Release v2.3.13

README.md

@@ -572,6 +572,362 @@ praisonai hooks init
 praisonai "Research and remember findings" --claude-memory --llm anthropic/claude-sonnet-4-20250514
 ```
 
+### Guardrail CLI:
+```bash
+# Validate output with LLM guardrail
+praisonai "Write code" --guardrail "Ensure code is secure and follows best practices"
+
+# Combine with other flags
+praisonai "Generate SQL query" --guardrail "No DROP or DELETE statements" --save
+```
+
+### Metrics CLI:
+```bash
+# Display token usage and cost metrics
+praisonai "Analyze this data" --metrics
+
+# Combine with other features
+praisonai "Complex task" --metrics --planning
+```
+
+### Image Processing CLI:
+```bash
+# Process images with vision-based tasks
+praisonai "Describe this image" --image path/to/image.png
+
+# Analyze image content
+praisonai "What objects are in this photo?" --image photo.jpg --llm openai/gpt-4o
+```
+
+### Telemetry CLI:
+```bash
+# Enable usage monitoring and analytics
+praisonai "Task" --telemetry
+
+# Combine with metrics for full observability
+praisonai "Complex analysis" --telemetry --metrics
+```
+
+### MCP (Model Context Protocol) CLI:
+```bash
+# Use MCP server tools
+praisonai "Search files" --mcp "npx -y @modelcontextprotocol/server-filesystem ."
+
+# MCP with environment variables
+praisonai "Search web" --mcp "npx -y @modelcontextprotocol/server-brave-search" --mcp-env "BRAVE_API_KEY=your_key"
+
+# Multiple MCP options
+praisonai "Task" --mcp "npx server" --mcp-env "KEY1=value1,KEY2=value2"
+```
+
+### Fast Context CLI:
+```bash
+# Search codebase for relevant context
+praisonai "Find authentication code" --fast-context ./src
+
+# Add code context to any task
+praisonai "Explain this function" --fast-context /path/to/project
+```
+
+### Knowledge CLI:
+```bash
+# Add documents to knowledge base
+praisonai knowledge add document.pdf
+praisonai knowledge add ./docs/
+
+# Search knowledge base
+praisonai knowledge search "API authentication"
+
+# List indexed documents
+praisonai knowledge list
+
+# Clear knowledge base
+praisonai knowledge clear
+
+# Show knowledge base info
+praisonai knowledge info
+
+# Show all commands
+praisonai knowledge help
+```
+
+### Session CLI:
+```bash
+# Start a new session
+praisonai session start my-project
+
+# List all sessions
+praisonai session list
+
+# Resume a session
+praisonai session resume my-project
+
+# Show session details
+praisonai session show my-project
+
+# Delete a session
+praisonai session delete my-project
+
+# Show all commands
+praisonai session help
+```
+
+### Tools CLI:
+```bash
+# List all available tools
+praisonai tools list
+
+# Get info about a specific tool
+praisonai tools info internet_search
+
+# Search for tools
+praisonai tools search "web"
+
+# Show all commands
+praisonai tools help
+```
+
+### Handoff CLI:
+```bash
+# Enable agent-to-agent task delegation
+praisonai "Research and write article" --handoff "researcher,writer,editor"
+
+# Complex multi-agent workflow
+praisonai "Analyze data and create report" --handoff "analyst,visualizer,writer"
+```
+
+### Auto Memory CLI:
+```bash
+# Enable automatic memory extraction
+praisonai "Learn about user preferences" --auto-memory
+
+# Combine with user isolation
+praisonai "Remember my settings" --auto-memory --user-id user123
+```
+
+### Todo CLI:
+```bash
+# Generate todo list from task
+praisonai "Plan the project" --todo
+
+# Add a todo item
+praisonai todo add "Implement feature X"
+
+# List all todos
+praisonai todo list
+
+# Complete a todo
+praisonai todo complete 1
+
+# Delete a todo
+praisonai todo delete 1
+
+# Clear all todos
+praisonai todo clear
+
+# Show all commands
+praisonai todo help
+```
+
+### Router CLI:
+```bash
+# Auto-select best model based on task complexity
+praisonai "Simple question" --router
+
+# Specify preferred provider
+praisonai "Complex analysis" --router --router-provider anthropic
+
+# Router automatically selects:
+# - Simple tasks → gpt-4o-mini, claude-3-haiku
+# - Complex tasks → gpt-4-turbo, claude-3-opus
+```
+
+### Flow Display CLI:
+```bash
+# Enable visual workflow tracking
+praisonai agents.yaml --flow-display
+
+# Combine with other features
+praisonai "Multi-step task" --planning --flow-display
