Release v3.0.5

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>=3.0.3" \
+    "praisonai>=3.0.5" \
     "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>=3.0.3" \
+    "praisonai>=3.0.5" \
     "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>=3.0.3" \
+    "praisonai>=3.0.5" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

examples/python/session/session_persistence_example.py

@@ -0,0 +1,165 @@
+"""
+Session Persistence Example
+
+Demonstrates automatic session persistence with zero configuration.
+When you provide a session_id to an Agent, conversation history is
+automatically saved to disk and restored on subsequent runs.
+
+Usage:
+    python session_persistence_example.py
+
+Requirements:
+    - praisonaiagents package installed
+    - OPENAI_API_KEY environment variable set
+"""
+
+from praisonaiagents import Agent
+
+
+def example_basic_persistence():
+    """Basic session persistence example."""
+    print("=" * 60)
+    print("EXAMPLE 1: Basic Session Persistence")
+    print("=" * 60)
+    
+    session_id = "demo-session-basic"
+    
+    # Create agent with session_id
+    agent = Agent(
+        name="Assistant",
+        instructions="You are a helpful assistant. Be very brief.",
+        session_id=session_id,
+        verbose=False,
+    )
+    
+    # First message
+    print("\n[User]: My favorite color is blue and my lucky number is 7.")
+    response1 = agent.chat("My favorite color is blue and my lucky number is 7.")
+    print(f"[Assistant]: {response1}")
+    
+    # Second message - agent remembers
+    print("\n[User]: What is my favorite color?")
+    response2 = agent.chat("What is my favorite color?")
+    print(f"[Assistant]: {response2}")
+    
+    print(f"\n✅ Session saved to: ~/.praison/sessions/{session_id}.json")
+    print(f"   Chat history length: {len(agent.chat_history)} messages")
+
+
+def example_session_restoration():
+    """Demonstrates session restoration across Agent instances."""
+    print("\n" + "=" * 60)
+    print("EXAMPLE 2: Session Restoration")
+    print("=" * 60)
+    
+    session_id = "demo-session-restore"
+    
+    # First agent instance
+    print("\n--- First Agent Instance ---")
+    agent1 = Agent(
+        name="MemoryBot",
+        instructions="You are a helpful assistant. Be very brief.",
+        session_id=session_id,
+        verbose=False,
+    )
+    
+    print("[User]: Remember that my pet's name is Max.")
+    response1 = agent1.chat("Remember that my pet's name is Max.")
+    print(f"[Assistant]: {response1}")
+    
+    # Simulate new process by creating new agent instance
+    print("\n--- New Agent Instance (simulating restart) ---")
+    agent2 = Agent(
+        name="MemoryBot",
+        instructions="You are a helpful assistant. Be very brief.",
+        session_id=session_id,
+        verbose=False,
+    )
+    
+    print("[User]: What is my pet's name?")
+    response2 = agent2.chat("What is my pet's name?")
+    print(f"[Assistant]: {response2}")
+    
+    if "max" in response2.lower():
+        print("\n✅ Session restored successfully! Agent remembered 'Max'.")
+    else:
+        print("\n⚠️ Agent may not have remembered correctly.")
+
+
+def example_in_memory_only():
+    """Demonstrates in-memory memory without persistence."""
+    print("\n" + "=" * 60)
+    print("EXAMPLE 3: In-Memory Only (No session_id)")
+    print("=" * 60)
+    
+    # Agent without session_id - in-memory only
+    agent = Agent(
+        name="TempBot",
+        instructions="You are a helpful assistant. Be very brief.",
+        verbose=False,
+    )
+    
+    print("\n[User]: My name is Alice.")
+    response1 = agent.chat("My name is Alice.")
+    print(f"[Assistant]: {response1}")
+    
+    print("\n[User]: What is my name?")
+    response2 = agent.chat("What is my name?")
+    print(f"[Assistant]: {response2}")
+    
+    print(f"\n✅ In-memory chat history: {len(agent.chat_history)} messages")
+    print("   (This will be lost when the Agent instance is garbage collected)")
+
+
+def example_direct_store_access():
+    """Demonstrates direct access to the session store."""
+    print("\n" + "=" * 60)
+    print("EXAMPLE 4: Direct Session Store Access")
+    print("=" * 60)
+    
+    from praisonaiagents.session import get_default_session_store
+    
+    store = get_default_session_store()
+    session_id = "demo-direct-access"
+    
+    # Add messages directly
+    store.add_user_message(session_id, "Hello from direct access!")
+    store.add_assistant_message(session_id, "Hi! I received your message.")
+    
+    # Get history
+    history = store.get_chat_history(session_id)
+    print(f"\n✅ Added {len(history)} messages directly to session store")
+    
+    for msg in history:
+        print(f"   [{msg['role']}]: {msg['content']}")
+    
+    # List all sessions
+    sessions = store.list_sessions()
+    print(f"\n📋 Total sessions in store: {len(sessions)}")
+    
+    # Clean up demo session
+    store.delete_session(session_id)
+    print(f"   Deleted demo session: {session_id}")
+
+
+def main():
+    """Run all examples."""
+    print("\n" + "=" * 60)
+    print("SESSION PERSISTENCE EXAMPLES")
+    print("=" * 60)
+    
+    # Run examples
+    example_basic_persistence()
+    example_session_restoration()
+    example_in_memory_only()
+    example_direct_store_access()
+    
+    print("\n" + "=" * 60)
+    print("ALL EXAMPLES COMPLETE")
+    print("=" * 60)
+    print("\nSession files are stored in: ~/.praison/sessions/")
+    print("You can inspect them with: cat ~/.praison/sessions/<session_id>.json")
+
+
+if __name__ == "__main__":
+    main()

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

@@ -58,6 +58,12 @@ class LLM:
     # Class-level flag for one-time logging configuration
     _logging_configured = False
     
+    # Class-level cache for LiteLLM module (avoids repeated import overhead)
+    _litellm_module = None
+    
+    # Class-level flag for LiteLLM global settings (set once)
+    _litellm_configured = False
+    
     # Default window sizes for different models (75% of actual to be safe)
     MODEL_WINDOWS = {
         # OpenAI
@@ -272,14 +278,16 @@ Respond with ONLY a valid JSON tool call in this format:
         if not LLM._logging_configured:
             LLM._configure_logging()
             
