diff --git a/app/__pycache__/main.cpython-312.pyc b/app/__pycache__/main.cpython-312.pyc
index d9c2a4c..9cbd458 100644
Binary files a/app/__pycache__/main.cpython-312.pyc and b/app/__pycache__/main.cpython-312.pyc differ
diff --git a/app/db/__pycache__/tables.cpython-312.pyc b/app/db/__pycache__/tables.cpython-312.pyc
index 2086f0d..8e6b31c 100644
Binary files a/app/db/__pycache__/tables.cpython-312.pyc and b/app/db/__pycache__/tables.cpython-312.pyc differ
diff --git a/app/db/tables.py b/app/db/tables.py
index c66d162..3153b82 100644
--- a/app/db/tables.py
+++ b/app/db/tables.py
@@ -2,7 +2,7 @@ import datetime
 
 from sqlalchemy import Column, DateTime, Integer, String
 
-from db.db import Base
+from app.db.db import Base
 
 
 class InvestorTable(Base):
diff --git a/app/main.py b/app/main.py
index a09042e..400730f 100644
--- a/app/main.py
+++ b/app/main.py
@@ -1,11 +1,12 @@
 import io
 
 import pandas as pd
-from db.db import db_dependency, init_database
+from app.db.db import db_dependency, init_database
 from fastapi import FastAPI, File, UploadFile
-from services.openrouter import InvestorProcessor
+from app.services.openrouter import InvestorProcessor
 
-from app.services.querying import QueryProcessor
+from app.pydantic_schemas import QueryRequest, QueryResponseList
+from app.services.langgraph_agent import LangGraphQueryAgent
 
 app = FastAPI()
 
@@ -31,11 +32,11 @@ async def parse_csv(db: db_dependency, file: UploadFile = File(...)):
     return {"results": [r.dict() for r in results]}
 
 
-@app.post("/query")
-async def query_investors(db: db_dependency, question: str):
-    processor = QueryProcessor(sql_session=db)
-    results = processor.process_query(question)
-    return {"results": [r.dict() for r in results]}
+@app.post("/query", response_model=QueryResponseList)
+async def query_investors(db: db_dependency, request: QueryRequest):
+    agent = LangGraphQueryAgent(sql_session=db)
+    result = agent.run(request.question)
+    return result
 
 
 if __name__ == "__main__":
diff --git a/app/pydantic_schemas.py b/app/pydantic_schemas.py
index 08588b5..af54a14 100644
--- a/app/pydantic_schemas.py
+++ b/app/pydantic_schemas.py
@@ -35,4 +35,4 @@ class QueryRequest(BaseModel):
 
