Release v2.3.77

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>=2.3.74" \
+    "praisonai>=2.3.77" \
     "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>=2.3.74" \
+    "praisonai>=2.3.77" \
     "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>=2.3.74" \
+    "praisonai>=2.3.77" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

src/praisonai-agents/praisonaiagents/_warning_patch.py

@@ -11,7 +11,7 @@ import sys
 warnings.filterwarnings("ignore", message=".*There is no current event loop.*", category=DeprecationWarning)
 warnings.filterwarnings("ignore", message=".*Pydantic serializer warnings.*")
 warnings.filterwarnings("ignore", message=".*PydanticSerializationUnexpectedValue.*")
-warnings.filterwarnings("ignore", message=".*Expected 9 fields but got.*")
+warnings.filterwarnings("ignore", message=".*Expected \\d+ fields but got.*")
 warnings.filterwarnings("ignore", message=".*Expected `StreamingChoices` but got.*")
 warnings.filterwarnings("ignore", message=".*serialized value may not be as expected.*")
 warnings.filterwarnings("ignore", message=".*Use 'content=<...>' to upload raw bytes/text content.*")
@@ -28,7 +28,7 @@ SUPPRESSED_PATTERNS = [
     "The `dict` method is deprecated; use `model_dump` instead",
     "Pydantic serializer warnings",
     "PydanticSerializationUnexpectedValue",
-    "Expected 9 fields but got 5 for type `Message`",
+    "Expected",  # Catches all "Expected N fields but got M" patterns
     "Expected `StreamingChoices` but got `Choices`",
     "serialized value may not be as expected",
     "Mixing V1 models and V2 models",

src/praisonai-agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.1.23"
+version = "0.1.24"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 readme = "README.md"
 requires-python = ">=3.10"

src/praisonai-agents/uv.lock

@@ -2422,7 +2422,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.1.23"
+version = "0.1.24"
 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/v2.3.74.tar.gz"
-    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.3.74.tar.gz | shasum -a 256`.split.first
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.3.77.tar.gz"
+    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.3.77.tar.gz | shasum -a 256`.split.first
     license "MIT"
   
     depends_on "[email protected]"

src/praisonai/praisonai/cli/features/at_mentions.py

@@ -105,6 +105,7 @@ class FileSearchService:
     - Fuzzy match file paths
     - Cache results with TTL
     - Respect .gitignore (basic)
+    - Support absolute paths (@/Users/...) and home paths (@~/...)
     """
     
     def __init__(
@@ -125,9 +126,10 @@ class FileSearchService:
         # Cache: query -> (results, timestamp)
         self._cache: Dict[str, Tuple[List[FileSuggestion], float]] = {}
         
-        # File list cache
+        # File list cache per directory
         self._file_list: Optional[List[str]] = None
         self._file_list_time: float = 0
+        self._file_list_dir: Optional[str] = None
     
     def _should_ignore(self, name: str) -> bool:
         """Check if file/dir should be ignored."""
@@ -136,20 +138,23 @@ class FileSearchService:
                 return True
         return False
     
-    def _get_file_list(self) -> List[str]:
+    def _get_file_list(self, search_dir: str = None) -> List[str]:
         """Get list of all files (cached)."""
         now = time.time()
+        target_dir = search_dir or self._root
         
-        # Return cached if fresh
-        if self._file_list is not None and (now - self._file_list_time) < self._cache_ttl:
+        # Return cached if fresh and same directory
+        if (self._file_list is not None and 
+            self._file_list_dir == target_dir and
+            (now - self._file_list_time) < self._cache_ttl):
             return self._file_list
         
         files = []
         
         try:
-            for root, dirs, filenames in os.walk(self._root):
+            for root, dirs, filenames in os.walk(target_dir):
                 # Calculate depth
-                rel_root = os.path.relpath(root, self._root)
+                rel_root = os.path.relpath(root, target_dir)
                 depth = 0 if rel_root == '.' else rel_root.count(os.sep) + 1
                 
                 if depth > self._max_depth:
@@ -161,19 +166,20 @@ class FileSearchService:
                 
                 # Add directories
                 for d in dirs:
-                    rel_path = os.path.relpath(os.path.join(root, d), self._root)
+                    rel_path = os.path.relpath(os.path.join(root, d), target_dir)
                     files.append(rel_path + os.sep)
                 
                 # Add files
                 for f in filenames:
                     if not self._should_ignore(f):
-                        rel_path = os.path.relpath(os.path.join(root, f), self._root)
+                        rel_path = os.path.relpath(os.path.join(root, f), target_dir)
                         files.append(rel_path)
         except OSError:
             pass
         
         self._file_list = files
         self._file_list_time = now
+        self._file_list_dir = target_dir
         return files
     
     def _fuzzy_match(self, text: str, query: str) -> int:
@@ -221,6 +227,11 @@ class FileSearchService:
         """
         Search for files matching query.
         
+        Supports:
+        - Relative paths: @file.txt, @src/main.py
+        - Home paths: @~/Documents, @~/.config
+        - Absolute paths: @/Users/name/file.txt, @/etc/hosts
+        
         Args:
             query: Search query (fuzzy matched)
             max_results: Maximum results to return
@@ -228,8 +239,51 @@ class FileSearchService:
         Returns:
             List of FileSuggestion sorted by score
         """
+        # Determine search directory based on query
+        search_dir = self._root
+        search_query = query
+        prefix = ""
+        
+        # Handle home directory paths (@~/)
+        if query.startswith('~'):
+            expanded = os.path.expanduser(query)
+            # Find the directory part and filename part
+            if os.path.isdir(expanded):
+                search_dir = expanded
+                search_query = ""
+                prefix = query.rstrip('/') + '/'
+            else:
+                parent = os.path.dirname(expanded)
+                if os.path.isdir(parent):
+                    search_dir = parent
+                    search_query = os.path.basename(expanded)
+                    prefix = os.path.dirname(query).rstrip('/') + '/'
+                else:
+                    # Just expand ~ and search from home
+                    search_dir = os.path.expanduser('~')
+                    search_query = query[2:] if query.startswith('~/') else query[1:]
+                    prefix = "~/"
+        
+        # Handle absolute paths (@/Users/..., @/etc/...)
+        elif query.startswith('/'):
+            if os.path.isdir(query):
+                search_dir = query
+                search_query = ""
+                prefix = query.rstrip('/') + '/'
+            else:
+                parent = os.path.dirname(query)
+                if os.path.isdir(parent):
+                    search_dir = parent
+                    search_query = os.path.basename(query)
+                    prefix = parent.rstrip('/') + '/'
+                else:
+                    # Search from root with the query
+                    search_dir = '/'
+                    search_query = query[1:]
+                    prefix = "/"
+        
         # Check cache
-        cache_key = f"{query}:{max_results}"
+        cache_key = f"{search_dir}:{search_query}:{max_results}"
         now = time.time()
         
         if cache_key in self._cache:
@@ -237,25 +291,26 @@ class FileSearchService:
             if (now - cached_time) < self._cache_ttl:
                 return results
         
-        # Get all files
-        all_files = self._get_file_list()
+        # Get all files from the appropriate directory
+        all_files = self._get_file_list(search_dir)
         
         # Score and filter
         scored: List[Tuple[int, str]] = []
         for path in all_files:
-            score = self._fuzzy_match(path, query)
+            score = self._fuzzy_match(path, search_query)
             if score > 0:
                 scored.append((score, path))
         
         # Sort by score descending
         scored.sort(key=lambda x: (-x[0], x[1]))
         
-        # Build results
+        # Build results with proper prefix
         results = []
         for score, path in scored[:max_results]:
             file_type = "directory" if path.endswith(os.sep) else "file"
+            display_path = prefix + path if prefix else path
             results.append(FileSuggestion(
-                path=path,
+                path=display_path,
                 file_type=file_type,
                 score=score
             ))

src/praisonai/praisonai/cli/features/auto_memory.py

@@ -45,8 +45,10 @@ class AutoMemoryHandler(FlagHandler):
     def _get_auto_memory(self, user_id: str = None):
         """Get AutoMemory instance lazily."""
         try:
-            from praisonaiagents.memory import AutoMemory
-            return AutoMemory(user_id=user_id or "default")
+            from praisonaiagents.memory import AutoMemory, FileMemory
+            # AutoMemory requires a FileMemory instance
+            memory = FileMemory(user_id=user_id or "default")
+            return AutoMemory(memory=memory)
         except ImportError:
             self.print_status(
                 "AutoMemory requires praisonaiagents. Install with: pip install praisonaiagents",
@@ -71,33 +73,36 @@ class AutoMemoryHandler(FlagHandler):
                 config['auto_memory_user_id'] = flag_value.get('user_id', 'default')
         return config
     
-    def extract_memories(self, text: str, user_id: str = None) -> Dict[str, Any]:
+    def extract_memories(self, text: str, user_id: str = None, user_message: str = None) -> list:
         """
-        Extract memories from text.
+        Extract and store memories from text.
         
         Args:
-            text: Text to extract memories from
+            text: Assistant response text to extract memories from
             user_id: User ID for memory isolation
+            user_message: Original user message (for context)
             
         Returns:
-            Dictionary of extracted memories
+            List of extracted memories
         """
         auto_memory = self._get_auto_memory(user_id)
         if not auto_memory:
-            return {}
+            return []
         
         try:
-            if hasattr(auto_memory, 'extract'):
-                memories = auto_memory.extract(text)
-                self.print_status(f"🧠 Extracted {len(memories)} memories", "success")
-                return memories
-            elif hasattr(auto_memory, 'process'):
-                result = auto_memory.process(text)
-                return result
+            # Use process_interaction which extracts AND stores memories
+            memories = auto_memory.process_interaction(
+                user_message=user_message or text,
+                assistant_response=text if user_message else None,
+                store=True  # Store the memories
+            )
+            if memories:
+                self.print_status(f"🧠 Extracted and stored {len(memories)} memories", "success")
+            return memories
         except Exception as e:
             self.log(f"Memory extraction failed: {e}", "error")
         
-        return {}
+        return []
     
     def store_memory(self, content: str, user_id: str = None, importance: float = 0.5) -> bool:
         """
@@ -127,13 +132,14 @@ class AutoMemoryHandler(FlagHandler):
             self.log(f"Memory storage failed: {e}", "error")
             return False
     
-    def post_process_result(self, result: Any, flag_value: Any) -> Any:
+    def post_process_result(self, result: Any, flag_value: Any, user_message: str = None) -> Any:
         """
         Post-process result to extract and store memories.
         