-        # Import litellm after logging is configured
-        try:
-            import litellm
-        except ImportError:
-            raise ImportError(
-                "LiteLLM is required but not installed. "
-                "Please install with: pip install 'praisonaiagents[llm]'"
-            )
+        # Use cached LiteLLM module or import once (avoids repeated import overhead)
+        if LLM._litellm_module is None:
+            try:
+                import litellm
+                LLM._litellm_module = litellm
+            except ImportError:
+                raise ImportError(
+                    "LiteLLM is required but not installed. "
+                    "Please install with: pip install 'praisonaiagents[llm]'"
+                )
 
         self.model = model
         self.timeout = timeout
@@ -341,11 +349,12 @@ Respond with ONLY a valid JSON tool call in this format:
         self._formatted_tools_cache = {}
         self._max_cache_size = 100
         
-        # Enable error dropping for cleaner output
-        import litellm
-        litellm.drop_params = True
-        # Enable parameter modification for providers like Anthropic
-        litellm.modify_params = True
+        # Configure LiteLLM global settings only once (avoids repeated setup cost)
+        if not LLM._litellm_configured:
+            litellm = LLM._litellm_module
+            litellm.drop_params = True
+            litellm.modify_params = True
+            LLM._litellm_configured = True
         self._setup_event_tracking(events)
         
         # Log all initialization parameters when in debug mode or verbose >= 10

src/praisonai-agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.6.1"
+version = "0.6.2"
 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 = "0.6.1"
+version = "0.6.2"
 source = { editable = "." }
 dependencies = [
     { name = "aiohttp" },

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/v3.0.3.tar.gz"
-    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v3.0.3.tar.gz | shasum -a 256`.split.first
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v3.0.5.tar.gz"
+    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v3.0.5.tar.gz | shasum -a 256`.split.first
     license "MIT"
   
     depends_on "[email protected]"

src/praisonai/praisonai/cli/execution/profiler.py

@@ -419,6 +419,8 @@ class ProfileReport:
             "timestamp": self.timestamp,
             "invocation": self.invocation.to_dict(),
             "timing": self.timing.to_dict(),
+            "timeline": self.timing.to_timeline(),
+            "timeline_diagram": self.to_timeline_diagram(),
             "response_preview": self.response_preview,
         }
         
@@ -444,6 +446,85 @@ class ProfileReport:
         import json
         return json.dumps(self.to_dict(), indent=2, default=str)
     
+    def to_timeline_diagram(self) -> str:
+        """
+        Generate ASCII timeline diagram showing execution phases.
+        
+        Format:
+        ENTER ─────────────────────────────────────────────────────────► RESPONSE
+               │ imports │ init │        network        │
+               │  XXXms  │ XXms │        XXXms          │
+               └─────────┴──────┴───────────────────────┘
+                                                  TOTAL: XXXXms
+        """
+        lines = []
+        total = self.timing.total_ms
+        if total <= 0:
+            return "No timing data available"
+        
+        # Calculate phase widths (proportional to time, min 8 chars)
+        phases = []
+        if self.timing.imports_ms > 0:
+            phases.append(("imports", self.timing.imports_ms))
+        if self.timing.agent_init_ms > 0:
+            phases.append(("init", self.timing.agent_init_ms))
+        if self.timing.execution_ms > 0:
+            phases.append(("network", self.timing.execution_ms))
+        
+        if not phases:
+            phases = [("total", total)]
+        
+        # Scale to fit in ~60 chars
+        scale = 50.0 / total if total > 0 else 1
+        
+        # Build the diagram
+        lines.append("")
+        lines.append("## Timeline Diagram")
+        lines.append("")
+        
+        # Top line with arrow
+        top_line = "ENTER "
+        for name, ms in phases:
+            width = max(8, int(ms * scale))
+            top_line += "─" * width
+        top_line += "► RESPONSE"
+        lines.append(top_line)
+        
+        # Phase names line
+        name_line = "      "
+        for name, ms in phases:
+            width = max(8, int(ms * scale))
+            name_line += "│" + name.center(width - 1)
+        name_line += "│"
+        lines.append(name_line)
+        
+        # Phase times line
+        time_line = "      "
+        for name, ms in phases:
+            width = max(8, int(ms * scale))
+            time_str = f"{ms:.0f}ms"
+            time_line += "│" + time_str.center(width - 1)
+        time_line += "│"
+        lines.append(time_line)
+        
+        # Bottom line
+        bottom_line = "      "
+        for i, (name, ms) in enumerate(phases):
+            width = max(8, int(ms * scale))
+            if i == 0:
+                bottom_line += "└" + "─" * (width - 1)
+            else:
+                bottom_line += "┴" + "─" * (width - 1)
+        bottom_line += "┘"
+        lines.append(bottom_line)
+        
+        # Total line
+        total_line = " " * (len(bottom_line) - 15) + f"TOTAL: {total:.0f}ms"
+        lines.append(total_line)
+        lines.append("")
+        
+        return "\n".join(lines)
+    
     def to_text(self) -> str:
         """Format as human-readable text."""
         lines = []
@@ -459,7 +540,10 @@ class ProfileReport:
         lines.append(f"Version:    {self.invocation.praisonai_version}")
         lines.append("")
         
-        # TIMELINE SECTION (new - shows ENTER → First Response)
+        # TIMELINE DIAGRAM (visual representation)
+        lines.append(self.to_timeline_diagram())
+        
+        # TIMELINE SECTION (shows ENTER → First Response)
         lines.append("## Execution Timeline")
         lines.append("-" * 45)
         timeline = self.timing.to_timeline()

