Release v3.7.7

.github/workflows/update-api-md.yml

@@ -0,0 +1,57 @@
+name: Update API Reference
+
+on:
+  push:
+    branches: [ "main" ]
+    paths:
+      - 'src/praisonai-agents/praisonaiagents/**/*.py'
+      - 'src/praisonai/praisonai/**/*.py'
+      - 'src/praisonai-ts/src/**/*.ts'
+      - '.github/workflows/update-api-md.yml'
+
+permissions:
+  contents: write
+
+jobs:
+  update-api-md:
+    runs-on: ubuntu-latest
+    
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v4
+      with:
+        ref: main
+        persist-credentials: true
+        fetch-depth: 0
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: '3.11'
+
+    - name: Generate api.md
+      run: |
+        cd $GITHUB_WORKSPACE
+        python3 src/praisonai/scripts/generate_api_md.py
+        
+    - name: Check for changes
+      id: check_changes
+      run: |
+        if git diff --quiet api.md; then
+          echo "changes=false" >> $GITHUB_OUTPUT
+          echo "No changes to api.md"
+        else
+          echo "changes=true" >> $GITHUB_OUTPUT
+          echo "api.md has been updated"
+          git diff api.md | head -50
+        fi
+
+    - name: Commit and push changes
+      if: steps.check_changes.outputs.changes == 'true'
+      uses: stefanzweifel/git-auto-commit-action@v5
+      with:
+        commit_message: "docs: auto-update api.md [skip ci]"
+        file_pattern: 'api.md'
+        commit_user_name: "github-actions[bot]"
+        commit_user_email: "41898282+github-actions[bot]@users.noreply.github.com"
+        commit_author: "github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>"

api.md

@@ -552,7 +552,7 @@ Methods:
 
 Types:
 ```python
-from praisonaiagents import AutoRagConfig, Citation, CodeExecutionStep, DeepResearchResponse, ExpandResult, ExpandStrategy, FileSearchCall, MCPCall, Provider, RagRetrievalPolicy, ReasoningStep, RewriteResult, RewriteStrategy, Task, WebSearchCall
+from praisonaiagents import AutoRagConfig, Citation, CodeExecutionStep, DeepResearchResponse, ExpandResult, ExpandStrategy, FileSearchCall, MCPCall, Provider, RagRetrievalPolicy, ReasoningStep, RewriteResult, RewriteStrategy, Task, WebSearchCall, warmup
 ```
 
 Methods:
@@ -569,6 +569,7 @@ Methods:
 * <code title="class Task">Task.<a href="./src/praisonai-agents/praisonaiagents/task/task.py">execute_callback_sync</a>(task_output: TaskOutput) -> None</code>
 * <code title="class Task">Task.<a href="./src/praisonai-agents/praisonaiagents/task/task.py">initialize_memory</a>()</code>
 * <code title="class Task">Task.<a href="./src/praisonai-agents/praisonaiagents/task/task.py">store_in_memory</a>(content: str, agent_name: str = None, task_id: str = None)</code>
+* <code title="function">praisonaiagents.<a href="./src/praisonai-agents/praisonaiagents/__init__.py">warmup</a>(include_litellm: bool = False, include_openai: bool = True) -> dict</code>
 
 # Wrapper (praisonai)
 
@@ -731,6 +732,10 @@ Methods:
 * <code title="cli">praisonai skills install <a href="./src/praisonai/praisonai/cli/commands/skills.py">--help</a></code>
 * <code title="cli">praisonai skills list <a href="./src/praisonai/praisonai/cli/commands/skills.py">--help</a></code>
 * <code title="cli">praisonai skills validate <a href="./src/praisonai/praisonai/cli/commands/skills.py">--help</a></code>
+* <code title="cli">praisonai standardise check <a href="./src/praisonai/praisonai/cli/commands/standardise.py">--help</a></code>
+* <code title="cli">praisonai standardise fix <a href="./src/praisonai/praisonai/cli/commands/standardise.py">--help</a></code>
+* <code title="cli">praisonai standardise init <a href="./src/praisonai/praisonai/cli/commands/standardise.py">--help</a></code>
+* <code title="cli">praisonai standardise report <a href="./src/praisonai/praisonai/cli/commands/standardise.py">--help</a></code>
 * <code title="cli">praisonai templates create <a href="./src/praisonai/praisonai/cli/commands/templates.py">--help</a></code>
 * <code title="cli">praisonai templates list <a href="./src/praisonai/praisonai/cli/commands/templates.py">--help</a></code>
 * <code title="cli">praisonai test info <a href="./src/praisonai/praisonai/cli/commands/test.py">--help</a></code>

docker/Dockerfile.chat

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=3.7.2" \
+    "praisonai>=3.7.7" \
     "praisonai[chat]" \
     "embedchain[github,youtube]"
 

docker/Dockerfile.dev

@@ -20,7 +20,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=3.7.2" \
+    "praisonai>=3.7.7" \
     "praisonai[ui]" \
     "praisonai[chat]" \
     "praisonai[realtime]" \

docker/Dockerfile.ui

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=3.7.2" \
+    "praisonai>=3.7.7" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

src/praisonai-agents/agent-profile.py

@@ -0,0 +1,63 @@
+"""
+Streaming Example with Profiling
+
+Key Points:
+1. output="stream" preset enables streaming by default
+2. stream=True in start() is redundant when using output="stream" (but doesn't hurt)
+3. Profiler.enable() MUST be called before profiling works
+4. Use Profiler.streaming() context manager for TTFT metrics
+"""
+from praisonai.profiler import Profiler, profile, profile_imports
+
+# IMPORTANT: Enable profiler BEFORE any profiling operations
+Profiler.enable()
+
+# Profile imports
+with profile_imports():
+    from praisonaiagents import Agent
+
+@profile
+def main():
+    agent = Agent(
+        instructions="You are a helpful assistant",
+        output="stream"  # This already enables streaming
+    )
+
+    print("\nStreaming response:")
+    print("-" * 40)
+    
+    # Use Profiler.streaming() to track TTFT (Time To First Token)
+    with Profiler.streaming("llm_stream") as tracker:
+        first_chunk = True
+        for chunk in agent.start("Write a short paragraph about the history of computing"):
+            if first_chunk:
+                tracker.first_token()  # Mark TTFT
+                first_chunk = False
+            tracker.chunk()  # Count chunks
+            print(chunk, end="", flush=True)
+    
+    print("\n" + "-" * 40)
+
+# Run with profiling blocks
+with Profiler.block("full_workflow"):
+    with Profiler.block("setup"):
+        pass
+    
+    with Profiler.block("execution"):
+        result = main()
+    
+    with Profiler.block("cleanup"):
+        pass
+
+# Generate report
+Profiler.report()
+
+# Show streaming-specific stats
+streaming_records = Profiler.get_streaming_records()
+if streaming_records:
+    print("\nStreaming Metrics:")
+    for record in streaming_records:
+        print(f"  {record.name}: TTFT={record.ttft_ms:.2f}ms, Total={record.total_ms:.2f}ms, Chunks={record.chunk_count}")
+
+stats = Profiler.get_statistics()
+print(f"\nMedian execution time: {stats['p50']:.2f}ms")

src/praisonai-agents/praisonaiagents/__init__.py

@@ -476,6 +476,62 @@ def _init_telemetry():
 _init_telemetry()
 
 
+def warmup(include_litellm: bool = False, include_openai: bool = True) -> dict:
+    """
+    Pre-import heavy dependencies to reduce first-call latency.
+    
+    NOTE: For default OpenAI usage (llm="gpt-4o-mini"), warmup is NOT needed.
+    The default path uses the native OpenAI SDK which is fast (~100ms import).
+    
+    Warmup is only beneficial when using LiteLLM backend, which is triggered by:
+    - Using "/" in model name (e.g., llm="openai/gpt-4o-mini")
+    - Passing a dict config (e.g., llm={"model": "gpt-4o-mini"})
+    - Using base_url parameter
+    
+    Args:
+        include_litellm: Pre-import LiteLLM (~2-3s). Only needed for multi-provider support.
+        include_openai: Pre-import OpenAI SDK (~100ms). Default path, usually fast.
+    
+    Returns:
+        dict: Timing information for each component warmed up
+    
+    Example:
+        # For LiteLLM multi-provider usage:
+        from praisonaiagents import warmup
+        warmup(include_litellm=True)  # Pre-load LiteLLM
+        
+        agent = Agent(llm="anthropic/claude-3-sonnet")  # Now faster
+        
+        # For default OpenAI usage, no warmup needed:
+        agent = Agent(llm="gpt-4o-mini")  # Already fast!
+    """
+    import time
+    timings = {}
+    
+    if include_openai:
+        start = time.perf_counter()
+        try:
+            import openai
+            timings['openai'] = (time.perf_counter() - start) * 1000
+        except ImportError:
+            timings['openai'] = -1  # Not available
+    
+    if include_litellm:
+        start = time.perf_counter()
+        try:
+            import litellm
+            # Also configure litellm to avoid first-call overhead
+            litellm.telemetry = False
+            litellm.set_verbose = False
+            litellm.drop_params = True
+            litellm.modify_params = True
+            timings['litellm'] = (time.perf_counter() - start) * 1000
+        except ImportError:
+            timings['litellm'] = -1  # Not available
+    
+    return timings
+
+
 __all__ = [
     # Core classes (lazy loaded)
     'Agent',
@@ -646,4 +702,6 @@ __all__ = [
     # Parse Utilities
     'is_policy_string',
     'parse_policy_string',
+    # Performance utilities
+    'warmup',
 ]

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

@@ -1430,6 +1430,8 @@ Now provide your final answer using this result."""
         task_id: Optional[str] = None,
         execute_tool_fn: Optional[Callable] = None,
         stream: bool = True,
+        stream_callback: Optional[Callable] = None,
+        emit_events: bool = False,
         **kwargs
     ) -> str:
         """Enhanced get_response with all OpenAI-like features"""
@@ -1489,6 +1491,19 @@ Now provide your final answer using this result."""
             # Disable litellm debug messages
             litellm.set_verbose = False
             
+            # Setup StreamEvent emission if enabled
+            _emit = emit_events and stream_callback is not None
+            if _emit:
+                from ..streaming.events import StreamEvent, StreamEventType
+                _first_token_emitted = False
+                _last_content_time = None
+                _request_start_perf = time.perf_counter()
+                stream_callback(StreamEvent(
+                    type=StreamEventType.REQUEST_START,
+                    timestamp=_request_start_perf,
+                    metadata={"model": self.model, "provider": "litellm"}
+                ))
+            
             # Format tools if provided
             formatted_tools = self._format_tools_for_litellm(tools)
             

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

@@ -582,6 +582,8 @@ class OpenAIClient:
         console: Optional[Console] = None,
         display_fn: Optional[Callable] = None,
         reasoning_steps: bool = False,
+        stream_callback: Optional[Callable] = None,
+        emit_events: bool = False,
         **kwargs
     ) -> Optional[ChatCompletion]:
         """
@@ -596,11 +598,18 @@ class OpenAIClient:
             console: Console for output
             display_fn: Display function for live updates
             reasoning_steps: Whether to show reasoning steps
+            stream_callback: Optional callback for StreamEvent emission
+            emit_events: Whether to emit StreamEvents (requires stream_callback)
             **kwargs: Additional parameters for the API
             
         Returns:
             ChatCompletion object or None if error
         """
+        # Lazy import StreamEvent types only when needed
+        _emit = emit_events and stream_callback is not None
+        if _emit:
+            from ..streaming.events import StreamEvent, StreamEventType
+        
         try:
             # Default start time and console if not provided
             if start_time is None:
@@ -608,6 +617,15 @@ class OpenAIClient:
             if console is None:
                 console = self.console
             
+            # Emit REQUEST_START event
+            request_start_perf = time.perf_counter()
+            if _emit:
+                stream_callback(StreamEvent(
+                    type=StreamEventType.REQUEST_START,
+                    timestamp=request_start_perf,
+                    metadata={"model": model, "message_count": len(messages)}
+                ))
+            
             # Create the response stream
             response_stream = self.sync_client.chat.completions.create(
                 model=model,
@@ -615,29 +633,81 @@ class OpenAIClient:
                 temperature=temperature,
                 tools=tools if tools else None,
                 stream=True,
+                stream_options={"include_usage": True} if _emit else None,
                 **kwargs
             )
             
+            # Emit HEADERS_RECEIVED event (stream object created means headers received)
+            if _emit:
+                stream_callback(StreamEvent(
+                    type=StreamEventType.HEADERS_RECEIVED,
+                    timestamp=time.perf_counter()
+                ))
+            
             full_response_text = ""
             reasoning_content = ""
             chunks = []
+            first_token_emitted = False
+            last_content_time = None
+            ttft_logged = False
             
             # If display function provided, use Live display
             if display_fn:
                 with Live(
                     display_fn("", start_time),
                     console=console,
-                    refresh_per_second=4,
+                    refresh_per_second=10,  # Increased for more responsive streaming
                     transient=True,
                     vertical_overflow="ellipsis",
                     auto_refresh=True
                 ) as live:
                     for chunk in response_stream:
                         chunks.append(chunk)
-                        if chunk.choices[0].delta.content:
-                            full_response_text += chunk.choices[0].delta.content
+                        
+                        # Check for content delta
+                        if chunk.choices and chunk.choices[0].delta.content:
+                            content = chunk.choices[0].delta.content
+                            full_response_text += content
+                            last_content_time = time.perf_counter()
+                            
+                            # Log TTFT on first token
+                            if not ttft_logged:
+                                ttft_ms = (last_content_time - request_start_perf) * 1000
+                                logging.debug(f"TTFT (Time To First Token): {ttft_ms:.0f}ms")
+                                ttft_logged = True
+                            
+                            # Emit FIRST_TOKEN on first content
+                            if _emit and not first_token_emitted:
+                                stream_callback(StreamEvent(
+                                    type=StreamEventType.FIRST_TOKEN,
+                                    timestamp=last_content_time,
+                                    content=content
+                                ))
+                                first_token_emitted = True
+                            elif _emit:
+                                # Emit DELTA_TEXT for subsequent tokens
+                                stream_callback(StreamEvent(
+                                    type=StreamEventType.DELTA_TEXT,
+                                    timestamp=last_content_time,
+                                    content=content
+                                ))
+                            
                             live.update(display_fn(full_response_text, start_time))
                         
+                        # Handle tool calls streaming
+                        if _emit and chunk.choices and hasattr(chunk.choices[0].delta, 'tool_calls') and chunk.choices[0].delta.tool_calls:
+                            for tc in chunk.choices[0].delta.tool_calls:
+                                stream_callback(StreamEvent(
+                                    type=StreamEventType.DELTA_TOOL_CALL,
+                                    timestamp=time.perf_counter(),
+                                    tool_call={
+                                        "index": tc.index,
+                                        "id": getattr(tc, 'id', None),
+                                        "name": getattr(tc.function, 'name', None) if tc.function else None,
+                                        "arguments": getattr(tc.function, 'arguments', None) if tc.function else None
+                                    }
+                                ))
+                        
                         # Update live display with reasoning content if enabled
                         if reasoning_steps and hasattr(chunk.choices[0].delta, "reasoning_content"):
                             rc = chunk.choices[0].delta.reasoning_content
@@ -648,9 +718,60 @@ class OpenAIClient:
                 # Clear the last generating display with a blank line
                 console.print()
             else:
-                # Just collect chunks without display
+                # Just collect chunks without display, but still emit events
                 for chunk in response_stream:
                     chunks.append(chunk)
+                    
+                    if chunk.choices and chunk.choices[0].delta.content:
+                        content = chunk.choices[0].delta.content
+                        last_content_time = time.perf_counter()
+                        
+                        # Log TTFT on first token
+                        if not ttft_logged:
+                            ttft_ms = (last_content_time - request_start_perf) * 1000
+                            logging.debug(f"TTFT (Time To First Token): {ttft_ms:.0f}ms")
+                            ttft_logged = True
+                        
+                        if _emit and not first_token_emitted:
+                            stream_callback(StreamEvent(
+                                type=StreamEventType.FIRST_TOKEN,
+                                timestamp=last_content_time,
+                                content=content
+                            ))
+                            first_token_emitted = True
+                        else:
+                            stream_callback(StreamEvent(
+                                type=StreamEventType.DELTA_TEXT,
+                                timestamp=last_content_time,
+                                content=content
+                            ))
+                    
+                    # Handle tool calls streaming
+                    if _emit and chunk.choices and hasattr(chunk.choices[0].delta, 'tool_calls') and chunk.choices[0].delta.tool_calls:
+                        for tc in chunk.choices[0].delta.tool_calls:
+                            stream_callback(StreamEvent(
+                                type=StreamEventType.DELTA_TOOL_CALL,
+                                timestamp=time.perf_counter(),
+                                tool_call={
+                                    "index": tc.index,
+                                    "id": getattr(tc, 'id', None),
+                                    "name": getattr(tc.function, 'name', None) if tc.function else None,
+                                    "arguments": getattr(tc.function, 'arguments', None) if tc.function else None
+                                }
+                            ))
+            
+            # Emit LAST_TOKEN and STREAM_END events
+            if _emit:
+                if last_content_time:
+                    stream_callback(StreamEvent(
+                        type=StreamEventType.LAST_TOKEN,
+                        timestamp=last_content_time
+                    ))
+                stream_callback(StreamEvent(
+                    type=StreamEventType.STREAM_END,
+                    timestamp=time.perf_counter(),
+                    metadata={"chunk_count": len(chunks)}
+                ))
             
             final_response = process_stream_chunks(chunks)
             return final_response
@@ -669,6 +790,8 @@ class OpenAIClient:
         console: Optional[Console] = None,
         display_fn: Optional[Callable] = None,
         reasoning_steps: bool = False,
+        stream_callback: Optional[Callable] = None,
+        emit_events: bool = False,
         **kwargs
     ) -> Optional[ChatCompletion]:
         """
@@ -683,11 +806,18 @@ class OpenAIClient:
             console: Console for output
             display_fn: Display function for live updates
             reasoning_steps: Whether to show reasoning steps
+            stream_callback: Optional callback for StreamEvent emission (can be sync or async)
+            emit_events: Whether to emit StreamEvents (requires stream_callback)
             **kwargs: Additional parameters for the API
             
         Returns:
             ChatCompletion object or None if error
         """
+        # Lazy import StreamEvent types only when needed
+        _emit = emit_events and stream_callback is not None
+        if _emit:
+            from ..streaming.events import StreamEvent, StreamEventType
+        
         try:
             # Default start time and console if not provided
             if start_time is None:
@@ -695,6 +825,19 @@ class OpenAIClient:
             if console is None:
                 console = self.console
             
+            # Emit REQUEST_START event
+            request_start_perf = time.perf_counter()
+            if _emit:
+                event = StreamEvent(
+                    type=StreamEventType.REQUEST_START,
+                    timestamp=request_start_perf,
+                    metadata={"model": model, "message_count": len(messages)}
+                )
+                if asyncio.iscoroutinefunction(stream_callback):
+                    await stream_callback(event)
+                else:
+                    stream_callback(event)
+            
             # Create the response stream
             response_stream = await self.async_client.chat.completions.create(
                 model=model,
@@ -702,29 +845,81 @@ class OpenAIClient:
                 temperature=temperature,
                 tools=tools if tools else None,
                 stream=True,
+                stream_options={"include_usage": True} if _emit else None,
                 **kwargs
             )
             
+            # Emit HEADERS_RECEIVED event
+            if _emit:
+                event = StreamEvent(
+                    type=StreamEventType.HEADERS_RECEIVED,
+                    timestamp=time.perf_counter()
+                )
+                if asyncio.iscoroutinefunction(stream_callback):
+                    await stream_callback(event)
+                else:
+                    stream_callback(event)
+            
             full_response_text = ""
             reasoning_content = ""
             chunks = []
+            first_token_emitted = False
+            last_content_time = None
+            
+            async def emit_event(event: 'StreamEvent'):
+                if asyncio.iscoroutinefunction(stream_callback):
+                    await stream_callback(event)
+                else:
+                    stream_callback(event)
             
             # If display function provided, use Live display
             if display_fn:
                 with Live(
                     display_fn("", start_time),
                     console=console,
-                    refresh_per_second=4,
+                    refresh_per_second=10,
                     transient=True,
                     vertical_overflow="ellipsis",
                     auto_refresh=True
                 ) as live:
                     async for chunk in response_stream:
                         chunks.append(chunk)
-                        if chunk.choices[0].delta.content:
-                            full_response_text += chunk.choices[0].delta.content
+                        
+                        if chunk.choices and chunk.choices[0].delta.content:
+                            content = chunk.choices[0].delta.content
+                            full_response_text += content
+                            last_content_time = time.perf_counter()
+                            
+                            if _emit and not first_token_emitted:
+                                await emit_event(StreamEvent(
+                                    type=StreamEventType.FIRST_TOKEN,
+                                    timestamp=last_content_time,
+                                    content=content
+                                ))
+                                first_token_emitted = True
+                            elif _emit:
+                                await emit_event(StreamEvent(
+                                    type=StreamEventType.DELTA_TEXT,
+                                    timestamp=last_content_time,
+                                    content=content
+                                ))
+                            
                             live.update(display_fn(full_response_text, start_time))
                         
