Release v2.3.3

- Add _display_memory_info() method to show memory stats in rich panel (long-term, short-term, entities, storage path)
- Disable system prompt caching when memory is enabled (dynamic context)
- Inject memory context into system prompt with "## Memory" section
- Display memory info on first chat when verbose=True
- Add integration tests for memory with OpenAI API (injection and persistence)
- Update README with zero

.github/workflows/auto-issue-comment.yml

@@ -22,5 +22,5 @@ jobs:
               issue_number: context.issue.number,
               owner: context.repo.owner,
               repo: context.repo.repo,
-              body: '@claude review this issue and do a detailed analysis and fix this if the existing code doesn\'t have the solution implemented. Making sure it has backward compatibility, no existing features removed. After making those changes, again review the applied changes. Use @web to search if you dont know any information or to find the latest documentation or to find the latest version. Run the code if you think you need to run it to test it. Minimal code change to start with if required any changes. **Please create a PR using gh tool with your changes**.'
+              body: '@claude review this issue and do a detailed analysis and fix this if the existing code doesn\'t have the solution implemented. Making sure it has backward compatibility, no existing features removed. After making those changes, again review the applied changes. Use @web to search if you dont know any information or to find the latest documentation or to find the latest version. Run the code if you think you need to run it to test it. Minimal code change to start with if required any changes. MAINLY IT SHOULD NOT IMPACT ON THE CURRENT SPEED OF EXECUTION of existing features, not increasing the overhead. **Please create a PR using gh tool with your changes**.'
             })

.github/workflows/auto-pr-comment.yml

@@ -19,5 +19,5 @@ jobs:
               issue_number: context.issue.number,
               owner: context.repo.owner,
               repo: context.repo.repo,
-              body: '@claude review this pull request and do a detailed analysis and fix this if the existing code doesn\'t have the solution implemented. Making sure it has backward compatibility, no existing features removed. After making those changes, again review the applied changes. Use @web to search if you dont know any information or to find the latest documentation or to find the latest version. Run the code if you think you need to run it to test it. Minimal code change to start with if required any changes.'
+              body: '@claude review this pull request and do a detailed analysis and fix this if the existing code doesn\'t have the solution implemented. Making sure it has backward compatibility, no existing features removed. After making those changes, again review the applied changes. Use @web to search if you dont know any information or to find the latest documentation or to find the latest version. Run the code if you think you need to run it to test it. Minimal code change to start with if required any changes. MAINLY IT SHOULD NOT IMPACT ON THE CURRENT SPEED OF EXECUTION of existing features, not increasing the overhead.'
             }) 

.github/workflows/claude.yml

@@ -12,13 +12,12 @@ on:
 
 jobs:
   claude-response:
-    # Prevent bots from triggering this action
+    # Prevent certain bots from triggering this action (but allow github-actions[bot] for auto-comments)
     if: |
       github.actor != 'dependabot[bot]' &&
-      github.actor != 'github-actions[bot]' &&
       github.actor != 'cursor[bot]' &&
-      !contains(github.actor, '[bot]') &&
-      github.actor != 'renovate[bot]'
+      github.actor != 'renovate[bot]' &&
+      !(contains(github.actor, '[bot]') && github.actor != 'github-actions[bot]')
     runs-on: ubuntu-latest
     permissions:
       contents: write
@@ -154,7 +153,7 @@ jobs:
                 issue_number: issueNumber,
                 owner: context.repo.owner,
                 repo: context.repo.repo,
-                body: '@claude review if the changes made in the above branch are as per the requirements and make changes to the above branch if issues still exist.'
+                body: '@claude review if the changes made in the above branch are as per the requirements and make changes to the above branch if issues still exist. MAINLY IT SHOULD NOT IMPACT ON THE CURRENT SPEED OF EXECUTION of existing features, not increasing the overhead.'
               });
             } else {
               console.log(`Skipping review comment for regular issue #${issueNumber}`);

.github/workflows/coverage.yml

@@ -41,7 +41,7 @@ jobs:
         run: |
           echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
           echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
-          echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
+          echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-5-nano' }}" >> $GITHUB_ENV
           echo "LOGLEVEL=DEBUG" >> $GITHUB_ENV
           echo "PYTHONPATH=${{ github.workspace }}/src/praisonai-agents:$PYTHONPATH" >> $GITHUB_ENV
 

.github/workflows/python-package.yml

@@ -29,7 +29,7 @@ jobs:
       run: |
         echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
         echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
-        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
+        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-5-nano' }}" >> $GITHUB_ENV
         echo "LOGLEVEL=DEBUG" >> $GITHUB_ENV
 
     - name: Install dependencies

.github/workflows/test-comprehensive.yml

@@ -27,6 +27,9 @@ jobs:
   comprehensive-test:
     runs-on: ubuntu-latest
     timeout-minutes: 15
+    env:
+      OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+      OPENAI_MODEL_NAME: gpt-4o-mini
     strategy:
       matrix:
         python-version: ["3.11"]
@@ -55,11 +58,13 @@ jobs:
         # Install knowledge dependencies from praisonai-agents
         uv pip install --system "praisonaiagents[knowledge]"
 
-    - name: Set environment variables
+    - name: Verify environment variables
       run: |
-        echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
-        echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
-        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
+        if [ -n "$OPENAI_API_KEY" ]; then
+          echo "✅ OPENAI_API_KEY is set (length: ${#OPENAI_API_KEY})"
+        else
+          echo "⚠️ OPENAI_API_KEY is NOT set"
+        fi
 
     - name: Run Comprehensive Test Suite
       run: |

.github/workflows/test-core.yml

@@ -34,7 +34,7 @@ jobs:
         cd src/praisonai
         uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
         uv pip install --system duckduckgo_search
-        uv pip install --system pytest pytest-asyncio pytest-cov
+        uv pip install --system pytest pytest-asyncio pytest-cov pytest-timeout
         # Install knowledge dependencies from praisonai-agents
         uv pip install --system "praisonaiagents[knowledge]"
 
@@ -42,13 +42,13 @@ jobs:
       run: |
         echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
         echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
-        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
+        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-5-nano' }}" >> $GITHUB_ENV
         echo "LOGLEVEL=DEBUG" >> $GITHUB_ENV
         echo "PYTHONPATH=${{ github.workspace }}/src/praisonai-agents:$PYTHONPATH" >> $GITHUB_ENV
         # Also export to current shell session for immediate availability
         export OPENAI_API_KEY="${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}"
         export OPENAI_API_BASE="${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}"
-        export OPENAI_MODEL_NAME="${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}"
+        export OPENAI_MODEL_NAME="${{ secrets.OPENAI_MODEL_NAME || 'gpt-5-nano' }}"
         export LOGLEVEL=DEBUG
         # Verify immediate availability
         echo "🔧 Immediate verification in same step:"
@@ -126,7 +126,7 @@ jobs:
       env:
         OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}
         OPENAI_API_BASE: ${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}
-        OPENAI_MODEL_NAME: ${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}
+        OPENAI_MODEL_NAME: ${{ secrets.OPENAI_MODEL_NAME || 'gpt-5-nano' }}
         LOGLEVEL: DEBUG
       run: |
         echo "🔍 Testing PraisonAI API key usage directly..."
@@ -166,7 +166,7 @@ jobs:
         
         print('\\n🧪 Testing PraisonAIModel with explicit API key (CrewAI method):')
         model_with_explicit_key = PraisonAIModel(
-            model='openai/gpt-4o-mini',
+            model='openai/gpt-5-nano',
             base_url=praisonai.config_list[0].get('base_url'),
             api_key=api_key_from_config # This will be a string from praisonai.config_list
         )
@@ -251,7 +251,7 @@ jobs:
       env:
         OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}
         OPENAI_API_BASE: ${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}
-        OPENAI_MODEL_NAME: ${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}
+        OPENAI_MODEL_NAME: ${{ secrets.OPENAI_MODEL_NAME || 'gpt-5-nano' }}
         LOGLEVEL: DEBUG
       run: |
         echo "🧪 Running legacy tests with real API key..."
@@ -262,7 +262,7 @@ jobs:
         export OPENAI_API_KEY="$OPENAI_API_KEY"
         export OPENAI_API_BASE="$OPENAI_API_BASE"
         export OPENAI_MODEL_NAME="$OPENAI_MODEL_NAME"
-        cd src/praisonai && python -m pytest tests/test.py -v --tb=short --disable-warnings
+        cd src/praisonai && python -m pytest tests/test.py -v --tb=short --disable-warnings -m "not real" --timeout=60
 
     - name: Upload coverage reports to Codecov
       if: matrix.python-version == '3.11'

.github/workflows/test-extended.yml

@@ -42,7 +42,7 @@ jobs:
       run: |
         echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
         echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
-        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
+        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-5-nano' }}" >> $GITHUB_ENV
         echo "PYTHONPATH=${{ github.workspace }}/src/praisonai-agents:$PYTHONPATH" >> $GITHUB_ENV
         echo "PRAISONAI_TELEMETRY_DISABLED=true" >> $GITHUB_ENV
         echo "PRAISONAI_DISABLE_TELEMETRY=true" >> $GITHUB_ENV

.github/workflows/test-frameworks.yml

@@ -56,7 +56,7 @@ jobs:
       run: |
         echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
         echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
-        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
+        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-5-nano' }}" >> $GITHUB_ENV
 
     - name: Test ${{ matrix.framework }} Framework
       run: |

.github/workflows/unittest.yml

@@ -27,7 +27,7 @@ jobs:
         cd src/praisonai
         uv pip install --system ."[ui,gradio,api,agentops,google,openai,anthropic,cohere,chat,code,realtime,call,crewai,autogen]"
         uv pip install --system duckduckgo_search
-        uv pip install --system pytest pytest-asyncio pytest-cov
+        uv pip install --system pytest pytest-asyncio pytest-cov pytest-timeout
         # Install knowledge dependencies from praisonai-agents
         uv pip install --system "praisonaiagents[knowledge]"
 
@@ -35,7 +35,7 @@ jobs:
       run: |
         echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY || 'sk-test-key-for-github-actions-testing-only-not-real' }}" >> $GITHUB_ENV
         echo "OPENAI_API_BASE=${{ secrets.OPENAI_API_BASE || 'https://api.openai.com/v1' }}" >> $GITHUB_ENV
-        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-4o-mini' }}" >> $GITHUB_ENV
+        echo "OPENAI_MODEL_NAME=${{ secrets.OPENAI_MODEL_NAME || 'gpt-5-nano' }}" >> $GITHUB_ENV
         echo "LOGLEVEL=DEBUG" >> $GITHUB_ENV
         echo "PYTHONPATH=${{ github.workspace }}/src/praisonai-agents:$PYTHONPATH" >> $GITHUB_ENV
 
@@ -316,8 +316,8 @@ jobs:
         try:
             from praisonai.inc.models import PraisonAIModel
             
-            # Test PraisonAIModel with openai/gpt-4o-mini (from YAML)
-            model = PraisonAIModel(model='openai/gpt-4o-mini')
+            # Test PraisonAIModel with openai/gpt-5-nano (from YAML)
+            model = PraisonAIModel(model='openai/gpt-5-nano')
             
             print('🤖 PraisonAIModel Configuration:')
             print(f'  model: {model.model}')
@@ -489,7 +489,7 @@ jobs:
 
     - name: Run Legacy Example Tests
       run: |
-        cd src/praisonai && python -m pytest tests/test.py -v --tb=short --disable-warnings
+        cd src/praisonai && python -m pytest tests/test.py -v --tb=short --disable-warnings -m "not real" --timeout=60
       continue-on-error: true
 
     - name: Upload coverage reports to Codecov

README.md

@@ -53,6 +53,13 @@ PraisonAI is a production-ready Multi-AI Agents framework with self-reflection,
 - 🛠️ 100+ Custom Tools
 - 📄 YAML Configuration
 - 💯 100+ LLM Support
+- 🔬 **Deep Research Agents** (OpenAI & Gemini)
+- 🔄 **Query Rewriter Agent** (HyDE, Step-back, Multi-query)
+- 🌐 **Native Web Search** (OpenAI, Gemini, Anthropic, xAI, Perplexity)
+- 📥 **Web Fetch** (Retrieve full content from URLs - Anthropic)
+- 💾 **Prompt Caching** (Reduce costs & latency - OpenAI, Anthropic, Bedrock, Deepseek)
+- 🧠 **Claude Memory Tool** (Persistent cross-conversation memory - Anthropic Beta)
+- 💾 **File-Based Memory** (Zero-dependency persistent memory for all agents)
 
 ## Using Python Code
 
@@ -96,6 +103,121 @@ Run:
 python app.py
 ```
 
+### 3. Deep Research Agent
+
+Automated research with real-time streaming, web search, and citations using OpenAI or Gemini Deep Research APIs.
+
+```python
+from praisonaiagents import DeepResearchAgent
+
+# OpenAI Deep Research
+agent = DeepResearchAgent(
+    model="o4-mini-deep-research",  # or "o3-deep-research"
+    verbose=True
+)
+
+result = agent.research("What are the latest AI trends in 2025?")
+print(result.report)
+print(f"Citations: {len(result.citations)}")
+```
+
+```python
+# Gemini Deep Research
+from praisonaiagents import DeepResearchAgent
+
+agent = DeepResearchAgent(
+    model="deep-research-pro",  # Auto-detected as Gemini
+    verbose=True
+)
+
+result = agent.research("Research quantum computing advances")
+print(result.report)
+```
+
+**Features:**
+- 🔍 Multi-provider support (OpenAI, Gemini, LiteLLM)
+- 📡 Real-time streaming with reasoning summaries
+- 📚 Structured citations with URLs
+- 🛠️ Built-in tools: web search, code interpreter, MCP, file search
+- 🔄 Automatic provider detection from model name
+
+### 4. Query Rewriter Agent
+
+Transform user queries to improve RAG retrieval quality using multiple strategies.
+
+```python
+from praisonaiagents import QueryRewriterAgent, RewriteStrategy
+
+agent = QueryRewriterAgent(model="gpt-4o-mini")
+
+# Basic - expands abbreviations, adds context
+result = agent.rewrite("AI trends")
+print(result.primary_query)  # "What are the current trends in Artificial Intelligence?"
+
+# HyDE - generates hypothetical document for semantic matching
+result = agent.rewrite("What is quantum computing?", strategy=RewriteStrategy.HYDE)
+
+# Step-back - generates broader context question
+result = agent.rewrite("GPT-4 vs Claude 3?", strategy=RewriteStrategy.STEP_BACK)
+
+# Sub-queries - decomposes complex questions
+result = agent.rewrite("RAG setup and best embedding models?", strategy=RewriteStrategy.SUB_QUERIES)
+
+# Contextual - resolves references using chat history
+result = agent.rewrite("What about cost?", chat_history=[...])
+```
+
+**Strategies:**
+- **BASIC**: Expand abbreviations, fix typos, add context
+- **HYDE**: Generate hypothetical document for semantic matching
+- **STEP_BACK**: Generate higher-level concept questions
+- **SUB_QUERIES**: Decompose multi-part questions
+- **MULTI_QUERY**: Generate multiple paraphrased versions
+- **CONTEXTUAL**: Resolve references using conversation history
+- **AUTO**: Automatically detect best strategy
+
+### 5. Agent Memory (Zero Dependencies)
+
+Enable persistent memory for agents - works out of the box without any extra packages.
+
+```python
+from praisonaiagents import Agent
+
+# Enable memory with a single parameter
+agent = Agent(
+    name="Personal Assistant",
+    instructions="You are a helpful assistant that remembers user preferences.",
+    memory=True,  # Enables file-based memory (no extra deps!)
+    user_id="user123"  # Isolate memory per user
+)
+
+# Store memories programmatically
+agent.store_memory("User prefers dark mode", memory_type="short_term")
+agent.store_memory("User's name is John", memory_type="long_term", importance=0.9)
+
+# Memory is automatically injected into conversations
+result = agent.start("What do you know about me?")
+# Agent will recall: "Your name is John and you prefer dark mode"
+
+# Memory persists across sessions - create new agent with same user_id
+agent2 = Agent(name="Assistant", memory=True, user_id="user123")
+# agent2 automatically loads all previous memories!
+```
+
+**Memory Types:**
+- **Short-term**: Rolling buffer of recent context (auto-expires)
+- **Long-term**: Persistent important facts (sorted by importance)
+- **Entity**: People, places, organizations with attributes
+- **Episodic**: Date-based interaction history
+
+**Storage Options:**
+| Option | Dependencies | Description |
+|--------|-------------|-------------|
+| `memory=True` | None | File-based JSON storage (default) |
+| `memory="file"` | None | Explicit file-based storage |
+| `memory="sqlite"` | Built-in | SQLite with indexing |
+| `memory="chromadb"` | chromadb | Vector/semantic search |
+
 ## Using No Code
 
 ### Auto Mode:
@@ -105,6 +227,51 @@ export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
 praisonai --auto create a movie script about Robots in Mars
 ```
 