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

@@ -94,6 +94,8 @@ __all__ = [
     'CapabilitiesHandler',
     # Performance benchmarking
     'PerformanceHandler',
+    # Comprehensive benchmarking
+    'BenchmarkHandler',
     # Lite agent (BYO-LLM)
     'LiteHandler',
     # Agent-centric tools (LSP/ACP powered)
@@ -233,6 +235,9 @@ def __getattr__(name):
     elif name == 'PerformanceHandler':
         from .performance import PerformanceHandler
         return PerformanceHandler
+    elif name == 'BenchmarkHandler':
+        from .benchmark import BenchmarkHandler
+        return BenchmarkHandler
     elif name == 'LiteHandler':
         from .lite import LiteHandler
         return LiteHandler

src/praisonai/praisonai/cli/features/benchmark.py

@@ -0,0 +1,956 @@
+"""
+Comprehensive Benchmarking CLI for PraisonAI.
+
+Provides deep profiling and comparison across all execution paths:
+- OpenAI SDK (baseline)
+- PraisonAI Agent (Python SDK)
+- PraisonAI CLI (fast path)
+- PraisonAI CLI with profiling
+- PraisonAI Workflow (single agent)
+- PraisonAI Workflow (multi-agent)
+- PraisonAI via LiteLLM
+- LiteLLM standalone
+
+Usage: praisonai benchmark profile "What is 2+2?"
+"""
+
+import os
+import sys
+import json
+import time
+import subprocess
+import statistics
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Tuple
+from pathlib import Path
+
+from .base import FlagHandler
+
+
+@dataclass
+class PhaseTimings:
+    """Timing breakdown for execution phases."""
+    import_ms: float = 0.0
+    init_ms: float = 0.0
+    network_ms: float = 0.0
+    exec_ms: float = 0.0
+    render_ms: float = 0.0
+    total_ms: float = 0.0
+    ttft_ms: float = 0.0  # Time to first token
+    subprocess_ms: float = 0.0  # CLI subprocess overhead
+
+
+@dataclass
+class BenchmarkRun:
+    """Single benchmark run result."""
+    path_name: str
+    iteration: int
+    is_cold: bool
+    timings: PhaseTimings
+    memory_mb: float = 0.0
+    success: bool = True
+    error: str = ""
+    response_preview: str = ""
+
+
+@dataclass
+class BenchmarkResult:
+    """Aggregated benchmark result for a path."""
+    path_name: str
+    runs: List[BenchmarkRun] = field(default_factory=list)
+    
+    # Aggregated stats
+    mean_total_ms: float = 0.0
+    min_total_ms: float = 0.0
+    max_total_ms: float = 0.0
+    std_total_ms: float = 0.0
+    
+    mean_import_ms: float = 0.0
+    mean_init_ms: float = 0.0
+    mean_network_ms: float = 0.0
+    
+    cold_total_ms: float = 0.0
+    warm_total_ms: float = 0.0
+    
+    delta_vs_sdk_ms: float = 0.0
+    
+    def compute_stats(self, sdk_baseline_ms: float = 0.0):
+        """Compute aggregated statistics from runs."""
+        if not self.runs:
+            return
+        
+        totals = [r.timings.total_ms for r in self.runs if r.success]
+        if not totals:
+            return
+        
+        self.mean_total_ms = statistics.mean(totals)
+        self.min_total_ms = min(totals)
+        self.max_total_ms = max(totals)
+        self.std_total_ms = statistics.stdev(totals) if len(totals) > 1 else 0.0
+        
+        self.mean_import_ms = statistics.mean([r.timings.import_ms for r in self.runs if r.success])
+        self.mean_init_ms = statistics.mean([r.timings.init_ms for r in self.runs if r.success])
+        self.mean_network_ms = statistics.mean([r.timings.network_ms for r in self.runs if r.success])
+        
+        cold_runs = [r.timings.total_ms for r in self.runs if r.is_cold and r.success]
+        warm_runs = [r.timings.total_ms for r in self.runs if not r.is_cold and r.success]
+        
+        self.cold_total_ms = statistics.mean(cold_runs) if cold_runs else 0.0
+        self.warm_total_ms = statistics.mean(warm_runs) if warm_runs else 0.0
+        
+        self.delta_vs_sdk_ms = self.mean_total_ms - sdk_baseline_ms
+
+
+@dataclass
+class BenchmarkReport:
+    """Complete benchmark report."""
+    timestamp: str
+    prompt: str
+    iterations: int
+    results: Dict[str, BenchmarkResult] = field(default_factory=dict)
+    sdk_baseline_ms: float = 0.0
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert to dictionary for JSON serialization."""
+        return {
+            "timestamp": self.timestamp,
+            "prompt": self.prompt,
+            "iterations": self.iterations,
+            "sdk_baseline_ms": self.sdk_baseline_ms,
+            "results": {
+                name: {
+                    "path_name": r.path_name,
+                    "mean_total_ms": r.mean_total_ms,
+                    "min_total_ms": r.min_total_ms,
+                    "max_total_ms": r.max_total_ms,
+                    "std_total_ms": r.std_total_ms,
+                    "mean_import_ms": r.mean_import_ms,
+                    "mean_init_ms": r.mean_init_ms,
+                    "mean_network_ms": r.mean_network_ms,
+                    "cold_total_ms": r.cold_total_ms,
+                    "warm_total_ms": r.warm_total_ms,
+                    "delta_vs_sdk_ms": r.delta_vs_sdk_ms,
+                    "runs": [
+                        {
+                            "iteration": run.iteration,
+                            "is_cold": run.is_cold,
+                            "total_ms": run.timings.total_ms,
+                            "import_ms": run.timings.import_ms,
+                            "init_ms": run.timings.init_ms,
+                            "network_ms": run.timings.network_ms,
+                            "memory_mb": run.memory_mb,
+                            "success": run.success,
+                        }
+                        for run in r.runs
+                    ]
+                }
+                for name, r in self.results.items()
+            }
+        }
+
+
+class BenchmarkHandler(FlagHandler):
+    """
+    Comprehensive benchmarking handler for PraisonAI.
+    
+    Commands:
+        praisonai benchmark profile [query]  - Run full benchmark suite
+        praisonai benchmark sdk [query]      - Benchmark OpenAI SDK only
+        praisonai benchmark agent [query]    - Benchmark Agent only
+        praisonai benchmark cli [query]      - Benchmark CLI only
+        praisonai benchmark compare [query]  - Quick comparison table
+    """
+    
+    DEFAULT_PROMPT = "Hi"
+    DEFAULT_ITERATIONS = 3
+    DEFAULT_MODEL = "gpt-4o-mini"
+    
+    # Benchmark paths
+    PATHS = [
+        "openai_sdk",
+        "praisonai_agent",
+        "praisonai_cli",
+        "praisonai_cli_profile",
+        "praisonai_workflow_single",
+        "praisonai_workflow_multi",
+        "praisonai_litellm",
+        "litellm_standalone",
+    ]
+    
+    @property
+    def feature_name(self) -> str:
+        return "benchmark"
+    
+    @property
+    def flag_name(self) -> str:
+        return "benchmark"
+    
+    @property
+    def flag_help(self) -> str:
+        return "Run comprehensive performance benchmarks"
+    
+    def check_dependencies(self) -> Tuple[bool, str]:
+        """Check if benchmarking is available."""
+        return True, ""
+    
+    def execute(self, *args, **kwargs) -> Any:
+        """Execute the benchmark command."""
+        return self.handle(*args, **kwargs)
+    