         Args:
             result: Agent output
             flag_value: Boolean or dict with configuration
+            user_message: Original user message for context
             
         Returns:
             Original result (memories are stored)
@@ -144,15 +150,18 @@ class AutoMemoryHandler(FlagHandler):
         user_id = None
         if isinstance(flag_value, dict):
             user_id = flag_value.get('user_id')
+            user_message = flag_value.get('user_message', user_message)
         
-        # Extract memories from result
+        # Extract and store memories from result
         text = str(result)
-        memories = self.extract_memories(text, user_id)
+        memories = self.extract_memories(text, user_id, user_message=user_message)
         
         if memories:
             self.print_status("\n🧠 Auto-extracted Memories:", "info")
-            for key, value in memories.items():
-                self.print_status(f"  • {key}: {value[:50]}...", "info")
+            for mem in memories:
+                mem_type = mem.get('type', 'unknown')
+                content = mem.get('content', str(mem))[:50]
+                self.print_status(f"  • {mem_type}: {content}...", "info")
         
         return result
     

src/praisonai/praisonai/cli/features/eval.py

@@ -26,42 +26,68 @@ class EvalHandler:
     
     def run_accuracy(
         self,
-        agent_file: str,
-        input_text: str,
-        expected_output: str,
+        agent_file: Optional[str] = None,
+        input_text: str = "",
+        expected_output: str = "",
         iterations: int = 1,
         model: Optional[str] = None,
-        output_file: Optional[str] = None
+        output_file: Optional[str] = None,
+        prompt: Optional[str] = None,
+        llm: Optional[str] = None
     ) -> Dict[str, Any]:
         """
         Run accuracy evaluation on an agent.
         
         Args:
-            agent_file: Path to agents.yaml file
+            agent_file: Path to agents.yaml file (optional if prompt is provided)
             input_text: Input to provide to the agent
             expected_output: Expected output to compare against
             iterations: Number of evaluation iterations
             model: LLM model for judging
             output_file: Path to save results
+            prompt: Direct prompt (alternative to agent_file)
+            llm: LLM model for the agent (when using prompt)
             
         Returns:
             Evaluation result dictionary
         """
         try:
             from praisonaiagents.eval import AccuracyEvaluator
-            from praisonai.agents_generator import AgentsGenerator
+            from praisonaiagents import Agent
         except ImportError as e:
             logger.error(f"Failed to import evaluation modules: {e}")
             return {"error": str(e)}
         
         try:
-            generator = AgentsGenerator(agent_file)
-            agents = generator.generate_agents()
-            
-            if not agents:
-                return {"error": "No agents found in configuration"}
-            
-            agent = agents[0] if isinstance(agents, list) else agents
+            # Create agent either from file or from prompt
+            if prompt:
+                # Direct prompt mode - create agent on the fly
+                agent = Agent(
+                    name="EvalAgent",
+                    role="Assistant",
+                    goal="Complete the given task",
+                    backstory="You are a helpful assistant.",
+                    llm=llm or model or "gpt-4o-mini",
+                    verbose=False
+                )
+                # Use prompt as input if input_text not provided
+                if not input_text:
+                    input_text = prompt
+            elif agent_file:
+                # Load from agents.yaml
+                try:
+                    from praisonai.agents_generator import AgentsGenerator
+                    generator = AgentsGenerator(agent_file)
+                    agents = generator.generate_agents()
+                    
+                    if not agents:
+                        return {"error": "No agents found in configuration"}
+                    
+                    agent = agents[0] if isinstance(agents, list) else agents
+                except Exception as e:
+                    return {"error": f"Failed to load agents from {agent_file}: {e}"}
+            else:
+                return {"error": "Either --agent or --prompt must be provided"}
             
             evaluator = AccuracyEvaluator(
                 agent=agent,
@@ -331,8 +357,16 @@ def handle_eval_command(args) -> None:
     handler = EvalHandler(verbose=getattr(args, 'verbose', False))
     
     eval_type = getattr(args, 'eval_type', 'accuracy')
-    agent_file = getattr(args, 'agent', 'agents.yaml')
+    agent_file = getattr(args, 'agent', None)
     output_file = getattr(args, 'output', None)
+    prompt = getattr(args, 'prompt', None)
+    llm = getattr(args, 'llm', None)
+    
+    # If no agent file and no prompt, check if agents.yaml exists
+    if not agent_file and not prompt:
+        import os
+        if os.path.exists('agents.yaml'):
+            agent_file = 'agents.yaml'
     
     if eval_type == 'accuracy':
         result = handler.run_accuracy(
@@ -341,7 +375,9 @@ def handle_eval_command(args) -> None:
             expected_output=getattr(args, 'expected', ''),
             iterations=getattr(args, 'iterations', 1),
             model=getattr(args, 'model', None),
-            output_file=output_file
+            output_file=output_file,
+            prompt=prompt,
+            llm=llm
         )
     elif eval_type == 'performance':
         result = handler.run_performance(
@@ -410,8 +446,10 @@ def add_eval_parser(subparsers) -> None:
         'accuracy',
         help='Run accuracy evaluation'
     )
-    accuracy_parser.add_argument('--agent', '-a', default='agents.yaml', help='Agent config file')
-    accuracy_parser.add_argument('--input', '-i', required=True, help='Input text')
+    accuracy_parser.add_argument('--agent', '-a', help='Agent config file (optional if --prompt used)')
+    accuracy_parser.add_argument('--prompt', '-p', help='Direct prompt (alternative to --agent)')
+    accuracy_parser.add_argument('--llm', help='LLM model for agent (when using --prompt)')
+    accuracy_parser.add_argument('--input', '-i', help='Input text (defaults to --prompt if not provided)')
     accuracy_parser.add_argument('--expected', '-e', required=True, help='Expected output')
     accuracy_parser.add_argument('--iterations', '-n', type=int, default=1, help='Number of iterations')
     accuracy_parser.add_argument('--model', '-m', help='Judge model')

src/praisonai/praisonai/cli/features/fast_context.py

@@ -69,6 +69,9 @@ class FastContextHandler(FlagHandler):
             query: Search query
             path: Path to search
             **kwargs: Additional search options
+                - use_llm: Use LLM for intelligent search (slower, default: False)
+                - max_turns: Max search turns for LLM mode
+                - model: LLM model for intelligent search
             
         Returns:
             List of matching results
@@ -86,15 +89,20 @@ class FastContextHandler(FlagHandler):
         try:
             from praisonaiagents import FastContext
             
+            # Use fast non-LLM search by default for better performance
+            use_llm = kwargs.get('use_llm', False)
+            
             fc = FastContext(
-                path=path,
+                workspace_path=path,
                 model=kwargs.get('model', 'gpt-4o-mini'),
-                max_turns=kwargs.get('max_turns', 4),
-                parallelism=kwargs.get('parallelism', 8),
-                timeout=kwargs.get('timeout', 30.0)
+                max_turns=kwargs.get('max_turns', 2),  # Reduced from 4 for speed
+                max_parallel=kwargs.get('parallelism', 8),
+                timeout=kwargs.get('timeout', 15.0),  # Reduced timeout
+                verbose=self.verbose
             )
             
-            result = fc.search(query)
+            # Use non-LLM search by default (much faster)
+            result = fc.search(query, use_llm=use_llm)
             
             # Convert to list of dicts
             matches = []

src/praisonai/praisonai/cli/features/guardrail.py

@@ -75,7 +75,7 @@ class GuardrailHandler(FlagHandler):
         
         Args:
             description: Natural language description of validation criteria
-            llm: Optional LLM instance for validation
+            llm: Optional LLM instance for validation (defaults to gpt-4o-mini)
             
         Returns:
             LLMGuardrail instance or None if unavailable
@@ -87,6 +87,12 @@ class GuardrailHandler(FlagHandler):
             )
             return None
         
+        # Provide default LLM if none specified
+        if llm is None:
+            import os
+            # Try to get from environment or use default
+            llm = os.environ.get('OPENAI_MODEL_NAME', 'gpt-4o-mini')
+        
         return LLMGuardrail(description=description, llm=llm)
     
     def validate_output(self, output: str, description: str, llm: Any = None) -> Tuple[bool, str]:

src/praisonai/praisonai/cli/features/profiling.py

@@ -0,0 +1,350 @@
+"""
+PraisonAI CLI Profiling Commands
+
+Provides CLI commands for profiling agent execution.
+
+Usage:
+    praisonai profile run "task" --output report.html
+    praisonai profile report --format json
+    praisonai profile benchmark --iterations 10
+"""
+
+import time
+import json
+from typing import Optional, Dict, Any
+
+
+class ProfilingHandler:
+    """
+    Handler for CLI profiling commands.
+    
+    Features:
+    - Run tasks with profiling enabled
+    - Generate reports (console, JSON, HTML)
+    - Benchmark agent performance
+    - Export flamegraphs
+    """
+    
+    def __init__(self):
+        self._profiler = None
+    
+    @property
+    def profiler(self):
+        """Lazy load profiler."""
+        if self._profiler is None:
+            from praisonai.profiler import Profiler
+            self._profiler = Profiler
+        return self._profiler
+    
+    def enable(self) -> None:
+        """Enable profiling."""
+        self.profiler.enable()
+    
+    def disable(self) -> None:
+        """Disable profiling."""
+        self.profiler.disable()
+    
+    def clear(self) -> None:
+        """Clear profiling data."""
+        self.profiler.clear()
+    
+    def run_with_profiling(self, task: str, config: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
+        """
+        Run a task with profiling enabled.
+        
+        Args:
+            task: Task description or prompt
+            config: Optional configuration dict
+            
+        Returns:
+            Dict with result and profiling data
+        """
+        self.profiler.enable()
+        self.profiler.clear()
+        
+        result = None
+        error = None
+        
+        try:
+            # Import and run PraisonAI
+            from praisonai import PraisonAI
+            
+            with self.profiler.block("total_execution"):
+                with self.profiler.block("initialization"):
+                    praison = PraisonAI(auto=task, **(config or {}))
+                
+                with self.profiler.block("agent_run"):
+                    result = praison.run()
+        except Exception as e:
+            error = str(e)
+        finally:
+            self.profiler.disable()
+        
+        return {
+            'result': result,
+            'error': error,
+            'summary': self.profiler.get_summary(),
+            'statistics': self.profiler.get_statistics()
+        }
+    
+    def report(self, format: str = "console", output: Optional[str] = None) -> str:
+        """
+        Generate profiling report.
+        
+        Args:
+            format: Output format (console, json, html)
+            output: Optional file path to save report
+            
+        Returns:
+            Report content as string
+        """
+        if format == "console":
+            content = self.profiler.report(output="console")
+        elif format == "json":
+            content = self.profiler.export_json()
+        elif format == "html":
+            content = self.profiler.export_html()
+        else:
+            raise ValueError(f"Unknown format: {format}")
+        
+        if output:
+            with open(output, 'w') as f:
+                f.write(content)
+            print(f"Report saved to: {output}")
+        
+        return content
+    
+    def benchmark(self, task: str, iterations: int = 5, 
+                  warmup: int = 1, config: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
+        """
+        Benchmark agent performance.
+        
+        Args:
+            task: Task to benchmark
+            iterations: Number of iterations
+            warmup: Number of warmup runs
+            config: Optional configuration
+            
+        Returns:
+            Benchmark results with statistics
+        """
+        from praisonai.profiler import Profiler
+        
+        times = []
+        errors = []
+        
+        # Warmup runs
+        for i in range(warmup):
+            print(f"Warmup {i + 1}/{warmup}...")
+            try:
+                self.run_with_profiling(task, config)
+            except Exception as e:
+                print(f"Warmup error: {e}")
+        
+        # Benchmark runs
+        for i in range(iterations):
+            print(f"Iteration {i + 1}/{iterations}...")
+            Profiler.clear()
+            
+            start = time.perf_counter()
+            try:
+                self.run_with_profiling(task, config)
+                duration_ms = (time.perf_counter() - start) * 1000
+                times.append(duration_ms)
+            except Exception as e:
+                errors.append(str(e))
+                duration_ms = (time.perf_counter() - start) * 1000
+                times.append(duration_ms)
+        
+        # Calculate statistics
+        if times:
+            sorted_times = sorted(times)
+            n = len(sorted_times)
+            
+            results = {
+                'iterations': iterations,
+                'successful': iterations - len(errors),
+                'failed': len(errors),
+                'times_ms': times,
+                'mean_ms': sum(times) / n,
+                'min_ms': min(times),
+                'max_ms': max(times),
+                'p50_ms': sorted_times[n // 2],
+                'p95_ms': sorted_times[int(n * 0.95)] if n > 1 else sorted_times[-1],
+                'p99_ms': sorted_times[int(n * 0.99)] if n > 1 else sorted_times[-1],
+                'errors': errors
+            }
+        else:
+            results = {
+                'iterations': iterations,
+                'successful': 0,
+                'failed': len(errors),
+                'errors': errors
+            }
+        
+        return results
+    
+    def export_flamegraph(self, filepath: str) -> None:
+        """Export flamegraph to SVG file."""
+        self.profiler.export_flamegraph(filepath)
+        print(f"Flamegraph saved to: {filepath}")
+    
+    def get_summary(self) -> Dict[str, Any]:
+        """Get profiling summary."""
+        return self.profiler.get_summary()
+    
+    def get_statistics(self, category: Optional[str] = None) -> Dict[str, float]:
+        """Get profiling statistics."""
+        return self.profiler.get_statistics(category)
+    
+    def print_summary(self) -> None:
+        """Print profiling summary to console."""
+        summary = self.get_summary()
+        stats = self.get_statistics()
+        
+        print("\n" + "=" * 60)
+        print("PROFILING SUMMARY")
+        print("=" * 60)
+        
+        print(f"\nTotal Time: {summary['total_time_ms']:.2f}ms")
+        print(f"Operations: {summary['timing_count']}")
+        print(f"Imports: {summary['import_count']}")
+        print(f"Flow Steps: {summary['flow_steps']}")
+        
+        print("\nStatistics:")
+        print(f"  P50 (Median): {stats['p50']:.2f}ms")
+        print(f"  P95: {stats['p95']:.2f}ms")
+        print(f"  P99: {stats['p99']:.2f}ms")
+        print(f"  Mean: {stats['mean']:.2f}ms")
+        print(f"  Std Dev: {stats['std_dev']:.2f}ms")
+        
+        print("\nSlowest Operations:")
+        for name, duration in summary['slowest_operations'][:5]:
+            print(f"  {name}: {duration:.2f}ms")
+        
+        print("=" * 60)
+
+
+def handle_profile_command(args) -> int:
+    """
+    Handle profile CLI command.
+    
+    Args:
+        args: Parsed command line arguments
+        
+    Returns:
+        Exit code (0 for success)
+    """
+    handler = ProfilingHandler()
+    
+    subcommand = getattr(args, 'profile_command', None)
+    
+    if subcommand == 'run':
+        task = getattr(args, 'task', '')
+        output = getattr(args, 'output', None)
+        format_type = getattr(args, 'format', 'console')
+        
+        print(f"Running with profiling: {task[:50]}...")
+        result = handler.run_with_profiling(task)
+        
+        if result['error']:
+            print(f"Error: {result['error']}")
+        
+        handler.report(format=format_type, output=output)
+        return 0 if not result['error'] else 1
+    
+    elif subcommand == 'report':
+        output = getattr(args, 'output', None)
+        format_type = getattr(args, 'format', 'console')
+        
+        handler.report(format=format_type, output=output)
+        return 0
+    
+    elif subcommand == 'benchmark':
+        task = getattr(args, 'task', '')
+        iterations = getattr(args, 'iterations', 5)
+        warmup = getattr(args, 'warmup', 1)
+        output = getattr(args, 'output', None)
+        
+        print(f"Benchmarking: {task[:50]}...")
+        results = handler.benchmark(task, iterations=iterations, warmup=warmup)
+        
+        print("\n" + "=" * 60)
+        print("BENCHMARK RESULTS")
+        print("=" * 60)
+        print(f"Iterations: {results['iterations']}")
+        print(f"Successful: {results['successful']}")
+        print(f"Failed: {results['failed']}")
+        
+        if 'mean_ms' in results:
+            print("\nTiming:")
+            print(f"  Mean: {results['mean_ms']:.2f}ms")
+            print(f"  Min: {results['min_ms']:.2f}ms")
+            print(f"  Max: {results['max_ms']:.2f}ms")
+            print(f"  P50: {results['p50_ms']:.2f}ms")
+            print(f"  P95: {results['p95_ms']:.2f}ms")
+        
+        if output:
+            with open(output, 'w') as f:
+                json.dump(results, f, indent=2)
+            print(f"\nResults saved to: {output}")
+        
+        return 0
+    
+    elif subcommand == 'flamegraph':
+        output = getattr(args, 'output', 'profile.svg')
+        handler.export_flamegraph(output)
+        return 0
+    
+    elif subcommand == 'summary':
+        handler.print_summary()
+        return 0
+    
+    else:
+        print("Usage: praisonai profile <run|report|benchmark|flamegraph|summary>")
+        return 1
+
+
+def add_profile_parser(subparsers) -> None:
+    """Add profile subcommand to argument parser."""
+    profile_parser = subparsers.add_parser(
+        'profile',
+        help='Profile agent execution'
+    )
+    
+    profile_subparsers = profile_parser.add_subparsers(dest='profile_command')
+    
+    # Run with profiling
+    run_parser = profile_subparsers.add_parser('run', help='Run task with profiling')
+    run_parser.add_argument('task', help='Task to run')
+    run_parser.add_argument('--output', '-o', help='Output file path')
+    run_parser.add_argument('--format', '-f', choices=['console', 'json', 'html'], 
+                           default='console', help='Output format')
+    
+    # Generate report
+    report_parser = profile_subparsers.add_parser('report', help='Generate profiling report')
+    report_parser.add_argument('--output', '-o', help='Output file path')
+    report_parser.add_argument('--format', '-f', choices=['console', 'json', 'html'],
+                              default='console', help='Output format')
+    
+    # Benchmark
+    bench_parser = profile_subparsers.add_parser('benchmark', help='Benchmark agent performance')
+    bench_parser.add_argument('task', help='Task to benchmark')
+    bench_parser.add_argument('--iterations', '-n', type=int, default=5, help='Number of iterations')
+    bench_parser.add_argument('--warmup', '-w', type=int, default=1, help='Warmup runs')
+    bench_parser.add_argument('--output', '-o', help='Output file for results')
+    
+    # Flamegraph
+    flame_parser = profile_subparsers.add_parser('flamegraph', help='Export flamegraph')
+    flame_parser.add_argument('--output', '-o', default='profile.svg', help='Output SVG file')
+    
+    # Summary
+    profile_subparsers.add_parser('summary', help='Print profiling summary')  # noqa: F841
+
+
+# Exports
+__all__ = [
+    'ProfilingHandler',
+    'handle_profile_command',
+    'add_profile_parser',
+]

src/praisonai/praisonai/cli/main.py

@@ -5,6 +5,42 @@ import argparse
 import warnings
 import os
 
+# Suppress Pydantic serialization warnings from LiteLLM BEFORE any imports
+# These warnings occur when LiteLLM's response objects have field mismatches
+# Using both filterwarnings AND patching warnings.warn for complete suppression
+
+warnings.filterwarnings("ignore", message=".*Pydantic serializer warnings.*")
+warnings.filterwarnings("ignore", message=".*PydanticSerializationUnexpectedValue.*")
+warnings.filterwarnings("ignore", message=".*Expected \\d+ fields but got.*")
+warnings.filterwarnings("ignore", message=".*Expected `StreamingChoices`.*")
+warnings.filterwarnings("ignore", message=".*Expected `Message`.*")
+warnings.filterwarnings("ignore", message=".*serialized value may not be as expected.*")
+warnings.filterwarnings("ignore", category=UserWarning, module="pydantic.*")
+
+# Patch warnings.showwarning to intercept ALL warnings including those from crewai's patched warn
+# This is the final output function that actually displays warnings
+_SUPPRESSED_PATTERNS = [
+    "Pydantic serializer warnings",
+    "PydanticSerializationUnexpectedValue",
+    "Expected",  # Catches "Expected N fields but got M"
+    "StreamingChoices",
+    "serialized value may not be as expected",
+    "duckduckgo_search",  # Suppress duckduckgo rename warning
+]
+
+_original_showwarning = warnings.showwarning
+
+def _patched_showwarning(message, category, filename, lineno, file=None, line=None):
+    msg_str = str(message)
+    for pattern in _SUPPRESSED_PATTERNS:
+        if pattern in msg_str:
+            return
+    if category is UserWarning and "pydantic" in filename.lower():
+        return
+    _original_showwarning(message, category, filename, lineno, file, line)
+
+warnings.showwarning = _patched_showwarning
+
 # Suppress crewai RuntimeWarning about module loading order (only in non-debug mode)
 # This warning is harmless and occurs when running as `python -m praisonai.cli.main`
 if os.environ.get('LOGLEVEL', 'INFO').upper() != 'DEBUG':
@@ -1342,12 +1378,47 @@ class PraisonAI:
                 
                 console.print(table)
                 
