Release v2.3.23

README.md

@@ -723,6 +723,12 @@ praisonai workflow run deploy --workflow-var environment=staging --workflow-var
 # Execute with planning mode (AI creates sub-steps for each workflow step)
 praisonai workflow run "Research Blog" --planning --verbose
 
+# Execute with reasoning mode (chain-of-thought)
+praisonai workflow run "Analysis" --reasoning --verbose
+
+# Execute with memory enabled
+praisonai workflow run "Research" --memory
+
 # Show workflow details
 praisonai workflow show deploy
 
@@ -734,8 +740,23 @@ praisonai "What is AI?" --workflow "Research,Summarize" --save
 
 # Inline workflow with step actions
 praisonai "GPT-5" --workflow "Research:Search for info,Write:Write blog" --tools tavily
+
+# Workflow CLI help
+praisonai workflow help
 ```
 
+**Workflow CLI Options:**
+| Flag | Description |
+|------|-------------|
+| `--workflow-var key=value` | Set workflow variable (can be repeated) |
+| `--llm <model>` | LLM model (e.g., openai/gpt-4o-mini) |
+| `--tools <tools>` | Tools (comma-separated, e.g., tavily) |
+| `--planning` | Enable planning mode |
+| `--reasoning` | Enable reasoning mode |
+| `--memory` | Enable memory |
+| `--verbose` | Enable verbose output |
+| `--save` | Save output to file |
+
 ### Hooks CLI:
 ```bash
 # List configured hooks

docker/Dockerfile

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     flask \
-    "praisonai>=2.3.22" \
+    "praisonai>=2.3.23" \
     "praisonai[api]" \
     gunicorn \
     markdown

docker/Dockerfile.chat

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=2.3.22" \
+    "praisonai>=2.3.23" \
     "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>=2.3.22" \
+    "praisonai>=2.3.23" \
     "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>=2.3.22" \
+    "praisonai>=2.3.23" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

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

@@ -213,6 +213,19 @@ class WorkflowStep:
     # Guardrail (like process="workflow")
     guardrail: Optional[Callable[['StepResult'], Tuple[bool, Any]]] = None  # Returns (is_valid, feedback)
     
+    # Output configuration (migrated from Task)
+    output_file: Optional[str] = None  # Save output to file
+    output_json: Optional[Any] = None  # Pydantic model for JSON output
+    output_pydantic: Optional[Any] = None  # Pydantic model for structured output
+    
+    # Image handling (migrated from Task)
+    images: Optional[List[str]] = None  # Image paths/URLs for vision tasks
+    
+    # Execution control (migrated from Task)
+    async_execution: bool = False  # Run step asynchronously
+    quality_check: bool = True  # Enable quality validation
+    rerun: bool = True  # Allow step to be rerun
+    
     # Status tracking
     status: str = "pending"  # pending, running, completed, failed, skipped
     retry_count: int = 0
@@ -245,7 +258,14 @@ class WorkflowStep:
             "next_steps": self.next_steps,
             "branch_condition": self.branch_condition,
             "loop_over": self.loop_over,
-            "loop_var": self.loop_var
+            "loop_var": self.loop_var,
+            "output_file": self.output_file,
+            "output_json": self.output_json is not None,
+            "output_pydantic": self.output_pydantic is not None,
+            "images": self.images,
+            "async_execution": self.async_execution,
+            "quality_check": self.quality_check,
+            "rerun": self.rerun
         }
 
 
@@ -264,6 +284,8 @@ class Workflow:
     memory_config: Optional[Dict[str, Any]] = None  # Memory configuration
     planning: bool = False  # Enable planning mode
     planning_llm: Optional[str] = None  # LLM for planning
+    reasoning: bool = False  # Enable chain-of-thought reasoning
+    verbose: bool = False  # Enable verbose output
     
     # Callbacks (like process="workflow")
     on_workflow_start: Optional[Callable[['Workflow', str], None]] = None  # (workflow, input)
@@ -325,6 +347,9 @@ class Workflow:
         # Use default LLM if not specified
         model = llm or self.default_llm or "gpt-4o-mini"
         
+        # Use workflow verbose setting if not overridden
+        verbose = verbose or self.verbose
+        
         # Add input to variables
         all_variables = {**self.variables, "input": input}
         
@@ -341,6 +366,13 @@ class Workflow:
             except Exception as e:
                 logger.error(f"on_workflow_start callback failed: {e}")
         
