Release v2.3.34

Add performance comparison table to README showing PraisonAI as fastest framework for agent instantiation. Include benchmarks for LangGraph and CrewAI frameworks. Implement auto-save functionality to generate BENCHMARK_RESULTS.md and update README.md with latest benchmark results.

README.md

@@ -34,6 +34,7 @@ PraisonAI is a production-ready Multi-AI Agents framework with self-reflection,
 
 ## 📑 Table of Contents
 
+- [⚡ Performance](#-performance)
 - [🚀 Quick Start](#-quick-start)
 - [📦 Installation](#-installation)
   - [Python SDK](#python-sdk)
@@ -73,6 +74,32 @@ PraisonAI is a production-ready Multi-AI Agents framework with self-reflection,
 
 ---
 
+## ⚡ Performance
+
+PraisonAI Agents is the **fastest AI agent framework** for agent instantiation.
+
+| Framework | Avg Time (μs) | Relative |
+|-----------|---------------|----------|
+| **PraisonAI** | **3.77** | **1.00x (fastest)** |
+| OpenAI Agents SDK | 5.26 | 1.39x |
+| Agno | 5.64 | 1.49x |
+| PraisonAI (LiteLLM) | 7.56 | 2.00x |
+| PydanticAI | 226.94 | 60.16x |
+| LangGraph | 4,558.71 | 1,209x |
+| CrewAI | 15,607.92 | 4,138x |
+
+<details>
+<summary>Run benchmarks yourself</summary>
+
+```bash
+cd praisonai-agents
+python benchmarks/simple_benchmark.py
+```
+
+</details>
+
+---
+
 ## 🚀 Quick Start
 