-                # Show recent memories
+                # Show recent short-term memories
                 print("\n[bold]Recent Short-term Memories:[/bold]")
                 short_term = memory.get_short_term(limit=5)
-                for i, item in enumerate(short_term, 1):
-                    content = item.get('content', str(item))[:100]
-                    print(f"  {i}. {content}")
+                if short_term:
+                    for i, item in enumerate(short_term, 1):
+                        content = item.get('content', str(item))[:100]
+                        print(f"  {i}. {content}")
+                else:
+                    print("  [dim]No short-term memories[/dim]")
+                
+                # Show long-term memories
+                print("\n[bold]Long-term Memories:[/bold]")
+                long_term = memory.get_long_term(limit=10)
+                if long_term:
+                    for i, item in enumerate(long_term, 1):
+                        # Handle both dict and MemoryItem objects
+                        if hasattr(item, 'content'):
+                            content = str(item.content)[:100]
+                            importance = getattr(item, 'importance', 0)
+                        else:
+                            content = item.get('content', str(item))[:100]
+                            importance = item.get('importance', 0)
+                        print(f"  {i}. [{importance:.1f}] {content}")
+                else:
+                    print("  [dim]No long-term memories[/dim]")
+                
+                # Show entities
+                print("\n[bold]Entities:[/bold]")
+                entities = memory.get_all_entities()
+                if entities:
+                    for entity in entities[:10]:
+                        # Handle both dict and Entity objects
+                        if hasattr(entity, 'name'):
+                            name = entity.name
+                            entity_type = getattr(entity, 'entity_type', 'unknown')
+                        else:
+                            name = entity.get('name', 'Unknown')
+                            entity_type = entity.get('entity_type', 'unknown')
+                        print(f"  • {name} ({entity_type})")
+                else:
+                    print("  [dim]No entities[/dim]")
                 
             elif action == 'add':
                 if not action_args:
@@ -3118,9 +3189,8 @@ Provide ONLY the commit message, no explanations."""
                     telemetry = TelemetryHandler(verbose=getattr(self.args, 'verbose', False))
                     telemetry.enable()
                 
-                # Auto Memory - Automatic memory extraction
+                # Auto Memory - Automatic memory extraction (handled post-processing, not as Agent param)
                 if getattr(self.args, 'auto_memory', False):
-                    agent_config["auto_memory"] = True
                     print("[bold cyan]Auto Memory enabled - will extract and store memories[/bold cyan]")
                 
                 # MCP - Model Context Protocol tools
@@ -3266,7 +3336,10 @@ Provide ONLY the commit message, no explanations."""
             if hasattr(self, 'args') and getattr(self.args, 'auto_memory', False):
                 from .features.auto_memory import AutoMemoryHandler
                 auto_mem = AutoMemoryHandler(verbose=getattr(self.args, 'verbose', False))
-                auto_mem.post_process_result(result, {'user_id': getattr(self.args, 'user_id', None)})
+                auto_mem.post_process_result(
+                    result, 
+                    {'user_id': getattr(self.args, 'user_id', None), 'user_message': prompt}
+                )
             
             # Todo - Generate todo list from response
             if hasattr(self, 'args') and getattr(self.args, 'todo', False):
@@ -4668,16 +4741,14 @@ Provide a concise summary (max 200 words):"""
     
     def _process_interactive_prompt_async(self, prompt, tools_list, console, session_state=None):
         """
-        Process a prompt asynchronously with live status display.
+        Process a prompt asynchronously with non-blocking status display.
         
-        Shows real-time status (tools, commands) while processing in background.
+        Shows status updates without blocking user input.
         Allows user to queue more messages while processing.
         """
         import threading
         import time
-        from rich.live import Live
-        from rich.panel import Panel
-        from rich.text import Text
+        import sys
         from praisonai.cli.features.message_queue import ProcessingState
         
         state_manager = session_state['state_manager']
@@ -4689,8 +4760,11 @@ Provide a concise summary (max 200 words):"""
         live_status.clear()
         live_status.update_status("Thinking...")
         
+        # Print initial status (non-blocking - user can still type)
+        console.print(f"[dim cyan]⏳ {live_status.current_status}[/dim cyan]")
+        
         # Result container for thread communication
-        result_container = {'response': None, 'error': None, 'done': False}
+        result_container = {'response': None, 'error': None, 'done': False, 'last_status': ''}
         
         def process_in_background():
             """Run agent processing in background thread."""
@@ -4736,37 +4810,36 @@ Provide a concise summary (max 200 words):"""
         bg_thread = threading.Thread(target=process_in_background, daemon=True)
         bg_thread.start()
         
-        # Show live status while processing
-        try:
-            with Live(console=console, refresh_per_second=4, transient=True) as live:
-                while not result_container['done']:
-                    # Build status display
-                    status_text = Text()
-                    status_text.append("⏳ ", style="cyan")
-                    status_text.append(live_status.current_status or "Processing...", style="dim")
-                    
-                    # Show queued messages if any
-                    queue_count = message_queue.count
-                    if queue_count > 0:
-                        status_text.append(f"\n📋 Queued: {queue_count}", style="dim yellow")
-                    
-                    live.update(Panel(status_text, border_style="dim"))
-                    time.sleep(0.1)
-        except KeyboardInterrupt:
-            console.print("\n[dim]Processing interrupted[/dim]")
+        # Wait for completion with periodic status updates (non-blocking style)
+        start_time = time.time()
+        last_status_update = ""
+        while not result_container['done']:
+            time.sleep(0.2)
+            current_status = live_status.current_status
+            # Only print status if it changed
+            if current_status != last_status_update:
+                # Use carriage return to update in place
+                elapsed = time.time() - start_time
+                queue_count = message_queue.count
+                queue_info = f" | 📋 Queued: {queue_count}" if queue_count > 0 else ""
+                sys.stdout.write(f"\r[dim cyan]⏳ {current_status} ({elapsed:.1f}s){queue_info}[/dim cyan]")
+                sys.stdout.flush()
+                last_status_update = current_status
+        
+        # Clear the status line
+        sys.stdout.write("\r" + " " * 80 + "\r")
+        sys.stdout.flush()
         
         # Wait for thread to finish
         bg_thread.join(timeout=1.0)
         
         # Handle result
         if result_container['error']:
-            console.print(f"\n[red]Error: {result_container['error']}[/red]")
+            console.print(f"[red]Error: {result_container['error']}[/red]")
         elif result_container['response']:
             # Print response with streaming effect
-            console.print()
             response_str = result_container['response']
             words = response_str.split()
-            import sys
             for i, word in enumerate(words):
                 console.print(word + " ", end="")
                 sys.stdout.flush()

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==2.3.74 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.3.77 gunicorn markdown\n")
             file.write("EXPOSE 8080\n")
             file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
             

src/praisonai/praisonai/profiler.py

@@ -8,7 +8,14 @@ Features:
 - Function execution timing
 - Flow tracking
 - File/module usage tracking
-- Memory usage (optional)
+- Memory usage (tracemalloc)
+- API call profiling (wall-clock time)
+- Streaming profiling (TTFT, total time)
+- Statistics (p50, p95, p99)
+- cProfile integration
+- Flamegraph generation
+- Line-level profiling
+- JSON/HTML export
 
 Usage:
     from praisonai.profiler import Profiler, profile, profile_imports
@@ -22,12 +29,27 @@ Usage:
     with Profiler.block("my_operation"):
         do_something()
     
+    # Profile API calls
+    with Profiler.api_call("https://api.example.com") as call:
+        response = requests.get(...)
+    
+    # Profile streaming
+    with Profiler.streaming("chat") as tracker:
+        tracker.first_token()
+        for chunk in stream:
+            tracker.chunk()
+    
     # Profile imports
     with profile_imports():
         import heavy_module
     
-    # Get report
+    # Get report with statistics
     Profiler.report()
+    stats = Profiler.get_statistics()
+    
+    # Export
+    Profiler.export_json()
+    Profiler.export_html()
 """
 
 import time
@@ -35,9 +57,15 @@ import functools
 import threading
 import sys
 import os
-from dataclasses import dataclass, field
+import json
+import tracemalloc
+import cProfile
+import pstats
+import io
+import statistics
+from dataclasses import dataclass, field, asdict
 from typing import Dict, List, Optional, Callable, Any
-from contextlib import contextmanager
+from contextlib import contextmanager, asynccontextmanager
 
 
 # ============================================================================
@@ -55,6 +83,38 @@ class TimingRecord:
     timestamp: float = field(default_factory=time.time)
 
 
+@dataclass
+class APICallRecord:
+    """Record of an API/HTTP call."""
+    endpoint: str
+    method: str
+    duration_ms: float
+    status_code: int = 0
+    request_size: int = 0
+    response_size: int = 0
+    timestamp: float = field(default_factory=time.time)
+
+
+@dataclass
+class StreamingRecord:
+    """Record of streaming operation (LLM responses)."""
+    name: str
+    ttft_ms: float  # Time to first token
+    total_ms: float
+    chunk_count: int = 0
+    total_tokens: int = 0
+    timestamp: float = field(default_factory=time.time)
+
+
+@dataclass
+class MemoryRecord:
+    """Record of memory usage."""
+    name: str
+    current_kb: float
+    peak_kb: float
+    timestamp: float = field(default_factory=time.time)
+
+
 @dataclass
 class ImportRecord:
     """Record of a module import."""
@@ -75,6 +135,80 @@ class FlowRecord:
     timestamp: float = field(default_factory=time.time)
 