+        # Planning mode - create execution plan before running
+        if self.planning:
+            plan = self._create_plan(input, model, verbose)
+            if plan and verbose:
+                print(f"📋 Execution Plan: {plan}")
+            all_variables["execution_plan"] = plan
+        
         # Process steps (may include Route, Parallel, Loop, Repeat)
         i = 0
         while i < len(self.steps):
@@ -456,19 +488,57 @@ class Workflow:
                             output = str(result)
                             
                     elif step.agent:
-                        # Direct agent
-                        output = step.agent.chat(step.action or input)
+                        # Direct agent with tools
+                        # Substitute variables in action
+                        action = step.action or input
+                        for key, value in all_variables.items():
+                            action = action.replace(f"{{{{{key}}}}}", str(value))
+                        if previous_output:
+                            action = action.replace("{{previous_output}}", str(previous_output))
+                        action = action.replace("{{input}}", input)
+                        
+                        # Add reasoning prompt if enabled
+                        if self.reasoning:
+                            action = f"Think step by step and reason through this task:\n\n{action}"
+                        
+                        # Add validation feedback if retrying
+                        if validation_feedback:
+                            action = f"{action}\n\nPrevious attempt feedback: {validation_feedback}"
+                        
+                        # Handle images if present
+                        step_images = getattr(step, 'images', None)
+                        if step_images:
+                            output = step.agent.chat(action, images=step_images)
+                        else:
+                            output = step.agent.chat(action)
+                        
+                        # Handle output_pydantic if present
+                        output_pydantic = getattr(step, 'output_pydantic', None)
+                        if output_pydantic and output:
+                            try:
+                                # Try to parse output as Pydantic model
+                                if hasattr(output_pydantic, 'model_validate_json'):
+                                    parsed = output_pydantic.model_validate_json(output)
+                                    output = parsed.model_dump_json()
+                            except Exception as e:
+                                logger.debug(f"Pydantic parsing failed: {e}")
                             
                     elif step.action:
                         # Action with agent_config - create temporary agent
                         from ..agent.agent import Agent
                         config = step.agent_config or self.default_agent_config or {}
+                        
+                        # Get tools from step or config
+                        step_tools = step.tools or config.get("tools", [])
+                        
                         temp_agent = Agent(
                             name=config.get("name", step.name),
                             role=config.get("role", "Assistant"),
                             goal=config.get("goal", "Complete the task"),
                             llm=config.get("llm", model),
-                            verbose=verbose
+                            tools=step_tools if step_tools else None,
+                            verbose=verbose,
+                            reasoning=self.reasoning
                         )
                         # Substitute variables in action
                         action = step.action
@@ -476,6 +546,12 @@ class Workflow:
                             action = action.replace(f"{{{{{key}}}}}", str(value))
                         if previous_output:
                             action = action.replace("{{previous_output}}", str(previous_output))
+                        action = action.replace("{{input}}", input)
+                        
+                        # Add reasoning prompt if enabled
+                        if self.reasoning:
+                            action = f"Think step by step and reason through this task:\n\n{action}"
+                        
                         if validation_feedback:
                             action = f"{action}\n\nPrevious attempt feedback: {validation_feedback}"
                         output = temp_agent.chat(action)
@@ -526,6 +602,23 @@ class Workflow:
                 except Exception as e:
                     logger.error(f"on_step_complete callback failed: {e}")
             
+            # Handle output_file - save output to file
+            if hasattr(step, 'output_file') and step.output_file and output:
+                try:
+                    import os
+                    output_path = step.output_file
+                    # Substitute variables in path
+                    for key, value in all_variables.items():
+                        output_path = output_path.replace(f"{{{{{key}}}}}", str(value))
+                    # Create directory if needed
+                    os.makedirs(os.path.dirname(output_path) if os.path.dirname(output_path) else ".", exist_ok=True)
+                    with open(output_path, "w") as f:
+                        f.write(str(output))
+                    if verbose:
+                        print(f"📁 Saved output to: {output_path}")
+                except Exception as e:
+                    logger.error(f"Failed to save output to file: {e}")
+            
             # Store result
             results.append({
                 "step": step.name,
@@ -569,6 +662,41 @@ class Workflow:
         
         return final_result
     
+    async def astart(
+        self,
+        input: str = "",
+        llm: Optional[str] = None,
+        verbose: bool = False
+    ) -> Dict[str, Any]:
+        """Async version of start() for async workflow execution.
+        
+        Args:
+            input: The input text/prompt for the workflow
+            llm: LLM model to use (default: gpt-4o-mini)
+            verbose: Print step outputs
+            
+        Returns:
+            Dict with 'output' (final result) and 'steps' (all step results)
+        """
+        import asyncio
+        
+        # Run the synchronous version in a thread pool
+        loop = asyncio.get_event_loop()
+        result = await loop.run_in_executor(
+            None,
+            lambda: self.run(input, llm, verbose)
+        )
+        return result
+    
+    async def arun(
+        self,
+        input: str = "",
+        llm: Optional[str] = None,
+        verbose: bool = False
+    ) -> Dict[str, Any]:
+        """Alias for astart() for backward compatibility."""
+        return await self.astart(input, llm, verbose)
+    
     def _normalize_steps(self) -> List['WorkflowStep']:
         """Convert mixed steps (Agent, function, WorkflowStep) to WorkflowStep list."""
         normalized = []
@@ -598,21 +726,89 @@ class Workflow:
         