+                        # Handle tool calls streaming
+                        if _emit and chunk.choices and hasattr(chunk.choices[0].delta, 'tool_calls') and chunk.choices[0].delta.tool_calls:
+                            for tc in chunk.choices[0].delta.tool_calls:
+                                await emit_event(StreamEvent(
+                                    type=StreamEventType.DELTA_TOOL_CALL,
+                                    timestamp=time.perf_counter(),
+                                    tool_call={
+                                        "index": tc.index,
+                                        "id": getattr(tc, 'id', None),
+                                        "name": getattr(tc.function, 'name', None) if tc.function else None,
+                                        "arguments": getattr(tc.function, 'arguments', None) if tc.function else None
+                                    }
+                                ))
+                        
                         # Update live display with reasoning content if enabled
                         if reasoning_steps and hasattr(chunk.choices[0].delta, "reasoning_content"):
                             rc = chunk.choices[0].delta.reasoning_content
@@ -738,6 +933,50 @@ class OpenAIClient:
                 # Just collect chunks without display
                 async for chunk in response_stream:
                     chunks.append(chunk)
+                    
+                    if _emit and chunk.choices and chunk.choices[0].delta.content:
+                        content = chunk.choices[0].delta.content
+                        last_content_time = time.perf_counter()
+                        
+                        if not first_token_emitted:
+                            await emit_event(StreamEvent(
+                                type=StreamEventType.FIRST_TOKEN,
+                                timestamp=last_content_time,
+                                content=content
+                            ))
+                            first_token_emitted = True
+                        else:
+                            await emit_event(StreamEvent(
+                                type=StreamEventType.DELTA_TEXT,
+                                timestamp=last_content_time,
+                                content=content
+                            ))
+                    
+                    if _emit and chunk.choices and hasattr(chunk.choices[0].delta, 'tool_calls') and chunk.choices[0].delta.tool_calls:
+                        for tc in chunk.choices[0].delta.tool_calls:
+                            await emit_event(StreamEvent(
+                                type=StreamEventType.DELTA_TOOL_CALL,
+                                timestamp=time.perf_counter(),
+                                tool_call={
+                                    "index": tc.index,
+                                    "id": getattr(tc, 'id', None),
+                                    "name": getattr(tc.function, 'name', None) if tc.function else None,
+                                    "arguments": getattr(tc.function, 'arguments', None) if tc.function else None
+                                }
+                            ))
+            
+            # Emit LAST_TOKEN and STREAM_END events
+            if _emit:
+                if last_content_time:
+                    await emit_event(StreamEvent(
+                        type=StreamEventType.LAST_TOKEN,
+                        timestamp=last_content_time
+                    ))
+                await emit_event(StreamEvent(
+                    type=StreamEventType.STREAM_END,
+                    timestamp=time.perf_counter(),
+                    metadata={"chunk_count": len(chunks)}
+                ))
             
             final_response = process_stream_chunks(chunks)
             return final_response
@@ -904,7 +1143,9 @@ class OpenAIClient:
                 if display_fn and console:
                     # When verbose (display_fn provided), use streaming for better UX
                     try:
-                        with Live(display_fn("", start_time), console=console, refresh_per_second=4, transient=True) as live:
+                        request_start_perf = time.perf_counter()
+                        ttft_logged = False
+                        with Live(display_fn("", start_time), console=console, refresh_per_second=10, transient=True) as live:
                             # Use streaming when display_fn is provided for progressive display
                             response_stream = self.create_completion(
                                 messages=messages,
@@ -922,6 +1163,11 @@ class OpenAIClient:
                             for chunk in response_stream:
                                 chunks.append(chunk)
                                 if chunk.choices[0].delta.content:
+                                    # Log TTFT on first token
+                                    if not ttft_logged:
+                                        ttft_ms = (time.perf_counter() - request_start_perf) * 1000
+                                        logging.debug(f"TTFT (Time To First Token): {ttft_ms:.0f}ms")
+                                        ttft_logged = True
                                     full_response_text += chunk.choices[0].delta.content
                                     live.update(display_fn(full_response_text, start_time))
                             

src/praisonai-agents/praisonaiagents/profiling/__init__.py

@@ -0,0 +1,479 @@
+"""
+PraisonAI Agents Core Profiling Module
+
+Lightweight, protocol-based profiling for the core SDK.
+Zero overhead when disabled. Opt-in only.
+
+Features:
+- Pre-LLM latency breakdown
+- LLM client initialization timing
+- Tool resolution timing
+- Message serialization timing
+- Streaming TTFT tracking
+- Import timing
+- Async-safe
+
+Usage:
+    from praisonaiagents.profiling import Profiler, profile_block
+    
+    # Enable profiling
+    Profiler.enable()
+    
+    # Profile a block
+    with profile_block("my_operation"):
+        do_something()
+    
+    # Get report
+    Profiler.report()
+"""
+
+import time
+import os
+import threading
+from dataclasses import dataclass, field, asdict
+from typing import Dict, List, Optional, Any, Callable
+from contextlib import contextmanager
+import functools
+import json
+
+
+# ============================================================================
+# Data Classes
+# ============================================================================
+
+@dataclass
+class TimingRecord:
+    """Record of a single timing measurement."""
+    name: str
+    duration_ms: float
+    category: str = "block"
+    parent: str = ""
+    timestamp: float = field(default_factory=time.time)
+    metadata: Dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass
+class StreamingRecord:
+    """Record of streaming operation."""
+    name: str
+    ttft_ms: float  # Time to first token
+    total_ms: float
+    chunk_count: int = 0
+    timestamp: float = field(default_factory=time.time)
+
+
+@dataclass
+class PreLLMBreakdown:
+    """Breakdown of pre-LLM latency phases."""
+    agent_init_ms: float = 0.0
+    llm_client_init_ms: float = 0.0
+    tool_resolution_ms: float = 0.0
+    message_build_ms: float = 0.0
+    context_management_ms: float = 0.0
+    knowledge_retrieval_ms: float = 0.0
+    guardrail_check_ms: float = 0.0
+    total_pre_llm_ms: float = 0.0
+    
+    def to_dict(self) -> Dict[str, float]:
+        return asdict(self)
+
+
+# ============================================================================
+# Streaming Tracker
+# ============================================================================
+
+class StreamingTracker:
+    """Track streaming operations for TTFT metrics."""
+    
+    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
+    
+    def start(self) -> None:
+        self._start_time = time.perf_counter()
+    
+    def first_token(self) -> None:
+        if self._first_token_time is None:
+            self._first_token_time = time.perf_counter()
+    
+    def chunk(self) -> None:
+        self._chunk_count += 1
+    
+    def end(self) -> None:
+        self._end_time = time.perf_counter()
+        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
+            )
+    
+    @property
+    def ttft_ms(self) -> float:
+        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:
+        if self._start_time:
+            end = self._end_time or time.perf_counter()
+            return (end - self._start_time) * 1000
+        return 0.0
+
+
+# ============================================================================
+# Core Profiler
+# ============================================================================
+
+class Profiler:
+    """
+    Lightweight profiler for PraisonAI Agents core SDK.
+    
+    Thread-safe singleton with zero overhead when disabled.
+    """
+    
+    _lock = threading.Lock()
+    _enabled: bool = False
+    _timings: List[TimingRecord] = []
+    _streaming: List[StreamingRecord] = []
+    _pre_llm_breakdown: Optional[PreLLMBreakdown] = None
+    _current_phases: Dict[str, float] = {}  # Track in-progress phases
+    _phase_stack: List[str] = []  # Track nested phases
+    
+    @classmethod
+    def enable(cls) -> None:
+        """Enable profiling."""
+        cls._enabled = True
+    
+    @classmethod
+    def disable(cls) -> None:
+        """Disable profiling."""
+        cls._enabled = False
+    
+    @classmethod
+    def is_enabled(cls) -> bool:
+        """Check if profiling is enabled."""
+        return cls._enabled or os.environ.get('PRAISONAI_PROFILE', '').lower() in ('1', 'true', 'yes')
+    
+    @classmethod
+    def clear(cls) -> None:
+        """Clear all profiling data."""
+        with cls._lock:
+            cls._timings.clear()
+            cls._streaming.clear()
+            cls._pre_llm_breakdown = None
+            cls._current_phases.clear()
+            cls._phase_stack.clear()
+    
+    @classmethod
+    def record_timing(cls, name: str, duration_ms: float, category: str = "block",
+                      parent: str = "", metadata: Optional[Dict[str, Any]] = None) -> None:
+        """Record a timing measurement."""
+        if not cls.is_enabled():
+            return
+        with cls._lock:
+            cls._timings.append(TimingRecord(
+                name=name,
+                duration_ms=duration_ms,
+                category=category,
+                parent=parent,
+                metadata=metadata or {}
+            ))
+    
+    @classmethod
+    def record_streaming(cls, name: str, ttft_ms: float, total_ms: float,
+                         chunk_count: 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
+            ))
+    
+    @classmethod
+    def start_phase(cls, phase_name: str) -> None:
+        """Start timing a phase (for pre-LLM breakdown)."""
+        if not cls.is_enabled():
+            return
+        with cls._lock:
+            cls._current_phases[phase_name] = time.perf_counter()
+            cls._phase_stack.append(phase_name)
+    
+    @classmethod
+    def end_phase(cls, phase_name: str) -> float:
+        """End timing a phase and return duration in ms."""
+        if not cls.is_enabled():
+            return 0.0
+        with cls._lock:
+            start = cls._current_phases.pop(phase_name, None)
+            if phase_name in cls._phase_stack:
+                cls._phase_stack.remove(phase_name)
+            if start is None:
+                return 0.0
+            duration_ms = (time.perf_counter() - start) * 1000
+            # Record as timing
+            parent = cls._phase_stack[-1] if cls._phase_stack else ""
+            cls._timings.append(TimingRecord(
+                name=phase_name,
+                duration_ms=duration_ms,
+                category="phase",
+                parent=parent
+            ))
+            return duration_ms
+    
+    @classmethod
+    @contextmanager
+    def phase(cls, name: str, category: str = "phase"):
+        """Context manager for timing a phase."""
+        if not cls.is_enabled():
+            yield
+            return
+        
+        start = time.perf_counter()
+        parent = ""
+        with cls._lock:
+            parent = cls._phase_stack[-1] if cls._phase_stack else ""
+            cls._phase_stack.append(name)
+        try:
+            yield
+        finally:
+            duration_ms = (time.perf_counter() - start) * 1000
+            with cls._lock:
+                if name in cls._phase_stack:
+                    cls._phase_stack.remove(name)
+                cls._timings.append(TimingRecord(
+                    name=name,
+                    duration_ms=duration_ms,
+                    category=category,
+                    parent=parent
+                ))
+    
+    @classmethod
+    @contextmanager
+    def streaming(cls, name: str):
+        """Context manager for streaming profiling."""
+        tracker = StreamingTracker(name)
+        tracker.start()
+        try:
+            yield tracker
+        finally:
+            tracker.end()
+    
+    @classmethod
+    def set_pre_llm_breakdown(cls, breakdown: PreLLMBreakdown) -> None:
+        """Set the pre-LLM breakdown data."""
+        if not cls.is_enabled():
+            return
+        with cls._lock:
+            cls._pre_llm_breakdown = breakdown
+    
+    @classmethod
+    def get_timings(cls, category: Optional[str] = None) -> List[TimingRecord]:
+        """Get timing records."""
+        with cls._lock:
+            if category:
+                return [t for t in cls._timings if t.category == category]
+            return cls._timings.copy()
+    
+    @classmethod
+    def get_streaming_records(cls) -> List[StreamingRecord]:
+        """Get streaming records."""
+        with cls._lock:
+            return cls._streaming.copy()
+    
+    @classmethod
+    def get_pre_llm_breakdown(cls) -> Optional[PreLLMBreakdown]:
+        """Get pre-LLM breakdown."""
+        with cls._lock:
+            return cls._pre_llm_breakdown
+    
+    @classmethod
+    def get_summary(cls) -> Dict[str, Any]:
+        """Get profiling summary."""
+        with cls._lock:
+            total_time = sum(t.duration_ms for t in cls._timings)
+            
+            # Group by category
+            by_category: Dict[str, float] = {}
+            for t in cls._timings:
+                by_category[t.category] = by_category.get(t.category, 0) + t.duration_ms
+            
+            # Top slowest
+            slowest = sorted(cls._timings, key=lambda x: x.duration_ms, reverse=True)[:15]
+            
+            # Pre-LLM breakdown
+            pre_llm = cls._pre_llm_breakdown.to_dict() if cls._pre_llm_breakdown else {}
+            
+            # Streaming stats
+            streaming_stats = []
+            for s in cls._streaming:
+                streaming_stats.append({
+                    'name': s.name,
+                    'ttft_ms': s.ttft_ms,
+                    'total_ms': s.total_ms,
+                    'chunk_count': s.chunk_count
+                })
+            
+            return {
+                'total_time_ms': total_time,
+                'timing_count': len(cls._timings),
+                'by_category': by_category,
+                'slowest_operations': [(s.name, s.duration_ms, s.category) for s in slowest],
+                'pre_llm_breakdown': pre_llm,
+                'streaming': streaming_stats
+            }
+    
+    @classmethod
+    def report(cls, output: str = "console") -> str:
+        """Generate and output profiling report."""
+        summary = cls.get_summary()
+        
+        lines = [
+            "=" * 70,
+            "PraisonAI Agents Core Profiling Report",
+            "=" * 70,
+            "",
+            f"Total Time: {summary['total_time_ms']:.2f}ms",
+            f"Timing Records: {summary['timing_count']}",
+            "",
+        ]
+        
+        # Pre-LLM Breakdown (key for debugging the 7s delay)
+        if summary['pre_llm_breakdown']:
+            lines.append("## Pre-LLM Latency Breakdown")
+            lines.append("-" * 50)
+            for phase, ms in summary['pre_llm_breakdown'].items():
+                if ms > 0:
+                    lines.append(f"  {phase:<30}: {ms:>10.2f} ms")
+            lines.append("")
+        
+        # By Category
+        lines.append("## By Category")
+        lines.append("-" * 50)
+        for cat, time_ms in summary['by_category'].items():
+            lines.append(f"  {cat}: {time_ms:.2f}ms")
+        lines.append("")
+        
+        # Slowest Operations
+        lines.append("## Slowest Operations")
+        lines.append("-" * 50)
+        for name, time_ms, cat in summary['slowest_operations']:
+            lines.append(f"  {name:<40}: {time_ms:>10.2f}ms [{cat}]")
+        lines.append("")
+        
+        # Streaming Stats
+        if summary['streaming']:
+            lines.append("## Streaming Metrics")
+            lines.append("-" * 50)
+            for s in summary['streaming']:
+                lines.append(f"  {s['name']}: TTFT={s['ttft_ms']:.2f}ms, Total={s['total_ms']:.2f}ms, Chunks={s['chunk_count']}")
+            lines.append("")
+        
+        lines.append("=" * 70)
+        
+        report_text = "\n".join(lines)
+        
+        if output == "console":
+            print(report_text)
+        
+        return report_text
+    
+    @classmethod
+    def export_json(cls) -> str:
+        """Export profiling data as JSON."""
+        summary = cls.get_summary()
+        with cls._lock:
+            data = {
+                'summary': summary,
+                'timings': [asdict(t) for t in cls._timings],
+                'streaming': [asdict(s) for s in cls._streaming],
+            }
+            return json.dumps(data, indent=2, default=str)
+
+
+# ============================================================================
+# Decorators
+# ============================================================================
+
+def profile_func(func: Optional[Callable] = None, *, category: str = "function"):
+    """Decorator to profile a function."""
+    def decorator(fn: Callable) -> Callable:
+        @functools.wraps(fn)
+        def wrapper(*args, **kwargs):
+            if not Profiler.is_enabled():
+                return fn(*args, **kwargs)
+            start = time.perf_counter()
+            try:
+                return fn(*args, **kwargs)
+            finally:
+                duration_ms = (time.perf_counter() - start) * 1000
+                Profiler.record_timing(fn.__name__, duration_ms, category)
+        return wrapper
+    
+    if func is not None:
+        return decorator(func)
+    return decorator
+
+
+def profile_async(func: Optional[Callable] = None, *, category: str = "async"):
+    """Decorator to profile an async function."""
+    def decorator(fn: Callable) -> Callable:
+        @functools.wraps(fn)
+        async def wrapper(*args, **kwargs):
+            if not Profiler.is_enabled():
+                return await fn(*args, **kwargs)
+            start = time.perf_counter()
+            try:
+                return await fn(*args, **kwargs)
+            finally:
+                duration_ms = (time.perf_counter() - start) * 1000
+                Profiler.record_timing(fn.__name__, duration_ms, category)
+        return wrapper
+    
+    if func is not None:
+        return decorator(func)
+    return decorator
+
+
+@contextmanager
+def profile_block(name: str, category: str = "block"):
+    """Context manager for profiling a block of code."""
+    if not Profiler.is_enabled():
+        yield
+        return
+    
+    start = time.perf_counter()
+    try:
+        yield
+    finally:
+        duration_ms = (time.perf_counter() - start) * 1000
+        Profiler.record_timing(name, duration_ms, category)
+
+
+# ============================================================================
+# Exports
+# ============================================================================
+
+__all__ = [
+    'Profiler',
+    'StreamingTracker',
+    'TimingRecord',
+    'StreamingRecord',
+    'PreLLMBreakdown',
+    'profile_func',
+    'profile_async',
+    'profile_block',
+]

src/praisonai-agents/praisonaiagents/streaming/__init__.py

@@ -0,0 +1,75 @@
+"""
+PraisonAI Streaming Module.
+
+Provides the StreamEvent protocol for pass-through streaming from LLM providers.
+
+This module is lazily loaded to ensure zero performance impact when streaming
+features are not used.
+
+Usage:
+    from praisonaiagents.streaming import (
+        StreamEvent,
+        StreamEventType,
+        StreamMetrics,
+        StreamEventEmitter,
+        create_text_printer_callback,
+    )
+"""
+
+__all__ = [
+    "StreamEvent",
+    "StreamEventType", 
+    "StreamMetrics",
+    "StreamEventEmitter",
+    "StreamCallback",
+    "AsyncStreamCallback",
+    "create_text_printer_callback",
+    "create_metrics_callback",
+    "StreamLogger",
+    "create_logging_callback",
+]
+
+
+def __getattr__(name: str):
+    """Lazy loading of streaming components."""
+    if name in __all__:
+        # Events module exports
+        if name in (
+            "StreamEvent", "StreamEventType", "StreamMetrics", 
+            "StreamEventEmitter", "StreamCallback", "AsyncStreamCallback",
+            "create_text_printer_callback", "create_metrics_callback"
+        ):
+            from .events import (
+                StreamEvent,
+                StreamEventType,
+                StreamMetrics,
+                StreamEventEmitter,
+                StreamCallback,
+                AsyncStreamCallback,
+                create_text_printer_callback,
+                create_metrics_callback,
+            )
+            
+            _events_exports = {
+                "StreamEvent": StreamEvent,
+                "StreamEventType": StreamEventType,
+                "StreamMetrics": StreamMetrics,
+                "StreamEventEmitter": StreamEventEmitter,
+                "StreamCallback": StreamCallback,
+                "AsyncStreamCallback": AsyncStreamCallback,
+                "create_text_printer_callback": create_text_printer_callback,
+                "create_metrics_callback": create_metrics_callback,
+            }
+            return _events_exports.get(name)
+        
+        # Logging module exports
+        if name in ("StreamLogger", "create_logging_callback"):
+            from .logging import StreamLogger, create_logging_callback
+            
+            _logging_exports = {
+                "StreamLogger": StreamLogger,
+                "create_logging_callback": create_logging_callback,
+            }
+            return _logging_exports.get(name)
+    
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

src/praisonai-agents/praisonaiagents/streaming/events.py

@@ -0,0 +1,300 @@
+"""
+StreamEvent protocol for PraisonAI streaming.
+
+This module defines the minimal streaming event protocol that both OpenAI SDK
+and LiteLLM streaming paths map their native chunks/events into.
+
+Design principles:
+- Zero overhead when not used (lazy imports, no global state)
+- Multi-agent safe (per-agent callbacks, no shared mutable state)
+- Async-safe (separate sync/async callback interfaces)
+- Provider-agnostic (same events for OpenAI, LiteLLM, etc.)
+"""
+
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Any, Dict, List, Optional, Protocol
+import time
+
+
+class StreamEventType(Enum):
+    """Types of streaming events emitted during LLM response streaming."""
+    
+    REQUEST_START = "request_start"      # Before API call is made
+    HEADERS_RECEIVED = "headers_received"  # When HTTP 200 headers arrive
+    FIRST_TOKEN = "first_token"          # First content delta received (TTFT marker)
+    DELTA_TEXT = "delta_text"            # Text content delta
+    DELTA_TOOL_CALL = "delta_tool_call"  # Tool call delta (function name, args chunk)
+    TOOL_CALL_END = "tool_call_end"      # Tool call complete
+    LAST_TOKEN = "last_token"            # Final content delta
+    STREAM_END = "stream_end"            # Stream completed successfully
+    ERROR = "error"                      # Error during streaming
+
+
+@dataclass
+class StreamEvent:
+    """
+    A single streaming event emitted during LLM response streaming.
+    
+    Attributes:
+        type: The type of event (see StreamEventType)
+        timestamp: High-precision timestamp (time.perf_counter())
+        content: Text content for DELTA_TEXT events
+        tool_call: Tool call data for DELTA_TOOL_CALL events
+        metadata: Additional event-specific metadata
+        error: Error message for ERROR events
+    """
+    type: StreamEventType
+    timestamp: float = field(default_factory=time.perf_counter)
+    content: Optional[str] = None
+    tool_call: Optional[Dict[str, Any]] = None
+    metadata: Optional[Dict[str, Any]] = None
+    error: Optional[str] = None
+    
+    def __repr__(self) -> str:
+        parts = [f"StreamEvent({self.type.value}"]
+        if self.content:
+            preview = self.content[:20] + "..." if len(self.content) > 20 else self.content
+            parts.append(f", content={preview!r}")
+        if self.tool_call:
+            parts.append(f", tool_call={self.tool_call.get('name', 'unknown')}")
+        if self.error:
+            parts.append(f", error={self.error!r}")
+        parts.append(")")
+        return "".join(parts)
+
+
+class StreamCallback(Protocol):
+    """Protocol for synchronous stream event callbacks."""
+    
+    def __call__(self, event: StreamEvent) -> None:
+        """Called when a stream event is emitted."""
+        ...
+
+
+class AsyncStreamCallback(Protocol):
+    """Protocol for asynchronous stream event callbacks."""
+    
+    async def __call__(self, event: StreamEvent) -> None:
+        """Called when a stream event is emitted (async)."""
+        ...
+
+
+@dataclass
+class StreamMetrics:
+    """
+    Timing metrics for a streaming response.
+    
+    Tracks key timestamps to measure:
+    - TTFT (Time To First Token): How long until first token arrives
+    - Stream Duration: How long the token streaming takes
+    - Total Time: End-to-end request time
+    
+    All times are in seconds, measured with time.perf_counter() for precision.
+    """
+    request_start: float = 0.0
+    headers_received: float = 0.0
+    first_token: float = 0.0
+    last_token: float = 0.0
+    stream_end: float = 0.0
+    token_count: int = 0
+    
+    @property
+    def ttft(self) -> float:
+        """Time To First Token in seconds (first_token - request_start)."""
+        if self.first_token and self.request_start:
+            return self.first_token - self.request_start
+        return 0.0
+    
+    @property
+    def stream_duration(self) -> float:
+        """Duration of token streaming in seconds (last_token - first_token)."""
+        if self.last_token and self.first_token:
+            return self.last_token - self.first_token
+        return 0.0
+    
+    @property
+    def total_time(self) -> float:
+        """Total request time in seconds (stream_end - request_start)."""
+        if self.stream_end and self.request_start:
+            return self.stream_end - self.request_start
+        return 0.0
+    
+    @property
+    def tokens_per_second(self) -> float:
+        """Token generation rate (tokens / stream_duration)."""
+        if self.stream_duration > 0 and self.token_count > 0:
+            return self.token_count / self.stream_duration
+        return 0.0
+    
+    def update_from_event(self, event: StreamEvent) -> None:
+        """Update metrics from a StreamEvent."""
+        if event.type == StreamEventType.REQUEST_START:
+            self.request_start = event.timestamp
+        elif event.type == StreamEventType.HEADERS_RECEIVED:
+            self.headers_received = event.timestamp
+        elif event.type == StreamEventType.FIRST_TOKEN:
+            self.first_token = event.timestamp
+            self.token_count = 1
+        elif event.type == StreamEventType.DELTA_TEXT:
+            self.token_count += 1
+        elif event.type == StreamEventType.LAST_TOKEN:
+            self.last_token = event.timestamp
+        elif event.type == StreamEventType.STREAM_END:
+            self.stream_end = event.timestamp
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert metrics to dictionary for logging/display."""
+        return {
+            "ttft_ms": round(self.ttft * 1000, 1),
+            "stream_duration_ms": round(self.stream_duration * 1000, 1),
+            "total_time_ms": round(self.total_time * 1000, 1),
+            "token_count": self.token_count,
+            "tokens_per_second": round(self.tokens_per_second, 1),
+        }
+    
+    def format_summary(self) -> str:
+        """Format metrics as a human-readable summary string."""
+        return (
+            f"TTFT: {self.ttft*1000:.0f}ms | "
+            f"Stream: {self.stream_duration*1000:.0f}ms | "
+            f"Total: {self.total_time*1000:.0f}ms | "
+            f"Tokens: {self.token_count} ({self.tokens_per_second:.1f}/s)"
+        )
+
+
+class StreamEventEmitter:
+    """
+    Manages stream event callbacks for an agent or streaming session.
+    
+    Thread-safe and multi-agent safe - each agent has its own emitter instance.
+    Supports both sync and async callbacks.
+    
+    Usage:
+        emitter = StreamEventEmitter()
+        emitter.add_callback(my_callback)
+        
+        # During streaming:
+        emitter.emit(StreamEvent(type=StreamEventType.DELTA_TEXT, content="Hello"))
+    """
+    
+    def __init__(self) -> None:
+        self._callbacks: List[StreamCallback] = []
+        self._async_callbacks: List[AsyncStreamCallback] = []
+        self._metrics: Optional[StreamMetrics] = None
+        self._collect_metrics: bool = False
+    
+    def add_callback(self, callback: StreamCallback) -> None:
+        """Add a synchronous callback to receive stream events."""
+        self._callbacks.append(callback)
+    
+    def remove_callback(self, callback: StreamCallback) -> None:
+        """Remove a synchronous callback."""
+        if callback in self._callbacks:
+            self._callbacks.remove(callback)
+    
+    def add_async_callback(self, callback: AsyncStreamCallback) -> None:
+        """Add an asynchronous callback to receive stream events."""
+        self._async_callbacks.append(callback)
+    
+    def remove_async_callback(self, callback: AsyncStreamCallback) -> None:
+        """Remove an asynchronous callback."""
+        if callback in self._async_callbacks:
+            self._async_callbacks.remove(callback)
+    
+    def enable_metrics(self) -> None:
+        """Enable metrics collection for this streaming session."""
+        self._collect_metrics = True
+        self._metrics = StreamMetrics()
+    
+    def get_metrics(self) -> Optional[StreamMetrics]:
+        """Get collected metrics (None if metrics not enabled)."""
+        return self._metrics
+    
+    def reset_metrics(self) -> None:
+        """Reset metrics for a new streaming session."""
+        if self._collect_metrics:
+            self._metrics = StreamMetrics()
+    
+    def emit(self, event: StreamEvent) -> None:
+        """
+        Emit a stream event to all registered callbacks.
+        
+        This is the synchronous emission path. For async streaming,
+        use emit_async().
+        """
+        # Update metrics if enabled
+        if self._collect_metrics and self._metrics:
+            self._metrics.update_from_event(event)
+        
+        # Call all sync callbacks
+        for callback in self._callbacks:
+            try:
+                callback(event)
+            except Exception:
+                # Don't let callback errors break streaming
+                pass
+    
+    async def emit_async(self, event: StreamEvent) -> None:
+        """
+        Emit a stream event to all registered callbacks (async version).
+        
+        Calls both sync and async callbacks.
+        """
+        # Update metrics if enabled
+        if self._collect_metrics and self._metrics:
+            self._metrics.update_from_event(event)
+        
+        # Call sync callbacks
+        for callback in self._callbacks:
+            try:
+                callback(event)
+            except Exception:
+                pass
+        
+        # Call async callbacks
+        for callback in self._async_callbacks:
+            try:
+                await callback(event)
+            except Exception:
+                pass
+    
+    @property
+    def has_callbacks(self) -> bool:
+        """Check if any callbacks are registered."""
+        return bool(self._callbacks or self._async_callbacks)
+
+
+def create_text_printer_callback(flush: bool = True) -> StreamCallback:
+    """
+    Create a simple callback that prints text deltas to stdout.
+    
+    Args:
+        flush: Whether to flush stdout after each print (default True)
+    
+    Returns:
+        A StreamCallback that prints DELTA_TEXT content
+    """
+    def printer(event: StreamEvent) -> None:
+        if event.type == StreamEventType.DELTA_TEXT and event.content:
+            print(event.content, end="", flush=flush)
+        elif event.type == StreamEventType.STREAM_END:
+            print()  # Newline at end
+    
+    return printer
+
+
+def create_metrics_callback(metrics: StreamMetrics) -> StreamCallback:
+    """
+    Create a callback that updates a StreamMetrics instance.
+    
+    Args:
+        metrics: The StreamMetrics instance to update
+    
+    Returns:
+        A StreamCallback that updates the metrics
+    """
+    def updater(event: StreamEvent) -> None:
+        metrics.update_from_event(event)
+    
+    return updater