+### Query Rewriting (works with any command):
+```bash
+# Rewrite query for better results (uses QueryRewriterAgent)
+praisonai "AI trends" --query-rewrite
+
+# Rewrite with search tools (agent decides when to search)
+praisonai "latest developments" --query-rewrite --rewrite-tools "internet_search"
+
+# Works with any prompt
+praisonai "explain quantum computing" --query-rewrite -v
+```
+
+### Deep Research CLI:
+```bash
+# Default: OpenAI (o4-mini-deep-research)
+praisonai research "What are the latest AI trends in 2025?"
+
+# Use Gemini
+praisonai research --model deep-research-pro "Your research query"
+
+# Rewrite query before research
+praisonai research --query-rewrite "AI trends"
+
+# Rewrite with search tools
+praisonai research --query-rewrite --rewrite-tools "internet_search" "AI trends"
+
+# Use custom tools from file (gathers context before deep research)
+praisonai research --tools tools.py "Your research query"
+praisonai research -t my_tools.py "Your research query"
+
+# Use built-in tools by name (comma-separated)
+praisonai research --tools "internet_search,wiki_search" "Your query"
+praisonai research -t "yfinance,calculator_tools" "Stock analysis query"
+
+# Save output to file (output/research/{query}.md)
+praisonai research --save "Your research query"
+praisonai research -s "Your research query"
+
+# Combine options
+praisonai research --query-rewrite --tools tools.py --save "Your research query"
+
+# Verbose mode (show debug logs)
+praisonai research -v "Your research query"
+```
+
 ## Using JavaScript Code
 
 ```bash
@@ -523,6 +690,172 @@ praisonai agents.yaml
   </a>
 </div>
 
+
+## Custom Tools
+
+### Using `@tool` Decorator
+
+```python
+from praisonaiagents import Agent, tool
+
+@tool
+def search(query: str) -> str:
+    """Search the web for information."""
+    return f"Results for: {query}"
+
+@tool
+def calculate(expression: str) -> float:
+    """Evaluate a math expression."""
+    return eval(expression)
+
+agent = Agent(
+    instructions="You are a helpful assistant",
+    tools=[search, calculate]
+)
+agent.start("Search for AI news and calculate 15*4")
+```
+
+### Using `BaseTool` Class
+
+```python
+from praisonaiagents import Agent, BaseTool
+
+class WeatherTool(BaseTool):
+    name = "weather"
+    description = "Get current weather for a location"
+    
+    def run(self, location: str) -> str:
+        return f"Weather in {location}: 72°F, Sunny"
+
+agent = Agent(
+    instructions="You are a weather assistant",
+    tools=[WeatherTool()]
+)
+agent.start("What's the weather in Paris?")
+```
+
+### Creating a Tool Package (pip installable)
+
+```toml
+# pyproject.toml
+[project]
+name = "my-praisonai-tools"
+version = "1.0.0"
+dependencies = ["praisonaiagents"]
+
+[project.entry-points."praisonaiagents.tools"]
+my_tool = "my_package:MyTool"
+```
+
+```python
+# my_package/__init__.py
+from praisonaiagents import BaseTool
+
+class MyTool(BaseTool):
+    name = "my_tool"
+    description = "My custom tool"
+    
+    def run(self, param: str) -> str:
+        return f"Result: {param}"
+```
+
+After `pip install`, tools are auto-discovered:
+```python
+agent = Agent(tools=["my_tool"])  # Works automatically!
+```
+
+
+## Prompt Expansion
+
+Expand short prompts into detailed, actionable prompts:
+
+### CLI Usage
+
+```bash
+# Expand a short prompt into detailed prompt
+praisonai "write a movie script in 3 lines" --expand-prompt
+
+# With verbose output
+praisonai "blog about AI" --expand-prompt -v
+
+# With tools for context gathering
+praisonai "latest AI trends" --expand-prompt --expand-tools tools.py
+
+# Combine with query rewrite
+praisonai "AI news" --query-rewrite --expand-prompt
+```
+
+### Programmatic Usage
+
+```python
+from praisonaiagents import PromptExpanderAgent, ExpandStrategy
+
+# Basic usage
+agent = PromptExpanderAgent()
+result = agent.expand("write a movie script in 3 lines")
+print(result.expanded_prompt)
+
+# With specific strategy
+result = agent.expand("blog about AI", strategy=ExpandStrategy.DETAILED)
+
+# Available strategies: BASIC, DETAILED, STRUCTURED, CREATIVE, AUTO
+```
+
+**Key Difference:**
+- `--query-rewrite`: Optimizes queries for **search/retrieval** (RAG)
+- `--expand-prompt`: Expands prompts for **detailed task execution**
+
+
+## Web Search, Web Fetch & Prompt Caching
+
+### CLI Usage
+
+```bash
+# Web Search - Get real-time information
+praisonai "What are the latest AI news today?" --web-search --llm openai/gpt-4o-search-preview
+
+# Web Fetch - Retrieve and analyze URL content (Anthropic only)
+praisonai "Summarize https://docs.praison.ai" --web-fetch --llm anthropic/claude-sonnet-4-20250514
+
+# Prompt Caching - Reduce costs for repeated prompts
+praisonai "Analyze this document..." --prompt-caching --llm anthropic/claude-sonnet-4-20250514
+```
+
+### Programmatic Usage
+
+```python
+from praisonaiagents import Agent
+
+# Web Search
+agent = Agent(
+    instructions="You are a research assistant",
+    llm="openai/gpt-4o-search-preview",
+    web_search=True
+)
+
+# Web Fetch (Anthropic only)
+agent = Agent(
+    instructions="You are a content analyzer",
+    llm="anthropic/claude-sonnet-4-20250514",
+    web_fetch=True
+)
+
+# Prompt Caching
+agent = Agent(
+    instructions="You are an AI assistant..." * 50,  # Long system prompt
+    llm="anthropic/claude-sonnet-4-20250514",
+    prompt_caching=True
+)
+```
+
+**Supported Providers:**
+| Feature | Providers |
+|---------|-----------|
+| Web Search | OpenAI, Gemini, Anthropic, xAI, Perplexity |
+| Web Fetch | Anthropic |
+| Prompt Caching | OpenAI (auto), Anthropic, Bedrock, Deepseek |
+
+
 ## Development:
 
 Below is used for development only.
@@ -540,6 +873,19 @@ uv pip install -r pyproject.toml --extra code
 uv pip install -r pyproject.toml --extra "crewai,autogen"
 ```
 
+### Bump and Release
+
+```bash
+# From project root - bumps version and releases in one command
+python src/praisonai/scripts/bump_and_release.py 2.2.99
+
+# With praisonaiagents dependency
+python src/praisonai/scripts/bump_and_release.py 2.2.99 --agents 0.0.169
+
+# Then publish
+cd src/praisonai && uv publish
+```
+
 ## Contributing
 
 - Fork on GitHub: Use the "Fork" button on the repository page.

docker/Dockerfile

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     flask \
-    "praisonai>=2.2.76" \
+    "praisonai>=2.3.3" \
     "praisonai[api]" \
     gunicorn \
     markdown

docker/Dockerfile.chat

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=2.2.76" \
+    "praisonai>=2.3.3" \
     "praisonai[chat]" \
     "embedchain[github,youtube]"
 

docker/Dockerfile.dev

@@ -20,7 +20,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=2.2.76" \
+    "praisonai>=2.3.3" \
     "praisonai[ui]" \
     "praisonai[chat]" \
     "praisonai[realtime]" \

docker/Dockerfile.ui

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=2.2.76" \
+    "praisonai>=2.3.3" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

docker/README.md

@@ -121,7 +121,7 @@ healthcheck:
 ## 📦 Package Versions
 
 All Docker images use consistent, up-to-date versions:
-- PraisonAI: `>=2.2.76`
+- PraisonAI: `>=2.2.95`
 - PraisonAI Agents: `>=0.0.92`
 - Python: `3.11-slim`
 
@@ -218,7 +218,7 @@ docker-compose up -d
 ### Version Pinning
 To use specific versions, update the Dockerfile:
 ```dockerfile
-RUN pip install "praisonai==2.2.76" "praisonaiagents==0.0.92"
+RUN pip install "praisonai==2.2.95" "praisonaiagents==0.0.92"
 ```
 
 ## 🌐 Production Deployment

examples/cookbooks/Conversational_Chat_Agents/Multi_Agent_Production_System.ipynb

@@ -26,14 +26,14 @@
         "\n",
         "### ✨ Key Benefits:\n",
         "- 🚀 **Scalable & Customizable** – Industry-agnostic design  \n",
-        "- 💰 **Cost-Effective** – Runs on GPT-4o-mini  \n",
+        "- 💰 **Cost-Effective** – Runs on gpt-5-nano  \n",
         "- 🛠️ **Production-Ready** – Realistic automation framework  \n",
         "- 🔄 **Complete Pipeline** – End-to-end flow\n",
         "\n",
         "---\n",
         "\n",
         "### 🔧 Technologies:\n",
-        "**PraisonAI** • **OpenAI GPT-4o-mini** • **Python** • **SQLite**  \n",
+        "**PraisonAI** • **OpenAI gpt-5-nano** • **Python** • **SQLite**  \n",
         "**Multi-Agent Architecture**\n",
         "\n",
         "---\n",
@@ -100,7 +100,7 @@
           "text": [
             "📦 Dependencies installed!\n",
             "🔄 Using PraisonAI Multi-Agent Framework\n",
-            "🤖 Powered by OpenAI GPT-4o-mini\n"
+            "🤖 Powered by OpenAI gpt-5-nano\n"
           ]
         }
       ],
@@ -116,7 +116,7 @@
         "\n",
         "print(\"📦 Dependencies installed!\")\n",
         "print(\"🔄 Using PraisonAI Multi-Agent Framework\")\n",
-        "print(\"🤖 Powered by OpenAI GPT-4o-mini\")"
+        "print(\"🤖 Powered by OpenAI gpt-5-nano\")"
       ]
     },
     {
@@ -5102,7 +5102,7 @@
             "\n",
             "🎊 BLENDER PRODUCTION STUDIO COMPLETE!\n",
             "🎬 17-Agent AI System Successfully Executed\n",
-            "🤖 Powered by PraisonAI + OpenAI GPT-4o-mini\n"
+            "🤖 Powered by PraisonAI + OpenAI gpt-5-nano\n"
           ]
         }
       ],
@@ -5149,7 +5149,7 @@
         "\n",
         "print(\"\\n🎊 BLENDER PRODUCTION STUDIO COMPLETE!\")\n",
         "print(\"🎬 17-Agent AI System Successfully Executed\")\n",
-        "print(\"🤖 Powered by PraisonAI + OpenAI GPT-4o-mini\")"
+        "print(\"🤖 Powered by PraisonAI + OpenAI gpt-5-nano\")"
       ]
     }
   ],

examples/cookbooks/Conversational_Chat_Agents/ReAct_agent.ipynb

@@ -182,7 +182,7 @@
         "        ]\n",
         "        for turn in range(max_turns):\n",
         "            response = client.chat.completions.create(\n",
-        "                model=\"gpt-4o-mini\",\n",
+        "                model=\"gpt-5-nano\",\n",
         "                messages=history,\n",
         "                max_tokens=500\n",
         "            )\n",

examples/cookbooks/Conversational_Chat_Agents/Semantic_Social_Network_Agent_System.ipynb

@@ -2642,7 +2642,7 @@
       "\n",
       "\ud83c\udf8a SEMANTIC NETWORK ANALYSIS COMPLETE!\n",
       "\ud83c\udf10 Cross-platform profile discovery and analysis\n",
-      "\ud83e\udd16 Powered by PraisonAI + OpenAI GPT-4o-mini\n"
+      "\ud83e\udd16 Powered by PraisonAI + OpenAI gpt-5-nano\n"
      ]
     }
    ],
@@ -2687,7 +2687,7 @@
     "\n",
     "print(\"\\n\ud83c\udf8a SEMANTIC NETWORK ANALYSIS COMPLETE!\")\n",
     "print(\"\ud83c\udf10 Cross-platform profile discovery and analysis\")\n",
-    "print(\"\ud83e\udd16 Powered by PraisonAI + OpenAI GPT-4o-mini\")"
+    "print(\"\ud83e\udd16 Powered by PraisonAI + OpenAI gpt-5-nano\")"
    ]
   },
   {

examples/cookbooks/Ecommerce_Shopping_Agents/Ai_Ecommerce_Shopping_Assistant.ipynb

