hash(content)
-        
-        # Skip if we've seen this chunk_id or content before
-        if (chunk_id and chunk_id in seen_chunk_ids) or content_hash in seen_content_hashes:
-            continue
+    if reranker_service:
+        try:
+            # Use the sub-section questions for reranking context
+            rerank_query = "\n".join(sub_section_questions)
             
-        # Add to our tracking sets and keep this document
-        if chunk_id:
-            seen_chunk_ids.add(chunk_id)
-        seen_content_hashes.add(content_hash)
-        deduplicated_docs.append(doc)
+            # Convert documents to format expected by reranker if needed
+            reranker_input_docs = [
+                {
+                    "chunk_id": doc.get("chunk_id", f"chunk_{i}"),
+                    "content": doc.get("content", ""),
+                    "score": doc.get("score", 0.0),
+                    "document": {
+                        "id": doc.get("document", {}).get("id", ""),
+                        "title": doc.get("document", {}).get("title", ""),
+                        "document_type": doc.get("document", {}).get("document_type", ""),
+                        "metadata": doc.get("document", {}).get("metadata", {})
+                    }
+                } for i, doc in enumerate(documents)
+            ]
+            
+            # Rerank documents using the section title
+            reranked_docs = reranker_service.rerank_documents(rerank_query, reranker_input_docs)
+            
+            # Sort by score in descending order
+            reranked_docs.sort(key=lambda x: x.get("score", 0), reverse=True)
+            
+            print(f"Reranked {len(reranked_docs)} documents for section: {configuration.sub_section_title}")
+        except Exception as e:
+            print(f"Error during reranking: {str(e)}")
+            # Use original docs if reranking fails
     
-    # Use deduplicated documents for reranking
-    reranked_docs = deduplicated_docs
-    if deduplicated_docs and reranker_service:
-        # Use the main sub_section_title for reranking context
-        rerank_query = configuration.sub_section_title
-        
-        # Convert documents to format expected by reranker
-        reranker_input_docs = [
-            {
-                "chunk_id": doc.get("chunk_id", f"chunk_{i}"),
-                "content": doc.get("content", ""),
-                "score": doc.get("score", 0.0),
-                "document": {
-                    "id": doc.get("document", {}).get("id", ""),
-                    "title": doc.get("document", {}).get("title", ""),
-                    "document_type": doc.get("document", {}).get("document_type", ""),
-                    "metadata": doc.get("document", {}).get("metadata", {})
-                }
-            } for i, doc in enumerate(deduplicated_docs)
-        ]
-        
-        # Rerank documents using the main title query
-        reranked_docs = reranker_service.rerank_documents(rerank_query, reranker_input_docs)
-        
-        # Sort by score in descending order
-        reranked_docs.sort(key=lambda x: x.get("score", 0), reverse=True)
-    
-    # Update state with fetched documents
     return {
-        "relevant_documents_fetched": reranked_docs
+        "reranked_documents": reranked_docs
     }
 
-
-
 async def write_sub_section(state: State, config: RunnableConfig) -> Dict[str, Any]:
     """
-    Write the sub-section using the fetched documents.
+    Write the sub-section using the provided documents.
     
-    This node takes the relevant documents fetched in the previous node and uses
-    an LLM to generate a comprehensive answer to the sub-section questions with
+    This node takes the relevant documents provided in the configuration and uses
+    an LLM to generate a comprehensive answer to the sub-section title with
     proper citations. The citations follow IEEE format using source IDs from the
     documents.
     
@@ -174,17 +82,17 @@ async def write_sub_section(state: State, config: RunnableConfig) -> Dict[str, A
         Dict containing the final answer in the "final_answer" key.
     """
     
-    # Get configuration and relevant documents
+    # Get configuration and relevant documents from configuration
     configuration = Configuration.from_runnable_config(config)
-    documents = state.relevant_documents_fetched
+    documents = configuration.relevant_documents
     
     # Initialize LLM
     llm = app_config.fast_llm_instance
     
-    # If no documents were found, return a message indicating this
+    # If no documents were provided, return a message indicating this
     if not documents or len(documents) == 0:
         return {
-            "final_answer": "No relevant documents were found to answer this question. Please try refining your search or providing more specific questions."
+            "final_answer": "No relevant documents were provided to answer this question. Please provide documents or try a different approach."
         }
     