src/praisonai-agents/praisonaiagents/streaming/logging.py

@@ -0,0 +1,207 @@
+"""
+Streaming logging utilities for PraisonAI.
+
+Provides guarded logging for streaming events with clear timestamps
+for request_start, headers_received, first_token, last_token, and stream_end.
+
+Usage:
+    from praisonaiagents.streaming.logging import StreamLogger
+    
+    logger = StreamLogger(verbose=True, metrics=True)
+    logger.log_request_start(model="gpt-4o-mini")
+    # ... streaming ...
+    logger.log_first_token()
+    # ... more tokens ...
+    logger.log_stream_end()
+    print(logger.get_metrics_summary())
+"""
+
+import logging
+import time
+from typing import Optional, Dict, Any
+
+from .events import StreamMetrics, StreamEvent, StreamEventType
+
+
+class StreamLogger:
+    """
+    Guarded logging for streaming events with timing metrics.
+    
+    Only logs when verbose=True or metrics=True to ensure zero overhead
+    when logging is disabled.
+    
+    Attributes:
+        verbose: Whether to log detailed streaming events
+        metrics: Whether to collect and display timing metrics
+    """
+    
+    def __init__(
+        self,
+        verbose: bool = False,
+        metrics: bool = False,
+        logger: Optional[logging.Logger] = None
+    ):
+        self.verbose = verbose
+        self.metrics = metrics
+        self._logger = logger or logging.getLogger(__name__)
+        self._metrics = StreamMetrics() if metrics else None
+        self._enabled = verbose or metrics
+    
+    def log_request_start(
+        self,
+        model: Optional[str] = None,
+        message_count: Optional[int] = None
+    ) -> None:
+        """Log when the streaming request is initiated."""
+        if not self._enabled:
+            return
+        
+        timestamp = time.perf_counter()
+        if self._metrics:
+            self._metrics.request_start = timestamp
+        
+        if self.verbose:
+            extra = f" model={model}" if model else ""
+            extra += f" messages={message_count}" if message_count else ""
+            self._logger.info(f"[STREAM] REQUEST_START{extra}")
+    
+    def log_headers_received(self) -> None:
+        """Log when HTTP response headers are received."""
+        if not self._enabled:
+            return
+        
+        timestamp = time.perf_counter()
+        if self._metrics:
+            self._metrics.headers_received = timestamp
+        
+        if self.verbose:
+            self._logger.info("[STREAM] HEADERS_RECEIVED")
+    
+    def log_first_token(self, content: Optional[str] = None) -> None:
+        """Log when the first content token is received (TTFT marker)."""
+        if not self._enabled:
+            return
+        
+        timestamp = time.perf_counter()
+        if self._metrics:
+            self._metrics.first_token = timestamp
+            self._metrics.token_count = 1
+        
+        if self.verbose:
+            preview = content[:20] + "..." if content and len(content) > 20 else content
+            self._logger.info(f"[STREAM] FIRST_TOKEN content={preview!r}")
+    
+    def log_delta_text(self, content: Optional[str] = None) -> None:
+        """Log a text delta (only when verbose, not for every token)."""
+        if not self._enabled:
+            return
+        
+        if self._metrics:
+            self._metrics.token_count += 1
+        
+        # Don't log every delta to avoid noise - just track metrics
+    
+    def log_last_token(self) -> None:
+        """Log when the last content token is received."""
+        if not self._enabled:
+            return
+        
+        timestamp = time.perf_counter()
+        if self._metrics:
+            self._metrics.last_token = timestamp
+        
+        if self.verbose:
+            self._logger.info("[STREAM] LAST_TOKEN")
+    
+    def log_stream_end(self, chunk_count: Optional[int] = None) -> None:
+        """Log when the stream ends."""
+        if not self._enabled:
+            return
+        
+        timestamp = time.perf_counter()
+        if self._metrics:
+            self._metrics.stream_end = timestamp
+        
+        if self.verbose:
+            extra = f" chunks={chunk_count}" if chunk_count else ""
+            self._logger.info(f"[STREAM] STREAM_END{extra}")
+    
+    def log_error(self, error: str) -> None:
+        """Log a streaming error."""
+        if self.verbose:
+            self._logger.error(f"[STREAM] ERROR: {error}")
+    
+    def get_metrics(self) -> Optional[StreamMetrics]:
+        """Get the collected metrics (None if metrics not enabled)."""
+        return self._metrics
+    
+    def get_metrics_summary(self) -> str:
+        """Get a formatted summary of streaming metrics."""
+        if not self._metrics:
+            return "Metrics not enabled"
+        return self._metrics.format_summary()
+    
+    def get_metrics_dict(self) -> Optional[Dict[str, Any]]:
+        """Get metrics as a dictionary."""
+        if not self._metrics:
+            return None
+        return self._metrics.to_dict()
+    
+    def update_from_event(self, event: StreamEvent) -> None:
+        """Update logger state from a StreamEvent."""
+        if not self._enabled:
+            return
+        
+        if self._metrics:
+            self._metrics.update_from_event(event)
+        
+        if self.verbose:
+            if event.type == StreamEventType.REQUEST_START:
+                self.log_request_start()
+            elif event.type == StreamEventType.HEADERS_RECEIVED:
+                self.log_headers_received()
+            elif event.type == StreamEventType.FIRST_TOKEN:
+                self.log_first_token(event.content)
+            elif event.type == StreamEventType.LAST_TOKEN:
+                self.log_last_token()
+            elif event.type == StreamEventType.STREAM_END:
+                self.log_stream_end()
+            elif event.type == StreamEventType.ERROR:
+                self.log_error(event.error or "Unknown error")
+
+
+def create_logging_callback(
+    verbose: bool = False,
+    metrics: bool = False,
+    logger: Optional[logging.Logger] = None
+) -> tuple:
+    """
+    Create a StreamLogger and a callback function for use with streaming.
+    
+    Args:
+        verbose: Whether to log detailed events
+        metrics: Whether to collect timing metrics
+        logger: Optional custom logger
+    
+    Returns:
+        Tuple of (StreamLogger, callback_function)
+    
+    Usage:
+        stream_logger, callback = create_logging_callback(verbose=True, metrics=True)
+        
+        # Pass callback to streaming method
+        response = client.process_stream_response(
+            ...,
+            stream_callback=callback,
+            emit_events=True
+        )
+        
+        # After streaming, get metrics
+        print(stream_logger.get_metrics_summary())
+    """
+    stream_logger = StreamLogger(verbose=verbose, metrics=metrics, logger=logger)
+    
+    def callback(event: StreamEvent) -> None:
+        stream_logger.update_from_event(event)
+    
+    return stream_logger, callback

src/praisonai-agents/profile-streaming-delay.py

@@ -0,0 +1,232 @@
+#!/usr/bin/env python3
+"""
+Detailed profiler to identify the exact source of streaming delay.
+Traces every step from agent.start() to HTTP request.
+"""
+
+import os
+import sys
+import time
+import functools
+
+os.environ['LOGLEVEL'] = 'DEBUG'
+
+# Microsecond precision timestamps
+def ts():
+    return time.perf_counter()
+
+start_time = ts()
+
+def log(msg):
+    elapsed = (ts() - start_time) * 1000
+    print(f"[{elapsed:8.2f}ms] {msg}")
+
+log("Script starting...")
+
+# ============================================================
+# PATCH 1: OpenAI client initialization
+# ============================================================
+import openai
+
+original_openai_init = openai.OpenAI.__init__
+
+@functools.wraps(original_openai_init)
+def traced_openai_init(self, *args, **kwargs):
+    t0 = ts()
+    result = original_openai_init(self, *args, **kwargs)
+    log(f"OpenAI.__init__() took {(ts()-t0)*1000:.2f}ms")
+    return result
+
+openai.OpenAI.__init__ = traced_openai_init
+
+# ============================================================
+# PATCH 2: OpenAI completions.create
+# ============================================================
+original_create = openai.resources.chat.completions.Completions.create
+
+@functools.wraps(original_create)
+def traced_create(self, *args, **kwargs):
+    log(f">>> OpenAI.create() CALLED - stream={kwargs.get('stream', False)}")
+    t0 = ts()
+    result = original_create(self, *args, **kwargs)
+    log(f"<<< OpenAI.create() RETURNED in {(ts()-t0)*1000:.2f}ms")
+    return result
+
+openai.resources.chat.completions.Completions.create = traced_create
+
+# ============================================================
+# PATCH 3: httpx.Client.send (actual HTTP request)
+# ============================================================
+import httpx
+
+original_send = httpx.Client.send
+
+@functools.wraps(original_send)
+def traced_send(self, request, *args, **kwargs):
+    log(f">>> HTTPX.send() {request.method} {request.url}")
+    t0 = ts()
+    result = original_send(self, request, *args, **kwargs)
+    log(f"<<< HTTPX.send() returned in {(ts()-t0)*1000:.2f}ms - status={result.status_code}")
+    return result
+
+httpx.Client.send = traced_send
+
+# ============================================================
+# PATCH 4: Rich Live display
+# ============================================================
+from rich.live import Live
+
+original_live_init = Live.__init__
+original_live_enter = Live.__enter__
+original_live_exit = Live.__exit__
+
+@functools.wraps(original_live_init)
+def traced_live_init(self, *args, **kwargs):
+    log(f">>> Live.__init__() called")
+    t0 = ts()
+    result = original_live_init(self, *args, **kwargs)
+    log(f"<<< Live.__init__() took {(ts()-t0)*1000:.2f}ms")
+    return result
+
+@functools.wraps(original_live_enter)
+def traced_live_enter(self):
+    log(f">>> Live.__enter__() called")
+    t0 = ts()
+    result = original_live_enter(self)
+    log(f"<<< Live.__enter__() took {(ts()-t0)*1000:.2f}ms")
+    return result
+
+@functools.wraps(original_live_exit)
+def traced_live_exit(self, *args):
+    log(f">>> Live.__exit__() called")
+    t0 = ts()
+    result = original_live_exit(self, *args)
+    log(f"<<< Live.__exit__() took {(ts()-t0)*1000:.2f}ms")
+    return result
+
+Live.__init__ = traced_live_init
+Live.__enter__ = traced_live_enter
+Live.__exit__ = traced_live_exit
+
+log("Patches applied, importing praisonaiagents...")
+
+# ============================================================
+# Import and trace Agent
+# ============================================================
+t0 = ts()
+from praisonaiagents import Agent
+log(f"Agent import took {(ts()-t0)*1000:.2f}ms")
+
+# ============================================================
+# PATCH 5: Agent methods
+# ============================================================
+from praisonaiagents.agent.agent import Agent as AgentClass
+
+original_chat_completion = AgentClass._chat_completion
+original_format_tools = AgentClass._format_tools_for_completion
+
+@functools.wraps(original_chat_completion)
+def traced_chat_completion(self, messages, temperature=1.0, tools=None, stream=True, 
+                           reasoning_steps=False, task_name=None, task_description=None, task_id=None):
+    log(f">>> Agent._chat_completion() stream={stream}, _using_custom_llm={self._using_custom_llm}")
+    t0 = ts()
+    result = original_chat_completion(self, messages, temperature, tools, stream, 
+                                      reasoning_steps, task_name, task_description, task_id)
+    log(f"<<< Agent._chat_completion() took {(ts()-t0)*1000:.2f}ms")
+    return result
+
+@functools.wraps(original_format_tools)
+def traced_format_tools(self, tools):
+    log(f">>> Agent._format_tools_for_completion() tools={tools}")
+    t0 = ts()
+    result = original_format_tools(self, tools)
+    log(f"<<< Agent._format_tools_for_completion() took {(ts()-t0)*1000:.2f}ms")
+    return result
+
+AgentClass._chat_completion = traced_chat_completion
+AgentClass._format_tools_for_completion = traced_format_tools
+
+# ============================================================
+# PATCH 6: OpenAIClient methods
+# ============================================================
+from praisonaiagents.llm.openai_client import OpenAIClient
+
+original_chat_with_tools = OpenAIClient.chat_completion_with_tools
+original_process_stream = OpenAIClient.process_stream_response
+original_create_completion = OpenAIClient.create_completion
+original_format_tools_client = OpenAIClient.format_tools
+
+@functools.wraps(original_chat_with_tools)
+def traced_chat_with_tools(self, *args, **kwargs):
+    log(f">>> OpenAIClient.chat_completion_with_tools() stream={kwargs.get('stream', True)}")
+    t0 = ts()
+    result = original_chat_with_tools(self, *args, **kwargs)
+    log(f"<<< OpenAIClient.chat_completion_with_tools() took {(ts()-t0)*1000:.2f}ms")
+    return result
+
+@functools.wraps(original_process_stream)
+def traced_process_stream(self, *args, **kwargs):
+    log(f">>> OpenAIClient.process_stream_response()")
+    t0 = ts()
+    result = original_process_stream(self, *args, **kwargs)
+    log(f"<<< OpenAIClient.process_stream_response() took {(ts()-t0)*1000:.2f}ms")
+    return result
+
+@functools.wraps(original_create_completion)
+def traced_create_completion(self, *args, **kwargs):
+    log(f">>> OpenAIClient.create_completion() stream={kwargs.get('stream', False)}")
+    t0 = ts()
+    result = original_create_completion(self, *args, **kwargs)
+    log(f"<<< OpenAIClient.create_completion() took {(ts()-t0)*1000:.2f}ms")
+    return result
+
+@functools.wraps(original_format_tools_client)
+def traced_format_tools_client(self, tools):
+    log(f">>> OpenAIClient.format_tools() tools count={len(tools) if tools else 0}")
+    t0 = ts()
+    result = original_format_tools_client(self, tools)
+    log(f"<<< OpenAIClient.format_tools() took {(ts()-t0)*1000:.2f}ms")
+    return result
+
+OpenAIClient.chat_completion_with_tools = traced_chat_with_tools
+OpenAIClient.process_stream_response = traced_process_stream
+OpenAIClient.create_completion = traced_create_completion
+OpenAIClient.format_tools = traced_format_tools_client
+
+# ============================================================
+# PATCH 7: sync_client property (lazy init)
+# ============================================================
+original_sync_client_getter = OpenAIClient.sync_client.fget
+
+def traced_sync_client_getter(self):
+    log(f">>> OpenAIClient.sync_client property accessed")
+    t0 = ts()
+    result = original_sync_client_getter(self)
+    log(f"<<< OpenAIClient.sync_client took {(ts()-t0)*1000:.2f}ms")
+    return result
+
+OpenAIClient.sync_client = property(traced_sync_client_getter)
+
+log("All patches applied. Creating agent...")
+
+# ============================================================
+# RUN THE TEST
+# ============================================================
+t0 = ts()
+agent = Agent(
+    instructions="You are a helpful assistant"
+)
+log(f"Agent created in {(ts()-t0)*1000:.2f}ms")
+log(f"Agent.stream={agent.stream}, Agent.verbose={agent.verbose}")
+
+log("=" * 60)
+log("Starting agent.start()...")
+log("=" * 60)
+
+t0 = ts()
+result = agent.start("Say hello in one word")
+log(f"agent.start() completed in {(ts()-t0)*1000:.2f}ms")
+
+log("=" * 60)
+log("DONE")
+log("=" * 60)

src/praisonai-agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.11.1"
+version = "0.11.3"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 readme = "README.md"
 requires-python = ">=3.10"

src/praisonai-agents/streaming-openai-basic.py

@@ -0,0 +1,24 @@
+"""
+Streaming Example with TTFT Metrics
+
+This example demonstrates true pass-through streaming with timing metrics.
+When verbose output is enabled, tokens are displayed as they arrive from the provider.
+
+Key timing metrics:
+- TTFT (Time To First Token): Time from request to first token received
+- Stream Duration: Time from first to last token
+- Total Time: End-to-end request time
+"""
+from praisonaiagents import Agent
+from praisonaiagents.config import OutputConfig
+
+# Create agent with verbose output (shows streaming progress)
+agent = Agent(
+    instructions="You are a helpful assistant",
+    llm="gpt-4o-mini",
+    output=OutputConfig(verbose=True)  # Show generation progress
+)
+
+# Run the agent - verbose mode shows streaming progress via Rich Live display
+result = agent.start("Write a short paragraph about the history of computing")
+print(f"\n--- Response ---\n{result}")

src/praisonai-agents/streaming-openai.py

@@ -1,11 +0,0 @@
-from praisonaiagents import Agent
-
-agent = Agent(
-    instructions="You are a helpful assistant",
-    llm="gpt-4o-mini"
-)
-
-# Use chat method with stream=True for streaming responses
-for chunk in agent.chat("Write a short paragraph about the history of computing", stream=True):
-    print(chunk, end="", flush=True)
-print()  # Final newline

src/praisonai-agents/tests/unit/streaming/__init__.py

@@ -0,0 +1 @@
+"""Unit tests for streaming module."""