@@ -1262,7 +1262,7 @@
       "\n",
       "🎊 AI SHOPPING ASSISTANT ACTIVE!\n",
       "🛒 Ready to help customers with natural language shopping\n",
-      "🤖 Powered by PraisonAI + OpenAI GPT-4o-mini\n"
+      "🤖 Powered by PraisonAI + OpenAI gpt-5-nano\n"
      ]
     }
    ],
@@ -1305,7 +1305,7 @@
     "\n",
     "print(\"\\n🎊 AI SHOPPING ASSISTANT ACTIVE!\")\n",
     "print(\"🛒 Ready to help customers with natural language shopping\")\n",
-    "print(\"🤖 Powered by PraisonAI + OpenAI GPT-4o-mini\")"
+    "print(\"🤖 Powered by PraisonAI + OpenAI gpt-5-nano\")"
    ]
   },
   {

examples/cookbooks/Finance_Market_Job_Agents/Home_Buying_Real_Estate_Agent.ipynb

@@ -156,7 +156,7 @@
     "  - Property comparison\n",
     "  - Cost of living and taxes\n",
     "  - First-time homebuyer guidance\n",
-    "llm: gpt-4o\n",
+    "llm: gpt-5-nano\n",
     "verbosity: true\n",
     "format: markdown\n",
     "\"\"\"\n"
@@ -188,7 +188,7 @@
     "Help users evaluate neighborhoods, estimate mortgage payments, compare homes, and understand homeownership costs.\n",
     "Explain clearly, format in markdown, and be friendly but professional.\n",
     "\"\"\",\n",
-    "    llm=\"gpt-4o\",\n",
+    "    llm=\"gpt-5-nano\",\n",
     "    verbose=True,\n",
     "    markdown=True\n",
     ")\n"

examples/cookbooks/Health_Fitness_Agents/Air_Quality_Analysis_Agent.ipynb

@@ -311,7 +311,7 @@
      "data": {
       "text/html": [
        "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008080; text-decoration-color: #008080\">╭───────────────────────────────────────────────────── Task ──────────────────────────────────────────────────────╮</span>\n",
-       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> You are an expert health advisor using the GPT-4o Mini model.                                                   <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> You are an expert health advisor using the gpt-5-nano Mini model.                                                   <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
        "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
        "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"background-color: #272822\">                                                                                                               </span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
        "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"background-color: #272822\"> </span><span style=\"color: #f8f8f2; text-decoration-color: #f8f8f2; background-color: #272822\">Based on the following air quality conditions in Mumbai, Maharashtra, India:</span><span style=\"background-color: #272822\">                                  </span> <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
@@ -340,7 +340,7 @@
       ],
       "text/plain": [
        "\u001b[36m╭─\u001b[0m\u001b[36m────────────────────────────────────────────────────\u001b[0m\u001b[36m Task \u001b[0m\u001b[36m─────────────────────────────────────────────────────\u001b[0m\u001b[36m─╮\u001b[0m\n",
-       "\u001b[36m│\u001b[0m You are an expert health advisor using the GPT-4o Mini model.                                                   \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m You are an expert health advisor using the gpt-5-nano Mini model.                                                   \u001b[36m│\u001b[0m\n",
        "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
        "\u001b[36m│\u001b[0m \u001b[48;2;39;40;34m                                                                                                               \u001b[0m \u001b[36m│\u001b[0m\n",
        "\u001b[36m│\u001b[0m \u001b[48;2;39;40;34m \u001b[0m\u001b[38;2;248;248;242;48;2;39;40;34mBased on the following air quality conditions in Mumbai, Maharashtra, India:\u001b[0m\u001b[48;2;39;40;34m                                 \u001b[0m\u001b[48;2;39;40;34m \u001b[0m \u001b[36m│\u001b[0m\n",
@@ -519,7 +519,7 @@
     "    aqi_data = aqi_data.data  # Convert to dict\n",
     "\n",
     "    prompt = f\"\"\"\n",
-    "    You are an expert health advisor using the GPT-4o Mini model.\n",
+    "    You are an expert health advisor using the gpt-5-nano Mini model.\n",
     "\n",
     "    Based on the following air quality conditions in {user_city}, {user_state}, {user_country}:\n",
     "    - Overall AQI: {aqi_data.get('aqi', 'N/A')}\n",

examples/cookbooks/Productivity_Workflow_Agents/Hackathon_Agent_Notebook.ipynb

@@ -193,7 +193,7 @@
         "    goal=\"Evaluate hackathon projects through video demonstrations\",\n",
         "    backstory=\"\"\"You are an expert hackathon judge. You give fair scores and detailed feedback\n",
         "    on technical complexity, innovation, presentation, and UX.\"\"\",\n",
-        "    llm=\"gpt-4o-mini\",\n",
+        "    llm=\"gpt-5-nano\",\n",
         "    self_reflect=False\n",
         ")"
       ],

examples/cookbooks/Programming_Code_Analysis_Agents/FunctionAgent_Workflow.ipynb

@@ -65,7 +65,7 @@
       "source": [
         "from llama_index.llms.openai import OpenAI\n",
         "\n",
-        "llm = OpenAI(model=\"gpt-4o-mini\", api_key=\"Enter your api key here\")  # Replace with your OpenAI API key\n"
+        "llm = OpenAI(model=\"gpt-5-nano\", api_key=\"Enter your api key here\")  # Replace with your OpenAI API key\n"
       ]
     },
     {

examples/cookbooks/Research_Knowledge_QA_Agents/AI_CourtSimulation.ipynb

@@ -129,7 +129,7 @@
     "judge = Agent(\n",
     "    name=\"Judge\",\n",
     "    role=\"Preside over court proceedings\",\n",
-    "    llm=\"gpt-4o-mini\",\n",
+    "    llm=\"gpt-5-nano\",\n",
     "    instructions=[\n",
     "        \"You are an impartial judge\",\n",
     "        \"Make fair decisions based on evidence\",\n",
@@ -142,7 +142,7 @@
     "prosecutor = Agent(\n",
     "    name=\"Prosecutor\",\n",
     "    role=\"Present case against defendant\",\n",
-    "    llm=\"gpt-4o-mini\",\n",
+    "    llm=\"gpt-5-nano\",\n",
     "    instructions=[\n",
     "        \"You are a prosecutor seeking conviction\",\n",
     "        \"Present evidence methodically\",\n",
@@ -155,7 +155,7 @@
     "defense = Agent(\n",
     "    name=\"Defense\",\n",
     "    role=\"Defend the accused\",\n",
-    "    llm=\"gpt-4o-mini\",\n",
+    "    llm=\"gpt-5-nano\",\n",
     "    instructions=[\n",
     "        \"You are a defense attorney\",\n",
     "        \"Create reasonable doubt\",\n",
@@ -168,7 +168,7 @@
     "witness = Agent(\n",
     "    name=\"Witness\",\n",
     "    role=\"Provide testimony\",\n",
-    "    llm=\"gpt-4o-mini\",\n",
+    "    llm=\"gpt-5-nano\",\n",
     "    instructions=[\n",
     "        \"You are a witness testifying\",\n",
     "        \"Provide factual testimony\",\n",

examples/cookbooks/Research_Knowledge_QA_Agents/AI_Data_Analysis_Agent.ipynb

@@ -76,7 +76,7 @@
      "output_type": "stream",
      "text": [
       "✅ API key configured!\n",
-      "✅ Using model: gpt-4o-mini\n"
+      "✅ Using model: gpt-5-nano\n"
      ]
     }
    ],
@@ -85,7 +85,7 @@
     "openai_key = \"sk-..\"\n",
     "\n",
     "os.environ[\"OPENAI_API_KEY\"] = openai_key\n",
-    "model_choice = \"gpt-4o-mini\"\n",
+    "model_choice = \"gpt-5-nano\"\n",
     "\n",
     "print(\"✅ API key configured!\")\n",
     "print(f\"✅ Using model: {model_choice}\")"
@@ -336,7 +336,7 @@
     "\n",
     "temperature: 0.3\n",
     "max_tokens: 4000\n",
-    "model: \"gpt-4o-mini\"\n",
+    "model: \"gpt-5-nano\"\n",
     "\"\"\"\n",
     "\n",
     "print(\"✅ YAML Prompt configured!\")"

examples/cookbooks/Research_Knowledge_QA_Agents/Ai_Documentation_Processor.ipynb

@@ -8,7 +8,7 @@
    "source": [
     "# 🤖 Add AI Documentation Processor: Multi-Agent Content Analysis & Code Generation System\n",
     "\n",
-    "This PR adds a comprehensive AI-powered documentation processing system using PraisonAI and OpenAI GPT-4o-mini. Features 3 specialized agents for content analysis, code generation, and example creation, perfect for automated documentation processing and code generation workflows.\n",
+    "This PR adds a comprehensive AI-powered documentation processing system using PraisonAI and OpenAI gpt-5-nano. Features 3 specialized agents for content analysis, code generation, and example creation, perfect for automated documentation processing and code generation workflows.\n",
     "\n",
     "**Key Features:** Multi-agent document analysis, automated code generation, usage example creation, content validation, and scalable processing workflows for various document formats."
    ]

examples/cookbooks/Research_Knowledge_QA_Agents/Crypto_Research_Agent_Intelligence_Agent.ipynb

@@ -152,7 +152,7 @@
     "  - Evaluate risk factors\n",
     "  - Identify scams or red flags\n",
     "  - Summarize project fundamentals\n",
-    "llm: gpt-4o\n",
+    "llm: gpt-5-nano\n",
     "verbosity: true\n",
     "format: markdown\n",
     "\"\"\"\n"
@@ -184,7 +184,7 @@
     "and market trends. Provide critical insight into legitimacy, potential growth, and associated risks.\n",
     "Use markdown formatting and structure your research clearly.\n",
     "\"\"\",\n",
-    "    llm=\"gpt-4o\",\n",
+    "    llm=\"gpt-5-nano\",\n",
     "    verbose=True,\n",
     "    markdown=True\n",
     ")\n"

examples/cookbooks/SocialMedia_Content_Agents/Ai_Meme_Creator_Agent.ipynb

@@ -491,7 +491,7 @@
     "\n",
     "temperature: 0.7\n",
     "max_tokens: 3000\n",
-    "model: \"gpt-4o-mini\"\n",
+    "model: \"gpt-5-nano\"\n",
     "\"\"\"\n",
     "\n",
     "print(\"✅ YAML Prompt configured!\")"
@@ -627,7 +627,7 @@
     "            )\n",
     "        else:  # OpenAI\n",
     "            llm = ChatOpenAI(\n",
-    "                model=\"gpt-4o\",\n",
+    "                model=\"gpt-5-nano\",\n",
     "                api_key=api_key,\n",
     "                temperature=0.0\n",
     "            )\n",

examples/cookbooks/SocialMedia_Content_Agents/Joke_Agent.ipynb

@@ -120,7 +120,7 @@
         "\n",
         "    def run(self, message: str):\n",
         "        response = client.chat.completions.create(\n",
-        "            model=\"gpt-4o-mini\",\n",
+        "            model=\"gpt-5-nano\",\n",
         "            messages=[\n",
         "                {\"role\": \"system\", \"content\": self.prompt},\n",
         "                {\"role\": \"user\", \"content\": message}\n",

examples/cookbooks/SocialMedia_Content_Agents/YouTube_Influencer_Intelligence_Agent.ipynb

@@ -153,7 +153,7 @@
     "  - Spot content patterns and trends\n",
     "  - Recommend growth strategies\n",
     "  - Detect potential monetization methods\n",
-    "llm: gpt-4o\n",
+    "llm: gpt-5-nano\n",
     "verbosity: true\n",
     "format: markdown\n",
     "\"\"\"\n"
@@ -184,7 +184,7 @@
     "You are a YouTube intelligence analyst. Your job is to research a specific channel, niche, or influencer topic.\n",
     "Provide growth recommendations, engagement analysis, trending strategies, and monetization tips. Format in markdown.\n",
     "\"\"\",\n",
-    "    llm=\"gpt-4o\",\n",
+    "    llm=\"gpt-5-nano\",\n",
     "    verbose=True,\n",
     "    markdown=True\n",
     ")\n"

examples/cookbooks/Travel_Planning_Agents/Intelligent_Travel_Planning_Agent.ipynb

@@ -136,7 +136,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'name': 'Travel Research Agent', 'description': 'A world-class travel researcher that searches for destinations, activities, and accommodations', 'instructions': ['Given a travel destination and number of days, generate 3 relevant search terms', 'Search the web for each term using web_search tool', 'Analyze results and return 10 most relevant findings', 'Focus on activities, accommodations, attractions, and local insights', 'Ensure high-quality, accurate information', 'Use travel_budget_calculator to estimate costs'], 'tools': ['web_search', 'travel_budget_calculator'], 'model': 'gpt-4o', 'temperature': 0.7}\n"
+      "{'name': 'Travel Research Agent', 'description': 'A world-class travel researcher that searches for destinations, activities, and accommodations', 'instructions': ['Given a travel destination and number of days, generate 3 relevant search terms', 'Search the web for each term using web_search tool', 'Analyze results and return 10 most relevant findings', 'Focus on activities, accommodations, attractions, and local insights', 'Ensure high-quality, accurate information', 'Use travel_budget_calculator to estimate costs'], 'tools': ['web_search', 'travel_budget_calculator'], 'model': 'gpt-5-nano', 'temperature': 0.7}\n"
      ]
     }
    ],
@@ -156,7 +156,7 @@
     "tools:\n",
     "  - web_search\n",
     "  - travel_budget_calculator\n",
-    "model: gpt-4o\n",
+    "model: gpt-5-nano\n",
     "temperature: 0.7\n",
     "\"\"\"\n",
     "\n",

examples/cookbooks/Travel_Planning_Agents/VoiGno_Restaurant_Agent_PraisonAI.ipynb

@@ -17,7 +17,7 @@
     "id": "Y9FkDzt1lF47"
    },
    "source": [
-    "This notebook implements a conversational restaurant booking assistant using the Praison AI Agent framework. It handles natural interruptions such as modifying guest count or changing time mid-conversation. Built for Google Colab Free Tier, it uses OpenAI's GPT-4o model and follows a YAML-based agent design for clarity and modularity."
+    "This notebook implements a conversational restaurant booking assistant using the Praison AI Agent framework. It handles natural interruptions such as modifying guest count or changing time mid-conversation. Built for Google Colab Free Tier, it uses OpenAI's gpt-5-nano model and follows a YAML-based agent design for clarity and modularity."
    ]
   },
   {
@@ -135,7 +135,7 @@
     "  - Context management\n",
     "  - Handling mid-conversation changes\n",
     "  - Summarizing confirmed reservations\n",
-    "llm: gpt-4o\n",
+    "llm: gpt-5-nano\n",
     "verbosity: true\n",
     "format: markdown\n",
     "\"\"\"\n"
@@ -167,7 +167,7 @@
     "manage corrections like “change to 3 guests” or “move to 8 PM,”\n",
     "confirm the details once the user is satisfied. Respond in markdown.\n",
     "\"\"\",\n",
-    "    llm=\"gpt-4o\",\n",
+    "    llm=\"gpt-5-nano\",\n",
     "    verbose=True,\n",
     "    markdown=True\n",
     ")\n"

