Release v3.0.4

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>=3.0.4" \
     "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>=3.0.4" \
     "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>=3.0.4" \
     "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/v3.0.4.tar.gz"
+    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v3.0.4.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/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==3.0.4 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__ = "3.0.4"

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]]