diff --git a/agent_api_app.py b/agent_api_app.py
new file mode 100644
index 00000000..37ebc6c7
--- /dev/null
+++ b/agent_api_app.py
@@ -0,0 +1,61 @@
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from typing import Optional
+from reflection_tuner import ReflectionTuner
+from token_cache_and_adaptive_factory import TokenCache, AdaptiveAgentFactory
+from swarms import Agent
+from swarm_models import OpenAIChat
+from swarms_memory import ChromaDB
+import os
+
+# Initialize FastAPI application
+app = FastAPI()
+
+# Define the token cache and model
+token_cache = TokenCache(cache_duration_minutes=30)
+model = OpenAIChat(api_key=os.getenv("OPENAI_API_KEY"), model_name="gpt-4o-mini", temperature=0.1)
+memory = ChromaDB(metric="cosine", output_dir="api_memory")
+
+# Agent creation factory
+adaptive_factory = AdaptiveAgentFactory(model, token_cache)
+
+# Input model for API request
+class AgentRequest(BaseModel):
+    agent_name: str
+    system_prompt: str
+    task: str
+    reflection_steps: Optional[int] = 2
+
+# Endpoint for creating and running an agent with Reflection-Tuning
+@app.post("/run_agent")
+async def run_agent(request: AgentRequest):
+    # Create or retrieve the agent from cache
+    agent = adaptive_factory.create_agent(
+        agent_name=request.agent_name,
+        system_prompt=request.system_prompt,
+        task=request.task,
+        memory=memory
+    )
+    
+    # Initialize Reflection-Tuning
+    reflection_tuner = ReflectionTuner(agent, reflection_steps=request.reflection_steps)
+    response = reflection_tuner.reflect_and_tune(request.task)
+    return {"response": response}
+
+# Endpoint for running an existing agent without creating a new one
+@app.post("/run_existing_agent")
+async def run_existing_agent(agent_name: str, task: str):
+    # Retrieve agent from cache
+    agent_token = token_cache.get_token(agent_name)
+    if not agent_token:
+        raise HTTPException(status_code=404, detail="Agent not found in cache. Create a new agent instead.")
+    
+    # Run the agent
+    response = agent_token.run(task)
+    return {"response": response}
+
+# Endpoint to clear cache for a specific agent
+@app.delete("/clear_cache/{agent_name}")
+async def clear_cache(agent_name: str):
+    token_cache.token_cache.pop(agent_name, None)
+    return {"detail": f"Cache for agent {agent_name} cleared."}