examples/cookbooks/rag_examples/agentic_rag/PraisonAI_RecipeAgent.ipynb

@@ -0,0 +1,481 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "LJRJRQtFTagH"
+   },
+   "source": [
+    "# AI RAG Agent with Web Access using PraisonAI"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Ph4RVTBCTcmB"
+   },
+   "source": [
+    "This notebook demonstrates how to build a Retrieval-Augmented Generation (RAG) agent with web access using GPT-4o and PraisonAI. The agent specializes in Thai recipes and can search the web for additional information."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "tMxfwPPATy_o"
+   },
+   "source": [
+    "This RAG agent:\n",
+    "*   Creates an AI assistant specialized in Thai recipes and cooking\n",
+    "*   Has access to a PDF knowledge base about Thai recipes\n",
+    "*   Can search the web using DuckDuckGo for additional information\n",
+    "*   Provides comprehensive answers about Thai cuisine\n",
+    "*   Uses PraisonAI framework for agent management"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "h68oMaCITmqt"
+   },
+   "source": [
+    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Dhivya-Bharathy/PraisonAI/blob/main/examples/cookbooks/rag_examples/agentic_rag/PraisonAI_RecipeAgent.ipynb)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "cAdzoWXJUMfF"
+   },
+   "source": [
+    "# Dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "9D-2TlOrUNcu"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install praisonaiagents openai requests"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "yY-4-kEIUW40"
+   },
+   "source": [
+    "# Set Up API Key"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "f5bhQ05yUX6J",
+    "outputId": "dcf9d0d9-332e-4136-9289-4c39d898005c"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Enter your OpenAI API key: ··········\n",
+      "✅ API key set successfully!\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "\n",
+    "# Set your OpenAI API key here\n",
+    "os.environ['OPENAI_API_KEY'] = 'Enter your api key here'\n",
+    "\n",
+    "# Or use this method to input it securely\n",
+    "from getpass import getpass\n",
+    "api_key = getpass('Enter your OpenAI API key: ')\n",
+    "os.environ['OPENAI_API_KEY'] = api_key\n",
+    "\n",
+    "print(\"✅ API key set successfully!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Eh0j-SegUi13"
+   },
+   "source": [
+    "# Tools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Z1A9oifIUkQN",
+    "outputId": "9be78226-36fa-43c4-865d-2f7e6e2ba9de"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Tools imported successfully!\n"
+     ]
+    }
+   ],
+   "source": [
+    "from praisonaiagents import Agent\n",
+    "import requests\n",
+    "import tempfile\n",
+    "from pathlib import Path\n",
+    "\n",
+    "print(\"✅ Tools imported successfully!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "efnnREHXUnYA"
+   },
+   "source": [
+    "# YAML Prompt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "kcM14-yQUqG-",
+    "outputId": "c5b6288d-a789-4a93-e20c-1d285370b667"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Agent instructions configured!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Agent configuration and instructions\n",
+    "AGENT_INSTRUCTIONS = \"\"\"\n",
+    "You are a helpful AI assistant specialized in Thai recipes and cooking.\n",
+    "\n",
+    "You have access to a PDF knowledge base about Thai recipes from: https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf\n",
+    "\n",
+    "You can also search the web for additional information about Thai cooking, ingredients, and techniques.\n",
+    "\n",
+    "When answering questions:\n",
+    "1. Use your knowledge about Thai cuisine to provide helpful information\n",
+    "2. If needed, search the web for additional details, current information, or clarification\n",
+    "3. Provide comprehensive, helpful answers about Thai cuisine\n",
+    "4. Always be informative and helpful about Thai cooking!\n",
+    "\n",
+    "You can use the internet_search function to search the web when needed.\n",
+    "\n",
+    "Focus on providing practical cooking advice, ingredient information, and authentic Thai recipe guidance.\n",
+    "\"\"\"\n",
+    "\n",
+    "print(\"✅ Agent instructions configured!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "MCBRcXFJUulY"
+   },
+   "source": [
+    "# Main"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "YaRLbgMmUvXv",
+    "outputId": "8ed8d9f8-85a7-48de-ccc6-f617cd672a85"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🤖 Thai Recipe RAG Agent created successfully!\n",
+      "The agent is ready to answer questions about Thai cooking.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create the RAG agent\n",
+    "rag_agent = Agent(\n",
+    "    instructions=AGENT_INSTRUCTIONS,\n",
+    "    llm=\"gpt-4o\",\n",
+    "    markdown=True,\n",
+    "    verbose=True\n",
+    ")\n",
+    "\n",
+    "print(\"🤖 Thai Recipe RAG Agent created successfully!\")\n",
+    "print(\"The agent is ready to answer questions about Thai cooking.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QvZM94pGU0IX"
+   },
+   "source": [
+    "# Test the Agent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000,
+     "referenced_widgets": [
+      "47739975c9f04ee5b4e69e8f445cbeda",
+      "08888d583cba4c01b3fdf8f30b06ca4f"
+     ]
+    },
+    "id": "QPdrleKxU1VN",
+    "outputId": "a71a86cd-eff8-4309-9b50-173a30066c90"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🤔 Question: What are the essential ingredients for Thai cooking?\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">╭─ </span><span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8; font-weight: bold\">Agent Info</span><span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\"> ────────────────────────────────────────────────────────────────────────────────────────────────────╮</span>\n",
+       "<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>                                                                                                                 <span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>\n",
+       "<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>  <span style=\"color: #ff9b9b; text-decoration-color: #ff9b9b; font-weight: bold\">👤 Agent:</span> <span style=\"color: #ffe5e5; text-decoration-color: #ffe5e5\">Agent</span>                                                                                                <span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>\n",
+       "<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>  <span style=\"color: #b4b4b3; text-decoration-color: #b4b4b3; font-weight: bold\">Role:</span> <span style=\"color: #ffe5e5; text-decoration-color: #ffe5e5\">Assistant</span>                                                                                                <span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>\n",
+       "<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>                                                                                                                 <span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>\n",
+       "<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;2;210;227;200m╭─\u001b[0m\u001b[38;2;210;227;200m \u001b[0m\u001b[1;38;2;210;227;200mAgent Info\u001b[0m\u001b[38;2;210;227;200m \u001b[0m\u001b[38;2;210;227;200m───────────────────────────────────────────────────────────────────────────────────────────────────\u001b[0m\u001b[38;2;210;227;200m─╮\u001b[0m\n",
+       "\u001b[38;2;210;227;200m│\u001b[0m                                                                                                                 \u001b[38;2;210;227;200m│\u001b[0m\n",
+       "\u001b[38;2;210;227;200m│\u001b[0m  \u001b[1;38;2;255;155;155m👤 Agent:\u001b[0m \u001b[38;2;255;229;229mAgent\u001b[0m                                                                                                \u001b[38;2;210;227;200m│\u001b[0m\n",
+       "\u001b[38;2;210;227;200m│\u001b[0m  \u001b[1;38;2;180;180;179mRole:\u001b[0m \u001b[38;2;255;229;229mAssistant\u001b[0m                                                                                                \u001b[38;2;210;227;200m│\u001b[0m\n",
+       "\u001b[38;2;210;227;200m│\u001b[0m                                                                                                                 \u001b[38;2;210;227;200m│\u001b[0m\n",
+       "\u001b[38;2;210;227;200m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+      ]
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+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">Response generated in 8.4s</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2mResponse generated in 8.4s\u001b[0m\n"
+      ]
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+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008080; text-decoration-color: #008080\">╭───────────────────────────────────────────────────── Task ──────────────────────────────────────────────────────╮</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> What are the essential ingredients for Thai cooking?                                                            <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[36m╭─\u001b[0m\u001b[36m────────────────────────────────────────────────────\u001b[0m\u001b[36m Task \u001b[0m\u001b[36m─────────────────────────────────────────────────────\u001b[0m\u001b[36m─╮\u001b[0m\n",
+       "\u001b[36m│\u001b[0m What are the essential ingredients for Thai cooking?                                                            \u001b[36m│\u001b[0m\n",
+       "\u001b[36m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008080; text-decoration-color: #008080\">╭─────────────────────────────────────────────────── Response ────────────────────────────────────────────────────╮</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> Thai cooking is known for its balance of five fundamental taste senses in each dish or the overall meal: sour,  <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> sweet, salty, bitter, and spicy. Essential ingredients that help achieve this balance include:                  <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">  1 </span><span style=\"font-weight: bold\">Fish Sauce (Nam Pla)</span>: A salty, savory sauce made from fermented fish. It's a cornerstone of Thai cooking,   <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">    </span>used to add depth of flavor.                                                                                <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">  2 </span><span style=\"font-weight: bold\">Lime Juice</span>: Used to add a fresh, tangy citrus flavor to dishes, especially in salads and some curries.      <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">  3 </span><span style=\"font-weight: bold\">Chilies</span>: Thai cuisine often features fresh Thai bird's eye chilies for heat, as well as dried chilies for   <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">    </span>certain recipes.                                                                                            <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">  4 </span><span style=\"font-weight: bold\">Lemongrass</span>: A citrusy, fragrant herb that is often used in soups, curries, and teas.                        <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">  5 </span><span style=\"font-weight: bold\">Galangal</span>: Similar to ginger, galangal has a sharp, peppery flavor and is used in soups and curries.         <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">  6 </span><span style=\"font-weight: bold\">Kaffir Lime Leaves</span>: They add a distinct, zesty citrus aroma and flavor to Thai dishes.                      <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">  7 </span><span style=\"font-weight: bold\">Coconut Milk</span>: Adds richness and creaminess, commonly used in curries and desserts.                          <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">  8 </span><span style=\"font-weight: bold\">Palm Sugar</span>: Used as a sweetener, palm sugar has a nuanced flavor, unlike regular sugar.                     <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">  9 </span><span style=\"font-weight: bold\">Tamarind Paste</span>: Provides a sour element to many dishes, particularly pad Thai and some sauces.              <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> 10 </span><span style=\"font-weight: bold\">Thai Basil</span>: Adds a spicy, peppery flavor, different from the sweet basil used in Western cooking.           <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> 11 </span><span style=\"font-weight: bold\">Coriander (Cilantro)</span>: Used as a garnish and for its fresh, citrusy leaves and roots, which add depth to     <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">    </span>sauces and stocks.                                                                                          <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> 12 </span><span style=\"font-weight: bold\">Shrimp Paste</span>: A very pungent paste made from fermented shrimp, used sparingly to enrich the flavor of       <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">    </span>dishes.                                                                                                     <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> These ingredients are commonly used in combination to create the complex and rich flavors characteristic of     <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> Thai cuisine. Fresh and high-quality versions of these ingredients contribute significantly to the authenticity <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> of Thai dishes.                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[36m╭─\u001b[0m\u001b[36m──────────────────────────────────────────────────\u001b[0m\u001b[36m Response \u001b[0m\u001b[36m───────────────────────────────────────────────────\u001b[0m\u001b[36m─╮\u001b[0m\n",
+       "\u001b[36m│\u001b[0m Thai cooking is known for its balance of five fundamental taste senses in each dish or the overall meal: sour,  \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m sweet, salty, bitter, and spicy. Essential ingredients that help achieve this balance include:                  \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m  1 \u001b[0m\u001b[1mFish Sauce (Nam Pla)\u001b[0m: A salty, savory sauce made from fermented fish. It's a cornerstone of Thai cooking,   \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m    \u001b[0mused to add depth of flavor.                                                                                \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m  2 \u001b[0m\u001b[1mLime Juice\u001b[0m: Used to add a fresh, tangy citrus flavor to dishes, especially in salads and some curries.      \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m  3 \u001b[0m\u001b[1mChilies\u001b[0m: Thai cuisine often features fresh Thai bird's eye chilies for heat, as well as dried chilies for   \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m    \u001b[0mcertain recipes.                                                                                            \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m  4 \u001b[0m\u001b[1mLemongrass\u001b[0m: A citrusy, fragrant herb that is often used in soups, curries, and teas.                        \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m  5 \u001b[0m\u001b[1mGalangal\u001b[0m: Similar to ginger, galangal has a sharp, peppery flavor and is used in soups and curries.         \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m  6 \u001b[0m\u001b[1mKaffir Lime Leaves\u001b[0m: They add a distinct, zesty citrus aroma and flavor to Thai dishes.                      \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m  7 \u001b[0m\u001b[1mCoconut Milk\u001b[0m: Adds richness and creaminess, commonly used in curries and desserts.                          \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m  8 \u001b[0m\u001b[1mPalm Sugar\u001b[0m: Used as a sweetener, palm sugar has a nuanced flavor, unlike regular sugar.                     \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m  9 \u001b[0m\u001b[1mTamarind Paste\u001b[0m: Provides a sour element to many dishes, particularly pad Thai and some sauces.              \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m 10 \u001b[0m\u001b[1mThai Basil\u001b[0m: Adds a spicy, peppery flavor, different from the sweet basil used in Western cooking.           \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m 11 \u001b[0m\u001b[1mCoriander (Cilantro)\u001b[0m: Used as a garnish and for its fresh, citrusy leaves and roots, which add depth to     \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m    \u001b[0msauces and stocks.                                                                                          \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m 12 \u001b[0m\u001b[1mShrimp Paste\u001b[0m: A very pungent paste made from fermented shrimp, used sparingly to enrich the flavor of       \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m    \u001b[0mdishes.                                                                                                     \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m These ingredients are commonly used in combination to create the complex and rich flavors characteristic of     \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m Thai cuisine. Fresh and high-quality versions of these ingredients contribute significantly to the authenticity \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m of Thai dishes.                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "�� Answer: Thai cooking is known for its balance of five fundamental taste senses in each dish or the overall meal: sour, sweet, salty, bitter, and spicy. Essential ingredients that help achieve this balance include:\n",
+      "\n",
+      "1. **Fish Sauce (Nam Pla)**: A salty, savory sauce made from fermented fish. It's a cornerstone of Thai cooking, used to add depth of flavor.\n",
+      "\n",
+      "2. **Lime Juice**: Used to add a fresh, tangy citrus flavor to dishes, especially in salads and some curries.\n",
+      "\n",
+      "3. **Chilies**: Thai cuisine often features fresh Thai bird's eye chilies for heat, as well as dried chilies for certain recipes.\n",
+      "\n",
+      "4. **Lemongrass**: A citrusy, fragrant herb that is often used in soups, curries, and teas.\n",
+      "\n",
+      "5. **Galangal**: Similar to ginger, galangal has a sharp, peppery flavor and is used in soups and curries.\n",
+      "\n",
+      "6. **Kaffir Lime Leaves**: They add a distinct, zesty citrus aroma and flavor to Thai dishes.\n",
+      "\n",
+      "7. **Coconut Milk**: Adds richness and creaminess, commonly used in curries and desserts.\n",
+      "\n",
+      "8. **Palm Sugar**: Used as a sweetener, palm sugar has a nuanced flavor, unlike regular sugar.\n",
+      "\n",
+      "9. **Tamarind Paste**: Provides a sour element to many dishes, particularly pad Thai and some sauces.\n",
+      "\n",
+      "10. **Thai Basil**: Adds a spicy, peppery flavor, different from the sweet basil used in Western cooking.\n",
+      "\n",
+      "11. **Coriander (Cilantro)**: Used as a garnish and for its fresh, citrusy leaves and roots, which add depth to sauces and stocks.\n",
+      "\n",
+      "12. **Shrimp Paste**: A very pungent paste made from fermented shrimp, used sparingly to enrich the flavor of dishes.\n",
+      "\n",
+      "These ingredients are commonly used in combination to create the complex and rich flavors characteristic of Thai cuisine. Fresh and high-quality versions of these ingredients contribute significantly to the authenticity of Thai dishes.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Test question 1: Basic Thai cooking\n",
+    "question1 = \"What are the essential ingredients for Thai cooking?\"\n",
+    "print(f\"🤔 Question: {question1}\")\n",
+    "\n",
+    "try:\n",
+    "    response1 = rag_agent.start(question1)\n",
+    "    print(f\"�� Answer: {response1}\")\n",
+    "except Exception as e:\n",
+    "    print(f\"❌ Error: {e}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

examples/cookbooks/rag_examples/agentic_rag/agentic_rag_gpt5_praisonai_agents.ipynb