 Get started with PraisonAI in under 1 minute:
@@ -1392,6 +1419,36 @@ praisonai commit
 praisonai commit --push
 ```
 
+### Serve CLI (API Server):
+```bash
+# Start API server for agents defined in YAML
+praisonai serve agents.yaml
+
+# With custom port and host
+praisonai serve agents.yaml --port 8005 --host 0.0.0.0
+
+# Alternative flag style
+praisonai agents.yaml --serve
+
+# The server provides:
+# POST /agents          - Run all agents sequentially
+# POST /agents/{name}   - Run specific agent (e.g., /agents/researcher)
+# GET  /agents/list     - List available agents
+```
+
+### n8n Integration CLI:
+```bash
+# Export workflow to n8n and open in browser
+praisonai agents.yaml --n8n
+
+# With custom n8n URL
+praisonai agents.yaml --n8n --n8n-url http://localhost:5678
+
+# Set N8N_API_KEY for auto-import
+export N8N_API_KEY="your-api-key"
+praisonai agents.yaml --n8n
+```
+
 ### @Mentions in Prompts:
 ```bash
 # Include file content in prompt
@@ -1523,6 +1580,21 @@ agent = Agent(
         env={"BRAVE_API_KEY": "your-key"}
     )
 )
+
+# Multiple MCP servers + regular functions
+def my_custom_tool(query: str) -> str:
+    """Custom tool function."""
+    return f"Result: {query}"
+
+agent = Agent(
+    name="MultiToolAgent",
+    instructions="Agent with multiple MCP servers",
+    tools=[
+        MCP("uvx mcp-server-time"),                    # Time tools
+        MCP("npx @modelcontextprotocol/server-memory"), # Memory tools
+        my_custom_tool                                  # Regular function
+    ]
+)
 ```
 
 ### MCP Server (Expose Tools as MCP Server)

docker/Dockerfile

@@ -14,9 +14,10 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
 RUN mkdir -p /root/.praison
 
 # Install Python packages (using latest versions)
+# Note: praisonai is installed from PyPI - remove version pin to use latest available
 RUN pip install --no-cache-dir \
     flask \
-    "praisonai>=2.3.28" \
+    praisonai \
     "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>=2.3.28" \
+    "praisonai>=2.3.34" \
     "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>=2.3.28" \
+    "praisonai>=2.3.34" \
     "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>=2.3.28" \
+    "praisonai>=2.3.34" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

examples/python/mcp/multiple-mcp-servers.py

@@ -0,0 +1,108 @@
+"""
+Multiple MCP Servers Example
+
+This example demonstrates how to use multiple MCP servers with a single agent.
+You can combine different MCP servers (time, memory, filesystem, etc.) along
+with regular Python functions as tools.
+
+Requirements:
+- pip install praisonaiagents
+- uvx (for mcp-server-time)
+- npx (for @modelcontextprotocol servers)
+"""
+
+from praisonaiagents import Agent
+from praisonaiagents.mcp import MCP
+
+
+def my_custom_tool(query: str) -> str:
+    """
+    A custom Python function that can be used alongside MCP tools.
+    
+    Args:
+        query: The search query
+        
+    Returns:
+        A formatted result string
+    """
+    return f"Custom tool result for: {query}"
+
+
+def main():
+    # Example 1: Single MCP server (basic usage)
+    print("=" * 60)
+    print("Example 1: Single MCP Server")
+    print("=" * 60)
+    
+    agent_single = Agent(
+        name="TimeAgent",
+        instructions="You are a helpful assistant that can tell time.",
+        tools=MCP("uvx mcp-server-time")
+    )
+    
+    response = agent_single.start("What time is it in Tokyo?")
+    print(f"Response: {response}\n")
+    
+    # Example 2: Multiple MCP servers
+    print("=" * 60)
+    print("Example 2: Multiple MCP Servers")
+    print("=" * 60)
+    
+    agent_multi = Agent(
+        name="MultiMCPAgent",
+        instructions="""You are a helpful assistant with access to multiple tools:
+        - Time tools for getting current time and converting timezones
+        - Memory tools for storing and retrieving information
+        Use the appropriate tool based on the user's request.""",
+        tools=[
+            MCP("uvx mcp-server-time"),                     # Time tools
+            MCP("npx @modelcontextprotocol/server-memory"), # Memory tools
+        ]
+    )
+    
+    response = agent_multi.start("What time is it in New York and London?")
+    print(f"Response: {response}\n")
+    
+    # Example 3: Multiple MCP servers + custom functions
+    print("=" * 60)
+    print("Example 3: MCP Servers + Custom Functions")
+    print("=" * 60)
+    
+    agent_mixed = Agent(
+        name="MixedToolsAgent",
+        instructions="""You are a helpful assistant with access to:
+        - Time tools for timezone operations
+        - A custom search tool for general queries
+        Choose the right tool for each task.""",
+        tools=[
+            MCP("uvx mcp-server-time"),  # MCP server
+            my_custom_tool                # Regular Python function
+        ]
+    )
+    
+    response = agent_mixed.start("What time is it in Paris?")
+    print(f"Response: {response}\n")
+    
+    # Example 4: MCP with environment variables
+    print("=" * 60)
+    print("Example 4: MCP with Environment Variables")
+    print("=" * 60)
+    
+    # Note: Replace with your actual API key
+    # agent_with_env = Agent(
+    #     name="SearchAgent",
+    #     instructions="You can search the web for information.",
+    #     tools=[
+    #         MCP(
+    #             command="npx",
+    #             args=["-y", "@modelcontextprotocol/server-brave-search"],
+    #             env={"BRAVE_API_KEY": "your-api-key-here"}
+    #         ),
+    #         MCP("uvx mcp-server-time")
+    #     ]
+    # )
+    print("(Skipped - requires BRAVE_API_KEY)")
+
+
+if __name__ == "__main__":
+    main()

examples/yaml/agents_workflow.yaml

@@ -11,7 +11,7 @@
 
 framework: praisonai
 process: workflow
-topic: "Research AI trends and create a comprehensive report"
+input: "Research AI trends and create a comprehensive report"  # The input passed to {{input}} in steps
 
 # Workflow configuration (new)
 workflow:

examples/yaml/workflows/loop_workflow.yaml

@@ -39,4 +39,4 @@ steps:
       over: topics
       
   - agent: summarizer
-    action: "Create a comprehensive summary of all the analyzed topics"
+    action: "Create a comprehensive summary of all the analyzed topics based on these results: {{previous_output}}"

examples/yaml/workflows/parallel_workflow.yaml

@@ -3,6 +3,7 @@
 
 name: Parallel Research Workflow
 description: Multiple agents research different aspects concurrently, then an aggregator combines results
+input: "AI-powered customer service solutions"  # The input passed to {{input}} in steps
 
 workflow:
   verbose: true
@@ -43,4 +44,4 @@ steps:
         action: "Research customer needs for: {{input}}"
         
   - agent: aggregator
-    action: "Synthesize all the research findings into a comprehensive report"
+    action: "Synthesize all the research findings into a comprehensive report: {{previous_output}}"

examples/yaml/workflows/routing_workflow.yaml

@@ -3,6 +3,7 @@
 
 name: Agentic Routing Workflow
 description: A classifier agent routes requests to specialized handler agents
+input: "Write a poem about the ocean"  # The input passed to {{input}} in steps
 
 workflow:
   verbose: true

examples/yaml/workflows/simple_workflow.yaml

@@ -3,6 +3,7 @@
 
 name: Simple Research Workflow
 description: A simple workflow where a researcher gathers info and a writer summarizes it
+input: "The benefits of renewable energy"  # The input passed to {{input}} in steps
 
 workflow:
   verbose: true

src/praisonai-agents/.gitignore

@@ -87,3 +87,4 @@ graph.py
 chroma_db/
 .qodo
 *_COMPARISON_ANALYSIS.md
+*_REPORT.md

src/praisonai-agents/benchmarks/BENCHMARK_RESULTS.md

@@ -0,0 +1,35 @@
+# PraisonAI Agents - Benchmark Results
+
+**Generated:** 2025-12-18 14:40:22
+**Iterations:** 100
+**Test:** Agent instantiation (without tools)
+
+## Results
+
+| Framework | Avg Time (μs) | Relative |
+|-----------|---------------|----------|
+| **PraisonAI** | **3.77** | **1.00x (fastest)** |
+| OpenAI Agents SDK | 5.26 | 1.39x |
+| Agno | 5.64 | 1.49x |
+| PraisonAI (LiteLLM) | 7.56 | 2.00x |
+| PydanticAI | 226.94 | 60.16x |
+| LangGraph | 4,558.71 | 1,209x |
+| CrewAI | 15,607.92 | 4,138x |
+
+## Package Versions
+
+| Package | Version |
+|---------|--------|
+| PraisonAI | 0.1.5 |
+| Agno | 2.3.14 |
+| PydanticAI | 1.35.0 |
+| OpenAI Agents SDK | 0.6.3 |
+| LangGraph | 1.0.5 |
+| CrewAI | 1.6.1 |
+
+## How to Reproduce
+
+```bash
+cd praisonai-agents
+python benchmarks/simple_benchmark.py
+```

src/praisonai-agents/benchmarks/EXECUTION_BENCHMARK_RESULTS.md

@@ -0,0 +1,31 @@
+# PraisonAI Agents - Real Execution Benchmark
+
+**Generated:** 2025-12-18 14:43:52
+**Model:** gpt-4o-mini
+**Iterations:** 3
+**Prompt:** "What is 2+2? Reply with just the number."
+
+## Results
+
+| Framework | Method | Avg Time | Relative |
+|-----------|--------|----------|----------|
+| **PraisonAI** | `agent.start()` | **0.45s** | **1.00x (fastest)** |
+| PraisonAI (LiteLLM) | `agent.start()` | 0.55s | 1.22x |
+| CrewAI | `crew.kickoff()` | 0.58s | 1.28x |
+| Agno | `agent.run()` | 0.92s | 2.05x |
+
+## Package Versions
+
+| Package | Version |
+|---------|--------|
+| PraisonAI | 0.1.5 |
+| Agno | 2.3.14 |
+| CrewAI | 1.6.1 |
+
+## How to Reproduce
+
+```bash
+export OPENAI_API_KEY=your_key
+cd praisonai-agents
+python benchmarks/execution_benchmark.py
+```

src/praisonai-agents/benchmarks/REAL_BENCHMARK_RESULTS.md

@@ -0,0 +1,72 @@
+# PraisonAI Agents - Real-World Execution Benchmark
+
+**Generated:** 2025-12-18 14:33:49
+**Model:** gpt-4o-mini
+**Iterations:** 3 per test
+
+## Results Summary
+
+| Test | PraisonAI (avg) | Agno (avg) | Difference |
+|------|-----------------|------------|------------|
+| Single Agent | 2.04s | 3.18s | PraisonAI 1.56x faster |
+| 2 Agents | 2.49s | 20.49s | PraisonAI 8.21x faster |
+| 3 Agents | 6.52s | 13.97s | PraisonAI 2.14x faster |
+
+## Detailed Results
+
+### praisonai_single
+- Average: 2.04s
+- Min: 1.95s
+- Max: 2.19s
+- Std Dev: 0.13s
+- Times: ['2.19s', '1.95s', '2.00s']
+
+### agno_single
+- Average: 3.18s
+- Min: 3.00s
+- Max: 3.51s
+- Std Dev: 0.28s
+- Times: ['3.51s', '3.00s', '3.04s']
+
+### praisonai_two_agents
+- Average: 2.49s
+- Min: 2.22s
+- Max: 2.65s
+- Std Dev: 0.24s
+- Times: ['2.65s', '2.22s', '2.62s']
+
+### agno_two_agents
+- Average: 20.49s
+- Min: 9.96s
+- Max: 35.05s
+- Std Dev: 13.02s
+- Times: ['16.46s', '9.96s', '35.05s']
+
+### praisonai_three_agents
+- Average: 6.52s
+- Min: 5.62s
+- Max: 7.39s
+- Std Dev: 0.88s
+- Times: ['5.62s', '6.54s', '7.39s']
+
+### agno_three_agents
+- Average: 13.97s
+- Min: 11.69s
+- Max: 15.32s
+- Std Dev: 1.99s
+- Times: ['14.91s', '11.69s', '15.32s']
+
+## Package Versions
+
+| Package | Version |
+|---------|--------|
+| PraisonAI | 0.1.5 |
+| Agno | 2.3.14 |
+
+## How to Reproduce
+
+```bash
+export OPENAI_API_KEY=your_key
+cd praisonai-agents
+python benchmarks/real_benchmark.py
+```

src/praisonai-agents/benchmarks/SIMPLE_BENCHMARK_RESULTS.md

@@ -0,0 +1,22 @@
+# PraisonAI Agents - Benchmark Results
+
+**Generated:** 2025-12-18 13:23:43
+
+## Agent Instantiation Time
+
+| Framework | Avg Time (μs) | Relative |
+|-----------|---------------|----------|
+| **PraisonAI** | **4.16** | **1.00x (fastest)** |
+| OpenAI Agents SDK | 5.55 | 1.33x |
+| Agno | 5.58 | 1.34x |
+| PraisonAI (LiteLLM) | 8.52 | 2.05x |
+| PydanticAI | 225.87 | 54.25x |
+| LangGraph | 4,349.54 | 1,045x |
+| CrewAI | 15,911.11 | 3,821x |
+
+## How to Reproduce
+
+```bash
+cd praisonai-agents
+python benchmarks/simple_benchmark.py
+```

src/praisonai-agents/benchmarks/TOOLS_BENCHMARK_RESULTS.md

@@ -0,0 +1,33 @@
+# PraisonAI Agents - Tools Benchmark Results
+
+**Generated:** 2025-12-18 14:40:35
+**Iterations:** 100
+**Test:** Agent instantiation WITH TOOLS
+
+## Results
+
+| Framework | Avg Time (μs) | Relative |
+|-----------|---------------|----------|
+| **PraisonAI** | **3.24** | **1.00x (fastest)** |
+| Agno | 5.12 | 1.58x |
+| PraisonAI (LiteLLM) | 8.59 | 2.65x |
+| OpenAI Agents SDK | 279.95 | 86.44x |
+| LangGraph | 2,310.82 | 713x |
+| CrewAI | 15,773.44 | 4,870x |
+
+## Package Versions
+
+| Package | Version |
+|---------|--------|
+| PraisonAI | 0.1.5 |
+| Agno | 2.3.14 |
+| OpenAI Agents SDK | 0.6.3 |
+| LangGraph | 1.0.5 |
+| CrewAI | 1.6.1 |
+
+## How to Reproduce
+
+```bash
+cd praisonai-agents
+python benchmarks/tools_benchmark.py
+```

src/praisonai-agents/benchmarks/deep_profiling.py

@@ -0,0 +1,368 @@
+#!/usr/bin/env python3
+"""
+PraisonAI Agents - Deep Profiling Benchmark
+
+Profiles each step of agent initialization to identify performance bottlenecks.
+
+Key Optimizations Applied:
+1. Lazy RulesManager - filesystem operations deferred until first access
+2. Lazy os.getcwd() - fast_context_path resolved only when needed
+3. Lazy console initialization - Rich Console created on demand
+
+Usage:
+    python benchmarks/deep_profiling.py
+"""
+
+import time
+import tracemalloc
+import cProfile
+import pstats
+import io
+from typing import Literal, Callable, Dict, List
+from dataclasses import dataclass
+
+# ============================================================================
+# Timing Utilities
+# ============================================================================
+
+@dataclass
+class StepTiming:
+    name: str
+    duration_us: float  # microseconds
+    memory_kb: float    # kilobytes
+
+
+class StepProfiler:
+    """Profile individual steps during initialization"""
+    
+    def __init__(self):
+        self.steps: List[StepTiming] = []
+        self._start_time = None
+        self._start_memory = None
+    
+    def start_step(self, name: str):
+        tracemalloc.start()
+        self._start_time = time.perf_counter()
+        self._step_name = name
+    
+    def end_step(self):
+        end_time = time.perf_counter()
+        current, peak = tracemalloc.get_traced_memory()
+        tracemalloc.stop()
+        
+        duration_us = (end_time - self._start_time) * 1_000_000
+        memory_kb = peak / 1024
+        
+        self.steps.append(StepTiming(
+            name=self._step_name,
+            duration_us=duration_us,
+            memory_kb=memory_kb
+        ))
+    
+    def print_report(self, title: str):
+        print(f"\n{'='*70}")
+        print(f"STEP-BY-STEP PROFILING: {title}")
+        print(f"{'='*70}")
+        
+        total_time = sum(s.duration_us for s in self.steps)
+        total_memory = sum(s.memory_kb for s in self.steps)
+        
+        print(f"\n{'Step':<40} {'Time (μs)':<15} {'Memory (KB)':<15} {'% Time':<10}")
+        print("-" * 80)
+        
+        for step in sorted(self.steps, key=lambda x: x.duration_us, reverse=True):
+            pct = (step.duration_us / total_time * 100) if total_time > 0 else 0
+            print(f"{step.name:<40} {step.duration_us:<15.2f} {step.memory_kb:<15.2f} {pct:<10.1f}%")
+        
+        print("-" * 80)
+        print(f"{'TOTAL':<40} {total_time:<15.2f} {total_memory:<15.2f} {'100.0':<10}%")
+
+
+def time_function(func: Callable, iterations: int = 100) -> Dict:
+    """Time a function with detailed statistics"""
+    times = []
+    
+    # Warmup
+    for _ in range(10):
+        func()
+    
+    # Measure
+    for _ in range(iterations):
+        start = time.perf_counter()
+        func()
+        end = time.perf_counter()
+        times.append((end - start) * 1_000_000)  # Convert to microseconds
+    
+    return {
+        'avg': sum(times) / len(times),
+        'min': min(times),
+        'max': max(times),
+        'median': sorted(times)[len(times)//2]
+    }
+
+
+# ============================================================================
+# Profile PraisonAI Agent Initialization - Step by Step
+# ============================================================================
+
+def profile_praisonai_detailed():
+    """Profile each step of PraisonAI Agent initialization"""
+    print("\n" + "="*70)
+    print("DETAILED PROFILING: PraisonAI Agent")
+    print("="*70)
+    
+    profiler = StepProfiler()
+    
+    # Step 1: Import
+    profiler.start_step("1. Import praisonaiagents")
+    from praisonaiagents import Agent as PraisonAgent
+    profiler.end_step()
+    
+    # Step 2: Define tool
+    profiler.start_step("2. Define tool function")
+    def get_weather(city: Literal["nyc", "sf"]):
+        """Use this to get weather information."""
+        if city == "nyc":
+            return "It might be cloudy in nyc"
+        elif city == "sf":
+            return "It's always sunny in sf"
+    tools = [get_weather]
+    profiler.end_step()
+    
+    # Step 3: Full instantiation
+    profiler.start_step("3. Full Agent instantiation")
+    agent = PraisonAgent(
+        name="Test Agent",
+        instructions="Be concise, reply with one sentence.",
+        llm="gpt-4o-mini",
+        tools=tools,
+        verbose=False
+    )
+    profiler.end_step()
+    
+    profiler.print_report("PraisonAI Agent")
+    
+    # Now profile the __init__ internals using cProfile
+    print("\n" + "="*70)
+    print("cProfile ANALYSIS: PraisonAI Agent.__init__")
+    print("="*70)
+    
+    pr = cProfile.Profile()
+    pr.enable()
+    
+    for _ in range(100):
+        agent = PraisonAgent(
+            name="Test Agent",
+            instructions="Be concise, reply with one sentence.",
+            llm="gpt-4o-mini",
+            tools=tools,
+            verbose=False
+        )
+    
+    pr.disable()
+    
+    # Print top 30 functions by cumulative time
+    s = io.StringIO()
+    ps = pstats.Stats(pr, stream=s).sort_stats('cumulative')
+    ps.print_stats(30)
+    print(s.getvalue())
+    
+    return agent
+
+
+# ============================================================================
+# Profile Agno Agent Initialization - Step by Step
+# ============================================================================
+
+def profile_agno_detailed():
+    """Profile each step of Agno Agent initialization"""
+    print("\n" + "="*70)
+    print("DETAILED PROFILING: Agno Agent")
+    print("="*70)
+    
+    profiler = StepProfiler()
+    
+    # Step 1: Import
+    profiler.start_step("1. Import agno")
+    from agno.agent import Agent as AgnoAgent
+    from agno.models.openai import OpenAIChat
+    profiler.end_step()
+    
+    # Step 2: Define tool
+    profiler.start_step("2. Define tool function")
+    def get_weather(city: Literal["nyc", "sf"]):
+        """Use this to get weather information."""
+        if city == "nyc":
+            return "It might be cloudy in nyc"
+        elif city == "sf":
+            return "It's always sunny in sf"
+    tools = [get_weather]
+    profiler.end_step()
+    
+    # Step 3: Full instantiation
+    profiler.start_step("3. Full Agent instantiation")
+    agent = AgnoAgent(model=OpenAIChat(id="gpt-4o-mini"), tools=tools)
+    profiler.end_step()
+    
+    profiler.print_report("Agno Agent")
+    
+    # Now profile the __init__ internals using cProfile
+    print("\n" + "="*70)
+    print("cProfile ANALYSIS: Agno Agent.__init__")
+    print("="*70)
+    
+    pr = cProfile.Profile()
+    pr.enable()
+    
+    for _ in range(100):
+        agent = AgnoAgent(model=OpenAIChat(id="gpt-4o-mini"), tools=tools)
+    
+    pr.disable()
+    
+    # Print top 30 functions by cumulative time
+    s = io.StringIO()
+    ps = pstats.Stats(pr, stream=s).sort_stats('cumulative')
+    ps.print_stats(30)
+    print(s.getvalue())
+    
+    return agent
+
+
+# ============================================================================
+# Profile Individual Components of PraisonAI
+# ============================================================================
+
+def profile_praisonai_components():
+    """Profile individual components of PraisonAI Agent initialization"""
+    print("\n" + "="*70)
+    print("COMPONENT-LEVEL PROFILING: PraisonAI Agent")
+    print("="*70)
+    
+    from praisonaiagents import Agent as PraisonAgent
+    
+    def get_weather(city: Literal["nyc", "sf"]):
+        if city == "nyc":
+            return "It might be cloudy in nyc"
+        elif city == "sf":
+            return "It's always sunny in sf"
+    tools = [get_weather]
+    
+    # Test different configurations
+    configs = [
+        ("Minimal (no tools, no llm)", {"name": "Test", "verbose": False}),
+        ("With name only", {"name": "Test Agent", "verbose": False}),
+        ("With instructions", {"name": "Test", "instructions": "Be concise", "verbose": False}),
+        ("With llm (gpt-4o-mini)", {"name": "Test", "llm": "gpt-4o-mini", "verbose": False}),
+        ("With llm (openai/gpt-4o-mini)", {"name": "Test", "llm": "openai/gpt-4o-mini", "verbose": False}),
+        ("With tools", {"name": "Test", "tools": tools, "verbose": False}),
+        ("Full config (OpenAI SDK)", {"name": "Test", "instructions": "Be concise", "llm": "gpt-4o-mini", "tools": tools, "verbose": False}),
+        ("Full config (LiteLLM)", {"name": "Test", "instructions": "Be concise", "llm": "openai/gpt-4o-mini", "tools": tools, "verbose": False}),
+    ]
+    
+    print(f"\n{'Configuration':<40} {'Avg Time (μs)':<15} {'Min (μs)':<15} {'Max (μs)':<15}")
+    print("-" * 85)
+    
+    for name, config in configs:
+        def create_agent():
+            return PraisonAgent(**config)
+        
+        stats = time_function(create_agent, iterations=100)
+        print(f"{name:<40} {stats['avg']:<15.2f} {stats['min']:<15.2f} {stats['max']:<15.2f}")
+
+
+# ============================================================================
+# Compare Specific Operations
+# ============================================================================
+
+def compare_specific_operations():
+    """Compare specific operations between PraisonAI and Agno"""
+    print("\n" + "="*70)
+    print("OPERATION COMPARISON: PraisonAI vs Agno")
+    print("="*70)
+    
+    from praisonaiagents import Agent as PraisonAgent
+    from agno.agent import Agent as AgnoAgent
+    from agno.models.openai import OpenAIChat
+    
+    def get_weather(city: Literal["nyc", "sf"]):
+        if city == "nyc":
+            return "It might be cloudy in nyc"
+        elif city == "sf":
+            return "It's always sunny in sf"
+    tools = [get_weather]
+    
+    operations = []
+    
+    # Operation 1: Bare minimum instantiation
+    def praisonai_minimal():
+        return PraisonAgent(name="Test", verbose=False)
+    
+    def agno_minimal():
+        return AgnoAgent(model=OpenAIChat(id="gpt-4o-mini"))
+    
+    praison_stats = time_function(praisonai_minimal, 100)
+    agno_stats = time_function(agno_minimal, 100)
+    operations.append(("Minimal instantiation", praison_stats['avg'], agno_stats['avg']))
+    
+    # Operation 2: With tools
+    def praisonai_with_tools():
+        return PraisonAgent(name="Test", tools=tools, verbose=False)
+    
+    def agno_with_tools():
+        return AgnoAgent(model=OpenAIChat(id="gpt-4o-mini"), tools=tools)
+    
+    praison_stats = time_function(praisonai_with_tools, 100)
+    agno_stats = time_function(agno_with_tools, 100)
+    operations.append(("With tools", praison_stats['avg'], agno_stats['avg']))
+    
+    # Operation 3: Full config
+    def praisonai_full():
+        return PraisonAgent(
+            name="Test Agent",
+            instructions="Be concise",
+            llm="gpt-4o-mini",
+            tools=tools,
+            verbose=False
+        )
+    
+    def agno_full():
+        return AgnoAgent(
+            model=OpenAIChat(id="gpt-4o-mini"),
+            tools=tools,
+            instructions=["Be concise"]
+        )
+    
+    praison_stats = time_function(praisonai_full, 100)
+    agno_stats = time_function(agno_full, 100)
+    operations.append(("Full config", praison_stats['avg'], agno_stats['avg']))
+    
+    print(f"\n{'Operation':<30} {'PraisonAI (μs)':<18} {'Agno (μs)':<18} {'Ratio':<10}")
+    print("-" * 76)
+    
+    for op_name, praison_time, agno_time in operations:
+        ratio = praison_time / agno_time if agno_time > 0 else 0
+        print(f"{op_name:<30} {praison_time:<18.2f} {agno_time:<18.2f} {ratio:<10.1f}x")
+
+
+# ============================================================================
+# Main
+# ============================================================================
+
+if __name__ == "__main__":
+    import os
+    os.environ.setdefault("OPENAI_API_KEY", "sk-test")
+    
+    print("="*70)
+    print("PraisonAI Agents - Deep Profiling Benchmark")
+    print("="*70)
+    print("\nThis analysis identifies performance bottlenecks in agent initialization.\n")
+    
+    # Run all profiling
+    profile_praisonai_detailed()
+    profile_agno_detailed()
+    profile_praisonai_components()
+    compare_specific_operations()
+    
+    print("\n" + "="*70)
+    print("PROFILING COMPLETE")
+    print("="*70)

src/praisonai-agents/benchmarks/execution_benchmark.py

@@ -0,0 +1,278 @@
+#!/usr/bin/env python3
+"""
+PraisonAI Agents - Real Execution Benchmark
+
+Benchmarks ACTUAL agent execution with LLM API calls.
+Tests single agent running a simple task across frameworks.
+
+Usage:
+    export OPENAI_API_KEY=your_key
+    python benchmarks/execution_benchmark.py
+"""
+
+import time
+import os
+from importlib.metadata import version as get_version
+
+# Check API key
+if not os.environ.get('OPENAI_API_KEY'):
+    print("ERROR: Set OPENAI_API_KEY environment variable")
+    exit(1)
+
+MODEL = "gpt-4o-mini"
+ITERATIONS = 3
+PROMPT = "What is 2+2? Reply with just the number."
+
+
+def get_package_versions():
+    """Get version numbers for benchmarked packages."""
+    packages = {
+        'PraisonAI': 'praisonaiagents',
+        'Agno': 'agno',
+        'CrewAI': 'crewai',
+    }
+    versions = {}
+    for name, pkg in packages.items():
+        try:
+            versions[name] = get_version(pkg)
+        except:
+            versions[name] = 'not installed'
+    return versions
+
+
+def benchmark_praisonai():
+    """Benchmark PraisonAI agent execution using agent.start()"""
+    from praisonaiagents import Agent
+    
+    print("\n--- PraisonAI ---")
+    print(f"Method: agent.start()")
+    
+    times = []
+    for i in range(ITERATIONS):
+        agent = Agent(
+            name="Calculator",
+            instructions="You are a helpful assistant. Be very brief.",
+            llm=MODEL,
+            verbose=False
+        )
+        
+        start = time.perf_counter()
+        response = agent.start(PROMPT)
+        elapsed = time.perf_counter() - start
+        
+        times.append(elapsed)
+        print(f"  Run {i+1}: {elapsed:.2f}s - Response: {str(response)[:50]}")
+    
+    avg = sum(times) / len(times)
+    print(f"  Average: {avg:.2f}s")
+    return avg
+
+
+def benchmark_praisonai_litellm():
+    """Benchmark PraisonAI with LiteLLM backend using agent.start()"""
+    from praisonaiagents import Agent
+    
+    print("\n--- PraisonAI (LiteLLM) ---")
+    print(f"Method: agent.start()")
+    
+    times = []
+    for i in range(ITERATIONS):
+        agent = Agent(
+            name="Calculator",
+            instructions="You are a helpful assistant. Be very brief.",
+            llm=f"openai/{MODEL}",
+            verbose=False
+        )
+        
+        start = time.perf_counter()
+        response = agent.start(PROMPT)
+        elapsed = time.perf_counter() - start
+        
+        times.append(elapsed)
+        print(f"  Run {i+1}: {elapsed:.2f}s - Response: {str(response)[:50]}")
+    
+    avg = sum(times) / len(times)
+    print(f"  Average: {avg:.2f}s")
+    return avg
+
+
+def benchmark_agno():
+    """Benchmark Agno agent execution using agent.run()"""
+    from agno.agent import Agent
+    from agno.models.openai import OpenAIChat
+    
+    print("\n--- Agno ---")
+    print(f"Method: agent.run()")
+    
+    times = []
+    for i in range(ITERATIONS):
+        agent = Agent(
+            model=OpenAIChat(id=MODEL),
+            instructions=["You are a helpful assistant. Be very brief."],
+        )
+        
+        start = time.perf_counter()
+        response = agent.run(PROMPT)
+        elapsed = time.perf_counter() - start
+        
+        times.append(elapsed)
+        # Extract content from response
+        content = str(response.content)[:50] if hasattr(response, 'content') else str(response)[:50]
+        print(f"  Run {i+1}: {elapsed:.2f}s - Response: {content}")
+    
+    avg = sum(times) / len(times)
+    print(f"  Average: {avg:.2f}s")
+    return avg
+
+
+def benchmark_crewai():
+    """Benchmark CrewAI agent execution using crew.kickoff()"""
+    from crewai import Agent, Task, Crew
+    
+    print("\n--- CrewAI ---")
+    print(f"Method: crew.kickoff()")
+    
+    times = []
+    for i in range(ITERATIONS):
+        agent = Agent(
+            role="Calculator",
+            goal="Answer math questions",
+            backstory="You are a helpful assistant.",
+            llm=MODEL,
+            verbose=False
+        )
+        
+        task = Task(
+            description=PROMPT,
+            expected_output="A number",
+            agent=agent
+        )
+        
+        crew = Crew(
+            agents=[agent],
+            tasks=[task],
+            verbose=False
+        )
+        
+        start = time.perf_counter()
+        response = crew.kickoff()
+        elapsed = time.perf_counter() - start
+        
+        times.append(elapsed)
+        print(f"  Run {i+1}: {elapsed:.2f}s - Response: {str(response)[:50]}")
+    
+    avg = sum(times) / len(times)
+    print(f"  Average: {avg:.2f}s")
+    return avg
+
+
+def save_results(results: dict):
+    """Save results to markdown file."""
+    import os
+    from datetime import datetime
+    
+    filepath = os.path.join(os.path.dirname(__file__), 'EXECUTION_BENCHMARK_RESULTS.md')
+    versions = get_package_versions()
+    
+    # Sort by time
+    sorted_results = sorted(results.items(), key=lambda x: x[1])
+    fastest = sorted_results[0][1]
+    
+    with open(filepath, 'w') as f:
+        f.write('# PraisonAI Agents - Real Execution Benchmark\n\n')
+        f.write(f'**Generated:** {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}\n')
+        f.write(f'**Model:** {MODEL}\n')
+        f.write(f'**Iterations:** {ITERATIONS}\n')
+        f.write(f'**Prompt:** "{PROMPT}"\n\n')
+        f.write('## Results\n\n')
+        f.write('| Framework | Method | Avg Time | Relative |\n')
+        f.write('|-----------|--------|----------|----------|\n')
+        
+        methods = {
+            'PraisonAI': 'agent.start()',
+            'PraisonAI (LiteLLM)': 'agent.start()',
+            'Agno': 'agent.run()',
+            'CrewAI': 'crew.kickoff()',
+        }
+        
+        for name, avg in sorted_results:
+            ratio = avg / fastest
+            method = methods.get(name, 'unknown')
+            if ratio == 1.0:
+                f.write(f'| **{name}** | `{method}` | **{avg:.2f}s** | **1.00x (fastest)** |\n')
+            else:
+                f.write(f'| {name} | `{method}` | {avg:.2f}s | {ratio:.2f}x |\n')
+        
+        f.write('\n## Package Versions\n\n')
+        f.write('| Package | Version |\n')
+        f.write('|---------|--------|\n')
+        for pkg, ver in versions.items():
+            f.write(f'| {pkg} | {ver} |\n')
+        
+        f.write('\n## How to Reproduce\n\n')
+        f.write('```bash\n')
+        f.write('export OPENAI_API_KEY=your_key\n')
+        f.write('cd praisonai-agents\n')
+        f.write('python benchmarks/execution_benchmark.py\n')
+        f.write('```\n')
+    
+    print(f'\nResults saved to: {filepath}')
+
+
+if __name__ == '__main__':
+    print('=' * 60)
+    print('PraisonAI Agents - Real Execution Benchmark')
+    print('=' * 60)
+    print(f'\nModel: {MODEL}')
+    print(f'Iterations: {ITERATIONS}')
+    print(f'Prompt: "{PROMPT}"')
+    
+    versions = get_package_versions()
+    print(f'\nPackage versions:')
+    for pkg, ver in versions.items():
+        print(f'  {pkg}: {ver}')
+    
+    results = {}
+    
+    # Run benchmarks
+    try:
+        results['PraisonAI'] = benchmark_praisonai()
+    except Exception as e:
+        print(f"PraisonAI error: {e}")
+    
+    try:
+        results['PraisonAI (LiteLLM)'] = benchmark_praisonai_litellm()
+    except Exception as e:
+        print(f"PraisonAI (LiteLLM) error: {e}")
+    
+    try:
+        results['Agno'] = benchmark_agno()
+    except Exception as e:
+        print(f"Agno error: {e}")
+    
+    try:
+        results['CrewAI'] = benchmark_crewai()
+    except Exception as e:
+        print(f"CrewAI error: {e}")
+    
+    # Summary
+    print('\n' + '=' * 60)
+    print('SUMMARY')
+    print('=' * 60)
+    
+    if results:
+        sorted_results = sorted(results.items(), key=lambda x: x[1])
+        fastest = sorted_results[0][1]
+        
+        print(f"\n{'Framework':<25} {'Avg Time':<12} {'Relative':<10}")
+        print('-' * 47)
+        
+        for name, avg in sorted_results:
+            ratio = avg / fastest
+            print(f'{name:<25} {avg:<12.2f}s {ratio:.2f}x')
+    
+    print('\n' + '=' * 60)
+    
+    # Save results
+    if results:
+        save_results(results)

src/praisonai-agents/benchmarks/performance_benchmark.py

@@ -0,0 +1,523 @@
+"""
+PraisonAI Agents - Comprehensive Performance Benchmark
+
+Measures agent instantiation time and memory footprint across
+multiple AI agent frameworks using industry-standard methodology.
+
+Usage:
+    python benchmarks/performance_benchmark.py
+"""
+
+import gc
+import time
+import tracemalloc
+import statistics
+from typing import Literal, List, Callable
+from dataclasses import dataclass, field
+
+
+@dataclass
+class PerformanceResult:
+    """Performance measurement results"""
+    run_times: List[float] = field(default_factory=list)
+    memory_usages: List[float] = field(default_factory=list)
+    
+    @property
+    def avg_run_time(self) -> float:
+        return statistics.mean(self.run_times) if self.run_times else 0
+    
+    @property
+    def avg_memory_usage(self) -> float:
+        return statistics.mean(self.memory_usages) if self.memory_usages else 0
+    
+    @property
+    def min_run_time(self) -> float:
+        return min(self.run_times) if self.run_times else 0
+    
+    @property
+    def max_run_time(self) -> float:
+        return max(self.run_times) if self.run_times else 0
+    
+    @property
+    def std_dev_run_time(self) -> float:
+        return statistics.stdev(self.run_times) if len(self.run_times) > 1 else 0
+    
+    @property
+    def median_run_time(self) -> float:
+        return statistics.median(self.run_times) if self.run_times else 0
+
+
+def compute_tracemalloc_baseline(samples: int = 3) -> float:
+    """Compute baseline memory usage for tracemalloc"""
+    def empty_func():
+        return
+    
+    results = []
+    for _ in range(samples):
+        gc.collect()
+        tracemalloc.start()
+        empty_func()
+        _, peak = tracemalloc.get_traced_memory()
+        tracemalloc.stop()
+        results.append(peak / 1024 / 1024)  # Convert to MiB
+    
+    return sum(results) / len(results) if results else 0
+
+
+def measure_time(func: Callable, iterations: int = 1000, warmup: int = 10) -> List[float]:
+    """Measure execution time for a function"""
+    # Warmup runs
+    for _ in range(warmup):
+        func()
+    
+    # Measured runs
+    times = []
+    for _ in range(iterations):
+        start = time.perf_counter()
+        func()
+        end = time.perf_counter()
+        times.append(end - start)
+    
+    return times
+
+
+def measure_memory(func: Callable, iterations: int = 1000, warmup: int = 10) -> List[float]:
+    """Measure memory usage for a function"""
+    baseline = compute_tracemalloc_baseline()
+    
+    # Warmup runs
+    for _ in range(warmup):
+        func()
+    
+    # Measured runs
+    memory_usages = []
+    for _ in range(iterations):
+        gc.collect()
+        tracemalloc.start()
+        func()
+        _, peak = tracemalloc.get_traced_memory()
+        tracemalloc.stop()
+        
+        peak_mib = peak / 1024 / 1024
+        adjusted = max(0, peak_mib - baseline)
+        memory_usages.append(adjusted)
+    
+    return memory_usages
+
+
+def run_benchmark(func: Callable, name: str, iterations: int = 1000, warmup: int = 10) -> PerformanceResult:
+    """Run full benchmark for a function"""
+    print(f"\n{'='*60}")
+    print(f"Benchmarking: {name}")
+    print(f"{'='*60}")
+    print(f"Warmup runs: {warmup}")
+    print(f"Measured iterations: {iterations}")
+    
+    print("\nMeasuring runtime...")
+    run_times = measure_time(func, iterations, warmup)
+    
+    print("Measuring memory...")
+    memory_usages = measure_memory(func, iterations, warmup)
+    
+    result = PerformanceResult(run_times=run_times, memory_usages=memory_usages)
+    
+    print(f"\nResults for {name}:")
+    print(f"  Average Time: {result.avg_run_time:.6f} seconds ({result.avg_run_time * 1_000_000:.2f} μs)")
+    print(f"  Min Time:     {result.min_run_time:.6f} seconds")
+    print(f"  Max Time:     {result.max_run_time:.6f} seconds")
+    print(f"  Std Dev:      {result.std_dev_run_time:.6f} seconds")
+    print(f"  Median Time:  {result.median_run_time:.6f} seconds")
+    print(f"  Avg Memory:   {result.avg_memory_usage:.6f} MiB ({result.avg_memory_usage * 1024:.2f} KiB)")
+    
+    return result
+
+
+# ============================================================================
+# Framework Imports
+# ============================================================================
+from praisonaiagents import Agent as PraisonAgent
+from agno.agent import Agent as AgnoAgent
+from agno.models.openai import OpenAIChat
+
+# Additional frameworks for comparison
+try:
+    from pydantic_ai import Agent as PydanticAgent
+    PYDANTIC_AVAILABLE = True
+except ImportError:
+    PYDANTIC_AVAILABLE = False
+
+try:
+    from crewai.agent import Agent as CrewAgent
+    from crewai.tools import tool as crewai_tool
+    CREWAI_AVAILABLE = True
+except ImportError:
+    CREWAI_AVAILABLE = False
+
+try:
+    from langchain_core.tools import tool as langchain_tool
+    from langchain_openai import ChatOpenAI
+    from langgraph.prebuilt import create_react_agent
+    LANGGRAPH_AVAILABLE = True
+except ImportError:
+    LANGGRAPH_AVAILABLE = False
+
+try:
+    from agents import Agent as OpenAIAgent, function_tool
+    OPENAI_AGENTS_AVAILABLE = True
+except ImportError:
+    OPENAI_AGENTS_AVAILABLE = False
+
+
+def get_weather(city: Literal["nyc", "sf"]):
+    """Use this to get weather information."""
+    if city == "nyc":
+        return "It might be cloudy in nyc"
+    elif city == "sf":
+        return "It's always sunny in sf"
+
+
+tools = [get_weather]
+
+
+def instantiate_praisonai_agent():
+    """Instantiate a PraisonAI agent with one tool."""
+    return PraisonAgent(
+        name="Test Agent",
+        instructions="Be concise, reply with one sentence.",
+        llm="gpt-4o-mini",
+        tools=tools,
+        verbose=False
+    )
+
+
+def instantiate_praisonai_litellm_agent():
+    """Instantiate a PraisonAI agent with LiteLLM backend."""
+    return PraisonAgent(
+        name="Test Agent",
+        instructions="Be concise, reply with one sentence.",
+        llm="openai/gpt-4o-mini",
+        tools=tools,
+        verbose=False
+    )
+
+
+def instantiate_agno_agent():
+    """Instantiate an Agno agent with one tool."""
+    return AgnoAgent(model=OpenAIChat(id="gpt-4o-mini"), tools=tools)
+
+
+def instantiate_pydantic_agent():
+    """Instantiate a PydanticAI agent with one tool."""
+    agent = PydanticAgent("openai:gpt-4o-mini", system_prompt="Be concise, reply with one sentence.")
+    @agent.tool_plain
+    def get_weather_pydantic(city: Literal["nyc", "sf"]):
+        """Use this to get weather information."""
+        if city == "nyc":
+            return "It might be cloudy in nyc"
+        elif city == "sf":
+            return "It's always sunny in sf"
+    return agent
+
+
+def instantiate_openai_agents():
+    """Instantiate an OpenAI Agents SDK agent with one tool."""
+    return OpenAIAgent(
+        name="Test agent",
+        instructions="Be concise, reply with one sentence.",
+        model="gpt-4o-mini",
+        tools=[function_tool(get_weather)],
+    )
+
+
+def instantiate_langgraph_agent():
+    """Instantiate a LangGraph agent with one tool."""
+    @langchain_tool
+    def get_weather_lg(city: Literal["nyc", "sf"]):
+        """Use this to get weather information."""
+        if city == "nyc":
+            return "It might be cloudy in nyc"
+        elif city == "sf":
+            return "It's always sunny in sf"
+    return create_react_agent(model=ChatOpenAI(model="gpt-4o-mini"), tools=[get_weather_lg])
+
+
+def instantiate_crewai_agent():
+    """Instantiate a CrewAI agent with one tool."""
+    @crewai_tool("Weather Tool")
+    def get_weather_crew(city: Literal["nyc", "sf"]):
+        """Use this to get weather information."""
+        if city == "nyc":
+            return "It might be cloudy in nyc"
+        elif city == "sf":
+            return "It's always sunny in sf"
+    return CrewAgent(
+        llm="gpt-4o-mini",
+        role="Test Agent",
+        goal="Be concise, reply with one sentence.",
+        tools=[get_weather_crew],
+        backstory="Test",
+    )
+
+
+def get_package_versions():
+    """Get version numbers for all benchmarked packages."""
+    from importlib.metadata import version as get_version
+    
+    packages = {
+        'PraisonAI': 'praisonaiagents',
+        'Agno': 'agno',
+        'PydanticAI': 'pydantic-ai',
+        'OpenAI Agents SDK': 'openai-agents',
+        'LangGraph': 'langgraph',
+        'CrewAI': 'crewai'
+    }
+    
+    versions = {}
+    for display_name, pkg_name in packages.items():
+        try:
+            versions[display_name] = get_version(pkg_name)
+        except Exception:
+            versions[display_name] = 'not installed'
+    
+    return versions
+
+
+def save_benchmark_results(results: dict, baseline):
+    """Save benchmark results to BENCHMARK_RESULTS.md and update README.md"""
+    import os
+    import re
+    from datetime import datetime
+    
+    benchmarks_dir = os.path.dirname(__file__)
+    filepath = os.path.join(benchmarks_dir, 'BENCHMARK_RESULTS.md')
+    
+    # Get package versions
+    versions = get_package_versions()
+    
+    display_names = {
+        'agno': 'Agno',
+        'praisonai': 'PraisonAI',
+        'praisonai_litellm': 'PraisonAI (LiteLLM)',
+        'pydantic': 'PydanticAI',
+        'openai_agents': 'OpenAI Agents SDK',
+        'langgraph': 'LangGraph',
+        'crewai': 'CrewAI'
+    }
+    
+    # Build table rows
+    table_rows = []
+    sorted_results = sorted(results.items(), key=lambda x: x[1].avg_run_time)
+    
+    for name, result in sorted_results:
+        display_name = display_names.get(name, name)
+        avg_us = result.avg_run_time * 1e6
+        ratio = result.avg_run_time / baseline.avg_run_time if baseline.avg_run_time > 0 else 0
+        
+        if name == 'praisonai':
+            table_rows.append(f'| **{display_name}** | **{avg_us:.2f}** | **1.00x (fastest)** |')
+        else:
+            avg_str = f'{avg_us:,.2f}' if avg_us >= 1000 else f'{avg_us:.2f}'
+            ratio_str = f'{ratio:,.0f}x' if ratio >= 100 else f'{ratio:.2f}x'
+            table_rows.append(f'| {display_name} | {avg_str} | {ratio_str} |')
+    
+    # Save to BENCHMARK_RESULTS.md
+    with open(filepath, 'w') as f:
+        f.write('# PraisonAI Agents - Benchmark Results\n\n')
+        f.write(f'**Generated:** {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}\n')
+        f.write('**Methodology:** 10 warmup runs + 1000 measured iterations\n\n')
+        f.write('## Agent Instantiation Time\n\n')
+        f.write('| Framework | Avg Time (μs) | Relative |\n')
+        f.write('|-----------|---------------|----------|\n')
+        f.write('\n'.join(table_rows) + '\n')
+        f.write('\n## Package Versions\n\n')
+        f.write('| Package | Version |\n')
+        f.write('|---------|--------|\n')
+        for pkg, ver in versions.items():
+            f.write(f'| {pkg} | {ver} |\n')
+        f.write('\n## How to Reproduce\n\n')
+        f.write('```bash\n')
+        f.write('cd praisonai-agents\n')
+        f.write('python benchmarks/performance_benchmark.py\n')
+        f.write('```\n')
+    
+    print(f'\nResults saved to: {filepath}')
+    
+    # Update README.md
+    readme_path = os.path.join(benchmarks_dir, '..', '..', '..', 'README.md')
+    if os.path.exists(readme_path):
+        with open(readme_path, 'r') as f:
+            content = f.read()
+        
+        new_table = '''| Framework | Avg Time (μs) | Relative |
+|-----------|---------------|----------|
+''' + '\n'.join(table_rows)
+        
+        pattern = r'(\| Framework \| Avg Time \(μs\) \| Relative \|\n\|[-|]+\|\n)(\|[^\n]+\|\n)+'
+        
+        if re.search(pattern, content):
+            content = re.sub(pattern, new_table + '\n', content)
+            
+            with open(readme_path, 'w') as f:
+                f.write(content)
+            
+            print(f'README.md updated: {readme_path}')
+
+
+if __name__ == "__main__":
+    print("="*70)
+    print("PraisonAI Agents - Comprehensive Performance Benchmark")
+    print("="*70)
+    print("\nThis benchmark measures:")
+    print("  1. Agent instantiation time (how fast an agent object is created)")
+    print("  2. Memory footprint (how much memory an agent uses)")
+    print("\nMethodology:")
+    print("  - 10 warmup runs (not measured)")
+    print("  - 1000 measured iterations")
+    print("  - tracemalloc for memory measurement")
+    print("  - Baseline subtraction for accuracy")
+    print("\nFrameworks available:")
+    print("  - PraisonAI: ✅")
+    print(f"  - Agno: ✅")
+    print(f"  - PydanticAI: {'✅' if PYDANTIC_AVAILABLE else '❌'}")
+    print(f"  - OpenAI Agents SDK: {'✅' if OPENAI_AGENTS_AVAILABLE else '❌'}")
+    print(f"  - LangGraph: {'✅' if LANGGRAPH_AVAILABLE else '❌'}")
+    print(f"  - CrewAI: {'✅' if CREWAI_AVAILABLE else '❌'}")
+    
+    results = {}
+    
+    # Test PraisonAI first
+    try:
+        print("\n" + "="*70)
+        print("Testing PraisonAI Agents")
+        print("="*70)
+        results['praisonai'] = run_benchmark(
+            instantiate_praisonai_agent,
+            "PraisonAI",
+            iterations=1000,
+            warmup=10
+        )
+    except Exception as e:
+        print(f"Error testing PraisonAI: {e}")
+    
+    try:
+        print("\n" + "="*70)
+        print("Testing PraisonAI Agents (LiteLLM)")
+        print("="*70)
+        results['praisonai_litellm'] = run_benchmark(
+            instantiate_praisonai_litellm_agent,
+            "PraisonAI (LiteLLM)",
+            iterations=1000,
+            warmup=10
+        )
+    except Exception as e:
+        print(f"Error testing PraisonAI (LiteLLM): {e}")
+    
+    # Test Agno
+    try:
+        print("\n" + "="*70)
+        print("Testing Agno")
+        print("="*70)
+        results['agno'] = run_benchmark(
+            instantiate_agno_agent,
+            "Agno",
+            iterations=1000,
+            warmup=10
+        )
+    except Exception as e:
+        print(f"Error testing Agno: {e}")
+    
+    # Test PydanticAI
+    if PYDANTIC_AVAILABLE:
+        try:
+            print("\n" + "="*70)
+            print("Testing PydanticAI")
+            print("="*70)
+            results['pydantic'] = run_benchmark(
+                instantiate_pydantic_agent,
+                "PydanticAI Agent Instantiation",
+                iterations=1000,
+                warmup=10
+            )
+        except Exception as e:
+            print(f"Error testing PydanticAI: {e}")
+    
+    # Test OpenAI Agents SDK
+    if OPENAI_AGENTS_AVAILABLE:
+        try:
+            print("\n" + "="*70)
+            print("Testing OpenAI Agents SDK")
+            print("="*70)
+            results['openai_agents'] = run_benchmark(
+                instantiate_openai_agents,
+                "OpenAI Agents SDK Instantiation",
+                iterations=1000,
+                warmup=10
+            )
+        except Exception as e:
+            print(f"Error testing OpenAI Agents SDK: {e}")
+    
+    # Test LangGraph
+    if LANGGRAPH_AVAILABLE:
+        try:
+            print("\n" + "="*70)
+            print("Testing LangGraph")
+            print("="*70)
+            results['langgraph'] = run_benchmark(
+                instantiate_langgraph_agent,
+                "LangGraph Agent Instantiation",
+                iterations=1000,
+                warmup=10
+            )
+        except Exception as e:
+            print(f"Error testing LangGraph: {e}")
+    
+    # Test CrewAI
+    if CREWAI_AVAILABLE:
+        try:
+            print("\n" + "="*70)
+            print("Testing CrewAI")
+            print("="*70)
+            results['crewai'] = run_benchmark(
+                instantiate_crewai_agent,
+                "CrewAI Agent Instantiation",
+                iterations=1000,
+                warmup=10
+            )
+        except Exception as e:
+            print(f"Error testing CrewAI: {e}")
+    
+    # Final Comparison Table
+    print("\n" + "="*70)
+    print("RESULTS SUMMARY")
+    print("="*70)
+    
+    if 'praisonai' in results:
+        baseline = results['praisonai']
+        
+        print(f"\n{'Framework':<25} {'Avg Time (μs)':<15} {'Avg Memory (KiB)':<18} {'Relative':<15}")
+        print("-" * 73)
+        
+        # Sort by time
+        sorted_results = sorted(results.items(), key=lambda x: x[1].avg_run_time)
+        