+```
+
+## 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 |
+
+## MCP (Model Context Protocol)
+
+PraisonAI supports MCP Protocol Revision 2025-11-25 with multiple transports.
+
+### MCP Client (Consume MCP Servers)
+```python
+from praisonaiagents import Agent, MCP
+
+# stdio - Local NPX/Python servers
+agent = Agent(tools=MCP("npx @modelcontextprotocol/server-memory"))
+
+# Streamable HTTP - Production servers
+agent = Agent(tools=MCP("https://api.example.com/mcp"))
+
+# WebSocket - Real-time bidirectional
+agent = Agent(tools=MCP("wss://api.example.com/mcp", auth_token="token"))
+
+# SSE (Legacy) - Backward compatibility
+agent = Agent(tools=MCP("http://localhost:8080/sse"))
+
+# With environment variables
+agent = Agent(
+    tools=MCP(
+        command="npx",
+        args=["-y", "@modelcontextprotocol/server-brave-search"],
+        env={"BRAVE_API_KEY": "your-key"}
+    )
+)
+```
+
+### MCP Server (Expose Tools as MCP Server)
+
+Expose your Python functions as MCP tools for Claude Desktop, Cursor, and other MCP clients:
+
+```python
+from praisonaiagents.mcp import ToolsMCPServer
+
+def search_web(query: str, max_results: int = 5) -> dict:
+    """Search the web for information."""
+    return {"results": [f"Result for {query}"]}
+
+def calculate(expression: str) -> dict:
+    """Evaluate a mathematical expression."""
+    return {"result": eval(expression)}
+
+# Create and run MCP server
+server = ToolsMCPServer(name="my-tools")
+server.register_tools([search_web, calculate])
+server.run()  # stdio for Claude Desktop
+# server.run_sse(host="0.0.0.0", port=8080)  # SSE for web clients
+```
+
+### MCP Features
+| Feature | Description |
+|---------|-------------|
+| Session Management | Automatic Mcp-Session-Id handling |
+| Protocol Versioning | Mcp-Protocol-Version header |
+| Resumability | SSE stream recovery via Last-Event-ID |
+| Security | Origin validation, DNS rebinding prevention |
+| WebSocket | Auto-reconnect with exponential backoff |
+
+## CLI Features
+
+| Feature | Docs |
+|---------|:----:|
+| 🔄 Query Rewrite - RAG optimization | [📖](https://docs.praison.ai/cli/query-rewrite) |
+| 🔬 Deep Research - Automated research | [📖](https://docs.praison.ai/cli/deep-research) |
+| 📋 Planning - Step-by-step execution | [📖](https://docs.praison.ai/cli/planning) |
+| 💾 Memory - Persistent agent memory | [📖](https://docs.praison.ai/cli/memory) |
+| 📜 Rules - Auto-discovered instructions | [📖](https://docs.praison.ai/cli/rules) |
+| 🔄 Workflow - Multi-step workflows | [📖](https://docs.praison.ai/cli/workflow) |
+| 🪝 Hooks - Event-driven actions | [📖](https://docs.praison.ai/cli/hooks) |
+| 🧠 Claude Memory - Anthropic memory tool | [📖](https://docs.praison.ai/cli/claude-memory) |
+| 🛡️ Guardrail - Output validation | [📖](https://docs.praison.ai/cli/guardrail) |
+| 📊 Metrics - Token usage tracking | [📖](https://docs.praison.ai/cli/metrics) |
+| 🖼️ Image - Vision processing | [📖](https://docs.praison.ai/cli/image) |
+| 📡 Telemetry - Usage monitoring | [📖](https://docs.praison.ai/cli/telemetry) |
+| 🔌 MCP - Model Context Protocol | [📖](https://docs.praison.ai/cli/mcp) |
+| ⚡ Fast Context - Codebase search | [📖](https://docs.praison.ai/cli/fast-context) |
+| 📚 Knowledge - RAG management | [📖](https://docs.praison.ai/cli/knowledge) |
+| 💬 Session - Conversation management | [📖](https://docs.praison.ai/cli/session) |
+| 🔧 Tools - Tool discovery | [📖](https://docs.praison.ai/cli/tools) |
+| 🤝 Handoff - Agent delegation | [📖](https://docs.praison.ai/cli/handoff) |
+| 🧠 Auto Memory - Memory extraction | [📖](https://docs.praison.ai/cli/auto-memory) |
+| 📋 Todo - Task management | [📖](https://docs.praison.ai/cli/todo) |
+| 🎯 Router - Smart model selection | [📖](https://docs.praison.ai/cli/router) |
+| 📈 Flow Display - Visual workflow | [📖](https://docs.praison.ai/cli/flow-display) |
+| ✨ Prompt Expansion - Detailed prompts | [📖](https://docs.praison.ai/cli/prompt-expansion) |
+| 🌐 Web Search - Real-time search | [📖](https://docs.praison.ai/cli/web-search) |
+| 📥 Web Fetch - URL content retrieval | [📖](https://docs.praison.ai/cli/web-fetch) |
+| 💾 Prompt Caching - Cost reduction | [📖](https://docs.praison.ai/cli/prompt-caching) |
+
 ## Using JavaScript Code
 