         return normalized
     
+    def _create_plan(self, input: str, model: str, verbose: bool) -> Optional[str]:
+        """Create an execution plan for the workflow using LLM.
+        
+        Args:
+            input: The workflow input
+            model: LLM model to use
+            verbose: Print verbose output
+            
+        Returns:
+            Execution plan as string, or None if planning fails
+        """
+        try:
+            from ..agent.agent import Agent
+            
+            # Describe the steps
+            step_descriptions = []
+            for i, step in enumerate(self.steps):
+                if isinstance(step, WorkflowStep):
+                    step_descriptions.append(f"{i+1}. {step.name}: {step.description or step.action or 'Execute step'}")
+                elif hasattr(step, 'name'):
+                    role = getattr(step, 'role', 'Agent')
+                    step_descriptions.append(f"{i+1}. {step.name} ({role})")
+                elif callable(step):
+                    step_descriptions.append(f"{i+1}. {getattr(step, '__name__', 'function')}")
+                elif isinstance(step, (Route, Parallel, Loop, Repeat)):
+                    step_descriptions.append(f"{i+1}. {type(step).__name__} pattern")
+                else:
+                    step_descriptions.append(f"{i+1}. Step {i+1}")
+            
+            steps_text = "\n".join(step_descriptions)
+            
+            planning_prompt = f"""You are a workflow planner. Given the following workflow steps and input, create a brief execution plan.
+
+Workflow: {self.name}
+Description: {self.description}
+
+Steps:
+{steps_text}
+
+Input: {input}
+
+Create a brief execution plan (2-3 sentences) describing how to best accomplish this task using the available steps. Focus on the key objectives and any important considerations."""
+
+            planner = Agent(
+                name="Planner",
+                role="Workflow Planner",
+                goal="Create efficient execution plans",
+                llm=self.planning_llm or model,
+                verbose=False
+            )
+            
+            plan = planner.chat(planning_prompt)
+            return plan
+            
+        except Exception as e:
+            logger.error(f"Planning failed: {e}")
+            return None
+    
     def _normalize_single_step(self, step: Any, index: int) -> 'WorkflowStep':
-        """Normalize a single step to WorkflowStep."""
+        """Normalize a single step to WorkflowStep.
+        
+        Supports:
+        - WorkflowStep objects (passed through)
+        - Agent objects (wrapped with agent reference and tools)
+        - Callable functions (wrapped as handler)
+        - Strings (used as action)
+        """
         if isinstance(step, WorkflowStep):
             return step
+        elif hasattr(step, 'chat'):
+            # It's an Agent - wrap with agent reference and preserve tools
+            agent_tools = getattr(step, 'tools', None)
+            return WorkflowStep(
+                name=getattr(step, 'name', f'agent_{index+1}'),
+                agent=step,
+                tools=agent_tools,
+                action="{{previous_output}}" if index > 0 else "{{input}}"
+            )
         elif callable(step):
             return WorkflowStep(
                 name=getattr(step, '__name__', f'step_{index+1}'),
                 handler=step
             )
-        elif hasattr(step, 'chat'):
-            return WorkflowStep(
-                name=getattr(step, 'name', f'agent_{index+1}'),
-                agent=step,
-                action="{{input}}"
-            )
         else:
             return WorkflowStep(
                 name=f'step_{index+1}',
@@ -1031,6 +1227,13 @@ class WorkflowManager:
         loop_var_pattern = re.compile(r'loop_var:\s*(\w+)', re.IGNORECASE)
         branch_pattern = re.compile(r'```branch\s*\n(.*?)\n```', re.DOTALL)
         
+        # New patterns for route, parallel, images, output_file
+        route_pattern = re.compile(r'```route\s*\n(.*?)\n```', re.DOTALL)
+        parallel_pattern = re.compile(r'```parallel\s*\n(.*?)\n```', re.DOTALL)
+        images_pattern = re.compile(r'```images\s*\n(.*?)\n```', re.DOTALL)
+        output_file_pattern = re.compile(r'output_file:\s*(.+)', re.IGNORECASE)
+        repeat_pattern = re.compile(r'```repeat\s*\n(.*?)\n```', re.DOTALL)
+        
         # Find all step headers
         matches = list(step_pattern.finditer(body))
         
@@ -1106,6 +1309,41 @@ class WorkflowManager:
             if loop_var_match:
                 loop_var = loop_var_match.group(1)
             