+        for name, result in sorted_results:
+            time_ratio = result.avg_run_time / baseline.avg_run_time if baseline.avg_run_time > 0 else 0
+            
+            time_str = f"{time_ratio:.2f}x"
+            
+            display_name = {
+                'agno': 'Agno',
+                'praisonai': 'PraisonAI',
+                'praisonai_litellm': 'PraisonAI (LiteLLM)',
+                'pydantic': 'PydanticAI',
+                'openai_agents': 'OpenAI Agents SDK',
+                'langgraph': 'LangGraph',
+                'crewai': 'CrewAI'
+            }.get(name, name)
+            
+            print(f"{display_name:<25} {result.avg_run_time*1e6:<15.2f} {result.avg_memory_usage*1024:<18.2f} {time_str:<15}")
+        
+        print("\n" + "="*70)
+        
+        # Save results to files
+        save_benchmark_results(results, baseline)

src/praisonai-agents/benchmarks/real_benchmark.py

@@ -0,0 +1,481 @@
+#!/usr/bin/env python3
+"""
+PraisonAI Agents - Real-World Execution Benchmark
+
+Benchmarks actual agent execution with LLM calls across frameworks.
+Tests single agent, 2-agent, and 3-agent workflows.
+
+Usage:
+    python benchmarks/real_benchmark.py
+"""
+
+import time
+import statistics
+import os
+from typing import Literal, List
+from dataclasses import dataclass, field
+from importlib.metadata import version as get_version
+
+# Ensure API key is set
+if not os.environ.get('OPENAI_API_KEY'):
+    print("ERROR: OPENAI_API_KEY environment variable not set")
+    exit(1)
+
+
+@dataclass
+class ExecutionResult:
+    """Execution benchmark results"""
+    times: List[float] = field(default_factory=list)
+    
+    @property
+    def avg_time(self) -> float:
+        return statistics.mean(self.times) if self.times else 0
+    
+    @property
+    def min_time(self) -> float:
+        return min(self.times) if self.times else 0
+    
+    @property
+    def max_time(self) -> float:
+        return max(self.times) if self.times else 0
+    
+    @property
+    def std_dev(self) -> float:
+        return statistics.stdev(self.times) if len(self.times) > 1 else 0
+
+
+def get_package_versions():
+    """Get version numbers for benchmarked packages."""
+    packages = {
+        'PraisonAI': 'praisonaiagents',
+        'Agno': 'agno',
+    }
+    versions = {}
+    for display_name, pkg_name in packages.items():
+        try:
+            versions[display_name] = get_version(pkg_name)
+        except Exception:
+            versions[display_name] = 'not installed'
+    return versions
+
+
+# ============================================================================
+# Tool Definitions
+# ============================================================================
+
+def get_weather(city: Literal["nyc", "sf"]) -> str:
+    """Get weather information for a city."""
+    if city == "nyc":
+        return "New York: Cloudy, 45°F, 60% humidity"
+    return "San Francisco: Sunny, 72°F, 40% humidity"
+
+
+def get_time(timezone: Literal["est", "pst"]) -> str:
+    """Get current time in a timezone."""
+    if timezone == "est":
+        return "Eastern Time: 2:30 PM"
+    return "Pacific Time: 11:30 AM"
+
+
+def calculate(expression: str) -> str:
+    """Calculate a math expression."""
+    try:
+        result = eval(expression)
+        return f"Result: {result}"
+    except Exception:
+        return "Error: Invalid expression"
+
+
+# ============================================================================
+# PraisonAI Benchmarks
+# ============================================================================
+
+def benchmark_praisonai_single(iterations: int = 3) -> ExecutionResult:
+    """Benchmark single PraisonAI agent execution."""
+    from praisonaiagents import Agent
+    
+    result = ExecutionResult()
+    
+    for i in range(iterations):
+        print(f"  PraisonAI single agent iteration {i+1}/{iterations}...")
+        
+        agent = Agent(
+            name="Assistant",
+            instructions="You are a helpful assistant. Be very brief.",
+            llm="gpt-4o-mini",
+            tools=[get_weather],
+            verbose=False
+        )
+        
+        start = time.perf_counter()
+        response = agent.start("What's the weather in NYC? Reply in one sentence.")
+        elapsed = time.perf_counter() - start
+        
+        result.times.append(elapsed)
+        print(f"    Time: {elapsed:.2f}s")
+    
+    return result
+
+
+def benchmark_praisonai_two_agents(iterations: int = 3) -> ExecutionResult:
+    """Benchmark 2 PraisonAI agents working together."""
+    from praisonaiagents import Agent, PraisonAIAgents
+    
+    result = ExecutionResult()
+    
+    for i in range(iterations):
+        print(f"  PraisonAI 2-agent iteration {i+1}/{iterations}...")
+        
+        researcher = Agent(
+            name="Researcher",
+            role="Research Assistant",
+            goal="Gather weather information",
+            instructions="Get weather data and pass to reporter. Be brief.",
+            llm="gpt-4o-mini",
+            tools=[get_weather],
+            verbose=False
+        )
+        
+        reporter = Agent(
+            name="Reporter",
+            role="News Reporter",
+            goal="Create weather report",
+            instructions="Create a one-sentence weather summary.",
+            llm="gpt-4o-mini",
+            verbose=False
+        )
+        
+        agents = PraisonAIAgents(
+            agents=[researcher, reporter],
+            process="sequential",
+            verbose=False
+        )
+        
+        start = time.perf_counter()
+        response = agents.start("Get NYC weather and create a brief report.")
+        elapsed = time.perf_counter() - start
+        
+        result.times.append(elapsed)
+        print(f"    Time: {elapsed:.2f}s")
+    
+    return result
+
+
+def benchmark_praisonai_three_agents(iterations: int = 3) -> ExecutionResult:
+    """Benchmark 3 PraisonAI agents working together."""
+    from praisonaiagents import Agent, PraisonAIAgents
+    
+    result = ExecutionResult()
+    
+    for i in range(iterations):
+        print(f"  PraisonAI 3-agent iteration {i+1}/{iterations}...")
+        
+        weather_agent = Agent(
+            name="WeatherAgent",
+            role="Weather Specialist",
+            goal="Get weather data",
+            instructions="Fetch weather information. Be brief.",
+            llm="gpt-4o-mini",
+            tools=[get_weather],
+            verbose=False
+        )
+        
+        time_agent = Agent(
+            name="TimeAgent",
+            role="Time Specialist",
+            goal="Get time information",
+            instructions="Fetch timezone information. Be brief.",
+            llm="gpt-4o-mini",
+            tools=[get_time],
+            verbose=False
+        )
+        
+        summarizer = Agent(
+            name="Summarizer",
+            role="Summary Writer",
+            goal="Combine information",
+            instructions="Create a one-sentence summary combining weather and time.",
+            llm="gpt-4o-mini",
+            verbose=False
+        )
+        
+        agents = PraisonAIAgents(
+            agents=[weather_agent, time_agent, summarizer],
+            process="sequential",
+            verbose=False
+        )
+        
+        start = time.perf_counter()
+        response = agents.start("Get NYC weather and EST time, then summarize.")
+        elapsed = time.perf_counter() - start
+        
+        result.times.append(elapsed)
+        print(f"    Time: {elapsed:.2f}s")
+    
+    return result
+
+
+# ============================================================================
+# Agno Benchmarks
+# ============================================================================
+
+def benchmark_agno_single(iterations: int = 3) -> ExecutionResult:
+    """Benchmark single Agno agent execution."""
+    from agno.agent import Agent
+    from agno.models.openai import OpenAIChat
+    
+    result = ExecutionResult()
+    
+    for i in range(iterations):
+        print(f"  Agno single agent iteration {i+1}/{iterations}...")
+        
+        agent = Agent(
+            model=OpenAIChat(id="gpt-4o-mini"),
+            instructions=["You are a helpful assistant. Be very brief."],
+            tools=[get_weather],
+        )
+        
+        start = time.perf_counter()
+        response = agent.run("What's the weather in NYC? Reply in one sentence.")
+        elapsed = time.perf_counter() - start
+        
+        result.times.append(elapsed)
+        print(f"    Time: {elapsed:.2f}s")
+    
+    return result
+
+
+def benchmark_agno_two_agents(iterations: int = 3) -> ExecutionResult:
+    """Benchmark 2 Agno agents working together (using Team)."""
+    from agno.agent import Agent
+    from agno.models.openai import OpenAIChat
+    from agno.team import Team
+    
+    result = ExecutionResult()
+    
+    for i in range(iterations):
+        print(f"  Agno 2-agent iteration {i+1}/{iterations}...")
+        
+        researcher = Agent(
+            name="Researcher",
+            model=OpenAIChat(id="gpt-4o-mini"),
+            instructions=["Get weather data. Be brief."],
+            tools=[get_weather],
+        )
+        
+        reporter = Agent(
+            name="Reporter",
+            model=OpenAIChat(id="gpt-4o-mini"),
+            instructions=["Create a one-sentence weather summary."],
+        )
+        
+        team = Team(
+            members=[researcher, reporter],
+            model=OpenAIChat(id="gpt-4o-mini"),
+        )
+        
+        start = time.perf_counter()
+        response = team.run("Get NYC weather and create a brief report.")
+        elapsed = time.perf_counter() - start
+        
+        result.times.append(elapsed)
+        print(f"    Time: {elapsed:.2f}s")
+    
+    return result
+
+
+def benchmark_agno_three_agents(iterations: int = 3) -> ExecutionResult:
+    """Benchmark 3 Agno agents working together."""
+    from agno.agent import Agent
+    from agno.models.openai import OpenAIChat
+    from agno.team import Team
+    
+    result = ExecutionResult()
+    
+    for i in range(iterations):
+        print(f"  Agno 3-agent iteration {i+1}/{iterations}...")
+        
+        weather_agent = Agent(
+            name="WeatherAgent",
+            model=OpenAIChat(id="gpt-4o-mini"),
+            instructions=["Fetch weather information. Be brief."],
+            tools=[get_weather],
+        )
+        
+        time_agent = Agent(
+            name="TimeAgent",
+            model=OpenAIChat(id="gpt-4o-mini"),
+            instructions=["Fetch timezone information. Be brief."],
+            tools=[get_time],
+        )
+        
+        summarizer = Agent(
+            name="Summarizer",
+            model=OpenAIChat(id="gpt-4o-mini"),
+            instructions=["Create a one-sentence summary combining weather and time."],
+        )
+        
+        team = Team(
+            members=[weather_agent, time_agent, summarizer],
+            model=OpenAIChat(id="gpt-4o-mini"),
+        )
+        
+        start = time.perf_counter()
+        response = team.run("Get NYC weather and EST time, then summarize.")
+        elapsed = time.perf_counter() - start
+        
+        result.times.append(elapsed)
+        print(f"    Time: {elapsed:.2f}s")
+    
+    return result
+
+
+# ============================================================================
+# Save Results
+# ============================================================================
+
+def save_results(results: dict, versions: dict):
+    """Save benchmark results to file."""
+    import os
+    from datetime import datetime
+    
+    filepath = os.path.join(os.path.dirname(__file__), 'REAL_BENCHMARK_RESULTS.md')
+    
+    with open(filepath, 'w') as f:
+        f.write('# PraisonAI Agents - Real-World Execution Benchmark\n\n')
+        f.write(f'**Generated:** {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}\n')
+        f.write('**Model:** gpt-4o-mini\n')
+        f.write('**Iterations:** 3 per test\n\n')
+        
+        f.write('## Results Summary\n\n')
+        f.write('| Test | PraisonAI (avg) | Agno (avg) | Difference |\n')
+        f.write('|------|-----------------|------------|------------|\n')
+        
+        tests = ['single', 'two_agents', 'three_agents']
+        test_names = ['Single Agent', '2 Agents', '3 Agents']
+        
+        for test, name in zip(tests, test_names):
+            praison_key = f'praisonai_{test}'
+            agno_key = f'agno_{test}'
+            
+            if praison_key in results and agno_key in results:
+                praison_avg = results[praison_key].avg_time
+                agno_avg = results[agno_key].avg_time
+                
+                if praison_avg < agno_avg:
+                    diff = f'PraisonAI {agno_avg/praison_avg:.2f}x faster'
+                else:
+                    diff = f'Agno {praison_avg/agno_avg:.2f}x faster'
+                
+                f.write(f'| {name} | {praison_avg:.2f}s | {agno_avg:.2f}s | {diff} |\n')
+        
+        f.write('\n## Detailed Results\n\n')
+        
+        for key, result in results.items():
+            f.write(f'### {key}\n')
+            f.write(f'- Average: {result.avg_time:.2f}s\n')
+            f.write(f'- Min: {result.min_time:.2f}s\n')
+            f.write(f'- Max: {result.max_time:.2f}s\n')
+            f.write(f'- Std Dev: {result.std_dev:.2f}s\n')
+            f.write(f'- Times: {[f"{t:.2f}s" for t in result.times]}\n\n')
+        
+        f.write('## Package Versions\n\n')
+        f.write('| Package | Version |\n')
+        f.write('|---------|--------|\n')
+        for pkg, ver in versions.items():
+            f.write(f'| {pkg} | {ver} |\n')
+        
+        f.write('\n## How to Reproduce\n\n')
+        f.write('```bash\n')
+        f.write('export OPENAI_API_KEY=your_key\n')
+        f.write('cd praisonai-agents\n')
+        f.write('python benchmarks/real_benchmark.py\n')
+        f.write('```\n')
+    
+    print(f'\nResults saved to: {filepath}')
+
+
+# ============================================================================
+# Main
+# ============================================================================
+
+if __name__ == "__main__":
+    print("="*70)
+    print("PraisonAI Agents - Real-World Execution Benchmark")
+    print("="*70)
+    print("\nThis benchmark measures actual agent execution time including:")
+    print("  - LLM API calls")
+    print("  - Tool execution")
+    print("  - Multi-agent coordination")
+    print("\nModel: gpt-4o-mini")
+    print("Iterations: 3 per test")
+    
+    versions = get_package_versions()
+    print(f"\nPackage versions:")
+    for pkg, ver in versions.items():
+        print(f"  {pkg}: {ver}")
+    
+    results = {}
+    
+    # Single Agent Tests
+    print("\n" + "="*70)
+    print("SINGLE AGENT BENCHMARK")
+    print("="*70)
+    
+    print("\nTesting PraisonAI single agent...")
+    results['praisonai_single'] = benchmark_praisonai_single()
+    
+    print("\nTesting Agno single agent...")
+    results['agno_single'] = benchmark_agno_single()
+    
+    # 2-Agent Tests
+    print("\n" + "="*70)
+    print("2-AGENT BENCHMARK")
+    print("="*70)
+    
+    print("\nTesting PraisonAI 2 agents...")
+    results['praisonai_two_agents'] = benchmark_praisonai_two_agents()
+    
+    print("\nTesting Agno 2 agents (Team)...")
+    results['agno_two_agents'] = benchmark_agno_two_agents()
+    
+    # 3-Agent Tests
+    print("\n" + "="*70)
+    print("3-AGENT BENCHMARK")
+    print("="*70)
+    
+    print("\nTesting PraisonAI 3 agents...")
+    results['praisonai_three_agents'] = benchmark_praisonai_three_agents()
+    
+    print("\nTesting Agno 3 agents (Team)...")
+    results['agno_three_agents'] = benchmark_agno_three_agents()
+    
+    # Summary
+    print("\n" + "="*70)
+    print("RESULTS SUMMARY")
+    print("="*70)
+    
+    print(f"\n{'Test':<20} {'PraisonAI (avg)':<18} {'Agno (avg)':<15} {'Difference':<20}")
+    print("-" * 73)
+    
+    tests = [
+        ('Single Agent', 'praisonai_single', 'agno_single'),
+        ('2 Agents', 'praisonai_two_agents', 'agno_two_agents'),
+        ('3 Agents', 'praisonai_three_agents', 'agno_three_agents'),
+    ]
+    
+    for name, praison_key, agno_key in tests:
+        praison_avg = results[praison_key].avg_time
+        agno_avg = results[agno_key].avg_time
+        
+        if praison_avg < agno_avg:
+            diff = f'PraisonAI {agno_avg/praison_avg:.2f}x faster'
+        else:
+            diff = f'Agno {praison_avg/agno_avg:.2f}x faster'
+        
+        print(f"{name:<20} {praison_avg:<18.2f}s {agno_avg:<15.2f}s {diff:<20}")
+    
+    print("\n" + "="*70)
+    
+    # Save results
+    save_results(results, versions)

src/praisonai-agents/benchmarks/response_benchmark.py

@@ -0,0 +1,235 @@
+"""
+PraisonAI Agents - Response Time Benchmark
+
+Measures actual LLM response time with gpt-4o-mini across frameworks.
+
+Usage:
+    python benchmarks/response_benchmark.py
+"""
+
+import time
+import statistics
+import os
+from typing import List, Literal
+from dataclasses import dataclass, field
+
+# Ensure we use gpt-4o-mini
+os.environ['OPENAI_MODEL_NAME'] = 'gpt-4o-mini'
+
+
+@dataclass
+class ResponseResult:
+    """Response time measurement results"""
+    response_times: List[float] = field(default_factory=list)
+    
+    @property
+    def avg_time(self) -> float:
+        return statistics.mean(self.response_times) if self.response_times else 0
+    
+    @property
+    def min_time(self) -> float:
+        return min(self.response_times) if self.response_times else 0
+    
+    @property
+    def max_time(self) -> float:
+        return max(self.response_times) if self.response_times else 0
+    
+    @property
+    def std_dev(self) -> float:
+        return statistics.stdev(self.response_times) if len(self.response_times) > 1 else 0
+
+
+def get_weather(city: Literal["nyc", "sf"]):
+    """Use this to get weather information."""
+    if city == "nyc":
+        return "It might be cloudy in nyc"
+    elif city == "sf":
+        return "It's always sunny in sf"
+
+
+def benchmark_praisonai_response(iterations: int = 5) -> ResponseResult:
+    """Benchmark PraisonAI agent response time"""
+    from praisonaiagents import Agent
+    
+    result = ResponseResult()
+    
+    for i in range(iterations):
+        print(f"  PraisonAI iteration {i+1}/{iterations}...")
+        
+        # Create fresh agent each time
+        agent = Agent(
+            name="Test Agent",
+            instructions="Be concise, reply with one sentence.",
+            llm="gpt-4o-mini",
+            verbose=False
+        )
+        
+        start = time.perf_counter()
+        response = agent.start("What is the capital of France?")
+        end = time.perf_counter()
+        
+        result.response_times.append(end - start)
+        print(f"    Response time: {end - start:.3f}s")
+    
+    return result
+
+
+def benchmark_agno_response(iterations: int = 5) -> ResponseResult:
+    """Benchmark Agno agent response time"""
+    from agno.agent import Agent
+    from agno.models.openai import OpenAIChat
+    
+    result = ResponseResult()
+    
+    for i in range(iterations):
+        print(f"  Agno iteration {i+1}/{iterations}...")
+        
+        # Create fresh agent each time
+        agent = Agent(
+            model=OpenAIChat(id="gpt-4o-mini"),
+            system_message="Be concise, reply with one sentence.",
+        )
+        
+        start = time.perf_counter()
+        response = agent.run("What is the capital of France?")
+        end = time.perf_counter()
+        
+        result.response_times.append(end - start)
+        print(f"    Response time: {end - start:.3f}s")
+    
+    return result
+
+
+def benchmark_praisonai_with_tool(iterations: int = 5) -> ResponseResult:
+    """Benchmark PraisonAI agent response time with tool calling"""
+    from praisonaiagents import Agent
+    
+    result = ResponseResult()
+    
+    for i in range(iterations):
+        print(f"  PraisonAI (with tool) iteration {i+1}/{iterations}...")
+        
+        agent = Agent(
+            name="Weather Agent",
+            instructions="You are a weather assistant. Use the get_weather tool to answer questions.",
+            llm="gpt-4o-mini",
+            tools=[get_weather],
+            verbose=False
+        )
+        
+        start = time.perf_counter()
+        response = agent.start("What's the weather in NYC?")
+        end = time.perf_counter()
+        
+        result.response_times.append(end - start)
+        print(f"    Response time: {end - start:.3f}s")
+    
+    return result
+
+
+def benchmark_agno_with_tool(iterations: int = 5) -> ResponseResult:
+    """Benchmark Agno agent response time with tool calling"""
+    from agno.agent import Agent
+    from agno.models.openai import OpenAIChat
+    
+    result = ResponseResult()
+    
+    for i in range(iterations):
+        print(f"  Agno (with tool) iteration {i+1}/{iterations}...")
+        
+        agent = Agent(
+            model=OpenAIChat(id="gpt-4o-mini"),
+            system_message="You are a weather assistant. Use the get_weather tool to answer questions.",
+            tools=[get_weather],
+        )
+        
+        start = time.perf_counter()
+        response = agent.run("What's the weather in NYC?")
+        end = time.perf_counter()
+        
+        result.response_times.append(end - start)
+        print(f"    Response time: {end - start:.3f}s")
+    
+    return result
+
+
+if __name__ == "__main__":
+    print("="*70)
+    print("PraisonAI Agents - Response Time Benchmark")
+    print("="*70)
+    print("\nModel: gpt-4o-mini")
+    print("Measures end-to-end response time including:")
+    print("  - Agent instantiation")
+    print("  - API call to OpenAI")
+    print("  - Response processing")
+    
+    iterations = 5
+    results = {}
+    
+    # Test 1: Simple response (no tools)
+    print("\n" + "="*70)
+    print("TEST 1: Simple Response (No Tools)")
+    print("="*70)
+    
+    print("\nBenchmarking PraisonAI...")
+    try:
+        results['praisonai_simple'] = benchmark_praisonai_response(iterations)
+    except Exception as e:
+        print(f"Error: {e}")
+        import traceback
+        traceback.print_exc()
+    
+    print("\nBenchmarking Agno...")
+    try:
+        results['agno_simple'] = benchmark_agno_response(iterations)
+    except Exception as e:
+        print(f"Error: {e}")
+        import traceback
+        traceback.print_exc()
+    
+    # Test 2: Response with tool calling
+    print("\n" + "="*70)
+    print("TEST 2: Response with Tool Calling")
+    print("="*70)
+    
+    print("\nBenchmarking PraisonAI with tool...")
+    try:
+        results['praisonai_tool'] = benchmark_praisonai_with_tool(iterations)
+    except Exception as e:
+        print(f"Error: {e}")
+        import traceback
+        traceback.print_exc()
+    
+    print("\nBenchmarking Agno with tool...")
+    try:
+        results['agno_tool'] = benchmark_agno_with_tool(iterations)
+    except Exception as e:
+        print(f"Error: {e}")
+        import traceback
+        traceback.print_exc()
+    
+    # Summary
+    print("\n" + "="*70)
+    print("SUMMARY RESULTS")
+    print("="*70)
+    
+    print(f"\n{'Test':<30} {'PraisonAI (avg)':<20} {'Agno (avg)':<20} {'Ratio':<15}")
+    print("-" * 85)
+    
+    if 'praisonai_simple' in results and 'agno_simple' in results:
+        p_time = results['praisonai_simple'].avg_time
+        a_time = results['agno_simple'].avg_time
+        ratio = p_time / a_time if a_time > 0 else float('inf')
+        print(f"{'Simple Response':<30} {p_time:<20.3f}s {a_time:<20.3f}s {ratio:.2f}x")
+    
+    if 'praisonai_tool' in results and 'agno_tool' in results:
+        p_time = results['praisonai_tool'].avg_time
+        a_time = results['agno_tool'].avg_time
+        ratio = p_time / a_time if a_time > 0 else float('inf')
+        print(f"{'With Tool Calling':<30} {p_time:<20.3f}s {a_time:<20.3f}s {ratio:.2f}x")
+    
+    print("\n" + "="*70)
+    print("NOTE")
+    print("="*70)
+    print("\nResponse time is dominated by OpenAI API latency.")
+    print("Agent instantiation overhead is negligible in real-world usage.")

src/praisonai-agents/benchmarks/results.console

@@ -0,0 +1,194 @@
+❯ python benchmarks/performance_benchmark.py
+/Users/praison/praisonai-package/src/praisonai-agents/praisonaiagents/_warning_patch.py:47: UserWarning: Mixing V1 models and V2 models (or constructs, like `TypeAdapter`) is not supported. Please upgrade `Settings` to V2.
+  _original_warn(message, category, stacklevel, source)
+[13:04:15] INFO     [13:04:15]          config.py:50
+                    config.py:50 INFO               
+                    Using config path:              
+                    /Users/praison/.con             
+                    fig/crewai/settings             
+                    .json                           
+======================================================================
+PraisonAI Agents - Comprehensive Performance Benchmark
+======================================================================
+
+This benchmark measures:
+  1. Agent instantiation time (how fast an agent object is created)
+  2. Memory footprint (how much memory an agent uses)
+
+Methodology:
+  - 10 warmup runs (not measured)
+  - 1000 measured iterations
+  - tracemalloc for memory measurement
+  - Baseline subtraction for accuracy
+
+Frameworks available:
+  - PraisonAI: ✅
+  - Agno: ✅
+  - PydanticAI: ✅
+  - OpenAI Agents SDK: ✅
+  - LangGraph: ✅
+  - CrewAI: ✅
+
+======================================================================
+Testing PraisonAI Agents
+======================================================================
+
+============================================================
+Benchmarking: PraisonAI
+============================================================
+Warmup runs: 10
+Measured iterations: 1000
+
+Measuring runtime...
+Measuring memory...
+
+Results for PraisonAI:
+  Average Time: 0.000003 seconds (3.20 μs)
+  Min Time:     0.000003 seconds
+  Max Time:     0.000025 seconds
+  Std Dev:      0.000001 seconds
+  Median Time:  0.000003 seconds
+  Avg Memory:   0.003678 MiB (3.77 KiB)
+
+======================================================================
+Testing PraisonAI Agents (LiteLLM)
+======================================================================
+
+============================================================
+Benchmarking: PraisonAI (LiteLLM)
+============================================================
+Warmup runs: 10
+Measured iterations: 1000
+
+Measuring runtime...
+Measuring memory...
+
+Results for PraisonAI (LiteLLM):
+  Average Time: 0.000007 seconds (7.31 μs)
+  Min Time:     0.000007 seconds
+  Max Time:     0.000060 seconds
+  Std Dev:      0.000002 seconds
+  Median Time:  0.000007 seconds
+  Avg Memory:   0.006419 MiB (6.57 KiB)
+
+======================================================================
+Testing Agno
+======================================================================
+
+============================================================
+Benchmarking: Agno
+============================================================
+Warmup runs: 10
+Measured iterations: 1000
+
+Measuring runtime...
+Measuring memory...
+
+Results for Agno:
+  Average Time: 0.000005 seconds (5.30 μs)
+  Min Time:     0.000005 seconds
+  Max Time:     0.000039 seconds
+  Std Dev:      0.000002 seconds
+  Median Time:  0.000005 seconds
+  Avg Memory:   0.006599 MiB (6.76 KiB)
+
+======================================================================
+Testing PydanticAI
+======================================================================
+
+============================================================
+Benchmarking: PydanticAI Agent Instantiation
+============================================================
+Warmup runs: 10
+Measured iterations: 1000
+
+Measuring runtime...
+Measuring memory...
+
+Results for PydanticAI Agent Instantiation:
+  Average Time: 0.000247 seconds (247.34 μs)
+  Min Time:     0.000218 seconds
+  Max Time:     0.000552 seconds
+  Std Dev:      0.000031 seconds
+  Median Time:  0.000237 seconds
+  Avg Memory:   0.028486 MiB (29.17 KiB)
+
+======================================================================
+Testing OpenAI Agents SDK
+======================================================================
+
+============================================================
+Benchmarking: OpenAI Agents SDK Instantiation
+============================================================
+Warmup runs: 10
+Measured iterations: 1000
+
+Measuring runtime...
+Measuring memory...
+
+Results for OpenAI Agents SDK Instantiation:
+  Average Time: 0.000282 seconds (282.25 μs)
+  Min Time:     0.000247 seconds
+  Max Time:     0.001110 seconds
+  Std Dev:      0.000055 seconds
+  Median Time:  0.000263 seconds
+  Avg Memory:   0.029032 MiB (29.73 KiB)
+
+======================================================================
+Testing LangGraph
+======================================================================
+
+============================================================
+Benchmarking: LangGraph Agent Instantiation
+============================================================
+Warmup runs: 10
+Measured iterations: 1000
+
+Measuring runtime...
+Measuring memory...
+
+Results for LangGraph Agent Instantiation:
+  Average Time: 0.003200 seconds (3200.15 μs)
+  Min Time:     0.002808 seconds
+  Max Time:     0.116363 seconds
+  Std Dev:      0.003628 seconds
+  Median Time:  0.002972 seconds
+  Avg Memory:   0.207509 MiB (212.49 KiB)
+
+======================================================================
+Testing CrewAI
+======================================================================
+
+============================================================
+Benchmarking: CrewAI Agent Instantiation
+============================================================
+Warmup runs: 10
+Measured iterations: 1000
+
+Measuring runtime...
+Measuring memory...
+
+Results for CrewAI Agent Instantiation:
+  Average Time: 0.016772 seconds (16771.69 μs)
+  Min Time:     0.014404 seconds
+  Max Time:     0.091841 seconds
+  Std Dev:      0.003445 seconds
+  Median Time:  0.015816 seconds
+  Avg Memory:   0.065050 MiB (66.61 KiB)
+
+======================================================================
+RESULTS SUMMARY
+======================================================================
+
+Framework                 Avg Time (μs)   Avg Memory (KiB)   Relative       
+-------------------------------------------------------------------------
+PraisonAI                 3.20            3.77               1.00x          
+Agno                      5.30            6.76               1.66x          
+PraisonAI (LiteLLM)       7.31            6.57               2.28x          
+PydanticAI                247.34          29.17              77.25x         
+OpenAI Agents SDK         282.25          29.73              88.15x         
+LangGraph                 3200.15         212.49             999.46x        
+CrewAI                    16771.69        66.61              5238.06x       
+
+======================================================================
+❯ 

src/praisonai-agents/benchmarks/simple_benchmark.py

@@ -0,0 +1,243 @@
+#!/usr/bin/env python3
+"""
+PraisonAI Agents - Performance Benchmark
+
+Compares agent instantiation times across popular AI agent frameworks.
+
+Usage:
+    python benchmarks/simple_benchmark.py
+"""
+
+import time
+from typing import Literal
+
+
+ITERATIONS = 100
+
+
+def sample_tool(city: Literal['nyc', 'sf']):
+    """Sample tool for benchmark testing."""
+    if city == 'nyc':
+        return 'cloudy'
+    return 'sunny'
+
+
+def measure_instantiation(create_fn, iterations=ITERATIONS):
+    """Measure average instantiation time in microseconds."""
+    times = []
+    for _ in range(iterations):
+        start = time.perf_counter()
+        create_fn()
+        times.append((time.perf_counter() - start) * 1_000_000)
+    return sum(times) / len(times)
+
+
+def run_benchmark():
+    """Run the benchmark across all available frameworks (without tools)."""
+    results = {}
+    
+    print('=' * 60)
+    print('PraisonAI Agents - Performance Benchmark')
+    print('=' * 60)
+    print(f'\nIterations: {ITERATIONS}')
+    print('Metric: Agent instantiation time (microseconds)\n')
+    
+    # PraisonAI (without tools)
+    print("Testing PraisonAI...")
+    from praisonaiagents import Agent as PraisonAgent
+    
+    results['PraisonAI'] = measure_instantiation(
+        lambda: PraisonAgent(name='Test', llm='gpt-4o-mini', verbose=False)
+    )
+    
+    results['PraisonAI (LiteLLM)'] = measure_instantiation(
+        lambda: PraisonAgent(name='Test', llm='openai/gpt-4o-mini', verbose=False)
+    )
+    
+    # Other frameworks for comparison
+    print("Testing other frameworks...")
+    
+    try:
+        from agno.agent import Agent as AgnoAgent
+        from agno.models.openai import OpenAIChat
+        results['Agno'] = measure_instantiation(
+            lambda: AgnoAgent(model=OpenAIChat(id='gpt-4o-mini'))
+        )
+    except ImportError:
+        pass
+    
+    try:
+        from pydantic_ai import Agent as PydanticAgent
+        results['PydanticAI'] = measure_instantiation(
+            lambda: PydanticAgent('openai:gpt-4o-mini')
+        )
+    except ImportError:
+        pass
+    
+    try:
+        from agents import Agent as OpenAIAgent
+        results['OpenAI Agents SDK'] = measure_instantiation(
+            lambda: OpenAIAgent(name='Test', model='gpt-4o-mini')
+        )
+    except ImportError:
+        pass
+    
+    try:
+        from langchain_core.tools import tool as langchain_tool
+        from langchain_openai import ChatOpenAI
+        from langgraph.prebuilt import create_react_agent
+        
+        @langchain_tool
+        def get_weather_lg(city: Literal['nyc', 'sf']):
+            """Get weather info."""
+            return 'sunny' if city == 'sf' else 'cloudy'
+        
+        results['LangGraph'] = measure_instantiation(
+            lambda: create_react_agent(model=ChatOpenAI(model='gpt-4o-mini'), tools=[get_weather_lg])
+        )
+    except ImportError:
+        pass
+    
+    try:
+        from crewai.agent import Agent as CrewAgent
+        from crewai.tools import tool as crewai_tool
+        
+        @crewai_tool("Weather Tool")
+        def get_weather_crew(city: Literal['nyc', 'sf']):
+            """Get weather info."""
+            return 'sunny' if city == 'sf' else 'cloudy'
+        
+        results['CrewAI'] = measure_instantiation(
+            lambda: CrewAgent(
+                role='Weather Agent',
+                goal='Provide weather info',
+                backstory='A weather expert',
+                tools=[get_weather_crew],
+                verbose=False
+            )
+        )
+    except ImportError:
+        pass
+    
+    # Print results
+    print('\n' + '=' * 60)
+    print('RESULTS')
+    print('=' * 60)
+    
+    baseline = results.get('PraisonAI', 1)
+    print(f"\n{'Framework':<25} {'Avg Time (μs)':<15} {'Relative':<10}")
+    print('-' * 50)
+    
+    for name, avg in sorted(results.items(), key=lambda x: x[1]):
+        ratio = avg / baseline
+        print(f'{name:<25} {avg:<15.2f} {ratio:.2f}x')
+    
+    print('\n' + '=' * 60)
+    return results
+
+
+def get_package_versions():
+    """Get version numbers for benchmarked packages."""
+    from importlib.metadata import version as get_version
+    
+    packages = {
+        'PraisonAI': 'praisonaiagents',
+        'Agno': 'agno',
+        'PydanticAI': 'pydantic-ai',
+        'OpenAI Agents SDK': 'openai-agents',
+        'LangGraph': 'langgraph',
+        'CrewAI': 'crewai'
+    }
+    
+    versions = {}
+    for display_name, pkg_name in packages.items():
+        try:
+            versions[display_name] = get_version(pkg_name)
+        except Exception:
+            versions[display_name] = 'not installed'
+    
+    return versions
+
+
+def save_results(results: dict, filename: str = 'BENCHMARK_RESULTS.md'):
+    """Save benchmark results to a markdown file and update README."""
+    import os
+    import re
+    from datetime import datetime
+    
+    filepath = os.path.join(os.path.dirname(__file__), filename)
+    versions = get_package_versions()
+    
+    # Find the fastest result (baseline)
+    sorted_results = sorted(results.items(), key=lambda x: x[1])
+    fastest_time = sorted_results[0][1] if sorted_results else 1
+    
+    # Build the table rows
+    table_rows = []
+    for name, avg in sorted_results:
+        ratio = avg / fastest_time
+        if ratio == 1.0:
+            table_rows.append(f'| **{name}** | **{avg:.2f}** | **1.00x (fastest)** |')
+        else:
+            avg_str = f'{avg:,.2f}' if avg >= 1000 else f'{avg:.2f}'
+            ratio_str = f'{ratio:,.0f}x' if ratio >= 100 else f'{ratio:.2f}x'
+            table_rows.append(f'| {name} | {avg_str} | {ratio_str} |')
+    
+    # Save to BENCHMARK_RESULTS.md
+    with open(filepath, 'w') as f:
+        f.write('# PraisonAI Agents - Benchmark Results\n\n')
+        f.write(f'**Generated:** {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}\n')
+        f.write(f'**Iterations:** {ITERATIONS}\n')
+        f.write('**Test:** Agent instantiation (without tools)\n\n')
+        f.write('## Results\n\n')
+        f.write('| Framework | Avg Time (μs) | Relative |\n')
+        f.write('|-----------|---------------|----------|\n')
+        f.write('\n'.join(table_rows) + '\n')
+        f.write('\n## Package Versions\n\n')
+        f.write('| Package | Version |\n')
+        f.write('|---------|--------|\n')
+        for pkg, ver in versions.items():
+            f.write(f'| {pkg} | {ver} |\n')
+        f.write('\n## How to Reproduce\n\n')
+        f.write('```bash\n')
+        f.write('cd praisonai-agents\n')
+        f.write('python benchmarks/simple_benchmark.py\n')
+        f.write('```\n')
+    
+    print(f'\nResults saved to: {filepath}')
+    
+    # Also update the main README.md
+    readme_path = os.path.join(os.path.dirname(__file__), '..', '..', '..', 'README.md')
+    if os.path.exists(readme_path):
+        update_readme(readme_path, table_rows)
+    
+    return filepath
+
+
+def update_readme(readme_path: str, table_rows: list):
+    """Update the performance section in README.md with latest results."""
+    import re
+    
+    with open(readme_path, 'r') as f:
+        content = f.read()
+    
+    # Build the new table
+    new_table = '''| Framework | Avg Time (μs) | Relative |
+|-----------|---------------|----------|
+''' + '\n'.join(table_rows)
+    
+    # Pattern to match the performance table
+    pattern = r'(\| Framework \| Avg Time \(μs\) \| Relative \|\n\|[-|]+\|\n)(\|[^\n]+\|\n)+'
+    
+    if re.search(pattern, content):
+        content = re.sub(pattern, new_table + '\n', content)
+        
+        with open(readme_path, 'w') as f:
+            f.write(content)
+        
+        print(f'README.md updated: {readme_path}')
+
+
+if __name__ == '__main__':
+    results = run_benchmark()
+    save_results(results)

src/praisonai-agents/benchmarks/tools_benchmark.py

@@ -0,0 +1,213 @@
+#!/usr/bin/env python3
+"""
+PraisonAI Agents - Tools Benchmark
+
+Compares agent instantiation times WITH TOOLS across frameworks.
+
+Usage:
+    python benchmarks/tools_benchmark.py
+"""
+
+import time
+from typing import Literal
+from importlib.metadata import version as get_version
+
+
+ITERATIONS = 100
+
+
+def sample_tool(city: Literal['nyc', 'sf']):
+    """Sample tool for benchmark testing."""
+    if city == 'nyc':
+        return 'cloudy'
+    return 'sunny'
+
+
+def measure_instantiation(create_fn, iterations=ITERATIONS):
+    """Measure average instantiation time in microseconds."""
+    times = []
+    
+    # Warmup
+    for _ in range(10):
+        create_fn()
+    
+    # Measure
+    for _ in range(iterations):
+        start = time.perf_counter()
+        create_fn()
+        elapsed = time.perf_counter() - start
+        times.append(elapsed * 1_000_000)  # Convert to microseconds
+    
+    return sum(times) / len(times)
+
+
+def run_benchmark():
+    """Run the benchmark across all available frameworks WITH TOOLS."""
+    tools = [sample_tool]
+    results = {}
+    
+    print('=' * 60)
+    print('PraisonAI Agents - Tools Benchmark')
+    print('=' * 60)
+    print(f'\nIterations: {ITERATIONS}')
+    print('Metric: Agent instantiation time WITH TOOLS (microseconds)\n')
+    
+    # PraisonAI with tools
+    print("Testing PraisonAI...")
+    from praisonaiagents import Agent as PraisonAgent
+    
+    results['PraisonAI'] = measure_instantiation(
+        lambda: PraisonAgent(name='Test', llm='gpt-4o-mini', tools=tools, verbose=False)
+    )
+    
+    results['PraisonAI (LiteLLM)'] = measure_instantiation(
+        lambda: PraisonAgent(name='Test', llm='openai/gpt-4o-mini', tools=tools, verbose=False)
+    )
+    
+    # Other frameworks with tools
+    print("Testing other frameworks...")
+    
+    try:
+        from agno.agent import Agent as AgnoAgent
+        from agno.models.openai import OpenAIChat
+        results['Agno'] = measure_instantiation(
+            lambda: AgnoAgent(model=OpenAIChat(id='gpt-4o-mini'), tools=tools)
+        )
+    except ImportError:
+        pass
+    
+    try:
+        from agents import Agent as OpenAIAgent, function_tool
+        results['OpenAI Agents SDK'] = measure_instantiation(
+            lambda: OpenAIAgent(name='Test', model='gpt-4o-mini', tools=[function_tool(sample_tool)])
+        )
+    except ImportError:
+        pass
+    
+    try:
+        from langchain_core.tools import tool as langchain_tool
+        from langchain_openai import ChatOpenAI
+        from langgraph.prebuilt import create_react_agent
+        
+        @langchain_tool
+        def get_weather_lg(city: Literal['nyc', 'sf']):
+            """Get weather info."""
+            return 'sunny' if city == 'sf' else 'cloudy'
+        
+        results['LangGraph'] = measure_instantiation(
+            lambda: create_react_agent(model=ChatOpenAI(model='gpt-4o-mini'), tools=[get_weather_lg])
+        )
+    except ImportError:
+        pass
+    
+    try:
+        from crewai.agent import Agent as CrewAgent
+        from crewai.tools import tool as crewai_tool
+        
+        @crewai_tool("Weather Tool")
+        def get_weather_crew(city: Literal['nyc', 'sf']):
+            """Get weather info."""
+            return 'sunny' if city == 'sf' else 'cloudy'
+        
+        results['CrewAI'] = measure_instantiation(
+            lambda: CrewAgent(
+                role='Weather Agent',
+                goal='Provide weather info',
+                backstory='A weather expert',
+                tools=[get_weather_crew],
+                verbose=False
+            )
+        )
+    except ImportError:
+        pass
+    
+    # Print results
+    print('\n' + '=' * 60)
+    print('RESULTS (WITH TOOLS)')
+    print('=' * 60)
+    
+    sorted_results = sorted(results.items(), key=lambda x: x[1])
+    baseline = sorted_results[0][1] if sorted_results else 1
+    
+    print(f"\n{'Framework':<25} {'Avg Time (μs)':<15} {'Relative':<10}")
+    print('-' * 50)
+    
+    for name, avg in sorted_results:
+        ratio = avg / baseline
+        print(f'{name:<25} {avg:<15.2f} {ratio:.2f}x')
+    
+    print('\n' + '=' * 60)
+    return results
+
+
+def get_package_versions():
+    """Get version numbers for benchmarked packages."""
+    packages = {
+        'PraisonAI': 'praisonaiagents',
+        'Agno': 'agno',
+        'OpenAI Agents SDK': 'openai-agents',
+        'LangGraph': 'langgraph',
+        'CrewAI': 'crewai'
+    }
+    
+    versions = {}
+    for display_name, pkg_name in packages.items():
+        try:
+            versions[display_name] = get_version(pkg_name)
+        except Exception:
+            versions[display_name] = 'not installed'
+    
+    return versions
+
+
+def save_results(results: dict, filename: str = 'TOOLS_BENCHMARK_RESULTS.md'):
+    """Save benchmark results to a markdown file."""
+    import os
+    from datetime import datetime
+    
+    filepath = os.path.join(os.path.dirname(__file__), filename)
+    versions = get_package_versions()
+    
+    # Find the fastest result (baseline)
+    sorted_results = sorted(results.items(), key=lambda x: x[1])
+    fastest_time = sorted_results[0][1] if sorted_results else 1
+    
+    # Build the table rows
+    table_rows = []
+    for name, avg in sorted_results:
+        ratio = avg / fastest_time
+        if ratio == 1.0:
+            table_rows.append(f'| **{name}** | **{avg:.2f}** | **1.00x (fastest)** |')
+        else:
+            avg_str = f'{avg:,.2f}' if avg >= 1000 else f'{avg:.2f}'
+            ratio_str = f'{ratio:,.0f}x' if ratio >= 100 else f'{ratio:.2f}x'
+            table_rows.append(f'| {name} | {avg_str} | {ratio_str} |')
+    
+    # Save to file
+    with open(filepath, 'w') as f:
+        f.write('# PraisonAI Agents - Tools Benchmark Results\n\n')
+        f.write(f'**Generated:** {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}\n')
+        f.write(f'**Iterations:** {ITERATIONS}\n')
+        f.write('**Test:** Agent instantiation WITH TOOLS\n\n')
+        f.write('## Results\n\n')
+        f.write('| Framework | Avg Time (μs) | Relative |\n')
+        f.write('|-----------|---------------|----------|\n')
+        f.write('\n'.join(table_rows) + '\n')
+        f.write('\n## Package Versions\n\n')
+        f.write('| Package | Version |\n')
+        f.write('|---------|--------|\n')
+        for pkg, ver in versions.items():
+            f.write(f'| {pkg} | {ver} |\n')
+        f.write('\n## How to Reproduce\n\n')
+        f.write('```bash\n')
+        f.write('cd praisonai-agents\n')
+        f.write('python benchmarks/tools_benchmark.py\n')
+        f.write('```\n')
+    
+    print(f'\nResults saved to: {filepath}')
+    return filepath
+
+
+if __name__ == '__main__':
+    results = run_benchmark()
+    save_results(results)

src/praisonai-agents/callback_test_log.txt

@@ -0,0 +1,9 @@
+Starting test with verbose=False
+CALLBACK EXECUTED!
+Message: 
+You need to do the following task: Say hello world.
+Expected Output: A greeting.
+Please provide only the final result of your work. Do not add any conversation or extra explanation.
+Response: Hello, World!
+Other kwargs: {'markdown': True, 'generation_time': 2.685833215713501, 'agent_name': 'TestAgent', 'agent_role': 'Assistant', 'agent_tools': None, 'task_name': 'simple_task', 'task_description': 'Say hello world', 'task_id': 0}
+--------------------------------------------------

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

@@ -551,8 +551,9 @@ Your Goal: {self.goal}
         self.auto_save = auto_save  # Session name for auto-saving
         
         # Initialize rules manager for persistent context (like Cursor/Windsurf)
+        # NOTE: Lazy initialization - rules are loaded only when accessed (performance optimization)
         self._rules_manager = None
-        self._init_rules_manager()
+        self._rules_manager_initialized = False
         
         # Handle web_search fallback: inject DuckDuckGo tool for unsupported models
         if web_search and not self._model_supports_web_search():
@@ -599,7 +600,7 @@ Your Goal: {self.goal}
 
         # Fast Context configuration (lazy loaded)
         self.fast_context_enabled = fast_context
-        self.fast_context_path = fast_context_path or os.getcwd()
+        self._fast_context_path = fast_context_path  # Store raw, resolve lazily
         self.fast_context_model = fast_context_model
         self.fast_context_max_turns = fast_context_max_turns
         self.fast_context_parallelism = fast_context_parallelism
@@ -614,6 +615,17 @@ Your Goal: {self.goal}
             self._console = Console()
         return self._console
     
+    @property
+    def fast_context_path(self):
+        """Lazily resolve fast_context_path - avoids os.getcwd() on every init."""
+        if self._fast_context_path is None:
+            self._fast_context_path = os.getcwd()
+        return self._fast_context_path
+    
+    @fast_context_path.setter
+    def fast_context_path(self, value):
+        self._fast_context_path = value
+    
     @property
     def _openai_client(self):
         """Lazily initialize OpenAI client only when needed."""
@@ -785,6 +797,21 @@ Your Goal: {self.goal}
             logging.warning(f"FastContext search failed: {e}")
             return None
     
+    @property
+    def rules_manager(self):
+        """
+        Lazy-initialized RulesManager for persistent rules/instructions.
+        
+        This property initializes the RulesManager only when first accessed,
+        avoiding expensive filesystem operations during agent instantiation.
+        
+        Returns:
+            RulesManager instance or None if not available
+        """
+        if not self._rules_manager_initialized:
+            self._init_rules_manager()
+        return self._rules_manager
+    
     def _init_rules_manager(self):
         """
         Initialize RulesManager for persistent rules/instructions.
@@ -793,7 +820,10 @@ Your Goal: {self.goal}
         - ~/.praison/rules/ (global)
         - .praison/rules/ (workspace)
         - Subdirectory rules
+        
+        NOTE: This is called lazily via the rules_manager property for performance.
         """