@@ -0,0 +1,556 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "EA8l4cSVl3Lx"
+   },
+   "source": [
+    "# Agentic RAG with GPT-5 - Colab Notebook"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "I-8nOxJ2l7nO"
+   },
+   "source": [
+    "This Colab notebook sets up a Streamlit app using PraisonAI Agents framework.\n",
+    "It allows you to interact with an AI agent powered by GPT-5 for Q&A.\n",
+    "Currently, the knowledge base functionality is disabled due to compatibility issues."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "g5I1tMkxmDQe"
+   },
+   "source": [
+    "- GPT-5 Integration (via OpenAI API)\n",
+    "- Streamlit Web App\n",
+    "- Agent Framework: PraisonAI Agents\n",
+    "- Markdown-formatted responses"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "yeEno7U0q2U_"
+   },
+   "source": [
+    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Dhivya-Bharathy/PraisonAI/blob/main/examples/cookbooks/Qwen2.5_InstructionAgent.ipynb)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "WV2GnzZCmGBR"
+   },
+   "source": [
+    "# Dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "20wAadXemJ0k"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install praisonaiagents python-dotenv streamlit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "l71uCRarndvy"
+   },
+   "source": [
+    "# Set API Key"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "id": "brfc-ZZKnfre"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from dotenv import load_dotenv\n",
+    "\n",
+    "load_dotenv()\n",
+    "\n",
+    "OPENAI_API_KEY = \"Enter your api key here\"\n",
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "d1fGzyQCn1aQ"
+   },
+   "source": [
+    "# Tools and Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "id": "GcNPLkZXntIS"
+   },
+   "outputs": [],
+   "source": [
+    "import streamlit as st\n",
+    "from praisonaiagents import Agent\n",
+    "\n",
+    "# YAML Prompt Configuration\n",
+    "agent_config = {\n",
+    "    \"name\": \"Knowledge Agent\",\n",
+    "    \"instructions\": [\n",
+    "        \"You are a helpful AI assistant. Answer questions based on your general knowledge.\",\n",
+    "        \"Provide clear, well-structured answers in markdown format.\",\n",
+    "        \"Use proper markdown formatting with headers, lists, and emphasis where appropriate.\",\n",
+    "        \"Structure your response with clear sections and bullet points when helpful.\",\n",
+    "    ],\n",
+    "    \"llm\": \"gpt-5-nano\",\n",
+    "    \"markdown\": True,\n",
+    "    \"verbose\": True\n",
+    "}\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "jkmrh0gIn_4a"
+   },
+   "source": [
+    "# Initialize Agent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "id": "qaJvf8RyoAsB"
+   },
+   "outputs": [],
+   "source": [
+    "def create_agent():\n",
+    "    \"\"\"Create an agent with reasoning capabilities\"\"\"\n",
+    "    return Agent(\n",
+    "        name=agent_config[\"name\"],\n",
+    "        instructions=agent_config[\"instructions\"],\n",
+    "        llm=agent_config[\"llm\"],\n",
+    "        markdown=agent_config[\"markdown\"],\n",
+    "        verbose=agent_config[\"verbose\"]\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "kvGJh3bbqTmh"
+   },
+   "source": [
+    "# Main Function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000,
+     "referenced_widgets": [
+      "ad296d97510246de8eb0138ce8a8a166",
+      "f6e502b3dca442f0ae6c5739f5a6d159"
+     ]
+    },
+    "id": "_QyxzqKKqUrW",
+    "outputId": "97be7e02-b034-4004-e9e3-56337e3f2887"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "�� Initializing Agentic RAG with GPT-5...\n",
+      "📚 Loading agent...\n",
+      "✅ Agent loaded successfully!\n",
+      "\n",
+      "🎯 Example queries you can try:\n",
+      "1. What is PraisonAI and how do Agents work?\n",
+      "2. What are Teams in PraisonAI and how do they work?\n",
+      "3. Give me a step-by-step guide to building a RAG system.\n",
+      "4. What are AI Agents?\n",
+      "\n",
+      "============================================================\n",
+      "\n",
+      "🤔 Enter your question (or 'quit' to exit):\n",
+      "> What is PraisonAI and how do Agents work?\n",
+      "🔍 Processing your question...\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">╭─ </span><span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8; font-weight: bold\">Agent Info</span><span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\"> ────────────────────────────────────────────────────────────────────────────────────────────────────╮</span>\n",
+       "<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>                                                                                                                 <span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>\n",
+       "<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>  <span style=\"color: #ff9b9b; text-decoration-color: #ff9b9b; font-weight: bold\">👤 Agent:</span> <span style=\"color: #ffe5e5; text-decoration-color: #ffe5e5\">Knowledge Agent</span>                                                                                      <span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>\n",
+       "<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>  <span style=\"color: #b4b4b3; text-decoration-color: #b4b4b3; font-weight: bold\">Role:</span> <span style=\"color: #ffe5e5; text-decoration-color: #ffe5e5\">Assistant</span>                                                                                                <span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>\n",
+       "<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>                                                                                                                 <span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">│</span>\n",
+       "<span style=\"color: #d2e3c8; text-decoration-color: #d2e3c8\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[38;2;210;227;200m╭─\u001b[0m\u001b[38;2;210;227;200m \u001b[0m\u001b[1;38;2;210;227;200mAgent Info\u001b[0m\u001b[38;2;210;227;200m \u001b[0m\u001b[38;2;210;227;200m───────────────────────────────────────────────────────────────────────────────────────────────────\u001b[0m\u001b[38;2;210;227;200m─╮\u001b[0m\n",
+       "\u001b[38;2;210;227;200m│\u001b[0m                                                                                                                 \u001b[38;2;210;227;200m│\u001b[0m\n",
+       "\u001b[38;2;210;227;200m│\u001b[0m  \u001b[1;38;2;255;155;155m👤 Agent:\u001b[0m \u001b[38;2;255;229;229mKnowledge Agent\u001b[0m                                                                                      \u001b[38;2;210;227;200m│\u001b[0m\n",
+       "\u001b[38;2;210;227;200m│\u001b[0m  \u001b[1;38;2;180;180;179mRole:\u001b[0m \u001b[38;2;255;229;229mAssistant\u001b[0m                                                                                                \u001b[38;2;210;227;200m│\u001b[0m\n",
+       "\u001b[38;2;210;227;200m│\u001b[0m                                                                                                                 \u001b[38;2;210;227;200m│\u001b[0m\n",
+       "\u001b[38;2;210;227;200m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ad296d97510246de8eb0138ce8a8a166",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">Response generated in 9.4s</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2mResponse generated in 9.4s\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008080; text-decoration-color: #008080\">╭───────────────────────────────────────────────────── Task ──────────────────────────────────────────────────────╮</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> What is PraisonAI and how do Agents work?                                                                       <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[36m╭─\u001b[0m\u001b[36m────────────────────────────────────────────────────\u001b[0m\u001b[36m Task \u001b[0m\u001b[36m─────────────────────────────────────────────────────\u001b[0m\u001b[36m─╮\u001b[0m\n",
+       "\u001b[36m│\u001b[0m What is PraisonAI and how do Agents work?                                                                       \u001b[36m│\u001b[0m\n",
+       "\u001b[36m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008080; text-decoration-color: #008080\">╭─────────────────────────────────────────────────── Response ────────────────────────────────────────────────────╮</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> ┃                                             <span style=\"font-weight: bold\">What is PraisonAI?</span>                                              ┃ <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛ <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> PraisonAI is a platform designed to leverage artificial intelligence to enhance personal and business           <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> productivity. It typically involves developing AI models that can assist in various tasks, automate processes,  <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> and provide insights based on data analysis.                                                                    <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                            <span style=\"font-weight: bold; text-decoration: underline\">Key Features of PraisonAI</span>                                            <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> • </span><span style=\"font-weight: bold\">Task Automation</span>: Automates repetitive tasks, allowing users to focus on more strategic activities.           <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> • </span><span style=\"font-weight: bold\">Data Insights</span>: Analyzes large sets of data to provide actionable insights.                                   <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> • </span><span style=\"font-weight: bold\">Natural Language Processing</span>: Enables users to interact with the AI through natural language, making it       <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">   </span>user-friendly.                                                                                               <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> • </span><span style=\"font-weight: bold\">Customizable Solutions</span>: Tailors AI applications to meet specific user needs across various industries.       <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> ┃                                      <span style=\"font-weight: bold\">How Do Agents Work in PraisonAI?</span>                                       ┃ <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛ <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> Agents in PraisonAI refer to autonomous or semi-autonomous AI systems that perform designated tasks or          <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> functions. Here’s how they typically operate:                                                                   <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                               <span style=\"font-weight: bold; text-decoration: underline\">1. Types of Agents</span>                                                <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> • </span><span style=\"font-weight: bold\">Reactive Agents</span>: Respond to the current environment without internal memory. They act solely based on        <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">   </span>current inputs.                                                                                              <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> • </span><span style=\"font-weight: bold\">Deliberative Agents</span>: Have a model of their environment, plan their actions, and retain memories of past      <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">   </span>interactions.                                                                                                <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> • </span><span style=\"font-weight: bold\">Learning Agents</span>: Improve their performance over time through experience, adapting to new information.        <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                           <span style=\"font-weight: bold; text-decoration: underline\">2. Functionality of Agents</span>                                            <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> • </span><span style=\"font-weight: bold\">Task Execution</span>: Agents can carry out specific tasks as programmed, such as scheduling, data entry, or        <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">   </span>customer support.                                                                                            <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> • </span><span style=\"font-weight: bold\">Interaction</span>: They often interact with users through chat interfaces or APIs, providing a seamless user       <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">   </span>experience.                                                                                                  <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> • </span><span style=\"font-weight: bold\">Integration</span>: Can be integrated with other systems or software to enhance functionality, such as CRM systems, <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">   </span>databases, or other AI tools.                                                                                <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> • </span><span style=\"font-weight: bold\">Continuous Learning</span>: Agents may utilize machine learning algorithms to adapt and evolve based on user        <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">   </span>interactions and data received.                                                                              <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                           <span style=\"font-weight: bold; text-decoration: underline\">3. Benefits of Using Agents</span>                                           <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> • </span><span style=\"font-weight: bold\">Increased Efficiency</span>: Automate routine tasks to reduce workload.                                             <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> • </span><span style=\"font-weight: bold\">24/7 Availability</span>: Provide consistent support and functionality without downtime.                            <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> • </span><span style=\"font-weight: bold\">Scalability</span>: Easily scale operations by deploying more agents as needed.                                     <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> <span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\"> • </span><span style=\"font-weight: bold\">Data-Driven Decisions</span>: Use data insights to inform strategy and operations.                                  <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                   <span style=\"font-weight: bold; text-decoration: underline\">Conclusion</span>                                                    <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span>                                                                                                                 <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> PraisonAI represents a robust platform for enhancing productivity through AI, with agents serving as the heart  <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> of this solution, performing various tasks and supporting users in achieving their goals efficiently. Whether   <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> they are simple reactive agents or complex learning entities, their design focuses on making operations         <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">│</span> smoother and more effective.                                                                                    <span style=\"color: #008080; text-decoration-color: #008080\">│</span>\n",
+       "<span style=\"color: #008080; text-decoration-color: #008080\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[36m╭─\u001b[0m\u001b[36m──────────────────────────────────────────────────\u001b[0m\u001b[36m Response \u001b[0m\u001b[36m───────────────────────────────────────────────────\u001b[0m\u001b[36m─╮\u001b[0m\n",
+       "\u001b[36m│\u001b[0m ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m ┃                                             \u001b[1mWhat is PraisonAI?\u001b[0m                                              ┃ \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛ \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m PraisonAI is a platform designed to leverage artificial intelligence to enhance personal and business           \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m productivity. It typically involves developing AI models that can assist in various tasks, automate processes,  \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m and provide insights based on data analysis.                                                                    \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                            \u001b[1;4mKey Features of PraisonAI\u001b[0m                                            \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mTask Automation\u001b[0m: Automates repetitive tasks, allowing users to focus on more strategic activities.           \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mData Insights\u001b[0m: Analyzes large sets of data to provide actionable insights.                                   \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mNatural Language Processing\u001b[0m: Enables users to interact with the AI through natural language, making it       \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0muser-friendly.                                                                                               \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mCustomizable Solutions\u001b[0m: Tailors AI applications to meet specific user needs across various industries.       \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m ┃                                      \u001b[1mHow Do Agents Work in PraisonAI?\u001b[0m                                       ┃ \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m ┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛ \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m Agents in PraisonAI refer to autonomous or semi-autonomous AI systems that perform designated tasks or          \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m functions. Here’s how they typically operate:                                                                   \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                               \u001b[1;4m1. \u001b[0m\u001b[1;4mTypes of Agents\u001b[0m                                                \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mReactive Agents\u001b[0m: Respond to the current environment without internal memory. They act solely based on        \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0mcurrent inputs.                                                                                              \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mDeliberative Agents\u001b[0m: Have a model of their environment, plan their actions, and retain memories of past      \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0minteractions.                                                                                                \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mLearning Agents\u001b[0m: Improve their performance over time through experience, adapting to new information.        \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                           \u001b[1;4m2. \u001b[0m\u001b[1;4mFunctionality of Agents\u001b[0m                                            \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mTask Execution\u001b[0m: Agents can carry out specific tasks as programmed, such as scheduling, data entry, or        \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0mcustomer support.                                                                                            \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mInteraction\u001b[0m: They often interact with users through chat interfaces or APIs, providing a seamless user       \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0mexperience.                                                                                                  \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mIntegration\u001b[0m: Can be integrated with other systems or software to enhance functionality, such as CRM systems, \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0mdatabases, or other AI tools.                                                                                \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mContinuous Learning\u001b[0m: Agents may utilize machine learning algorithms to adapt and evolve based on user        \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m   \u001b[0minteractions and data received.                                                                              \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                           \u001b[1;4m3. \u001b[0m\u001b[1;4mBenefits of Using Agents\u001b[0m                                           \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mIncreased Efficiency\u001b[0m: Automate routine tasks to reduce workload.                                             \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1m24/7 Availability\u001b[0m: Provide consistent support and functionality without downtime.                            \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mScalability\u001b[0m: Easily scale operations by deploying more agents as needed.                                     \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m \u001b[1;33m • \u001b[0m\u001b[1mData-Driven Decisions\u001b[0m: Use data insights to inform strategy and operations.                                  \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                   \u001b[1;4mConclusion\u001b[0m                                                    \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m                                                                                                                 \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m PraisonAI represents a robust platform for enhancing productivity through AI, with agents serving as the heart  \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m of this solution, performing various tasks and supporting users in achieving their goals efficiently. Whether   \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m they are simple reactive agents or complex learning entities, their design focuses on making operations         \u001b[36m│\u001b[0m\n",
+       "\u001b[36m│\u001b[0m smoother and more effective.                                                                                    \u001b[36m│\u001b[0m\n",
+       "\u001b[36m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "💡 Answer:\n",
+      "========================================\n",
+      "# What is PraisonAI?\n",
+      "\n",
+      "PraisonAI is a platform designed to leverage artificial intelligence to enhance personal and business productivity. It typically involves developing AI models that can assist in various tasks, automate processes, and provide insights based on data analysis.\n",
+      "\n",
+      "## Key Features of PraisonAI\n",
+      "\n",
+      "- **Task Automation**: Automates repetitive tasks, allowing users to focus on more strategic activities.\n",
+      "- **Data Insights**: Analyzes large sets of data to provide actionable insights.\n",
+      "- **Natural Language Processing**: Enables users to interact with the AI through natural language, making it user-friendly.\n",
+      "- **Customizable Solutions**: Tailors AI applications to meet specific user needs across various industries.\n",
+      "\n",
+      "# How Do Agents Work in PraisonAI?\n",
+      "\n",
+      "Agents in PraisonAI refer to autonomous or semi-autonomous AI systems that perform designated tasks or functions. Here’s how they typically operate:\n",
+      "\n",
+      "## 1. **Types of Agents**\n",
+      "\n",
+      "- **Reactive Agents**: Respond to the current environment without internal memory. They act solely based on current inputs.\n",
+      "- **Deliberative Agents**: Have a model of their environment, plan their actions, and retain memories of past interactions.\n",
+      "- **Learning Agents**: Improve their performance over time through experience, adapting to new information.\n",
+      "\n",
+      "## 2. **Functionality of Agents**\n",
+      "\n",
+      "- **Task Execution**: Agents can carry out specific tasks as programmed, such as scheduling, data entry, or customer support.\n",
+      "- **Interaction**: They often interact with users through chat interfaces or APIs, providing a seamless user experience.\n",
+      "- **Integration**: Can be integrated with other systems or software to enhance functionality, such as CRM systems, databases, or other AI tools.\n",
+      "- **Continuous Learning**: Agents may utilize machine learning algorithms to adapt and evolve based on user interactions and data received.\n",
+      "\n",
+      "## 3. **Benefits of Using Agents**\n",
+      "\n",
+      "- **Increased Efficiency**: Automate routine tasks to reduce workload.\n",
+      "- **24/7 Availability**: Provide consistent support and functionality without downtime.\n",
+      "- **Scalability**: Easily scale operations by deploying more agents as needed.\n",
+      "- **Data-Driven Decisions**: Use data insights to inform strategy and operations.\n",
+      "\n",
+      "## Conclusion\n",
+      "\n",
+      "PraisonAI represents a robust platform for enhancing productivity through AI, with agents serving as the heart of this solution, performing various tasks and supporting users in achieving their goals efficiently. Whether they are simple reactive agents or complex learning entities, their design focuses on making operations smoother and more effective.\n",
+      "========================================\n",
+      "\n",
+      "🤔 Enter your question (or 'quit' to exit):\n",
+      "> quit\n",
+      "👋 Goodbye!\n"
+     ]
+    }
+   ],
+   "source": [
+    "def main():\n",
+    "    print(\"�� Initializing Agentic RAG with GPT-5...\")\n",
+    "\n",
+    "    # Check if API key is set\n",
+    "    if not OPENAI_API_KEY or OPENAI_API_KEY == \"your_openai_api_key_here\":\n",
+    "        print(\"❌ Please set your OpenAI API key first!\")\n",
+    "        print(\"�� Get your key from: https://platform.openai.com/\")\n",
+    "        return\n",
+    "\n",
+    "    try:\n",
+    "        # Create agent\n",
+    "        print(\"📚 Loading agent...\")\n",
+    "        agent = create_agent()\n",
+    "        print(\"✅ Agent loaded successfully!\")\n",
+    "\n",
+    "        # Example queries\n",
+    "        example_queries = [\n",
+    "            \"What is PraisonAI and how do Agents work?\",\n",
+    "            \"What are Teams in PraisonAI and how do they work?\",\n",
+    "            \"Give me a step-by-step guide to building a RAG system.\",\n",
+    "            \"What are AI Agents?\"\n",
+    "        ]\n",
+    "\n",
+    "        print(\"\\n🎯 Example queries you can try:\")\n",
+    "        for i, query in enumerate(example_queries, 1):\n",
+    "            print(f\"{i}. {query}\")\n",
+    "\n",
+    "        print(\"\\n\" + \"=\"*60)\n",
+    "\n",
+    "        # Interactive query loop\n",
+    "        while True:\n",
+    "            print(\"\\n🤔 Enter your question (or 'quit' to exit):\")\n",
+    "            user_query = input(\"> \")\n",
+    "\n",
+    "            if user_query.lower() in ['quit', 'exit', 'q']:\n",
+    "                print(\"👋 Goodbye!\")\n",
+    "                break\n",
+    "\n",
+    "            if user_query.strip():\n",
+    "                print(\"🔍 Processing your question...\")\n",
+    "                try:\n",
+    "                    response = agent.start(user_query)\n",
+    "                    print(\"\\n💡 Answer:\")\n",
+    "                    print(\"=\"*40)\n",
+    "                    print(response)\n",
+    "                    print(\"=\"*40)\n",
+    "                except Exception as e:\n",
+    "                    print(f\"❌ Error: {str(e)}\")\n",
+    "                    print(f\"🔍 Error type: {type(e).__name__}\")\n",
+    "                    print(\"💡 This might be due to API issues or model access problems.\")\n",
+    "            else:\n",
+    "                print(\"❌ Please enter a valid question.\")\n",
+    "\n",
+    "    except Exception as e:\n",
+    "        print(f\"❌ Failed to initialize agent: {str(e)}\")\n",
+    "        print(\"�� Make sure your API key is correct and you have sufficient credits.\")\n",
+    "\n",
+    "# Run the main function\n",
+    "if __name__ == \"__main__\":\n",
+    "    main()"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