+            # Extract route pattern (same as branch_condition)
+            route_match = route_pattern.search(step_content)
+            if route_match and not branch_condition:
+                branch_condition = self._parse_branch_condition(route_match.group(1).strip())
+            
+            # Extract parallel pattern
+            parallel_steps = None
+            parallel_match = parallel_pattern.search(step_content)
+            if parallel_match:
+                parallel_str = parallel_match.group(1).strip()
+                # Parse as list of step names
+                parallel_steps = [s.strip().lstrip('- ').strip('"\'') for s in parallel_str.split('\n') if s.strip()]
+                # Set next_steps to parallel steps if not already set
+                if not next_steps:
+                    next_steps = parallel_steps
+            
+            # Extract images
+            images = None
+            images_match = images_pattern.search(step_content)
+            if images_match:
+                images_str = images_match.group(1).strip()
+                images = [s.strip() for s in images_str.split('\n') if s.strip()]
+            
+            # Extract output_file
+            output_file = None
+            output_file_match = output_file_pattern.search(step_content)
+            if output_file_match:
+                output_file = output_file_match.group(1).strip()
+            
+            # Extract repeat config
+            repeat_config = None
+            repeat_match = repeat_pattern.search(step_content)
+            if repeat_match:
+                repeat_config = self._parse_agent_config(repeat_match.group(1).strip())
+            
             # Get description (text before action block)
             description = step_content
             if action_match:
@@ -1119,8 +1357,14 @@ class WorkflowManager:
             description = re.sub(r'next_steps:.*', '', description)
             description = re.sub(r'loop_over:.*', '', description)
             description = re.sub(r'loop_var:.*', '', description)
+            description = re.sub(r'output_file:.*', '', description)
             description = description.strip()
             
+            # Set max_retries from repeat config
+            max_retries = 3
+            if repeat_config and 'max_iterations' in repeat_config:
+                max_retries = int(repeat_config['max_iterations'])
+            
             steps.append(WorkflowStep(
                 name=step_name,
                 description=description,
@@ -1134,7 +1378,10 @@ class WorkflowManager:
                 next_steps=next_steps,
                 branch_condition=branch_condition,
                 loop_over=loop_over,
-                loop_var=loop_var
+                loop_var=loop_var,
+                output_file=output_file,
+                images=images,
+                max_retries=max_retries
             ))
         
         return steps

src/praisonai-agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.1.0"
+version = "0.1.1"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 requires-python = ">=3.10"
 authors = [

src/praisonai-agents/tests/test_workflow_patterns.py

@@ -1017,6 +1017,667 @@ class TestDocumentationExamples:
         assert attempts[0] == 2  # First attempt + 1 retry
 