 
+# ============================================================================
+# Streaming Tracker
+# ============================================================================
+
+class StreamingTracker:
+    """
+    Track streaming operations (LLM responses).
+    
+    Usage:
+        tracker = StreamingTracker("chat")
+        tracker.start()
+        tracker.first_token()  # Mark TTFT
+        for chunk in stream:
+            tracker.chunk()
+        tracker.end(total_tokens=100)
+    """
+    
+    def __init__(self, name: str):
+        self.name = name
+        self._start_time: Optional[float] = None
+        self._first_token_time: Optional[float] = None
+        self._end_time: Optional[float] = None
+        self._chunk_count: int = 0
+        self._total_tokens: int = 0
+    
+    def start(self) -> None:
+        """Start tracking."""
+        self._start_time = time.perf_counter()
+    
+    def first_token(self) -> None:
+        """Mark time to first token."""
+        if self._first_token_time is None:
+            self._first_token_time = time.perf_counter()
+    
+    def chunk(self) -> None:
+        """Record a chunk received."""
+        self._chunk_count += 1
+    
+    def end(self, total_tokens: int = 0) -> None:
+        """End tracking and record to Profiler."""
+        self._end_time = time.perf_counter()
+        self._total_tokens = total_tokens
+        
+        if self._start_time is not None:
+            ttft_ms = 0.0
+            if self._first_token_time is not None:
+                ttft_ms = (self._first_token_time - self._start_time) * 1000
+            
+            total_ms = (self._end_time - self._start_time) * 1000
+            
+            Profiler.record_streaming(
+                name=self.name,
+                ttft_ms=ttft_ms,
+                total_ms=total_ms,
+                chunk_count=self._chunk_count,
+                total_tokens=self._total_tokens
+            )
+    
+    @property
+    def ttft_ms(self) -> float:
+        """Get time to first token in ms."""
+        if self._start_time and self._first_token_time:
+            return (self._first_token_time - self._start_time) * 1000
+        return 0.0
+    
+    @property
+    def elapsed_ms(self) -> float:
+        """Get elapsed time in ms."""
+        if self._start_time:
+            end = self._end_time or time.perf_counter()
+            return (end - self._start_time) * 1000
+        return 0.0
+
+
 # ============================================================================
 # Profiler Class
 # ============================================================================
@@ -84,6 +218,16 @@ class Profiler:
     Centralized profiler for performance monitoring.
     
     Thread-safe singleton pattern for global access.
+    
+    Features:
+    - Function/block timing
+    - API call profiling (wall-clock)
+    - Streaming profiling (TTFT)
+    - Memory profiling
+    - Import timing
+    - Statistics (p50, p95, p99)
+    - cProfile integration
+    - Export (JSON, HTML)
     """
     
     _instance: Optional['Profiler'] = None
@@ -93,9 +237,14 @@ class Profiler:
     _timings: List[TimingRecord] = []
     _imports: List[ImportRecord] = []
     _flow: List[FlowRecord] = []
+    _api_calls: List[APICallRecord] = []
+    _streaming: List[StreamingRecord] = []
+    _memory: List[MemoryRecord] = []
     _enabled: bool = False
     _flow_step: int = 0
     _files_accessed: Dict[str, int] = {}
+    _line_profile_data: Dict[str, Any] = {}
+    _cprofile_stats: List[Dict[str, Any]] = []
     
     def __new__(cls):
         if cls._instance is None:
@@ -126,8 +275,13 @@ class Profiler:
             cls._timings.clear()
             cls._imports.clear()
             cls._flow.clear()
+            cls._api_calls.clear()
+            cls._streaming.clear()
+            cls._memory.clear()
             cls._flow_step = 0
             cls._files_accessed.clear()
+            cls._line_profile_data.clear()
+            cls._cprofile_stats.clear()
     
     @classmethod
     def record_timing(cls, name: str, duration_ms: float, category: str = "function", 
@@ -295,6 +449,433 @@ class Profiler:
             print(report_text)
         