+    def _get_project_root(self) -> str:
+        """Get the project root directory."""
+        # Navigate up from this file to find the project root
+        current = Path(__file__).resolve()
+        # Go up to praisonai-package
+        for _ in range(6):
+            current = current.parent
+            if (current / "pyproject.toml").exists():
+                return str(current)
+        return os.getcwd()
+    
+    def _run_subprocess_benchmark(self, code: str, cwd: str = None) -> Tuple[Dict[str, float], str, str]:
+        """
+        Run a benchmark in a subprocess and return timing data.
+        
+        Returns:
+            Tuple of (timings_dict, stdout, stderr)
+        """
+        if cwd is None:
+            cwd = self._get_project_root()
+        
+        t0 = time.perf_counter()
+        result = subprocess.run(
+            [sys.executable, "-c", code],
+            capture_output=True,
+            text=True,
+            cwd=cwd,
+            env={**os.environ, "PYTHONDONTWRITEBYTECODE": "1"},
+            timeout=120
+        )
+        subprocess_time = (time.perf_counter() - t0) * 1000
+        
+        timings = {"subprocess_ms": subprocess_time}
+        
+        # Parse JSON output from the benchmark code
+        if result.returncode == 0 and result.stdout.strip():
+            try:
+                # Look for JSON in the output
+                lines = result.stdout.strip().split("\n")
+                for line in lines:
+                    if line.startswith("{") and line.endswith("}"):
+                        data = json.loads(line)
+                        timings.update(data)
+                        break
+            except json.JSONDecodeError:
+                pass
+        
+        return timings, result.stdout, result.stderr
+    
+    def benchmark_openai_sdk(self, prompt: str, iteration: int, is_cold: bool) -> BenchmarkRun:
+        """Benchmark raw OpenAI SDK."""
+        code = f'''
+import time
+import json
+import os
+
+t0 = time.perf_counter()
+import openai
+t_import = time.perf_counter()
+
+client = openai.OpenAI()
+t_init = time.perf_counter()
+
+response = client.chat.completions.create(
+    model="{self.DEFAULT_MODEL}",
+    messages=[{{"role": "user", "content": "{prompt}"}}],
+    max_tokens=10
+)
+t_network = time.perf_counter()
+
+result = response.choices[0].message.content
+t_end = time.perf_counter()
+
+print(json.dumps({{
+    "import_ms": (t_import - t0) * 1000,
+    "init_ms": (t_init - t_import) * 1000,
+    "network_ms": (t_network - t_init) * 1000,
+    "exec_ms": (t_end - t_network) * 1000,
+    "total_ms": (t_end - t0) * 1000,
+    "response": result[:50]
+}}))
+'''
+        timings, stdout, stderr = self._run_subprocess_benchmark(code)
+        
+        return BenchmarkRun(
+            path_name="openai_sdk",
+            iteration=iteration,
+            is_cold=is_cold,
+            timings=PhaseTimings(
+                import_ms=timings.get("import_ms", 0),
+                init_ms=timings.get("init_ms", 0),
+                network_ms=timings.get("network_ms", 0),
+                exec_ms=timings.get("exec_ms", 0),
+                total_ms=timings.get("total_ms", 0),
+                subprocess_ms=timings.get("subprocess_ms", 0),
+            ),
+            success="total_ms" in timings,
+            error=stderr if "total_ms" not in timings else "",
+            response_preview=timings.get("response", "")[:50]
+        )
+    
+    def benchmark_praisonai_agent(self, prompt: str, iteration: int, is_cold: bool) -> BenchmarkRun:
+        """Benchmark PraisonAI Agent (Python SDK)."""
+        code = f'''
+import time
+import json
+import sys
+sys.path.insert(0, "{self._get_project_root()}/src/praisonai")
+
+t0 = time.perf_counter()
+from praisonaiagents import Agent
+t_import = time.perf_counter()
+
+agent = Agent(instructions="You are helpful", llm="{self.DEFAULT_MODEL}", verbose=False)
+t_init = time.perf_counter()
+
+result = agent.start("{prompt}")
+t_network = time.perf_counter()
+
+print(json.dumps({{
+    "import_ms": (t_import - t0) * 1000,
+    "init_ms": (t_init - t_import) * 1000,
+    "network_ms": (t_network - t_init) * 1000,
+    "total_ms": (t_network - t0) * 1000,
+    "response": str(result)[:50] if result else ""
+}}))
+'''
+        timings, stdout, stderr = self._run_subprocess_benchmark(code)
+        
+        return BenchmarkRun(
+            path_name="praisonai_agent",
+            iteration=iteration,
+            is_cold=is_cold,
+            timings=PhaseTimings(
+                import_ms=timings.get("import_ms", 0),
+                init_ms=timings.get("init_ms", 0),
+                network_ms=timings.get("network_ms", 0),
+                total_ms=timings.get("total_ms", 0),
+                subprocess_ms=timings.get("subprocess_ms", 0),
+            ),
+            success="total_ms" in timings,
+            error=stderr if "total_ms" not in timings else "",
+            response_preview=timings.get("response", "")[:50]
+        )
+    
+    def benchmark_praisonai_cli(self, prompt: str, iteration: int, is_cold: bool, with_profile: bool = False) -> BenchmarkRun:
+        """Benchmark PraisonAI CLI."""
+        t0 = time.perf_counter()
+        
+        cmd = ["praisonai", prompt, "--llm", self.DEFAULT_MODEL]
+        if with_profile:
+            cmd.append("--profile")
+        
+        result = subprocess.run(
+            cmd,
+            capture_output=True,
+            text=True,
+            cwd=self._get_project_root(),
+            timeout=120
+        )
+        
+        total_ms = (time.perf_counter() - t0) * 1000
+        
+        return BenchmarkRun(
+            path_name="praisonai_cli_profile" if with_profile else "praisonai_cli",
+            iteration=iteration,
+            is_cold=is_cold,
+            timings=PhaseTimings(
+                total_ms=total_ms,
+                subprocess_ms=total_ms,
+            ),
+            success=result.returncode == 0,
+            error=result.stderr[:200] if result.returncode != 0 else "",
+            response_preview=result.stdout[:50] if result.stdout else ""
+        )
+    
+    def benchmark_praisonai_workflow_single(self, prompt: str, iteration: int, is_cold: bool) -> BenchmarkRun:
+        """Benchmark PraisonAI Workflow with single agent."""
+        code = f'''
+import time
+import json
+import sys
+sys.path.insert(0, "{self._get_project_root()}/src/praisonai")
+
+t0 = time.perf_counter()
+from praisonaiagents import Agent, Task, PraisonAIAgents
+t_import = time.perf_counter()
+
+agent = Agent(name="Helper", instructions="You are helpful", llm="{self.DEFAULT_MODEL}", verbose=False)
+task = Task(name="respond", description="{prompt}", expected_output="response", agent=agent)
+agents = PraisonAIAgents(agents=[agent], tasks=[task], verbose=0)
+t_init = time.perf_counter()
+
+result = agents.start()
+t_network = time.perf_counter()
+
+print(json.dumps({{
+    "import_ms": (t_import - t0) * 1000,
+    "init_ms": (t_init - t_import) * 1000,
+    "network_ms": (t_network - t_init) * 1000,
+    "total_ms": (t_network - t0) * 1000,
+    "response": str(result)[:50] if result else ""
+}}))