 
+# =============================================================================
+# Test: Agent Integration in Workflows
+# =============================================================================
+
+class TestAgentWorkflows:
+    """Test Agent objects as workflow steps."""
+    
+    def test_agent_normalization(self):
+        """Test that Agent objects are properly normalized to WorkflowStep."""
+        # Create a mock agent
+        class MockAgent:
+            def __init__(self, name, tools=None):
+                self.name = name
+                self.tools = tools or []
+            
+            def chat(self, message):
+                return f"Response from {self.name}: {message}"
+        
+        agent = MockAgent("TestAgent", tools=["tool1", "tool2"])
+        
+        workflow = Workflow(steps=[agent])
+        
+        # Normalize and check
+        normalized = workflow._normalize_single_step(agent, 0)
+        assert normalized.name == "TestAgent"
+        assert normalized.agent == agent
+        assert normalized.tools == ["tool1", "tool2"]
+    
+    def test_workflow_with_mock_agent(self):
+        """Test workflow execution with mock agent."""
+        class MockAgent:
+            def __init__(self, name):
+                self.name = name
+            
+            def chat(self, message):
+                return f"Processed: {message}"
+        
+        agent = MockAgent("Processor")
+        
+        workflow = Workflow(steps=[agent])
+        result = workflow.start("Hello World")
+        
+        assert "Processed:" in result["output"]
+        assert "Hello World" in result["output"]
+    
+    def test_sequential_mock_agents(self):
+        """Test sequential execution of multiple mock agents."""
+        class MockAgent:
+            def __init__(self, name, prefix):
+                self.name = name
+                self.prefix = prefix
+            
+            def chat(self, message):
+                return f"{self.prefix}: {message}"
+        
+        agent1 = MockAgent("Agent1", "First")
+        agent2 = MockAgent("Agent2", "Second")
+        agent3 = MockAgent("Agent3", "Third")
+        
+        workflow = Workflow(steps=[agent1, agent2, agent3])
+        result = workflow.start("Input")
+        
+        # Final output should be from agent3
+        assert "Third:" in result["output"]
+        assert len(result["steps"]) == 3
+    
+    def test_parallel_mock_agents(self):
+        """Test parallel execution of mock agents."""
+        class MockAgent:
+            def __init__(self, name, output):
+                self.name = name
+                self.output = output
+            
+            def chat(self, message):
+                return self.output
+        
+        agent1 = MockAgent("Agent1", "Result A")
+        agent2 = MockAgent("Agent2", "Result B")
+        agent3 = MockAgent("Agent3", "Result C")
+        
+        def aggregator(ctx: WorkflowContext) -> StepResult:
+            outputs = ctx.variables.get("parallel_outputs", [])
+            return StepResult(output=f"Combined: {len(outputs)} results")
+        
+        workflow = Workflow(steps=[
+            parallel([agent1, agent2, agent3]),
+            aggregator
+        ])
+        
+        result = workflow.start("Test")
+        assert "Combined: 3 results" in result["output"]
+    
+    def test_route_to_mock_agents(self):
+        """Test routing to different mock agents."""
+        class MockAgent:
+            def __init__(self, name, output):
+                self.name = name
+                self.output = output
+            
+            def chat(self, message):
+                return self.output
+        
+        tech_agent = MockAgent("TechAgent", "Technical response")
+        creative_agent = MockAgent("CreativeAgent", "Creative response")
+        
+        def classifier(ctx: WorkflowContext) -> StepResult:
+            if "tech" in ctx.input.lower():
+                return StepResult(output="category: technical")
+            return StepResult(output="category: creative")
+        
+        workflow = Workflow(steps=[
+            classifier,
+            route({
+                "technical": [tech_agent],
+                "creative": [creative_agent]
+            })
+        ])
+        
+        # Test technical route
+        result = workflow.start("tech question")
+        assert "Technical response" in result["output"]
+        
+        # Test creative route
+        result = workflow.start("creative request")
+        assert "Creative response" in result["output"]
+    
+    def test_loop_with_mock_agent(self):
+        """Test loop pattern with mock agent."""
+        class MockAgent:
+            def __init__(self, name):
+                self.name = name
+            
+            def chat(self, message):
+                return f"Processed: {message}"
+        
+        processor = MockAgent("Processor")
+        
+        workflow = Workflow(
+            steps=[loop(processor, over="items")],
+            variables={"items": ["A", "B", "C"]}
+        )
+        
+        result = workflow.start("Process items")
+        outputs = result["variables"].get("loop_outputs", [])
+        assert len(outputs) == 3
+
+
+# =============================================================================
+# Test: Workflow Configuration
+# =============================================================================
+
+class TestWorkflowConfiguration:
+    """Test workflow configuration options."""
+    
+    def test_workflow_verbose_setting(self):
+        """Test that workflow verbose setting is used."""
+        def step1(ctx): return StepResult(output="Done")
+        
+        workflow = Workflow(steps=[step1], verbose=True)
+        assert workflow.verbose == True
+    
+    def test_workflow_reasoning_setting(self):
+        """Test that workflow reasoning setting is stored."""
+        def step1(ctx): return StepResult(output="Done")
+        
+        workflow = Workflow(steps=[step1], reasoning=True)
+        assert workflow.reasoning == True
+    
+    def test_workflow_planning_setting(self):
+        """Test that workflow planning setting is stored."""
+        def step1(ctx): return StepResult(output="Done")
+        
+        workflow = Workflow(steps=[step1], planning=True, planning_llm="gpt-4o")
+        assert workflow.planning == True
+        assert workflow.planning_llm == "gpt-4o"
+    
+    def test_workflow_default_llm(self):
+        """Test default LLM setting."""
+        def step1(ctx): return StepResult(output="Done")
+        
+        workflow = Workflow(steps=[step1], default_llm="gpt-4o-mini")
+        assert workflow.default_llm == "gpt-4o-mini"
+    
+    def test_workflow_default_agent_config(self):
+        """Test default agent config."""
+        def step1(ctx): return StepResult(output="Done")
+        
+        workflow = Workflow(
+            steps=[step1],
+            default_agent_config={
+                "name": "DefaultAgent",
+                "role": "Assistant",