+        self._rules_manager_initialized = True
         try:
             from ..memory.rules_manager import RulesManager
             import os
@@ -828,10 +858,10 @@ Your Goal: {self.goal}
         Returns:
             Formatted rules context string
         """
-        if not self._rules_manager:
+        if not self.rules_manager:
             return ""
         
-        return self._rules_manager.build_rules_context(
+        return self.rules_manager.build_rules_context(
             file_path=file_path,
             include_manual=include_manual
         )
@@ -1239,8 +1269,8 @@ Your Goal: {self.goal}
 Your Role: {self.role}\n
 Your Goal: {self.goal}"""
         
-        # Add rules context if rules manager is enabled
-        if self._rules_manager:
+        # Add rules context if rules manager is enabled (lazy initialization)
+        if self._rules_manager_initialized and self._rules_manager:
             rules_context = self.get_rules_context()
             if rules_context:
                 system_prompt += f"\n\n## Rules (Guidelines you must follow)\n{rules_context}"
@@ -1539,23 +1569,54 @@ Your Goal: {self.goal}"""
         # Special handling for MCP tools
         # Check if tools is an MCP instance with the requested function name
         from ..mcp.mcp import MCP
+        
+        # Helper function to execute MCP tool
+        def _execute_mcp_tool(mcp_instance, func_name, args):
+            """Execute a tool from an MCP instance."""
+            # Handle SSE MCP client
+            if hasattr(mcp_instance, 'is_sse') and mcp_instance.is_sse:
+                if hasattr(mcp_instance, 'sse_client'):
+                    for tool in mcp_instance.sse_client.tools:
+                        if tool.name == func_name:
+                            logging.debug(f"Found matching SSE MCP tool: {func_name}")
+                            return True, tool(**args)
+            # Handle HTTP Stream MCP client
+            if hasattr(mcp_instance, 'is_http_stream') and mcp_instance.is_http_stream:
+                if hasattr(mcp_instance, 'http_stream_client'):
+                    for tool in mcp_instance.http_stream_client.tools:
+                        if tool.name == func_name:
+                            logging.debug(f"Found matching HTTP Stream MCP tool: {func_name}")
+                            return True, tool(**args)
+            # Handle WebSocket MCP client
+            if hasattr(mcp_instance, 'is_websocket') and mcp_instance.is_websocket:
+                if hasattr(mcp_instance, 'websocket_client'):
+                    for tool in mcp_instance.websocket_client.tools:
+                        if tool.name == func_name:
+                            logging.debug(f"Found matching WebSocket MCP tool: {func_name}")
+                            return True, tool(**args)
+            # Handle stdio MCP client
+            if hasattr(mcp_instance, 'runner'):
+                for mcp_tool in mcp_instance.runner.tools:
+                    if hasattr(mcp_tool, 'name') and mcp_tool.name == func_name:
+                        logging.debug(f"Found matching MCP tool: {func_name}")
+                        return True, mcp_instance.runner.call_tool(func_name, args)
+            return False, None
+        
+        # Check if tools is a single MCP instance
         if isinstance(self.tools, MCP):
             logging.debug(f"Looking for MCP tool {function_name}")
-            
-            # Handle SSE MCP client
-            if hasattr(self.tools, 'is_sse') and self.tools.is_sse:
-                if hasattr(self.tools, 'sse_client'):
-                    for tool in self.tools.sse_client.tools:
-                        if tool.name == function_name:
-                            logging.debug(f"Found matching SSE MCP tool: {function_name}")
-                            return tool(**arguments)
-            # Handle stdio MCP client
-            elif hasattr(self.tools, 'runner'):
-                # Check if any of the MCP tools match the function name
-                for mcp_tool in self.tools.runner.tools:
-                    if hasattr(mcp_tool, 'name') and mcp_tool.name == function_name:
-                        logging.debug(f"Found matching MCP tool: {function_name}")
-                        return self.tools.runner.call_tool(function_name, arguments)
+            found, result = _execute_mcp_tool(self.tools, function_name, arguments)
+            if found:
+                return result
+        
+        # Check if tools is a list that may contain MCP instances
+        if isinstance(self.tools, (list, tuple)):
+            for tool in self.tools:
+                if isinstance(tool, MCP):
+                    logging.debug(f"Looking for MCP tool {function_name} in MCP instance")
+                    found, result = _execute_mcp_tool(tool, function_name, arguments)
+                    if found:
+                        return result
 
         # Try to find the function in the agent's tools list first
         func = None
@@ -1870,7 +1931,8 @@ Your Goal: {self.goal}"""
                 if tool_param is not None:
                     from ..mcp.mcp import MCP
                     if isinstance(tool_param, MCP) and hasattr(tool_param, 'to_openai_tool'):
-                        logging.debug("Converting MCP tool to OpenAI format")
+                        # Single MCP instance
+                        logging.debug("Converting single MCP tool to OpenAI format")
                         openai_tool = tool_param.to_openai_tool()
                         if openai_tool:
                             # Handle both single tool and list of tools
@@ -1879,6 +1941,22 @@ Your Goal: {self.goal}"""
                             else:
                                 tool_param = [openai_tool]
                             logging.debug(f"Converted MCP tool: {tool_param}")
+                    elif isinstance(tool_param, (list, tuple)):
+                        # List that may contain MCP instances - convert each MCP to OpenAI format
+                        converted_tools = []
+                        for t in tool_param:
+                            if isinstance(t, MCP) and hasattr(t, 'to_openai_tool'):
+                                logging.debug("Converting MCP instance in list to OpenAI format")
+                                openai_tools = t.to_openai_tool()
+                                if isinstance(openai_tools, list):
+                                    converted_tools.extend(openai_tools)
+                                elif openai_tools:
+                                    converted_tools.append(openai_tools)
+                            else:
+                                # Keep non-MCP tools as-is
+                                converted_tools.append(t)
+                        tool_param = converted_tools
+                        logging.debug(f"Converted {len(converted_tools)} tools from list")
                 
                 # Store chat history length for potential rollback
                 chat_history_length = len(self.chat_history)

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

@@ -1370,6 +1370,61 @@ Context:
             agent_names = ", ".join([agent.name for agent in self.agents])
             print(f"📊 Available agents for this endpoint ({len(self.agents)}): {agent_names}")
             
+            # Create per-agent endpoints for individual agent access
+            # This allows n8n and other tools to call specific agents
+            agents_dict = {agent.name.lower().replace(' ', '_'): agent for agent in self.agents}
+            
+            # Add GET endpoint to list available agents
+            @_agents_shared_apps[port].get(f"{path}/list")
+            async def list_agents():
+                return {
+                    "agents": [
+                        {"name": agent.name, "id": agent.name.lower().replace(' ', '_')}
+                        for agent in self.agents
+                    ]
+                }
+            
+            # Add per-agent POST endpoints
+            for agent_id, agent_instance in agents_dict.items():
+                agent_path = f"{path}/{agent_id}"
+                
+                # Create a closure to capture the agent instance
+                def create_agent_handler(agent):
+                    async def handle_single_agent(request: Request):
+                        try:
+                            request_data = await request.json()
+                            query = request_data.get("query", "")
+                            if not query:
+                                raise HTTPException(status_code=400, detail="Missing 'query' field")
+                        except:
+                            raise HTTPException(status_code=400, detail="Invalid JSON body")
+                        
+                        try:
+                            if asyncio.iscoroutinefunction(agent.chat):
+                                response = await agent.achat(query)
+                            else:
+                                loop = asyncio.get_running_loop()
+                                response = await loop.run_in_executor(None, lambda q=query: agent.chat(q))
+                            
+                            return {
+                                "agent": agent.name,
+                                "query": query,
+                                "response": response
+                            }
+                        except Exception as e:
+                            logging.error(f"Error with agent {agent.name}: {str(e)}", exc_info=True)
+                            return JSONResponse(
+                                status_code=500,
+                                content={"error": f"Agent error: {str(e)}"}
+                            )
+                    return handle_single_agent
+                
+                # Register the endpoint
+                _agents_shared_apps[port].post(agent_path)(create_agent_handler(agent_instance))
+                _agents_registered_endpoints[port][agent_path] = f"{endpoint_id}_{agent_id}"
+            
+            print(f"🔗 Per-agent endpoints: {', '.join([f'{path}/{aid}' for aid in agents_dict.keys()])}")
+            
             # Start the server if it's not already running for this port
             if not _agents_server_started.get(port, False):
                 # Mark the server as started first to prevent duplicate starts

src/praisonai-agents/praisonaiagents/memory/workflows.py

@@ -440,7 +440,7 @@ class Workflow:
             previous_output = output
             
             if verbose:
-                print(f"✅ {step.name}: {str(output)[:100]}...")
+                print(f"✅ {step.name}: {str(output)}")
             
             # Handle early stop
             if stop:
@@ -571,7 +571,7 @@ class Workflow:
                 output = f"Error: {e}"
         
         if verbose:
-            print(f"✅ {normalized.name}: {str(output)[:100]}...")
+            print(f"✅ {normalized.name}: {str(output)}")
         
         return {
             "step": normalized.name,

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

@@ -286,6 +286,7 @@ class Workflow:
     planning_llm: Optional[str] = None  # LLM for planning
     reasoning: bool = False  # Enable chain-of-thought reasoning
     verbose: bool = False  # Enable verbose output
+    stream: bool = True  # Enable streaming responses (default True like PraisonAIAgents)
     
     # Callbacks (like process="workflow")
     on_workflow_start: Optional[Callable[['Workflow', str], None]] = None  # (workflow, input)
@@ -316,7 +317,8 @@ class Workflow:
         self,
         input: str = "",
         llm: Optional[str] = None,
-        verbose: bool = False
+        verbose: bool = False,
+        stream: bool = None
     ) -> Dict[str, Any]:
         """
         Run the workflow with the given input.
@@ -340,16 +342,22 @@ class Workflow:
             input: The input text/prompt for the workflow
             llm: LLM model to use (default: gpt-4o-mini)
             verbose: Print step outputs
+            stream: Enable streaming responses (default: use workflow's stream setting)
             
         Returns:
             Dict with 'output' (final result) and 'steps' (all step results)
         """
         # Use default LLM if not specified
         model = llm or self.default_llm or "gpt-4o-mini"
+        logger.debug(f"Workflow using model: {model} (llm={llm}, default_llm={self.default_llm})")
         
         # Use workflow verbose setting if not overridden
         verbose = verbose or self.verbose
         
+        # Use workflow stream setting if not overridden
+        if stream is None:
+            stream = self.stream
+        
         # Add input to variables
         all_variables = {**self.variables, "input": input}
         
@@ -382,7 +390,7 @@ class Workflow:
             if isinstance(step, Route):
                 # Decision-based routing
                 route_result = self._execute_route(
-                    step, previous_output, input, all_variables, model, verbose
+                    step, previous_output, input, all_variables, model, verbose, stream
                 )
                 results.extend(route_result["steps"])
                 previous_output = route_result["output"]
@@ -393,7 +401,7 @@ class Workflow:
             elif isinstance(step, Parallel):
                 # Parallel execution
                 parallel_result = self._execute_parallel(
-                    step, previous_output, input, all_variables, model, verbose
+                    step, previous_output, input, all_variables, model, verbose, stream
                 )
                 results.extend(parallel_result["steps"])
                 previous_output = parallel_result["output"]
@@ -404,7 +412,7 @@ class Workflow:
             elif isinstance(step, Loop):
                 # Loop over items
                 loop_result = self._execute_loop(
-                    step, previous_output, input, all_variables, model, verbose
+                    step, previous_output, input, all_variables, model, verbose, stream
                 )
                 results.extend(loop_result["steps"])
                 previous_output = loop_result["output"]
@@ -415,7 +423,7 @@ class Workflow:
             elif isinstance(step, Repeat):
                 # Repeat until condition
                 repeat_result = self._execute_repeat(
-                    step, previous_output, input, all_variables, model, verbose
+                    step, previous_output, input, all_variables, model, verbose, stream
                 )
                 results.extend(repeat_result["steps"])
                 previous_output = repeat_result["output"]
@@ -494,7 +502,11 @@ class Workflow:
                         for key, value in all_variables.items():
                             action = action.replace(f"{{{{{key}}}}}", str(value))
                         if previous_output:
-                            action = action.replace("{{previous_output}}", str(previous_output))
+                            if "{{previous_output}}" in action:
+                                action = action.replace("{{previous_output}}", str(previous_output))
+                            else:
+                                # Auto-append context if not explicitly referenced
+                                action = f"{action}\n\nContext from previous step:\n{previous_output}"
                         action = action.replace("{{input}}", input)
                         
                         # Add reasoning prompt if enabled
@@ -508,9 +520,9 @@ class Workflow:
                         # Handle images if present
                         step_images = getattr(step, 'images', None)
                         if step_images:
-                            output = step.agent.chat(action, images=step_images)
+                            output = step.agent.chat(action, images=step_images, stream=stream)
                         else:
-                            output = step.agent.chat(action)
+                            output = step.agent.chat(action, stream=stream)
                         
                         # Handle output_pydantic if present
                         output_pydantic = getattr(step, 'output_pydantic', None)
@@ -538,14 +550,19 @@ class Workflow:
                             llm=config.get("llm", model),
                             tools=step_tools if step_tools else None,
                             verbose=verbose,
-                            reasoning=self.reasoning
+                            reasoning=self.reasoning,
+                            stream=stream
                         )
                         # Substitute variables in action
                         action = step.action
                         for key, value in all_variables.items():
                             action = action.replace(f"{{{{{key}}}}}", str(value))
                         if previous_output:
-                            action = action.replace("{{previous_output}}", str(previous_output))
+                            if "{{previous_output}}" in action:
+                                action = action.replace("{{previous_output}}", str(previous_output))
+                            else:
+                                # Auto-append context if not explicitly referenced
+                                action = f"{action}\n\nContext from previous step:\n{previous_output}"
                         action = action.replace("{{input}}", input)
                         
                         # Add reasoning prompt if enabled
@@ -554,7 +571,8 @@ class Workflow:
                         
                         if validation_feedback:
                             action = f"{action}\n\nPrevious attempt feedback: {validation_feedback}"
-                        output = temp_agent.chat(action)
+                        
+                        output = temp_agent.chat(action, stream=stream)
                         
                 except Exception as e:
                     step_error = e
@@ -629,7 +647,7 @@ class Workflow:
             previous_output = output
             
             if verbose:
-                print(f"✅ {step.name}: {str(output)[:100]}...")
+                print(f"✅ {step.name}: {str(output)}")
             
             # Handle early stop
             if stop:
@@ -828,7 +846,8 @@ Create a brief execution plan (2-3 sentences) describing how to best accomplish
         all_variables: Dict[str, Any],
         model: str,
         verbose: bool,
-        index: int = 0
+        index: int = 0,
+        stream: bool = True
     ) -> Dict[str, Any]:
         """Execute a single step and return result."""
         normalized = self._normalize_single_step(step, index)
@@ -862,9 +881,13 @@ Create a brief execution plan (2-3 sentences) describing how to best accomplish
                 for key, value in all_variables.items():
                     action = action.replace(f"{{{{{key}}}}}", str(value))
                 if previous_output:
-                    action = action.replace("{{previous_output}}", str(previous_output))
+                    if "{{previous_output}}" in action:
+                        action = action.replace("{{previous_output}}", str(previous_output))
+                    else:
+                        # Auto-append context if not explicitly referenced
+                        action = f"{action}\n\nContext from previous step:\n{previous_output}"
                 action = action.replace("{{input}}", input)
-                output = normalized.agent.chat(action)
+                output = normalized.agent.chat(action, stream=stream)
             except Exception as e:
                 output = f"Error: {e}"
         elif normalized.action:
@@ -876,19 +899,25 @@ Create a brief execution plan (2-3 sentences) describing how to best accomplish
                     role=config.get("role", "Assistant"),
                     goal=config.get("goal", "Complete the task"),
                     llm=config.get("llm", model),
-                    verbose=verbose
+                    verbose=verbose,
+                    stream=stream
                 )
                 action = normalized.action
                 for key, value in all_variables.items():
                     action = action.replace(f"{{{{{key}}}}}", str(value))
                 if previous_output:
-                    action = action.replace("{{previous_output}}", str(previous_output))
-                output = temp_agent.chat(action)
+                    if "{{previous_output}}" in action:
+                        action = action.replace("{{previous_output}}", str(previous_output))
+                    else:
+                        # Auto-append context if not explicitly referenced
+                        action = f"{action}\n\nContext from previous step:\n{previous_output}"
+                
+                output = temp_agent.chat(action, stream=stream)
             except Exception as e:
                 output = f"Error: {e}"
         
         if verbose:
-            print(f"✅ {normalized.name}: {str(output)[:100]}...")
+            print(f"✅ {normalized.name}: {str(output)}")
         
         return {
             "step": normalized.name,
@@ -904,7 +933,8 @@ Create a brief execution plan (2-3 sentences) describing how to best accomplish
         input: str,
         all_variables: Dict[str, Any],
         model: str,
-        verbose: bool
+        verbose: bool,
+        stream: bool = True
     ) -> Dict[str, Any]:
         """Execute routing based on previous output."""
         results = []
@@ -930,7 +960,7 @@ Create a brief execution plan (2-3 sentences) describing how to best accomplish
         # Execute matched route steps
         for idx, step in enumerate(matched_route):
             step_result = self._execute_single_step_internal(
-                step, output, input, all_variables, model, verbose, idx
+                step, output, input, all_variables, model, verbose, idx, stream=stream
             )
             results.append({"step": step_result["step"], "output": step_result["output"]})
             output = step_result["output"]
@@ -948,7 +978,8 @@ Create a brief execution plan (2-3 sentences) describing how to best accomplish
         input: str,
         all_variables: Dict[str, Any],
         model: str,
-        verbose: bool
+        verbose: bool,
+        stream: bool = True
     ) -> Dict[str, Any]:
         """Execute steps in parallel (simulated with sequential for now)."""
         import concurrent.futures
@@ -965,7 +996,7 @@ Create a brief execution plan (2-3 sentences) describing how to best accomplish
             for idx, step in enumerate(parallel_step.steps):
                 future = executor.submit(
                     self._execute_single_step_internal,
-                    step, previous_output, input, all_variables.copy(), model, False, idx
+                    step, previous_output, input, all_variables.copy(), model, False, idx, stream
                 )
                 futures.append((idx, future))
             
@@ -994,7 +1025,8 @@ Create a brief execution plan (2-3 sentences) describing how to best accomplish
         input: str,
         all_variables: Dict[str, Any],
         model: str,
-        verbose: bool
+        verbose: bool,
+        stream: bool = True
     ) -> Dict[str, Any]:
         """Execute step for each item in loop."""
         import csv
@@ -1034,7 +1066,7 @@ Create a brief execution plan (2-3 sentences) describing how to best accomplish
             loop_vars["loop_index"] = idx
             
             step_result = self._execute_single_step_internal(
-                loop_step.step, previous_output, input, loop_vars, model, verbose, idx
+                loop_step.step, previous_output, input, loop_vars, model, verbose, idx, stream=stream
             )
             results.append({"step": f"{step_result['step']}_{idx}", "output": step_result["output"]})
             outputs.append(step_result["output"])
@@ -1052,7 +1084,8 @@ Create a brief execution plan (2-3 sentences) describing how to best accomplish
         input: str,
         all_variables: Dict[str, Any],
         model: str,
-        verbose: bool
+        verbose: bool,
+        stream: bool = True
     ) -> Dict[str, Any]:
         """Repeat step until condition is met."""
         results = []
@@ -1063,7 +1096,7 @@ Create a brief execution plan (2-3 sentences) describing how to best accomplish
         
         for iteration in range(repeat_step.max_iterations):
             step_result = self._execute_single_step_internal(
-                repeat_step.step, output, input, all_variables, model, verbose, iteration
+                repeat_step.step, output, input, all_variables, model, verbose, iteration, stream=stream
             )
             results.append({"step": f"{step_result['step']}_{iteration}", "output": step_result["output"]})
             output = step_result["output"]

src/praisonai-agents/praisonaiagents/workflows/yaml_parser.py

@@ -131,11 +131,18 @@ class YAMLWorkflowParser:
             
         normalized = data.copy()
         
-        # 1. Normalize workflow name: topic -> name
-        if 'topic' in normalized and 'name' not in normalized:
+        # 1. Normalize workflow name: topic -> name (only if topic is used as name, not as input)
+        # Note: 'topic' is legacy, 'input' is canonical for workflow input data
+        if 'topic' in normalized and 'name' not in normalized and 'input' not in normalized:
+            # Legacy behavior: topic was used as both name and input
             normalized['name'] = normalized.get('topic', 'Unnamed Workflow')
         
-        # 2. Normalize container: roles -> agents
+        # 2. Normalize workflow input: 'input' is canonical, 'topic' is alias
+        # Priority: input > topic (input takes precedence if both exist)
+        if 'input' not in normalized and 'topic' in normalized:
+            normalized['input'] = normalized['topic']
+        
+        # 3. Normalize container: roles -> agents
         # Note: _convert_roles_to_agents handles backstory -> instructions
         if 'roles' in normalized and 'agents' not in normalized:
             normalized['agents'] = self._convert_roles_to_agents(normalized['roles'])
@@ -143,7 +150,7 @@ class YAMLWorkflowParser:
             if 'steps' not in normalized:
                 normalized['steps'] = self._extract_steps_from_roles(normalized['roles'])
         
-        # 3. Normalize agent fields: backstory -> instructions (for agents section)
+        # 4. Normalize agent fields: backstory -> instructions (for agents section)
         if 'agents' in normalized:
             for agent_id, agent_config in normalized['agents'].items():
                 if isinstance(agent_config, dict):
@@ -151,7 +158,7 @@ class YAMLWorkflowParser:
                     if 'backstory' in agent_config and 'instructions' not in agent_config:
                         agent_config['instructions'] = agent_config['backstory']
         
-        # 4. Normalize step fields: description -> action
+        # 5. Normalize step fields: description -> action
         if 'steps' in normalized:
             for step in normalized['steps']:
                 if isinstance(step, dict):
@@ -225,6 +232,7 @@ class YAMLWorkflowParser:
         workflow_config = data.get('workflow', {})
         planning = workflow_config.get('planning', False)
         planning_llm = workflow_config.get('planning_llm')
+        default_llm = workflow_config.get('default_llm')
         reasoning = workflow_config.get('reasoning', False)
         verbose = workflow_config.get('verbose', False)
         memory_config = workflow_config.get('memory_config')
@@ -254,6 +262,7 @@ class YAMLWorkflowParser:
             variables=variables,
             planning=planning,
             planning_llm=planning_llm,
+            default_llm=default_llm,
             reasoning=reasoning,
             verbose=verbose,
             memory_config=memory_config,
@@ -268,6 +277,10 @@ class YAMLWorkflowParser:
         workflow.process = process
         workflow.manager_llm = manager_llm
         
+        # Store workflow input (from 'input' or 'topic' field)
+        # This is the default input passed to workflow.start() if no input is provided
+        workflow.default_input = data.get('input', data.get('topic', ''))
+        
         return workflow
     
     def _convert_roles_to_agents(self, roles: Dict[str, Dict]) -> Dict[str, Dict]:

src/praisonai-agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.1.7"
+version = "0.1.14"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 readme = "README.md"
 requires-python = ">=3.10"

src/praisonai-agents/tests/test_multiple_mcp_tools.py

@@ -0,0 +1,391 @@
+"""
+Test-Driven Development tests for Multiple MCP Tools support.
+
+This test file verifies that:
+1. Single MCP instance works (baseline)
+2. Multiple MCP instances in a list work for tool definition conversion
+3. Multiple MCP instances in a list work for tool execution
+4. Mixed tools (MCP + regular functions) work together
+
+Uses mcp-server-time as the test MCP server (simple, no API keys needed).
+"""
+
+import pytest
+import os
+import sys
+from unittest.mock import Mock
+
+# Add the package to path for testing
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
+
+from praisonaiagents.mcp.mcp import MCP
+from praisonaiagents.agent.agent import Agent
+
+
+# ============================================================================
+# Test Fixtures
+# ============================================================================
+
+@pytest.fixture
+def time_mcp():
+    """Create a Time MCP server instance."""
+    return MCP("uvx mcp-server-time", timeout=30)
+
+
+@pytest.fixture
+def mock_mcp_with_tools():
+    """Create a mock MCP instance with predefined tools."""
+    mock = Mock(spec=MCP)
+    mock.is_sse = False
+    mock.is_http_stream = False
+    mock.is_websocket = False
+    
+    # Create mock runner with tools
+    mock.runner = Mock()
+    mock_tool1 = Mock()
+    mock_tool1.name = "get_current_time"
+    mock_tool1.description = "Get current time in a timezone"
+    mock_tool1.inputSchema = {
+        "type": "object",
+        "properties": {
+            "timezone": {"type": "string", "description": "IANA timezone name"}
+        },
+        "required": ["timezone"]
+    }
+    
+    mock_tool2 = Mock()
+    mock_tool2.name = "convert_time"
+    mock_tool2.description = "Convert time between timezones"
+    mock_tool2.inputSchema = {
+        "type": "object",
+        "properties": {
+            "source_timezone": {"type": "string"},
+            "time": {"type": "string"},
+            "target_timezone": {"type": "string"}
+        },
+        "required": ["source_timezone", "time", "target_timezone"]
+    }
+    
+    mock.runner.tools = [mock_tool1, mock_tool2]
+    mock._tools = []
+    
+    # Mock to_openai_tool method
+    def to_openai_tool():
+        return [
+            {
+                "type": "function",
+                "function": {
+                    "name": "get_current_time",
+                    "description": "Get current time in a timezone",
+                    "parameters": mock_tool1.inputSchema
+                }
+            },
+            {
+                "type": "function",
+                "function": {
+                    "name": "convert_time",
+                    "description": "Convert time between timezones",
+                    "parameters": mock_tool2.inputSchema
+                }
+            }
+        ]
+    mock.to_openai_tool = to_openai_tool
+    
+    # Mock __iter__ to return tool functions
+    def get_current_time(timezone: str) -> str:
+        return f"Time in {timezone}: 12:00"
+    def convert_time(source_timezone: str, time: str, target_timezone: str) -> str:
+        return f"Converted {time} from {source_timezone} to {target_timezone}"
+    get_current_time.__name__ = "get_current_time"
+    convert_time.__name__ = "convert_time"
+    mock._tools = [get_current_time, convert_time]
+    mock.__iter__ = lambda self: iter(self._tools)
+    
+    return mock
+
+
+@pytest.fixture
+def mock_mcp_filesystem():
+    """Create a mock MCP instance simulating filesystem server."""
+    mock = Mock(spec=MCP)
+    mock.is_sse = False
+    mock.is_http_stream = False
+    mock.is_websocket = False
+    
+    mock.runner = Mock()
+    mock_tool = Mock()
+    mock_tool.name = "read_file"
+    mock_tool.description = "Read a file"
+    mock_tool.inputSchema = {
+        "type": "object",
+        "properties": {
+            "path": {"type": "string"}
+        },
+        "required": ["path"]
+    }
+    mock.runner.tools = [mock_tool]
+    mock._tools = []
+    
+    def to_openai_tool():
+        return [
+            {
+                "type": "function",
+                "function": {
+                    "name": "read_file",
+                    "description": "Read a file",
+                    "parameters": mock_tool.inputSchema
+                }
+            }
+        ]
+    mock.to_openai_tool = to_openai_tool
+    
+    def read_file(path: str) -> str:
+        return f"Contents of {path}"
+    read_file.__name__ = "read_file"
+    mock._tools = [read_file]
+    mock.__iter__ = lambda self: iter(self._tools)
+    
+    return mock
+
+
+def sample_function(query: str) -> str:
+    """A sample regular function tool."""
+    return f"Result for: {query}"
+
+
+# ============================================================================
+# Test 1: Single MCP Instance (Baseline)
+# ============================================================================
+
+class TestSingleMCPInstance:
+    """Tests for single MCP instance - baseline functionality."""
+    
+    def test_single_mcp_creates_tools(self, mock_mcp_with_tools):
+        """Test that a single MCP instance properly exposes tools."""
+        tools = mock_mcp_with_tools.to_openai_tool()
+        assert isinstance(tools, list)
+        assert len(tools) == 2
+        assert tools[0]["function"]["name"] == "get_current_time"
+        assert tools[1]["function"]["name"] == "convert_time"
+    
+    def test_single_mcp_is_iterable(self, mock_mcp_with_tools):
+        """Test that MCP instance is iterable."""
+        tools_list = mock_mcp_with_tools._tools
+        assert len(tools_list) == 2
+        assert callable(tools_list[0])
+        assert callable(tools_list[1])
+    
+    def test_agent_accepts_single_mcp(self, mock_mcp_with_tools):
+        """Test that Agent accepts a single MCP instance as tools."""
+        # This should not raise an error
+        agent = Agent(
+            name="TestAgent",
+            instructions="Test agent",
+            tools=mock_mcp_with_tools
+        )
+        # Agent wraps tools in a list if not already a list
+        assert mock_mcp_with_tools in agent.tools or agent.tools == mock_mcp_with_tools
+
+
+# ============================================================================
+# Test 2: Multiple MCP Instances - Tool Definition Conversion
+# ============================================================================
+
+class TestMultipleMCPToolDefinition:
+    """Tests for multiple MCP instances - tool definition conversion."""
+    
+    def test_agent_accepts_multiple_mcp_in_list(self, mock_mcp_with_tools, mock_mcp_filesystem):
+        """Test that Agent accepts multiple MCP instances in a list."""
+        agent = Agent(
+            name="TestAgent",
+            instructions="Test agent",
+            tools=[mock_mcp_with_tools, mock_mcp_filesystem]
+        )
+        assert isinstance(agent.tools, list)
+        assert len(agent.tools) == 2
+    
+    def test_format_tools_handles_multiple_mcp(self, mock_mcp_with_tools, mock_mcp_filesystem):
+        """Test that _format_tools_for_llm handles multiple MCP instances."""
+        agent = Agent(
+            name="TestAgent",
+            instructions="Test agent",
+            tools=[mock_mcp_with_tools, mock_mcp_filesystem]
+        )
+        
+        # Call the internal method to format tools
+        formatted = agent._format_tools_for_completion(agent.tools)
+        
+        # Should have 3 tools total (2 from time + 1 from filesystem)
+        assert isinstance(formatted, list)
+        assert len(formatted) == 3
+        
+        # Check tool names
+        tool_names = [t["function"]["name"] for t in formatted]
+        assert "get_current_time" in tool_names
+        assert "convert_time" in tool_names
+        assert "read_file" in tool_names
+    
+    def test_system_prompt_includes_all_mcp_tools(self, mock_mcp_with_tools, mock_mcp_filesystem):
+        """Test that system prompt includes tools from all MCP instances."""
+        agent = Agent(
+            name="TestAgent",
+            instructions="Test agent",
+            tools=[mock_mcp_with_tools, mock_mcp_filesystem]
+        )
+        
+        system_prompt = agent._build_system_prompt(agent.tools)
+        
+        # Should mention all tools
+        assert "get_current_time" in system_prompt
+        assert "convert_time" in system_prompt
+        assert "read_file" in system_prompt
+
+
+# ============================================================================
+# Test 3: Multiple MCP Instances - Tool Execution
+# ============================================================================
+
+class TestMultipleMCPToolExecution:
+    """Tests for multiple MCP instances - tool execution."""
+    
+    def test_execute_tool_finds_mcp_tool_in_list(self, mock_mcp_with_tools, mock_mcp_filesystem):
+        """Test that execute_tool_call finds tools from MCP instances in a list."""
+        agent = Agent(
+            name="TestAgent",
+            instructions="Test agent",
+            tools=[mock_mcp_with_tools, mock_mcp_filesystem]
+        )
+        
+        # Mock the runner.call_tool method
+        mock_mcp_with_tools.runner.call_tool = Mock(return_value='{"timezone": "UTC", "datetime": "2024-01-01T12:00:00Z"}')
+        
+        # This should find and execute the tool from the first MCP instance
+        result = agent.execute_tool("get_current_time", {"timezone": "UTC"})
+        
+        # Verify the tool was called
+        mock_mcp_with_tools.runner.call_tool.assert_called_once_with("get_current_time", {"timezone": "UTC"})
+    
+    def test_execute_tool_finds_tool_from_second_mcp(self, mock_mcp_with_tools, mock_mcp_filesystem):
+        """Test that execute_tool_call finds tools from the second MCP instance."""
+        agent = Agent(
+            name="TestAgent",
+            instructions="Test agent",
+            tools=[mock_mcp_with_tools, mock_mcp_filesystem]
+        )
+        
+        # Mock the runner.call_tool method
+        mock_mcp_filesystem.runner.call_tool = Mock(return_value='Contents of /test/file.txt')
+        
+        # This should find and execute the tool from the second MCP instance
+        result = agent.execute_tool("read_file", {"path": "/test/file.txt"})
+        
+        # Verify the tool was called
+        mock_mcp_filesystem.runner.call_tool.assert_called_once_with("read_file", {"path": "/test/file.txt"})
+
+
+# ============================================================================
+# Test 4: Mixed Tools (MCP + Regular Functions)
+# ============================================================================
+
+class TestMixedTools:
+    """Tests for mixed tools - MCP instances and regular functions."""
+    
+    def test_agent_accepts_mixed_tools(self, mock_mcp_with_tools):
+        """Test that Agent accepts a mix of MCP instances and regular functions."""
+        agent = Agent(
+            name="TestAgent",
+            instructions="Test agent",
+            tools=[mock_mcp_with_tools, sample_function]
+        )
+        assert isinstance(agent.tools, list)
+        assert len(agent.tools) == 2
+    
+    def test_format_tools_handles_mixed(self, mock_mcp_with_tools):
+        """Test that _format_tools_for_llm handles mixed tools."""
+        agent = Agent(
+            name="TestAgent",
+            instructions="Test agent",
+            tools=[mock_mcp_with_tools, sample_function]
+        )
+        
+        formatted = agent._format_tools_for_completion(agent.tools)
+        
+        # Should have 3 tools (2 from MCP + 1 regular function)
+        assert isinstance(formatted, list)
+        assert len(formatted) == 3
+        
+        tool_names = [t["function"]["name"] for t in formatted]
+        assert "get_current_time" in tool_names
+        assert "convert_time" in tool_names
+        assert "sample_function" in tool_names
+    
+    def test_execute_mixed_tools(self, mock_mcp_with_tools):
+        """Test executing both MCP tools and regular functions."""
+        agent = Agent(
+            name="TestAgent",
+            instructions="Test agent",
+            tools=[mock_mcp_with_tools, sample_function]
+        )
+        
+        # Execute regular function
+        result = agent.execute_tool("sample_function", {"query": "test"})
+        assert "Result for: test" in str(result)
+        
+        # Mock and execute MCP tool
+        mock_mcp_with_tools.runner.call_tool = Mock(return_value='{"timezone": "UTC"}')
+        result = agent.execute_tool("get_current_time", {"timezone": "UTC"})
+        mock_mcp_with_tools.runner.call_tool.assert_called_once()
+
+
+# ============================================================================
+# Integration Tests (requires actual MCP server)
+# ============================================================================
+
+@pytest.mark.integration
+@pytest.mark.skipif(
+    os.environ.get("SKIP_MCP_INTEGRATION") == "1",
+    reason="MCP integration tests skipped"
+)
+class TestMCPIntegration:
+    """Integration tests using actual MCP servers."""
+    
+    def test_real_time_mcp_server(self):
+        """Test with real mcp-server-time."""
+        try:
+            mcp = MCP("uvx mcp-server-time", timeout=30)
+            tools = mcp.to_openai_tool()
+            
+            assert isinstance(tools, list)
+            assert len(tools) >= 1
+            
+            tool_names = [t["function"]["name"] for t in tools]
+            assert "get_current_time" in tool_names
+        except Exception as e:
+            pytest.skip(f"MCP server not available: {e}")
+    
+    def test_multiple_real_mcp_servers(self):
+        """Test with multiple real MCP servers."""
+        try:
+            time_mcp = MCP("uvx mcp-server-time", timeout=30)
+            
+            # Create agent with multiple MCP instances
+            agent = Agent(
+                name="MultiMCPAgent",
+                instructions="Test agent with multiple MCP tools",
+                tools=[time_mcp]
+            )
+            
+            # Verify tools are available
+            formatted = agent._format_tools_for_completion(agent.tools)
+            assert len(formatted) >= 1
+            
+        except Exception as e:
+            pytest.skip(f"MCP servers not available: {e}")
+
+
+# ============================================================================
+# Run tests
+# ============================================================================
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v", "--tb=short"])

src/praisonai-agents/tests/test_workflow_patterns.py

@@ -1032,7 +1032,7 @@ class TestAgentWorkflows:
                 self.name = name
                 self.tools = tools or []
             
-            def chat(self, message):
+            def chat(self, message, **kwargs):
                 return f"Response from {self.name}: {message}"
         
         agent = MockAgent("TestAgent", tools=["tool1", "tool2"])
@@ -1051,7 +1051,7 @@ class TestAgentWorkflows:
             def __init__(self, name):
                 self.name = name
             
-            def chat(self, message):
+            def chat(self, message, **kwargs):
                 return f"Processed: {message}"
         
         agent = MockAgent("Processor")
@@ -1069,7 +1069,7 @@ class TestAgentWorkflows:
                 self.name = name
                 self.prefix = prefix
             
-            def chat(self, message):
+            def chat(self, message, **kwargs):
                 return f"{self.prefix}: {message}"
         
         agent1 = MockAgent("Agent1", "First")
@@ -1090,7 +1090,7 @@ class TestAgentWorkflows:
                 self.name = name
                 self.output = output
             
-            def chat(self, message):
+            def chat(self, message, **kwargs):
                 return self.output
         
         agent1 = MockAgent("Agent1", "Result A")
@@ -1116,7 +1116,7 @@ class TestAgentWorkflows:
                 self.name = name
                 self.output = output
             
-            def chat(self, message):
+            def chat(self, message, **kwargs):
                 return self.output
         
         tech_agent = MockAgent("TechAgent", "Technical response")
@@ -1149,7 +1149,7 @@ class TestAgentWorkflows:
             def __init__(self, name):
                 self.name = name
             
-            def chat(self, message):
+            def chat(self, message, **kwargs):
                 return f"Processed: {message}"
         
         processor = MockAgent("Processor")
@@ -1463,7 +1463,7 @@ class TestToolsPerStep:
                 self.name = name
                 self.tools = tools or []
             
-            def chat(self, message):
+            def chat(self, message, **kwargs):
                 return f"Response with {len(self.tools)} tools"
         
         def mock_tool():

src/praisonai-agents/tests/test_yaml_workflow_parser.py

@@ -1389,6 +1389,79 @@ steps:
         assert workflow is not None
         assert workflow.name == "My Topic Workflow"
     
+    def test_input_field_canonical(self):
+        """Test that 'input' field is the canonical way to specify workflow input."""
+        from praisonaiagents.workflows import YAMLWorkflowParser
+        
+        yaml_content = """
+name: Input Test Workflow
+input: "This is the workflow input"
+agents:
+  researcher:
+    name: Researcher
+    role: Research Analyst
+    goal: Research topics
+    instructions: "Provide research"
+
+steps:
+  - agent: researcher
+    action: "Research {{input}}"
+"""
+        parser = YAMLWorkflowParser()
+        workflow = parser.parse_string(yaml_content)
+        
+        assert workflow is not None
+        assert workflow.default_input == "This is the workflow input"
+    
+    def test_topic_as_input_alias(self):
+        """Test that 'topic' works as an alias for 'input' (backward compatibility)."""
+        from praisonaiagents.workflows import YAMLWorkflowParser
+        
+        yaml_content = """
+name: Topic Alias Test
+topic: "This is the topic as input"
+agents:
+  researcher:
+    name: Researcher
+    role: Research Analyst
+    goal: Research topics
+    instructions: "Provide research"
+
+steps:
+  - agent: researcher
+    action: "Research {{input}}"
+"""
+        parser = YAMLWorkflowParser()
+        workflow = parser.parse_string(yaml_content)
+        
+        assert workflow is not None
+        assert workflow.default_input == "This is the topic as input"
+    
+    def test_input_takes_precedence_over_topic(self):
+        """Test that 'input' takes precedence over 'topic' when both are present."""
+        from praisonaiagents.workflows import YAMLWorkflowParser
+        
+        yaml_content = """
+name: Precedence Test
+input: "Input value wins"
+topic: "Topic value loses"
+agents:
+  researcher:
+    name: Researcher
+    role: Research Analyst
+    goal: Research topics
+    instructions: "Provide research"
+
+steps:
+  - agent: researcher
+    action: "Research {{input}}"
+"""
+        parser = YAMLWorkflowParser()
+        workflow = parser.parse_string(yaml_content)
+        
+        assert workflow is not None
+        assert workflow.default_input == "Input value wins"
+    
     def test_backstory_normalized_to_instructions(self):
         """Test that 'backstory' is normalized to 'instructions'."""
         from praisonaiagents.workflows import YAMLWorkflowParser

src/praisonai-agents/uv.lock

@@ -2422,7 +2422,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.1.7"
+version = "0.1.14"
 source = { editable = "." }
 dependencies = [
     { name = "aiohttp" },

src/praisonai/README.md

@@ -34,6 +34,7 @@ PraisonAI is a production-ready Multi-AI Agents framework with self-reflection,
 
 ## 📑 Table of Contents
 
+- [⚡ Performance](#-performance)
 - [🚀 Quick Start](#-quick-start)
 - [📦 Installation](#-installation)
   - [Python SDK](#python-sdk)
@@ -73,6 +74,32 @@ PraisonAI is a production-ready Multi-AI Agents framework with self-reflection,
 
 ---
 
+## ⚡ Performance
+
+PraisonAI Agents is the **fastest AI agent framework** for agent instantiation.
+
+| Framework | Avg Time (μs) | Relative |
+|-----------|---------------|----------|
+| **PraisonAI** | **3.77** | **1.00x (fastest)** |
+| OpenAI Agents SDK | 5.26 | 1.39x |
+| Agno | 5.64 | 1.49x |
+| PraisonAI (LiteLLM) | 7.56 | 2.00x |
+| PydanticAI | 226.94 | 60.16x |
+| LangGraph | 4,558.71 | 1,209x |
+| CrewAI | 15,607.92 | 4,138x |
+
+<details>
+<summary>Run benchmarks yourself</summary>
+
+```bash
+cd praisonai-agents
+python benchmarks/simple_benchmark.py
+```
+
+</details>
+
+---
+
 ## 🚀 Quick Start
 
 Get started with PraisonAI in under 1 minute:
@@ -1392,6 +1419,36 @@ praisonai commit
 praisonai commit --push
 ```
 