         return report_text
+    
+    # ========================================================================
+    # API Call Profiling
+    # ========================================================================
+    
+    @classmethod
+    def record_api_call(cls, endpoint: str, method: str, duration_ms: float,
+                        status_code: int = 0, request_size: int = 0,
+                        response_size: int = 0) -> None:
+        """Record an API/HTTP call timing."""
+        if not cls.is_enabled():
+            return
+        
+        with cls._lock:
+            cls._api_calls.append(APICallRecord(
+                endpoint=endpoint,
+                method=method,
+                duration_ms=duration_ms,
+                status_code=status_code,
+                request_size=request_size,
+                response_size=response_size
+            ))
+    
+    @classmethod
+    def get_api_calls(cls) -> List[APICallRecord]:
+        """Get API call records."""
+        with cls._lock:
+            return cls._api_calls.copy()
+    
+    @classmethod
+    @contextmanager
+    def api_call(cls, endpoint: str, method: str = "GET"):
+        """Context manager for profiling API calls."""
+        start = time.perf_counter()
+        call_info = {'status_code': 0, 'request_size': 0, 'response_size': 0}
+        
+        try:
+            yield call_info
+        finally:
+            duration_ms = (time.perf_counter() - start) * 1000
+            cls.record_api_call(
+                endpoint=endpoint,
+                method=method,
+                duration_ms=duration_ms,
+                status_code=call_info.get('status_code', 0),
+                request_size=call_info.get('request_size', 0),
+                response_size=call_info.get('response_size', 0)
+            )
+    
+    # ========================================================================
+    # Streaming Profiling
+    # ========================================================================
+    
+    @classmethod
+    def record_streaming(cls, name: str, ttft_ms: float, total_ms: float,
+                         chunk_count: int = 0, total_tokens: int = 0) -> None:
+        """Record streaming metrics."""
+        if not cls.is_enabled():
+            return
+        
+        with cls._lock:
+            cls._streaming.append(StreamingRecord(
+                name=name,
+                ttft_ms=ttft_ms,
+                total_ms=total_ms,
+                chunk_count=chunk_count,
+                total_tokens=total_tokens
+            ))
+    
+    @classmethod
+    def get_streaming_records(cls) -> List[StreamingRecord]:
+        """Get streaming records."""
+        with cls._lock:
+            return cls._streaming.copy()
+    
+    @classmethod
+    @contextmanager
+    def streaming(cls, name: str):
+        """Context manager for profiling streaming operations."""
+        tracker = StreamingTracker(name)
+        tracker.start()
+        try:
+            yield tracker
+        finally:
+            tracker.end()
+    
+    @classmethod
+    @asynccontextmanager
+    async def streaming_async(cls, name: str):
+        """Async context manager for profiling streaming operations."""
+        tracker = StreamingTracker(name)
+        tracker.start()
+        try:
+            yield tracker
+        finally:
+            tracker.end()
+    
+    # ========================================================================
+    # Memory Profiling
+    # ========================================================================
+    
+    @classmethod
+    def record_memory(cls, name: str, current_kb: float, peak_kb: float) -> None:
+        """Record memory usage."""
+        if not cls.is_enabled():
+            return
+        
+        with cls._lock:
+            cls._memory.append(MemoryRecord(
+                name=name,
+                current_kb=current_kb,
+                peak_kb=peak_kb
+            ))
+    
+    @classmethod
+    def get_memory_records(cls) -> List[MemoryRecord]:
+        """Get memory records."""
+        with cls._lock:
+            return cls._memory.copy()
+    
+    @classmethod
+    @contextmanager
+    def memory(cls, name: str):
+        """Context manager for profiling memory usage."""
+        tracemalloc.start()
+        try:
+            yield
+        finally:
+            current, peak = tracemalloc.get_traced_memory()
+            tracemalloc.stop()
+            cls.record_memory(name, current / 1024, peak / 1024)
+    
+    @classmethod
+    def memory_snapshot(cls) -> Dict[str, float]:
+        """Take a memory snapshot."""
+        tracemalloc.start()
+        current, peak = tracemalloc.get_traced_memory()
+        tracemalloc.stop()
+        return {
+            'current_kb': current / 1024,
+            'peak_kb': peak / 1024
+        }
+    
+    # ========================================================================
+    # Statistics
+    # ========================================================================
+    
+    @classmethod
+    def get_statistics(cls, category: Optional[str] = None) -> Dict[str, float]:
+        """
+        Get statistical analysis of timing data.
+        
+        Returns p50, p95, p99, mean, std_dev, min, max.
+        """
+        with cls._lock:
+            if category:
+                durations = [t.duration_ms for t in cls._timings if t.category == category]
+            else:
+                durations = [t.duration_ms for t in cls._timings]
+            
+            if not durations:
+                return {
+                    'p50': 0.0, 'p95': 0.0, 'p99': 0.0,
+                    'mean': 0.0, 'std_dev': 0.0, 'min': 0.0, 'max': 0.0,
+                    'count': 0
+                }
+            
+            sorted_durations = sorted(durations)
+            n = len(sorted_durations)
+            
+            def percentile(p: float) -> float:
+                idx = int(n * p / 100)
+                return sorted_durations[min(idx, n - 1)]
+            
+            mean = statistics.mean(durations)
+            std_dev = statistics.stdev(durations) if n > 1 else 0.0
+            
+            return {
+                'p50': percentile(50),
+                'p95': percentile(95),
+                'p99': percentile(99),
+                'mean': mean,
+                'std_dev': std_dev,
+                'min': min(durations),
+                'max': max(durations),
+                'count': n
+            }
+    
+    # ========================================================================
+    # cProfile Integration
+    # ========================================================================
+    
+    @classmethod
+    @contextmanager
+    def cprofile(cls, name: str):
+        """Context manager for cProfile profiling."""
+        pr = cProfile.Profile()
+        pr.enable()
+        
+        try:
+            yield pr
+        finally:
+            pr.disable()
+            
+            # Store stats
+            s = io.StringIO()
+            ps = pstats.Stats(pr, stream=s).sort_stats('cumulative')
+            ps.print_stats(30)
+            
+            with cls._lock:
+                cls._cprofile_stats.append({
+                    'name': name,
+                    'stats': s.getvalue(),
+                    'total_calls': ps.total_calls if hasattr(ps, 'total_calls') else 0,
+                    'timestamp': time.time()
+                })
+    
+    @classmethod
+    def get_cprofile_stats(cls) -> List[Dict[str, Any]]:
+        """Get cProfile statistics."""
+        with cls._lock:
+            return cls._cprofile_stats.copy()
+    
+    # ========================================================================
+    # Line-Level Profiling
+    # ========================================================================
+    
+    @classmethod
+    def get_line_profile_data(cls) -> Dict[str, Any]:
+        """Get line-level profiling data."""
+        with cls._lock:
+            return cls._line_profile_data.copy()
+    
+    @classmethod
+    def set_line_profile_data(cls, func_name: str, data: Any) -> None:
+        """Store line-level profiling data."""
+        if not cls.is_enabled():
+            return
+        with cls._lock:
+            cls._line_profile_data[func_name] = data
+    
+    # ========================================================================
+    # Flamegraph
+    # ========================================================================
+    
+    @classmethod
+    def get_flamegraph_data(cls) -> List[Dict[str, Any]]:
+        """
+        Generate flamegraph-compatible data from flow records.
+        
+        Returns list of {name, value, children} for flamegraph visualization.
+        """
+        with cls._lock:
+            # Convert flow records to flamegraph format
+            data = []
+            for record in cls._flow:
+                data.append({
+                    'name': record.name,
+                    'value': record.duration_ms,
+                    'file': record.file,
+                    'line': record.line
+                })
+            return data
+    
+    @classmethod
+    def export_flamegraph(cls, filepath: str) -> None:
+        """
+        Export flamegraph to SVG file.
+        
+        Note: Requires flamegraph data. For full flamegraph support,
+        use py-spy: py-spy record -o profile.svg -- python script.py
+        """
+        data = cls.get_flamegraph_data()
+        
+        # Generate simple SVG flamegraph
+        svg_content = cls._generate_simple_flamegraph_svg(data)
+        
+        with open(filepath, 'w') as f:
+            f.write(svg_content)
+    
+    @classmethod
+    def _generate_simple_flamegraph_svg(cls, data: List[Dict[str, Any]]) -> str:
+        """Generate a simple SVG flamegraph."""
+        if not data:
+            return '<svg xmlns="http://www.w3.org/2000/svg" width="800" height="100"><text x="10" y="50">No profiling data</text></svg>'
+        
+        total_time = sum(d['value'] for d in data)
+        if total_time == 0:
+            total_time = 1
+        
+        width = 800
+        height = max(100, len(data) * 25 + 50)
+        
+        svg_parts = [
+            f'<svg xmlns="http://www.w3.org/2000/svg" width="{width}" height="{height}">',
+            '<style>rect:hover { opacity: 0.8; } text { font-family: monospace; font-size: 12px; }</style>',
+            f'<text x="10" y="20">PraisonAI Profiling Flamegraph (Total: {total_time:.2f}ms)</text>'
+        ]
+        
+        y = 40
+        for item in sorted(data, key=lambda x: x['value'], reverse=True)[:20]:
+            bar_width = max(10, (item['value'] / total_time) * (width - 20))
+            color = f'hsl({int(item["value"] / total_time * 120)}, 70%, 50%)'
+            
+            svg_parts.append(
+                f'<rect x="10" y="{y}" width="{bar_width}" height="20" fill="{color}" />'
+            )
+            svg_parts.append(
+                f'<text x="15" y="{y + 15}">{item["name"]}: {item["value"]:.2f}ms</text>'
+            )
+            y += 25
+        
+        svg_parts.append('</svg>')
+        return '\n'.join(svg_parts)
+    
+    # ========================================================================
+    # Export Functions
+    # ========================================================================
+    
+    @classmethod
+    def export_json(cls) -> str:
+        """Export profiling data as JSON."""
+        # Get summary and stats first (they acquire their own locks)
+        summary = cls.get_summary()
+        stats = cls.get_statistics()
+        
+        with cls._lock:
+            data = {
+                'summary': summary,
+                'statistics': stats,
+                'timings': [asdict(t) for t in cls._timings],
+                'api_calls': [asdict(a) for a in cls._api_calls],
+                'streaming': [asdict(s) for s in cls._streaming],
+                'memory': [asdict(m) for m in cls._memory],
+                'imports': [asdict(i) for i in cls._imports],
+                'flow': [asdict(f) for f in cls._flow]
+            }
+            return json.dumps(data, indent=2, default=str)
+    
+    @classmethod
+    def export_html(cls) -> str:
+        """Export profiling data as HTML report."""
+        summary = cls.get_summary()
+        stats = cls.get_statistics()
+        
+        html = f'''<!DOCTYPE html>
+<html>
+<head>
+    <title>PraisonAI Profiling Report</title>
+    <style>
+        body {{ font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; margin: 20px; }}
+        h1 {{ color: #333; }}
+        h2 {{ color: #666; border-bottom: 1px solid #ddd; padding-bottom: 5px; }}
+        table {{ border-collapse: collapse; width: 100%; margin: 10px 0; }}
+        th, td {{ border: 1px solid #ddd; padding: 8px; text-align: left; }}
+        th {{ background-color: #f5f5f5; }}
+        .metric {{ display: inline-block; margin: 10px; padding: 15px; background: #f9f9f9; border-radius: 5px; }}
+        .metric-value {{ font-size: 24px; font-weight: bold; color: #0066cc; }}
+        .metric-label {{ font-size: 12px; color: #666; }}
+    </style>
+</head>
+<body>
+    <h1>PraisonAI Profiling Report</h1>
+    
+    <h2>Summary</h2>
+    <div class="metric">
+        <div class="metric-value">{summary['total_time_ms']:.2f}ms</div>
+        <div class="metric-label">Total Time</div>
+    </div>
+    <div class="metric">
+        <div class="metric-value">{summary['timing_count']}</div>
+        <div class="metric-label">Operations</div>
+    </div>
+    <div class="metric">
+        <div class="metric-value">{len(cls._api_calls)}</div>
+        <div class="metric-label">API Calls</div>
+    </div>
+    <div class="metric">
+        <div class="metric-value">{len(cls._streaming)}</div>
+        <div class="metric-label">Streams</div>
+    </div>
+    
+    <h2>Statistics</h2>
+    <table>
+        <tr><th>Metric</th><th>Value</th></tr>
+        <tr><td>P50 (Median)</td><td>{stats['p50']:.2f}ms</td></tr>
+        <tr><td>P95</td><td>{stats['p95']:.2f}ms</td></tr>
+        <tr><td>P99</td><td>{stats['p99']:.2f}ms</td></tr>
+        <tr><td>Mean</td><td>{stats['mean']:.2f}ms</td></tr>
+        <tr><td>Std Dev</td><td>{stats['std_dev']:.2f}ms</td></tr>
+        <tr><td>Min</td><td>{stats['min']:.2f}ms</td></tr>
+        <tr><td>Max</td><td>{stats['max']:.2f}ms</td></tr>
+    </table>
+    
+    <h2>Slowest Operations</h2>
+    <table>
+        <tr><th>Operation</th><th>Duration (ms)</th></tr>
+        {''.join(f"<tr><td>{name}</td><td>{dur:.2f}</td></tr>" for name, dur in summary['slowest_operations'])}
+    </table>
+    
+    <h2>API Calls</h2>
+    <table>
+        <tr><th>Endpoint</th><th>Method</th><th>Duration (ms)</th><th>Status</th></tr>
+        {''.join(f"<tr><td>{a.endpoint}</td><td>{a.method}</td><td>{a.duration_ms:.2f}</td><td>{a.status_code}</td></tr>" for a in cls._api_calls[:20])}
+    </table>
+    
+    <h2>Streaming</h2>
+    <table>
+        <tr><th>Name</th><th>TTFT (ms)</th><th>Total (ms)</th><th>Chunks</th><th>Tokens</th></tr>
+        {''.join(f"<tr><td>{s.name}</td><td>{s.ttft_ms:.2f}</td><td>{s.total_ms:.2f}</td><td>{s.chunk_count}</td><td>{s.total_tokens}</td></tr>" for s in cls._streaming[:20])}
+    </table>
+</body>
+</html>'''
+        return html
+    
+    @classmethod
+    def export_to_file(cls, filepath: str, format: str = "json") -> None:
+        """Export profiling data to file."""
+        if format == "json":
+            content = cls.export_json()
+        elif format == "html":
+            content = cls.export_html()
+        else:
+            raise ValueError(f"Unknown format: {format}")
+        
+        with open(filepath, 'w') as f:
+            f.write(content)
 
 
 # ============================================================================
@@ -422,6 +1003,161 @@ def profile_imports():
     return ImportProfiler()
 