src/praisonai-agents/tests/unit/streaming/test_events.py

@@ -0,0 +1,387 @@
+"""
+Unit tests for streaming events module.
+
+Tests cover:
+- StreamEvent creation and representation
+- StreamEventType enum values
+- StreamMetrics calculations (TTFT, stream duration, total time)
+- StreamEventEmitter callback management
+- create_text_printer_callback and create_metrics_callback helpers
+"""
+
+import time
+from praisonaiagents.streaming.events import (
+    StreamEvent,
+    StreamEventType,
+    StreamMetrics,
+    StreamEventEmitter,
+    create_text_printer_callback,
+    create_metrics_callback,
+)
+
+
+class TestStreamEventType:
+    """Tests for StreamEventType enum."""
+    
+    def test_event_types_exist(self):
+        """All required event types should exist."""
+        assert StreamEventType.REQUEST_START.value == "request_start"
+        assert StreamEventType.HEADERS_RECEIVED.value == "headers_received"
+        assert StreamEventType.FIRST_TOKEN.value == "first_token"
+        assert StreamEventType.DELTA_TEXT.value == "delta_text"
+        assert StreamEventType.DELTA_TOOL_CALL.value == "delta_tool_call"
+        assert StreamEventType.TOOL_CALL_END.value == "tool_call_end"
+        assert StreamEventType.LAST_TOKEN.value == "last_token"
+        assert StreamEventType.STREAM_END.value == "stream_end"
+        assert StreamEventType.ERROR.value == "error"
+
+
+class TestStreamEvent:
+    """Tests for StreamEvent dataclass."""
+    
+    def test_create_basic_event(self):
+        """Create a basic event with type only."""
+        event = StreamEvent(type=StreamEventType.REQUEST_START)
+        assert event.type == StreamEventType.REQUEST_START
+        assert event.timestamp > 0
+        assert event.content is None
+        assert event.tool_call is None
+        assert event.metadata is None
+        assert event.error is None
+    
+    def test_create_event_with_content(self):
+        """Create an event with content."""
+        event = StreamEvent(
+            type=StreamEventType.DELTA_TEXT,
+            content="Hello"
+        )
+        assert event.type == StreamEventType.DELTA_TEXT
+        assert event.content == "Hello"
+    
+    def test_create_event_with_tool_call(self):
+        """Create an event with tool call data."""
+        tool_data = {"name": "calculator", "arguments": '{"x": 1}'}
+        event = StreamEvent(
+            type=StreamEventType.DELTA_TOOL_CALL,
+            tool_call=tool_data
+        )
+        assert event.type == StreamEventType.DELTA_TOOL_CALL
+        assert event.tool_call == tool_data
+    
+    def test_create_event_with_metadata(self):
+        """Create an event with metadata."""
+        event = StreamEvent(
+            type=StreamEventType.REQUEST_START,
+            metadata={"model": "gpt-4o-mini", "message_count": 2}
+        )
+        assert event.metadata["model"] == "gpt-4o-mini"
+        assert event.metadata["message_count"] == 2
+    
+    def test_create_error_event(self):
+        """Create an error event."""
+        event = StreamEvent(
+            type=StreamEventType.ERROR,
+            error="Connection timeout"
+        )
+        assert event.type == StreamEventType.ERROR
+        assert event.error == "Connection timeout"
+    
+    def test_event_repr(self):
+        """Event repr should be informative."""
+        event = StreamEvent(
+            type=StreamEventType.DELTA_TEXT,
+            content="Hello world"
+        )
+        repr_str = repr(event)
+        assert "DELTA_TEXT" in repr_str or "delta_text" in repr_str
+        assert "Hello world" in repr_str
+
+
+class TestStreamMetrics:
+    """Tests for StreamMetrics dataclass."""
+    
+    def test_default_metrics(self):
+        """Default metrics should be zero."""
+        metrics = StreamMetrics()
+        assert metrics.request_start == 0.0
+        assert metrics.headers_received == 0.0
+        assert metrics.first_token == 0.0
+        assert metrics.last_token == 0.0
+        assert metrics.stream_end == 0.0
+        assert metrics.token_count == 0
+    
+    def test_ttft_calculation(self):
+        """TTFT should be first_token - request_start."""
+        metrics = StreamMetrics()
+        metrics.request_start = 100.0
+        metrics.first_token = 100.5
+        assert metrics.ttft == 0.5
+    
+    def test_ttft_zero_when_no_first_token(self):
+        """TTFT should be 0 when first_token not set."""
+        metrics = StreamMetrics()
+        metrics.request_start = 100.0
+        assert metrics.ttft == 0.0
+    
+    def test_stream_duration_calculation(self):
+        """Stream duration should be last_token - first_token."""
+        metrics = StreamMetrics()
+        metrics.first_token = 100.0
+        metrics.last_token = 102.0
+        assert metrics.stream_duration == 2.0
+    
+    def test_total_time_calculation(self):
+        """Total time should be stream_end - request_start."""
+        metrics = StreamMetrics()
+        metrics.request_start = 100.0
+        metrics.stream_end = 103.0
+        assert metrics.total_time == 3.0
+    
+    def test_tokens_per_second(self):
+        """Tokens per second calculation."""
+        metrics = StreamMetrics()
+        metrics.first_token = 100.0
+        metrics.last_token = 102.0  # 2 second duration
+        metrics.token_count = 100
+        assert metrics.tokens_per_second == 50.0
+    
+    def test_tokens_per_second_zero_duration(self):
+        """Tokens per second should be 0 when duration is 0."""
+        metrics = StreamMetrics()
+        assert metrics.tokens_per_second == 0.0
+    
+    def test_update_from_event(self):
+        """Metrics should update from events."""
+        metrics = StreamMetrics()
+        
+        # Request start
+        event1 = StreamEvent(type=StreamEventType.REQUEST_START, timestamp=100.0)
+        metrics.update_from_event(event1)
+        assert metrics.request_start == 100.0
+        
+        # Headers received
+        event2 = StreamEvent(type=StreamEventType.HEADERS_RECEIVED, timestamp=100.2)
+        metrics.update_from_event(event2)
+        assert metrics.headers_received == 100.2
+        
+        # First token
+        event3 = StreamEvent(type=StreamEventType.FIRST_TOKEN, timestamp=100.5)
+        metrics.update_from_event(event3)
+        assert metrics.first_token == 100.5
+        assert metrics.token_count == 1
+        
+        # Delta text
+        event4 = StreamEvent(type=StreamEventType.DELTA_TEXT, timestamp=100.6)
+        metrics.update_from_event(event4)
+        assert metrics.token_count == 2
+        
+        # Last token
+        event5 = StreamEvent(type=StreamEventType.LAST_TOKEN, timestamp=102.0)
+        metrics.update_from_event(event5)
+        assert metrics.last_token == 102.0
+        
+        # Stream end
+        event6 = StreamEvent(type=StreamEventType.STREAM_END, timestamp=102.1)
+        metrics.update_from_event(event6)
+        assert metrics.stream_end == 102.1
+    
+    def test_to_dict(self):
+        """Metrics should convert to dict."""
+        metrics = StreamMetrics()
+        metrics.request_start = 100.0
+        metrics.first_token = 100.5
+        metrics.last_token = 102.0
+        metrics.stream_end = 102.1
+        metrics.token_count = 50
+        
+        d = metrics.to_dict()
+        assert "ttft_ms" in d
+        assert "stream_duration_ms" in d
+        assert "total_time_ms" in d
+        assert "token_count" in d
+        assert "tokens_per_second" in d
+    
+    def test_format_summary(self):
+        """Metrics should format as summary string."""
+        metrics = StreamMetrics()
+        metrics.request_start = 100.0
+        metrics.first_token = 100.5
+        metrics.last_token = 102.0
+        metrics.stream_end = 102.1
+        metrics.token_count = 50
+        
+        summary = metrics.format_summary()
+        assert "TTFT" in summary
+        assert "Stream" in summary
+        assert "Total" in summary
+        assert "Tokens" in summary
+
+
+class TestStreamEventEmitter:
+    """Tests for StreamEventEmitter."""
+    
+    def test_create_emitter(self):
+        """Create an emitter."""
+        emitter = StreamEventEmitter()
+        assert not emitter.has_callbacks
+    
+    def test_add_callback(self):
+        """Add a callback."""
+        emitter = StreamEventEmitter()
+        events = []
+        
+        def callback(event):
+            events.append(event)
+        
+        emitter.add_callback(callback)
+        assert emitter.has_callbacks
+    
+    def test_remove_callback(self):
+        """Remove a callback."""
+        emitter = StreamEventEmitter()
+        events = []
+        
+        def callback(event):
+            events.append(event)
+        
+        emitter.add_callback(callback)
+        emitter.remove_callback(callback)
+        assert not emitter.has_callbacks
+    
+    def test_emit_calls_callbacks(self):
+        """Emit should call all callbacks."""
+        emitter = StreamEventEmitter()
+        events1 = []
+        events2 = []
+        
+        emitter.add_callback(lambda e: events1.append(e))
+        emitter.add_callback(lambda e: events2.append(e))
+        
+        event = StreamEvent(type=StreamEventType.DELTA_TEXT, content="Hello")
+        emitter.emit(event)
+        
+        assert len(events1) == 1
+        assert len(events2) == 1
+        assert events1[0].content == "Hello"
+    
+    def test_emit_with_metrics(self):
+        """Emit should update metrics when enabled."""
+        emitter = StreamEventEmitter()
+        emitter.enable_metrics()
+        
+        emitter.emit(StreamEvent(type=StreamEventType.REQUEST_START, timestamp=100.0))
+        emitter.emit(StreamEvent(type=StreamEventType.FIRST_TOKEN, timestamp=100.5))
+        emitter.emit(StreamEvent(type=StreamEventType.STREAM_END, timestamp=102.0))
+        
+        metrics = emitter.get_metrics()
+        assert metrics is not None
+        assert metrics.request_start == 100.0
+        assert metrics.first_token == 100.5
+        assert metrics.stream_end == 102.0
+    
+    def test_callback_error_does_not_break_emission(self):
+        """Callback errors should not break other callbacks."""
+        emitter = StreamEventEmitter()
+        events = []
+        
+        def bad_callback(event):
+            raise ValueError("Test error")
+        
+        def good_callback(event):
+            events.append(event)
+        
+        emitter.add_callback(bad_callback)
+        emitter.add_callback(good_callback)
+        
+        event = StreamEvent(type=StreamEventType.DELTA_TEXT)
+        emitter.emit(event)  # Should not raise
+        
+        assert len(events) == 1
+    
+    def test_reset_metrics(self):
+        """Reset metrics should create new metrics."""
+        emitter = StreamEventEmitter()
+        emitter.enable_metrics()
+        
+        emitter.emit(StreamEvent(type=StreamEventType.REQUEST_START, timestamp=100.0))
+        emitter.reset_metrics()
+        
+        metrics = emitter.get_metrics()
+        assert metrics.request_start == 0.0
+
+
+class TestHelperFunctions:
+    """Tests for helper functions."""
+    
+    def test_create_text_printer_callback(self, capsys):
+        """Text printer callback should print content."""
+        callback = create_text_printer_callback(flush=True)
+        
+        callback(StreamEvent(type=StreamEventType.DELTA_TEXT, content="Hello"))
+        callback(StreamEvent(type=StreamEventType.DELTA_TEXT, content=" World"))
+        callback(StreamEvent(type=StreamEventType.STREAM_END))
+        
+        captured = capsys.readouterr()
+        assert "Hello World" in captured.out
+    
+    def test_create_metrics_callback(self):
+        """Metrics callback should update metrics."""
+        metrics = StreamMetrics()
+        callback = create_metrics_callback(metrics)
+        
+        callback(StreamEvent(type=StreamEventType.REQUEST_START, timestamp=100.0))
+        callback(StreamEvent(type=StreamEventType.FIRST_TOKEN, timestamp=100.5))
+        
+        assert metrics.request_start == 100.0
+        assert metrics.first_token == 100.5
+
+
+class TestStreamingEventOrdering:
+    """Tests for event ordering and pass-through behavior."""
+    
+    def test_events_emitted_immediately(self):
+        """Events should be emitted immediately, not buffered."""
+        emitter = StreamEventEmitter()
+        timestamps = []
+        
+        def callback(event):
+            timestamps.append((event.type, time.perf_counter()))
+        
+        emitter.add_callback(callback)
+        
+        # Simulate streaming with small delays
+        emitter.emit(StreamEvent(type=StreamEventType.REQUEST_START))
+        time.sleep(0.01)
+        emitter.emit(StreamEvent(type=StreamEventType.FIRST_TOKEN))
+        time.sleep(0.01)
+        emitter.emit(StreamEvent(type=StreamEventType.DELTA_TEXT))
+        
+        # Verify events were captured in order
+        assert len(timestamps) == 3
+        assert timestamps[0][0] == StreamEventType.REQUEST_START
+        assert timestamps[1][0] == StreamEventType.FIRST_TOKEN
+        assert timestamps[2][0] == StreamEventType.DELTA_TEXT
+        
+        # Verify timestamps are increasing (no buffering)
+        assert timestamps[0][1] < timestamps[1][1] < timestamps[2][1]
+    
+    def test_no_buffering_before_first_token(self):
+        """Events should not be buffered before first token."""
+        emitter = StreamEventEmitter()
+        emitter.enable_metrics()
+        
+        # Emit request start
+        t1 = time.perf_counter()
+        emitter.emit(StreamEvent(type=StreamEventType.REQUEST_START, timestamp=t1))
+        
+        # Small delay (simulating network)
+        time.sleep(0.05)
+        
+        # Emit first token
+        t2 = time.perf_counter()
+        emitter.emit(StreamEvent(type=StreamEventType.FIRST_TOKEN, timestamp=t2))
+        
+        metrics = emitter.get_metrics()
+        
+        # TTFT should reflect the actual delay
+        assert metrics.ttft >= 0.04  # Allow some tolerance

src/praisonai-agents/tests/unit/test_profiling.py

@@ -0,0 +1,371 @@
+"""
+Unit tests for the core SDK profiling module.
+
+Tests cover:
+- Profiler enable/disable
+- Timing recording
+- Streaming tracking
+- Phase tracking
+- Report generation
+- Zero overhead when disabled
+"""
+
+import pytest
+import time
+from unittest.mock import patch
+
+
+class TestProfilerBasics:
+    """Test basic Profiler functionality."""
+    
+    def test_profiler_disabled_by_default(self):
+        """Profiler should be disabled by default."""
+        from praisonaiagents.profiling import Profiler
+        Profiler.disable()  # Ensure clean state
+        assert not Profiler.is_enabled()
+    
+    def test_profiler_enable_disable(self):
+        """Test enable/disable functionality."""
+        from praisonaiagents.profiling import Profiler
+        
+        Profiler.disable()
+        assert not Profiler.is_enabled()
+        
+        Profiler.enable()
+        assert Profiler.is_enabled()
+        
+        Profiler.disable()
+        assert not Profiler.is_enabled()
+    
+    def test_profiler_env_var_enable(self):
+        """Test enabling via environment variable."""
+        from praisonaiagents.profiling import Profiler
+        
+        Profiler.disable()
+        
+        with patch.dict('os.environ', {'PRAISONAI_PROFILE': '1'}):
+            assert Profiler.is_enabled()
+        
+        with patch.dict('os.environ', {'PRAISONAI_PROFILE': 'true'}):
+            assert Profiler.is_enabled()
+        
+        with patch.dict('os.environ', {'PRAISONAI_PROFILE': 'yes'}):
+            assert Profiler.is_enabled()
+    
+    def test_profiler_clear(self):
+        """Test clearing profiling data."""
+        from praisonaiagents.profiling import Profiler
+        
+        Profiler.enable()
+        Profiler.record_timing("test", 100.0)
+        assert len(Profiler.get_timings()) > 0
+        
+        Profiler.clear()
+        assert len(Profiler.get_timings()) == 0
+        Profiler.disable()
+
+
+class TestTimingRecording:
+    """Test timing recording functionality."""
+    
+    def test_record_timing_when_enabled(self):
+        """Timing should be recorded when profiler is enabled."""
+        from praisonaiagents.profiling import Profiler
+        
+        Profiler.enable()
+        Profiler.clear()
+        
+        Profiler.record_timing("test_op", 123.45, category="test")
+        
+        timings = Profiler.get_timings()
+        assert len(timings) == 1
+        assert timings[0].name == "test_op"
+        assert timings[0].duration_ms == 123.45
+        assert timings[0].category == "test"
+        
+        Profiler.disable()
+    
+    def test_record_timing_when_disabled(self):
+        """Timing should NOT be recorded when profiler is disabled."""
+        from praisonaiagents.profiling import Profiler
+        
+        Profiler.disable()
+        Profiler.clear()
+        
+        Profiler.record_timing("test_op", 123.45)
+        
+        timings = Profiler.get_timings()
+        assert len(timings) == 0
+    
+    def test_record_timing_with_metadata(self):
+        """Timing should include metadata."""
+        from praisonaiagents.profiling import Profiler
+        
+        Profiler.enable()
+        Profiler.clear()
+        
+        Profiler.record_timing("test_op", 100.0, metadata={"key": "value"})
+        
+        timings = Profiler.get_timings()
+        assert timings[0].metadata == {"key": "value"}
+        
+        Profiler.disable()
+
+
+class TestPhaseTracking:
+    """Test phase tracking functionality."""
+    
+    def test_phase_context_manager(self):
+        """Phase context manager should record timing."""
+        from praisonaiagents.profiling import Profiler
+        
+        Profiler.enable()
+        Profiler.clear()
+        
+        with Profiler.phase("test_phase"):
+            time.sleep(0.01)  # 10ms
+        
+        timings = Profiler.get_timings(category="phase")
+        assert len(timings) == 1
+        assert timings[0].name == "test_phase"
+        assert timings[0].duration_ms >= 10  # At least 10ms
+        
+        Profiler.disable()
+    
+    def test_nested_phases(self):
+        """Nested phases should track parent."""
+        from praisonaiagents.profiling import Profiler
+        
+        Profiler.enable()
+        Profiler.clear()
+        
+        with Profiler.phase("outer"):
+            with Profiler.phase("inner"):
+                time.sleep(0.005)
+        
+        timings = Profiler.get_timings(category="phase")
+        assert len(timings) == 2
+        
+        inner = next(t for t in timings if t.name == "inner")
+        assert inner.parent == "outer"
+        
+        Profiler.disable()
+
+
+class TestStreamingTracker:
+    """Test streaming tracker functionality."""
+    
+    def test_streaming_ttft(self):
+        """Streaming tracker should measure TTFT."""
+        from praisonaiagents.profiling import Profiler, StreamingTracker
+        
+        Profiler.enable()
+        Profiler.clear()
+        
+        tracker = StreamingTracker("test_stream")
+        tracker.start()
+        time.sleep(0.01)  # 10ms to first token
+        tracker.first_token()
+        time.sleep(0.02)  # 20ms more
+        tracker.end()
+        
+        assert tracker.ttft_ms >= 10
+        assert tracker.elapsed_ms >= 30
+        
+        records = Profiler.get_streaming_records()
+        assert len(records) == 1
+        assert records[0].name == "test_stream"
+        assert records[0].ttft_ms >= 10
+        
+        Profiler.disable()
+    
+    def test_streaming_context_manager(self):
+        """Streaming context manager should work."""
+        from praisonaiagents.profiling import Profiler
+        
+        Profiler.enable()
+        Profiler.clear()
+        
+        with Profiler.streaming("test_stream") as tracker:
+            time.sleep(0.01)
+            tracker.first_token()
+            tracker.chunk()
+            tracker.chunk()
+        
+        records = Profiler.get_streaming_records()
+        assert len(records) == 1
+        assert records[0].chunk_count == 2
+        
+        Profiler.disable()
+
+
+class TestProfileDecorators:
+    """Test profiling decorators."""
+    
+    def test_profile_func_decorator(self):
+        """profile_func decorator should record function timing."""
+        from praisonaiagents.profiling import Profiler, profile_func
+        
+        Profiler.enable()
+        Profiler.clear()
+        
+        @profile_func
+        def slow_function():
+            time.sleep(0.01)
+            return "done"
+        
+        result = slow_function()
+        assert result == "done"
+        
+        timings = Profiler.get_timings(category="function")
+        assert len(timings) == 1
+        assert timings[0].name == "slow_function"
+        assert timings[0].duration_ms >= 10
+        
+        Profiler.disable()
+    
+    def test_profile_func_disabled(self):
+        """profile_func should have zero overhead when disabled."""
+        from praisonaiagents.profiling import Profiler, profile_func
+        
+        Profiler.disable()
+        Profiler.clear()
+        
+        @profile_func
+        def fast_function():
+            return "done"
+        
+        result = fast_function()
+        assert result == "done"
+        
+        timings = Profiler.get_timings()
+        assert len(timings) == 0
+
+
+class TestProfileBlock:
+    """Test profile_block context manager."""
+    
+    def test_profile_block(self):
+        """profile_block should record block timing."""
+        from praisonaiagents.profiling import Profiler, profile_block
+        
+        Profiler.enable()
+        Profiler.clear()
+        
+        with profile_block("my_block", category="custom"):
+            time.sleep(0.01)
+        
+        timings = Profiler.get_timings(category="custom")
+        assert len(timings) == 1
+        assert timings[0].name == "my_block"
+        
+        Profiler.disable()
+    
+    def test_profile_block_disabled(self):
+        """profile_block should have zero overhead when disabled."""
+        from praisonaiagents.profiling import Profiler, profile_block
+        
+        Profiler.disable()
+        Profiler.clear()
+        
+        with profile_block("my_block"):
+            pass
+        
+        timings = Profiler.get_timings()
+        assert len(timings) == 0
+
+
+class TestReporting:
+    """Test report generation."""
+    
+    def test_get_summary(self):
+        """get_summary should return structured data."""
+        from praisonaiagents.profiling import Profiler
+        
+        Profiler.enable()
+        Profiler.clear()
+        
+        Profiler.record_timing("op1", 100.0, category="cat1")
+        Profiler.record_timing("op2", 200.0, category="cat2")
+        
+        summary = Profiler.get_summary()
+        
+        assert summary['total_time_ms'] == 300.0
+        assert summary['timing_count'] == 2
+        assert 'cat1' in summary['by_category']
+        assert 'cat2' in summary['by_category']
+        assert len(summary['slowest_operations']) == 2
+        
+        Profiler.disable()
+    
+    def test_export_json(self):
+        """export_json should return valid JSON."""
+        from praisonaiagents.profiling import Profiler
+        import json
+        
+        Profiler.enable()
+        Profiler.clear()
+        
+        Profiler.record_timing("test", 100.0)
+        
+        json_str = Profiler.export_json()
+        data = json.loads(json_str)
+        
+        assert 'summary' in data
+        assert 'timings' in data
+        assert len(data['timings']) == 1
+        
+        Profiler.disable()
+
+
+class TestWarmup:
+    """Test warmup function."""
+    
+    def test_warmup_returns_timings(self):
+        """warmup should return timing information."""
+        from praisonaiagents import warmup
+        
+        result = warmup(include_litellm=False, include_openai=False)
+        assert isinstance(result, dict)
+    
+    def test_warmup_with_litellm(self):
+        """warmup with litellm should return litellm timing."""
+        from praisonaiagents import warmup
+        
+        result = warmup(include_litellm=True, include_openai=False)
+        assert 'litellm' in result
+        # Should be positive (imported) or -1 (not available)
+        assert result['litellm'] > 0 or result['litellm'] == -1
+
+
+class TestZeroOverhead:
+    """Test that profiling has zero overhead when disabled."""
+    
+    def test_record_timing_overhead(self):
+        """record_timing should be fast when disabled."""
+        from praisonaiagents.profiling import Profiler
+        
+        Profiler.disable()
+        
+        start = time.perf_counter()
+        for _ in range(10000):
+            Profiler.record_timing("test", 100.0)
+        elapsed = (time.perf_counter() - start) * 1000
+        
+        # Should complete 10k calls in under 10ms when disabled
+        assert elapsed < 10, f"record_timing took {elapsed}ms for 10k calls"
+    
+    def test_phase_context_overhead(self):
+        """phase context manager should be fast when disabled."""
+        from praisonaiagents.profiling import Profiler
+        
+        Profiler.disable()
+        
+        start = time.perf_counter()
+        for _ in range(1000):
+            with Profiler.phase("test"):
+                pass
+        elapsed = (time.perf_counter() - start) * 1000
+        
+        # Should complete 1k calls in under 10ms when disabled
+        assert elapsed < 10, f"phase took {elapsed}ms for 1k calls"

src/praisonai-agents/uv.lock

@@ -2422,7 +2422,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.11.1"
+version = "0.11.3"
 source = { editable = "." }
 dependencies = [
     { name = "aiohttp" },

src/praisonai/examples/python/guardrails/guardrails-advanced.py

@@ -0,0 +1,65 @@
+"""
+Guardrails - Advanced Example
+
+Demonstrates advanced guardrails patterns including:
+- Multi-agent configuration
+- Custom callbacks/handlers
+- Error handling
+- All available options
+
+Expected Output:
+    Comprehensive demonstration of guardrails with multiple agents.
+"""
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# ============================================================
+# Section 1: Custom Configuration
+# ============================================================
+
+# Define custom guardrails configuration
+# config = ...
+
+# ============================================================
+# Section 2: Multi-Agent Setup
+# ============================================================
+
+agent1 = Agent(
+    name="Agent1",
+    instructions="You are the first agent",
+    # guardrails=config,
+)
+
+agent2 = Agent(
+    name="Agent2",
+    instructions="You are the second agent",
+    # guardrails=config,
+)
+
+# ============================================================
+# Section 3: Task Definition
+# ============================================================
+
+task = Task(
+    description="Demonstrate guardrails with multiple agents",
+    agent=agent1,
+)
+
+# ============================================================
+# Section 4: Execution
+# ============================================================
+
+agents = PraisonAIAgents(agents=[agent1, agent2], tasks=[task])
+result = agents.start()
+print(result)
+
+# ============================================================
+# Section 5: All Options Reference
+# ============================================================
+"""
+Guardrails Options:
+──────────────────
+| Option          | Type     | Default | Description           |
+|-----------------|----------|---------|----------------------|
+| option1         | str      | None    | Description here     |
+| option2         | bool     | False   | Description here     |
+"""

src/praisonai/examples/python/guardrails/guardrails-basic.py

@@ -0,0 +1,19 @@
+"""
+Guardrails - Basic Example
+
+Demonstrates minimal usage of guardrails with a single agent.
+
+Expected Output:
+    Agent response demonstrating guardrails functionality.
+"""
+from praisonaiagents import Agent
+
+# Basic guardrails usage
+agent = Agent(
+    instructions="You are a helpful assistant",
+    # guardrails=...  # Configure guardrails here
+)
+
+# Run the agent
+result = agent.start("Hello! Demonstrate guardrails functionality.")
+print(result)

src/praisonai/praisonai.rb

@@ -3,8 +3,8 @@ class Praisonai < Formula
   
     desc "AI tools for various AI applications"
     homepage "https://github.com/MervinPraison/PraisonAI"
-    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v3.7.2.tar.gz"
-    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v3.7.2.tar.gz | shasum -a 256`.split.first
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v3.7.7.tar.gz"
+    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v3.7.7.tar.gz | shasum -a 256`.split.first
     license "MIT"
   
     depends_on "[email protected]"

src/praisonai/praisonai/cli/app.py

@@ -141,11 +141,18 @@ def main_callback(
     )
     set_output_controller(state.output_controller)
     
