diff --git a/requirements.txt b/requirements.txt
index 11d4d11..75ba4cf 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -15,4 +15,6 @@ google-generativeai
 # For local embeddings if needed
 sentence-transformers
 # Graph database for relationship mapping
-neo4j
\ No newline at end of file
+neo4j
+qdrant-client
+sentence-transformers
\ No newline at end of file
diff --git a/scripts/init_athena_db.py b/scripts/init_athena_db.py
new file mode 100644
index 0000000..b9e01ba
--- /dev/null
+++ b/scripts/init_athena_db.py
@@ -0,0 +1,159 @@
+
+import os
+import re
+from typing import List, Dict
+import logging
+from dotenv import load_dotenv
+from qdrant_client import QdrantClient
+from qdrant_client.http import models
+from sentence_transformers import SentenceTransformer
+
+# Setup logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+# Load environment variables
+load_dotenv()
+
+# Configuration
+AGENT_CATALOG_PATH = os.path.join(os.path.dirname(__file__), '../docs/AGENT_CATALOG.md')
+QDRANT_HOST = os.getenv("QDRANT_HOST", "localhost")
+QDRANT_PORT = int(os.getenv("QDRANT_PORT", 6333))
+COLLECTION_NAME = "routing_index"
+EMBEDDING_MODEL_NAME = "all-MiniLM-L6-v2" # Defines the vector size as 384
+VECTOR_SIZE = 384
+
+def read_agent_catalog(file_path: str) -> List[Dict]:
+    """Reads the agent catalog and extracts agent information."""
+    if not os.path.exists(file_path):
+        logger.error(f"Agent catalog not found at {file_path}")
+        return []
+
+    with open(file_path, 'r', encoding='utf-8') as f:
+        content = f.read()
+
+    agents = []
+    # Regex to find agent blocks
+    # Looking for ### Agent Name ... - **Crews:** Crew Name
+    agent_blocks = re.split(r'### ', content)[1:] # Split and skip header
+
+    for block in agent_blocks:
+        lines = block.strip().split('\n')
+        name = lines[0].strip()
+        
+        description = ""
+        crew = ""
+        
+        for line in lines:
+            if line.startswith("- **Papel:**"):
+                role = line.split(":", 1)[1].strip()
+                description += f"Papel: {role}. "
+            elif line.startswith("- **Especialidade:**"):
+                specialty = line.split(":", 1)[1].strip()
+                description += f"Especialidade: {specialty}. "
+            elif line.startswith("- **Crews:**"):
+                crew = line.split(":", 1)[1].strip()
+        
+        if name and description and crew:
+            agents.append({
+                "name": name,
+                "description": description,
+                "crew": crew
+            })
+            logger.info(f"Found agent: {name} (Crew: {crew})")
+
+    return agents
+
+def get_embeddings(texts: List[str]) -> List[List[float]]:
+    """Generates embeddings for a list of texts."""
+    logger.info(f"Generating embeddings using model {EMBEDDING_MODEL_NAME}...")
+    model = SentenceTransformer(EMBEDDING_MODEL_NAME)
+    embeddings = model.encode(texts)
+    return embeddings.tolist()
+
+def init_qdrant_collection(client: QdrantClient, collection_name: str, vector_size: int):
+    """Creates the Qdrant collection if it does not exist."""
+    collections = client.get_collections().collections
+    exists = any(c.name == collection_name for c in collections)
+
+    if not exists:
+        logger.info(f"Creating collection '{collection_name}' with vector size {vector_size}...")
+        client.create_collection(
+            collection_name=collection_name,
+            vectors_config=models.VectorParams(
+                size=vector_size,
+                distance=models.Distance.COSINE
+            )
+        )
+    else:
+        logger.info(f"Collection '{collection_name}' already exists.")
+
+def populate_collection(client: QdrantClient, collection_name: str, agents: List[Dict]):
+    """Populates the collection with agent embeddings."""
+    if not agents:
+        logger.warning("No agents to index.")
+        return
+
+    descriptions = [agent["description"] for agent in agents]
+    embeddings = get_embeddings(descriptions)
+    
+    points = []
+    for i, agent in enumerate(agents):
+        # We process crew string to maybe pick the first one if multiple?
+        # For now, let's keep the raw string, or split if it's comma separated
+        # The instruction says payload: {"target_crew": "NomeDaCrew"}
+        # If an agent belongs to multiple crews, we might want to create multiple points or just string match
+        # Let's keep it simple for now as per instructions.
+        
+        target_crew = agent["crew"]
+        
+        points.append(models.PointStruct(
+            id=i + 1, # Simple integer ID
+            vector=embeddings[i],
+            payload={
+                "agent_name": agent["name"],
+                "target_crew": target_crew,
+                "description": agent["description"]
+            }
+        ))
+    
+    logger.info(f"Upserting {len(points)} points into '{collection_name}'...")
+    client.upsert(
+        collection_name=collection_name,
+        points=points
+    )
+    logger.info("Indexing complete.")
+
+def main():
+    logger.info("Starting Athena DB Initialization...")
+    
+    # 1. Read Catalog
+    agents = read_agent_catalog(AGENT_CATALOG_PATH)
+    if not agents:
+        logger.error("Failed to extract agents from catalog.")
+        return
+
+    # 2. Connect to Qdrant
+    try:
+        client = QdrantClient(host=QDRANT_HOST, port=QDRANT_PORT)
+        # Check connection likely by getting collections
+        client.get_collections()
+        logger.info(f"Connected to Qdrant at {QDRANT_HOST}:{QDRANT_PORT}")
+    except Exception as e:
+        logger.error(f"Failed to connect to Qdrant: {e}")
+        # For the sake of this task execution without running Qdrant, we might fail here.
+        # But the task is to CREATE the script.
+        # If the user has Qdrant running, it will work. 
+        # If not, the script is still valid.
+        return
+
+    # 3. Init Collection
+    init_qdrant_collection(client, COLLECTION_NAME, VECTOR_SIZE)
+
+    # 4. Populate
+    populate_collection(client, COLLECTION_NAME, agents)
+    
+    logger.info("Athena DB Initialization finished successfully.")
+
+if __name__ == "__main__":
+    main()