examples/cookbooks/yaml/article_generation_agents.ipynb

@@ -585,7 +585,7 @@
         "\n",
         "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
         "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
         "\n",
         "# Run PraisonAI\n",
         "result = praisonai.run()\n",

examples/cookbooks/yaml/auto_email_responder_agents.ipynb

@@ -1090,7 +1090,7 @@
         "\n",
         "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
         "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
         "\n",
         "# Run PraisonAI\n",
         "result = praisonai.run()\n",

examples/cookbooks/yaml/book_writing_agents.ipynb

@@ -2077,7 +2077,7 @@
         "\n",
         "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
         "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
         "\n",
         "# Run PraisonAI\n",
         "result = praisonai.run()\n",

examples/cookbooks/yaml/business_product_launch_agents.ipynb

@@ -151,7 +151,7 @@
         "\n",
         "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
         "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
         "\n",
         "# Run PraisonAI\n",
         "result = praisonai.run()\n",

examples/cookbooks/yaml/cli_operation_agents.ipynb

@@ -533,7 +533,7 @@
         "\n",
         "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
         "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
         "\n",
         "# Run PraisonAI\n",
         "result = praisonai.run()\n",

examples/cookbooks/yaml/comprehensive_research_report_agents.ipynb

@@ -235,7 +235,7 @@
         "\n",
         "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
         "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
         "\n",
         "# Run PraisonAI\n",
         "result = praisonai.run()\n",

examples/cookbooks/yaml/email_workflow_management_agents.ipynb

@@ -1250,7 +1250,7 @@
         "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
         "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
         "os.environ[\"TAVILY_API_KEY\"] = userdata.get('TAVILY_API_KEY') or \"ENTER TAVILY_API_KEY HERE\"\n",
-        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
         "\n",
         "# Run PraisonAI\n",
         "result = praisonai.run()\n",

examples/cookbooks/yaml/game_development_agents.ipynb

@@ -1559,7 +1559,7 @@
         "\n",
         "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
         "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
         "\n",
         "# Run PraisonAI\n",
         "result = praisonai.run()\n",

examples/cookbooks/yaml/instagram_post_agents.ipynb

@@ -2159,7 +2159,7 @@
     "\n",
     "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
     "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
     "\n",
     "# Run PraisonAI\n",
     "result = praisonai.run()\n",

examples/cookbooks/yaml/job_hunt_agents.ipynb

@@ -698,7 +698,7 @@
     "\n",
     "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
     "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
     "\n",
     "# Run PraisonAI\n",
     "result = praisonai.run()\n",

examples/cookbooks/yaml/job_profile_matching_agents.ipynb

@@ -592,7 +592,7 @@
         "\n",
         "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
         "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
         "\n",
         "# Run PraisonAI\n",
         "result = praisonai.run()\n",

examples/cookbooks/yaml/language_translator_agents.ipynb

@@ -572,7 +572,7 @@
     "\n",
     "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
     "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
     "\n",
     "# Run PraisonAI\n",
     "result = praisonai.run()\n",

examples/cookbooks/yaml/markdown_validator_agents.ipynb

@@ -580,7 +580,7 @@
     "\n",
     "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
     "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
     "\n",
     "# Run PraisonAI\n",
     "result = praisonai.run()\n",

examples/cookbooks/yaml/marketing_campaign_development_agents.ipynb

@@ -252,7 +252,7 @@
      "text": [
       "\u001b[95m \n",
       "\n",
-      "[{'title': 'CrewAI uses third-party models to automate business tasks', 'href': 'https://techcrunch.com/2024/10/22/crewai-uses-third-party-models-to-automate-business-tasks/', 'body': 'CrewAI, a startup developing a hub where companies can build automations using third-party AI models, has raised $18 million from investors including Andrew Ng. ... 4:00 AM PDT · October 22, 2024 ...'}, {'title': 'CrewAI Launches Multi-Agentic Platform to Deliver on the Promise of ...', 'href': 'https://markets.businessinsider.com/news/stocks/crewai-launches-multi-agentic-platform-to-deliver-on-the-promise-of-generative-ai-for-enterprise-1033870246?op=1', 'body': 'SAN FRANCISCO and SÃO PAULO, Oct. 22, 2024 (GLOBE NEWSWIRE) -- CrewAI, a leading multi-agent platform, today announced $18 million in total funding that included an inception round led by ...'}, {'title': 'Agentic AI startup CrewAI closes $18M funding round', 'href': 'https://siliconangle.com/2024/10/22/agentic-ai-startup-crewai-closes-18m-funding-round/', 'body': \"An e-commerce company, for example, could create an agent that uses GPT-4o to generate shopping advice based on customers' past purchases. CrewAI has created an open-source framework of the same ...\"}, {'title': 'CrewAI Company Profile 2024: Valuation, Funding & Investors - PitchBook', 'href': 'https://pitchbook.com/profiles/company/590845-78', 'body': \"Explore institutional-grade private market research from our team of analysts. ... Artificial Intelligence & Machine Learning; Artificial Intelligence & Machine Learning Report. September 19, 2024. CrewAI Executive Team (2) Update this profile Name ... CrewAI's primary industry is Business/Productivity Software. Is CrewAI a private or public ...\"}, {'title': 'CrewAI - Autonomous AI Agents in action for a market ... - LinkedIn', 'href': 'https://www.linkedin.com/pulse/crewai-autonomous-ai-agents-action-market-research-task-kithion-ovnrc', 'body': 'The will provide a detailed market trends report, the will generate a comprehensive analysis of top competitors, and the will develop a thorough marketing plan. Conclusion. CrewAI, combined with ...'}]\n",
+      "[{'title': 'CrewAI uses third-party models to automate business tasks', 'href': 'https://techcrunch.com/2024/10/22/crewai-uses-third-party-models-to-automate-business-tasks/', 'body': 'CrewAI, a startup developing a hub where companies can build automations using third-party AI models, has raised $18 million from investors including Andrew Ng. ... 4:00 AM PDT · October 22, 2024 ...'}, {'title': 'CrewAI Launches Multi-Agentic Platform to Deliver on the Promise of ...', 'href': 'https://markets.businessinsider.com/news/stocks/crewai-launches-multi-agentic-platform-to-deliver-on-the-promise-of-generative-ai-for-enterprise-1033870246?op=1', 'body': 'SAN FRANCISCO and SÃO PAULO, Oct. 22, 2024 (GLOBE NEWSWIRE) -- CrewAI, a leading multi-agent platform, today announced $18 million in total funding that included an inception round led by ...'}, {'title': 'Agentic AI startup CrewAI closes $18M funding round', 'href': 'https://siliconangle.com/2024/10/22/agentic-ai-startup-crewai-closes-18m-funding-round/', 'body': \"An e-commerce company, for example, could create an agent that uses gpt-5-nano to generate shopping advice based on customers' past purchases. CrewAI has created an open-source framework of the same ...\"}, {'title': 'CrewAI Company Profile 2024: Valuation, Funding & Investors - PitchBook', 'href': 'https://pitchbook.com/profiles/company/590845-78', 'body': \"Explore institutional-grade private market research from our team of analysts. ... Artificial Intelligence & Machine Learning; Artificial Intelligence & Machine Learning Report. September 19, 2024. CrewAI Executive Team (2) Update this profile Name ... CrewAI's primary industry is Business/Productivity Software. Is CrewAI a private or public ...\"}, {'title': 'CrewAI - Autonomous AI Agents in action for a market ... - LinkedIn', 'href': 'https://www.linkedin.com/pulse/crewai-autonomous-ai-agents-action-market-research-task-kithion-ovnrc', 'body': 'The will provide a detailed market trends report, the will generate a comprehensive analysis of top competitors, and the will develop a thorough marketing plan. Conclusion. CrewAI, combined with ...'}]\n",
       "\u001b[00m\n",
       "\u001b[32;1m\u001b[1;3mThought: I have found more information about CrewAI's target customers and market positioning. CrewAI is a startup that has developed a hub where companies can build automations using third-party AI models, and it has recently raised $18 million in funding. Their target customers are likely businesses looking to automate complex tasks and improve efficiency using AI. They are positioned as a platform for deploying and managing AI agents to automate a variety of tasks.\n",
       "\n",

examples/cookbooks/yaml/meeting_prep_and_strategy_agents.ipynb

@@ -1075,7 +1075,7 @@
         "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
         "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
         "os.environ[\"EXA_API_KEY\"] = userdata.get('EXA_API_KEY') or \"ENTER EXA_API_KEY HERE\"\n",
-        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
         "\n",
         "# Run PraisonAI\n",
         "result = praisonai.run()\n",

examples/cookbooks/yaml/meeting_prep_using_slack_and_trello_agents.ipynb

@@ -667,7 +667,7 @@
         "\n",
         "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
         "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
         "\n",
         "# Run PraisonAI\n",
         "result = praisonai.run()\n",

examples/cookbooks/yaml/memory_management_and_knowledge_retreival_agents.ipynb

@@ -1947,7 +1947,7 @@
         "\n",
         "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
         "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
         "\n",
         "# Run PraisonAI\n",
         "result = praisonai.run()\n",

examples/cookbooks/yaml/model_fee_retreival_agents.ipynb

@@ -73,7 +73,7 @@
     "            url=url,\n",
     "            word_count_threshold=1,\n",
     "            extraction_strategy= LLMExtractionStrategy(\n",
-    "                provider=\"openai/gpt-4o\",\n",
+    "                provider=\"openai/gpt-5-nano\",\n",
     "                api_token=os.getenv('OPENAI_API_KEY'),\n",
     "                schema=ModelFee.schema(),\n",
     "                extraction_type=\"schema\",\n",
@@ -666,7 +666,7 @@
     "\n",
     "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
     "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
     "\n",
     "# Run PraisonAI\n",
     "result = praisonai.run()\n",

examples/cookbooks/yaml/news_retreival_rag_agents.ipynb

@@ -475,7 +475,7 @@
     "\n",
     "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
     "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
     "\n",
     "# Run PraisonAI\n",
     "result = praisonai.run()\n",

examples/cookbooks/yaml/recruitment_flow_agents.ipynb

@@ -2130,7 +2130,7 @@
     "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
     "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
     "os.environ[\"SERPER_API_KEY\"] = userdata.get('SERPER_API_KEY') or \"ENTER SERPER_API_KEY HERE\"\n",
-    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
     "\n",
     "# Run PraisonAI\n",
     "result = praisonai.run()\n",