     # Prepare documents for citation formatting
@@ -208,18 +116,25 @@ async def write_sub_section(state: State, config: RunnableConfig) -> Dict[str, A
         """
         formatted_documents.append(formatted_doc)
     
-    # Create the query that combines the section title and questions
-    # section_title = configuration.sub_section_title
-    questions = "\n".join([f"- {q}" for q in configuration.sub_questions])
+    # Create the query that uses the section title and questions
+    section_title = configuration.sub_section_title
+    sub_section_questions = configuration.sub_section_questions
     documents_text = "\n".join(formatted_documents)
     
+    # Format the questions as bullet points for clarity
+    questions_text = "\n".join([f"- {question}" for question in sub_section_questions])
+    
     # Construct a clear, structured query for the LLM
     human_message_content = f"""
     Please write a comprehensive answer for the title: 
     
-    Address the following questions:
+    <title>
+        {section_title}
+    </title>
+    
+    Focus on answering these specific questions related to the title:
     <questions>
-        {questions}
+        {questions_text}
     </questions>
 
     Use the provided documents as your source material and cite them properly using the IEEE citation format [X] where X is the source_id.
@@ -230,7 +145,7 @@ async def write_sub_section(state: State, config: RunnableConfig) -> Dict[str, A
     
     # Create messages for the LLM
     messages = [
-        SystemMessage(content=citation_system_prompt),
+        SystemMessage(content=get_citation_system_prompt()),
         HumanMessage(content=human_message_content)
     ]
     
diff --git a/surfsense_backend/app/agents/researcher/sub_section_writer/prompts.py b/surfsense_backend/app/agents/researcher/sub_section_writer/prompts.py
index cc3ad61..18a91eb 100644
--- a/surfsense_backend/app/agents/researcher/sub_section_writer/prompts.py
+++ b/surfsense_backend/app/agents/researcher/sub_section_writer/prompts.py
@@ -1,4 +1,9 @@
-citation_system_prompt = f"""
+import datetime
+
+
+def get_citation_system_prompt():
+    return f"""
+Today's date: {datetime.datetime.now().strftime("%Y-%m-%d")}
 You are a research assistant tasked with analyzing documents and providing comprehensive answers with proper citations in IEEE format.
 
 <instructions>
diff --git a/surfsense_backend/app/agents/researcher/sub_section_writer/state.py b/surfsense_backend/app/agents/researcher/sub_section_writer/state.py
index fb5b08e..b33abe6 100644
--- a/surfsense_backend/app/agents/researcher/sub_section_writer/state.py
+++ b/surfsense_backend/app/agents/researcher/sub_section_writer/state.py
@@ -18,6 +18,6 @@ class State:
     db_session: AsyncSession
     
     # OUTPUT: Populated by agent nodes
-    relevant_documents_fetched: Optional[List[Any]] = None
+    reranked_documents: Optional[List[Any]] = None
     final_answer: Optional[str] = None
 