+### Serve CLI (API Server):
+```bash
+# Start API server for agents defined in YAML
+praisonai serve agents.yaml
+
+# With custom port and host
+praisonai serve agents.yaml --port 8005 --host 0.0.0.0
+
+# Alternative flag style
+praisonai agents.yaml --serve
+
+# The server provides:
+# POST /agents          - Run all agents sequentially
+# POST /agents/{name}   - Run specific agent (e.g., /agents/researcher)
+# GET  /agents/list     - List available agents
+```
+
+### n8n Integration CLI:
+```bash
+# Export workflow to n8n and open in browser
+praisonai agents.yaml --n8n
+
+# With custom n8n URL
+praisonai agents.yaml --n8n --n8n-url http://localhost:5678
+
+# Set N8N_API_KEY for auto-import
+export N8N_API_KEY="your-api-key"
+praisonai agents.yaml --n8n
+```
+
 ### @Mentions in Prompts:
 ```bash
 # Include file content in prompt
@@ -1523,6 +1580,21 @@ agent = Agent(
         env={"BRAVE_API_KEY": "your-key"}
     )
 )
+
+# Multiple MCP servers + regular functions
+def my_custom_tool(query: str) -> str:
+    """Custom tool function."""
+    return f"Result: {query}"
+
+agent = Agent(
+    name="MultiToolAgent",
+    instructions="Agent with multiple MCP servers",
+    tools=[
+        MCP("uvx mcp-server-time"),                    # Time tools
+        MCP("npx @modelcontextprotocol/server-memory"), # Memory tools
+        my_custom_tool                                  # Regular function
+    ]
+)
 ```
 