 
+# ============================================================================
+# API Profiling Decorators
+# ============================================================================
+
+def profile_api(func: Optional[Callable] = None, *, endpoint: str = ""):
+    """
+    Decorator to profile a function as an API call.
+    
+    Usage:
+        @profile_api(endpoint="openai/chat")
+        def call_openai():
+            pass
+    """
+    def decorator(fn: Callable) -> Callable:
+        @functools.wraps(fn)
+        def wrapper(*args, **kwargs):
+            if not Profiler.is_enabled():
+                return fn(*args, **kwargs)
+            
+            ep = endpoint or fn.__name__
+            start = time.perf_counter()
+            try:
+                return fn(*args, **kwargs)
+            finally:
+                duration_ms = (time.perf_counter() - start) * 1000
+                Profiler.record_api_call(ep, "CALL", duration_ms)
+        
+        return wrapper
+    
+    if func is not None:
+        return decorator(func)
+    return decorator
+
+
+def profile_api_async(func: Optional[Callable] = None, *, endpoint: str = ""):
+    """
+    Decorator to profile an async function as an API call.
+    """
+    def decorator(fn: Callable) -> Callable:
+        @functools.wraps(fn)
+        async def wrapper(*args, **kwargs):
+            if not Profiler.is_enabled():
+                return await fn(*args, **kwargs)
+            
+            ep = endpoint or fn.__name__
+            start = time.perf_counter()
+            try:
+                return await fn(*args, **kwargs)
+            finally:
+                duration_ms = (time.perf_counter() - start) * 1000
+                Profiler.record_api_call(ep, "CALL", duration_ms)
+        
+        return wrapper
+    
+    if func is not None:
+        return decorator(func)
+    return decorator
+
+
+# ============================================================================
+# cProfile Decorator
+# ============================================================================
+
+def profile_detailed(func: Optional[Callable] = None):
+    """
+    Decorator for detailed cProfile profiling.
+    
+    Usage:
+        @profile_detailed
+        def heavy_computation():
+            pass
+    """
+    def decorator(fn: Callable) -> Callable:
+        @functools.wraps(fn)
+        def wrapper(*args, **kwargs):
+            if not Profiler.is_enabled():
+                return fn(*args, **kwargs)
+            
+            pr = cProfile.Profile()
+            pr.enable()
+            try:
+                return fn(*args, **kwargs)
+            finally:
+                pr.disable()
+                s = io.StringIO()
+                ps = pstats.Stats(pr, stream=s).sort_stats('cumulative')
+                ps.print_stats(20)
+                Profiler._cprofile_stats.append({
+                    'name': fn.__name__,
+                    'stats': s.getvalue(),
+                    'timestamp': time.time()
+                })
+        
+        return wrapper
+    
+    if func is not None:
+        return decorator(func)
+    return decorator
+
+
+# ============================================================================
+# Line-Level Profiling Decorator
+# ============================================================================
+
+def profile_lines(func: Optional[Callable] = None):
+    """
+    Decorator for line-level profiling.
+    
+    Note: Requires line_profiler package for full functionality.
+    Falls back to basic timing if not available.
+    
+    Usage:
+        @profile_lines
+        def my_function():
+            pass
+    """
+    def decorator(fn: Callable) -> Callable:
+        @functools.wraps(fn)
+        def wrapper(*args, **kwargs):
+            if not Profiler.is_enabled():
+                return fn(*args, **kwargs)
+            
+            # Try to use line_profiler if available
+            try:
+                from line_profiler import LineProfiler
+                lp = LineProfiler()
+                lp.add_function(fn)
+                lp.enable()
+                try:
+                    result = fn(*args, **kwargs)
+                finally:
+                    lp.disable()
+                    s = io.StringIO()
+                    lp.print_stats(stream=s)
+                    Profiler.set_line_profile_data(fn.__name__, s.getvalue())
+                return result
+            except ImportError:
+                # Fallback to basic timing
+                start = time.perf_counter()
+                try:
+                    return fn(*args, **kwargs)
+                finally:
+                    duration_ms = (time.perf_counter() - start) * 1000
+                    Profiler.set_line_profile_data(fn.__name__, {
+                        'note': 'line_profiler not installed',
+                        'total_ms': duration_ms
+                    })
+        
+        return wrapper
+    
+    if func is not None:
+        return decorator(func)
+    return decorator
+
+
 # ============================================================================
 # Quick Profiling Functions
 # ============================================================================
@@ -452,14 +1188,27 @@ def check_module_available(module_name: str) -> bool:
 # ============================================================================
 
 __all__ = [
+    # Core
     'Profiler',
+    'StreamingTracker',
+    # Decorators
     'profile',
     'profile_async',
+    'profile_api',
+    'profile_api_async',
+    'profile_detailed',
+    'profile_lines',
+    # Import profiling
     'profile_imports',
     'ImportProfiler',
+    # Utilities
     'time_import',
     'check_module_available',
+    # Data classes
     'TimingRecord',
     'ImportRecord',
     'FlowRecord',
+    'APICallRecord',
+    'StreamingRecord',
+    'MemoryRecord',
 ]

src/praisonai/praisonai/version.py

@@ -1 +1 @@
-__version__ = "2.3.74"
+__version__ = "2.3.77"

src/praisonai/pyproject.toml

@@ -12,7 +12,7 @@ dependencies = [
     "rich>=13.7",
     "markdown>=3.5",
     "pyparsing>=3.0.0",
-    "praisonaiagents>=0.1.23",
+    "praisonaiagents>=0.1.24",
     "python-dotenv>=0.19.0",
     "instructor>=1.3.3",
     "PyYAML>=6.0",