-    # If no command provided, show help or handle legacy behavior
+    # If no command provided, start interactive mode
     if ctx.invoked_subcommand is None:
-        # Check if there are remaining args (legacy behavior)
-        # This is handled by the legacy adapter in main.py
-        pass
+        from praisonai.cli.interactive.async_tui import AsyncTUI, AsyncTUIConfig
+        
+        tui_config = AsyncTUIConfig(
+            model="gpt-4o-mini",
+            show_logo=True,
+            show_status_bar=state.output_format != OutputFormat.json,
+        )
+        
+        tui = AsyncTUI(config=tui_config)
+        tui.run()
 
 
 def get_output_controller() -> OutputController:
@@ -266,6 +273,115 @@ def register_commands():
     app.add_typer(test_app, name="test", help="Run test suite with tier and provider options")
     app.add_typer(examples_app, name="examples", help="Run and manage example files")
     
+    # Register standardise command
+    try:
+        standardise_app = typer.Typer(name="standardise", help="Documentation and examples standardisation (FDEP)")
+        
+        @standardise_app.command("check")
+        def standardise_check(
+            path: str = typer.Option(".", "--path", "-p", help="Project root path"),
+            feature: str = typer.Option(None, "--feature", help="Specific feature slug"),
+            scope: str = typer.Option("all", "--scope", help="Scope: all, docs, examples, sdk, cli"),
+            ci: bool = typer.Option(False, "--ci", help="CI mode"),
+        ):
+            """Check for standardisation issues."""
+            from .commands.standardise import _run_check
+            import argparse
+            args = argparse.Namespace(path=path, feature=feature, scope=scope, ci=ci, dry_run=True)
+            _run_check(args)
+        
+        @standardise_app.command("report")
+        def standardise_report(
+            path: str = typer.Option(".", "--path", "-p", help="Project root path"),
+            format: str = typer.Option("text", "--format", "-f", help="Format: text, json, markdown"),
+            output: str = typer.Option(None, "--output", "-o", help="Output file"),
+            ci: bool = typer.Option(False, "--ci", help="CI mode"),
+        ):
+            """Generate detailed report."""
+            from .commands.standardise import _run_report
+            import argparse
+            args = argparse.Namespace(path=path, format=format, output=output, ci=ci, feature=None, scope="all", dry_run=True)
+            _run_report(args)
+        
+        @standardise_app.command("fix")
+        def standardise_fix(
+            path: str = typer.Option(".", "--path", "-p", help="Project root path"),
+            feature: str = typer.Option(None, "--feature", help="Specific feature slug"),
+            apply: bool = typer.Option(False, "--apply", help="Actually apply changes"),
+            no_backup: bool = typer.Option(False, "--no-backup", help="Don't create backups"),
+        ):
+            """Fix standardisation issues."""
+            from .commands.standardise import _run_fix
+            import argparse
+            args = argparse.Namespace(path=path, feature=feature, apply=apply, no_backup=no_backup, scope="all", ci=False, dry_run=not apply)
+            _run_fix(args)
+        
+        @standardise_app.command("init")
+        def standardise_init(
+            feature: str = typer.Argument(..., help="Feature slug to initialise"),
+            path: str = typer.Option(".", "--path", "-p", help="Project root path"),
+            apply: bool = typer.Option(False, "--apply", help="Actually create files"),
+        ):
+            """Initialise a new feature with all required artifacts."""
+            from .commands.standardise import _run_init
+            import argparse
+            args = argparse.Namespace(feature=feature, path=path, apply=apply, scope="all", ci=False, dry_run=not apply)
+            _run_init(args)
+        
+        @standardise_app.command("ai")
+        def standardise_ai(
+            feature: str = typer.Argument(..., help="Feature slug to generate content for"),
+            gen_type: str = typer.Option("all", "--type", "-t", help="Type: docs, examples, all"),
+            apply: bool = typer.Option(False, "--apply", help="Actually create files"),
+            verify: bool = typer.Option(False, "--verify", help="Verify with AI"),
+            model: str = typer.Option("gpt-4o-mini", "--model", help="LLM model"),
+            path: str = typer.Option(".", "--path", "-p", help="Project root path"),
+        ):
+            """AI-powered generation of docs/examples."""
+            from .commands.standardise import _run_ai
+            import argparse
+            args = argparse.Namespace(feature=feature, type=gen_type, apply=apply, verify=verify, model=model, path=path, scope="all", ci=False, dry_run=not apply)
+            _run_ai(args)
+        
+        @standardise_app.command("checkpoint")
+        def standardise_checkpoint(
+            message: str = typer.Option(None, "--message", "-m", help="Checkpoint message"),
+            path: str = typer.Option(".", "--path", "-p", help="Repository path"),
+        ):
+            """Create an undo checkpoint."""
+            from .commands.standardise import _run_checkpoint
+            import argparse
+            args = argparse.Namespace(message=message, path=path)
+            _run_checkpoint(args)
+        
+        @standardise_app.command("undo")
+        def standardise_undo(
+            checkpoint: str = typer.Option(None, "--checkpoint", help="Checkpoint ID"),
+            list_checkpoints: bool = typer.Option(False, "--list", help="List checkpoints"),
+            path: str = typer.Option(".", "--path", "-p", help="Repository path"),
+        ):
+            """Undo to a previous checkpoint."""
+            from .commands.standardise import _run_undo
+            import argparse
+            args = argparse.Namespace(checkpoint=checkpoint, list=list_checkpoints, path=path)
+            _run_undo(args)
+        
+        @standardise_app.command("redo")
+        def standardise_redo(
+            path: str = typer.Option(".", "--path", "-p", help="Repository path"),
+        ):
+            """Redo after an undo."""
+            from .commands.standardise import _run_redo
+            import argparse
+            args = argparse.Namespace(path=path)
+            _run_redo(args)
+        
+        app.add_typer(standardise_app, name="standardise", help="Documentation and examples standardisation (FDEP)")
+        # Also register as 'standardize' for US spelling
+        app.add_typer(standardise_app, name="standardize", help="Documentation and examples standardisation (FDEP)")
+    except Exception:
+        pass  # Graceful degradation if standardise module not available
+    
     # Register TUI and queue commands
     tui_app = create_tui_debug_app()
     queue_app = create_queue_app()

src/praisonai/praisonai/cli/commands/chat.py

@@ -127,7 +127,7 @@ def chat_main(
     profile: bool = typer.Option(False, "--profile", help="Enable CLI profiling (timing breakdown)"),
     profile_deep: bool = typer.Option(False, "--profile-deep", help="Enable deep profiling (cProfile stats, higher overhead)"),
     # UI backend selection
-    ui: str = typer.Option("auto", "--ui", help="UI backend: auto, plain, rich, mg (middle-ground)"),
+    ui_backend: str = typer.Option("auto", "--ui-backend", help="UI backend: auto, plain, rich, mg (middle-ground)"),
     json_output: bool = typer.Option(False, "--json", help="Output JSON (forces plain backend)"),
     no_color: bool = typer.Option(False, "--no-color", help="Disable colors"),
     theme: str = typer.Option("default", "--theme", help="UI theme: default, dark, light, minimal"),
@@ -149,25 +149,12 @@ def chat_main(
         praisonai chat "Summarize this" --file README.md
         praisonai chat "What is 2+2?" --profile
     """
-    import asyncio
     import os
     
     # Set workspace if provided
     if workspace:
         os.environ["PRAISONAI_WORKSPACE"] = workspace
     
-    # Configure UI backend
-    from praisonai.cli.ui import select_backend, UIConfig
-    ui_config = UIConfig(
-        ui_backend=ui,
-        json_output=json_output,
-        no_color=no_color,
-        theme=theme,
-        compact=compact,
-        verbose=verbose,
-    )
-    backend = select_backend(ui_config)
-    
     # Handle profiling for single prompt mode
     if prompt and (profile or profile_deep):
         _run_profiled_chat(
@@ -184,60 +171,30 @@ def chat_main(
         typer.echo("   Use: praisonai chat \"your prompt\" --profile", err=True)
     
     # Parse memory flag: --no-memory takes precedence, then --memory value
-    memory_value = _parse_memory_flag(memory, no_memory)
+    # TODO: Pass memory_value to TUI when memory support is added
+    _parse_memory_flag(memory, no_memory)
     
-    # Try InteractiveCore first (preferred terminal-native implementation)
-    try:
-        from praisonai.cli.interactive import InteractiveCore, InteractiveConfig
-        from praisonai.cli.interactive.frontends import RichFrontend
-        
-        config = InteractiveConfig(
-            model=model,
-            session_id=session_id,
-            continue_session=continue_session,
-            workspace=workspace or None,
-            verbose=verbose,
-            memory=memory_value,
-            files=list(file) if file else [],
-            autonomy=autonomy,
-        )
-        
-        core = InteractiveCore(config=config)
-        
-        if prompt:
-            # Single prompt mode - use backend for output
-            from praisonai.cli.ui.events import UIEventType
-            
-            async def run_prompt():
-                if continue_session:
-                    core.continue_session()
-                
-                backend.emit(UIEventType.MESSAGE_START, {})
-                response = await core.prompt(prompt)
-                backend.emit(UIEventType.MESSAGE_CHUNK, {'content': response})
-                backend.emit(UIEventType.MESSAGE_END, {})
-            
-            asyncio.run(run_prompt())
-        else:
-            # Interactive REPL mode - pass ui_config to frontend
-            frontend = RichFrontend(core=core, config=config, ui_config=ui_config)
-            asyncio.run(frontend.run())
-            
-    except ImportError:
-        # Fallback to legacy terminal-native interactive mode
-        _run_legacy_terminal_chat(
-            prompt=prompt,
-            model=model,
-            verbose=verbose,
-            memory=memory,
-            tools=tools,
-            user_id=user_id,
-            session_id=session_id,
-            continue_session=continue_session,
-            workspace=workspace,
-            no_acp=no_acp,
-            no_lsp=no_lsp,
-        )
+    # Use the async TUI (non-blocking, scrollable output)
+    from praisonai.cli.interactive.async_tui import AsyncTUI, AsyncTUIConfig
+    
+    tui_config = AsyncTUIConfig(
+        model=model or "gpt-4o-mini",
+        show_logo=not compact,
+        show_status_bar=not compact,
+        session_id=session_id,
+        workspace=workspace,
+    )
+    
+    tui = AsyncTUI(config=tui_config)
+    
+    if prompt:
+        # Single prompt mode - direct response, no streaming
+        response = tui.run_single(prompt)
+        if response:
+            print(response)
+    else:
+        # Interactive split-pane TUI mode
+        tui.run()
 
 
 def _run_profiled_chat(
@@ -275,7 +232,6 @@ def _run_profiled_chat(
         "name": "ChatAgent",
         "role": "Assistant",
         "goal": "Help the user",
-        "verbose": verbose,
     }
     if model:
         agent_config["llm"] = model

src/praisonai/praisonai/cli/commands/standardise.py

@@ -2,10 +2,14 @@
 CLI commands for the FDEP standardisation system.
 
 Commands:
-- praisonai standardise check   - Check for standardisation issues
-- praisonai standardise report  - Generate detailed report
-- praisonai standardise fix     - Fix issues (with --apply)
-- praisonai standardise init    - Initialise a new feature
+- praisonai standardise check      - Check for standardisation issues
+- praisonai standardise report     - Generate detailed report
+- praisonai standardise fix        - Fix issues (with --apply)
+- praisonai standardise init       - Initialise a new feature
+- praisonai standardise ai         - AI-powered generation
+- praisonai standardise checkpoint - Create undo checkpoint
+- praisonai standardise undo       - Undo to previous checkpoint
+- praisonai standardise redo       - Redo after undo
 """
 
 import sys
@@ -88,6 +92,91 @@ def standardise_command(args=None):
         help="Actually create files (default: dry-run)",
     )
     
+    # ai subcommand
+    ai_parser = subparsers.add_parser(
+        "ai",
+        help="AI-powered generation of docs/examples",
+    )
+    ai_parser.add_argument(
+        "feature",
+        type=str,
+        help="Feature slug to generate content for",
+    )
+    ai_parser.add_argument(
+        "--type", "-t",
+        choices=["docs", "examples", "all"],
+        default="all",
+        help="Type of content to generate",
+    )
+    ai_parser.add_argument(
+        "--apply",
+        action="store_true",
+        help="Actually create files (default: dry-run preview)",
+    )
+    ai_parser.add_argument(
+        "--verify",
+        action="store_true",
+        help="Verify generated content with AI",
+    )
+    ai_parser.add_argument(
+        "--model",
+        type=str,
+        default="gpt-4o-mini",
+        help="LLM model to use (default: gpt-4o-mini)",
+    )
+    _add_common_args(ai_parser)
+    
+    # checkpoint subcommand
+    checkpoint_parser = subparsers.add_parser(
+        "checkpoint",
+        help="Create an undo checkpoint",
+    )
+    checkpoint_parser.add_argument(
+        "--message", "-m",
+        type=str,
+        help="Checkpoint message",
+    )
+    checkpoint_parser.add_argument(
+        "--path", "-p",
+        type=str,
+        default=".",
+        help="Repository path",
+    )
+    
+    # undo subcommand
+    undo_parser = subparsers.add_parser(
+        "undo",
+        help="Undo to a previous checkpoint",
+    )
+    undo_parser.add_argument(
+        "--checkpoint",
+        type=str,
+        help="Specific checkpoint ID to restore",
+    )
+    undo_parser.add_argument(
+        "--list",
+        action="store_true",
+        help="List available checkpoints",
+    )
+    undo_parser.add_argument(
+        "--path", "-p",
+        type=str,
+        default=".",
+        help="Repository path",
+    )
+    
+    # redo subcommand
+    redo_parser = subparsers.add_parser(
+        "redo",
+        help="Redo after an undo",
+    )
+    redo_parser.add_argument(
+        "--path", "-p",
+        type=str,
+        default=".",
+        help="Repository path",
+    )
+    
     # Parse args
     parsed = parser.parse_args(args)
     
@@ -105,6 +194,14 @@ def standardise_command(args=None):
         return _run_fix(parsed)
     elif parsed.subcommand == "init":
         return _run_init(parsed)
+    elif parsed.subcommand == "ai":
+        return _run_ai(parsed)
+    elif parsed.subcommand == "checkpoint":
+        return _run_checkpoint(parsed)
+    elif parsed.subcommand == "undo":
+        return _run_undo(parsed)
+    elif parsed.subcommand == "redo":
+        return _run_redo(parsed)
     
     return 0
 
@@ -258,6 +355,210 @@ def _run_init(parsed) -> int:
     return 0
 
 
+def _run_ai(parsed) -> int:
+    """Run the AI generation subcommand with ACP/LSP runtime support."""
+    import asyncio
+    from praisonai.standardise.ai_generator import AIGenerator
+    from praisonai.standardise.models import ArtifactType, FeatureSlug
+    
+    config = _create_config(parsed)
+    
+    slug = FeatureSlug.from_string(parsed.feature)
+    if not slug.is_valid:
+        print(f"Error: Invalid feature slug: {slug.validation_error}")
+        return 2
+    
+    # Start ACP/LSP runtime for context gathering
+    runtime = None
+    try:
+        from praisonai.cli.features.interactive_runtime import create_runtime
+        runtime = create_runtime(
+            workspace=str(config.sdk_root or config.project_root or "."),
+            lsp=True,
+            acp=False  # ACP not needed for generation
+        )
+        # Start runtime in background
+        asyncio.get_event_loop().run_until_complete(runtime.start())
+        if runtime.lsp_ready:
+            print("🔧 LSP server ready for code intelligence")
+    except Exception:
+        # Runtime is optional, continue without it
+        pass
+    
+    generator = AIGenerator(
+        model=parsed.model,
+        sdk_root=config.sdk_root,
+        docs_root=config.docs_root,
+        examples_root=config.examples_root,
+        use_fast_context=True,
+    )
+    
+    # Determine which artifacts to generate
+    artifact_types = []
+    gen_type = getattr(parsed, "type", "all")
+    
+    if gen_type in ["docs", "all"]:
+        artifact_types.extend([
+            ArtifactType.DOCS_CONCEPT,
+            ArtifactType.DOCS_FEATURE,
+            ArtifactType.DOCS_CLI,
+            ArtifactType.DOCS_SDK,
+        ])
+    
+    if gen_type in ["examples", "all"]:
+        artifact_types.extend([
+            ArtifactType.EXAMPLE_BASIC,
+            ArtifactType.EXAMPLE_ADVANCED,
+        ])
+    
+    print(f"🤖 AI Generation for '{slug.normalised}'")
+    print("=" * 50)
+    
+    results = []
+    for artifact_type in artifact_types:
+        print(f"\n📝 Generating {artifact_type.value}...")
+        
+        try:
+            # Generate with execution verification for examples
+            content, output_path, verification_info = generator.generate(
+                slug, artifact_type,
+                dry_run=not parsed.apply,
+                verify_execution=True,
+            )
+            
+            # Show verification results for examples
+            if verification_info:
+                if verification_info.get("execution_passed"):
+                    print("  ✅ Code verified: runs successfully")
+                elif verification_info.get("requires_external"):
+                    libs = ", ".join(verification_info.get("missing_libraries", []))
+                    print(f"  ⚠️  Requires external libraries: {libs}")
+                elif not verification_info.get("syntax_valid"):
+                    print(f"  ❌ Syntax error: {verification_info.get('error', 'Unknown')[:100]}")
+                else:
+                    print(f"  ❌ Execution failed after {verification_info.get('attempt', 1)} attempts")
+                    if verification_info.get("error"):
+                        print(f"     Error: {verification_info['error'][:150]}")
+            
+            # AI content verification (optional)
+            if parsed.verify:
+                print("  🔍 AI content review...")
+                is_valid, summary, issues = generator.verify(content, artifact_type, slug)
+                if is_valid:
+                    print(f"  ✅ {summary}")
+                else:
+                    print(f"  ⚠️  {summary}")
+                    for issue in issues[:3]:
+                        print(f"     - {issue}")
+            
+            # Check if we should write
+            can_write = True
+            if verification_info and not verification_info.get("success"):
+                if not verification_info.get("requires_external"):
+                    can_write = False
+                    print("  ⛔ Not writing: code doesn't run")
+            
+            if parsed.apply and output_path and can_write:
+                print(f"  ✓ Created: {output_path}")
+                results.append((artifact_type.value, True, output_path))
+            elif not parsed.apply:
+                print(f"  📋 Preview ({len(content)} chars)")
+                # Show first few lines
+                preview_lines = content.split("\n")[:10]
+                for line in preview_lines:
+                    print(f"     {line[:60]}")
+                if len(content.split("\n")) > 10:
+                    print("     ...")
+                results.append((artifact_type.value, True, None))
+            else:
+                results.append((artifact_type.value, False, "Verification failed"))
+            
+        except Exception as e:
+            print(f"  ❌ Error: {e}")
+            results.append((artifact_type.value, False, str(e)))
+    
+    print("\n" + "=" * 50)
+    print("Summary:")
+    success_count = sum(1 for _, success, _ in results if success)
+    print(f"  Generated: {success_count}/{len(results)}")
+    
+    if not parsed.apply:
+        print("\nRun with --apply to create these files.")
+    
+    # Cleanup runtime
+    if runtime:
+        try:
+            asyncio.get_event_loop().run_until_complete(runtime.stop())
+        except Exception:
+            pass
+    
+    return 0
+
+
+def _run_checkpoint(parsed) -> int:
+    """Run the checkpoint subcommand."""
+    from praisonai.standardise.undo_redo import UndoRedoManager
+    
+    manager = UndoRedoManager(Path(parsed.path).resolve())
+    
+    success, result = manager.create_checkpoint(parsed.message)
+    
+    if success:
+        print(f"✓ Checkpoint created: {result}")
+    else:
+        print(f"❌ Failed: {result}")
+        return 1
+    
+    return 0
+
+
+def _run_undo(parsed) -> int:
+    """Run the undo subcommand."""
+    from praisonai.standardise.undo_redo import UndoRedoManager
+    
+    manager = UndoRedoManager(Path(parsed.path).resolve())
+    
+    # List checkpoints if requested
+    if getattr(parsed, "list", False):
+        checkpoints = manager.list_checkpoints()
+        if not checkpoints:
+            print("No checkpoints found.")
+            return 0
+        
+        print("Available checkpoints:")
+        for cp_id, date, message in checkpoints:
+            print(f"  • {cp_id} ({date}): {message[:50]}")
+        return 0
+    
+    # Undo to checkpoint
+    success, result = manager.undo(parsed.checkpoint)
+    
+    if success:
+        print(f"✓ {result}")
+    else:
+        print(f"❌ {result}")
+        return 1
+    
+    return 0
+
+
+def _run_redo(parsed) -> int:
+    """Run the redo subcommand."""
+    from praisonai.standardise.undo_redo import UndoRedoManager
+    
+    manager = UndoRedoManager(Path(parsed.path).resolve())
+    
+    success, result = manager.redo()
+    
+    if success:
+        print(f"✓ {result}")
+    else:
+        print(f"❌ {result}")
+        return 1
+    
+    return 0
+
+
 # Click-based alternative for integration with main CLI
 def register_click_commands(app):
     """Register click commands with the main CLI app."""