+'''
+        timings, stdout, stderr = self._run_subprocess_benchmark(code)
+        
+        return BenchmarkRun(
+            path_name="praisonai_workflow_single",
+            iteration=iteration,
+            is_cold=is_cold,
+            timings=PhaseTimings(
+                import_ms=timings.get("import_ms", 0),
+                init_ms=timings.get("init_ms", 0),
+                network_ms=timings.get("network_ms", 0),
+                total_ms=timings.get("total_ms", 0),
+                subprocess_ms=timings.get("subprocess_ms", 0),
+            ),
+            success="total_ms" in timings,
+            error=stderr[:200] if "total_ms" not in timings else "",
+            response_preview=timings.get("response", "")[:50]
+        )
+    
+    def benchmark_praisonai_workflow_multi(self, prompt: str, iteration: int, is_cold: bool) -> BenchmarkRun:
+        """Benchmark PraisonAI Workflow with multiple agents."""
+        code = f'''
+import time
+import json
+import sys
+sys.path.insert(0, "{self._get_project_root()}/src/praisonai")
+
+t0 = time.perf_counter()
+from praisonaiagents import Agent, Task, PraisonAIAgents
+t_import = time.perf_counter()
+
+agent1 = Agent(name="Analyzer", instructions="Analyze the request", llm="{self.DEFAULT_MODEL}", verbose=False)
+agent2 = Agent(name="Responder", instructions="Provide a response", llm="{self.DEFAULT_MODEL}", verbose=False)
+task1 = Task(name="analyze", description="Analyze: {prompt}", expected_output="analysis", agent=agent1)
+task2 = Task(name="respond", description="Respond based on analysis", expected_output="response", agent=agent2)
+agents = PraisonAIAgents(agents=[agent1, agent2], tasks=[task1, task2], verbose=0)
+t_init = time.perf_counter()
+
+result = agents.start()
+t_network = time.perf_counter()
+
+print(json.dumps({{
+    "import_ms": (t_import - t0) * 1000,
+    "init_ms": (t_init - t_import) * 1000,
+    "network_ms": (t_network - t_init) * 1000,
+    "total_ms": (t_network - t0) * 1000,
+    "response": str(result)[:50] if result else ""
+}}))
+'''
+        timings, stdout, stderr = self._run_subprocess_benchmark(code)
+        
+        return BenchmarkRun(
+            path_name="praisonai_workflow_multi",
+            iteration=iteration,
+            is_cold=is_cold,
+            timings=PhaseTimings(
+                import_ms=timings.get("import_ms", 0),
+                init_ms=timings.get("init_ms", 0),
+                network_ms=timings.get("network_ms", 0),
+                total_ms=timings.get("total_ms", 0),
+                subprocess_ms=timings.get("subprocess_ms", 0),
+            ),
+            success="total_ms" in timings,
+            error=stderr[:200] if "total_ms" not in timings else "",
+            response_preview=timings.get("response", "")[:50]
+        )
+    
+    def benchmark_praisonai_litellm(self, prompt: str, iteration: int, is_cold: bool) -> BenchmarkRun:
+        """Benchmark PraisonAI Agent using LiteLLM path (openai/ prefix)."""
+        code = f'''
+import time
+import json
+import sys
+sys.path.insert(0, "{self._get_project_root()}/src/praisonai")
+
+t0 = time.perf_counter()
+from praisonaiagents import Agent
+t_import = time.perf_counter()
+
+agent = Agent(instructions="You are helpful", llm="openai/{self.DEFAULT_MODEL}", verbose=False)
+t_init = time.perf_counter()
+
+result = agent.start("{prompt}")
+t_network = time.perf_counter()
+
+print(json.dumps({{
+    "import_ms": (t_import - t0) * 1000,
+    "init_ms": (t_init - t_import) * 1000,
+    "network_ms": (t_network - t_init) * 1000,
+    "total_ms": (t_network - t0) * 1000,
+    "response": str(result)[:50] if result else ""
+}}))
+'''
+        timings, stdout, stderr = self._run_subprocess_benchmark(code)
+        
+        return BenchmarkRun(
+            path_name="praisonai_litellm",
+            iteration=iteration,
+            is_cold=is_cold,
+            timings=PhaseTimings(
+                import_ms=timings.get("import_ms", 0),
+                init_ms=timings.get("init_ms", 0),
+                network_ms=timings.get("network_ms", 0),
+                total_ms=timings.get("total_ms", 0),
+                subprocess_ms=timings.get("subprocess_ms", 0),
+            ),
+            success="total_ms" in timings,
+            error=stderr[:200] if "total_ms" not in timings else "",
+            response_preview=timings.get("response", "")[:50]
+        )
+    
+    def benchmark_litellm_standalone(self, prompt: str, iteration: int, is_cold: bool) -> BenchmarkRun:
+        """Benchmark LiteLLM standalone (raw)."""
+        code = f'''
+import time
+import json
+import os
+
+t0 = time.perf_counter()
+import litellm
+t_import = time.perf_counter()
+
+# Configure
+litellm.drop_params = True
+t_init = time.perf_counter()
+
+response = litellm.completion(
+    model="{self.DEFAULT_MODEL}",
+    messages=[{{"role": "user", "content": "{prompt}"}}],
+    max_tokens=10
+)
+t_network = time.perf_counter()
+
+result = response.choices[0].message.content
+t_end = time.perf_counter()
+
+print(json.dumps({{
+    "import_ms": (t_import - t0) * 1000,
+    "init_ms": (t_init - t_import) * 1000,
+    "network_ms": (t_network - t_init) * 1000,
+    "exec_ms": (t_end - t_network) * 1000,
+    "total_ms": (t_end - t0) * 1000,
+    "response": result[:50] if result else ""
+}}))
+'''
+        timings, stdout, stderr = self._run_subprocess_benchmark(code)
+        
+        return BenchmarkRun(
+            path_name="litellm_standalone",
+            iteration=iteration,
+            is_cold=is_cold,
+            timings=PhaseTimings(
+                import_ms=timings.get("import_ms", 0),
+                init_ms=timings.get("init_ms", 0),
+                network_ms=timings.get("network_ms", 0),
+                exec_ms=timings.get("exec_ms", 0),
+                total_ms=timings.get("total_ms", 0),
+                subprocess_ms=timings.get("subprocess_ms", 0),
+            ),
+            success="total_ms" in timings,
+            error=stderr[:200] if "total_ms" not in timings else "",
+            response_preview=timings.get("response", "")[:50]
+        )
+    
+    def create_timeline_diagram(self, result: BenchmarkResult) -> str:
+        """Create ASCII timeline diagram for a benchmark result."""
+        if not result.runs:
+            return "No data"
+        
+        # Use mean values
+        import_ms = result.mean_import_ms
+        init_ms = result.mean_init_ms
+        network_ms = result.mean_network_ms
+        total_ms = result.mean_total_ms
+        
+        # Calculate other time
+        other_ms = max(0, total_ms - import_ms - init_ms - network_ms)
+        
+        # Scale to 60 chars width
+        if total_ms <= 0:
+            return "No timing data"
+        
+        scale = 50.0 / total_ms
+        
+        def bar_width(ms):
+            return max(1, int(ms * scale))
+        
+        import_w = bar_width(import_ms)
+        init_w = bar_width(init_ms)
+        network_w = bar_width(network_ms)
+        other_w = bar_width(other_ms)
+        
+        # Build diagram
+        lines = []
+        
+        # Top line
+        total_w = import_w + init_w + network_w + other_w
