diff --git a/cookbook/learning/support-agent.mdx b/cookbook/learning/support-agent.mdx
index 683ccf690..baf8e2dc1 100644
--- a/cookbook/learning/support-agent.mdx
+++ b/cookbook/learning/support-agent.mdx
@@ -100,14 +100,18 @@ Key pattern: The `namespace=f"org:{org_id}:support"` isolates entity memory per
 
 ```bash
 git clone https://github.com/agno-agi/agno.git
-cd agno/cookbook/08_learning
+cd agno
 
 # Setup (requires Docker for Postgres)
-./setup_venv.sh
+./cookbook/08_learning/setup_venv.sh
 ./cookbook/scripts/run_pgvector.sh
 
+# Activate the environment and set API key
+source .venvs/learning/bin/activate
+export OPENAI_API_KEY=***
+
 # Run
-python 07_patterns/support_agent.py
+python cookbook/08_learning/07_patterns/support_agent.py
 ```
 
 Full source: [agno/cookbook/08_learning/07_patterns/support_agent.py](https://github.com/agno-agi/agno/blob/main/cookbook/08_learning/07_patterns/support_agent.py)
diff --git a/cookbook/streamlit/agentic-rag.mdx b/cookbook/streamlit/agentic-rag.mdx
index be39095fb..a3f28cef5 100644
--- a/cookbook/streamlit/agentic-rag.mdx
+++ b/cookbook/streamlit/agentic-rag.mdx
@@ -1,5 +1,5 @@
 ---
-title: Agentic RAG
+title: Knowledge Agent (Agentic RAG)
 description: Document Q&A with knowledge base, citations, and conversation memory.
 ---
 
@@ -8,25 +8,16 @@ A sophisticated RAG (Retrieval Augmented Generation) system that leverages searc
 
 ## The agent can:
 - Process and understand documents from multiple sources (PDFs, websites, text files)
-- Build a searchable knowledge base using vector embeddings
+- Build a searchable knowledge base using vector embeddings with hybrid search
 - Maintain conversation context and memory across sessions
 - Provide relevant citations and sources for its responses
-- Generate summaries and extract key insights
-- Answer follow-up questions and clarifications
+- Handle uncertainty gracefully (ambiguous queries, partial matches, conflicting sources)
 
 ## The agent uses:
-- Vector similarity search for relevant document retrieval
-- Conversation memory for contextual responses
+- Hybrid search (semantic + keyword) for relevant document retrieval
+- Conversation memory for contextual responses (5 turns)
 - Citation tracking for source attribution
-- Dynamic knowledge base updates
-
-<video
-  autoPlay
-  muted
-  controls
-  className="w-full aspect-video"
-  src="/videos/agentic_rag.mp4"
-></video>
+- ReasoningTools for think/analyze capabilities
 
 ## Example queries to try:
 - "What are the key points from this document?"
@@ -35,11 +26,10 @@ A sophisticated RAG (Retrieval Augmented Generation) system that leverages searc
 - "How does this relate to [topic X]?"
 - "What are the limitations or gaps in this analysis?"
 - "Can you explain [concept X] in more detail?"
-- "What other sources support or contradict these claims?"
 
 ## Code
 
-The complete code is available in the [Agno repository](https://github.com/agno-agi/agno).
+The complete code is available in the [Agno repository](https://github.com/agno-agi/agno/tree/main/cookbook/01_showcase/01_agents/knowledge_agent).
 
 ## Usage
 
@@ -60,7 +50,7 @@ The complete code is available in the [Agno repository](https://github.com/agno-
 
   <Step title="Install dependencies">
     ```bash
-    uv pip install -r cookbook/02_examples/streamlit_apps/agentic_rag/requirements.txt
+    pip install agno openai psycopg[binary] pgvector
     ```
   </Step>
 
@@ -86,24 +76,29 @@ The complete code is available in the [Agno repository](https://github.com/agno-
     ```
   </Step>
 
-
   <Step title="Set up API keys">
     ```bash
-    # Required
     export OPENAI_API_KEY=***
-    # Optional
-    export ANTHROPIC_API_KEY=***
-    export GOOGLE_API_KEY=***
+    ```
+  </Step>
 
+  <Step title="Load knowledge base">
+    ```bash
+    python cookbook/01_showcase/01_agents/knowledge_agent/scripts/load_knowledge.py
     ```
-    We recommend using gpt-5-mini for optimal performance.
   </Step>
 