 
 class QueryResponseList(BaseModel):
-    responses: List[QueryResponse]
\ No newline at end of file
+    responses: List[QueryResponse]
diff --git a/app/services/__pycache__/langgraph_agent.cpython-312.pyc b/app/services/__pycache__/langgraph_agent.cpython-312.pyc
index fe524c0..06c5a6d 100644
Binary files a/app/services/__pycache__/langgraph_agent.cpython-312.pyc and b/app/services/__pycache__/langgraph_agent.cpython-312.pyc differ
diff --git a/app/services/langgraph_agent.py b/app/services/langgraph_agent.py
index e69de29..9bf1866 100644
--- a/app/services/langgraph_agent.py
+++ b/app/services/langgraph_agent.py
@@ -0,0 +1,162 @@
+from __future__ import annotations
+
+from typing import Any, Dict, List, Optional
+
+import chromadb
+from langgraph.graph import END, START, StateGraph
+from pydantic import BaseModel, Field
+from sqlalchemy import func
+
+from app.db.tables import InvestorTable
+from app.pydantic_schemas import QueryResponse, QueryResponseList
+
+
+class AgentState(BaseModel):
+    question: str
+    sql_results: List[QueryResponse] = Field(default_factory=list)
+    vector_results: List[QueryResponse] = Field(default_factory=list)
+
+
+class LangGraphQueryAgent:
+    """Simple LangGraph agent that queries both SQL and Chroma and merges results."""
+
+    def __init__(
+        self,
+        sql_session: Optional[object] = None,
+        vector_db_client: Optional[object] = None,
+    ) -> None:
+        self.sql_session = sql_session
+
+        # Setup Chroma collection
+        self.vector_db_client = vector_db_client or chromadb.PersistentClient(
+            path="./chroma_db"
+        )
+        self.collection = self.vector_db_client.get_or_create_collection(
+            name="investor_descriptions",
+            metadata={
+                "description": "Investor descriptions and investment thesis focus",
+            },
+        )
+
+        # Build graph
+        graph = StateGraph(AgentState)
+        graph.add_node("sql_search", self._sql_search)
+        graph.add_node("vector_search", self._vector_search)
+        graph.add_node("merge", self._merge)
+
+        # Parallel fan-out: START -> sql_search & vector_search -> merge -> END
+        graph.add_edge(START, "sql_search")
+        graph.add_edge(START, "vector_search")
+        graph.add_edge("sql_search", "merge")
+        graph.add_edge("vector_search", "merge")
+        graph.add_edge("merge", END)
+
+        self.app = graph.compile()
+
+    # Nodes
+    def _sql_search(self, state: AgentState) -> Dict[str, Any]:
+        results: List[QueryResponse] = []
+        if self.sql_session is None:
+            return {"sql_results": results}
+
+        # Simple LIKE-based search across a few fields
+        # Note: SQLite uses case-insensitive LIKE by default for ASCII.
+        q = (
+            self.sql_session.query(InvestorTable)
+            .filter(
+                (func.lower(InvestorTable.name).like(f"%{state.question.lower()}%"))
+                | (
+                    func.lower(InvestorTable.sector_focus).like(
+                        f"%{state.question.lower()}%"
+                    )
+                )
+                | (
+                    func.lower(InvestorTable.stage_focus).like(
+                        f"%{state.question.lower()}%"
+                    )
+                )
+                | (func.lower(InvestorTable.region).like(f"%{state.question.lower()}%"))
+            )
+            .limit(10)
+        )
+
+        for row in q.all():
+            results.append(
+                QueryResponse(
+                    name=row.name,
+                    aum=row.aum,
+                    check_size=row.check_size,
+                    sector_focus=row.sector_focus,
+                    stage_focus=row.stage_focus,
+                    region=row.region,
+                    investment_thesis="",
+                    investor_description="",
+                    reason="Matched SQL fields via LIKE",
+                )
+            )
+
+        return {"sql_results": results}
+
+    def _vector_search(self, state: AgentState) -> Dict[str, Any]:
+        results: List[QueryResponse] = []
+        try:
+            q = self.collection.query(query_texts=[state.question], n_results=10)
+            # q has keys: ids, distances, documents, metadatas
+            docs = q.get("documents") or []
+            metas = q.get("metadatas") or []
+            if docs and metas:
+                for i, md in enumerate(metas[0]):
+                    name = md.get("name", "Unknown")
+                    results.append(
+                        QueryResponse(
+                            name=name,
+                            aum=0,
+                            check_size="",
+                            sector_focus="",
+                            stage_focus="",
+                            region=md.get("headquarters", ""),
+                            investment_thesis="",
+                            investor_description=(docs[0][i] if docs[0] else ""),
+                            reason="Vector similarity in Chroma",
+                        )
+                    )
+        except Exception:
+            # Best-effort; leave vector results empty on failure
+            pass
+
+        return {"vector_results": results}
+
+    def _merge(self, state: AgentState) -> Dict[str, Any]:
+        # Deduplicate by name, prefer SQL fields where available, keep first reason
+        merged: Dict[str, QueryResponse] = {}
+
+        for item in state.vector_results + state.sql_results:
+            if item.name not in merged:
+                merged[item.name] = item
+            else:
+                existing = merged[item.name]
+                merged[item.name] = QueryResponse(
+                    name=existing.name,
+                    aum=existing.aum or item.aum,
+                    check_size=existing.check_size or item.check_size,
+                    sector_focus=existing.sector_focus or item.sector_focus,
+                    stage_focus=existing.stage_focus or item.stage_focus,
+                    region=existing.region or item.region,
+                    investment_thesis=existing.investment_thesis
+                    or item.investment_thesis,
+                    investor_description=existing.investor_description
+                    or item.investor_description,
+                    reason=existing.reason or item.reason,
+                )
+
+        # Store back into sql_results to pass through the END with full state
+        return {
+            "sql_results": list(merged.values()),
+            "vector_results": [],
+        }
+
+    # Public API
+    def run(self, question: str) -> QueryResponseList:
+        state = AgentState(question=question)
+        final_state: AgentState = self.app.invoke(state)
+        return QueryResponseList(responses=final_state.sql_results)
diff --git a/app/services/openrouter.py b/app/services/openrouter.py
index 6fa8a01..6a36a61 100644
--- a/app/services/openrouter.py
+++ b/app/services/openrouter.py
@@ -3,12 +3,13 @@ from typing import List, Optional
 
 import chromadb
 import pandas as pd
-from db.tables import InvestorTable
 from langchain_core.prompts import PromptTemplate
 from langchain_openai import ChatOpenAI
 from pydantic_schemas import Investor, InvestorList
 from settings import settings
 
+from app.db.tables import InvestorTable
+
 # Add these imports for your databases
 # from sqlalchemy.ext.asyncio import AsyncSession
 # from your_vector_db import VectorDBClient
diff --git a/requirements.txt b/requirements.txt
index 10ba213..6a7dbd3 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -8,9 +8,13 @@ chromadb>=0.4.0
 
 # LLM integration
 openai>=1.0.0
+langchain>=0.2.0
+langchain-openai>=0.1.0
+langgraph>=0.2.0
 
 # Environment management
 python-dotenv>=1.0.0
+pydantic-settings>=2.0.0
 
 # Additional dependencies for data processing
 typing-extensions>=4.0.0