 ```bash

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>=2.3.12" \
+    "praisonai>=2.3.13" \
     "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>=2.3.12" \
+    "praisonai>=2.3.13" \
     "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>=2.3.12" \
+    "praisonai>=2.3.13" \
     "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>=2.3.12" \
+    "praisonai>=2.3.13" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

src/praisonai-agents/praisonaiagents/agent/agent.py

@@ -2627,10 +2627,10 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
             
             # Execute the step
             result = self.chat(step_prompt, **kwargs)
-            results.append(result)
+            results.append({"step": i + 1, "description": step.description, "result": result})
             
-            # Update context for next step
-            context += f"\n\nStep {i + 1} result: {result[:500] if result else 'No result'}"
+            # Update context for next step (use full result, not truncated)
+            context += f"\n\nStep {i + 1} result: {result if result else 'No result'}"
             
             console.print(f"   [green]✅ Completed[/green]")
         
@@ -2638,8 +2638,41 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
         console.print(f"[dim]Progress: [{'█' * bar_length}] 100%[/dim]")
         console.print(f"Completed {len(plan.steps)}/{len(plan.steps)} steps!\n")
         
-        # Return the final result
-        return results[-1] if results else None
+        # Compile all results into a comprehensive final output
+        if len(results) > 1:
+            # Create a compilation prompt
+            all_results_text = "\n\n".join([
+                f"## Step {r['step']}: {r['description']}\n\n{r['result']}" 
+                for r in results
+            ])
+            
+            compilation_prompt = f"""You are tasked with compiling a comprehensive, detailed report from the following research steps.
+
+IMPORTANT: Write a DETAILED and COMPREHENSIVE report. Do NOT summarize or compress the information. 
+Include ALL relevant details, data, statistics, and findings from each step.
+Organize the information logically with clear sections and subsections.
+
+## Research Results to Compile:
+
+{all_results_text}
+
+## Instructions:
+1. Combine all the information into a single, well-organized document
+2. Preserve ALL details, numbers, statistics, and specific findings
+3. Use clear headings and subheadings
+4. Do not omit any important information
+5. Make it comprehensive and detailed
+
+Write the complete compiled report:"""
+            
+            console.print("\n[bold blue]📝 COMPILING FINAL REPORT[/bold blue]")
+            console.print("[dim]Creating comprehensive output from all steps...[/dim]\n")
+            
+            final_result = self.chat(compilation_prompt, **kwargs)
+            return final_result
+        
+        # Return the single result if only one step
+        return results[0]["result"] if results else None
 
