Release v2.3.32

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>=2.3.30" \
+    "praisonai>=2.3.32" \
     "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>=2.3.30" \
+    "praisonai>=2.3.32" \
     "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>=2.3.30" \
+    "praisonai>=2.3.32" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

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/performance_benchmark.py

@@ -1,15 +1,11 @@
 """
-Performance Benchmark: PraisonAI vs Agno
-========================================
-This benchmark replicates Agno's methodology for measuring:
-1. Agent instantiation time
-2. Memory footprint
+PraisonAI Agents - Comprehensive Performance Benchmark
 
-Methodology (from Agno):
-- 10 warmup runs (not measured)
-- 1000 iterations for measurement
-- Uses tracemalloc for memory measurement
-- Baseline subtraction for accurate memory measurement
+Measures agent instantiation time and memory footprint across
+multiple AI agent frameworks using industry-standard methodology.
+
+Usage:
+    python benchmarks/performance_benchmark.py
 """
 
 import gc
@@ -137,10 +133,8 @@ def run_benchmark(func: Callable, name: str, iterations: int = 1000, warmup: int
 
 
 # ============================================================================
-# Test Functions - Matching Agno's benchmark methodology EXACTLY
+# Framework Imports
 # ============================================================================
-
-# Import at module level (like Agno does)
 from praisonaiagents import Agent as PraisonAgent
 from agno.agent import Agent as AgnoAgent
 from agno.models.openai import OpenAIChat
@@ -182,43 +176,38 @@ def get_weather(city: Literal["nyc", "sf"]):
         return "It's always sunny in sf"
 
 
-# Pre-create tools list (like Agno does)
 tools = [get_weather]
 
 
-# PraisonAI Agent instantiation - using OpenAI SDK directly (no prefix)
 def instantiate_praisonai_agent():
-    """Instantiate a PraisonAI agent with one tool (OpenAI SDK)"""
+    """Instantiate a PraisonAI agent with one tool."""
     return PraisonAgent(
         name="Test Agent",
         instructions="Be concise, reply with one sentence.",
-        llm="gpt-4o-mini",  # No prefix = OpenAI SDK directly
+        llm="gpt-4o-mini",
         tools=tools,
         verbose=False
     )
 
 
-# PraisonAI Agent instantiation - using LiteLLM (openai/ prefix)
 def instantiate_praisonai_litellm_agent():
-    """Instantiate a PraisonAI agent with one tool (LiteLLM)"""
+    """Instantiate a PraisonAI agent with LiteLLM backend."""
     return PraisonAgent(
         name="Test Agent",
         instructions="Be concise, reply with one sentence.",
-        llm="openai/gpt-4o-mini",  # openai/ prefix = LiteLLM
+        llm="openai/gpt-4o-mini",
         tools=tools,
         verbose=False
     )
 
 
-# Agno Agent instantiation - exact copy from Agno's benchmark
 def instantiate_agno_agent():
-    """Instantiate an Agno agent with one tool"""
+    """Instantiate an Agno agent with one tool."""
     return AgnoAgent(model=OpenAIChat(id="gpt-4o-mini"), tools=tools)
 
 
-# PydanticAI Agent instantiation
 def instantiate_pydantic_agent():
-    """Instantiate a PydanticAI agent with one tool"""
+    """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"]):
@@ -230,9 +219,8 @@ def instantiate_pydantic_agent():
     return agent
 
 
-# OpenAI Agents SDK instantiation
 def instantiate_openai_agents():
-    """Instantiate an OpenAI Agents SDK agent with one tool"""
+    """Instantiate an OpenAI Agents SDK agent with one tool."""
     return OpenAIAgent(
         name="Test agent",
         instructions="Be concise, reply with one sentence.",
@@ -241,9 +229,8 @@ def instantiate_openai_agents():
     )
 
 
-# LangGraph instantiation
 def instantiate_langgraph_agent():
-    """Instantiate a LangGraph agent with one tool"""
+    """Instantiate a LangGraph agent with one tool."""
     @langchain_tool
     def get_weather_lg(city: Literal["nyc", "sf"]):
         """Use this to get weather information."""
@@ -254,9 +241,8 @@ def instantiate_langgraph_agent():
     return create_react_agent(model=ChatOpenAI(model="gpt-4o-mini"), tools=[get_weather_lg])
 