src/praisonai/tests/unit/test_profiler_advanced.py

@@ -0,0 +1,555 @@
+"""
+Unit tests for PraisonAI Advanced Profiler features.
+
+TDD: Tests written first for new profiling capabilities:
+- API/HTTP call profiling (wall-clock time)
+- Streaming profiling (TTFT, total time)
+- Line-level profiling
+- Memory profiling
+- Flamegraph generation
+- cProfile integration
+- Statistics (p50, p95, p99)
+"""
+
+import time
+import asyncio
+import pytest
+
+
+# ============================================================================
+# API Call Profiling Tests
+# ============================================================================
+
+class TestAPICallProfiler:
+    """Test API/HTTP call profiling with wall-clock time."""
+    
+    def test_api_call_record(self):
+        """Should record API call timing."""
+        from praisonai.profiler import Profiler, APICallRecord
+        
+        Profiler.enable()
+        Profiler.clear()
+        
+        Profiler.record_api_call(
+            endpoint="https://api.openai.com/v1/chat/completions",
+            method="POST",
+            duration_ms=1500.0,
+            status_code=200,
+            request_size=1024,
+            response_size=2048
+        )
+        
+        calls = Profiler.get_api_calls()
+        assert len(calls) >= 1
+        assert calls[-1].endpoint == "https://api.openai.com/v1/chat/completions"
+        assert calls[-1].duration_ms == 1500.0
+        assert calls[-1].status_code == 200
+        
+        Profiler.disable()
+        Profiler.clear()
+    
+    def test_api_call_context_manager(self):
+        """Should profile API call with context manager."""
+        from praisonai.profiler import Profiler
+        
+        Profiler.enable()
+        Profiler.clear()
+        
+        with Profiler.api_call("https://api.example.com/test", method="GET") as call:
+            time.sleep(0.01)  # Simulate API latency
+        
+        calls = Profiler.get_api_calls()
+        assert len(calls) >= 1
+        assert calls[-1].duration_ms >= 10
+        
+        Profiler.disable()
+        Profiler.clear()
+    
+    def test_api_call_decorator(self):
+        """Should profile function as API call."""
+        from praisonai.profiler import Profiler, profile_api
+        
+        Profiler.enable()
+        Profiler.clear()
+        
+        @profile_api(endpoint="test_endpoint")
+        def mock_api_call():
+            time.sleep(0.01)
+            return {"status": "ok"}
+        
+        result = mock_api_call()
+        assert result == {"status": "ok"}
+        
+        calls = Profiler.get_api_calls()
+        assert len(calls) >= 1
+        
+        Profiler.disable()
+        Profiler.clear()
+
+
+# ============================================================================
+# Streaming Profiling Tests
+# ============================================================================
+
+class TestStreamingProfiler:
+    """Test streaming profiling (TTFT, chunk times, total time)."""
+    
+    def test_streaming_record(self):
+        """Should record streaming metrics."""
+        from praisonai.profiler import Profiler, StreamingRecord
+        
+        Profiler.enable()
+        Profiler.clear()
+        
+        Profiler.record_streaming(
+            name="chat_completion",
+            ttft_ms=150.0,  # Time to first token
+            total_ms=2000.0,
+            chunk_count=50,
+            total_tokens=500
+        )
+        
+        streams = Profiler.get_streaming_records()
+        assert len(streams) >= 1
+        assert streams[-1].ttft_ms == 150.0
+        assert streams[-1].total_ms == 2000.0
+        assert streams[-1].chunk_count == 50
+        
+        Profiler.disable()
+        Profiler.clear()
+    
+    def test_streaming_tracker(self):
+        """Should track streaming with context manager."""
+        from praisonai.profiler import Profiler, StreamingTracker
+        
+        Profiler.enable()
+        Profiler.clear()
+        
+        tracker = StreamingTracker("test_stream")
+        tracker.start()
+        time.sleep(0.01)
+        tracker.first_token()  # Mark TTFT
+        time.sleep(0.02)
+        tracker.chunk()  # Record chunk
+        tracker.chunk()
+        tracker.end(total_tokens=100)
+        
+        streams = Profiler.get_streaming_records()
+        assert len(streams) >= 1
+        assert streams[-1].ttft_ms >= 10
+        assert streams[-1].chunk_count == 2
+        
+        Profiler.disable()
+        Profiler.clear()
+    
+    def test_streaming_context_manager(self):
+        """Should profile streaming with context manager."""
+        from praisonai.profiler import Profiler
+        
+        Profiler.enable()
+        Profiler.clear()
+        
+        with Profiler.streaming("test_stream") as tracker:
+            time.sleep(0.01)
+            tracker.first_token()
+            for _ in range(3):
+                tracker.chunk()
+                time.sleep(0.005)
+        
+        streams = Profiler.get_streaming_records()
+        assert len(streams) >= 1
+        assert streams[-1].chunk_count == 3
+        
+        Profiler.disable()
+        Profiler.clear()
+
+
+# ============================================================================
+# Memory Profiling Tests
+# ============================================================================
+
+class TestMemoryProfiler:
+    """Test memory profiling capabilities."""
+    
+    def test_memory_record(self):
+        """Should record memory usage."""
+        from praisonai.profiler import Profiler, MemoryRecord
+        
+        Profiler.enable()
+        Profiler.clear()
+        
+        Profiler.record_memory(
+            name="agent_init",
+            current_kb=1024.0,
+            peak_kb=2048.0
+        )
+        
+        memories = Profiler.get_memory_records()
+        assert len(memories) >= 1
+        assert memories[-1].current_kb == 1024.0
+        assert memories[-1].peak_kb == 2048.0
+        
+        Profiler.disable()
+        Profiler.clear()
+    
+    def test_memory_context_manager(self):
+        """Should profile memory with context manager."""
+        from praisonai.profiler import Profiler
+        
+        Profiler.enable()
+        Profiler.clear()
+        
+        with Profiler.memory("test_operation"):
+            # Allocate some memory
+            data = [i for i in range(10000)]
+        
+        memories = Profiler.get_memory_records()
+        assert len(memories) >= 1
+        assert memories[-1].peak_kb > 0
+        
+        Profiler.disable()
+        Profiler.clear()
+    
+    def test_memory_snapshot(self):
+        """Should take memory snapshot."""
+        from praisonai.profiler import Profiler
+        
+        Profiler.enable()
+        
+        snapshot = Profiler.memory_snapshot()
+        assert 'current_kb' in snapshot
+        assert 'peak_kb' in snapshot
+        
+        Profiler.disable()
+
+
+# ============================================================================
+# Statistics Tests
+# ============================================================================
+
+class TestProfilerStatistics:
+    """Test statistical analysis of profiling data."""
+    
+    def test_percentiles(self):
+        """Should calculate percentiles (p50, p95, p99)."""
+        from praisonai.profiler import Profiler
+        
+        Profiler.enable()
+        Profiler.clear()
+        
+        # Add multiple timing records
+        for i in range(100):
+            Profiler.record_timing(f"op_{i}", float(i), "test")
+        
+        stats = Profiler.get_statistics(category="test")
+        
+        assert 'p50' in stats
+        assert 'p95' in stats
+        assert 'p99' in stats
+        assert 'mean' in stats
+        assert 'std_dev' in stats
+        assert 'min' in stats
+        assert 'max' in stats
+        
+        Profiler.disable()
+        Profiler.clear()
+    
+    def test_statistics_by_category(self):
+        """Should calculate statistics per category."""
+        from praisonai.profiler import Profiler
+        
+        Profiler.enable()
+        Profiler.clear()
+        
+        for i in range(10):
+            Profiler.record_timing(f"api_{i}", float(i * 100), "api")
+            Profiler.record_timing(f"func_{i}", float(i * 10), "function")
+        
+        api_stats = Profiler.get_statistics(category="api")
+        func_stats = Profiler.get_statistics(category="function")
+        
+        assert api_stats['mean'] > func_stats['mean']
+        
+        Profiler.disable()
+        Profiler.clear()
+
+
+# ============================================================================
+# cProfile Integration Tests
+# ============================================================================
+
+class TestCProfileIntegration:
+    """Test cProfile integration for detailed function profiling."""
+    
+    def test_cprofile_context_manager(self):
+        """Should profile with cProfile."""
+        from praisonai.profiler import Profiler
+        
+        Profiler.enable()
+        
+        with Profiler.cprofile("test_operation") as stats:
+            # Do some work
+            result = sum(i * i for i in range(1000))
+        
+        assert stats is not None
+        assert hasattr(stats, 'total_calls') or hasattr(stats, 'stats')
+        
+        Profiler.disable()
+    
+    def test_cprofile_decorator(self):
+        """Should profile function with cProfile decorator."""
+        from praisonai.profiler import profile_detailed
+        
+        @profile_detailed
+        def compute_heavy():
+            return sum(i * i for i in range(10000))
+        
+        result = compute_heavy()
+        assert result > 0
+
+
+# ============================================================================
+# Flamegraph Tests
+# ============================================================================
+
+class TestFlamegraph:
+    """Test flamegraph generation."""
+    
+    def test_flamegraph_data_generation(self):
+        """Should generate flamegraph-compatible data."""
+        from praisonai.profiler import Profiler
+        
+        Profiler.enable()
+        Profiler.clear()
+        
+        # Record some nested operations
+        with Profiler.block("outer"):
+            time.sleep(0.01)
+            with Profiler.block("inner"):
+                time.sleep(0.01)
+        
+        flamegraph_data = Profiler.get_flamegraph_data()
+        
+        assert flamegraph_data is not None
+        assert isinstance(flamegraph_data, (str, list, dict))
+        
+        Profiler.disable()
+        Profiler.clear()
+    
+    def test_flamegraph_export(self):
+        """Should export flamegraph to file."""
+        import tempfile
+        import os
+        from praisonai.profiler import Profiler
+        
+        Profiler.enable()
+        Profiler.clear()
+        
+        with Profiler.block("test_op"):
+            time.sleep(0.01)
+        
+        with tempfile.NamedTemporaryFile(mode='w', suffix='.svg', delete=False) as f:
+            filepath = f.name
+        
+        try:
+            Profiler.export_flamegraph(filepath)
+            assert os.path.exists(filepath)
+        finally:
+            if os.path.exists(filepath):
+                os.unlink(filepath)
+        
+        Profiler.disable()
+        Profiler.clear()
+
+
+# ============================================================================
+# Line-Level Profiling Tests
+# ============================================================================
+
+class TestLineProfiler:
+    """Test line-level profiling integration."""
+    
+    def test_line_profile_decorator(self):
+        """Should profile function line by line."""
+        from praisonai.profiler import profile_lines, Profiler
+        
+        Profiler.enable()
+        
+        @profile_lines
+        def sample_function():
+            a = 1
+            b = 2
+            c = a + b
+            return c
+        
+        result = sample_function()
+        assert result == 3
+        
+        # Line profiling data should be available
+        line_data = Profiler.get_line_profile_data()
+        # May be empty if line_profiler not installed, but shouldn't error
+        assert line_data is not None
+        
+        Profiler.disable()
+
+
+# ============================================================================
+# Report Export Tests
+# ============================================================================
+
+class TestReportExport:
+    """Test report export in various formats."""
+    
+    def test_json_export(self):
+        """Should export report as JSON."""
+        import json
+        from praisonai.profiler import Profiler
+        
+        Profiler.enable()
+        Profiler.clear()
+        
+        Profiler.record_timing("test_op", 100.0, "test")
+        
+        json_report = Profiler.export_json()
+        data = json.loads(json_report)
+        
+        assert 'timings' in data or 'summary' in data
+        
+        Profiler.disable()
+        Profiler.clear()
+    
+    def test_html_export(self):
+        """Should export report as HTML."""
+        from praisonai.profiler import Profiler
+        
+        Profiler.enable()
+        Profiler.clear()
+        
+        Profiler.record_timing("test_op", 100.0, "test")
+        
+        html_report = Profiler.export_html()
+        
+        assert '<html>' in html_report or '<!DOCTYPE' in html_report.lower()
+        
+        Profiler.disable()
+        Profiler.clear()
+    
+    def test_file_export(self):
+        """Should export report to file."""
+        import tempfile
+        import os
+        from praisonai.profiler import Profiler
+        
+        Profiler.enable()
+        Profiler.clear()
+        
+        Profiler.record_timing("test_op", 100.0, "test")
+        
+        with tempfile.NamedTemporaryFile(mode='w', suffix='.json', delete=False) as f:
+            filepath = f.name
+        
+        try:
+            Profiler.export_to_file(filepath, format="json")
+            assert os.path.exists(filepath)
+            with open(filepath) as f:
+                content = f.read()
+            assert len(content) > 0
+        finally:
+            if os.path.exists(filepath):
+                os.unlink(filepath)
+        
+        Profiler.disable()
+        Profiler.clear()
+
+
+# ============================================================================
+# Zero Performance Impact Tests
+# ============================================================================
+
+class TestZeroPerformanceImpact:
+    """Test that profiling has zero impact when disabled."""
+    
+    def test_disabled_no_overhead(self):
+        """Should have minimal overhead when disabled."""
+        from praisonai.profiler import Profiler, profile
+        
+        Profiler.disable()
+        
+        @profile
+        def fast_function():
+            return 1 + 1
+        
+        # Measure overhead
+        iterations = 10000
+        start = time.perf_counter()
+        for _ in range(iterations):
+            fast_function()
+        duration = time.perf_counter() - start
+        
+        # Should complete very fast (< 1ms per call on average)
+        avg_us = (duration / iterations) * 1_000_000
+        assert avg_us < 100  # Less than 100 microseconds per call
+    
+    def test_no_data_when_disabled(self):
+        """Should not record data when disabled."""
+        from praisonai.profiler import Profiler
+        
+        Profiler.disable()
+        Profiler.clear()
+        
+        Profiler.record_timing("test", 100.0, "test")
+        Profiler.record_api_call("endpoint", "GET", 100.0)
+        
+        assert len(Profiler.get_timings()) == 0
+        assert len(Profiler.get_api_calls()) == 0
+
+
+# ============================================================================
+# Async Profiling Tests
+# ============================================================================
+
+class TestAsyncProfiling:
+    """Test async function profiling."""
+    
+    @pytest.mark.asyncio
+    async def test_async_api_call(self):
+        """Should profile async API calls."""
+        from praisonai.profiler import Profiler, profile_api_async
+        
+        Profiler.enable()
+        Profiler.clear()
+        
+        @profile_api_async(endpoint="async_test")
+        async def async_api_call():
+            await asyncio.sleep(0.01)
+            return {"status": "ok"}
+        
+        result = await async_api_call()
+        assert result == {"status": "ok"}
+        
+        calls = Profiler.get_api_calls()
+        assert len(calls) >= 1
+        
+        Profiler.disable()
+        Profiler.clear()
+    
+    @pytest.mark.asyncio
+    async def test_async_streaming(self):
+        """Should profile async streaming."""
+        from praisonai.profiler import Profiler
+        
+        Profiler.enable()
+        Profiler.clear()
+        
+        async with Profiler.streaming_async("async_stream") as tracker:
+            await asyncio.sleep(0.01)
+            tracker.first_token()
+            for _ in range(3):
+                tracker.chunk()
+                await asyncio.sleep(0.005)
+        
+        streams = Profiler.get_streaming_records()
+        assert len(streams) >= 1
+        
+        Profiler.disable()
+        Profiler.clear()

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 = ">=0.1.23" },
+    { name = "praisonaiagents", specifier = ">=0.1.24" },
     { 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 = "0.1.23"
+version = "0.1.24"
 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/79/5e/563a931f0d79f6c64fa8f891ad6eb88cacbbd3f26af5efe52c07a31f11b4/praisonaiagents-0.1.23.tar.gz", hash = "sha256:7a840172892c67feb68e04167db827017d5df8a0e496eb91e309d0e272db60c9", size = 678076, upload-time = "2025-12-22T17:00:28.27Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/31/3e/9dec08dd8107b963cdb3ea3a77b5530e7f68b239c38c80f713be27920f1b/praisonaiagents-0.1.24.tar.gz", hash = "sha256:8a6d17c2df22f2171df11b44b44b817e72a6f34f1f5b4e389691bedbbff06621", size = 678110, upload-time = "2025-12-23T05:39:18.104Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/b4/b3/bf02847078595ab1cf109dfd3c7282eaece21f7b879fe96a3ae0d23e8571/praisonaiagents-0.1.23-py3-none-any.whl", hash = "sha256:a455c8fd04fdbd7dd7bf9755c24f19c9c496c404ef60a4fdfc4d4a51decb2e60", size = 616785, upload-time = "2025-12-22T17:00:26.55Z" },
+    { url = "https://files.pythonhosted.org/packages/37/33/3b45e341433a01408f59ced6eb39da3ecb17067f0ff3c954fa38e07aaffb/praisonaiagents-0.1.24-py3-none-any.whl", hash = "sha256:03a1c476bb5287ad9e519b85a17a93c42ce11b01db87009fcde17a1901e4828a", size = 616792, upload-time = "2025-12-23T05:39:16.12Z" },
 ]
 
 [[package]]