     def start(self, prompt: str, **kwargs):
         """Start the agent with a prompt. This is a convenience method that wraps chat()."""

src/praisonai-agents/praisonaiagents/agent/prompt_expander_agent.py

@@ -347,6 +347,37 @@ Expanded prompt:"""
             current_date=current_date
         )
         
+        # Add tool usage instructions if tools are available
+        if self.tools:
+            # Build dynamic tool descriptions from docstrings
+            tool_descriptions = []
+            for tool in self.tools:
+                tool_name = getattr(tool, '__name__', str(tool))
+                tool_doc = getattr(tool, '__doc__', '')
+                # Get first line of docstring as description
+                if tool_doc:
+                    first_line = tool_doc.strip().split('\n')[0].strip()
+                    tool_descriptions.append(f"- {tool_name}: {first_line}")
+                else:
+                    tool_descriptions.append(f"- {tool_name}")
+            
+            tools_list = '\n'.join(tool_descriptions)
+            
+            tool_instruction = f"""
+
+IMPORTANT: You have the following tools available:
+{tools_list}
+
+Before expanding this prompt, FIRST use the available tools to gather relevant current information about the topic. This will help you create a more informed and accurate expanded prompt.
+
+Steps:
+1. Use the appropriate tool(s) to gather relevant information about the topic
+2. Use the gathered information to inform your expansion
+3. Create an expanded prompt that incorporates the latest context
+
+"""
+            expansion_prompt = tool_instruction + expansion_prompt
+        
         # Call the agent
         expanded = self._call_agent(expansion_prompt).strip()
         