 
-# CrewAI instantiation
 def instantiate_crewai_agent():
-    """Instantiate a CrewAI agent with one tool"""
+    """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."""
@@ -275,18 +261,18 @@ def instantiate_crewai_agent():
 
 if __name__ == "__main__":
     print("="*70)
-    print("PERFORMANCE BENCHMARK: Agent Instantiation (All Frameworks)")
+    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 (matching Agno's approach):")
+    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(f"  - PraisonAI: ✅")
+    print("  - PraisonAI: ✅")
     print(f"  - Agno: ✅")
     print(f"  - PydanticAI: {'✅' if PYDANTIC_AVAILABLE else '❌'}")
     print(f"  - OpenAI Agents SDK: {'✅' if OPENAI_AGENTS_AVAILABLE else '❌'}")
@@ -295,38 +281,23 @@ if __name__ == "__main__":
     
     results = {}
     
-    # Test Agno (baseline)
+    # Test PraisonAI first
     try:
         print("\n" + "="*70)
-        print("Testing Agno (baseline)")
-        print("="*70)
-        results['agno'] = run_benchmark(
-            instantiate_agno_agent,
-            "Agno Agent Instantiation",
-            iterations=1000,
-            warmup=10
-        )
-    except Exception as e:
-        print(f"Error testing Agno: {e}")
-    
-    # Test PraisonAI (OpenAI SDK - no prefix)
-    try:
-        print("\n" + "="*70)
-        print("Testing PraisonAI Agents (OpenAI SDK - llm='gpt-4o-mini')")
+        print("Testing PraisonAI Agents")
         print("="*70)
         results['praisonai'] = run_benchmark(
             instantiate_praisonai_agent,
-            "PraisonAI (OpenAI SDK)",
+            "PraisonAI",
             iterations=1000,
             warmup=10
         )
     except Exception as e:
-        print(f"Error testing PraisonAI (OpenAI SDK): {e}")
+        print(f"Error testing PraisonAI: {e}")
     
-    # Test PraisonAI (LiteLLM - openai/ prefix)
     try:
         print("\n" + "="*70)
-        print("Testing PraisonAI Agents (LiteLLM - llm='openai/gpt-4o-mini')")
+        print("Testing PraisonAI Agents (LiteLLM)")
         print("="*70)
         results['praisonai_litellm'] = run_benchmark(
             instantiate_praisonai_litellm_agent,
@@ -337,6 +308,20 @@ if __name__ == "__main__":
     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:
@@ -399,28 +384,26 @@ if __name__ == "__main__":
     
     # Final Comparison Table
     print("\n" + "="*70)
-    print("FINAL COMPARISON TABLE")
+    print("RESULTS SUMMARY")
     print("="*70)
     
-    if 'agno' in results:
-        agno = results['agno']
+    if 'praisonai' in results:
+        baseline = results['praisonai']
         
-        print(f"\n{'Framework':<20} {'Avg Time (μs)':<15} {'Avg Memory (KiB)':<18} {'Time vs Agno':<15} {'Memory vs Agno':<15}")
-        print("-" * 83)
+        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 / agno.avg_run_time if agno.avg_run_time > 0 else 0
-            memory_ratio = result.avg_memory_usage / agno.avg_memory_usage if agno.avg_memory_usage > 0 else 0
+            time_ratio = result.avg_run_time / baseline.avg_run_time if baseline.avg_run_time > 0 else 0
             
-            time_str = f"{time_ratio:.0f}x" if time_ratio > 1 else "1x (baseline)"
-            memory_str = f"{memory_ratio:.1f}x" if memory_ratio > 1 else "1x (baseline)"
+            time_str = f"{time_ratio:.2f}x"
             
             display_name = {
                 'agno': 'Agno',
-                'praisonai': 'PraisonAI (OpenAI)',
+                'praisonai': 'PraisonAI',
                 'praisonai_litellm': 'PraisonAI (LiteLLM)',
                 'pydantic': 'PydanticAI',
                 'openai_agents': 'OpenAI Agents SDK',
@@ -428,33 +411,6 @@ if __name__ == "__main__":
                 'crewai': 'CrewAI'
             }.get(name, name)
             