src/praisonai/praisonai/cli/interactive/async_tui.py

@@ -0,0 +1,991 @@
+"""
+PraisonAI Async TUI Application.
+
+Full-screen terminal UI with:
+- Non-blocking LLM execution (UI stays responsive)
+- Scrollable output area with keyboard navigation
+- Fixed bottom input pane (always visible)
+- Activity indicators during processing
+
+Key fixes:
+- Uses threading for LLM calls to prevent UI freeze
+- Uses Buffer + BufferControl for scrollable output
+- Proper app.invalidate() for UI refresh
+"""
+
+import shutil
+import threading
+import time
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import List, Optional
+from datetime import datetime
+
+# ============================================================================
+# ASCII Art Logo - "Praison AI" branding
+# ============================================================================
+
+LOGO = r"""
+ ██████╗ ██████╗  █████╗ ██╗███████╗ ██████╗ ███╗   ██╗     █████╗ ██╗
+ ██╔══██╗██╔══██╗██╔══██╗██║██╔════╝██╔═══██╗████╗  ██║    ██╔══██╗██║
+ ██████╔╝██████╔╝███████║██║███████╗██║   ██║██╔██╗ ██║    ███████║██║
+ ██╔═══╝ ██╔══██╗██╔══██║██║╚════██║██║   ██║██║╚██╗██║    ██╔══██║██║
+ ██║     ██║  ██║██║  ██║██║███████║╚██████╔╝██║ ╚████║    ██║  ██║██║
+ ╚═╝     ╚═╝  ╚═╝╚═╝  ╚═╝╚═╝╚══════╝ ╚═════╝ ╚═╝  ╚═══╝    ╚═╝  ╚═╝╚═╝
+"""
+
+LOGO_SMALL = r"""
+ ╔═╗┬─┐┌─┐┬┌─┐┌─┐┌┐┌  ╔═╗╦
+ ╠═╝├┬┘├─┤│└─┐│ ││││  ╠═╣║
+ ╩  ┴└─┴ ┴┴└─┘└─┘┘└┘  ╩ ╩╩
+"""
+
+LOGO_MINIMAL = "▶ Praison AI"
+
+
+def get_logo(width: int = 80) -> str:
+    """Get appropriate logo based on terminal width."""
+    if width >= 75:
+        return LOGO
+    elif width >= 40:
+        return LOGO_SMALL
+    else:
+        return LOGO_MINIMAL
+
+
+def get_version() -> str:
+    """Get PraisonAI version."""
+    try:
+        from praisonai import __version__
+        return __version__
+    except Exception:
+        return "1.0.0"
+
+
+# ============================================================================
+# Configuration
+# ============================================================================
+
+@dataclass
+class AsyncTUIConfig:
+    """Configuration for the Async TUI application."""
+    model: str = "gpt-4o-mini"
+    show_logo: bool = True
+    show_status_bar: bool = True
+    session_id: Optional[str] = None
+    workspace: Optional[str] = None
+    history_file: Optional[str] = None
+    compact_mode: bool = False
+    multiline_mode: bool = False
+
+
+# ============================================================================
+# Message Types
+# ============================================================================
+
+@dataclass
+class ChatMessage:
+    """A chat message."""
+    role: str  # "user", "assistant", "system", "status"
+    content: str
+    timestamp: datetime = field(default_factory=datetime.now)
+
+
+# ============================================================================
+# Async TUI Application
+# ============================================================================
+
+class AsyncTUI:
+    """
+    Async TUI with non-blocking LLM execution and scrollable output.
+    
+    Key features:
+    - Input box ALWAYS visible at bottom (never freezes)
+    - Output scrolls above independently
+    - LLM runs in background thread
+    - UI stays responsive during processing
+    """
+    
+    def __init__(self, config: Optional[AsyncTUIConfig] = None):
+        self.config = config or AsyncTUIConfig()
+        self.messages: List[ChatMessage] = []
+        self._running = False
+        self._agent = None
+        self._processing = False
+        self._status_text = ""
+        self._app = None
+        self._output_buffer = None
+        self._prompt_queue: List[str] = []  # Queue for pending prompts
+        self._conversation_history: List[dict] = []  # Full conversation history
+        self._total_tokens = 0
+        self._total_cost = 0.0
+        self._workspace_files: List[str] = []  # Files in workspace for @ completion
+        self._runtime = None  # InteractiveRuntime for ACP/LSP
+        self._runtime_started = False
+        
+        # Terminal size
+        self.term_width, self.term_height = shutil.get_terminal_size((80, 24))
+        
+        # Session ID
+        import uuid
+        self.session_id = self.config.session_id or str(uuid.uuid4())[:8]
+        
+        # Scan workspace for files
+        self._scan_workspace()
+    
+    def _scan_workspace(self):
+        """Scan workspace for files (for @ completion)."""
+        import os
+        workspace = self.config.workspace or os.getcwd()
+        try:
+            for root, dirs, files in os.walk(workspace):
+                # Skip hidden directories
+                dirs[:] = [d for d in dirs if not d.startswith('.')]
+                for f in files:
+                    if not f.startswith('.'):
+                        rel_path = os.path.relpath(os.path.join(root, f), workspace)
+                        self._workspace_files.append(rel_path)
+                # Limit to 1000 files
+                if len(self._workspace_files) >= 1000:
+                    break
+        except Exception:
+            pass
+    
+    def _process_file_mentions(self, prompt: str) -> str:
+        """Process @file mentions and include file contents."""
+        import os
+        import re
+        
+        workspace = self.config.workspace or os.getcwd()
+        
+        # Find all @file mentions
+        pattern = r'@([^\s]+)'
+        matches = re.findall(pattern, prompt)
+        
+        if not matches:
+            return prompt
+        
+        file_contents = []
+        for match in matches:
+            file_path = os.path.join(workspace, match)
+            if os.path.isfile(file_path):
+                try:
+                    with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
+                        content = f.read()
+                    file_contents.append(f"\n--- Content of {match} ---\n{content}\n--- End of {match} ---\n")
+                except Exception as e:
+                    file_contents.append(f"\n[Error reading {match}: {e}]\n")
+        
+        if file_contents:
+            return prompt + "\n" + "\n".join(file_contents)
+        return prompt
+    
+    def _get_agent(self):
+        """Lazy-load the agent with tools."""
+        if self._agent is None:
+            try:
+                from praisonaiagents import Agent
+                
+                # Load interactive tools (read_file, write_file, execute_command, etc.)
+                tools = self._load_tools()
+                
+                self._agent = Agent(
+                    name="Praison",
+                    role="AI Assistant",
+                    goal="Help the user with their requests",
+                    instructions="You are Praison AI, a helpful assistant. Be concise and helpful. You have access to tools for file operations, code execution, and web search.",
+                    llm=self.config.model,
+                    tools=tools if tools else None,
+                )
+            except ImportError as e:
+                raise RuntimeError(f"Failed to import praisonaiagents: {e}")
+        
+        return self._agent
+    
+    async def _start_runtime(self):
+        """Start the InteractiveRuntime with ACP/LSP servers."""
+        if self._runtime_started:
+            return
+        
+        try:
+            from praisonai.cli.features.interactive_runtime import create_runtime
+            
+            self._runtime = create_runtime(
+                workspace=self.config.workspace or ".",
+                lsp=True,
+                acp=True,
+                approval="auto",  # Auto-approve for interactive mode
+            )
+            
+            # Start runtime (this starts ACP/LSP servers)
+            await self._runtime.start()
+            self._runtime_started = True
+            
+            # Return status
+            return {
+                "lsp": "ready" if self._runtime.lsp_ready else "failed",
+                "acp": "ready" if self._runtime.acp_ready else "failed",
+                "read_only": self._runtime.read_only
+            }
+        except ImportError as e:
+            # Runtime not available, continue without it
+            self._runtime_started = True  # Mark as attempted
+            return {"error": f"Runtime not available: {e}"}
+        except Exception as e:
+            self._runtime_started = True
+            return {"error": str(e)}
+    
+    def _load_tools(self):
+        """Load interactive tools for the agent."""
+        tools = []
+        
+        # Try to load all interactive tools (basic + ACP + LSP)
+        try:
+            from praisonai.cli.features.interactive_tools import get_interactive_tools
+            tools = get_interactive_tools(
+                groups=["basic", "acp", "lsp"],  # All tool groups
+                workspace=self.config.workspace,
+            )
+            if tools:
+                return tools
+        except ImportError:
+            pass
+        except Exception:
+            pass
+        
+        # Fallback: try to load basic tools directly from praisonaiagents
+        try:
+            from praisonaiagents.tools import (
+                read_file,
+                write_file, 
+                list_files,
+                execute_command,
+            )
+            tools = [read_file, write_file, list_files, execute_command]
+        except ImportError:
+            pass
+        
+        # Try to add internet search
+        try:
+            from praisonaiagents.tools import internet_search
+            tools.append(internet_search)
+        except ImportError:
+            pass
+        
+        return tools
+    
+    def _format_output(self) -> str:
+        """Format all messages for the output pane."""
+        lines = []
+        
+        # Logo (only if no messages yet)
+        if self.config.show_logo and not self.messages:
+            logo = get_logo(self.term_width)
+            lines.append(logo.strip())
+            lines.append("")
+            lines.append(f"  v{get_version()} · Model: {self.config.model}")
+            lines.append("")
+            lines.append("  Type your message and press Enter. Use /help for commands.")
+            lines.append("  Use PageUp/PageDown or Ctrl+Up/Down to scroll.")
+            lines.append("")
+        
+        # Messages
+        for msg in self.messages:
+            if msg.role == "user":
+                lines.append(f"› {msg.content}")
+                lines.append("")
+            elif msg.role == "assistant":
+                lines.append(f"● {msg.content}")
+                lines.append("")
+            elif msg.role == "system":
+                lines.append(f"  {msg.content}")
+                lines.append("")
+            elif msg.role == "status":
+                lines.append(f"  ⏳ {msg.content}")
+                lines.append("")
+        
+        # Processing indicator
+        if self._processing:
+            lines.append(f"  ⏳ {self._status_text}")
+            lines.append("")
+        
+        return "\n".join(lines)
+    
+    def _update_output(self):
+        """Update the output buffer and refresh UI."""
+        if self._output_buffer:
+            new_text = self._format_output()
+            self._output_buffer.set_document(
+                self._output_buffer.document.__class__(
+                    text=new_text,
+                    cursor_position=len(new_text)  # Cursor at end for auto-scroll
+                ),
+                bypass_readonly=True
+            )
+        if self._app:
+            self._app.invalidate()
+    
+    def _handle_command(self, command: str) -> bool:
+        """Handle slash commands. Returns True if handled."""
+        parts = command.split(maxsplit=1)
+        cmd = parts[0].lower().lstrip("/")
+        args = parts[1] if len(parts) > 1 else ""
+        
+        if cmd in ("exit", "quit", "q"):
+            self._running = False
+            if self._app:
+                self._app.exit()
+            return True
+        
+        elif cmd == "help":
+            help_text = """Available Commands:
+  /help            Show this help
+  /exit, /quit     Exit Praison AI
+  /clear           Clear conversation
+  /new             Start new conversation
+  /model [name]    Show or change model
+  /session         Show current session info
+  /sessions        List all saved sessions
+  /continue        Continue most recent session
+  /history         Show conversation history
+  /export [file]   Export conversation to file
+  /import <file>   Import conversation from file
+  /cost            Show token usage and cost
+  /status          Show ACP/LSP runtime status
+  /compact         Toggle compact output mode
+  /multiline       Toggle multiline input mode
+  /files           List workspace files for @ mentions
+  /queue           Show pending prompts in queue
+
+Keyboard Shortcuts:
+  PageUp/Down      Scroll output
+  Ctrl+Up/Down     Scroll output
+  Ctrl+C           Clear input
+  Ctrl+D           Exit (if input empty)
+  Ctrl+Q           Exit
+
+Tips:
+  Use @filename to include file contents in your prompt
+  Type multiple prompts while AI is thinking (queued)"""
+            self.messages.append(ChatMessage(role="system", content=help_text))
+            return True
+        
+        elif cmd == "clear":
+            self.messages.clear()
+            self._conversation_history.clear()
+            self.messages.append(ChatMessage(role="system", content="Conversation cleared."))
+            return True
+        
+        elif cmd == "new":
+            self.messages.clear()
+            self._conversation_history.clear()
+            import uuid
+            self.session_id = str(uuid.uuid4())[:8]
+            self.messages.append(ChatMessage(role="system", content=f"New session: {self.session_id}"))
+            return True
+        
+        elif cmd == "model":
+            if args:
+                self.config.model = args
+                self._agent = None
+                self.messages.append(ChatMessage(role="system", content=f"Model changed to: {args}"))
+            else:
+                self.messages.append(ChatMessage(role="system", content=f"Current model: {self.config.model}"))
+            return True
+        
+        elif cmd == "session":
+            info = f"Session: {self.session_id}\nModel: {self.config.model}\nMessages: {len(self._conversation_history)}"
+            self.messages.append(ChatMessage(role="system", content=info))
+            return True
+        
+        elif cmd == "history":
+            if not self._conversation_history:
+                self.messages.append(ChatMessage(role="system", content="No conversation history."))
+            else:
+                history_lines = []
+                for i, msg in enumerate(self._conversation_history[-10:], 1):
+                    role = msg.get("role", "unknown")
+                    content = msg.get("content", "")[:80]
+                    history_lines.append(f"{i}. [{role}] {content}...")
+                self.messages.append(ChatMessage(role="system", content="\n".join(history_lines)))
+            return True
+        
+        elif cmd == "export":
+            filename = args or f"conversation_{self.session_id}.txt"
+            try:
+                with open(filename, "w") as f:
+                    f.write("# Praison AI Conversation\n")
+                    f.write(f"# Session: {self.session_id}\n")
+                    f.write(f"# Model: {self.config.model}\n\n")
+                    for msg in self._conversation_history:
+                        f.write(f"[{msg.get('role', 'unknown')}]\n")
+                        f.write(f"{msg.get('content', '')}\n\n")
+                self.messages.append(ChatMessage(role="system", content=f"Exported to {filename}"))
+            except Exception as e:
+                self.messages.append(ChatMessage(role="system", content=f"Export failed: {e}"))
+            return True
+        
+        elif cmd == "cost":
+            cost_info = f"Total tokens: {self._total_tokens}\nEstimated cost: ${self._total_cost:.4f}"
+            self.messages.append(ChatMessage(role="system", content=cost_info))
+            return True
+        
+        elif cmd == "compact":
+            self.config.compact_mode = not self.config.compact_mode
+            mode = "enabled" if self.config.compact_mode else "disabled"
+            self.messages.append(ChatMessage(role="system", content=f"Compact mode {mode}"))
+            return True
+        
+        elif cmd == "multiline":
+            self.config.multiline_mode = not self.config.multiline_mode
+            mode = "enabled" if self.config.multiline_mode else "disabled"
+            self.messages.append(ChatMessage(role="system", content=f"Multiline mode {mode}. Use Ctrl+D to submit."))
+            return True
+        
+        elif cmd == "files":
+            if not self._workspace_files:
+                self.messages.append(ChatMessage(role="system", content="No files found in workspace."))
+            else:
+                files_list = self._workspace_files[:20]
+                more = f"\n... and {len(self._workspace_files) - 20} more" if len(self._workspace_files) > 20 else ""
+                self.messages.append(ChatMessage(role="system", content="Workspace files:\n" + "\n".join(f"  @{f}" for f in files_list) + more))
+            return True
+        
+        elif cmd == "queue":
+            if not self._prompt_queue:
+                self.messages.append(ChatMessage(role="system", content="No prompts in queue."))
+            else:
+                queue_list = [f"{i+1}. {p[:50]}..." for i, p in enumerate(self._prompt_queue)]
+                self.messages.append(ChatMessage(role="system", content="Queued prompts:\n" + "\n".join(queue_list)))
+            return True
+        
+        elif cmd == "sessions":
+            # List available sessions
+            try:
+                from praisonai.cli.session import get_session_store
+                store = get_session_store()
+                sessions = store.list_sessions()
+                if not sessions:
+                    self.messages.append(ChatMessage(role="system", content="No saved sessions found."))
+                else:
+                    lines = ["Available sessions:"]
+                    for s in sessions[:10]:
+                        sid = s.get("session_id", "unknown")
+                        title = s.get("title", "Untitled")
+                        lines.append(f"  {sid}: {title}")
+                    if len(sessions) > 10:
+                        lines.append(f"  ... and {len(sessions) - 10} more")
+                    self.messages.append(ChatMessage(role="system", content="\n".join(lines)))
+            except Exception as e:
+                self.messages.append(ChatMessage(role="system", content=f"Could not list sessions: {e}"))
+            return True
+        
+        elif cmd == "continue":
+            # Continue most recent session
+            try:
+                from praisonai.cli.session import get_session_store
+                store = get_session_store()
+                sessions = store.list_sessions()
+                if not sessions:
+                    self.messages.append(ChatMessage(role="system", content="No sessions to continue."))
+                else:
+                    # Get most recent
+                    sorted_sessions = sorted(
+                        sessions,
+                        key=lambda s: s.get("updated_at", s.get("created_at", 0)),
+                        reverse=True
+                    )
+                    if sorted_sessions:
+                        session = store.load(sorted_sessions[0].get("session_id"))
+                        if session:
+                            self.session_id = session.session_id
+                            # Load history
+                            history = session.get_chat_history()
+                            self._conversation_history = history
+                            self.messages.append(ChatMessage(
+                                role="system", 
+                                content=f"Continued session: {self.session_id} ({len(history)} messages)"
+                            ))
+                        else:
+                            self.messages.append(ChatMessage(role="system", content="Could not load session."))
+            except Exception as e:
+                self.messages.append(ChatMessage(role="system", content=f"Could not continue session: {e}"))
+            return True
+        
+        elif cmd == "import":
+            # Import session from file
+            if not args:
+                self.messages.append(ChatMessage(role="system", content="Usage: /import <filename>"))
+            else:
+                try:
+                    import json
+                    with open(args) as f:
+                        data = json.load(f)
+                    
+                    # Load messages from file
+                    messages = data.get("messages", [])
+                    self._conversation_history = messages
+                    self.session_id = data.get("session_id", self.session_id)
+                    self.messages.append(ChatMessage(
+                        role="system",
+                        content=f"Imported {len(messages)} messages from {args}"
+                    ))
+                except FileNotFoundError:
+                    self.messages.append(ChatMessage(role="system", content=f"File not found: {args}"))
+                except Exception as e:
+                    self.messages.append(ChatMessage(role="system", content=f"Import failed: {e}"))
+            return True
+        
+        elif cmd == "status":
+            # Show runtime status (ACP/LSP)
+            if self._runtime:
+                status = self._runtime.get_status()
+                lsp = status.get("lsp", {})
+                acp = status.get("acp", {})
+                info = f"Runtime Status:\n  LSP: {lsp.get('status', 'unknown')}\n  ACP: {acp.get('status', 'unknown')}\n  Read-only: {status.get('read_only', False)}"
+                self.messages.append(ChatMessage(role="system", content=info))
+            else:
+                self.messages.append(ChatMessage(role="system", content="Runtime not initialized."))
+            return True
+        
+        else:
+            self.messages.append(ChatMessage(role="system", content=f"Unknown command: /{cmd}. Use /help for available commands."))
+            return True
+    
+    def _execute_prompt(self, prompt: str) -> Optional[str]:
+        """Execute a prompt and return the response (suppresses agent output)."""
+        import sys
+        import io
+        import asyncio
+        
+        try:
+            agent = self._get_agent()
+            
+            # Suppress agent's Rich output
+            old_stdout = sys.stdout
+            old_stderr = sys.stderr
+            sys.stdout = io.StringIO()
+            sys.stderr = io.StringIO()
+            
+            try:
+                # Try async execution first for better non-blocking behavior
+                if hasattr(agent, 'astart'):
+                    try:
+                        loop = asyncio.new_event_loop()
+                        asyncio.set_event_loop(loop)
+                        response = loop.run_until_complete(agent.astart(prompt))
+                        loop.close()
+                    except Exception:
+                        # Fallback to sync
+                        response = agent.start(prompt)
+                else:
+                    response = agent.start(prompt)
+            finally:
+                sys.stdout = old_stdout
+                sys.stderr = old_stderr
+            
+            return str(response) if response else None
+        except Exception as e:
+            return f"Error: {e}"
+    
+    def _execute_in_background(self, prompt: str):
+        """Execute prompt in background thread (non-blocking)."""
+        def run():
+            self._processing = True
+            self._status_text = "Praison AI is thinking..."
+            self._update_output()
+            
+            # Process @file mentions
+            processed_prompt = self._process_file_mentions(prompt)
+            
+            # Add to conversation history
+            self._conversation_history.append({"role": "user", "content": prompt})
+            
+            # Animate spinner while processing
+            spinner = ["⠋", "⠙", "⠹", "⠸", "⠼", "⠴", "⠦", "⠧", "⠇", "⠏"]
+            start_time = time.time()
+            
+            # Start LLM execution in another thread
+            result = [None]
+            error = [None]
+            done = [False]
+            
+            def execute_llm():
+                try:
+                    result[0] = self._execute_prompt(processed_prompt)
+                except Exception as e:
+                    error[0] = str(e)
+                finally:
+                    done[0] = True
+            
+            llm_thread = threading.Thread(target=execute_llm)
+            llm_thread.start()
+            
+            # Update spinner while waiting
+            idx = 0
+            while not done[0]:
+                elapsed = time.time() - start_time
+                queue_info = f" | Queue: {len(self._prompt_queue)}" if self._prompt_queue else ""
+                self._status_text = f"{spinner[idx]} Praison AI is thinking... ({elapsed:.1f}s){queue_info}"
+                self._update_output()
+                idx = (idx + 1) % len(spinner)
+                time.sleep(0.1)
+            
+            llm_thread.join()
+            
+            # Done processing current prompt
+            self._processing = False
+            self._status_text = ""
+            
+            if error[0]:
+                self.messages.append(ChatMessage(role="assistant", content=f"Error: {error[0]}"))
+                self._conversation_history.append({"role": "assistant", "content": f"Error: {error[0]}"})
+            elif result[0]:
+                self.messages.append(ChatMessage(role="assistant", content=result[0]))
+                self._conversation_history.append({"role": "assistant", "content": result[0]})
+            
+            self._update_output()
+            
+            # Process next item in queue if any
+            if self._prompt_queue:
+                next_prompt = self._prompt_queue.pop(0)
+                self.messages.append(ChatMessage(role="user", content=next_prompt))
+                self._update_output()
+                self._execute_in_background(next_prompt)
+        
+        # Run in background thread
+        thread = threading.Thread(target=run, daemon=True)
+        thread.start()
+    
+    def _queue_or_execute(self, prompt: str):
+        """Queue prompt if processing, otherwise execute immediately."""
+        if self._processing:
+            # Add to queue
+            self._prompt_queue.append(prompt)
+            self.messages.append(ChatMessage(role="system", content=f"Queued: {prompt[:50]}..."))
+            self._update_output()
+        else:
+            # Execute immediately
+            self.messages.append(ChatMessage(role="user", content=prompt))
+            self._update_output()
+            self._execute_in_background(prompt)
+    
+    def run(self) -> None:
+        """Run the async TUI."""
+        import asyncio
+        
+        self._running = True
+        
+        # Start runtime (ACP/LSP servers) before TUI
+        try:
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+            runtime_status = loop.run_until_complete(self._start_runtime())
+            loop.close()
+            
+            # Show runtime status
+            if runtime_status and "error" not in runtime_status:
+                lsp = runtime_status.get("lsp", "unknown")
+                acp = runtime_status.get("acp", "unknown")
+                if lsp == "ready" or acp == "ready":
+                    self.messages.append(ChatMessage(
+                        role="system", 
+                        content=f"Runtime started: LSP={lsp}, ACP={acp}"
+                    ))
+        except Exception as e:
+            # Continue without runtime
+            self.messages.append(ChatMessage(
+                role="system",
+                content=f"Runtime unavailable: {e}"
+            ))
+        
+        try:
+            self._run_with_prompt_toolkit()
+        except ImportError:
+            self._run_simple()
+    
+    def _run_simple(self) -> None:
+        """Simple fallback mode without prompt_toolkit."""
+        print(self._format_output())
+        
+        while self._running:
+            try:
+                if self.config.show_status_bar:
+                    status = f" ? for help | {self.config.model} | Session: {self.session_id} "
+                    print(f"\033[90m{status}\033[0m")
+                
+                user_input = input("› ").strip()
+                
+                if not user_input:
+                    continue
+                
+                if user_input.startswith("/"):
+                    self._handle_command(user_input)
+                    for msg in self.messages:
+                        if msg.role == "system":
+                            print(f"  {msg.content}")
+                    self.messages = [m for m in self.messages if m.role != "system"]
+                    continue
+                
+                self.messages.append(ChatMessage(role="user", content=user_input))
+                print("  ⏳ Praison AI is thinking...")
+                response = self._execute_prompt(user_input)
+                
+                if response:
+                    self.messages.append(ChatMessage(role="assistant", content=response))
+                    print(f"\n● {response}\n")
+                
+            except KeyboardInterrupt:
+                print("\n  Use /exit to quit")
+            except EOFError:
+                self._running = False
+        
+        print("\n  Goodbye from Praison AI!")
+    
+    def _run_with_prompt_toolkit(self) -> None:
+        """Run with prompt_toolkit for non-blocking split-pane layout."""
+        from prompt_toolkit import Application
+        from prompt_toolkit.buffer import Buffer
+        from prompt_toolkit.document import Document
+        from prompt_toolkit.layout.containers import HSplit, VSplit, Window, Float, FloatContainer
+        from prompt_toolkit.layout.controls import BufferControl, FormattedTextControl
+        from prompt_toolkit.layout.layout import Layout
+        from prompt_toolkit.layout.margins import ScrollbarMargin
+        from prompt_toolkit.layout.menus import CompletionsMenu
+        from prompt_toolkit.key_binding import KeyBindings
+        from prompt_toolkit.styles import Style
+        from prompt_toolkit.history import FileHistory
+        from prompt_toolkit.completion import Completer, Completion
+        
+        # Create completer for commands and files
+        commands = ["help", "exit", "quit", "clear", "new", "model", "session", "sessions",
+                    "continue", "history", "export", "import", "cost", "status", "compact", 
+                    "multiline", "files", "queue"]
+        workspace_files = self._workspace_files
+        
+        class PraisonCompleter(Completer):
+            def get_completions(self, document, complete_event):
+                text = document.text_before_cursor
+                
+                # Get the current word being typed
+                # Handle case where text might be empty or have no words
+                if not text:
+                    return
+                
+                # Find the current word (last word or partial word)
+                words = text.split()
+                if not words:
+                    current_word = text
+                elif text.endswith(' '):
+                    current_word = ""
+                else:
+                    current_word = words[-1]
+                
+                # Show all slash commands when "/" is typed
+                if current_word == "/":
+                    for cmd in commands:
+                        yield Completion(f"/{cmd}", start_position=-1, display=f"/{cmd}", display_meta="command")
+                # Filter slash commands
+                elif current_word.startswith("/"):
+                    cmd_part = current_word[1:].lower()
+                    for cmd in commands:
+                        if cmd.lower().startswith(cmd_part):
+                            yield Completion(f"/{cmd}", start_position=-len(current_word), display=f"/{cmd}", display_meta="command")
+                
+                # Show all files when "@" is typed
+                elif current_word == "@":
+                    for f in workspace_files[:30]:
+                        yield Completion(f"@{f}", start_position=-1, display=f"@{f}", display_meta="file")
+                # Filter @ file mentions
+                elif current_word.startswith("@"):
+                    file_part = current_word[1:].lower()
+                    for f in workspace_files[:50]:
+                        if file_part in f.lower():
+                            yield Completion(f"@{f}", start_position=-len(current_word), display=f"@{f}", display_meta="file")
+        
+        # Setup history
+        history_file = self.config.history_file or str(Path.home() / ".praison" / "history")
+        Path(history_file).parent.mkdir(parents=True, exist_ok=True)
+        
+        # Output buffer (read-only, scrollable)
+        initial_output = self._format_output()
+        self._output_buffer = Buffer(
+            document=Document(initial_output, cursor_position=len(initial_output)),
+            read_only=True,
+            name="output"
+        )
+        
+        # Scroll offset for output
+        self._scroll_offset = 0
+        
+        # Input buffer with completer and auto-complete on typing
+        input_buffer = Buffer(
+            multiline=False,
+            history=FileHistory(history_file),
+            name="input",
+            completer=PraisonCompleter(),
+            complete_while_typing=True,  # Show completions as you type
+        )
+        
+        # Key bindings
+        kb = KeyBindings()
+        
+        @kb.add("enter")
+        def handle_enter(event):
+            user_input = input_buffer.text.strip()
+            if not user_input:
+                return
+            
+            input_buffer.reset()
+            
+            # Handle commands (always process immediately)
+            if user_input.startswith("/"):
+                self._handle_command(user_input)
+                self._update_output()
+                return
+            
+            # Queue or execute prompt
+            self._queue_or_execute(user_input)
+        
+        @kb.add("c-c")
+        def handle_ctrl_c(event):
+            input_buffer.reset()
+        
+        @kb.add("c-d")
+        def handle_ctrl_d(event):
+            if not input_buffer.text:
+                self._running = False
+                event.app.exit()
+        
+        @kb.add("c-q")
+        def handle_ctrl_q(event):
+            self._running = False
+            event.app.exit()
+        
+        # Scroll bindings for output
+        @kb.add("pageup")
+        def handle_pageup(event):
+            self._output_buffer.cursor_up(count=10)
+        
+        @kb.add("pagedown")
+        def handle_pagedown(event):
+            self._output_buffer.cursor_down(count=10)
+        
+        @kb.add("c-up")
+        def handle_ctrl_up(event):
+            self._output_buffer.cursor_up(count=3)
+        
+        @kb.add("c-down")
+        def handle_ctrl_down(event):
+            self._output_buffer.cursor_down(count=3)
+        
+        @kb.add("home")
+        def handle_home(event):
+            self._output_buffer.cursor_position = 0
+        
+        @kb.add("end")
+        def handle_end(event):
+            self._output_buffer.cursor_position = len(self._output_buffer.text)
+        
+        # Style
+        style = Style.from_dict({
+            "output-area": "bg:#0d0d0d",
+            "input-area": "bg:#1a1a1a",
+            "status-bar": "bg:#333333 #888888",
+            "prompt": "#00aaff bold",
+            "scrollbar.background": "bg:#333333",
+            "scrollbar.button": "bg:#666666",
+        })
+        
+        # Status bar
+        def get_status_bar():
+            if self._processing:
+                return f" {self._status_text} "
+            left = "? for help | PageUp/Down to scroll"
+            center = self.config.model
+            right = f"Session: {self.session_id}"
+            return f" {left}  |  {center}  |  {right} "
+        
+        # Layout: Output (top, scrollable) + Status bar + Input (bottom, fixed)
+        # Use FloatContainer to show completion menu as floating overlay
+        main_layout = HSplit([
+            # Output area (scrollable with scrollbar)
+            Window(
+                content=BufferControl(
+                    buffer=self._output_buffer,
+                    focusable=False,
+                ),
+                wrap_lines=True,
+                style="class:output-area",
+                right_margins=[ScrollbarMargin(display_arrows=True)],
+            ),
+            # Status bar (1 line)
+            Window(
+                content=FormattedTextControl(get_status_bar),
+                height=1,
+                style="class:status-bar",
+            ),
+            # Input area (fixed at bottom)
+            HSplit([
+                Window(height=1),  # Spacer
+                VSplit([
+                    Window(
+                        content=FormattedTextControl([("class:prompt", "› ")]),
+                        width=2,
+                    ),
+                    Window(
+                        content=BufferControl(buffer=input_buffer),
+                        wrap_lines=True,
+                    ),
+                ]),
+                Window(height=1),  # Spacer
+            ], height=3, style="class:input-area"),
+        ])
+        
+        # Wrap in FloatContainer to show completions menu
+        layout = Layout(
+            FloatContainer(
+                content=main_layout,
+                floats=[
+                    Float(
+                        xcursor=True,
+                        ycursor=True,
+                        content=CompletionsMenu(max_height=10, scroll_offset=1),
+                    ),
+                ],
+            )
+        )
+        
+        # Create application
+        self._app = Application(
+            layout=layout,
+            key_bindings=kb,
+            style=style,
+            full_screen=True,
+            mouse_support=True,
+            refresh_interval=0.1,  # Refresh every 100ms for spinner
+        )
+        
+        # Run
+        self._app.run()
+        
+        print("\n  Goodbye from Praison AI!")
+    
+    def run_single(self, prompt: str) -> Optional[str]:
+        """Run a single prompt (non-interactive)."""
+        return self._execute_prompt(prompt)
+
+
+def start_async_tui(
+    model: str = "gpt-4o-mini",
+    show_logo: bool = True,
+    **kwargs
+) -> None:
+    """Start the async TUI application."""
+    config = AsyncTUIConfig(
+        model=model,
+        show_logo=show_logo,
+    )
+    
+    tui = AsyncTUI(config=config)
+    tui.run()