+        lines.append(f"ENTER {'─' * total_w}► RESPONSE")
+        
+        # Phase boxes
+        phases = []
+        if import_ms > 0:
+            phases.append(("import", import_ms, import_w))
+        if init_ms > 0:
+            phases.append(("init", init_ms, init_w))
+        if network_ms > 0:
+            phases.append(("network", network_ms, network_w))
+        if other_ms > 10:
+            phases.append(("other", other_ms, other_w))
+        
+        if phases:
+            # Phase names line
+            name_line = "      "
+            for name, ms, w in phases:
+                name_line += "│" + name.center(w - 1)
+            name_line += "│"
+            lines.append(name_line)
+            
+            # Phase times line
+            time_line = "      "
+            for name, ms, w in phases:
+                time_str = f"{ms:.0f}ms"
+                time_line += "│" + time_str.center(w - 1)
+            time_line += "│"
+            lines.append(time_line)
+            
+            # Bottom line
+            bottom_line = "      "
+            for i, (name, ms, w) in enumerate(phases):
+                if i == 0:
+                    bottom_line += "└" + "─" * (w - 1)
+                else:
+                    bottom_line += "┴" + "─" * (w - 1)
+            bottom_line += "┘"
+            lines.append(bottom_line)
+        
+        # Total line
+        lines.append(f"{' ' * 40}TOTAL: {total_ms:.0f}ms")
+        
+        return "\n".join(lines)
+    
+    def create_comparison_table(self, report: BenchmarkReport) -> str:
+        """Create master ASCII comparison table."""
+        lines = []
+        
+        # Header
+        lines.append("+" + "-" * 30 + "+" + "-" * 10 + "+" + "-" * 10 + "+" + "-" * 10 + "+" + "-" * 10 + "+" + "-" * 12 + "+")
+        lines.append(f"| {'Path':<28} | {'Import':>8} | {'Init':>8} | {'Network':>8} | {'Total':>8} | {'Δ SDK':>10} |")
+        lines.append("+" + "-" * 30 + "+" + "-" * 10 + "+" + "-" * 10 + "+" + "-" * 10 + "+" + "-" * 10 + "+" + "-" * 12 + "+")
+        
+        # Sort by total time
+        sorted_results = sorted(report.results.values(), key=lambda r: r.mean_total_ms)
+        
+        for result in sorted_results:
+            delta_str = f"{result.delta_vs_sdk_ms:+.0f}ms" if result.delta_vs_sdk_ms != 0 else "baseline"
+            lines.append(
+                f"| {result.path_name:<28} | "
+                f"{result.mean_import_ms:>6.0f}ms | "
+                f"{result.mean_init_ms:>6.0f}ms | "
+                f"{result.mean_network_ms:>6.0f}ms | "
+                f"{result.mean_total_ms:>6.0f}ms | "
+                f"{delta_str:>10} |"
+            )
+        
+        lines.append("+" + "-" * 30 + "+" + "-" * 10 + "+" + "-" * 10 + "+" + "-" * 10 + "+" + "-" * 10 + "+" + "-" * 12 + "+")
+        
+        return "\n".join(lines)
+    
+    def create_variance_table(self, report: BenchmarkReport) -> str:
+        """Create variance analysis table."""
+        lines = []
+        
+        lines.append("\n## Variance Analysis")
+        lines.append("+" + "-" * 30 + "+" + "-" * 10 + "+" + "-" * 10 + "+" + "-" * 10 + "+" + "-" * 10 + "+" + "-" * 12 + "+")
+        lines.append(f"| {'Path':<28} | {'Mean':>8} | {'Min':>8} | {'Max':>8} | {'StdDev':>8} | {'Cold/Warm':>10} |")
+        lines.append("+" + "-" * 30 + "+" + "-" * 10 + "+" + "-" * 10 + "+" + "-" * 10 + "+" + "-" * 10 + "+" + "-" * 12 + "+")
+        
+        for name, result in sorted(report.results.items(), key=lambda x: x[1].mean_total_ms):
+            cold_warm = f"{result.cold_total_ms:.0f}/{result.warm_total_ms:.0f}" if result.cold_total_ms > 0 else "N/A"
+            lines.append(
+                f"| {name:<28} | "
+                f"{result.mean_total_ms:>6.0f}ms | "
+                f"{result.min_total_ms:>6.0f}ms | "
+                f"{result.max_total_ms:>6.0f}ms | "
+                f"{result.std_total_ms:>6.0f}ms | "
+                f"{cold_warm:>10} |"
+            )
+        
+        lines.append("+" + "-" * 30 + "+" + "-" * 10 + "+" + "-" * 10 + "+" + "-" * 10 + "+" + "-" * 10 + "+" + "-" * 12 + "+")
+        
+        return "\n".join(lines)
+    
+    def classify_overhead(self, report: BenchmarkReport) -> str:
+        """Classify overhead by category."""
+        lines = []
+        
+        sdk_result = report.results.get("openai_sdk")
+        if not sdk_result:
+            return "No SDK baseline for comparison"
+        
+        lines.append("\n## Overhead Classification")
+        lines.append("-" * 60)
+        
+        for name, result in report.results.items():
+            if name == "openai_sdk":
+                continue
+            
+            total_overhead = result.delta_vs_sdk_ms
+            
+            # Classify overhead
+            network_variance = abs(result.mean_network_ms - sdk_result.mean_network_ms)
+            import_overhead = result.mean_import_ms - sdk_result.mean_import_ms
+            init_overhead = result.mean_init_ms - sdk_result.mean_init_ms
+            
+            lines.append(f"\n{name}:")
+            lines.append(f"  Total overhead: {total_overhead:+.0f}ms")
+            lines.append(f"  - Network variance (unavoidable): ~{network_variance:.0f}ms")
+            lines.append(f"  - Import overhead: {import_overhead:+.0f}ms")
+            lines.append(f"  - Init overhead: {init_overhead:+.0f}ms")
+            
+            # Categorize
+            if "cli" in name.lower():
+                lines.append(f"  - CLI subprocess overhead: ~100-300ms (estimated)")
+            if "litellm" in name.lower():
+                lines.append(f"  - LiteLLM wrapper overhead: included in init")
+            if "workflow" in name.lower():
+                lines.append(f"  - Workflow orchestration: included in init")
+        
+        return "\n".join(lines)
+    
+    def run_full_benchmark(self, prompt: str = None, iterations: int = None, 
+                           paths: List[str] = None, verbose: bool = True) -> BenchmarkReport:
+        """
+        Run full benchmark suite.
+        
+        Args:
+            prompt: Query to benchmark (default: "Hi")
+            iterations: Number of iterations per path (default: 3)
+            paths: List of paths to benchmark (default: all)
+            verbose: Print progress
+            
+        Returns:
+            BenchmarkReport with all results
+        """
+        prompt = prompt or self.DEFAULT_PROMPT
+        iterations = iterations or self.DEFAULT_ITERATIONS
+        paths = paths or self.PATHS
+        
+        from datetime import datetime
+        
+        report = BenchmarkReport(
+            timestamp=datetime.utcnow().isoformat() + "Z",
+            prompt=prompt,
+            iterations=iterations,
+        )
+        
+        if verbose:
+            print("=" * 70)
+            print("PraisonAI Comprehensive Benchmark Suite")
+            print("=" * 70)
+            print(f"Prompt: \"{prompt}\"")
+            print(f"Iterations: {iterations}")
+            print(f"Paths: {len(paths)}")
+            print("=" * 70)
+        
+        # Benchmark each path
+        benchmark_methods = {