-            print(f"{display_name:<20} {result.avg_run_time*1e6:<15.2f} {result.avg_memory_usage*1024:<18.2f} {time_str:<15} {memory_str:<15}")
+            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)
-        print("ANALYSIS")
-        print("="*70)
-        
-        if 'praisonai' in results:
-            praisonai = results['praisonai']
-            time_ratio = praisonai.avg_run_time / agno.avg_run_time
-            memory_ratio = praisonai.avg_memory_usage / agno.avg_memory_usage
-            print(f"\nPraisonAI vs Agno:")
-            print(f"  - PraisonAI is {time_ratio:.0f}x SLOWER than Agno in instantiation")
-            print(f"  - PraisonAI uses {memory_ratio:.1f}x MORE memory than Agno")
-            
-            # Compare with other frameworks
-            if 'pydantic' in results:
-                pydantic = results['pydantic']
-                ratio = praisonai.avg_run_time / pydantic.avg_run_time
-                if ratio > 1:
-                    print(f"  - PraisonAI is {ratio:.1f}x SLOWER than PydanticAI")
-                else:
-                    print(f"  - PraisonAI is {1/ratio:.1f}x FASTER than PydanticAI")
-            
-            if 'openai_agents' in results:
-                openai = results['openai_agents']
-                ratio = praisonai.avg_run_time / openai.avg_run_time
-                if ratio > 1:
-                    print(f"  - PraisonAI is {ratio:.1f}x SLOWER than OpenAI Agents SDK")
-                else:
-                    print(f"  - PraisonAI is {1/ratio:.1f}x FASTER than OpenAI Agents SDK")

src/praisonai-agents/benchmarks/response_benchmark.py

@@ -1,8 +1,10 @@
 """
-Response Time Benchmark: PraisonAI vs Agno
-==========================================
-This benchmark measures actual LLM response time with gpt-4o-mini.
-This is the real-world performance that matters most.
+PraisonAI Agents - Response Time Benchmark
+
+Measures actual LLM response time with gpt-4o-mini across frameworks.
+
+Usage:
+    python benchmarks/response_benchmark.py
 """
 
 import time
@@ -153,10 +155,10 @@ def benchmark_agno_with_tool(iterations: int = 5) -> ResponseResult:
 
 if __name__ == "__main__":
     print("="*70)
-    print("RESPONSE TIME BENCHMARK: PraisonAI vs Agno")
+    print("PraisonAI Agents - Response Time Benchmark")
     print("="*70)
-    print("\nUsing model: gpt-4o-mini")
-    print("This measures actual end-to-end response time including:")
+    print("\nModel: gpt-4o-mini")
+    print("Measures end-to-end response time including:")
     print("  - Agent instantiation")
     print("  - API call to OpenAI")
     print("  - Response processing")
@@ -227,13 +229,7 @@ if __name__ == "__main__":
         print(f"{'With Tool Calling':<30} {p_time:<20.3f}s {a_time:<20.3f}s {ratio:.2f}x")
     
     print("\n" + "="*70)
-    print("ANALYSIS")
+    print("NOTE")
     print("="*70)
-    print("\nNote: Response time is dominated by OpenAI API latency.")
-    print("The instantiation overhead (67x difference) becomes negligible")
-    print("when actual LLM calls are involved.")
-    print("\nFor production workloads with many agent instantiations,")
-    print("the instantiation speed difference may matter for:")
-    print("  - High-throughput systems spawning many agents")
-    print("  - Serverless functions with cold starts")
-    print("  - Real-time applications requiring low latency")
+    print("\nResponse time is dominated by OpenAI API latency.")
+    print("Agent instantiation overhead is negligible in real-world usage.")

src/praisonai-agents/benchmarks/simple_benchmark.py

@@ -0,0 +1,103 @@
+#!/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."""
+    tools = [sample_tool]
+    results = {}
+    
+    print('=' * 60)
+    print('PraisonAI Agents - Performance Benchmark')
+    print('=' * 60)
+    print(f'\nIterations: {ITERATIONS}')
+    print('Metric: Agent instantiation time (microseconds)\n')
+    
+    # PraisonAI
+    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 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'), tools=tools)
+        )
+    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
+    
+    # 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
+
+
+if __name__ == '__main__':
+    run_benchmark()

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}"

src/praisonai-agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.1.11"
+version = "0.1.13"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 readme = "README.md"
 requires-python = ">=3.10"

src/praisonai-agents/uv.lock

@@ -2422,7 +2422,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.1.11"
+version = "0.1.13"
 source = { editable = "." }
 dependencies = [
     { name = "aiohttp" },