src/praisonai/praisonai/cli/interactive/praison_io.py

@@ -0,0 +1,502 @@
+"""
+PraisonIO - Interactive CLI I/O handler inspired by Aider.
+
+Key design principles:
+1. prompt_toolkit used SYNCHRONOUSLY (never inside asyncio.run)
+2. Rich for output rendering
+3. MarkdownStream for flicker-free streaming
+4. Clean separation of input/output concerns
+"""
+
+import sys
+import time
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Dict, List, Optional, Set
+
+# Lazy imports for performance
+_PROMPT_TOOLKIT_AVAILABLE = None
+_RICH_AVAILABLE = None
+
+
+def _check_prompt_toolkit():
+    global _PROMPT_TOOLKIT_AVAILABLE
+    if _PROMPT_TOOLKIT_AVAILABLE is None:
+        try:
+            import prompt_toolkit  # noqa: F401
+            _PROMPT_TOOLKIT_AVAILABLE = True
+        except ImportError:
+            _PROMPT_TOOLKIT_AVAILABLE = False
+    return _PROMPT_TOOLKIT_AVAILABLE
+
+
+def _check_rich():
+    global _RICH_AVAILABLE
+    if _RICH_AVAILABLE is None:
+        try:
+            import rich  # noqa: F401
+            _RICH_AVAILABLE = True
+        except ImportError:
+            _RICH_AVAILABLE = False
+    return _RICH_AVAILABLE
+
+
+@dataclass
+class IOConfig:
+    """Configuration for PraisonIO."""
+    # Appearance
+    pretty: bool = True
+    user_input_color: str = "#00cc00"
+    tool_output_color: str = "#0088ff"
+    tool_error_color: str = "#ff0000"
+    tool_warning_color: str = "#ffaa00"
+    assistant_color: str = "#00aaff"
+    
+    # Input settings
+    multiline_mode: bool = False
+    vi_mode: bool = False
+    
+    # History
+    input_history_file: Optional[str] = None
+    
+    # Completion
+    enable_completions: bool = True
+    
+    # Streaming
+    stream_delay: float = 0.05  # 20fps default
+
+
+class PraisonCompleter:
+    """Auto-completer for commands, files, and symbols."""
+    
+    def __init__(self, commands: Optional[Dict[str, str]] = None):
+        self.commands = commands or {}
+        self.files: Set[str] = set()
+        self.symbols: Set[str] = set()
+    
+    def add_files(self, files: List[str]) -> None:
+        """Add files to completion."""
+        self.files.update(files)
+    
+    def add_symbols(self, symbols: List[str]) -> None:
+        """Add symbols to completion."""
+        self.symbols.update(symbols)
+    
+    def get_completions(self, text: str, cursor_pos: int) -> List[tuple]:
+        """Get completions for current input."""
+        before_cursor = text[:cursor_pos]
+        words = before_cursor.split()
+        current_word = words[-1] if words else ""
+        
+        completions = []
+        
+        # Slash commands
+        if current_word.startswith("/"):
+            cmd_part = current_word[1:].lower()
+            for cmd, desc in self.commands.items():
+                if cmd.lower().startswith(cmd_part):
+                    completions.append((f"/{cmd}", desc))
+        
+        # @ file mentions
+        elif current_word.startswith("@"):
+            file_part = current_word[1:].lower()
+            for f in self.files:
+                if file_part in f.lower():
+                    completions.append((f"@{f}", "file"))
+        
+        # General word completion
+        elif current_word:
+            word_lower = current_word.lower()
+            for symbol in self.symbols:
+                if symbol.lower().startswith(word_lower):
+                    completions.append((symbol, "symbol"))
+        
+        return completions[:20]
+    
+    def create_prompt_toolkit_completer(self):
+        """Create prompt_toolkit Completer."""
+        if not _check_prompt_toolkit():
+            return None
+        
+        from prompt_toolkit.completion import Completer, Completion
+        
+        outer = self
+        
+        class PTCompleter(Completer):
+            def get_completions(self, document, complete_event):
+                text = document.text
+                cursor_pos = document.cursor_position
+                
+                for completion, meta in outer.get_completions(text, cursor_pos):
+                    words = text[:cursor_pos].split()
+                    current_word = words[-1] if words else ""
+                    start_pos = -len(current_word)
+                    yield Completion(completion, start_position=start_pos, display_meta=meta)
+        
+        return PTCompleter()
+
+
+class MarkdownStream:
+    """Flicker-free streaming markdown renderer.
+    
+    Uses Rich Live with a sliding window approach:
+    - Stable lines print above the live area
+    - Unstable lines (last N) render in the live window
+    """
+    
+    def __init__(self, live_window: int = 6):
+        self.live_window = live_window
+        self.printed: List[str] = []
+        self.live = None
+        self._live_started = False
+        self.min_delay = 1.0 / 20  # 20fps
+        self.when = 0
+    
+    def _render_to_lines(self, text: str) -> List[str]:
+        """Render markdown to lines."""
+        if not _check_rich():
+            return text.splitlines(keepends=True)
+        
+        import io
+        from rich.console import Console
+        from rich.markdown import Markdown
+        
+        string_io = io.StringIO()
+        console = Console(file=string_io, force_terminal=True, width=100)
+        console.print(Markdown(text))
+        return string_io.getvalue().splitlines(keepends=True)
+    
+    def update(self, text: str, final: bool = False) -> None:
+        """Update the streaming display."""
+        if not _check_rich():
+            # Fallback: just print
+            print(text, end="" if not final else "\n", flush=True)
+            return
+        
+        from rich.live import Live
+        from rich.text import Text
+        
+        # Start live on first update
+        if not self._live_started:
+            self.live = Live(Text(""), refresh_per_second=20)
+            self.live.start()
+            self._live_started = True
+        
+        # Throttle updates
+        now = time.time()
+        if not final and now - self.when < self.min_delay:
+            return
+        self.when = now
+        
+        lines = self._render_to_lines(text)
+        num_lines = len(lines)
+        
+        # Calculate stable lines
+        if not final:
+            num_lines -= self.live_window
+        
+        if final or num_lines > 0:
+            num_printed = len(self.printed)
+            show_count = num_lines - num_printed
+            
+            if show_count > 0:
+                show = lines[num_printed:num_lines]
+                show_text = "".join(show)
+                self.live.console.print(Text.from_ansi(show_text), end="")
+                self.printed = lines[:num_lines]
+        
+        if final:
+            self.live.update(Text(""))
+            self.live.stop()
+            self.live = None
+            return
+        
+        # Update live window
+        rest = lines[num_lines:]
+        rest_text = "".join(rest)
+        self.live.update(Text.from_ansi(rest_text))
+    
+    def __del__(self):
+        if self.live:
+            try:
+                self.live.stop()
+            except Exception:
+                pass
+
+
+class PraisonIO:
+    """
+    Main I/O handler for PraisonAI interactive CLI.
+    
+    Inspired by Aider's io.py - handles all terminal I/O with:
+    - Rich formatting for output
+    - prompt_toolkit for input (with completions, history)
+    - Streaming markdown rendering
+    """
+    
+    def __init__(self, config: Optional[IOConfig] = None):
+        self.config = config or IOConfig()
+        self.completer = PraisonCompleter()
+        self._console = None
+        self._prompt_session = None
+        self._history_file = None
+        
+        # Setup history file
+        if self.config.input_history_file:
+            self._history_file = Path(self.config.input_history_file)
+        else:
+            self._history_file = Path.home() / ".praison" / "input_history"
+        
+        self._history_file.parent.mkdir(parents=True, exist_ok=True)
+    
+    @property
+    def console(self):
+        """Lazy-load Rich console."""
+        if self._console is None and _check_rich():
+            from rich.console import Console
+            self._console = Console()
+        return self._console
+    
+    def _create_prompt_session(self):
+        """Create prompt_toolkit session."""
+        if not _check_prompt_toolkit():
+            return None
+        
+        from prompt_toolkit import PromptSession
+        from prompt_toolkit.history import FileHistory
+        from prompt_toolkit.auto_suggest import AutoSuggestFromHistory
+        from prompt_toolkit.key_binding import KeyBindings
+        
+        kb = KeyBindings()
+        
+        # Ctrl+C to cancel current input
+        @kb.add("c-c")
+        def _(event):
+            event.current_buffer.reset()
+        
+        # Alt+Enter for newline in multiline mode
+        @kb.add("escape", "enter")
+        def _(event):
+            event.current_buffer.insert_text("\n")
+        
+        # Enter to submit (unless in multiline mode)
+        @kb.add("enter")
+        def _(event):
+            if self.config.multiline_mode:
+                event.current_buffer.insert_text("\n")
+            else:
+                event.current_buffer.validate_and_handle()
+        
+        # Ctrl+D to submit in multiline mode
+        @kb.add("c-d")
+        def _(event):
+            event.current_buffer.validate_and_handle()
+        
+        session_kwargs = {
+            "history": FileHistory(str(self._history_file)),
+            "auto_suggest": AutoSuggestFromHistory(),
+            "key_bindings": kb,
+            "multiline": self.config.multiline_mode,
+        }
+        
+        if self.config.enable_completions:
+            completer = self.completer.create_prompt_toolkit_completer()
+            if completer:
+                from prompt_toolkit.completion import ThreadedCompleter
+                session_kwargs["completer"] = ThreadedCompleter(completer)
+        
+        if self.config.vi_mode:
+            from prompt_toolkit.enums import EditingMode
+            session_kwargs["editing_mode"] = EditingMode.VI
+        
+        return PromptSession(**session_kwargs)
+    
+    def _get_style(self):
+        """Get prompt_toolkit style."""
+        if not _check_prompt_toolkit() or not self.config.pretty:
+            return None
+        
+        from prompt_toolkit.styles import Style
+        
+        style_dict = {}
+        if self.config.user_input_color:
+            style_dict[""] = self.config.user_input_color
+        
+        return Style.from_dict(style_dict) if style_dict else None
+    
+    def get_input(self, prompt: str = "You: ") -> str:
+        """
+        Get input from user.
+        
+        This is SYNCHRONOUS - never call from inside asyncio.run().
+        """
+        if self._prompt_session is None:
+            self._prompt_session = self._create_prompt_session()
+        
+        if self._prompt_session:
+            try:
+                style = self._get_style()
+                return self._prompt_session.prompt(prompt, style=style)
+            except KeyboardInterrupt:
+                return ""
+            except EOFError:
+                return "/exit"
+        else:
+            # Fallback to basic input
+            try:
+                return input(prompt)
+            except KeyboardInterrupt:
+                return ""
+            except EOFError:
+                return "/exit"
+    
+    def rule(self) -> None:
+        """Print a horizontal rule."""
+        if self.console and self.config.pretty:
+            self.console.rule(style=self.config.user_input_color)
+        else:
+            print()
+    
+    def print_welcome(self, model: str = "gpt-4o-mini", tools_count: int = 0, session_id: str = "") -> None:
+        """Print welcome message."""
+        if self.console and self.config.pretty:
+            from rich.panel import Panel
+            from rich.text import Text
+            
+            welcome = Text()
+            welcome.append("PraisonAI Interactive Mode\n", style="bold cyan")
+            welcome.append("Type your prompt, use ", style="dim")
+            welcome.append("/help", style="bold green")
+            welcome.append(" for commands, ", style="dim")
+            welcome.append("/exit", style="bold yellow")
+            welcome.append(" to quit\n", style="dim")
+            welcome.append(f"Model: {model}", style="dim cyan")
+            if tools_count:
+                welcome.append(f" | Tools: {tools_count}", style="dim")
+            if session_id:
+                welcome.append(f" | Session: {session_id[:8]}", style="dim")
+            
+            self.console.print(Panel(welcome, border_style="blue", padding=(0, 1)))
+        else:
+            print("PraisonAI Interactive Mode")
+            print("Type your prompt, use /help for commands, /exit to quit")
+            print(f"Model: {model} | Tools: {tools_count}")
+    
+    def print_user_input(self, text: str) -> None:
+        """Echo user input with styling."""
+        if self.console and self.config.pretty:
+            from rich.text import Text
+            self.console.print(Text(text, style=self.config.user_input_color))
+    
+    def print_assistant_start(self) -> None:
+        """Print assistant response start marker."""
+        if self.console and self.config.pretty:
+            self.console.print("\n[bold cyan]Assistant:[/bold cyan]")
+        else:
+            print("\nAssistant:")
+    
+    def print_assistant_response(self, response: str) -> None:
+        """Print assistant response with markdown formatting."""
+        if self.console and self.config.pretty:
+            from rich.markdown import Markdown
+            from rich.panel import Panel
+            
+            try:
+                md = Markdown(response)
+                self.console.print(Panel(md, border_style="cyan", padding=(0, 1)))
+            except Exception:
+                self.console.print(response)
+        else:
+            print(response)
+    
+    def stream_response(self, text: str, final: bool = False) -> MarkdownStream:
+        """Get or update streaming response."""
+        if not hasattr(self, '_current_stream') or self._current_stream is None:
+            self._current_stream = MarkdownStream()
+        
+        self._current_stream.update(text, final=final)
+        
+        if final:
+            self._current_stream = None
+        
+        return self._current_stream
+    
+    def tool_output(self, message: str) -> None:
+        """Print tool output."""
+        if self.console and self.config.pretty:
+            self.console.print(f"[{self.config.tool_output_color}]⚙ {message}[/]")
+        else:
+            print(f"⚙ {message}")
+    
+    def tool_error(self, message: str) -> None:
+        """Print tool error."""
+        if self.console and self.config.pretty:
+            self.console.print(f"[{self.config.tool_error_color}]✗ {message}[/]")
+        else:
+            print(f"✗ {message}", file=sys.stderr)
+    
+    def tool_warning(self, message: str) -> None:
+        """Print tool warning."""
+        if self.console and self.config.pretty:
+            self.console.print(f"[{self.config.tool_warning_color}]⚠ {message}[/]")
+        else:
+            print(f"⚠ {message}", file=sys.stderr)
+    
+    def info(self, message: str) -> None:
+        """Print info message."""
+        if self.console and self.config.pretty:
+            self.console.print(f"[dim]ℹ {message}[/dim]")
+        else:
+            print(f"ℹ {message}")
+    
+    def success(self, message: str) -> None:
+        """Print success message."""
+        if self.console and self.config.pretty:
+            self.console.print(f"[green]✓ {message}[/green]")
+        else:
+            print(f"✓ {message}")
+    
+    def print_help(self, commands: Dict[str, str]) -> None:
+        """Print help for available commands."""
+        if self.console and self.config.pretty:
+            from rich.table import Table
+            
+            table = Table(title="Available Commands", border_style="blue")
+            table.add_column("Command", style="green")
+            table.add_column("Description", style="dim")
+            
+            for cmd, desc in sorted(commands.items()):
+                table.add_row(f"/{cmd}", desc)
+            
+            self.console.print(table)
+        else:
+            print("\nAvailable Commands:")
+            for cmd, desc in sorted(commands.items()):
+                print(f"  /{cmd:<12} - {desc}")
+    
+    def confirm(self, message: str, default: bool = False) -> bool:
+        """Ask for confirmation."""
+        suffix = " [Y/n]: " if default else " [y/N]: "
+        
+        if self.console and self.config.pretty:
+            self.console.print(f"[yellow]? {message}[/yellow]", end="")
+        
+        try:
+            response = input(suffix).strip().lower()
+            if not response:
+                return default
+            return response in ("y", "yes")
+        except (EOFError, KeyboardInterrupt):
+            return False
+    
+    def add_commands(self, commands: Dict[str, str]) -> None:
+        """Add commands for completion."""
+        self.completer.commands.update(commands)
+    
+    def add_files(self, files: List[str]) -> None:
+        """Add files for completion."""
+        self.completer.add_files(files)
+    
+    def add_symbols(self, symbols: List[str]) -> None:
+        """Add symbols for completion."""
+        self.completer.add_symbols(symbols)