 ### MCP Server (Expose Tools as MCP Server)

src/praisonai/agents.yaml

@@ -1,44 +1,49 @@
+# Movie Script Workflow - Cat in Mars
+# Uses the modern agents + steps format
+
+name: Create Movie Script About Cat in Mars
+description: A sequential workflow to research, design narrative, and write a movie script
 framework: praisonai
-process: sequential
-topic: create movie script about cat in mars
-memory: false
-roles:
+
+workflow:
+  verbose: true
+
+agents:
   researcher:
-    backstory: Skilled in research, with a focus on gathering accurate and relevant
-      information.
+    name: Researcher
+    role: Research Analyst
     goal: Gather information about Mars and cats
-    role: Researcher
-    min_reflect: 1
-    max_reflect: 2
-    tasks:
-      research_task:
-        description: Research about Mars, its environment, and the feasibility of
-          a cat being on Mars. Also, research about cat behavior and characteristics.
-        expected_output: Document with research findings on Mars and cats.
+    backstory: Skilled in research, with a focus on gathering accurate and relevant information.
+    instructions: |
+      Research about Mars, its environment, and the feasibility of a cat being on Mars.
+      Also, research about cat behavior and characteristics.
     tools:
-    - search_tool
+      - search_tool
+
   narrative_designer:
-    backstory: Skilled in narrative development, with a focus on creating engaging
-      stories.
+    name: Narrative Designer
+    role: Story Concept Developer
     goal: Create a story concept for a movie about a cat in Mars
-    role: Narrative Designer
-    tasks:
-      story_concept_development:
-        description: Use the research findings to craft a unique story concept for
-          a movie about a cat in Mars.
-        expected_output: Document with narrative arcs, character bios, and settings.
-    tools:
-    - ''
+    backstory: Skilled in narrative development, with a focus on creating engaging stories.
+    instructions: |
+      Use the research findings to craft a unique story concept for a movie about a cat in Mars.
+      Create narrative arcs, character bios, and settings.
+
   scriptwriter:
-    backstory: Expert in dialogue and script structure, translating concepts into
-      scripts.
+    name: Scriptwriter
+    role: Script Writer
     goal: Write a movie script about a cat in Mars
-    role: Scriptwriter
-    tasks:
-      scriptwriting_task:
-        description: Turn the story concept into a production-ready movie script,
-          including dialogue and scene details.
-        expected_output: Final movie script with dialogue and scene details.
-    tools:
-    - ''
-dependencies: []
+    backstory: Expert in dialogue and script structure, translating concepts into scripts.
+    instructions: |
+      Turn the story concept into a production-ready movie script,
+      including dialogue and scene details.
+
+steps:
+  - agent: researcher
+    action: "Research about Mars environment and cat behavior for the movie concept"
+
+  - agent: narrative_designer
+    action: "Create story concept based on research: {{previous_output}}"
+
+  - agent: scriptwriter
+    action: "Write the final movie script based on the story concept: {{previous_output}}"

src/praisonai/agents_n8n.json

@@ -0,0 +1,133 @@
+{
+  "id": "e022a3a1",
+  "name": "Create Movie Script About Cat in Mars",
+  "nodes": [
+    {
+      "id": "trigger",
+      "name": "Webhook",
+      "type": "n8n-nodes-base.webhook",
+      "typeVersion": 2,
+      "position": [
+        250,
+        300
+      ],
+      "webhookId": "dd7cf5cb72d19eae",
+      "parameters": {
+        "path": "create-movie-script-about-cat-in-mars",
+        "httpMethod": "POST",
+        "responseMode": "lastNode",
+        "options": {}
+      }
+    },
+    {
+      "id": "agent_0",
+      "name": "Researcher",
+      "type": "n8n-nodes-base.httpRequest",
+      "typeVersion": 4.2,
+      "position": [
+        450,
+        300
+      ],
+      "parameters": {
+        "method": "POST",
+        "url": "http://127.0.0.1:8005/agents/researcher",
+        "sendBody": true,
+        "specifyBody": "json",
+        "jsonBody": "={{ JSON.stringify({ query:  $json.body?.query || $json.query || 'Research about Mars environment and cat behavior for the movie concept' } }) }}",
+        "options": {
+          "timeout": 300000
+        }
+      },
+      "notes": "Agent: Researcher\nTask: Research about Mars environment and cat behavior for the movie concept"
+    },
+    {
+      "id": "agent_1",
+      "name": "Narrative Designer",
+      "type": "n8n-nodes-base.httpRequest",
+      "typeVersion": 4.2,
+      "position": [
+        700,
+        300
+      ],
+      "parameters": {
+        "method": "POST",
+        "url": "http://127.0.0.1:8005/agents/narrative_designer",
+        "sendBody": true,
+        "specifyBody": "json",
+        "jsonBody": "={{ JSON.stringify({ query:  $json.response || 'Create story concept based on research: {{previous_output}}' } }) }}",
+        "options": {
+          "timeout": 300000
+        }
+      },
+      "notes": "Agent: Narrative Designer\nTask: Create story concept based on research: {{previous_output}}"
+    },
+    {
+      "id": "agent_2",
+      "name": "Scriptwriter",
+      "type": "n8n-nodes-base.httpRequest",
+      "typeVersion": 4.2,
+      "position": [
+        950,
+        300
+      ],
+      "parameters": {
+        "method": "POST",
+        "url": "http://127.0.0.1:8005/agents/scriptwriter",
+        "sendBody": true,
+        "specifyBody": "json",
+        "jsonBody": "={{ JSON.stringify({ query:  $json.response || 'Write the final movie script based on the story concept: {{previous_output}}' } }) }}",
+        "options": {
+          "timeout": 300000
+        }
+      },
+      "notes": "Agent: Scriptwriter\nTask: Write the final movie script based on the story concept: {{previous_output}}"
+    }
+  ],
+  "connections": {
+    "Webhook": {
+      "main": [
+        [
+          {
+            "node": "Researcher",
+            "type": "main",
+            "index": 0
+          }
+        ]
+      ]
+    },
+    "Researcher": {
+      "main": [
+        [
+          {
+            "node": "Narrative Designer",
+            "type": "main",
+            "index": 0
+          }
+        ]
+      ]
+    },
+    "Narrative Designer": {
+      "main": [
+        [
+          {
+            "node": "Scriptwriter",
+            "type": "main",
+            "index": 0
+          }
+        ]
+      ]
+    }
+  },
+  "active": false,
+  "settings": {
+    "executionOrder": "v1"
+  },
+  "versionId": "1",
+  "pinData": {},
+  "meta": {
+    "instanceId": "praisonai-export",
+    "templateCredsSetupCompleted": true,
+    "description": "A sequential workflow to research, design narrative, and write a movie script"
+  },
+  "tags": []
+}

src/praisonai/examples/workflow_example.yaml

@@ -0,0 +1,93 @@
+# Advanced Workflow Example
+# Demonstrates: conditional routing, decision tasks, and multi-agent coordination
+
+name: Content Creation Pipeline
+description: A workflow that researches, writes, reviews, and publishes content
+
+workflow:
+  process: workflow  # Use workflow process for conditional execution
+  verbose: true
+
+agents:
+  researcher:
+    name: Researcher
+    role: Research Specialist
+    goal: Gather comprehensive information on topics
+    backstory: Expert at finding and synthesizing information from various sources
+    instructions: |
+      Research the given topic thoroughly.
+      Provide detailed findings with key points.
+    llm: gpt-4o-mini
+
+  writer:
+    name: Writer
+    role: Content Writer
+    goal: Create engaging and informative content
+    backstory: Skilled writer who transforms research into readable content
+    instructions: |
+      Write clear, engaging content based on the research.
+      Structure the content with introduction, body, and conclusion.
+    llm: gpt-4o-mini
+
+  reviewer:
+    name: Reviewer
+    role: Quality Reviewer
+    goal: Ensure content meets quality standards
+    backstory: Meticulous editor who ensures accuracy and clarity
+    instructions: |
+      Review the content for accuracy, clarity, and engagement.
+      Decide if it's ready for publication or needs revision.
+    llm: gpt-4o-mini
+
+  publisher:
+    name: Publisher
+    role: Content Publisher
+    goal: Finalize and format content for publication
+    backstory: Expert at preparing content for various platforms
+    instructions: |
+      Format the approved content for publication.
+      Add metadata and prepare final deliverable.
+    llm: gpt-4o-mini
+
+steps:
+  # Step 1: Research (starting point)
+  - name: research_task
+    agent: researcher
+    action: "Research the topic: AI trends in 2025"
+    expected_output: "Comprehensive research findings with key points and sources"
+    is_start: true
+    next_tasks: [write_task]
+
+  # Step 2: Write content based on research
+  - name: write_task
+    agent: writer
+    action: "Write an article based on the research findings"
+    expected_output: "Well-structured article with introduction, body, and conclusion"
+    context: [research_task]
+    next_tasks: [review_task]
+
+  # Step 3: Review with decision (conditional routing)
+  - name: review_task
+    agent: reviewer
+    action: "Review the article for quality and accuracy"
+    expected_output: "Quality assessment with approval or revision recommendation"
+    context: [write_task]
+    task_type: decision
+    condition:
+      approved: [publish_task]
+      needs_revision: [revise_task]
+
+  # Step 4a: Revision (conditional - only if review fails)
+  - name: revise_task
+    agent: writer
+    action: "Revise the article based on reviewer feedback"
+    expected_output: "Revised article addressing all feedback"
+    context: [write_task, review_task]
+    next_tasks: [review_task]  # Loop back to review
+
+  # Step 4b: Publish (conditional - only if review passes)
+  - name: publish_task
+    agent: publisher
+    action: "Prepare the approved article for publication"
+    expected_output: "Final formatted article ready for publication"
+    context: [write_task, review_task]

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/v2.3.28.tar.gz"
-    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.3.28.tar.gz | shasum -a 256`.split.first
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.3.34.tar.gz"
+    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.3.34.tar.gz | shasum -a 256`.split.first
     license "MIT"
   
     depends_on "[email protected]"

src/praisonai/praisonai/agents_generator.py

@@ -327,7 +327,8 @@ class AgentsGenerator:
             # Route to YAMLWorkflowParser for advanced workflow patterns
             return self._run_yaml_workflow(config)
 
-        topic = config.get('topic', '')
+        # Get workflow input: 'input' is canonical, 'topic' is alias for backward compatibility
+        topic = config.get('input', config.get('topic', ''))
         tools_dict = {}
         
         # Only try to use praisonai_tools if it's available and needed
@@ -441,6 +442,15 @@ class AgentsGenerator:
         if 'name' not in config:
             config['name'] = config.get('topic', 'Workflow')
         
+        # Pass model from config_list to workflow as default_llm
+        if self.config_list and self.config_list[0].get('model'):
+            model_from_cli = self.config_list[0]['model']
+            # Set default_llm in workflow config if not already set
+            if 'workflow' not in config:
+                config['workflow'] = {}
+            if 'default_llm' not in config['workflow']:
+                config['workflow']['default_llm'] = model_from_cli
+        
         # Convert config back to YAML string for parser
         # Note: YAMLWorkflowParser handles 'roles' to 'agents' conversion internally
         import yaml as yaml_module
@@ -450,11 +460,11 @@ class AgentsGenerator:
         parser = YAMLWorkflowParser()
         workflow = parser.parse_string(yaml_content)
         
-        # Get input from topic or variables
-        input_data = config.get('topic', '')
+        # Get input: 'input' is canonical, 'topic' is alias for backward compatibility
+        input_data = config.get('input', config.get('topic', ''))
         
         # Execute workflow
-        self.logger.info(f"Running workflow: {workflow.name}")
+        self.logger.debug(f"Running workflow: {workflow.name}")
         result = workflow.start(input_data)
         
         if result.get("status") == "completed":

src/praisonai/praisonai/cli/features/__init__.py

@@ -20,6 +20,7 @@ Features:
 - router: Smart model selection
 - flow_display: Visual workflow tracking
 - workflow: YAML workflow management
+- n8n: Export workflows to n8n visual editor
 """
 
 # Type hints available for IDE support
@@ -41,6 +42,7 @@ __all__ = [
     'RouterHandler',
     'FlowDisplayHandler',
     'WorkflowHandler',
+    'N8nHandler',
 ]
 
 def __getattr__(name):
@@ -90,4 +92,7 @@ def __getattr__(name):
     elif name == 'WorkflowHandler':
         from .workflow import WorkflowHandler
         return WorkflowHandler
+    elif name == 'N8nHandler':
+        from .n8n import N8nHandler
+        return N8nHandler
     raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

src/praisonai/praisonai/cli/features/flow_display.py

@@ -1,25 +1,31 @@
 """
 Flow Display Handler for CLI.
 
-Provides visual workflow tracking.
+Shows a visual flow diagram of the workflow WITHOUT executing it.
 Usage: praisonai agents.yaml --flow-display
 """
 
-from typing import Any, Dict, Tuple, List
+from typing import Any, Dict, Tuple
 from .base import FlagHandler
+import os
+import yaml
 
 
 class FlowDisplayHandler(FlagHandler):
     """
     Handler for --flow-display flag.
     
-    Enables visual workflow tracking for multi-agent executions.
+    Shows a visual flow diagram/chart of agents and tasks WITHOUT executing.
+    This allows users to preview the workflow structure before running it.
     
     Example:
         praisonai agents.yaml --flow-display
-        praisonai "Complex task" --flow-display
     """
     
+    def __init__(self, verbose: bool = False):
+        super().__init__(verbose)
+        self._console = None
+    
     @property
     def feature_name(self) -> str:
         return "flow_display"
@@ -30,160 +36,531 @@ class FlowDisplayHandler(FlagHandler):
     
     @property
     def flag_help(self) -> str:
-        return "Enable visual workflow tracking"
+        return "Show workflow flow diagram without executing"
+    
+    def _get_console(self):
+        """Get or create Rich Console."""
+        if self._console is None:
+            try:
+                from rich.console import Console
+                self._console = Console()
+            except ImportError:
+                pass
+        return self._console
     
     def check_dependencies(self) -> Tuple[bool, str]:
-        """Check if FlowDisplay is available."""
+        """Check if Rich is available."""
         try:
             import importlib.util
-            if importlib.util.find_spec("praisonaiagents") is not None:
-                return True, ""
-            return False, "praisonaiagents not installed"
+            rich_available = importlib.util.find_spec("rich") is not None
+            if not rich_available:
+                return False, "rich not installed. Install with: pip install rich"
+            return True, ""
         except ImportError:
-            return False, "praisonaiagents not installed. Install with: pip install praisonaiagents"
-    
-    def _get_flow_display(self):
-        """Get FlowDisplay class lazily."""
-        try:
-            from praisonaiagents import FlowDisplay
-            return FlowDisplay
-        except ImportError:
-            return None
-    
-    def create_flow_display(self, title: str = "Workflow") -> Any:
-        """
-        Create a FlowDisplay instance.
-        
-        Args:
-            title: Title for the workflow display
-            
-        Returns:
-            FlowDisplay instance or None
-        """
-        FlowDisplay = self._get_flow_display()
-        if not FlowDisplay:
-            self.print_status(
-                "FlowDisplay requires praisonaiagents. Install with: pip install praisonaiagents",
-                "error"
-            )
-            return None
-        
-        try:
-            return FlowDisplay(title=title)
-        except Exception as e:
-            self.log(f"Failed to create FlowDisplay: {e}", "error")
-            return None
+            return False, "rich not installed. Install with: pip install rich"
     
     def apply_to_agent_config(self, config: Dict[str, Any], flag_value: Any) -> Dict[str, Any]:
-        """
-        Apply flow display configuration.
-        
-        Args:
-            config: Agent configuration dictionary
-            flag_value: Boolean indicating flow display
-            
-        Returns:
-            Modified configuration
-        """
+        """Apply flow display configuration."""
         if flag_value:
             config['flow_display'] = True
         return config
     
-    def display_workflow_start(self, workflow_name: str, agents: List[str] = None):
+    def display_flow_diagram(self, yaml_file: str, show_footer: bool = True) -> bool:
         """
-        Display workflow start.
+        Display a visual flow diagram of the workflow from YAML file.
         
         Args:
-            workflow_name: Name of the workflow
-            agents: List of agent names
-        """
-        self.print_status(f"\n🚀 Starting Workflow: {workflow_name}", "info")
-        self.print_status("=" * 50, "info")
-        
-        if agents:
-            self.print_status("Agents:", "info")
-            for i, agent in enumerate(agents, 1):
-                self.print_status(f"  {i}. {agent}", "info")
-        
-        self.print_status("=" * 50, "info")
-    
-    def display_step(self, step_num: int, step_name: str, status: str = "running"):
-        """
-        Display a workflow step.
-        
-        Args:
-            step_num: Step number
-            step_name: Name of the step
-            status: Step status (running, completed, failed)
-        """
-        icons = {
-            'running': '⏳',
-            'completed': '✅',
-            'failed': '❌',
-            'pending': '⬜'
-        }
-        icon = icons.get(status, '•')
-        self.print_status(f"  {icon} Step {step_num}: {step_name}", "info")
-    
-    def display_workflow_end(self, success: bool = True, duration: float = None):
-        """
-        Display workflow end.
-        
-        Args:
-            success: Whether workflow completed successfully
-            duration: Workflow duration in seconds
-        """
-        self.print_status("=" * 50, "info")
-        
-        if success:
-            msg = "✅ Workflow completed successfully"
-        else:
-            msg = "❌ Workflow failed"
-        
-        if duration:
-            msg += f" ({duration:.2f}s)"
-        
-        self.print_status(msg, "success" if success else "error")
-    
-    def track_execution(self, func):
-        """
-        Decorator to track function execution in flow display.
-        
-        Args:
-            func: Function to track
+            yaml_file: Path to the agents.yaml file
+            show_footer: Whether to show the "run without --flow-display" footer
             
         Returns:
-            Wrapped function
+            True if displayed successfully, False otherwise
         """
-        import functools
+        console = self._get_console()
+        if not console:
+            print("Rich library not available for flow display")
+            return False
         
-        @functools.wraps(func)
-        def wrapper(*args, **kwargs):
-            func_name = func.__name__
+        # Load YAML config
+        try:
+            if not os.path.exists(yaml_file):
+                console.print(f"[red]Error: File not found: {yaml_file}[/red]")
+                return False
             
-            self.display_step(1, func_name, "running")
+            with open(yaml_file, 'r') as f:
+                config = yaml.safe_load(f)
+        except Exception as e:
+            console.print(f"[red]Error loading YAML: {e}[/red]")
+            return False
+        
+        # Detect YAML format: 'roles' (old) or 'agents'+'steps' (new workflow)
+        has_roles = 'roles' in config and config['roles']
+        has_agents = 'agents' in config and config['agents']
+        has_steps = 'steps' in config and config['steps']
+        
+        if not has_roles and not has_agents:
+            console.print("[red]Error: Invalid YAML format. No 'roles' or 'agents' found.[/red]")
+            return False
+        
+        try:
+            from rich.panel import Panel
+            from rich.table import Table
+            from rich.text import Text
+            from rich import box
             