+            "openai_sdk": self.benchmark_openai_sdk,
+            "praisonai_agent": self.benchmark_praisonai_agent,
+            "praisonai_cli": lambda p, i, c: self.benchmark_praisonai_cli(p, i, c, with_profile=False),
+            "praisonai_cli_profile": lambda p, i, c: self.benchmark_praisonai_cli(p, i, c, with_profile=True),
+            "praisonai_workflow_single": self.benchmark_praisonai_workflow_single,
+            "praisonai_workflow_multi": self.benchmark_praisonai_workflow_multi,
+            "praisonai_litellm": self.benchmark_praisonai_litellm,
+            "litellm_standalone": self.benchmark_litellm_standalone,
+        }
+        
+        for path_name in paths:
+            if path_name not in benchmark_methods:
+                if verbose:
+                    print(f"\n⚠ Unknown path: {path_name}")
+                continue
+            
+            if verbose:
+                print(f"\n📊 Benchmarking: {path_name}")
+            
+            result = BenchmarkResult(path_name=path_name)
+            method = benchmark_methods[path_name]
+            
+            for i in range(iterations):
+                is_cold = (i == 0)
+                if verbose:
+                    print(f"   Run {i + 1}/{iterations} ({'cold' if is_cold else 'warm'})...", end=" ", flush=True)
+                
+                try:
+                    run = method(prompt, i + 1, is_cold)
+                    result.runs.append(run)
+                    
+                    if verbose:
+                        if run.success:
+                            print(f"{run.timings.total_ms:.0f}ms")
+                        else:
+                            print(f"FAILED: {run.error[:50]}")
+                except Exception as e:
+                    if verbose:
+                        print(f"ERROR: {e}")
+                    result.runs.append(BenchmarkRun(
+                        path_name=path_name,
+                        iteration=i + 1,
+                        is_cold=is_cold,
+                        timings=PhaseTimings(),
+                        success=False,
+                        error=str(e)
+                    ))
+            
+            report.results[path_name] = result
+        
+        # Compute stats
+        sdk_baseline = 0.0
+        if "openai_sdk" in report.results:
+            report.results["openai_sdk"].compute_stats(0.0)
+            sdk_baseline = report.results["openai_sdk"].mean_total_ms
+            report.sdk_baseline_ms = sdk_baseline
+        
+        for name, result in report.results.items():
+            result.compute_stats(sdk_baseline)
+        
+        return report
+    
+    def print_report(self, report: BenchmarkReport):
+        """Print full benchmark report."""
+        print("\n" + "=" * 70)
+        print("BENCHMARK RESULTS")
+        print("=" * 70)
+        
+        # Timeline diagrams for each path
+        print("\n## Timeline Diagrams")
+        print("-" * 70)
+        
+        for name, result in sorted(report.results.items(), key=lambda x: x[1].mean_total_ms):
+            print(f"\n### {name}")
+            print(self.create_timeline_diagram(result))
+        
+        # Comparison table
+        print("\n" + "=" * 70)
+        print("## Master Comparison Table")
+        print(self.create_comparison_table(report))
+        
+        # Variance analysis
+        print(self.create_variance_table(report))
+        
+        # Overhead classification
+        print(self.classify_overhead(report))
+        
+        print("\n" + "=" * 70)
+    
+    def handle(self, subcommand: str = "profile", prompt: str = None, 
+               iterations: int = None, output_format: str = "text", 
+               output_file: str = None, **kwargs) -> Any:
+        """
+        Handle benchmark subcommands.
+        
+        Args:
+            subcommand: One of profile, sdk, agent, cli, compare
+            prompt: Query to benchmark
+            iterations: Number of iterations
+            output_format: text or json
+            output_file: Path to save results
+            
+        Returns:
+            BenchmarkReport
+        """
+        prompt = prompt or self.DEFAULT_PROMPT
+        iterations = iterations or self.DEFAULT_ITERATIONS
+        
+        if subcommand == "profile":
+            # Full benchmark
+            report = self.run_full_benchmark(prompt, iterations)
+            
+            if output_format == "json":
+                output = json.dumps(report.to_dict(), indent=2)
+                if output_file:
+                    with open(output_file, "w") as f:
+                        f.write(output)
+                    print(f"Results saved to {output_file}")
+                else:
+                    print(output)
+            else:
+                self.print_report(report)
+                if output_file:
+                    with open(output_file, "w") as f:
+                        f.write(json.dumps(report.to_dict(), indent=2))
+                    print(f"\nJSON results saved to {output_file}")
+            
+            return report
+        
+        elif subcommand == "sdk":
+            # SDK only
+            report = self.run_full_benchmark(prompt, iterations, paths=["openai_sdk"])
+            self.print_report(report)
+            return report
+        
+        elif subcommand == "agent":
+            # Agent only
+            report = self.run_full_benchmark(prompt, iterations, paths=["openai_sdk", "praisonai_agent"])
+            self.print_report(report)
+            return report
+        
+        elif subcommand == "cli":
+            # CLI only
+            report = self.run_full_benchmark(prompt, iterations, paths=["openai_sdk", "praisonai_cli", "praisonai_cli_profile"])
+            self.print_report(report)
+            return report
+        
+        elif subcommand == "compare":
+            # Quick comparison (fewer iterations)
+            report = self.run_full_benchmark(prompt, iterations=2, 
+                                            paths=["openai_sdk", "praisonai_agent", "praisonai_cli", "litellm_standalone"])
+            print("\n" + self.create_comparison_table(report))
+            return report
+        
+        else:
+            print(f"Unknown subcommand: {subcommand}")
+            print("Available: profile, sdk, agent, cli, compare")
+            return None
+
+
+def run_benchmark_command(args) -> int:
+    """
+    Run benchmark command from CLI args.
+    
+    Args:
+        args: Parsed CLI arguments
+        
+    Returns:
+        Exit code (0 for success)
+    """
+    handler = BenchmarkHandler()
+    
+    subcommand = getattr(args, 'benchmark_command', 'profile')
+    prompt = getattr(args, 'benchmark_prompt', None)
+    iterations = getattr(args, 'benchmark_iterations', None)
+    output_format = getattr(args, 'benchmark_format', 'text')
+    output_file = getattr(args, 'benchmark_output', None)
+    
+    result = handler.handle(
+        subcommand=subcommand,
+        prompt=prompt,
+        iterations=iterations,
+        output_format=output_format,
+        output_file=output_file
+    )
+    
+    return 0 if result else 1