-  <Step title="Launch the app">
+  <Step title="Run examples">
     ```bash
-    streamlit run cookbook/02_examples/streamlit_apps/agentic_rag/app.py
+    # Basic Q&A
+    python cookbook/01_showcase/01_agents/knowledge_agent/examples/basic_query.py
+
+    # Multi-turn conversation
+    python cookbook/01_showcase/01_agents/knowledge_agent/examples/conversation.py
+
+    # Edge cases and uncertainty
+    python cookbook/01_showcase/01_agents/knowledge_agent/examples/edge_cases.py
     ```
-    Open localhost:8501 to start using the Agentic RAG.
   </Step>
 </Steps>
 
diff --git a/cookbook/streamlit/answer-engine.mdx b/cookbook/streamlit/answer-engine.mdx
index d99096f90..e3c380240 100644
--- a/cookbook/streamlit/answer-engine.mdx
+++ b/cookbook/streamlit/answer-engine.mdx
@@ -20,14 +20,6 @@ Sage:
 - Chat history export
 - Interactive Streamlit UI
 
-<video
-  autoPlay
-  muted
-  controls
-  className="w-full aspect-video"
-  src="/videos/answer-engine.mp4"
-></video>
-
 ## Simple queries to try
 - "Tell me about the tariffs the US is imposing in 2025"
 - "Which is a better reasoning model: o3-mini or DeepSeek R1?"
@@ -42,7 +34,7 @@ Sage:
 
 ## Code
 
-The complete code is available in the [Agno repository](https://github.com/agno-agi/agno).
+The complete code is available in the [Agno repository](https://github.com/agno-agi/agno/tree/main/cookbook/01_showcase/01_agents/research_agent).
 
 ## Usage
 
@@ -63,29 +55,31 @@ The complete code is available in the [Agno repository](https://github.com/agno-
 
   <Step title="Install dependencies">
     ```bash
-    uv pip install -r cookbook/02_examples/streamlit_apps/answer_engine/requirements.txt
+    pip install agno openai parallel-web
     ```
   </Step>
 
   <Step title="Set up API keys">
     ```bash
-    # Required
     export OPENAI_API_KEY=***
-    export EXA_API_KEY=***
-
-    # Optional (for additional models)
-    export ANTHROPIC_API_KEY=***
-    export GOOGLE_API_KEY=***
-    export GROQ_API_KEY=***
+    export PARALLEL_API_KEY=***
     ```
-    We recommend using gpt-5-mini for optimal performance.
   </Step>
 
-  <Step title="Launch the app">
+  <Step title="Run examples">
     ```bash
-    streamlit run cookbook/02_examples/streamlit_apps/answer_engine/app.py
+    # Fast overview research (3-5 sources)
+    python cookbook/01_showcase/01_agents/research_agent/examples/quick_research.py
+
+    # Comprehensive investigation (10-15 sources)
+    python cookbook/01_showcase/01_agents/research_agent/examples/deep_research.py
+
+    # Comparison and decision support
+    python cookbook/01_showcase/01_agents/research_agent/examples/comparative.py
+
+    # Automated accuracy testing
+    python cookbook/01_showcase/01_agents/research_agent/examples/evaluate.py
     ```
-    Open localhost:8501 to start using Sage.
   </Step>
 </Steps>
 
diff --git a/cookbook/streamlit/overview.mdx b/cookbook/streamlit/overview.mdx
index 73098a267..0d5612a83 100644
--- a/cookbook/streamlit/overview.mdx
+++ b/cookbook/streamlit/overview.mdx
@@ -29,11 +29,9 @@ cd agno
 python3 -m venv .venv
 source .venv/bin/activate
 
-# Install for specific app
-uv pip install -r cookbook/02_examples/streamlit_apps/agentic_rag/requirements.txt
+# Install agno
+pip install agno
 