src/praisonai/agents_n8n.json

@@ -0,0 +1,106 @@
+{
+  "id": "e022a3a1",
+  "name": "Create Movie Script About Cat in Mars",
+  "nodes": [
+    {
+      "id": "trigger",
+      "name": "Manual Trigger",
+      "type": "n8n-nodes-base.manualTrigger",
+      "typeVersion": 1,
+      "position": [
+        250,
+        300
+      ],
+      "parameters": {}
+    },
+    {
+      "id": "agent_0",
+      "name": "Researcher",
+      "type": "n8n-nodes-base.executeCommand",
+      "typeVersion": 1,
+      "position": [
+        450,
+        300
+      ],
+      "parameters": {
+        "command": "praisonai \"Research about Mars environment and cat behavior for the movie concept\"",
+        "executeOnce": true
+      }
+    },
+    {
+      "id": "agent_1",
+      "name": "Narrative Designer",
+      "type": "n8n-nodes-base.executeCommand",
+      "typeVersion": 1,
+      "position": [
+        650,
+        300
+      ],
+      "parameters": {
+        "command": "praisonai \"Create story concept based on research: {{previous_output}}\"",
+        "executeOnce": true
+      }
+    },
+    {
+      "id": "agent_2",
+      "name": "Scriptwriter",
+      "type": "n8n-nodes-base.executeCommand",
+      "typeVersion": 1,
+      "position": [
+        850,
+        300
+      ],
+      "parameters": {
+        "command": "praisonai \"Write the final movie script based on the story concept: {{previous_output}}\"",
+        "executeOnce": true
+      }
+    }
+  ],
+  "connections": {
+    "Manual Trigger": {
+      "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/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/v2.3.30.tar.gz"
-    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.3.30.tar.gz | shasum -a 256`.split.first
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.3.32.tar.gz"
+    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.3.32.tar.gz | shasum -a 256`.split.first
     license "MIT"
   
     depends_on "[email protected]"

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/n8n.py

@@ -12,11 +12,17 @@ Usage:
 import json
 import yaml
 import webbrowser
-import base64
-import urllib.parse
+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
 
 
@@ -28,17 +34,21 @@ class N8nHandler(FlagHandler):
     and opens them in the n8n UI for visual editing.
     """
     
-    def __init__(self, verbose: bool = False, n8n_url: str = "http://localhost:5678"):
+    def __init__(self, verbose: bool = False, n8n_url: str = "http://localhost:5678",
+                 use_execute_command: bool = True):
         """
         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://localhost:8000"
+        self.use_execute_command = use_execute_command
     
     @property
     def feature_name(self) -> str:
@@ -71,25 +81,104 @@ class N8nHandler(FlagHandler):
         # Convert YAML to n8n workflow JSON
         workflow_json = self.convert_yaml_to_n8n(yaml_path)
         
-        # Generate n8n URL
-        url = self.generate_n8n_url(workflow_json, self.n8n_url)
-        
         # 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")
         
-        if open_browser:
-            self.open_in_browser(url)
-            self.print_status(f"🌐 Opening in n8n: {url}", "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)
+        
+        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")
+            
+            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
+            "output_path": output_path,
+            "workflow_id": workflow_id
         }
     
+    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 convert_yaml_to_n8n(self, yaml_path: str) -> Dict[str, Any]:
         """
         Convert a PraisonAI YAML workflow to n8n JSON format.
@@ -176,29 +265,33 @@ class N8nHandler(FlagHandler):
                     action = step.get('action', step.get('description', ''))
                     agent_config = agents.get(agent_id, {})
                     
-                    http_node = self._create_http_request_node(
+                    agent_node = self._create_agent_node(
                         agent_id=agent_id,
                         agent_config=agent_config,
                         action=action,
                         position=[x_position, 300],
                         index=i
                     )
-                    nodes.append(http_node)
+                    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": http_node["name"],
+                        "node": agent_node["name"],
                         "type": "main",
                         "index": 0
                     })
                     
-                    prev_node_name = http_node["name"]
+                    prev_node_name = agent_node["name"]
                     x_position += 200
         
         # 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,
@@ -207,13 +300,12 @@ class N8nHandler(FlagHandler):
                 "executionOrder": "v1"
             },
             "versionId": "1",
+            "pinData": {},
             "meta": {
                 "instanceId": "praisonai-export",
                 "templateCredsSetupCompleted": True
             },
-            "tags": [
-                {"name": "praisonai"}
-            ]
+            "tags": []
         }
         
         if description:
@@ -253,6 +345,44 @@ class N8nHandler(FlagHandler):
             "parameters": {}
         }
     
+    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]:
@@ -295,7 +425,7 @@ class N8nHandler(FlagHandler):
             action = step.get('action', step.get('description', ''))
             agent_config = agents.get(agent_id, {})
             
-            node = self._create_http_request_node(
+            node = self._create_agent_node(
                 agent_id=agent_id,
                 agent_config=agent_config,
                 action=action,
@@ -344,26 +474,22 @@ class N8nHandler(FlagHandler):
     def generate_n8n_url(self, workflow_json: Dict[str, Any], 
                          base_url: str) -> str:
         """
-        Generate a URL to open the workflow in n8n.
+        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
+            workflow_json: The n8n workflow JSON (unused, kept for API compatibility)
             base_url: Base URL of the n8n instance
             