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==3.0.3 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==3.0.5 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__ = "3.0.3"
+__version__ = "3.0.5"

src/praisonai/pyproject.toml

@@ -12,7 +12,7 @@ dependencies = [
     "rich>=13.7",
     "markdown>=3.5",
     "pyparsing>=3.0.0",
-    "praisonaiagents>=0.6.1",
+    "praisonaiagents>=0.6.2",
     "python-dotenv>=0.19.0",
     "instructor>=1.3.3",
     "PyYAML>=6.0",

src/praisonai/uv.lock

@@ -4276,7 +4276,7 @@ requires-dist = [
     { name = "praisonai-tools", marker = "extra == 'autogen'", specifier = ">=0.1.0" },
     { name = "praisonai-tools", marker = "extra == 'autogen-v4'", specifier = ">=0.1.0" },
     { name = "praisonai-tools", marker = "extra == 'crewai'", specifier = ">=0.1.0" },
-    { name = "praisonaiagents", specifier = ">=0.6.1" },
+    { name = "praisonaiagents", specifier = ">=0.6.2" },
     { 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" },
@@ -4324,7 +4324,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.6.1"
+version = "0.6.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohttp" },
@@ -4333,9 +4333,9 @@ dependencies = [
     { name = "pydantic" },
     { name = "rich" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/58/21/307235585bad28ae1825b890caa85163632f98de8a6cde604b403dad1d7d/praisonaiagents-0.6.1.tar.gz", hash = "sha256:3f57334bded1c224bc94f9f05890cb74808d0d6effc7cb57c50315f56cfa429a", size = 785452, upload-time = "2026-01-02T05:14:48.391Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/ea/d0/42418977331fb9a019331c49ee2eaf66c6dc6967725592a6a8bfb7e529ca/praisonaiagents-0.6.2.tar.gz", hash = "sha256:36d48b4c8b2915671033a05bb3983c4e613acdcc2fd8b3547271a6102f0c0928", size = 785586, upload-time = "2026-01-02T05:42:43.766Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/0f/3d/efb640be61b496d432a2bf26461d92391d9c20d06bf9924fa4dfa9b1631d/praisonaiagents-0.6.1-py3-none-any.whl", hash = "sha256:fcb82e09600fa9cf96935c4b1e2f3f5f8e549b1b1737ff083999f405f3279887", size = 730367, upload-time = "2026-01-02T05:14:46.519Z" },
+    { url = "https://files.pythonhosted.org/packages/62/aa/b3a6daeca388a975d4d21fc3e93847be2379726c5e7428d4efc28a31238d/praisonaiagents-0.6.2-py3-none-any.whl", hash = "sha256:440e45851dab829e9019b0ab35d80a2fa5d7c83a5aadb7269412cc119ede7890", size = 730473, upload-time = "2026-01-02T05:42:41.989Z" },
 ]
 
 [[package]]