src/praisonai/README.md

@@ -572,6 +572,362 @@ praisonai hooks init
 praisonai "Research and remember findings" --claude-memory --llm anthropic/claude-sonnet-4-20250514
 ```
 
+### Guardrail CLI:
+```bash
+# Validate output with LLM guardrail
+praisonai "Write code" --guardrail "Ensure code is secure and follows best practices"
+
+# Combine with other flags
+praisonai "Generate SQL query" --guardrail "No DROP or DELETE statements" --save
+```
+
+### Metrics CLI:
+```bash
+# Display token usage and cost metrics
+praisonai "Analyze this data" --metrics
+
+# Combine with other features
+praisonai "Complex task" --metrics --planning
+```
+
+### Image Processing CLI:
+```bash
+# Process images with vision-based tasks
+praisonai "Describe this image" --image path/to/image.png
+
+# Analyze image content
+praisonai "What objects are in this photo?" --image photo.jpg --llm openai/gpt-4o
+```
+
+### Telemetry CLI:
+```bash
+# Enable usage monitoring and analytics
+praisonai "Task" --telemetry
+
+# Combine with metrics for full observability
+praisonai "Complex analysis" --telemetry --metrics
+```
+
+### MCP (Model Context Protocol) CLI:
+```bash
+# Use MCP server tools
+praisonai "Search files" --mcp "npx -y @modelcontextprotocol/server-filesystem ."
+
+# MCP with environment variables
+praisonai "Search web" --mcp "npx -y @modelcontextprotocol/server-brave-search" --mcp-env "BRAVE_API_KEY=your_key"
+
+# Multiple MCP options
+praisonai "Task" --mcp "npx server" --mcp-env "KEY1=value1,KEY2=value2"
+```
+
+### Fast Context CLI:
+```bash
+# Search codebase for relevant context
+praisonai "Find authentication code" --fast-context ./src
+
+# Add code context to any task
+praisonai "Explain this function" --fast-context /path/to/project
+```
+
+### Knowledge CLI:
+```bash
+# Add documents to knowledge base
+praisonai knowledge add document.pdf
+praisonai knowledge add ./docs/
+
+# Search knowledge base
+praisonai knowledge search "API authentication"
+
+# List indexed documents
+praisonai knowledge list
+
+# Clear knowledge base
+praisonai knowledge clear
+
+# Show knowledge base info
+praisonai knowledge info
+
+# Show all commands
+praisonai knowledge help
+```
+
+### Session CLI:
+```bash
+# Start a new session
+praisonai session start my-project
+
+# List all sessions
+praisonai session list
+
+# Resume a session
+praisonai session resume my-project
+
+# Show session details
+praisonai session show my-project
+
+# Delete a session
+praisonai session delete my-project
+
+# Show all commands
+praisonai session help
+```
+
+### Tools CLI:
+```bash
+# List all available tools
+praisonai tools list
+
+# Get info about a specific tool
+praisonai tools info internet_search
+
+# Search for tools
+praisonai tools search "web"
+
+# Show all commands
+praisonai tools help
+```
+
+### Handoff CLI:
+```bash
+# Enable agent-to-agent task delegation
+praisonai "Research and write article" --handoff "researcher,writer,editor"
+
+# Complex multi-agent workflow
+praisonai "Analyze data and create report" --handoff "analyst,visualizer,writer"
+```
+
+### Auto Memory CLI:
+```bash
+# Enable automatic memory extraction
+praisonai "Learn about user preferences" --auto-memory
+
+# Combine with user isolation
+praisonai "Remember my settings" --auto-memory --user-id user123
+```
+
+### Todo CLI:
+```bash
+# Generate todo list from task
+praisonai "Plan the project" --todo
+
+# Add a todo item
+praisonai todo add "Implement feature X"
+
+# List all todos
+praisonai todo list
+
+# Complete a todo
+praisonai todo complete 1
+
+# Delete a todo
+praisonai todo delete 1
+
+# Clear all todos
+praisonai todo clear
+
+# Show all commands
+praisonai todo help
+```
+
+### Router CLI:
+```bash
+# Auto-select best model based on task complexity
+praisonai "Simple question" --router
+
+# Specify preferred provider
+praisonai "Complex analysis" --router --router-provider anthropic
+
+# Router automatically selects:
+# - Simple tasks → gpt-4o-mini, claude-3-haiku
+# - Complex tasks → gpt-4-turbo, claude-3-opus
+```
+
+### Flow Display CLI:
+```bash
+# Enable visual workflow tracking
+praisonai agents.yaml --flow-display
+
+# Combine with other features
+praisonai "Multi-step task" --planning --flow-display