examples/cookbooks/yaml/reddit_search_analysis_agents.ipynb

@@ -635,7 +635,7 @@
     "\n",
     "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
     "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
     "\n",
     "# Run PraisonAI\n",
     "result = praisonai.run()\n",

examples/cookbooks/yaml/research_report_generation_agents.ipynb

@@ -564,7 +564,7 @@
     "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
     "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
     "os.environ[\"TAVILY_API_KEY\"] = userdata.get('TAVILY_API_KEY') or \"ENTER TAVILY_API_KEY HERE\"\n",
-    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
     "\n",
     "# Run PraisonAI\n",
     "result = praisonai.run()\n",

examples/cookbooks/yaml/research_through_azure_instance_agents.ipynb

@@ -564,7 +564,7 @@
         "\n",
         "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
         "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
         "\n",
         "# Run PraisonAI\n",
         "result = praisonai.run()\n",

examples/cookbooks/yaml/screenplay_writer_agents.ipynb

@@ -614,7 +614,7 @@
         "\n",
         "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
         "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
         "\n",
         "# Run PraisonAI\n",
         "result = praisonai.run()\n",

examples/cookbooks/yaml/secondary_market_research_agents.ipynb

@@ -66,7 +66,7 @@
     "\n",
     "# Set up OpenAI API key (add to Google Colab secrets)\n",
     "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\""
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\""
    ]
   },
   {

examples/cookbooks/yaml/secondary_market_research_agents.yaml

@@ -22,7 +22,7 @@ roles:
     verbose: true
     allow_delegation: false
     llm:
-      model: "gpt-4o-mini"
+      model: "gpt-5-nano"
       temperature: 0.3
     tools:
       - "InternetSearchTool"
@@ -76,7 +76,7 @@ roles:
     verbose: true
     allow_delegation: false
     llm:
-      model: "gpt-4o-mini" 
+      model: "gpt-5-nano" 
       temperature: 0.3
     tools:
       - "InternetSearchTool"
@@ -130,7 +130,7 @@ roles:
     verbose: true
     allow_delegation: false
     llm:
-      model: "gpt-4o-mini"
+      model: "gpt-5-nano"
       temperature: 0.3
     tools:
       - "InternetSearchTool"
@@ -185,7 +185,7 @@ roles:
     verbose: true
     allow_delegation: false
     llm:
-      model: "gpt-4o-mini"
+      model: "gpt-5-nano"
       temperature: 0.4
     tools:
       - "InternetSearchTool"
@@ -239,7 +239,7 @@ roles:
     verbose: true
     allow_delegation: false
     llm:
-      model: "gpt-4o-mini"
+      model: "gpt-5-nano"
       temperature: 0.3
     tools:
       - "InternetSearchTool"
@@ -299,7 +299,7 @@ roles:
     verbose: true
     allow_delegation: false
     llm:
-      model: "gpt-4o-mini"
+      model: "gpt-5-nano"
       temperature: 0.3
     tasks:
       report_synthesis:

examples/cookbooks/yaml/shakespearean_tweets_generation_agents.ipynb

@@ -462,7 +462,7 @@
         "\n",
         "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
         "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
         "\n",
         "# Run PraisonAI\n",
         "result = praisonai.run()\n",

examples/cookbooks/yaml/stock_analysis_agents.ipynb

@@ -380,7 +380,7 @@
         "\n",
         "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
         "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+        "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
         "\n",
         "# Run PraisonAI\n",
         "result = praisonai.run()\n",

examples/cookbooks/yaml/story_book_author_agents.ipynb

@@ -540,7 +540,7 @@
     "\n",
     "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
     "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
     "\n",
     "# Run PraisonAI\n",
     "result = praisonai.run()\n",

examples/cookbooks/yaml/surprise_trip_planner_and_itinerary_agents.ipynb

@@ -2639,7 +2639,7 @@
     "\n",
     "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
     "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
     "\n",
     "# Run PraisonAI\n",
     "result = praisonai.run()\n",

examples/cookbooks/yaml/trip_planner_agents.ipynb

@@ -1835,7 +1835,7 @@
     "\n",
     "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
     "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
     "\n",
     "# Run PraisonAI\n",
     "result = praisonai.run()\n",

examples/cookbooks/yaml/wikipedia_search_analysis_agents.ipynb

@@ -623,7 +623,7 @@
     "\n",
     "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
     "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
     "\n",
     "# Run PraisonAI\n",
     "result = praisonai.run()\n",

examples/cookbooks/yaml/you_search_analysis_agents.ipynb

@@ -635,7 +635,7 @@
     "\n",
     "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
     "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
     "\n",
     "# Run PraisonAI\n",
     "result = praisonai.run()\n",

examples/cookbooks/yaml/youtube_search_analysis_agents.ipynb

@@ -582,7 +582,7 @@
     "\n",
     "# Add OPENAI_API_KEY Secrets to Google Colab on the Left Hand Side 🔑 or Enter Manually Below\n",
     "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY') or \"ENTER OPENAI_API_KEY HERE\"\n",
-    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-4o-mini\"\n",
+    "os.environ[\"OPENAI_MODEL_NAME\"] = \"gpt-5-nano\"\n",
     "\n",
     "# Run PraisonAI\n",
     "result = praisonai.run()\n",

examples/python/agents/MULTI_PROVIDER_README.md

@@ -10,7 +10,7 @@ PraisonAI now supports intelligent multi-provider and multi-model capabilities,
 - Supports fallback mechanisms for reliability
 
 ### 2. **Cost Optimization**
-- Routes simple tasks to cheaper models (e.g., GPT-4o-mini, Gemini Flash)
+- Routes simple tasks to cheaper models (e.g., gpt-5-nano, Gemini Flash)
 - Reserves expensive models for complex tasks
 - Tracks usage and provides cost estimates
 
@@ -46,7 +46,7 @@ agent = RouterAgent(
     name="Smart Assistant",
     role="Adaptive AI Assistant",
     goal="Complete tasks using the most appropriate model",
-    models=["gpt-4o-mini", "gpt-4o", "claude-3-5-sonnet-20241022"],
+    models=["gpt-5-nano", "gpt-5-nano", "claude-3-5-sonnet-20241022"],
     routing_strategy="auto"  # Automatic model selection
 )
 
@@ -90,7 +90,7 @@ analyzer = RouterAgent(
     goal="Analyze data efficiently",
     models={
         "gemini/gemini-1.5-flash": {},
-        "gpt-4o-mini": {},
+        "gpt-5-nano": {},
         "claude-3-haiku-20240307": {}
     },
     model_router=router,
@@ -130,10 +130,10 @@ The system includes pre-configured profiles for popular models:
 
 | Model | Provider | Best For | Cost/1k tokens |
 |-------|----------|----------|----------------|
-| gpt-4o-mini | OpenAI | Simple tasks, speed | $0.00075 |
+| gpt-5-nano | OpenAI | Simple tasks, speed | $0.00075 |
 | gemini-1.5-flash | Google | Cost-effective, multimodal | $0.000125 |
 | claude-3-haiku | Anthropic | Fast responses | $0.0008 |
-| gpt-4o | OpenAI | General purpose | $0.0075 |
+| gpt-5-nano | OpenAI | General purpose | $0.0075 |
 | claude-3-5-sonnet-20241022 | Anthropic | Complex reasoning | $0.009 |
 | deepseek-chat | DeepSeek | Code & math | $0.0014 |
 
@@ -160,8 +160,8 @@ print(report)
     'agent_name': 'Smart Assistant',
     'routing_strategy': 'auto',
     'model_usage': {
-        'gpt-4o-mini': {'calls': 5, 'tokens': 1500, 'cost': 0.0011},
-        'gpt-4o': {'calls': 2, 'tokens': 3000, 'cost': 0.0225}
+        'gpt-5-nano': {'calls': 5, 'tokens': 1500, 'cost': 0.0011},
+        'gpt-5-nano': {'calls': 2, 'tokens': 3000, 'cost': 0.0225}
     },
     'total_cost_estimate': 0.0236,
     'total_calls': 7
@@ -207,7 +207,7 @@ for i, agent in enumerate(auto.agents):
         name=agent.name,
         role=agent.role,
         goal=agent.goal,
-        models=["gpt-4o-mini", "gpt-4o", "claude-3-5-sonnet"],
+        models=["gpt-5-nano", "gpt-5-nano", "claude-3-5-sonnet"],
         routing_strategy="auto"
     )
 

examples/python/agents/context-agent.py

@@ -31,7 +31,7 @@ def demonstrate_basic_context_generation():
     
     # Create a ContextAgent using the factory function
     context_agent = create_context_agent(
-        llm="gpt-4o-mini",
+        llm="gpt-5-nano",
         name="Basic Context Engineer",
         verbose=True
     )
@@ -133,7 +133,7 @@ def demonstrate_context_agent_as_tool():
     print("=" * 60)
     
     # Create ContextAgent
-    context_agent = create_context_agent(llm="gpt-4o-mini")
+    context_agent = create_context_agent(llm="gpt-5-nano")
     
     # Example scenario: Product planning workflow
     project_path = str(project_root)
@@ -186,7 +186,7 @@ def show_context_engineering_benefits():
     
     # Context Engineering approach
     print("\n✅ Context Engineering Approach:")
-    context_agent = create_context_agent(llm="gpt-4o-mini")
+    context_agent = create_context_agent(llm="gpt-5-nano")
     
     # Generate comprehensive context
     analysis = context_agent.analyze_codebase_patterns(str(project_root))

examples/python/agents/image-agent.py

@@ -13,7 +13,7 @@ image_agent = Agent(
     goal="Analyze images and videos to extract meaningful information",
     backstory="""You are an expert in computer vision and image analysis.
     You excel at describing images, detecting objects, and understanding visual content.""",
-    llm="gpt-4o-mini",
+    llm="gpt-5-nano",
     self_reflect=False
 )
 

examples/python/agents/image-to-text-agent.py

@@ -13,7 +13,7 @@ image_agent = Agent(
     goal="Analyze images and videos to extract meaningful information",
     backstory="""You are an expert in computer vision and image analysis.
     You excel at describing images, detecting objects, and understanding visual content.""",
-    llm="gpt-4o-mini",
+    llm="gpt-5-nano",
     self_reflect=False
 )
 

examples/python/agents/multi-provider-agent.py

@@ -21,7 +21,7 @@ def example_auto_routing():
         role="Adaptive Research Assistant",
         goal="Research topics using the most appropriate AI model",
         backstory="I analyze task complexity and route to the best model",
-        models=["gpt-4o-mini", "gpt-4o", "claude-3-5-sonnet-20241022"],
+        models=["gpt-5-nano", "gpt-5-nano", "claude-3-5-sonnet-20241022"],
         routing_strategy="auto",  # Automatic model selection
         verbose=True
     )
@@ -81,7 +81,7 @@ def example_cost_optimized_workflow():
         goal="Analyze data efficiently while minimizing costs",
         models={
             "gemini/gemini-1.5-flash": {},
-            "gpt-4o-mini": {},
+            "gpt-5-nano": {},
             "deepseek-chat": {}
         },
         model_router=cost_router,
@@ -95,7 +95,7 @@ def example_cost_optimized_workflow():
         goal="Create high-quality content",
         models={
             "claude-3-5-sonnet-20241022": {},
-            "gpt-4o": {}
+            "gpt-5-nano": {}
         },
         routing_strategy="performance-optimized",  # Prefer better models
         verbose=True
@@ -144,7 +144,7 @@ def example_auto_agents_multi_provider():
     auto_agents = AutoAgents(
         instructions="Create a market research report on electric vehicles. Include data analysis, competitor analysis, and future projections.",
         max_agents=3,
-        llm="gpt-4o-mini",  # Default model for agent generation
+        llm="gpt-5-nano",  # Default model for agent generation
         verbose=True
     )
     
@@ -158,7 +158,7 @@ def example_auto_agents_multi_provider():
             goal=agent.goal,
             backstory=agent.backstory,
             tools=agent.tools,
-            models=["gpt-4o-mini", "gemini/gemini-1.5-flash", "claude-3-haiku-20240307", "gpt-4o"],
+            models=["gpt-5-nano", "gemini/gemini-1.5-flash", "claude-3-haiku-20240307", "gpt-5-nano"],
             routing_strategy="auto",
             verbose=True
         )
@@ -192,7 +192,7 @@ def example_custom_routing():
     # Create custom model profiles for specific use cases
     custom_models = [
         ModelProfile(
-            name="gpt-4o",
+            name="gpt-5-nano",
             provider="openai",
             complexity_range=(TaskComplexity.SIMPLE, TaskComplexity.VERY_COMPLEX),
             cost_per_1k_tokens=0.0075,

examples/python/agents/router-agent-cost-optimization.py

@@ -18,8 +18,8 @@ from praisonaiagents.tools import duckduckgo
 
 # Define model options with cost considerations
 # Using different models for different complexity levels
-cheap_model = "gpt-4o-mini"  # For simple tasks
-balanced_model = "gpt-4o"    # For moderate complexity  
+cheap_model = "gpt-5-nano"  # For simple tasks
+balanced_model = "gpt-5-nano"    # For moderate complexity  
 premium_model = "claude-3-5-sonnet-20241022"  # For complex tasks
 
 # Create RouterAgent with cost optimization strategy
@@ -76,7 +76,7 @@ print("\n" + "="*80)
 print("COST OPTIMIZATION ROUTER DEMONSTRATION COMPLETED")
 print("="*80)
 print("The RouterAgent automatically selected appropriate models based on task complexity:")
-print("- Simple factual questions → Cheaper model (gpt-4o-mini)")  
-print("- Moderate analysis tasks → Balanced model (gpt-4o)")
+print("- Simple factual questions → Cheaper model (gpt-5-nano)")  
+print("- Moderate analysis tasks → Balanced model (gpt-5-nano)")
 print("- Complex strategic analysis → Premium model (claude-3-5-sonnet)")
 print("- This approach optimizes costs while maintaining quality for each task type")

examples/python/agents/video-agent.py

@@ -7,7 +7,7 @@ video_agent = Agent(
     goal="Analyze images and videos to extract meaningful information",
     backstory="""You are an expert in computer vision and image analysis.
     You excel at describing images, detecting objects, and understanding visual content.""",
-    llm="gpt-4o-mini",
+    llm="gpt-5-nano",
     self_reflect=False
 )
 

examples/python/api/mcp-sse.py

@@ -2,7 +2,7 @@ from praisonaiagents import Agent, MCP
 
 qa_agent = Agent(
     instructions="""You are a Question Answering Agent.""",
-    llm="openai/gpt-4o-mini",
+    llm="openai/gpt-5-nano",
     tools=MCP("http://localhost:8080/agents/sse")
 )
 

examples/python/api/multi-agent-api.py

@@ -2,17 +2,17 @@ from praisonaiagents import Agent
 
 weather_agent = Agent(
     instructions="""You are a weather agent that can provide weather information for a given city.""",
-    llm="gpt-4o-mini"
+    llm="gpt-5-nano"
 )
 
 stock_agent = Agent(
     instructions="""You are a stock market agent that can provide information about stock prices and market trends.""",
-    llm="gpt-4o-mini"
+    llm="gpt-5-nano"
 )
 
 travel_agent = Agent(
     instructions="""You are a travel agent that can provide recommendations for destinations, hotels, and activities.""",
-    llm="gpt-4o-mini"
+    llm="gpt-5-nano"
 )
 
 weather_agent.launch(path="/weather", port=3030)

examples/python/api/simple-api-mcp.py

@@ -2,7 +2,7 @@ from praisonaiagents import Agent, MCP
 
 search_agent = Agent(
     instructions="""You are a weather agent that can provide weather information for a given city.""",
-    llm="openai/gpt-4o-mini",
+    llm="openai/gpt-5-nano",
     tools=MCP("http://localhost:8080/sse")
 )
 search_agent.launch(path="/weather", port=3030)

examples/python/api/simple-api.py

@@ -1,4 +1,4 @@
 from praisonaiagents import Agent
 
-agent = Agent(instructions="""You are a helpful assistant.""", llm="gpt-4o-mini")
+agent = Agent(instructions="""You are a helpful assistant.""", llm="gpt-5-nano")
 agent.launch(path="/ask", port=3030)

examples/python/camera/camera-basic.py

@@ -28,7 +28,7 @@ def capture_and_analyze():
         role="Camera Feed Analyzer",
         goal="Analyze camera captures in real-time",
         backstory="Expert in real-time visual analysis with computer vision skills",
-        llm="gpt-4o-mini"
+        llm="gpt-5-nano"
     )
     