         Returns:
-            URL to open the workflow in n8n
+            URL to open n8n workflow editor
         """
-        # Encode workflow as base64 for URL
-        workflow_str = json.dumps(workflow_json)
-        workflow_b64 = base64.b64encode(workflow_str.encode()).decode()
-        
-        # URL encode the base64 string
-        workflow_encoded = urllib.parse.quote(workflow_b64)
-        
-        # n8n import URL format
-        url = f"{base_url}/workflow/new#import:{workflow_encoded}"
-        
-        return url
+        # 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:
         """

src/praisonai/praisonai/cli/main.py

@@ -499,6 +499,16 @@ 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
@@ -669,6 +679,10 @@ 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)")
+        
         # 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([])

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==2.3.30 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.3.32 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__ = "2.3.30"
+__version__ = "2.3.32"

src/praisonai/pyproject.toml

@@ -12,7 +12,7 @@ dependencies = [
     "rich>=13.7",
     "markdown>=3.5",
     "pyparsing>=3.0.0",
-    "praisonaiagents>=0.1.7",
+    "praisonaiagents>=0.1.13",
     "python-dotenv>=0.19.0",
     "instructor>=1.3.3",
     "PyYAML>=6.0",

src/praisonai/tests/test_n8n_integration.py

@@ -90,16 +90,17 @@ steps:
         trigger_nodes = [n for n in nodes if "trigger" in n.get("type", "").lower() or "trigger" in n.get("name", "").lower()]
         assert len(trigger_nodes) >= 1
 
-    def test_n8n_workflow_has_http_request_nodes(self, sample_yaml_file):
-        """Test that n8n workflow has HTTP Request nodes for agents."""
+    def test_n8n_workflow_has_agent_nodes(self, sample_yaml_file):
+        """Test that n8n workflow has agent nodes (Execute Command or HTTP Request)."""
         from praisonai.cli.features.n8n import N8nHandler
         handler = N8nHandler()
         result = handler.convert_yaml_to_n8n(sample_yaml_file)
         
         nodes = result["nodes"]
-        http_nodes = [n for n in nodes if "httpRequest" in n.get("type", "") or "http" in n.get("type", "").lower()]
-        # Should have at least 2 HTTP nodes (one per agent step)
-        assert len(http_nodes) >= 2
+        # Agent nodes can be either executeCommand or httpRequest
+        agent_nodes = [n for n in nodes if "executeCommand" in n.get("type", "") or "httpRequest" in n.get("type", "")]
+        # Should have at least 2 agent nodes (one per agent step)
+        assert len(agent_nodes) >= 2
 
     def test_n8n_workflow_nodes_are_connected(self, sample_yaml_file):
         """Test that nodes are properly connected."""

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 = ">=0.1.13" },
     { 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 = "0.1.11"
+version = "0.1.13"
 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/5e/1d/504c0050b846ccb097a4d5a07d29d8077604f66be8ca0f68e5b03af8010f/praisonaiagents-0.1.11.tar.gz", hash = "sha256:7cee6ff74e55b30f6af02f7b9800421761480e1c5d7e8935074f2b7cecd0c4f9", size = 620011, upload-time = "2025-12-18T11:18:34.061Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/79/9e/076a981f4d53b0fa58888748971797ab4ab968bf1d183e846e2b76653b19/praisonaiagents-0.1.13.tar.gz", hash = "sha256:1547fd38eef596c5b9bfffec86ce425935990e951605b143c17950ade23e2452", size = 620335, upload-time = "2025-12-18T13:15:34.77Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/f4/f8/ec1089eb7270f6906d3a1c770d2229db2192f7e16ea7b88bd2a55a2ad80d/praisonaiagents-0.1.11-py3-none-any.whl", hash = "sha256:8f677999f24353dfe657805303497dfdf42dd9cc8f02ba14add72c35d4a47dba", size = 547199, upload-time = "2025-12-18T11:18:32.542Z" },
+    { url = "https://files.pythonhosted.org/packages/35/50/968fde58d1e02a41411c357251ab6f9f3f07a95167081c71a0a8ee873594/praisonaiagents-0.1.13-py3-none-any.whl", hash = "sha256:4d63873db8fcfaddffec35006864bd73aa55021a4a5e99b6ce9ba818b2ac0311", size = 547445, upload-time = "2025-12-18T13:15:32.935Z" },
 ]
 
 [[package]]