-# Run
-streamlit run cookbook/02_examples/streamlit_apps/agentic_rag/app.py
+# Run an example
+python cookbook/01_showcase/01_agents/text_to_sql/examples/basic_queries.py
 ```
-
-Open `localhost:8501` to use the application.
diff --git a/cookbook/streamlit/text-to-sql.mdx b/cookbook/streamlit/text-to-sql.mdx
index f701921c7..63dc4bf48 100644
--- a/cookbook/streamlit/text-to-sql.mdx
+++ b/cookbook/streamlit/text-to-sql.mdx
@@ -1,9 +1,9 @@
 ---
-title: SQL Agent
-description: Natural language to SQL with dynamic few-shot learning and Agentic RAG.
+title: Text to SQL Agent
+description: Natural language to SQL with dynamic few-shot learning and self-learning capabilities.
 ---
 
-A text-to-SQL system that:
+A text to SQL system that:
 1. Uses Agentic RAG to search for table metadata, sample queries and rules for writing better SQL queries.
 2. Uses dynamic few-shot examples and rules to improve query construction.
 3. Provides an interactive Streamlit UI for users to query the database.
@@ -12,17 +12,10 @@ We'll use the F1 dataset as an example, but you can easily extend it to other da
 
 ### Key capabilities
 - Natural language to SQL conversion
-- Retrieve table metadata, sample queries and rules using Agentic RAG
-- Better query construction with the help of dynamic few-shot examples and rules
-- Interactive Streamlit UI
-
-<video
-  autoPlay
-  muted
-  controls
-  className="w-full aspect-video"
-  src="/videos/sql_agent.mp4"
-></video>
+- Semantic model for table metadata guidance
+- Knowledge base with query patterns and data quality notes
+- Self-learning workflow (save validated queries for future use)
+- Agentic memory for user preferences across sessions
 
 ### Simple queries to try
 - "Who are the top 5 drivers with the most race wins?"
@@ -41,7 +34,7 @@ We'll use the F1 dataset as an example, but you can easily extend it to other da
 
 ## Code
 
-The complete code is available in the [Agno repository](https://github.com/agno-agi/agno).
+The complete code is available in the [Agno repository](https://github.com/agno-agi/agno/tree/main/cookbook/01_showcase/01_agents/text_to_sql).
 
 ## Usage
 
@@ -55,14 +48,20 @@ The complete code is available in the [Agno repository](https://github.com/agno-
 
   <Step title="Create virtual environment">
     ```bash
-    python3 -m venv .venv
-    source .venv/bin/activate
+    uv venv .venvs/text-to-sql --python 3.12
+    source .venvs/text-to-sql/bin/activate
     ```
   </Step>
 
   <Step title="Install dependencies">
     ```bash
-    uv pip install -r cookbook/02_examples/streamlit_apps/sql_agent/requirements.txt
+    uv pip install -r cookbook/01_showcase/01_agents/text_to_sql/requirements.txt
+    ```
+  </Step>
+
+  <Step title="Set up API keys">
+    ```bash
+    export OPENAI_API_KEY=***
     ```
   </Step>
 
@@ -88,42 +87,34 @@ The complete code is available in the [Agno repository](https://github.com/agno-
     ```
   </Step>
 
-  <Step title="Load F1 data">
+  <Step title="Check setup">
     ```bash
-    python cookbook/02_examples/streamlit_apps/sql_agent/load_f1_data.py
+    python cookbook/01_showcase/01_agents/text_to_sql/scripts/check_setup.py
     ```
   </Step>
 
-  <Step title="Load knowledge base">
-    The knowledge base contains table metadata, rules and sample queries that help the Agent generate better responses.
-
+  <Step title="Load data and knowledge">
     ```bash
-    python cookbook/02_examples/streamlit_apps/sql_agent/load_knowledge.py
+    python cookbook/01_showcase/01_agents/text_to_sql/scripts/load_f1_data.py
+    python cookbook/01_showcase/01_agents/text_to_sql/scripts/load_knowledge.py
     ```
-
-    Pro tips for enhancing the knowledge base:
-    - Add `table_rules` and `column_rules` to guide the Agent on query formats
-    - Add sample queries to `cookbook/02_examples/apps/sql_agent/knowledge_base/sample_queries.sql`
   </Step>
 
-  <Step title="Set up API keys">
+  <Step title="Run examples">
     ```bash
-    # Required
-    export OPENAI_API_KEY=***
+    # Basic queries
+    python cookbook/01_showcase/01_agents/text_to_sql/examples/basic_queries.py
 
-    # Optional
-    export ANTHROPIC_API_KEY=***
-    export GOOGLE_API_KEY=***
-    export GROQ_API_KEY=***
-    ```
-    We recommend using gpt-5-mini for optimal performance.
-  </Step>
+    # Self-learning demonstration
+    python cookbook/01_showcase/01_agents/text_to_sql/examples/learning_loop.py
 
-  <Step title="Launch the app">
-    ```bash
-    streamlit run cookbook/02_examples/streamlit_apps/sql_agent/app.py
+    # Data quality edge cases
+    python cookbook/01_showcase/01_agents/text_to_sql/examples/edge_cases.py
+
+    # Evaluate accuracy
+    python cookbook/01_showcase/01_agents/text_to_sql/examples/evaluate.py
     ```
-    Open localhost:8501 to start using the SQL Agent.
+    These scripts will run example text-to-SQL queries and demonstrate the SQL Agent in your terminal.
   </Step>
 </Steps>