     # Initialize camera (0 for default camera)

examples/python/camera/camera-continuous.py

@@ -42,7 +42,7 @@ class ContinuousCameraMonitor:
             role="Security Camera Analyst",
             goal="Monitor camera feeds for activities and security events",
             backstory="Expert security analyst with computer vision skills specialized in real-time monitoring",
-            llm="gpt-4o-mini"
+            llm="gpt-5-nano"
         )
     
     def capture_frame(self, cap):

examples/python/camera/camera-multi-agent.py

@@ -57,7 +57,7 @@ def analyze_with_multiple_agents():
         role="Security Specialist",
         goal="Identify security threats and suspicious activities",
         backstory="Expert security analyst with years of experience in surveillance and threat detection",
-        llm="gpt-4o-mini"
+        llm="gpt-5-nano"
     )
     
     object_detector = Agent(
@@ -65,7 +65,7 @@ def analyze_with_multiple_agents():
         role="Object Recognition Specialist", 
         goal="Identify and catalog all objects in the scene",
         backstory="Computer vision expert specializing in object detection and classification",
-        llm="gpt-4o-mini"
+        llm="gpt-5-nano"
     )
     
     scene_analyst = Agent(
@@ -73,7 +73,7 @@ def analyze_with_multiple_agents():
         role="Scene Understanding Expert",
         goal="Provide comprehensive scene analysis and context",
         backstory="Environmental analyst expert in understanding spatial relationships and scene context",
-        llm="gpt-4o-mini"
+        llm="gpt-5-nano"
     )
     
     # Create specialized tasks

examples/python/camera/camera-video-analysis.py

@@ -39,7 +39,7 @@ class CameraVideoAnalyzer:
             role="Video Content Analyzer",
             goal="Analyze video content for activities, objects, and events",
             backstory="Expert in video analysis and temporal event detection with computer vision expertise",
-            llm="gpt-4o-mini"
+            llm="gpt-5-nano"
         )
     
     def record_video_segment(self, duration_seconds=10):

examples/python/concepts/context-engineering-workflow.py

@@ -34,7 +34,7 @@ class ContextEngineeringWorkflow:
     to achieve higher success rates in AI-driven development tasks.
     """
     
-    def __init__(self, project_path: str, llm: str = "gpt-4o-mini"):
+    def __init__(self, project_path: str, llm: str = "gpt-5-nano"):
         self.project_path = project_path
         self.llm = llm
         self.context_data = {}
@@ -382,7 +382,7 @@ def run_example_workflow():
     project_path = str(project_root)
     workflow = ContextEngineeringWorkflow(
         project_path=project_path,
-        llm="gpt-4o-mini"
+        llm="gpt-5-nano"
     )
     
     # Example feature request
@@ -446,7 +446,7 @@ if __name__ == "__main__":
     print("(Note: Full execution requires complete environment setup)")
     
     project_path = str(project_root)
-    workflow = ContextEngineeringWorkflow(project_path, "gpt-4o-mini")
+    workflow = ContextEngineeringWorkflow(project_path, "gpt-5-nano")
     
     print(f"\n✅ Workflow Setup Complete:")
     print(f"   • Project path: {project_path}")

examples/python/concepts/csv-processing-agents.py

@@ -68,7 +68,7 @@ def method_1_simple_loop():
         goal="Process each task from the CSV file efficiently",
         backstory="Expert at handling repetitive tasks from CSV data",
         instructions="Process each task thoroughly and provide detailed output",
-        llm="gpt-4o-mini"
+        llm="gpt-5-nano"
     )
     
     # Create a loop task that will automatically process each CSV row
@@ -127,7 +127,7 @@ def method_2_manual_csv():
         4. Create a summary of the processing results
         5. Save results to a new CSV file if needed
         """,
-        llm="gpt-4o-mini"
+        llm="gpt-5-nano"
     )
     
     # Create a task for manual CSV processing
@@ -187,7 +187,7 @@ def method_3_url_processing():
         4. Create a detailed analysis report
         5. Save results with analysis data
         """,
-        llm="gpt-4o-mini"
+        llm="gpt-5-nano"
     )
     
     # Create the URL processing task
@@ -264,7 +264,7 @@ def method_4_advanced_processing():
         4. Create separate files for valid and invalid data
         5. Provide detailed validation reports
         """,
-        llm="gpt-4o-mini"
+        llm="gpt-5-nano"
     )
     
     # Create a processing agent
@@ -281,7 +281,7 @@ def method_4_advanced_processing():
         3. Generate comprehensive results
         4. Create detailed output reports
         """,
-        llm="gpt-4o-mini"
+        llm="gpt-5-nano"
     )
     
     # Create validation task

examples/python/concepts/knowledge-reranker-example.py

@@ -140,7 +140,7 @@ def main():
         instructions="You are an AI research assistant. Use your knowledge base to answer questions about artificial intelligence topics.",
         knowledge=sample_documents,  # Will use default knowledge config
         knowledge_config=rerank_config,  # Use reranking config
-        llm="gpt-4o-mini"
+        llm="gpt-5-nano"
     )
     
     print("7. Agent with reranking knowledge:")

examples/python/concepts/rag-agents.py

@@ -16,7 +16,7 @@ rag_agent = Agent(
     name="RAG Agent",
     role="Information Specialist",
     goal="Retrieve knowledge efficiently",
-    llm="gpt-4o-mini"
+    llm="gpt-5-nano"
 )
 
 # Define a task for the agent

examples/python/concepts/repetitive-agents.py

@@ -2,7 +2,7 @@ from praisonaiagents import Agent, Task, PraisonAIAgents
 
 agent = Agent(
     instructions="You are a loop agent that creating a loop of tasks.",
-    llm="gpt-4o-mini"
+    llm="gpt-5-nano"
 )
 
 task = Task(

examples/python/concepts/self-reflection-optimization-patterns.py

@@ -20,7 +20,7 @@ reflection_agent = Agent(
     self_reflect=True,
     min_reflect=2,
     max_reflect=4,
-    reflect_llm="gpt-4o-mini",
+    reflect_llm="gpt-5-nano",
     verbose=True
 )
 

examples/python/concepts/simple-csv-url-processor.py

@@ -46,7 +46,7 @@ def main():
         goal="Analyze each URL from the CSV list",
         backstory="Expert at analyzing websites and URLs",
         instructions="Analyze each URL and provide insights about the website",
-        llm="gpt-4o-mini"  # You can change this to any supported model
+        llm="gpt-5-nano"  # You can change this to any supported model
     )
     
     # Step 3: Create a task that will loop through the CSV

examples/python/custom_tools/example_latency_tracking.py

@@ -27,7 +27,7 @@ def example_with_tool():
         role="Information Researcher",
         goal="Research and provide information",
         tools=[latency_tracking_tool],  # Add our custom tool
-        llm="gpt-4o-mini"
+        llm="gpt-5-nano"
     )
     
     # Start tracking manually
@@ -60,7 +60,7 @@ def example_with_wrappers():
         name="Analyst",
         role="Data Analyst",
         goal="Analyze data and provide insights",
-        llm="gpt-4o-mini",
+        llm="gpt-5-nano",
         request_id="request_2"  # Unique request ID
     )
     
@@ -83,7 +83,7 @@ def example_with_context_manager():
         name="Calculator",
         role="Math Expert",
         goal="Solve mathematical problems",
-        llm="gpt-4o-mini"
+        llm="gpt-5-nano"
     )
     
     request_id = "request_3"
@@ -126,7 +126,7 @@ def example_mcp_tracking():
                 name="Assistant",
                 role="MCP Assistant",
                 goal="Handle MCP requests",
-                llm="gpt-4o-mini"
+                llm="gpt-5-nano"
             )
             plan = agent.chat("Plan: Search for Python documentation")
         
@@ -166,7 +166,7 @@ def example_concurrent_tracking():
                 name=f"Agent_{i}",
                 role="Concurrent Worker",
                 goal="Process requests concurrently",
-                llm="gpt-4o-mini"
+                llm="gpt-5-nano"
             )
             
             with tracker.track("processing", request_id):

examples/python/custom_tools/mcp_server_latency_example.py

@@ -56,7 +56,7 @@ def create_mcp_agent_with_tracking(request_id: str = "mcp_request"):
         name="MCPAssistant",
         role="MCP Request Handler",
         goal="Handle MCP requests with latency tracking",
-        llm="gpt-4o-mini"
+        llm="gpt-5-nano"
     )
     
     return agent

examples/python/custom_tools/minimal_latency_example.py

@@ -23,7 +23,7 @@ def handle_mcp_request(query: str, request_id: str = "mcp_1"):
         name="Assistant",
         role="MCP Handler",
         goal="Process requests",
-        llm="gpt-4o-mini"
+        llm="gpt-5-nano"
     )
     plan = f"I will search for information about {query}"
     time.sleep(0.1)  # Simulate planning time

examples/python/data/cot-agents.py

@@ -29,7 +29,7 @@ qa_generator = Agent(
     role="Question Creator",
     goal="Create challenging math and logic questions",
     backstory="Expert in educational content creation",
-    llm="gpt-4o-mini",
+    llm="gpt-5-nano",
     tools=[write_csv, count_questions]
 )
 
@@ -38,7 +38,7 @@ total_questions_evaluator = Agent(
     role="Total Questions Evaluator",
     goal="Evaluate the total number of questions in qa_pairs.csv file",
     backstory="Expert in evaluating the total number of questions in a file",
-    llm="gpt-4o-mini",
+    llm="gpt-5-nano",
     tools=[count_questions],
     verbose=False
 )
@@ -49,7 +49,7 @@ cot_generator = Agent(
     goal="Generate and manage chain of thought solutions for Q&A pairs",
     backstory="Expert in breaking down problems and generating detailed solution steps",
     tools=[cot_save],
-    llm="gpt-4o-mini",
+    llm="gpt-5-nano",
     verbose=False
 )
 
@@ -59,7 +59,7 @@ upload_to_huggingface = Agent(
     goal="Upload the generated chain of thought solutions to a Huggingface dataset",
     backstory="Expert in saving data to Huggingface",
     tools=[cot_upload_to_huggingface],
-    llm="gpt-4o-mini",
+    llm="gpt-5-nano",
     verbose=False
 )
 

examples/python/general/code_agents_example.py

@@ -23,13 +23,13 @@ def code_interpreter(code: str):
 
 code_agent = Agent(
     name="code_agent",
-    llm="gpt-4o-mini",
+    llm="gpt-5-nano",
     backstory="Expert in writing Python scripts",
     self_reflect=False
 )
 execution_agent = Agent(
     name="execution_agent",
-    llm="gpt-4o-mini",
+    llm="gpt-5-nano",
     backstory="Expert in executing Python scripts",
     self_reflect=False,
     tools=[code_interpreter]

examples/python/general/example_callback.py

@@ -127,9 +127,9 @@ def main():
         verbose=True,
         allow_delegation=False,
         tools=[internet_search_tool],
-        llm="gpt-4o",
+        llm="gpt-5-nano",
         markdown=True,
-        reflect_llm="gpt-4o",
+        reflect_llm="gpt-5-nano",
         min_reflect=2,
         max_reflect=4
     )
@@ -142,7 +142,7 @@ def main():
         making complex tech topics interesting and easy to understand.""",
         verbose=True,
         allow_delegation=True,
-        llm="gpt-4o",
+        llm="gpt-5-nano",
         tools=[],
         markdown=True
     )
@@ -195,7 +195,7 @@ def main():
         tasks=[task1, task2, task3, task4],
         verbose=True,
         process="sequential",
-        manager_llm="gpt-4o"
+        manager_llm="gpt-5-nano"
     )
 
     agents.start()

examples/python/general/example_sequential.py

@@ -39,9 +39,9 @@ researcher = Agent(
     verbose=True,
     allow_delegation=False,
     tools=[internet_search_tool],
-    llm="gpt-4o",
+    llm="gpt-5-nano",
     markdown=True,
-    reflect_llm="gpt-4o",
+    reflect_llm="gpt-5-nano",
     min_reflect=2,
     max_reflect=4
 )
@@ -53,7 +53,7 @@ writer = Agent(
     making complex tech topics interesting and easy to understand.""",
     verbose=True,
     allow_delegation=True,
-    llm="gpt-4o",
+    llm="gpt-5-nano",
     tools=[],
     markdown=True
 )
@@ -104,7 +104,7 @@ agents = PraisonAIAgents(
     tasks=[task1, task2, task3, task4],
     verbose=False,
     process="sequential",  # "sequential" or "hierarchical"
-    manager_llm="gpt-4o"
+    manager_llm="gpt-5-nano"
 )
 
 result = agents.start()

examples/python/general/memory_example.py

@@ -33,14 +33,14 @@ def main():
         role="Research Analyst",
         goal="Research and document key information about topics",
         backstory="Expert at analyzing and storing information in memory",
-        llm="gpt-4o-mini"
+        llm="gpt-5-nano"
     )
     
     retriever = Agent(
         role="Information Retriever",
         goal="Retrieve and verify stored information from memory",
         backstory="Specialist in searching and validating information from memory",
-        llm="gpt-4o-mini"
+        llm="gpt-5-nano"
     )
 
     # Task 1: Process the facts

examples/python/general/memory_simple.py

@@ -9,19 +9,19 @@ fact3 = "The year the first Josinga was released is 2007"
 fact_agent = Agent(
     name="Fact Agent",
     instructions="You are a fact agent, you store and retrieve facts in memory",
-    llm="gpt-4o-mini"
+    llm="gpt-5-nano"
 )
 
 research_agent = Agent(
     name="Research Agent",
     instructions="You are a research analyst, you research and document key points about topics",
-    llm="gpt-4o-mini"
+    llm="gpt-5-nano"
 )
 
 blog_agent = Agent(
     name="Blog Agent",
     instructions="You are a blog writer, you write a blog post about the research",
-    llm="gpt-4o-mini"
+    llm="gpt-5-nano"
 )
 
 fact_task = Task(