updated README

README.md

@@ -36,6 +36,9 @@ A curated collection of awesome LLM apps built with RAG and AI agents. This repo
 ### AI Agents
 - [💼 AI Customer Support Agent](https://github.com/Shubhamsaboo/awesome-llm-apps/tree/main/ai_agent_tutorials/ai_customer_support_agent)
 - [📈 AI Investment Agent](https://github.com/Shubhamsaboo/awesome-llm-apps/tree/main/ai_agent_tutorials/ai_investment_agent)
+- [👨‍💼 AI Services Agency](https://github.com/Shubhamsaboo/awesome-llm-apps/tree/main/ai_agent_tutorials/ai_services_agency)
+- [🏋️‍♂️ AI Health & Fitness Planner Agent](https://github.com/Shubhamsaboo/awesome-llm-apps/tree/main/ai_agent_tutorials/ai_health_fitness_agent)
+- [📈 AI Startup Trend Analysis Agent](https://github.com/Shubhamsaboo/awesome-llm-apps/tree/main/ai_agent_tutorials/ai_startup_trend_analysis_agent)
 - [🗞️ AI Journalist Agent](https://github.com/Shubhamsaboo/awesome-llm-apps/tree/main/ai_agent_tutorials/ai_journalist_agent)
 - [💲 AI Finance Agent Team](https://github.com/Shubhamsaboo/awesome-llm-apps/tree/main/ai_agent_tutorials/ai_finance_agent_team)
 - [💰 AI Personal Finance Agent](https://github.com/Shubhamsaboo/awesome-llm-apps/tree/main/ai_agent_tutorials/ai_personal_finance_agent)
@@ -45,13 +48,16 @@ A curated collection of awesome LLM apps built with RAG and AI agents. This repo
 - [📑 AI Meeting Agent](https://github.com/Shubhamsaboo/awesome-llm-apps/tree/main/ai_agent_tutorials/ai_meeting_agent)
 - [🌐 Local News Agent OpenAI Swarm](https://github.com/Shubhamsaboo/awesome-llm-apps/tree/main/ai_agent_tutorials/local_news_agent_openai_swarm)
 - [📊 AI Finance Agent with xAI Grok](https://github.com/Shubhamsaboo/awesome-llm-apps/tree/main/ai_agent_tutorials/xai_finance_agent)
+- [🧠 AI Reasoning Agent](https://github.com/Shubhamsaboo/awesome-llm-apps/tree/main/ai_agent_tutorials/ai_reasoning_agent)
 
 ### RAG (Retrieval Augmented Generation)
 - [🔍 Autonomous RAG](https://github.com/Shubhamsaboo/awesome-llm-apps/tree/main/rag_tutorials/autonomous_rag)
 - [🔗 Agentic RAG](https://github.com/Shubhamsaboo/awesome-llm-apps/tree/main/rag_tutorials/agentic_rag)
 - [🔄 Llama3.1 Local RAG](https://github.com/Shubhamsaboo/awesome-llm-apps/tree/main/rag_tutorials/llama3.1_local_rag)
 - [🧩 RAG-as-a-Service](https://github.com/Shubhamsaboo/awesome-llm-apps/tree/main/rag_tutorials/rag-as-a-service)
 - [🦙 Local RAG Agent](https://github.com/Shubhamsaboo/awesome-llm-apps/tree/main/rag_tutorials/local_rag_agent)
+- [👀 RAG App with Hybrid Search](https://github.com/Shubhamsaboo/awesome-llm-apps/tree/main/rag_tutorials/hybrid_search_rag)
+- [🖥️ Local RAG App with Hybrid Search](https://github.com/Shubhamsaboo/awesome-llm-apps/tree/main/rag_tutorials/local_hybrid_search_rag)
 
 ### LLM Apps with Memory
 - [💾 AI Arxiv Agent with Memory](https://github.com/Shubhamsaboo/awesome-llm-apps/tree/main/llm_apps_with_memory_tutorials/ai_arxiv_agent_memory)

rag_tutorials/hybrid_search_rag/README.md

@@ -1,4 +1,4 @@
-# LLM Hybrid Search-RAG Assistant - Claude 🤖
+# 👀 RAG App with Hybrid Search 
 
 A powerful document Q&A application that leverages Hybrid Search (RAG) and Claude's advanced language capabilities to provide comprehensive answers. Built with RAGLite for robust document processing and retrieval, and Streamlit for an intuitive chat interface, this system seamlessly combines document-specific knowledge with Claude's general intelligence to deliver accurate and contextual responses.
 

rag_tutorials/hybrid_search_rag/main.py

@@ -108,7 +108,7 @@ def main():
             except Exception as e:
                 st.error(f"Configuration error: {str(e)}")
 
-    st.title("LLM-Powered Hybrid Search-RAG Assistant")
+    st.title("👀 RAG App with Hybrid Search")
 
     if st.session_state.my_config:
         uploaded_files = st.file_uploader("Upload PDF documents", type=["pdf"], accept_multiple_files=True, key="pdf_uploader")

rag_tutorials/local_hybrid_search_rag/README.md

@@ -1,4 +1,4 @@
-# Local LLM Hybrid Search-RAG Assistant 🤖
+# 🖥️ Local RAG App with Hybrid Search
 
 A powerful document Q&A application that leverages Hybrid Search (RAG) and local LLMs for comprehensive answers. Built with RAGLite for robust document processing and retrieval, and Streamlit for an intuitive chat interface, this system combines document-specific knowledge with local LLM capabilities to deliver accurate and contextual responses.
 

rag_tutorials/local_hybrid_search_rag/local_main.py

@@ -130,7 +130,7 @@ def main():
             except Exception as e:
                 st.error(f"Configuration error: {str(e)}")
 
-    st.title("Local LLM-Powered Hybrid Search-RAG Assistant")
+    st.title("🖥️ Local RAG App with Hybrid Search")
 
     if st.session_state.my_config:
         uploaded_files = st.file_uploader(