+            # Determine format type
+            is_workflow_format = has_agents and has_steps
+            
+            # Extract workflow info based on format
+            if is_workflow_format:
+                name = config.get('name', 'Workflow')
+                description = config.get('description', '')
+                framework = config.get('framework', 'praisonai')
+                workflow_config = config.get('workflow', {})
+                memory = workflow_config.get('memory_config', {}).get('persist', False)
+                num_agents = len(config['agents'])
+                num_steps = len(config['steps'])
+            else:
+                name = config.get('topic', 'Workflow')
+                description = ''
+                framework = config.get('framework', 'praisonai')
+                process = config.get('process', 'sequential')
+                memory = config.get('memory', False)
+                num_agents = len(config['roles'])
+                num_steps = sum(len(details.get('tasks', {})) for details in config['roles'].values())
+            
+            # Build the flow diagram
+            console.print()
+            
+            # === HEADER ===
+            header = Text()
+            header.append("📊 WORKFLOW FLOW DIAGRAM\n", style="bold cyan")
+            header.append("─" * 40, style="dim")
+            
+            header_panel = Panel(
+                header,
+                border_style="cyan",
+                box=box.DOUBLE_EDGE,
+                padding=(0, 2)
+            )
+            console.print(header_panel)
+            
+            # === WORKFLOW INFO ===
+            info_table = Table(show_header=False, box=None, padding=(0, 2))
+            info_table.add_column("Key", style="bold yellow")
+            info_table.add_column("Value", style="white")
+            
+            info_table.add_row("📋 Name:", name)
+            if description:
+                info_table.add_row("📝 Description:", description)
+            
+            # Show input if present ('input' is canonical, 'topic' is alias)
+            workflow_input = config.get('input', config.get('topic', ''))
+            if workflow_input:
+                info_table.add_row("💬 Input:", workflow_input)
+            
+            info_table.add_row("⚙️  Framework:", framework)
+            
+            if is_workflow_format:
+                info_table.add_row("🔄 Format:", "WORKFLOW (steps-based)")
+                planning = workflow_config.get('planning', False)
+                info_table.add_row("🧠 Planning:", "✅ Enabled" if planning else "❌ Disabled")
+            else:
+                info_table.add_row("🔄 Process:", process.upper())
+            
+            info_table.add_row("💾 Memory:", "✅ Enabled" if memory else "❌ Disabled")
+            info_table.add_row("👥 Agents:", str(num_agents))
+            info_table.add_row("📝 Steps:", str(num_steps))
+            
+            console.print(Panel(info_table, title="[bold white]Workflow Configuration[/bold white]", 
+                               border_style="blue", box=box.ROUNDED))
+            console.print()
+            
+            # === FLOW DIAGRAM ===
+            if is_workflow_format:
+                self._display_steps_flow(console, config)
+            else:
+                process = config.get('process', 'sequential')
+                if process == 'sequential':
+                    self._display_sequential_flow(console, config)
+                elif process == 'hierarchical':
+                    self._display_hierarchical_flow(console, config)
+                else:
+                    self._display_sequential_flow(console, config)
+            
+            # === DETAILED AGENT INFO ===
+            console.print()
+            if is_workflow_format:
+                self._display_workflow_agent_details(console, config)
+            else:
+                self._display_agent_details(console, config)
+            
+            # === FOOTER ===
+            if show_footer:
+                console.print()
+                footer = Text()
+                footer.append("💡 ", style="bold")
+                footer.append("To execute this workflow, run without --flow-display flag", style="dim italic")
+                console.print(Panel(footer, border_style="dim", box=box.ROUNDED))
+            console.print()
+            
+            return True
+            
+        except ImportError as e:
+            console.print(f"[red]Error: Rich components not available: {e}[/red]")
+            return False
+    
+    def _display_sequential_flow(self, console, config: Dict):
+        """Display sequential flow diagram."""
+        from rich.panel import Panel
+        from rich.text import Text
+        from rich import box
+        
+        roles = list(config['roles'].items())
+        
+        # Box width (content area)
+        BOX_WIDTH = 36
+        
+        def pad_line(content: str, width: int = BOX_WIDTH) -> str:
+            """Pad content to fixed width."""
+            padding = width - len(content)
+            if padding > 0:
+                return content + " " * padding
+            return content[:width]
+        
+        flow_text = Text()
+        flow_text.append("  SEQUENTIAL EXECUTION FLOW\n\n", style="bold magenta")
+        
+        for i, (role_key, details) in enumerate(roles):
+            role_name = details.get('role', role_key)
+            tasks = list(details.get('tasks', {}).keys())
+            
+            # Agent box
+            if i == 0:
+                flow_text.append("       ┌── START ──┐\n", style="green")
+                flow_text.append("             │\n", style="dim")
+                flow_text.append("             ▼\n", style="dim")
+            
+            # Agent node
+            flow_text.append("       ╔════════════════════════════════════╗\n", style="cyan")
+            line = f" Agent {i+1}: {role_name}"
+            flow_text.append(f"       ║{pad_line(line)}║\n", style="cyan")
+            
+            # Tasks
+            for task in tasks:
+                line = f"   -> {task}"
+                flow_text.append(f"       ║{pad_line(line)}║\n", style="dim white")
+            
+            flow_text.append("       ╚════════════════════════════════════╝\n", style="cyan")
+            
+            # Arrow to next
+            if i < len(roles) - 1:
+                flow_text.append("             │\n", style="dim")
+                flow_text.append("          output\n", style="dim italic")
+                flow_text.append("             ▼\n", style="dim")
+            else:
+                flow_text.append("             │\n", style="dim")
+                flow_text.append("             ▼\n", style="dim")
+                flow_text.append("       └── END ────┘\n", style="red")
+        
+        console.print(Panel(flow_text, border_style="magenta", box=box.DOUBLE_EDGE,
+                           title="[bold white]Execution Flow[/bold white]"))
+    
+    def _display_hierarchical_flow(self, console, config: Dict):
+        """Display hierarchical flow diagram."""
+        from rich.panel import Panel
+        from rich.text import Text
+        from rich import box
+        
+        manager_llm = config.get('manager_llm', 'default')
+        
+        flow_text = Text()
+        flow_text.append("👑 HIERARCHICAL EXECUTION FLOW\n\n", style="bold magenta")
+        
+        # Manager at top
+        flow_text.append("         ┌──────────────────────┐\n", style="yellow")
+        flow_text.append("         │  👑 MANAGER AGENT    │\n", style="bold yellow")
+        flow_text.append(f"         │  LLM: {manager_llm:<14}│\n", style="dim yellow")
+        flow_text.append("         └──────────┬───────────┘\n", style="yellow")
+        flow_text.append("                    │\n", style="dim")
+        flow_text.append("         ┌──────────┴───────────┐\n", style="dim")
+        flow_text.append("         │    delegates to      │\n", style="dim italic")
+        flow_text.append("         └──────────┬───────────┘\n", style="dim")
+        
+        roles = list(config['roles'].items())
+        num_agents = len(roles)
+        
+        # Draw branches
+        if num_agents > 0:
+            branch_line = "    "
+            for i in range(num_agents):
+                if i == 0:
+                    branch_line += "┌" + "─" * 8
+                elif i == num_agents - 1:
+                    branch_line += "┬" + "─" * 8 + "┐"
+                else:
+                    branch_line += "┬" + "─" * 8
+            flow_text.append(branch_line + "\n", style="dim")
+            
+            # Agent boxes
+            for i, (role_key, details) in enumerate(roles):
+                flow_text.append("\n    ╔═══════════════╗", style="cyan")
+            flow_text.append("\n")
+            
+            for i, (role_key, details) in enumerate(roles):
+                role_name = details.get('role', role_key)[:12]
+                flow_text.append(f"    ║ 🤖 {role_name:<10} ║", style="cyan")
+            flow_text.append("\n")
+            
+            for i, (role_key, details) in enumerate(roles):
+                flow_text.append("    ╚═══════════════╝", style="cyan")
+            flow_text.append("\n")
+        
+        console.print(Panel(flow_text, border_style="magenta", box=box.DOUBLE_EDGE,
+                           title="[bold white]Execution Flow[/bold white]"))
+    
+    def _display_agent_details(self, console, config: Dict):
+        """Display detailed agent information."""
+        from rich.table import Table
+        from rich import box
+        
+        table = Table(
+            title="[bold cyan]📋 Agent & Task Details[/bold cyan]",
+            box=box.ROUNDED,
+            show_header=True,
+            header_style="bold magenta",
+            border_style="blue",
+            padding=(0, 1),
+            expand=True
+        )
+        
+        table.add_column("#", style="dim", width=3, justify="center")
+        table.add_column("Agent", style="bold yellow", min_width=15)
+        table.add_column("Role", style="cyan", min_width=15)
+        table.add_column("Goal", style="white", min_width=25, overflow="fold")
+        table.add_column("Tasks", style="green", min_width=20)
+        table.add_column("Tools", style="magenta", min_width=15)
+        
+        for i, (role_key, details) in enumerate(config['roles'].items(), 1):
+            role_name = details.get('role', role_key)
+            goal = details.get('goal', '-')
+            if len(goal) > 50:
+                goal = goal[:47] + "..."
+            
+            tasks = list(details.get('tasks', {}).keys())
+            tasks_str = "\n".join(f"• {t}" for t in tasks) if tasks else "-"
+            
+            tools = details.get('tools', [])
+            tools_str = ", ".join(t for t in tools if t) if tools else "-"
+            
+            table.add_row(
+                str(i),
+                role_name,
+                role_key,
+                goal,
+                tasks_str,
+                tools_str
+            )
+        
+        console.print(table)
+    
+    def _display_steps_flow(self, console, config: Dict):
+        """Display steps-based workflow flow diagram."""
+        from rich.panel import Panel
+        from rich.text import Text
+        from rich import box
+        
+        steps = config.get('steps', [])
+        agents = config.get('agents', {})
+        
+        # Box width (content area)
+        BOX_WIDTH = 36
+        
+        def pad_line(content: str, width: int = BOX_WIDTH) -> str:
+            """Pad content to fixed width, accounting for emoji width."""
+            # Emojis typically take 2 character widths
+            emoji_count = sum(1 for c in content if ord(c) > 0x1F300)
+            actual_len = len(content) + emoji_count  # Add extra for emoji width
+            padding = width - actual_len
+            if padding > 0:
+                return content + " " * padding
+            return content[:width]
+        
+        flow_text = Text()
+        flow_text.append("  STEPS-BASED EXECUTION FLOW\n\n", style="bold magenta")
+        
+        flow_text.append("       ┌── START ──┐\n", style="green")
+        flow_text.append("             │\n", style="dim")
+        flow_text.append("             ▼\n", style="dim")
+        
+        for i, step in enumerate(steps):
+            # Determine step type
+            agent_key = step.get('agent', '')
+            step_name = step.get('name', '')
+            
+            # Check for special step types
+            has_route = 'route' in step
+            has_parallel = 'parallel' in step
+            has_loop = 'loop' in step
+            has_repeat = 'repeat' in step
+            
+            if has_route:
+                # Route step
+                flow_text.append("       ╔════════════════════════════════════╗\n", style="yellow")
+                line = f" ROUTE: {step_name or 'routing'}"
+                flow_text.append(f"       ║{pad_line(line)}║\n", style="yellow")
+                
+                routes = step['route']
+                for route_name, route_agents in routes.items():
+                    agents_str = ", ".join(route_agents) if isinstance(route_agents, list) else str(route_agents)
+                    line = f"   -> {route_name}: {agents_str}"
+                    flow_text.append(f"       ║{pad_line(line)}║\n", style="yellow")
+                
+                flow_text.append("       ╚════════════════════════════════════╝\n", style="yellow")
+                
+            elif has_parallel:
+                # Parallel step
+                flow_text.append("       ╔════════════════════════════════════╗\n", style="green")
+                line = f" PARALLEL: {step_name or 'parallel'}"
+                flow_text.append(f"       ║{pad_line(line)}║\n", style="green")
+                
+                for p_step in step['parallel']:
+                    p_agent = p_step.get('agent', '?')
+                    line = f"   |-- {p_agent}"
+                    flow_text.append(f"       ║{pad_line(line)}║\n", style="green")
+                
+                flow_text.append("       ╚════════════════════════════════════╝\n", style="green")
+                
+            else:
+                # Regular agent step
+                agent_info = agents.get(agent_key, {})
+                agent_name = agent_info.get('name', agent_key)
+                
+                flow_text.append("       ╔════════════════════════════════════╗\n", style="cyan")
+                line = f" Step {i+1}: {agent_name}"
+                flow_text.append(f"       ║{pad_line(line)}║\n", style="cyan")
+                
+                # Show modifiers
+                if has_loop:
+                    loop_over = step['loop'].get('over', '?')
+                    line = f"   [loop over: {loop_over}]"
+                    flow_text.append(f"       ║{pad_line(line)}║\n", style="dim magenta")
+                
+                if has_repeat:
+                    until = step['repeat'].get('until', '?')
+                    max_iter = step['repeat'].get('max_iterations', '?')
+                    line = f"   [repeat until: {until} max:{max_iter}]"
+                    flow_text.append(f"       ║{pad_line(line)}║\n", style="dim magenta")
+                
+                flow_text.append("       ╚════════════════════════════════════╝\n", style="cyan")
+            
+            # Arrow to next
+            if i < len(steps) - 1:
+                flow_text.append("             │\n", style="dim")
+                flow_text.append("             ▼\n", style="dim")
+        
+        flow_text.append("             │\n", style="dim")
+        flow_text.append("             ▼\n", style="dim")
+        flow_text.append("       └── END ────┘\n", style="red")
+        
+        console.print(Panel(flow_text, border_style="magenta", box=box.DOUBLE_EDGE,
+                           title="[bold white]Execution Flow[/bold white]"))
+    
+    def _display_workflow_agent_details(self, console, config: Dict):
+        """Display detailed agent information for workflow format."""
+        from rich.table import Table
+        from rich import box
+        
+        table = Table(
+            title="[bold cyan]📋 Agent Details[/bold cyan]",
+            box=box.ROUNDED,
+            show_header=True,
+            header_style="bold magenta",
+            border_style="blue",
+            padding=(0, 1),
+            expand=True
+        )
+        
+        table.add_column("#", style="dim", width=3, justify="center")
+        table.add_column("Agent Key", style="bold yellow", min_width=15)
+        table.add_column("Name", style="cyan", min_width=15)
+        table.add_column("Role", style="white", min_width=20)
+        table.add_column("Goal", style="green", min_width=25, overflow="fold")
+        
+        for i, (agent_key, details) in enumerate(config['agents'].items(), 1):
+            name = details.get('name', agent_key)
+            role = details.get('role', '-')
+            goal = details.get('goal', '-')
+            if len(goal) > 40:
+                goal = goal[:37] + "..."
+            
+            table.add_row(
+                str(i),
+                agent_key,
+                name,
+                role,
+                goal
+            )
+        
+        console.print(table)
+    
+    def display_workflow_start(self, workflow_name: str, agents: list) -> None:
+        """
+        Display workflow start message.
+        
+        Args:
+            workflow_name: Name of the workflow being started
+            agents: List of agent names in the workflow
+        """
+        console = self._get_console()
+        if console:
             try:
-                result = func(*args, **kwargs)
-                self.display_step(1, func_name, "completed")
-                return result
-            except Exception:
-                self.display_step(1, func_name, "failed")
-                raise
-        
-        return wrapper
+                from rich.panel import Panel
+                from rich import box
+                
+                agents_str = ", ".join(agents) if agents else "No agents"
+                content = f"🚀 Starting: {workflow_name}\n👥 Agents: {agents_str}"
+                console.print(Panel(content, title="[bold cyan]Workflow Started[/bold cyan]", 
+                                   border_style="cyan", box=box.ROUNDED))
+            except ImportError:
+                print(f"Starting workflow: {workflow_name} with agents: {agents}")
     
-    def execute(self, workflow_name: str = None, **kwargs) -> Any:
+    def display_workflow_end(self, success: bool = True) -> None:
         """
-        Execute flow display setup.
+        Display workflow end message.
         
         Args:
-            workflow_name: Name of the workflow
+            success: Whether the workflow completed successfully
+        """
+        console = self._get_console()
+        if console:
+            try:
+                from rich.panel import Panel
+                from rich import box
+                
+                if success:
+                    content = "✅ Workflow completed successfully"
+                    style = "green"
+                else:
+                    content = "❌ Workflow failed"
+                    style = "red"
+                
+                console.print(Panel(content, title=f"[bold {style}]Workflow Ended[/bold {style}]", 
+                                   border_style=style, box=box.ROUNDED))
+            except ImportError:
+                status = "successfully" if success else "with errors"
+                print(f"Workflow ended {status}")
+    
+    def execute(self, yaml_file: str = None, **kwargs) -> bool:
+        """
+        Execute flow display - shows diagram without running workflow.
+        
+        Args:
+            yaml_file: Path to the YAML file
             
         Returns:
-            FlowDisplay instance or None
+            True if displayed successfully
         """
-        if workflow_name:
-            self.display_workflow_start(workflow_name)
-        
-        return self.create_flow_display(workflow_name or "Workflow")
+        if yaml_file:
+            return self.display_flow_diagram(yaml_file)
+        return False

src/praisonai/praisonai/cli/features/n8n.py

@@ -0,0 +1,670 @@
+"""
+n8n Integration Handler for PraisonAI CLI.
+
+Converts PraisonAI agents.yaml workflows to n8n workflow JSON format
+and opens them in the n8n UI.
+
+Usage:
+    praisonai agents.yaml --n8n
+    praisonai agents.yaml --n8n --n8n-url http://localhost:5678
+"""
+
+import json
+import yaml
+import webbrowser
+import hashlib
+import os
+from typing import Any, Dict, List, Optional
+from pathlib import Path
+
+try:
+    import requests
+    REQUESTS_AVAILABLE = True
+except ImportError:
+    REQUESTS_AVAILABLE = False
+
+from .base import FlagHandler
+
+
+class N8nHandler(FlagHandler):
+    """
+    Handler for n8n workflow export and visualization.
+    
+    Converts PraisonAI YAML workflows to n8n-compatible JSON format
+    and opens them in the n8n UI for visual editing.
+    """
+    
+    def __init__(self, verbose: bool = False, n8n_url: str = "http://localhost:5678",
+                 use_execute_command: bool = False):
+        """
+        Initialize the n8n handler.
+        
+        Args:
+            verbose: Enable verbose output
+            n8n_url: Base URL of the n8n instance
+            use_execute_command: Use Execute Command nodes (runs praisonai directly)
+                                 instead of HTTP Request nodes
+        """
+        super().__init__(verbose=verbose)
+        self.n8n_url = n8n_url
+        self.praisonai_api_url = "http://127.0.0.1:8005"
+        self.use_execute_command = use_execute_command
+    
+    @property
+    def feature_name(self) -> str:
+        return "n8n"
+    
+    @property
+    def flag_name(self) -> str:
+        return "n8n"
+    
+    @property
+    def flag_help(self) -> str:
+        return "Export workflow to n8n and open in browser"
+    
+    def execute(self, yaml_path: str, n8n_url: Optional[str] = None, 
+                open_browser: bool = True, **kwargs) -> Dict[str, Any]:
+        """
+        Execute the n8n export workflow.
+        
+        Args:
+            yaml_path: Path to the agents.yaml file
+            n8n_url: Optional custom n8n URL
+            open_browser: Whether to open the workflow in browser
+            
+        Returns:
+            Dictionary with workflow JSON and URL
+        """
+        if n8n_url:
+            self.n8n_url = n8n_url
+        
+        # Convert YAML to n8n workflow JSON
+        workflow_json = self.convert_yaml_to_n8n(yaml_path)
+        
+        # Save workflow JSON to file
+        output_path = self._save_workflow_json(workflow_json, yaml_path)
+        
+        self.print_status("✅ Workflow converted successfully!", "success")
+        self.print_status(f"📄 JSON saved to: {output_path}", "info")
+        
+        # Try to create workflow via n8n API
+        api_key = os.environ.get('N8N_API_KEY')
+        workflow_id = None
+        
+        if api_key and REQUESTS_AVAILABLE:
+            workflow_id = self._create_workflow_via_api(workflow_json, api_key)
+        
+        webhook_url = None
+        if workflow_id:
+            # Successfully created via API - open directly
+            url = f"{self.n8n_url}/workflow/{workflow_id}"
+            self.print_status("🚀 Workflow created in n8n!", "success")
+            
+            # Activate the workflow so webhook is available
+            if self._activate_workflow(workflow_id, api_key):
+                self.print_status("✅ Workflow activated!", "success")
+                
+                # Get webhook path from the workflow
+                trigger_node = next((n for n in workflow_json["nodes"] 
+                                    if n.get("type") == "n8n-nodes-base.webhook"), None)
+                if trigger_node:
+                    webhook_path = trigger_node["parameters"].get("path", "praisonai")
+                    webhook_url = f"{self.n8n_url}/webhook/{webhook_path}"
+                    self.print_status("", "info")
+                    self.print_status("🔗 Webhook URL (to trigger workflow):", "info")
+                    self.print_status(f"   POST {webhook_url}", "info")
+            else:
+                self.print_status("⚠️  Could not activate workflow (activate manually in n8n)", "warning")
+            
+            if open_browser:
+                self.open_in_browser(url)
+                self.print_status(f"🌐 Opening: {url}", "info")
+        else:
+            # Fallback to manual import instructions
+            url = self.generate_n8n_url(workflow_json, self.n8n_url)
+            
+            if not api_key:
+                self.print_status("", "info")
+                self.print_status("💡 Tip: Set N8N_API_KEY env var for auto-import", "info")
+                self.print_status("   Generate API key in n8n: Settings → API", "info")
+            
+            self.print_status("", "info")
+            self.print_status("📋 To import into n8n:", "info")
+            self.print_status("   1. Open n8n in your browser", "info")
+            self.print_status("   2. Click the three dots menu (⋮) in the top right", "info")
+            self.print_status("   3. Select 'Import from File'", "info")
+            self.print_status(f"   4. Choose: {output_path}", "info")
+            
+            if open_browser:
+                self.open_in_browser(url)
+                self.print_status(f"🌐 Opening n8n: {url}", "info")
+        
+        return {
+            "workflow": workflow_json,
+            "url": url,
+            "output_path": output_path,
+            "workflow_id": workflow_id,
+            "webhook_url": webhook_url
+        }
+    
+    def _create_workflow_via_api(self, workflow_json: Dict[str, Any], 
+                                  api_key: str) -> Optional[str]:
+        """
+        Create workflow in n8n via REST API.
+        
+        Args:
+            workflow_json: The n8n workflow JSON
+            api_key: n8n API key
+            
+        Returns:
+            Workflow ID if successful, None otherwise
+        """
+        if not REQUESTS_AVAILABLE:
+            return None
+        
+        try:
+            # Only include fields allowed by n8n API schema
+            # See: n8n/packages/cli/src/public-api/v1/handlers/workflows/spec/schemas/workflow.yml
+            workflow_data = {
+                "name": workflow_json.get("name", "PraisonAI Workflow"),
+                "nodes": workflow_json.get("nodes", []),
+                "connections": workflow_json.get("connections", {}),
+                "settings": workflow_json.get("settings", {"executionOrder": "v1"}),
+            }
+            
+            # Optional fields
+            if "staticData" in workflow_json:
+                workflow_data["staticData"] = workflow_json["staticData"]
+            
+            response = requests.post(
+                f"{self.n8n_url}/api/v1/workflows",
+                headers={
+                    "X-N8N-API-KEY": api_key,
+                    "Content-Type": "application/json"
+                },
+                json=workflow_data,
+                timeout=30
+            )
+            
+            if response.status_code == 200:
+                result = response.json()
+                return result.get('id')
+            else:
+                self.log(f"API error: {response.status_code} - {response.text}", "warning")
+                return None
+                
+        except Exception as e:
+            self.log(f"Failed to create workflow via API: {e}", "warning")
+            return None
+    
+    def _activate_workflow(self, workflow_id: str, api_key: str) -> bool:
+        """
+        Activate a workflow via n8n API.
+        
+        Args:
+            workflow_id: The workflow ID to activate
+            api_key: n8n API key
+            
+        Returns:
+            True if successful, False otherwise
+        """
+        if not REQUESTS_AVAILABLE:
+            return False
+        
+        try:
+            response = requests.post(
+                f"{self.n8n_url}/api/v1/workflows/{workflow_id}/activate",
+                headers={
+                    "X-N8N-API-KEY": api_key,
+                    "Content-Type": "application/json"
+                },
+                timeout=30
+            )
+            
+            if response.status_code == 200:
+                return True
+            else:
+                self.log(f"Activation error: {response.status_code} - {response.text}", "warning")
+                return False
+                
+        except Exception as e:
+            self.log(f"Failed to activate workflow: {e}", "warning")
+            return False
+    
+    def convert_yaml_to_n8n(self, yaml_path: str, use_webhook: bool = True) -> Dict[str, Any]:
+        """
+        Convert a PraisonAI YAML workflow to n8n JSON format.
+        
+        The n8n workflow will have:
+        - A webhook trigger to receive requests
+        - One HTTP Request node per agent (calls /agents/{agent_name})
+        - Each agent node passes its output to the next agent
+        
+        Args:
+            yaml_path: Path to the agents.yaml file
+            use_webhook: Use webhook trigger for programmatic execution (default: True)
+            
+        Returns:
+            n8n workflow JSON as dictionary
+        """
+        # Load YAML file
+        with open(yaml_path, 'r') as f:
+            yaml_content = yaml.safe_load(f)
+        
+        # Extract workflow metadata
+        workflow_name = yaml_content.get('name', 'PraisonAI Workflow')
+        description = yaml_content.get('description', '')
+        
+        # Generate a unique webhook path from workflow name
+        webhook_path = workflow_name.lower().replace(' ', '-').replace('/', '-')[:50]
+        
+        # Get agents and steps
+        agents = yaml_content.get('agents', yaml_content.get('roles', {}))
+        steps = yaml_content.get('steps', [])
+        
+        # If no steps defined, create steps from agents
+        if not steps and agents:
+            steps = self._create_steps_from_agents(agents)
+        
+        # Build n8n nodes and connections
+        nodes = []
+        connections = {}
+        
+        # Add trigger node (webhook for programmatic execution, manual otherwise)
+        trigger_node = self._create_trigger_node(use_webhook=use_webhook, webhook_path=webhook_path)
+        nodes.append(trigger_node)
+        
+        # Track previous node for connections
+        prev_node_name = trigger_node["name"]
+        x_position = 450
+        
+        # Create one HTTP node per agent step
+        for i, step in enumerate(steps):
+            if isinstance(step, dict):
+                agent_id = step.get('agent', '')
+                action = step.get('action', step.get('description', ''))
+                agent_config = agents.get(agent_id, {})
+                
+                # Create HTTP node that calls the specific agent endpoint
+                agent_node = self._create_per_agent_node(
+                    agent_id=agent_id,
+                    agent_config=agent_config,
+                    action=action,
+                    position=[x_position, 300],
+                    index=i,
+                    is_first=(i == 0)
+                )
+                nodes.append(agent_node)
+                
+                # Connect to previous node
+                if prev_node_name not in connections:
+                    connections[prev_node_name] = {"main": [[]]}
+                connections[prev_node_name]["main"][0].append({
+                    "node": agent_node["name"],
+                    "type": "main",
+                    "index": 0
+                })
+                
+                prev_node_name = agent_node["name"]
+                x_position += 250
+        
+        # Build the complete workflow
+        # Generate a unique ID based on workflow name
+        workflow_id = hashlib.md5(workflow_name.encode()).hexdigest()[:8]
+        
+        workflow = {
+            "id": workflow_id,
+            "name": workflow_name,
+            "nodes": nodes,
+            "connections": connections,
+            "active": False,
+            "settings": {
+                "executionOrder": "v1"
+            },
+            "versionId": "1",
+            "pinData": {},
+            "meta": {
+                "instanceId": "praisonai-export",
+                "templateCredsSetupCompleted": True
+            },
+            "tags": []
+        }
+        
+        if description:
+            workflow["meta"]["description"] = description
+        
+        return workflow
+    
+    def _create_steps_from_agents(self, agents: Dict[str, Any]) -> List[Dict]:
+        """Create steps from agents when no steps are defined."""
+        steps = []
+        for agent_id, agent_config in agents.items():
+            if isinstance(agent_config, dict):
+                # Check for tasks nested in agent
+                tasks = agent_config.get('tasks', {})
+                if tasks:
+                    for task_id, task_config in tasks.items():
+                        steps.append({
+                            'agent': agent_id,
+                            'action': task_config.get('description', task_config.get('action', f'Execute {task_id}'))
+                        })
+                else:
+                    # Create a default step for the agent
+                    steps.append({
+                        'agent': agent_id,
+                        'action': agent_config.get('instructions', agent_config.get('goal', f'Execute {agent_id}'))
+                    })
+        return steps
+    
+    def _create_trigger_node(self, use_webhook: bool = False, 
+                              webhook_path: str = "praisonai") -> Dict[str, Any]:
+        """Create a trigger node (webhook or manual).
+        
+        Args:
+            use_webhook: If True, creates a webhook trigger for programmatic execution
+            webhook_path: Path for the webhook URL (only used if use_webhook=True)
+        """
+        if use_webhook:
+            # Generate a unique webhook ID
+            webhook_id = hashlib.md5(webhook_path.encode()).hexdigest()[:16]
+            return {
+                "id": "trigger",
+                "name": "Webhook",
+                "type": "n8n-nodes-base.webhook",
+                "typeVersion": 2,
+                "position": [250, 300],
+                "webhookId": webhook_id,
+                "parameters": {
+                    "path": webhook_path,
+                    "httpMethod": "POST",
+                    "responseMode": "lastNode",
+                    "options": {}
+                }
+            }
+        return {
+            "id": "trigger",
+            "name": "Manual Trigger",
+            "type": "n8n-nodes-base.manualTrigger",
+            "typeVersion": 1,
+            "position": [250, 300],
+            "parameters": {}
+        }
+    
+    def _create_per_agent_node(self, agent_id: str, agent_config: Dict,
+                                action: str, position: List[int],
+                                index: int, is_first: bool = False) -> Dict[str, Any]:
+        """
+        Create an HTTP Request node that calls a specific agent endpoint.
+        
+        Args:
+            agent_id: Agent identifier (used in URL path)
+            agent_config: Agent configuration from YAML
+            action: The action/task description for this agent
+            position: Node position [x, y]
+            index: Node index
+            is_first: Whether this is the first agent (uses webhook input)
+            
+        Returns:
+            n8n HTTP Request node configuration
+        """
+        agent_name = agent_config.get('name', agent_id.title())
+        # Convert agent_id to URL-safe format (lowercase, underscores)
+        agent_url_id = agent_id.lower().replace(' ', '_')
+        
+        # Build the query - first agent uses webhook input, others use previous response
+        if is_first:
+            # First agent: use query from webhook body
+            query_expr = f"={{ $json.body?.query || $json.query || '{action}' }}"
+        else:
+            # Subsequent agents: use previous agent's response + original action context
+            query_expr = f"={{ $json.response || '{action}' }}"
+        
+        return {
+            "id": f"agent_{index}",
+            "name": agent_name,
+            "type": "n8n-nodes-base.httpRequest",
+            "typeVersion": 4.2,
+            "position": position,
+            "parameters": {
+                "method": "POST",
+                "url": f"{self.praisonai_api_url}/agents/{agent_url_id}",
+                "sendBody": True,
+                "specifyBody": "json",
+                "jsonBody": f"={{{{ JSON.stringify({{ query: {query_expr[2:]} }}) }}}}",
+                "options": {
+                    "timeout": 300000  # 5 minute timeout per agent
+                }
+            },
+            "notes": f"Agent: {agent_name}\nTask: {action[:100]}"
+        }
+    
+    def _create_praisonai_workflow_node(self, workflow_name: str,
+                                         agent_names: List[str],
+                                         position: List[int]) -> Dict[str, Any]:
+        """
+        Create a single HTTP Request node that triggers the entire PraisonAI workflow.
+        
+        The PraisonAI API will run all agents sequentially when called.
+        
+        Args:
+            workflow_name: Name of the workflow
+            agent_names: List of agent names (for documentation)
+            position: Node position [x, y]
+            
+        Returns:
+            n8n HTTP Request node configuration
+        """
+        # The query will be passed from the webhook trigger
+        # Using n8n expression to get the body from the webhook
+        return {
+            "id": "praisonai_workflow",
+            "name": f"PraisonAI: {workflow_name}",
+            "type": "n8n-nodes-base.httpRequest",
+            "typeVersion": 4.2,
+            "position": position,
+            "parameters": {
+                "method": "POST",
+                "url": f"{self.praisonai_api_url}/agents",
+                "sendBody": True,
+                "specifyBody": "json",
+                # Pass the query from webhook body, or use a default
+                "jsonBody": "={{ JSON.stringify({ query: $json.body?.query || $json.query || 'Start workflow' }) }}",
+                "options": {
+                    "timeout": 600000  # 10 minute timeout for full workflow
+                }
+            },
+            "notes": f"Runs agents: {', '.join(agent_names)}"
+        }
+    
+    def _create_agent_node(self, agent_id: str, agent_config: Dict,
+                            action: str, position: List[int], 
+                            index: int) -> Dict[str, Any]:
+        """Create a node for an agent - either Execute Command or HTTP Request."""
+        if self.use_execute_command:
+            return self._create_execute_command_node(
+                agent_id, agent_config, action, position, index
+            )
+        else:
+            return self._create_http_request_node(
+                agent_id, agent_config, action, position, index
+            )
+    
+    def _create_execute_command_node(self, agent_id: str, agent_config: Dict,
+                                      action: str, position: List[int],
+                                      index: int) -> Dict[str, Any]:
+        """Create an Execute Command node that runs praisonai directly."""
+        agent_name = agent_config.get('name', agent_id.title())
+        
+        # Escape quotes in the action for shell command
+        escaped_action = action.replace('"', '\\"').replace("'", "\\'")
+        
+        # Build the praisonai command
+        # Uses the input from previous node if available
+        command = f'praisonai "{escaped_action}"'
+        
+        return {
+            "id": f"agent_{index}",
+            "name": agent_name,
+            "type": "n8n-nodes-base.executeCommand",
+            "typeVersion": 1,
+            "position": position,
+            "parameters": {
+                "command": command,
+                "executeOnce": True
+            }
+        }
+    
+    def _create_http_request_node(self, agent_id: str, agent_config: Dict,
+                                   action: str, position: List[int], 
+                                   index: int) -> Dict[str, Any]:
+        """Create an HTTP Request node for an agent."""
+        agent_name = agent_config.get('name', agent_id.title())
+        
+        # Build the request body
+        request_body = {
+            "query": action,
+            "agent": agent_id
+        }
+        
+        return {
+            "id": f"agent_{index}",
+            "name": agent_name,
+            "type": "n8n-nodes-base.httpRequest",
+            "typeVersion": 4.2,
+            "position": position,
+            "parameters": {
+                "method": "POST",
+                "url": f"{self.praisonai_api_url}/agents",
+                "sendBody": True,
+                "specifyBody": "json",
+                "jsonBody": json.dumps(request_body),
+                "options": {
+                    "timeout": 300000  # 5 minute timeout for LLM calls
+                }
+            }
+        }
+    
+    def _create_parallel_nodes(self, parallel_steps: List[Dict], 
+                                x_position: int,
+                                agents: Dict[str, Any]) -> tuple:
+        """Create nodes for parallel execution."""
+        nodes = []
+        connections = {}
+        
+        for i, step in enumerate(parallel_steps):
+            agent_id = step.get('agent', '')
+            action = step.get('action', step.get('description', ''))
+            agent_config = agents.get(agent_id, {})
+            
+            node = self._create_agent_node(
+                agent_id=agent_id,
+                agent_config=agent_config,
+                action=action,
+                position=[x_position + (i * 200), 300 + (i * 100)],
+                index=100 + i  # Offset index for parallel nodes
+            )
+            nodes.append(node)
+        
+        return nodes, connections
+    
+    def _create_route_node(self, step: Dict, x_position: int, 
+                           index: int) -> Dict[str, Any]:
+        """Create an IF node for routing decisions."""
+        route_config = step.get('route', {})
+        conditions = list(route_config.keys())
+        
+        return {
+            "id": f"route_{index}",
+            "name": f"Route Decision {index}",
+            "type": "n8n-nodes-base.if",
+            "typeVersion": 2,
+            "position": [x_position, 300],
+            "parameters": {
+                "conditions": {
+                    "options": {
+                        "caseSensitive": True,
+                        "leftValue": "",
+                        "typeValidation": "strict"
+                    },
+                    "conditions": [
+                        {
+                            "id": f"condition_{i}",
+                            "leftValue": "={{ $json.result }}",
+                            "rightValue": condition,
+                            "operator": {
+                                "type": "string",
+                                "operation": "contains"
+                            }
+                        }
+                        for i, condition in enumerate(conditions) if condition != 'default'
+                    ]
+                }
+            }
+        }
+    
+    def generate_n8n_url(self, workflow_json: Dict[str, Any], 
+                         base_url: str) -> str:
+        """
+        Generate a URL to open n8n workflow editor.
+        
+        Note: n8n doesn't support URL hash-based import. Users need to:
+        1. Open n8n
+        2. Use "Import from File" to load the saved JSON
+        
+        Args:
+            workflow_json: The n8n workflow JSON (unused, kept for API compatibility)
+            base_url: Base URL of the n8n instance
+            
+        Returns:
+            URL to open n8n workflow editor
+        """
+        # n8n doesn't support URL-based workflow import via hash
+        # Return the new workflow URL - user will need to import manually
+        return f"{base_url}/workflow/new"
+    
+    def open_in_browser(self, url: str) -> None:
+        """
+        Open a URL in the default web browser.
+        
+        Args:
+            url: URL to open
+        """
+        try:
+            webbrowser.open(url)
+        except Exception as e:
+            self.print_status(f"Could not open browser: {e}", "warning")
+            self.print_status(f"Please open this URL manually: {url}", "info")
+    
+    def _save_workflow_json(self, workflow_json: Dict[str, Any], 
+                            yaml_path: str) -> str:
+        """
+        Save the workflow JSON to a file.
+        
+        Args:
+            workflow_json: The n8n workflow JSON
+            yaml_path: Original YAML file path
+            
+        Returns:
+            Path to the saved JSON file
+        """
+        # Create output path based on input YAML
+        yaml_file = Path(yaml_path)
+        output_path = yaml_file.parent / f"{yaml_file.stem}_n8n.json"
+        
+        with open(output_path, 'w') as f:
+            json.dump(workflow_json, f, indent=2)
+        
+        return str(output_path)
+    
+    def get_workflow_json_for_clipboard(self, yaml_path: str) -> str:
+        """
+        Get the workflow JSON as a string for clipboard copy.
+        
+        Args:
+            yaml_path: Path to the agents.yaml file
+            
+        Returns:
+            JSON string ready for clipboard
+        """
+        workflow_json = self.convert_yaml_to_n8n(yaml_path)
+        return json.dumps(workflow_json, indent=2)