+```
+
+## 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 |
+
+## MCP (Model Context Protocol)
+
+PraisonAI supports MCP Protocol Revision 2025-11-25 with multiple transports.
+
+### MCP Client (Consume MCP Servers)
+```python
+from praisonaiagents import Agent, MCP
+
+# stdio - Local NPX/Python servers
+agent = Agent(tools=MCP("npx @modelcontextprotocol/server-memory"))
+
+# Streamable HTTP - Production servers
+agent = Agent(tools=MCP("https://api.example.com/mcp"))
+
+# WebSocket - Real-time bidirectional
+agent = Agent(tools=MCP("wss://api.example.com/mcp", auth_token="token"))
+
+# SSE (Legacy) - Backward compatibility
+agent = Agent(tools=MCP("http://localhost:8080/sse"))
+
+# With environment variables
+agent = Agent(
+    tools=MCP(
+        command="npx",
+        args=["-y", "@modelcontextprotocol/server-brave-search"],
+        env={"BRAVE_API_KEY": "your-key"}
+    )
+)
+```
+
+### MCP Server (Expose Tools as MCP Server)
+
+Expose your Python functions as MCP tools for Claude Desktop, Cursor, and other MCP clients:
+
+```python
+from praisonaiagents.mcp import ToolsMCPServer
+
+def search_web(query: str, max_results: int = 5) -> dict:
+    """Search the web for information."""
+    return {"results": [f"Result for {query}"]}
+
+def calculate(expression: str) -> dict:
+    """Evaluate a mathematical expression."""
+    return {"result": eval(expression)}
+
+# Create and run MCP server
+server = ToolsMCPServer(name="my-tools")
+server.register_tools([search_web, calculate])
+server.run()  # stdio for Claude Desktop
+# server.run_sse(host="0.0.0.0", port=8080)  # SSE for web clients
+```
+
+### MCP Features
+| Feature | Description |
+|---------|-------------|
+| Session Management | Automatic Mcp-Session-Id handling |
+| Protocol Versioning | Mcp-Protocol-Version header |
+| Resumability | SSE stream recovery via Last-Event-ID |
+| Security | Origin validation, DNS rebinding prevention |
+| WebSocket | Auto-reconnect with exponential backoff |
+
+## CLI Features
+
+| Feature | Docs |
+|---------|:----:|
+| 🔄 Query Rewrite - RAG optimization | [📖](https://docs.praison.ai/cli/query-rewrite) |
+| 🔬 Deep Research - Automated research | [📖](https://docs.praison.ai/cli/deep-research) |
+| 📋 Planning - Step-by-step execution | [📖](https://docs.praison.ai/cli/planning) |
+| 💾 Memory - Persistent agent memory | [📖](https://docs.praison.ai/cli/memory) |
+| 📜 Rules - Auto-discovered instructions | [📖](https://docs.praison.ai/cli/rules) |
+| 🔄 Workflow - Multi-step workflows | [📖](https://docs.praison.ai/cli/workflow) |
+| 🪝 Hooks - Event-driven actions | [📖](https://docs.praison.ai/cli/hooks) |
+| 🧠 Claude Memory - Anthropic memory tool | [📖](https://docs.praison.ai/cli/claude-memory) |
+| 🛡️ Guardrail - Output validation | [📖](https://docs.praison.ai/cli/guardrail) |
+| 📊 Metrics - Token usage tracking | [📖](https://docs.praison.ai/cli/metrics) |
+| 🖼️ Image - Vision processing | [📖](https://docs.praison.ai/cli/image) |
+| 📡 Telemetry - Usage monitoring | [📖](https://docs.praison.ai/cli/telemetry) |
+| 🔌 MCP - Model Context Protocol | [📖](https://docs.praison.ai/cli/mcp) |
+| ⚡ Fast Context - Codebase search | [📖](https://docs.praison.ai/cli/fast-context) |
+| 📚 Knowledge - RAG management | [📖](https://docs.praison.ai/cli/knowledge) |
+| 💬 Session - Conversation management | [📖](https://docs.praison.ai/cli/session) |
+| 🔧 Tools - Tool discovery | [📖](https://docs.praison.ai/cli/tools) |
+| 🤝 Handoff - Agent delegation | [📖](https://docs.praison.ai/cli/handoff) |
+| 🧠 Auto Memory - Memory extraction | [📖](https://docs.praison.ai/cli/auto-memory) |
+| 📋 Todo - Task management | [📖](https://docs.praison.ai/cli/todo) |
+| 🎯 Router - Smart model selection | [📖](https://docs.praison.ai/cli/router) |
+| 📈 Flow Display - Visual workflow | [📖](https://docs.praison.ai/cli/flow-display) |
+| ✨ Prompt Expansion - Detailed prompts | [📖](https://docs.praison.ai/cli/prompt-expansion) |
+| 🌐 Web Search - Real-time search | [📖](https://docs.praison.ai/cli/web-search) |
+| 📥 Web Fetch - URL content retrieval | [📖](https://docs.praison.ai/cli/web-fetch) |
+| 💾 Prompt Caching - Cost reduction | [📖](https://docs.praison.ai/cli/prompt-caching) |
+
 ## Using JavaScript Code
 