+                "tools": ["tool1"]
+            }
+        )
+        assert workflow.default_agent_config["name"] == "DefaultAgent"
+        assert workflow.default_agent_config["tools"] == ["tool1"]
+
+
+# =============================================================================
+# Test: WorkflowStep with Tools
+# =============================================================================
+
+class TestWorkflowStepTools:
+    """Test WorkflowStep with tools configuration."""
+    
+    def test_step_with_tools(self):
+        """Test WorkflowStep with tools parameter."""
+        def my_tool(): return "tool result"
+        
+        step = WorkflowStep(
+            name="step_with_tools",
+            action="Do something",
+            tools=[my_tool]
+        )
+        
+        assert step.tools == [my_tool]
+    
+    def test_step_with_agent_config_tools(self):
+        """Test WorkflowStep with tools in agent_config."""
+        def my_tool(): return "tool result"
+        
+        step = WorkflowStep(
+            name="step_with_config",
+            action="Do something",
+            agent_config={
+                "name": "Agent",
+                "role": "Helper",
+                "tools": [my_tool]
+            }
+        )
+        
+        assert step.agent_config["tools"] == [my_tool]
+
+
+# =============================================================================
+# Test: Migrated Features (output_file, images, async, etc.)
+# =============================================================================
+
+class TestMigratedFeatures:
+    """Test features migrated from process='workflow'."""
+    
+    def test_step_output_file(self):
+        """Test WorkflowStep with output_file parameter."""
+        import tempfile
+        import os
+        
+        with tempfile.TemporaryDirectory() as tmpdir:
+            output_path = os.path.join(tmpdir, "output.txt")
+            
+            def generate_content(ctx: WorkflowContext) -> StepResult:
+                return StepResult(output="Generated content for file")
+            
+            workflow = Workflow(steps=[
+                WorkflowStep(
+                    name="generator",
+                    handler=generate_content,
+                    output_file=output_path
+                )
+            ])
+            
+            result = workflow.start("Generate content")
+            
+            # Check file was created
+            assert os.path.exists(output_path)
+            with open(output_path) as f:
+                content = f.read()
+            assert "Generated content" in content
+    
+    def test_step_images_parameter(self):
+        """Test WorkflowStep with images parameter."""
+        step = WorkflowStep(
+            name="vision_step",
+            action="Analyze this image",
+            images=["image1.jpg", "image2.png"]
+        )
+        
+        assert step.images == ["image1.jpg", "image2.png"]
+    
+    def test_step_output_pydantic_parameter(self):
+        """Test WorkflowStep with output_pydantic parameter."""
+        from pydantic import BaseModel
+        
+        class OutputModel(BaseModel):
+            title: str
+            content: str
+        
+        step = WorkflowStep(
+            name="structured_step",
+            action="Generate structured output",
+            output_pydantic=OutputModel
+        )
+        
+        assert step.output_pydantic == OutputModel
+    
+    def test_step_async_execution_parameter(self):
+        """Test WorkflowStep with async_execution parameter."""
+        step = WorkflowStep(
+            name="async_step",
+            action="Run async",
+            async_execution=True
+        )
+        
+        assert step.async_execution == True
+    
+    def test_step_quality_check_parameter(self):
+        """Test WorkflowStep with quality_check parameter."""
+        step = WorkflowStep(
+            name="quality_step",
+            action="Check quality",
+            quality_check=False
+        )
+        
+        assert step.quality_check == False
+    
+    def test_step_rerun_parameter(self):
+        """Test WorkflowStep with rerun parameter."""
+        step = WorkflowStep(
+            name="rerun_step",
+            action="Can rerun",
+            rerun=False
+        )
+        
+        assert step.rerun == False
+    
+    def test_workflow_astart_method_exists(self):
+        """Test that Workflow has astart method."""
+        def step1(ctx): return StepResult(output="Done")
+        
+        workflow = Workflow(steps=[step1])
+        
+        assert hasattr(workflow, 'astart')
+        assert callable(workflow.astart)
+    
+    def test_workflow_arun_method_exists(self):
+        """Test that Workflow has arun method."""
+        def step1(ctx): return StepResult(output="Done")
+        
+        workflow = Workflow(steps=[step1])
+        
+        assert hasattr(workflow, 'arun')
+        assert callable(workflow.arun)
+    
+    def test_step_to_dict_includes_new_fields(self):
+        """Test that to_dict includes all new fields."""
+        step = WorkflowStep(
+            name="test_step",
+            action="Test action",
+            output_file="output.txt",
+            images=["img.jpg"],
+            async_execution=True,
+            quality_check=False,
+            rerun=False
+        )
+        
+        d = step.to_dict()
+        
+        assert d["output_file"] == "output.txt"
+        assert d["images"] == ["img.jpg"]
+        assert d["async_execution"] == True
+        assert d["quality_check"] == False
+        assert d["rerun"] == False
+
+
+# =============================================================================
+# Test: WorkflowManager with Patterns in MD Files
+# =============================================================================
+
+class TestWorkflowManagerPatterns:
+    """Test WorkflowManager parsing of pattern syntax in markdown files."""
+    
+    def test_parse_route_pattern_in_md(self):
+        """Test parsing route pattern from markdown."""
+        import tempfile
+        import os
+        from praisonaiagents.workflows import WorkflowManager
+        
+        md_content = """---
+name: Route Test
+description: Test routing pattern
+---
+
+## Step 1: Classifier
+Classify the input.
+
+```action
+Classify this input as technical or creative
+```
+
+```route
+technical: [Tech Handler]
+creative: [Creative Handler]
+default: [General Handler]
+```
+
+## Step 2: Tech Handler
+Handle technical requests.
+
+```action
+Process technical request
+```
+
+## Step 3: Creative Handler
+Handle creative requests.
+
+```action
+Process creative request
+```
+
+## Step 4: General Handler
+Handle general requests.
+
+```action
+Process general request
+```
+"""
+        with tempfile.TemporaryDirectory() as tmpdir:
+            workflows_dir = os.path.join(tmpdir, ".praison", "workflows")
+            os.makedirs(workflows_dir)
+            
+            with open(os.path.join(workflows_dir, "route_test.md"), "w") as f:
+                f.write(md_content)
+            
+            manager = WorkflowManager(workspace_path=tmpdir)
+            workflows = manager.list_workflows()
+            
+            assert len(workflows) == 1
+            workflow = workflows[0]
+            assert workflow.name == "Route Test"
+            
+            # Check that route pattern is parsed
+            step1 = workflow.steps[0]
+            assert step1.name == "Classifier"
+            assert step1.branch_condition is not None
+            assert "technical" in step1.branch_condition
+    
+    def test_parse_parallel_pattern_in_md(self):
+        """Test parsing parallel pattern from markdown."""
+        import tempfile
+        import os
+        from praisonaiagents.workflows import WorkflowManager
+        
+        md_content = """---
+name: Parallel Test
+description: Test parallel pattern
+---
+
+## Step 1: Research
+Research in parallel.
+
+```parallel
+- Market Research
+- Competitor Analysis
+- Customer Survey
+```
+
+```action
+Research the topic
+```
+
+## Step 2: Market Research
+Research the market.
+
+```action
+Analyze market trends
+```
+
+## Step 3: Competitor Analysis
+Analyze competitors.
+
+```action
+Analyze competitors
+```
+
+## Step 4: Customer Survey
+Survey customers.
+
+```action
+Survey customers
+```
+"""
+        with tempfile.TemporaryDirectory() as tmpdir:
+            workflows_dir = os.path.join(tmpdir, ".praison", "workflows")
+            os.makedirs(workflows_dir)
+            
+            with open(os.path.join(workflows_dir, "parallel_test.md"), "w") as f:
+                f.write(md_content)
+            
+            manager = WorkflowManager(workspace_path=tmpdir)
+            workflows = manager.list_workflows()
+            
+            assert len(workflows) == 1
+            workflow = workflows[0]
+            assert workflow.name == "Parallel Test"
+            
+            # Check that parallel steps are parsed
+            step1 = workflow.steps[0]
+            assert step1.name == "Research"
+            assert hasattr(step1, 'parallel_steps') or step1.next_steps is not None
+    
+    def test_parse_loop_pattern_in_md(self):
+        """Test parsing loop pattern from markdown."""
+        import tempfile
+        import os
+        from praisonaiagents.workflows import WorkflowManager
+        
+        md_content = """---
+name: Loop Test
+description: Test loop pattern
+variables:
+  items: ["item1", "item2", "item3"]
+---
+
+## Step 1: Process Items
+Process each item in the list.
+
+loop_over: items
+loop_var: current_item
+
+```action
+Process {{current_item}}
+```
+"""
+        with tempfile.TemporaryDirectory() as tmpdir:
+            workflows_dir = os.path.join(tmpdir, ".praison", "workflows")
+            os.makedirs(workflows_dir)
+            
+            with open(os.path.join(workflows_dir, "loop_test.md"), "w") as f:
+                f.write(md_content)
+            
+            manager = WorkflowManager(workspace_path=tmpdir)
+            workflows = manager.list_workflows()
+            
+            assert len(workflows) == 1
+            workflow = workflows[0]
+            
+            step1 = workflow.steps[0]
+            assert step1.name == "Process Items"
+            assert step1.loop_over == "items"
+            assert step1.loop_var == "current_item"
+    
+    def test_parse_repeat_pattern_in_md(self):
+        """Test parsing repeat pattern from markdown."""
+        import tempfile
+        import os
+        from praisonaiagents.workflows import WorkflowManager
+        
+        md_content = """---
+name: Repeat Test
+description: Test repeat pattern
+---
+
+## Step 1: Generate
+Generate content repeatedly.
+
+```repeat
+max_iterations: 5
+until: count >= 10
+```
+
+```action
+Generate more content
+```
+"""
+        with tempfile.TemporaryDirectory() as tmpdir:
+            workflows_dir = os.path.join(tmpdir, ".praison", "workflows")
+            os.makedirs(workflows_dir)
+            
+            with open(os.path.join(workflows_dir, "repeat_test.md"), "w") as f:
+                f.write(md_content)
+            
+            manager = WorkflowManager(workspace_path=tmpdir)
+            workflows = manager.list_workflows()
+            
+            assert len(workflows) == 1
+            workflow = workflows[0]
+            
+            step1 = workflow.steps[0]
+            assert step1.name == "Generate"
+            # Check repeat config is parsed
+            assert hasattr(step1, 'max_retries') or hasattr(step1, 'repeat_config')
+    
+    def test_parse_output_file_in_md(self):
+        """Test parsing output_file from markdown."""
+        import tempfile
+        import os
+        from praisonaiagents.workflows import WorkflowManager
+        
+        md_content = """---
+name: Output Test
+description: Test output file
+---
+
+## Step 1: Generate Report
+Generate a report and save to file.
+
+output_file: output/report.txt
+
+```action
+Generate the report
+```
+"""
+        with tempfile.TemporaryDirectory() as tmpdir:
+            workflows_dir = os.path.join(tmpdir, ".praison", "workflows")
+            os.makedirs(workflows_dir)
+            
+            with open(os.path.join(workflows_dir, "output_test.md"), "w") as f:
+                f.write(md_content)
+            
+            manager = WorkflowManager(workspace_path=tmpdir)
+            workflows = manager.list_workflows()
+            
+            assert len(workflows) == 1
+            workflow = workflows[0]
+            
+            step1 = workflow.steps[0]
+            assert step1.name == "Generate Report"
+            assert step1.output_file == "output/report.txt"
+    
+    def test_parse_images_in_md(self):
+        """Test parsing images from markdown."""
+        import tempfile
+        import os
+        from praisonaiagents.workflows import WorkflowManager
+        
+        md_content = """---
+name: Vision Test
+description: Test image handling
+---
+
+## Step 1: Analyze Image
+Analyze the provided image.
+
+```images
+image1.jpg
+image2.png
+```
+
+```action
+Analyze these images
+```
+"""
+        with tempfile.TemporaryDirectory() as tmpdir:
+            workflows_dir = os.path.join(tmpdir, ".praison", "workflows")
+            os.makedirs(workflows_dir)
+            
+            with open(os.path.join(workflows_dir, "vision_test.md"), "w") as f:
+                f.write(md_content)
+            
+            manager = WorkflowManager(workspace_path=tmpdir)
+            workflows = manager.list_workflows()
+            
+            assert len(workflows) == 1
+            workflow = workflows[0]
+            
+            step1 = workflow.steps[0]
+            assert step1.name == "Analyze Image"
+            assert step1.images is not None
+            assert "image1.jpg" in step1.images
+
+
 # =============================================================================
 # Run Tests
 # =============================================================================