diff --git a/surfsense_backend/app/agents/researcher/test_researcher.py b/surfsense_backend/app/agents/researcher/test_researcher.py
new file mode 100644
index 0000000..15c993e
--- /dev/null
+++ b/surfsense_backend/app/agents/researcher/test_researcher.py
@@ -0,0 +1,126 @@
+#!/usr/bin/env python3
+"""
+Test script for the Researcher LangGraph agent.
+
+This script demonstrates how to invoke the researcher agent with a sample query.
+Run this script directly from VSCode using the "Run Python File" button or
+right-click and select "Run Python File in Terminal".
+
+Before running:
+1. Make sure your Python environment has all required dependencies
+2. Create a .env file with any required API keys
+3. Ensure database connection is properly configured
+"""
+
+import asyncio
+import os
+import sys
+from pathlib import Path
+
+# Add project root to Python path so that 'app' can be found as a module
+# Get the absolute path to the surfsense_backend directory which contains the app module
+project_root = str(Path(__file__).resolve().parents[3])  # Go up 3 levels from the script: app/agents/researcher -> app/agents -> app -> surfsense_backend
+print(f"Adding to Python path: {project_root}")
+sys.path.insert(0, project_root)
+
+# Now import the modules after fixing the path
+from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession
+from sqlalchemy.orm import sessionmaker
+from dotenv import load_dotenv
+
+# These imports should now work with the correct path
+from app.agents.researcher.graph import graph
+from app.agents.researcher.state import State
+
+
+# Load environment variables
+load_dotenv()
+
+# Database connection string - use a test database or mock
+DATABASE_URL = "postgresql+asyncpg://postgres:postgres@localhost:5432/surfsense"
+
+# Create async engine and session
+engine = create_async_engine(DATABASE_URL)
+async_session_maker = sessionmaker(
+    engine, class_=AsyncSession, expire_on_commit=False
+)
+
+async def run_test():
+    """Run a test of the researcher agent."""
+    print("Starting researcher agent test...")
+    
+    # Create a database session
+    async with async_session_maker() as db_session:
+        # Sample configuration
+        config = {
+            "configurable": {
+                "user_query": "What are the best clash royale decks recommended by Surgical Goblin?",
+                "num_sections": 1,
+                "connectors_to_search": [
+                    "YOUTUBE_VIDEO",
+                ],
+                "user_id": "d6ac2187-7407-4664-8734-af09926d161e",
+                "search_space_id": 2
+            }
+        }
+        
+        try:
+            # Initialize state with database session and engine
+            initial_state = State(db_session=db_session, engine=engine)
+            
+            # Run the graph directly
+            print("\nRunning the complete researcher workflow...")
+            result = await graph.ainvoke(initial_state, config)
+            
+            # Extract the answer outline for display
+            if "answer_outline" in result and result["answer_outline"]:
+                print(f"\nGenerated answer outline with {len(result['answer_outline'].answer_outline)} sections")
+                
+                # Print the outline
+                print("\nGenerated Answer Outline:")
+                for section in result["answer_outline"].answer_outline:
+                    print(f"\nSection {section.section_id}: {section.section_title}")
+                    print("Research Questions:")
+                    for q in section.questions:
+                        print(f"  - {q}")
+            
+            # Check if we got a final report
+            if "final_written_report" in result and result["final_written_report"]:
+                final_report = result["final_written_report"]
+                print("\nFinal Research Report generated successfully!")
+                print(f"Report length: {len(final_report)} characters")
+                
+                # Display the final report
+                print("\n==== FINAL RESEARCH REPORT ====\n")
+                print(final_report)
+            else:
+                print("\nNo final report was generated.")
+                print(f"Available result keys: {list(result.keys())}")
+            
+            return result
+            
+        except Exception as e:
+            print(f"Error running researcher agent: {str(e)}")
+            import traceback
+            traceback.print_exc()
+            raise
+
+async def main():
+    """Main entry point for the test script."""
+    try:
+        result = await run_test()
+        print("\nTest completed successfully.")
+        return result
+    except Exception as e:
+        print(f"\nTest failed with error: {e}")
+        import traceback
+        traceback.print_exc()
+        return None
+
+if __name__ == "__main__":
+    # Run the async test
+    result = asyncio.run(main())
+    
+    # Keep terminal open if run directly in VSCode
+    if 'VSCODE_PID' in os.environ:
+        input("\nPress Enter to close this window...") 
\ No newline at end of file
diff --git a/surfsense_backend/app/config/__init__.py b/surfsense_backend/app/config/__init__.py
index f4226ed..82517a8 100644
--- a/surfsense_backend/app/config/__init__.py
+++ b/surfsense_backend/app/config/__init__.py
@@ -42,10 +42,8 @@ class Config:
     
     # GPT Researcher
     FAST_LLM = os.getenv("FAST_LLM")
-    SMART_LLM = os.getenv("SMART_LLM")
     STRATEGIC_LLM = os.getenv("STRATEGIC_LLM")
     fast_llm_instance = ChatLiteLLM(model=extract_model_name(FAST_LLM))
-    smart_llm_instance = ChatLiteLLM(model=extract_model_name(SMART_LLM))
     strategic_llm_instance = ChatLiteLLM(model=extract_model_name(STRATEGIC_LLM))