 ```bash

src/praisonai/praisonai.rb

@@ -3,8 +3,8 @@ class Praisonai < Formula
   
     desc "AI tools for various AI applications"
     homepage "https://github.com/MervinPraison/PraisonAI"
-    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.3.12.tar.gz"
-    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.3.12.tar.gz | shasum -a 256`.split.first
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.3.13.tar.gz"
+    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.3.13.tar.gz | shasum -a 256`.split.first
     license "MIT"
   
     depends_on "[email protected]"

src/praisonai/praisonai/cli/main.py

@@ -593,6 +593,7 @@ class PraisonAI:
         parser.add_argument("--planning-tools", type=str, help="Tools for planning research (path to tools.py or comma-separated tool names)")
         parser.add_argument("--planning-reasoning", action="store_true", help="Enable chain-of-thought reasoning in planning")
         parser.add_argument("--auto-approve-plan", action="store_true", help="Auto-approve generated plans without user confirmation")
+        parser.add_argument("--max-tokens", type=int, default=16000, help="Maximum output tokens for agent responses (default: 16000)")
         
         # Memory arguments
         parser.add_argument("--memory", action="store_true", help="Enable file-based memory for agent")
@@ -1727,7 +1728,14 @@ class PraisonAI:
             
             # Add llm if specified
             if hasattr(self, 'args') and self.args.llm:
-                agent_config["llm"] = self.args.llm
+                # Check if max_tokens is specified - pass as dict config
+                max_tokens = getattr(self.args, 'max_tokens', 16000)
+                if max_tokens:
+                    # Pass llm as dict with model and max_tokens
+                    agent_config["llm"] = {"model": self.args.llm, "max_tokens": max_tokens}
+                    print(f"[bold cyan]Max tokens set to: {max_tokens}[/bold cyan]")
+                else:
+                    agent_config["llm"] = self.args.llm
             
             # Add feature flags if enabled
             if hasattr(self, 'args'):
@@ -1738,6 +1746,18 @@ class PraisonAI:
                 if getattr(self.args, 'prompt_caching', False):
                     agent_config["prompt_caching"] = True
                 
+                # Load tools if specified (--tools flag)
+                if getattr(self.args, 'tools', None):
+                    tools_list = self._load_tools(self.args.tools)
+                    if tools_list:
+                        existing_tools = agent_config.get('tools', [])
+                        if isinstance(existing_tools, list):
+                            existing_tools.extend(tools_list)
+                        else:
+                            existing_tools = tools_list
+                        agent_config['tools'] = existing_tools
+                        print(f"[bold cyan]Tools loaded: {len(tools_list)} tool(s) available for agent[/bold cyan]")
+                
                 # Planning Mode
                 if getattr(self.args, 'planning', False):
                     agent_config["planning"] = True
@@ -1748,6 +1768,10 @@ class PraisonAI:
                         planning_tools_list = self._load_tools(self.args.planning_tools)
                         if planning_tools_list:
                             agent_config["planning_tools"] = planning_tools_list
+                    # If no planning_tools but --tools is specified, use those for planning too
+                    elif getattr(self.args, 'tools', None) and agent_config.get('tools'):
+                        agent_config["planning_tools"] = agent_config['tools']
+                        print("[cyan]Using --tools for planning as well[/cyan]")
                     
                     if getattr(self.args, 'planning_reasoning', False):
                         agent_config["planning_reasoning"] = True

src/praisonai/praisonai/deploy.py

@@ -57,7 +57,7 @@ class CloudDeployer:
             file.write("FROM python:3.11-slim\n")
             file.write("WORKDIR /app\n")
             file.write("COPY . .\n")
-            file.write("RUN pip install flask praisonai==2.3.12 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.3.13 gunicorn markdown\n")
             file.write("EXPOSE 8080\n")
             file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
             

src/praisonai/praisonai/version.py

@@ -1 +1 @@
-__version__ = "2.3.12"
+__version__ = "2.3.13"