src/praisonai/praisonai/cli/main.py

@@ -210,6 +210,10 @@ class PraisonAI:
         version_string = f"PraisonAI version {__version__}"
 
         self.framework = args.framework or self.framework
+        
+        # Update config_list model if --model flag is provided
+        if getattr(args, 'model', None):
+            self.config_list[0]['model'] = args.model
 
         # Check for piped input from stdin
         stdin_input = self.read_stdin_if_available()
@@ -495,6 +499,31 @@ class PraisonAI:
                 print(result)
                 return result
         else:
+            # n8n Integration - Export workflow to n8n and open in browser
+            if getattr(args, 'n8n', False):
+                from .features.n8n import N8nHandler
+                n8n_handler = N8nHandler(
+                    verbose=getattr(args, 'verbose', False),
+                    n8n_url=getattr(args, 'n8n_url', 'http://localhost:5678')
+                )
+                result = n8n_handler.execute(self.agent_file)
+                return result
+            
+            # Flow Display - Show flow diagram WITHOUT executing (--flow-display flag)
+            if getattr(args, 'flow_display', False):
+                from .features.flow_display import FlowDisplayHandler
+                flow_handler = FlowDisplayHandler(verbose=getattr(args, 'verbose', False))
+                flow_handler.display_flow_diagram(self.agent_file)
+                return  # Exit without executing
+            
+            # Always show flow diagram before execution (default behavior)
+            try:
+                from .features.flow_display import FlowDisplayHandler
+                flow_handler = FlowDisplayHandler(verbose=getattr(args, 'verbose', False))
+                flow_handler.display_flow_diagram(self.agent_file, show_footer=False)
+            except Exception:
+                pass  # Continue even if flow display fails
+            
             agents_generator = AgentsGenerator(
                 self.agent_file,
                 self.framework,
@@ -549,7 +578,7 @@ class PraisonAI:
             return default_args
         
         # Define special commands
-        special_commands = ['chat', 'code', 'call', 'realtime', 'train', 'ui', 'context', 'research', 'memory', 'rules', 'workflow', 'hooks', 'knowledge', 'session', 'tools', 'todo', 'docs', 'mcp', 'commit']
+        special_commands = ['chat', 'code', 'call', 'realtime', 'train', 'ui', 'context', 'research', 'memory', 'rules', 'workflow', 'hooks', 'knowledge', 'session', 'tools', 'todo', 'docs', 'mcp', 'commit', 'serve']
         
         parser = argparse.ArgumentParser(prog="praisonai", description="praisonAI command-line interface")
         parser.add_argument("--framework", choices=["crewai", "autogen", "praisonai"], help="Specify the framework")
@@ -650,6 +679,15 @@ class PraisonAI:
         # Flow Display - visual workflow
         parser.add_argument("--flow-display", action="store_true", help="Enable visual workflow tracking")
         
+        # n8n Integration - export workflow to n8n
+        parser.add_argument("--n8n", action="store_true", help="Export workflow to n8n and open in browser")
+        parser.add_argument("--n8n-url", type=str, default="http://localhost:5678", help="n8n instance URL (default: http://localhost:5678)")
+        
+        # Serve - start API server for agents
+        parser.add_argument("--serve", action="store_true", help="Start API server for agents (use with agents.yaml)")
+        parser.add_argument("--port", type=int, default=8005, help="Server port (default: 8005)")
+        parser.add_argument("--host", type=str, default="127.0.0.1", help="Server host (default: 127.0.0.1)")
+        
         # If we're in a test environment, parse with empty args to avoid pytest interference
         if in_test_env:
             args, unknown_args = parser.parse_known_args([])
@@ -677,6 +715,13 @@ class PraisonAI:
             args.realtime = True
         if args.command == 'call':
             args.call = True
+        if args.command == 'serve':
+            args.serve = True
+
+        # Handle serve command - start API server for agents
+        if args.command == 'serve' or args.serve:
+            self._handle_serve_command(args, unknown_args)
+            sys.exit(0)
 
         # Handle both command and flag versions for call
         if args.command == 'call' or args.call:
@@ -3011,6 +3056,168 @@ Now, {final_instruction.lower()}:"""
             print("pip install praisonaiagents # For PraisonAIAgents\n")  
             sys.exit(1)
 
+    def _handle_serve_command(self, args, unknown_args):
+        """
+        Handle the serve command - start API server for agents.
+        
+        Usage:
+            praisonai serve agents.yaml
+            praisonai serve agents.yaml --port 8005 --host 0.0.0.0
+            praisonai agents.yaml --serve
+        """
+        import yaml
+        from praisonaiagents import Agent, PraisonAIAgents
+        
+        # Determine the YAML file path
+        yaml_file = None
+        
+        # Check if command is 'serve' and there's a file in unknown_args
+        if args.command == 'serve' and unknown_args:
+            yaml_file = unknown_args[0]
+        # Check if command itself is a yaml file (praisonai agents.yaml --serve)
+        elif args.command and args.command.endswith('.yaml'):
+            yaml_file = args.command
+        # Default to agents.yaml
+        else:
+            yaml_file = 'agents.yaml'
+        
+        # Check if file exists
+        if not os.path.exists(yaml_file):
+            print(f"[red]ERROR: File not found: {yaml_file}[/red]")
+            print("\nUsage:")
+            print("  praisonai serve agents.yaml")
+            print("  praisonai serve agents.yaml --port 8005")
+            print("  praisonai agents.yaml --serve")
+            sys.exit(1)
+        
+        print(f"📄 Loading agents from: {yaml_file}")
+        
+        # Load YAML file
+        with open(yaml_file, 'r') as f:
+            config = yaml.safe_load(f)
+        
+        # Extract agents
+        agents_config = config.get('agents', config.get('roles', {}))
+        agents_dict = {}  # Map agent_id to Agent instance
+        agents_list = []
+        
+        for agent_id, agent_config in agents_config.items():
+            if isinstance(agent_config, dict):
+                agent = Agent(
+                    name=agent_config.get('name', agent_id.title()),
+                    role=agent_config.get('role', ''),
+                    goal=agent_config.get('goal', ''),
+                    backstory=agent_config.get('backstory', ''),
+                    instructions=agent_config.get('instructions', ''),
+                    llm=agent_config.get('llm', 'gpt-4o-mini'),
+                    verbose=False
+                )
+                agents_dict[agent_id] = agent
+                agents_list.append(agent)
+                print(f"  ✓ Loaded agent: {agent.name}")
+        
+        if not agents_list:
+            print("[red]ERROR: No agents found in YAML file[/red]")
+            sys.exit(1)
+        
+        # Extract tasks from steps (if defined)
+        steps = config.get('steps', [])
+        tasks_list = []
+        
+        if steps:
+            from praisonaiagents import Task
+            print(f"\n📋 Loading workflow steps...")
+            
+            # First pass: create all tasks
+            task_name_map = {}  # Map step names to Task objects for context/next_tasks
+            for i, step in enumerate(steps):
+                if isinstance(step, dict):
+                    agent_id = step.get('agent', '')
+                    action = step.get('action', step.get('description', ''))
+                    task_name = step.get('name', f"step_{i+1}")
+                    
+                    if agent_id in agents_dict:
+                        task = Task(
+                            name=task_name,
+                            description=action,
+                            agent=agents_dict[agent_id],
+                            expected_output=step.get('expected_output', 'Completed task output'),
+                            task_type=step.get('task_type', 'task'),
+                            is_start=step.get('is_start', i == 0),
+                            async_execution=step.get('async_execution', False),
+                            output_file=step.get('output_file'),
+                        )
+                        tasks_list.append(task)
+                        task_name_map[task_name] = task
+                        print(f"  ✓ Loaded step {i+1}: {agent_id} → {action[:50]}...")
+            
+            # Second pass: set up next_tasks and context relationships
+            for i, step in enumerate(steps):
+                if isinstance(step, dict):
+                    task_name = step.get('name', f"step_{i+1}")
+                    if task_name in task_name_map:
+                        task = task_name_map[task_name]
+                        
+                        # Set next_tasks if specified
+                        next_tasks = step.get('next_tasks', [])
+                        if next_tasks:
+                            task.next_tasks = next_tasks
+                        
+                        # Set context if specified
+                        context_refs = step.get('context', [])
+                        if context_refs:
+                            task.context = [task_name_map[ref] for ref in context_refs if ref in task_name_map]
+                        
+                        # Set condition for workflow branching
+                        condition = step.get('condition', {})
+                        if condition:
+                            task.condition = condition
+        
+        port = args.port
+        host = args.host
+        
+        # Extract process type from workflow config (default: sequential)
+        workflow_config = config.get('workflow', {})
+        process_type = workflow_config.get('process', 'sequential')
+        verbose = workflow_config.get('verbose', False)
+        
+        # Also check top-level process key
+        if 'process' in config:
+            process_type = config['process']
+        
+        print(f"\n🚀 Starting PraisonAI API server...")
+        print(f"   Host: {host}")
+        print(f"   Port: {port}")
+        print(f"   Agents: {len(agents_list)}")
+        if tasks_list:
+            print(f"   Tasks: {len(tasks_list)}")
+        print(f"   Process: {process_type}")
+        
+        # Create and launch - with tasks if defined
+        if tasks_list:
+            praison = PraisonAIAgents(
+                agents=agents_list, 
+                tasks=tasks_list,
+                process=process_type,
+                verbose=1 if verbose else 0
+            )
+        else:
+            praison = PraisonAIAgents(
+                agents=agents_list,
+                process=process_type,
+                verbose=1 if verbose else 0
+            )
+        praison.launch(port=port, host=host)
+        
+        # Keep the main thread alive to prevent exit
+        print("\n✅ Server running. Press Ctrl+C to stop.")
+        try:
+            import time
+            while True:
+                time.sleep(1)
+        except KeyboardInterrupt:
+            print("\n👋 Server stopped.")
+
     def create_chainlit_chat_interface(self):
         """
         Create a Chainlit interface for the chat application.

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==2.3.28 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.3.34 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__ = "2.3.28"
+__version__ = "2.3.34"

src/praisonai/pyproject.toml

@@ -12,7 +12,7 @@ dependencies = [
     "rich>=13.7",
     "markdown>=3.5",
     "pyparsing>=3.0.0",
-    "praisonaiagents>=0.1.7",
+    "praisonaiagents>=0.1.14",
     "python-dotenv>=0.19.0",
     "instructor>=1.3.3",
     "PyYAML>=6.0",

src/praisonai/tests/test_n8n_integration.py

@@ -0,0 +1,246 @@
+"""
+Test-Driven Development tests for n8n integration.
+
+Tests for converting PraisonAI agents.yaml to n8n workflow JSON format.
+"""
+
+import pytest
+import json
+import os
+import tempfile
+from pathlib import Path
+
+
+class TestN8nConverter:
+    """Tests for YAML to n8n JSON conversion."""
+    
+    @pytest.fixture
+    def sample_agents_yaml(self):
+        """Sample agents.yaml content for testing."""
+        return """
+name: Test Workflow
+description: A test workflow for n8n integration
+
+agents:
+  researcher:
+    name: Researcher
+    role: Research Analyst
+    goal: Research topics
+    instructions: Research and gather information
+
+  writer:
+    name: Writer
+    role: Content Writer
+    goal: Write content
+    instructions: Write based on research
+
+steps:
+  - agent: researcher
+    action: "Research the topic"
+  
+  - agent: writer
+    action: "Write content based on research"
+"""
+
+    @pytest.fixture
+    def sample_yaml_file(self, sample_agents_yaml):
+        """Create a temporary YAML file for testing."""
+        with tempfile.NamedTemporaryFile(mode='w', suffix='.yaml', delete=False) as f:
+            f.write(sample_agents_yaml)
+            f.flush()
+            yield f.name
+        os.unlink(f.name)
+
+    def test_n8n_handler_import(self):
+        """Test that N8nHandler can be imported."""
+        from praisonai.cli.features.n8n import N8nHandler
+        assert N8nHandler is not None
+
+    def test_n8n_handler_initialization(self):
+        """Test N8nHandler initialization."""
+        from praisonai.cli.features.n8n import N8nHandler
+        handler = N8nHandler(verbose=True)
+        assert handler.feature_name == "n8n"
+        assert handler.verbose is True
+
+    def test_convert_yaml_to_n8n_returns_dict(self, sample_yaml_file):
+        """Test that convert_yaml_to_n8n returns a dictionary."""
+        from praisonai.cli.features.n8n import N8nHandler
+        handler = N8nHandler()
+        result = handler.convert_yaml_to_n8n(sample_yaml_file)
+        assert isinstance(result, dict)
+
+    def test_n8n_workflow_has_required_keys(self, sample_yaml_file):
+        """Test that n8n workflow JSON has required keys."""
+        from praisonai.cli.features.n8n import N8nHandler
+        handler = N8nHandler()
+        result = handler.convert_yaml_to_n8n(sample_yaml_file)
+        
+        assert "name" in result
+        assert "nodes" in result
+        assert "connections" in result
+
+    def test_n8n_workflow_has_trigger_node(self, sample_yaml_file):
+        """Test that n8n workflow has a trigger node (webhook or manual)."""
+        from praisonai.cli.features.n8n import N8nHandler
+        handler = N8nHandler()
+        result = handler.convert_yaml_to_n8n(sample_yaml_file)
+        
+        nodes = result["nodes"]
+        # Check for webhook or manual trigger nodes
+        trigger_nodes = [n for n in nodes if "trigger" in n.get("type", "").lower() 
+                        or "webhook" in n.get("type", "").lower()
+                        or "trigger" in n.get("name", "").lower()
+                        or "webhook" in n.get("name", "").lower()]
+        assert len(trigger_nodes) >= 1
+
+    def test_n8n_workflow_has_per_agent_nodes(self, sample_yaml_file):
+        """Test that n8n workflow has HTTP Request nodes for each agent."""
+        from praisonai.cli.features.n8n import N8nHandler
+        handler = N8nHandler()
+        result = handler.convert_yaml_to_n8n(sample_yaml_file)
+        
+        nodes = result["nodes"]
+        # Should have HTTP Request nodes for each agent
+        http_nodes = [n for n in nodes if "httpRequest" in n.get("type", "")]
+        assert len(http_nodes) >= 2  # At least 2 agents
+        
+        # Each node should call a specific agent endpoint
+        for node in http_nodes:
+            assert "/agents/" in node["parameters"]["url"]
+
+    def test_n8n_workflow_nodes_are_connected(self, sample_yaml_file):
+        """Test that nodes are properly connected."""
+        from praisonai.cli.features.n8n import N8nHandler
+        handler = N8nHandler()
+        result = handler.convert_yaml_to_n8n(sample_yaml_file)
+        
+        connections = result["connections"]
+        assert len(connections) > 0
+
+    def test_n8n_workflow_name_from_yaml(self, sample_yaml_file):
+        """Test that workflow name is taken from YAML."""
+        from praisonai.cli.features.n8n import N8nHandler
+        handler = N8nHandler()
+        result = handler.convert_yaml_to_n8n(sample_yaml_file)
+        
+        assert result["name"] == "Test Workflow"
+
+    def test_generate_n8n_url(self, sample_yaml_file):
+        """Test URL generation for n8n."""
+        from praisonai.cli.features.n8n import N8nHandler
+        handler = N8nHandler()
+        workflow_json = handler.convert_yaml_to_n8n(sample_yaml_file)
+        url = handler.generate_n8n_url(workflow_json, "http://localhost:5678")
+        
+        assert url.startswith("http://localhost:5678")
+        assert "workflow" in url.lower()
+
+    def test_node_positions_are_sequential(self, sample_yaml_file):
+        """Test that node positions are laid out sequentially."""
+        from praisonai.cli.features.n8n import N8nHandler
+        handler = N8nHandler()
+        result = handler.convert_yaml_to_n8n(sample_yaml_file)
+        
+        nodes = result["nodes"]
+        positions = [n.get("position", [0, 0]) for n in nodes]
+        
+        # X positions should increase for sequential workflow
+        x_positions = [p[0] for p in positions]
+        assert x_positions == sorted(x_positions), "Nodes should be positioned left to right"
+
+
+class TestN8nCLIIntegration:
+    """Tests for CLI integration of n8n feature."""
+    
+    def test_n8n_flag_in_argparser(self):
+        """Test that --n8n flag is recognized by argparser."""
+        import argparse
+        # This will be tested after we add the flag to main.py
+        pass
+
+    def test_execute_method_exists(self):
+        """Test that execute method exists."""
+        from praisonai.cli.features.n8n import N8nHandler
+        handler = N8nHandler()
+        assert hasattr(handler, 'execute')
+        assert callable(handler.execute)
+
+
+class TestN8nWorkflowPatterns:
+    """Tests for different workflow patterns."""
+    
+    @pytest.fixture
+    def parallel_workflow_yaml(self):
+        """YAML with parallel steps."""
+        return """
+name: Parallel Workflow
+
+agents:
+  agent1:
+    name: Agent1
+    role: Role1
+    goal: Goal1
+  agent2:
+    name: Agent2
+    role: Role2
+    goal: Goal2
+
+steps:
+  - parallel:
+      - agent: agent1
+        action: "Do task 1"
+      - agent: agent2
+        action: "Do task 2"
+"""
+
+    @pytest.fixture
+    def route_workflow_yaml(self):
+        """YAML with route/decision steps."""
+        return """
+name: Route Workflow
+
+agents:
+  classifier:
+    name: Classifier
+    role: Classifier
+    goal: Classify input
+  handler_a:
+    name: HandlerA
+    role: Handler
+    goal: Handle type A
+  handler_b:
+    name: HandlerB
+    role: Handler
+    goal: Handle type B
+
+steps:
+  - agent: classifier
+    action: "Classify the input"
+  
+  - route:
+      type_a: [handler_a]
+      type_b: [handler_b]
+"""
+
+    def test_parallel_workflow_creates_split_node(self, parallel_workflow_yaml):
+        """Test that parallel steps create appropriate n8n structure."""
+        import tempfile
+        import os
+        from praisonai.cli.features.n8n import N8nHandler
+        
+        with tempfile.NamedTemporaryFile(mode='w', suffix='.yaml', delete=False) as f:
+            f.write(parallel_workflow_yaml)
+            f.flush()
+            
+            handler = N8nHandler()
+            result = handler.convert_yaml_to_n8n(f.name)
+            
+            # Should have nodes for parallel execution
+            assert len(result["nodes"]) >= 2
+            
+        os.unlink(f.name)
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])

src/praisonai/tests/test_phase2_refactor.py

@@ -1,15 +1,21 @@
 #!/usr/bin/env python3
-"""Test script to verify Phase 2 refactoring maintains backward compatibility"""
+"""Test script to verify Phase 2 refactoring maintains backward compatibility
+
+NOTE: These tests make real API calls and require valid API keys.
+They are marked with @pytest.mark.real to be excluded from CI.
+"""
 
 import asyncio
 import sys
 import os
+import pytest
 
 # Add the correct path for imports
 sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'src/praisonai-agents'))
 
 from praisonaiagents.llm import LLM
 
+@pytest.mark.real
 def test_sync_methods():
     """Test synchronous methods still work correctly"""
     print("Testing synchronous methods...")
@@ -52,6 +58,7 @@ def test_sync_methods():
     
     print("✅ Synchronous methods working correctly\n")
 
+@pytest.mark.real
 async def test_async_methods():
     """Test asynchronous methods still work correctly"""
     print("Testing asynchronous methods...")
@@ -94,6 +101,7 @@ async def test_async_methods():
     
     print("✅ Asynchronous methods working correctly\n")
 
+@pytest.mark.real
 def test_edge_cases():
     """Test edge cases and special scenarios"""
     print("Testing edge cases...")

src/praisonai/uv.lock

@@ -4197,7 +4197,7 @@ requires-dist = [
     { name = "praisonai-tools", marker = "extra == 'autogen'", specifier = ">=0.0.22" },
     { name = "praisonai-tools", marker = "extra == 'autogen-v4'", specifier = ">=0.0.22" },
     { name = "praisonai-tools", marker = "extra == 'crewai'", specifier = ">=0.0.22" },
-    { name = "praisonaiagents", specifier = ">=0.1.7" },
+    { name = "praisonaiagents", specifier = ">=0.1.14" },
     { name = "pyautogen", marker = "extra == 'autogen'", specifier = "==0.2.29" },
     { name = "pydantic", marker = "extra == 'chat'", specifier = "<=2.10.1" },
     { name = "pydantic", marker = "extra == 'code'", specifier = "<=2.10.1" },
@@ -4252,7 +4252,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.1.7"
+version = "0.1.14"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohttp" },
@@ -4261,9 +4261,9 @@ dependencies = [
     { name = "pydantic" },
     { name = "rich" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/07/19/d59400d701c3433c1b0c08024f34d9e17d63bb6257c0f56e39c9715f66a8/praisonaiagents-0.1.7.tar.gz", hash = "sha256:42ac442ff80bdbcca0f25987d8ac722e4ce9480a650acecde1c6fc7fa124acdf", size = 615505, upload-time = "2025-12-17T13:15:11.644Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/aa/19/3493e61f00afc8f2dc6beed5407dd5d18e36f70c96107aa0d8f23c136f26/praisonaiagents-0.1.14.tar.gz", hash = "sha256:3e86b548fa6a44f37b84fc9588d275ede34a303d2f5ac3d0c596f4e2464131b9", size = 621584, upload-time = "2025-12-18T14:39:37.194Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/b1/cb/cdd00ec34abb60925133ed8643432f93f45f4c679ccd3f599d01a26fc354/praisonaiagents-0.1.7-py3-none-any.whl", hash = "sha256:b07ecc2b088f9bcbd4dab352685f35142b422d002581aaa8b992a8f7033d6d13", size = 545981, upload-time = "2025-12-17T13:15:09.434Z" },
+    { url = "https://files.pythonhosted.org/packages/0b/f7/618f7278370091eb62c118331646010d139dd88a8477c8bea91e18ac274e/praisonaiagents-0.1.14-py3-none-any.whl", hash = "sha256:034c862bb293248c29f8bf6ce4a910cb17bc8b980dd5cc38d790a4cb16a08729", size = 548184, upload-time = "2025-12-18T14:39:34.989Z" },
 ]
 
 [[package]]