src/praisonai-agents/uv.lock

@@ -2422,7 +2422,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.1.0"
+version = "0.1.1"
 source = { editable = "." }
 dependencies = [
     { name = "aiohttp" },

src/praisonai/README.md

@@ -723,6 +723,12 @@ praisonai workflow run deploy --workflow-var environment=staging --workflow-var
 # Execute with planning mode (AI creates sub-steps for each workflow step)
 praisonai workflow run "Research Blog" --planning --verbose
 
+# Execute with reasoning mode (chain-of-thought)
+praisonai workflow run "Analysis" --reasoning --verbose
+
+# Execute with memory enabled
+praisonai workflow run "Research" --memory
+
 # Show workflow details
 praisonai workflow show deploy
 
@@ -734,8 +740,23 @@ praisonai "What is AI?" --workflow "Research,Summarize" --save
 
 # Inline workflow with step actions
 praisonai "GPT-5" --workflow "Research:Search for info,Write:Write blog" --tools tavily
+
+# Workflow CLI help
+praisonai workflow help
 ```
 
+**Workflow CLI Options:**
+| Flag | Description |
+|------|-------------|
+| `--workflow-var key=value` | Set workflow variable (can be repeated) |
+| `--llm <model>` | LLM model (e.g., openai/gpt-4o-mini) |
+| `--tools <tools>` | Tools (comma-separated, e.g., tavily) |
+| `--planning` | Enable planning mode |
+| `--reasoning` | Enable reasoning mode |
+| `--memory` | Enable memory |
+| `--verbose` | Enable verbose output |
+| `--save` | Save output to file |
+
 ### Hooks CLI:
 ```bash
 # List configured hooks

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/v2.3.22.tar.gz"
-    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.3.22.tar.gz | shasum -a 256`.split.first
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.3.23.tar.gz"
+    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.3.23.tar.gz | shasum -a 256`.split.first
     license "MIT"
   
     depends_on "[email protected]"

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==2.3.22 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.3.23 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__ = "2.3.22"
+__version__ = "2.3.23"

src/praisonai/pyproject.toml

@@ -12,7 +12,7 @@ dependencies = [
     "rich>=13.7",
     "markdown>=3.5",
     "pyparsing>=3.0.0",
-    "praisonaiagents>=0.1.0",
+    "praisonaiagents>=0.1.1",
     "python-dotenv>=0.19.0",
     "instructor>=1.3.3",
     "PyYAML>=6.0",

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 = ">=0.1.0" },
+    { name = "praisonaiagents", specifier = ">=0.1.1" },
     { 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 = "0.1.0"
+version = "0.1.1"
 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/1a/69/062b8e0867ec673483d83a7529b15d17c15196460060cd7e1542f96c8036/praisonaiagents-0.1.0.tar.gz", hash = "sha256:9c87d8ef965997ab6df2f76badda7911889c6409333b8d2afa20a4f60c80fef9", size = 545876, upload-time = "2025-12-17T00:24:37.466Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/b0/41/14a8e49c644c26ee565511776c67cc978427f36aec68a1442ac666c35401/praisonaiagents-0.1.1.tar.gz", hash = "sha256:76f69188e4893b0e868f33ea02d26cccfa90b99d679c2db4185d8cf624fa740e", size = 550745, upload-time = "2025-12-17T00:37:35.974Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/42/11/9f9a8802208e50a44d2e105c6b9743fa08f02378829fb5fad1b3ee9c8b8d/praisonaiagents-0.1.0-py3-none-any.whl", hash = "sha256:bb39f79e92b8a3abe215fcb8f49be896dee539a5e8998f55b14fb8bbbeb86905", size = 516692, upload-time = "2025-12-17T00:24:35.719Z" },
+    { url = "https://files.pythonhosted.org/packages/f7/93/2cef070fae77bbef32a986ad7e1787f799442047f5dac831b9c137839353/praisonaiagents-0.1.1-py3-none-any.whl", hash = "sha256:c116551a35df0f8766496ba5102c54633f071735eae2a71b33eb4df5e2728e86", size = 518796, upload-time = "2025-12-17T00:37:33.824Z" },
 ]
 
 [[package]]