src/praisonai/praisonai/cli/interactive/repl.py

@@ -0,0 +1,297 @@
+"""
+Interactive REPL for PraisonAI.
+
+This is the main entry point for interactive chat mode.
+Uses PraisonIO for input/output (prompt_toolkit is used SYNCHRONOUSLY).
+"""
+
+import logging
+from dataclasses import dataclass
+from typing import Dict, List, Optional
+
+from .praison_io import PraisonIO, IOConfig
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class REPLConfig:
+    """Configuration for the interactive REPL."""
+    model: str = "gpt-4o-mini"
+    verbose: bool = False
+    memory: bool = False
+    tools: Optional[List] = None
+    session_id: Optional[str] = None
+    continue_session: bool = False
+    workspace: Optional[str] = None
+    autonomy: bool = True
+
+
+# Default slash commands
+DEFAULT_COMMANDS: Dict[str, str] = {
+    "help": "Show available commands",
+    "exit": "Exit interactive mode",
+    "quit": "Exit interactive mode",
+    "clear": "Clear conversation history",
+    "session": "Show current session info",
+    "model": "Show or change model",
+    "cost": "Show token usage and cost",
+    "history": "Show conversation history",
+    "export": "Export conversation to file",
+    "compact": "Toggle compact output mode",
+    "multiline": "Toggle multiline input mode",
+}
+
+
+class InteractiveREPL:
+    """
+    Interactive REPL for PraisonAI chat.
+    
+    Key design:
+    - Uses PraisonIO for all I/O (prompt_toolkit used synchronously)
+    - Agent execution is async but input is sync
+    - Clean separation between I/O and agent logic
+    """
+    
+    def __init__(
+        self,
+        config: Optional[REPLConfig] = None,
+        io_config: Optional[IOConfig] = None,
+    ):
+        self.config = config or REPLConfig()
+        self.io = PraisonIO(io_config)
+        self._agent = None
+        self._running = False
+        self._conversation_history: List[Dict] = []
+        self._session_id: Optional[str] = None
+        self._total_tokens = 0
+        self._total_cost = 0.0
+        
+        # Setup commands
+        self.io.add_commands(DEFAULT_COMMANDS)
+    
+    def _get_agent(self):
+        """Lazy-load the agent."""
+        if self._agent is None:
+            try:
+                from praisonaiagents import Agent
+                
+                agent_kwargs = {
+                    "name": "InteractiveAgent",
+                    "role": "Assistant",
+                    "goal": "Help the user with their requests",
+                    "instructions": "You are a helpful AI assistant. Be concise and helpful.",
+                }
+                
+                if self.config.model:
+                    agent_kwargs["llm"] = self.config.model
+                
+                if self.config.memory:
+                    agent_kwargs["memory"] = True
+                
+                if self.config.tools:
+                    agent_kwargs["tools"] = self.config.tools
+                
+                self._agent = Agent(**agent_kwargs)
+                
+            except ImportError as e:
+                self.io.tool_error(f"Failed to import praisonaiagents: {e}")
+                raise
+        
+        return self._agent
+    
+    def _handle_command(self, command: str) -> bool:
+        """
+        Handle slash commands.
+        
+        Returns True if command was handled, False otherwise.
+        """
+        parts = command.split(maxsplit=1)
+        cmd = parts[0].lower().lstrip("/")
+        args = parts[1] if len(parts) > 1 else ""
+        
+        if cmd in ("exit", "quit", "q"):
+            self._running = False
+            self.io.info("Goodbye!")
+            return True
+        
+        elif cmd == "help":
+            self.io.print_help(DEFAULT_COMMANDS)
+            return True
+        
+        elif cmd == "clear":
+            self._conversation_history.clear()
+            self.io.success("Conversation cleared.")
+            return True
+        
+        elif cmd == "session":
+            if self._session_id:
+                self.io.info(f"Session: {self._session_id}")
+            else:
+                self.io.info("No active session")
+            return True
+        
+        elif cmd == "model":
+            if args:
+                self.config.model = args
+                self._agent = None  # Reset agent to use new model
+                self.io.success(f"Model changed to: {args}")
+            else:
+                self.io.info(f"Current model: {self.config.model}")
+            return True
+        
+        elif cmd == "cost":
+            self.io.info(f"Total tokens: {self._total_tokens}")
+            self.io.info(f"Estimated cost: ${self._total_cost:.4f}")
+            return True
+        
+        elif cmd == "history":
+            if not self._conversation_history:
+                self.io.info("No conversation history")
+            else:
+                for i, msg in enumerate(self._conversation_history[-10:], 1):
+                    role = msg.get("role", "unknown")
+                    content = msg.get("content", "")[:100]
+                    self.io.info(f"{i}. [{role}] {content}...")
+            return True
+        
+        elif cmd == "export":
+            filename = args or "conversation.txt"
+            try:
+                with open(filename, "w") as f:
+                    for msg in self._conversation_history:
+                        f.write(f"[{msg.get('role', 'unknown')}]\n")
+                        f.write(f"{msg.get('content', '')}\n\n")
+                self.io.success(f"Exported to {filename}")
+            except Exception as e:
+                self.io.tool_error(f"Export failed: {e}")
+            return True
+        
+        elif cmd == "compact":
+            self.io.config.pretty = not self.io.config.pretty
+            mode = "disabled" if self.io.config.pretty else "enabled"
+            self.io.info(f"Compact mode {mode}")
+            return True
+        
+        elif cmd == "multiline":
+            self.io.config.multiline_mode = not self.io.config.multiline_mode
+            mode = "enabled" if self.io.config.multiline_mode else "disabled"
+            self.io.info(f"Multiline mode {mode}")
+            return True
+        
+        else:
+            self.io.tool_warning(f"Unknown command: /{cmd}")
+            self.io.info("Type /help for available commands")
+            return True
+    
+    def _execute_prompt(self, prompt: str) -> Optional[str]:
+        """Execute a prompt and return the response."""
+        try:
+            agent = self._get_agent()
+            
+            # Add to history
+            self._conversation_history.append({
+                "role": "user",
+                "content": prompt
+            })
+            
+            # Execute
+            response = agent.start(prompt)
+            
+            # Add response to history
+            if response:
+                self._conversation_history.append({
+                    "role": "assistant", 
+                    "content": str(response)
+                })
+            
+            return str(response) if response else None
+            
+        except Exception as e:
+            logger.exception("Error executing prompt")
+            self.io.tool_error(f"Error: {e}")
+            return None
+    
+    def run(self) -> None:
+        """
+        Run the interactive REPL.
+        
+        This is the main entry point. It runs synchronously,
+        using prompt_toolkit for input (which must be sync).
+        """
+        self._running = True
+        
+        # Get tools count for display
+        tools_count = len(self.config.tools) if self.config.tools else 0
+        
+        # Generate session ID
+        import uuid
+        self._session_id = self.config.session_id or str(uuid.uuid4())
+        
+        # Print welcome
+        self.io.print_welcome(
+            model=self.config.model,
+            tools_count=tools_count,
+            session_id=self._session_id
+        )
+        
+        while self._running:
+            try:
+                # Get input (SYNC - this is the key!)
+                user_input = self.io.get_input("You: ").strip()
+                
+                if not user_input:
+                    continue
+                
+                # Handle slash commands
+                if user_input.startswith("/"):
+                    self._handle_command(user_input)
+                    continue
+                
+                # Show thinking indicator
+                self.io.print_assistant_start()
+                
+                # Execute prompt
+                response = self._execute_prompt(user_input)
+                
+                # Display response
+                if response:
+                    self.io.print_assistant_response(response)
+                
+            except KeyboardInterrupt:
+                self.io.info("\nUse /exit to quit")
+            except EOFError:
+                self._running = False
+                self.io.info("Goodbye!")
+            except Exception as e:
+                logger.exception("Error in REPL loop")
+                self.io.tool_error(f"Error: {e}")
+    
+    def run_single(self, prompt: str) -> Optional[str]:
+        """Run a single prompt and return the response."""
+        return self._execute_prompt(prompt)
+
+
+def start_interactive(
+    model: str = "gpt-4o-mini",
+    verbose: bool = False,
+    memory: bool = False,
+    tools: Optional[List] = None,
+    session_id: Optional[str] = None,
+    **kwargs
+) -> None:
+    """
+    Start the interactive REPL.
+    
+    This is the main entry point for `praisonai chat` and `praisonai` (no args).
+    """
+    config = REPLConfig(
+        model=model,
+        verbose=verbose,
+        memory=memory,
+        tools=tools,
+        session_id=session_id,
+    )
+    
+    repl = InteractiveREPL(config=config)
+    repl.run()

src/praisonai/praisonai/cli/interactive/split_tui.py

@@ -0,0 +1,490 @@
+"""
+PraisonAI Split-Pane TUI Application.
+
+Full-screen terminal UI with:
+- Fixed bottom input pane (always visible)
+- Scrollable top output pane
+- Direct response (no streaming - faster)
+- Activity indicators showing agent status
+
+Uses prompt_toolkit Application for true split-pane layout.
+"""
+
+import shutil
+import threading
+import time
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import List, Optional
+from datetime import datetime
+
+# ============================================================================
+# ASCII Art Logo - "Praison AI" branding
+# ============================================================================
+
+LOGO = r"""
+ ██████╗ ██████╗  █████╗ ██╗███████╗ ██████╗ ███╗   ██╗     █████╗ ██╗
+ ██╔══██╗██╔══██╗██╔══██╗██║██╔════╝██╔═══██╗████╗  ██║    ██╔══██╗██║
+ ██████╔╝██████╔╝███████║██║███████╗██║   ██║██╔██╗ ██║    ███████║██║
+ ██╔═══╝ ██╔══██╗██╔══██║██║╚════██║██║   ██║██║╚██╗██║    ██╔══██║██║
+ ██║     ██║  ██║██║  ██║██║███████║╚██████╔╝██║ ╚████║    ██║  ██║██║
+ ╚═╝     ╚═╝  ╚═╝╚═╝  ╚═╝╚═╝╚══════╝ ╚═════╝ ╚═╝  ╚═══╝    ╚═╝  ╚═╝╚═╝
+"""
+
+LOGO_SMALL = r"""
+ ╔═╗┬─┐┌─┐┬┌─┐┌─┐┌┐┌  ╔═╗╦
+ ╠═╝├┬┘├─┤│└─┐│ ││││  ╠═╣║
+ ╩  ┴└─┴ ┴┴└─┘└─┘┘└┘  ╩ ╩╩
+"""
+
+LOGO_MINIMAL = "▶ Praison AI"
+
+
+def get_logo(width: int = 80) -> str:
+    """Get appropriate logo based on terminal width."""
+    if width >= 75:
+        return LOGO
+    elif width >= 40:
+        return LOGO_SMALL
+    else:
+        return LOGO_MINIMAL
+
+
+def get_version() -> str:
+    """Get PraisonAI version."""
+    try:
+        from praisonai import __version__
+        return __version__
+    except Exception:
+        return "1.0.0"
+
+
+# ============================================================================
+# Configuration
+# ============================================================================
+
+@dataclass
+class SplitTUIConfig:
+    """Configuration for the Split TUI application."""
+    model: str = "gpt-4o-mini"
+    show_logo: bool = True
+    show_status_bar: bool = True
+    session_id: Optional[str] = None
+    workspace: Optional[str] = None
+    history_file: Optional[str] = None
+
+
+# ============================================================================
+# Message Types
+# ============================================================================
+
+@dataclass
+class ChatMessage:
+    """A chat message."""
+    role: str  # "user", "assistant", "system", "status"
+    content: str
+    timestamp: datetime = field(default_factory=datetime.now)
+
+
+# ============================================================================
+# Split TUI Application
+# ============================================================================
+
+class SplitTUI:
+    """
+    Split-pane TUI with fixed bottom input and scrollable top output.
+    
+    Key features:
+    - Input box ALWAYS visible at bottom
+    - Output scrolls above independently
+    - Direct response (no streaming) for speed
+    - Activity indicators during processing
+    """
+    
+    def __init__(self, config: Optional[SplitTUIConfig] = None):
+        self.config = config or SplitTUIConfig()
+        self.messages: List[ChatMessage] = []
+        self._running = False
+        self._agent = None
+        self._processing = False
+        self._status_text = ""
+        
+        # Terminal size
+        self.term_width, self.term_height = shutil.get_terminal_size((80, 24))
+        
+        # Session ID
+        import uuid
+        self.session_id = self.config.session_id or str(uuid.uuid4())[:8]
+    
+    def _get_agent(self):
+        """Lazy-load the agent with verbose=False for no streaming."""
+        if self._agent is None:
+            try:
+                from praisonaiagents import Agent
+                
+                self._agent = Agent(
+                    name="Praison",
+                    role="AI Assistant",
+                    goal="Help the user with their requests",
+                    instructions="You are Praison AI, a helpful assistant. Be concise and helpful.",
+                    llm=self.config.model,
+                )
+            except ImportError as e:
+                raise RuntimeError(f"Failed to import praisonaiagents: {e}")
+        
+        return self._agent
+    
+    def _format_output(self) -> str:
+        """Format all messages for the output pane."""
+        lines = []
+        
+        # Logo
+        if self.config.show_logo and not self.messages:
+            logo = get_logo(self.term_width)
+            lines.append(logo.strip())
+            lines.append("")
+            lines.append(f"  v{get_version()} · Model: {self.config.model}")
+            lines.append("")
+            lines.append("  Type your message and press Enter. Use /help for commands.")
+            lines.append("")
+        
+        # Messages
+        for msg in self.messages:
+            if msg.role == "user":
+                lines.append(f"› {msg.content}")
+                lines.append("")
+            elif msg.role == "assistant":
+                lines.append(f"● {msg.content}")
+                lines.append("")
+            elif msg.role == "system":
+                lines.append(f"  {msg.content}")
+                lines.append("")
+            elif msg.role == "status":
+                lines.append(f"  ⏳ {msg.content}")
+        
+        # Processing indicator
+        if self._processing:
+            lines.append(f"  ⏳ {self._status_text}")
+        
+        return "\n".join(lines)
+    
+    def _handle_command(self, command: str) -> bool:
+        """Handle slash commands. Returns True if handled."""
+        parts = command.split(maxsplit=1)
+        cmd = parts[0].lower().lstrip("/")
+        args = parts[1] if len(parts) > 1 else ""
+        
+        if cmd in ("exit", "quit", "q"):
+            self._running = False
+            return True
+        
+        elif cmd == "help":
+            help_text = """Available Commands:
+  /help          Show this help
+  /exit, /quit   Exit Praison AI
+  /clear         Clear conversation
+  /model [name]  Show or change model
+  /new           Start new conversation"""
+            self.messages.append(ChatMessage(role="system", content=help_text))
+            return True
+        
+        elif cmd == "clear":
+            self.messages.clear()
+            return True
+        
+        elif cmd == "new":
+            self.messages.clear()
+            import uuid
+            self.session_id = str(uuid.uuid4())[:8]
+            self.messages.append(ChatMessage(role="system", content=f"New session: {self.session_id}"))
+            return True
+        
+        elif cmd == "model":
+            if args:
+                self.config.model = args
+                self._agent = None
+                self.messages.append(ChatMessage(role="system", content=f"Model changed to: {args}"))
+            else:
+                self.messages.append(ChatMessage(role="system", content=f"Current model: {self.config.model}"))
+            return True
+        
+        else:
+            self.messages.append(ChatMessage(role="system", content=f"Unknown command: /{cmd}. Use /help for available commands."))
+            return True
+    
+    def _execute_prompt(self, prompt: str) -> Optional[str]:
+        """Execute a prompt and return the response (direct, no streaming)."""
+        import sys
+        import io
+        
+        try:
+            agent = self._get_agent()
+            
+            # Suppress agent's Rich output by redirecting stdout/stderr
+            old_stdout = sys.stdout
+            old_stderr = sys.stderr
+            sys.stdout = io.StringIO()
+            sys.stderr = io.StringIO()
+            
+            try:
+                response = agent.start(prompt)
+            finally:
+                # Restore stdout/stderr
+                sys.stdout = old_stdout
+                sys.stderr = old_stderr
+            
+            return str(response) if response else None
+        except Exception as e:
+            return f"Error: {e}"
+    
+    def _run_with_status(self, prompt: str) -> Optional[str]:
+        """Run prompt with status updates."""
+        self._processing = True
+        self._status_text = "Praison AI is thinking..."
+        
+        # Execute in background
+        result = [None]
+        error = [None]
+        
+        def execute():
+            try:
+                result[0] = self._execute_prompt(prompt)
+            except Exception as e:
+                error[0] = str(e)
+        
+        thread = threading.Thread(target=execute)
+        thread.start()
+        
+        # Update status while waiting
+        start_time = time.time()
+        spinner = ["⠋", "⠙", "⠹", "⠸", "⠼", "⠴", "⠦", "⠧", "⠇", "⠏"]
+        idx = 0
+        
+        while thread.is_alive():
+            elapsed = time.time() - start_time
+            self._status_text = f"{spinner[idx]} Praison AI is thinking... ({elapsed:.1f}s)"
+            idx = (idx + 1) % len(spinner)
+            time.sleep(0.1)
+        
+        thread.join()
+        self._processing = False
+        self._status_text = ""
+        
+        if error[0]:
+            return f"Error: {error[0]}"
+        return result[0]
+    
+    def run(self) -> None:
+        """Run the split-pane TUI."""
+        self._running = True
+        
+        # Try to use prompt_toolkit for better experience
+        try:
+            self._run_with_prompt_toolkit()
+        except ImportError:
+            # Fallback to simple mode
+            self._run_simple()
+    
+    def _run_simple(self) -> None:
+        """Simple fallback mode without prompt_toolkit."""
+        # Print welcome
+        print(self._format_output())
+        
+        while self._running:
+            try:
+                # Status bar
+                if self.config.show_status_bar:
+                    status = f" ? for help | {self.config.model} | Session: {self.session_id} "
+                    print(f"\033[90m{status}\033[0m")
+                
+                # Get input
+                user_input = input("› ").strip()
+                
+                if not user_input:
+                    continue
+                
+                # Handle commands
+                if user_input.startswith("/"):
+                    self._handle_command(user_input)
+                    # Print system messages
+                    for msg in self.messages:
+                        if msg.role == "system":
+                            print(f"  {msg.content}")
+                    self.messages = [m for m in self.messages if m.role != "system"]
+                    continue
+                
+                # Add user message
+                self.messages.append(ChatMessage(role="user", content=user_input))
+                
+                # Execute with status
+                print("  ⏳ Praison AI is thinking...")
+                response = self._execute_prompt(user_input)
+                
+                if response:
+                    self.messages.append(ChatMessage(role="assistant", content=response))
+                    print(f"\n● {response}\n")
+                
+            except KeyboardInterrupt:
+                print("\n  Use /exit to quit")
+            except EOFError:
+                self._running = False
+        
+        print("\n  Goodbye from Praison AI!")
+    
+    def _run_with_prompt_toolkit(self) -> None:
+        """Run with prompt_toolkit for split-pane layout."""
+        from prompt_toolkit import Application
+        from prompt_toolkit.buffer import Buffer
+        from prompt_toolkit.layout.containers import HSplit, VSplit, Window
+        from prompt_toolkit.layout.controls import BufferControl, FormattedTextControl
+        from prompt_toolkit.layout.layout import Layout
+        from prompt_toolkit.key_binding import KeyBindings
+        from prompt_toolkit.styles import Style
+        from prompt_toolkit.history import FileHistory
+        
+        # Setup history
+        history_file = self.config.history_file or str(Path.home() / ".praison" / "history")
+        Path(history_file).parent.mkdir(parents=True, exist_ok=True)
+        
+        # Output buffer (read-only, for displaying conversation)
+        output_text = [self._format_output()]
+        
+        def get_output_text():
+            return output_text[0]
+        
+        # Input buffer
+        input_buffer = Buffer(
+            multiline=False,
+            history=FileHistory(history_file),
+        )
+        
+        # Key bindings
+        kb = KeyBindings()
+        
+        @kb.add("enter")
+        def handle_enter(event):
+            user_input = input_buffer.text.strip()
+            if not user_input:
+                return
+            
+            input_buffer.reset()
+            
+            # Handle commands
+            if user_input.startswith("/"):
+                self._handle_command(user_input)
+                output_text[0] = self._format_output()
+                return
+            
+            # Add user message
+            self.messages.append(ChatMessage(role="user", content=user_input))
+            
+            # Show processing status
+            self.messages.append(ChatMessage(role="status", content="Praison AI is thinking..."))
+            output_text[0] = self._format_output()
+            
+            # Execute (this blocks but that's OK for direct response)
+            response = self._execute_prompt(user_input)
+            
+            # Remove status message
+            self.messages = [m for m in self.messages if m.role != "status"]
+            
+            if response:
+                self.messages.append(ChatMessage(role="assistant", content=response))
+            
+            output_text[0] = self._format_output()
+        
+        @kb.add("c-c")
+        def handle_ctrl_c(event):
+            input_buffer.reset()
+        
+        @kb.add("c-d")
+        def handle_ctrl_d(event):
+            if not input_buffer.text:
+                self._running = False
+                event.app.exit()
+        
+        @kb.add("c-q")
+        def handle_ctrl_q(event):
+            self._running = False
+            event.app.exit()
+        
+        # Style
+        style = Style.from_dict({
+            "output-area": "bg:#0d0d0d",
+            "input-area": "bg:#1a1a1a",
+            "status-bar": "bg:#333333 #888888",
+            "prompt": "#00aaff bold",
+        })
+        
+        # Status bar text
+        def get_status_bar():
+            left = "? for help"
+            center = self.config.model
+            right = f"Session: {self.session_id}"
+            return f" {left}  |  {center}  |  {right} "
+        
+        # Layout: Output (top, scrollable) + Status bar + Input (bottom, fixed)
+        layout = Layout(
+            HSplit([
+                # Output area (takes remaining space)
+                Window(
+                    content=FormattedTextControl(get_output_text),
+                    wrap_lines=True,
+                    style="class:output-area",
+                ),
+                # Status bar (1 line)
+                Window(
+                    content=FormattedTextControl(get_status_bar),
+                    height=1,
+                    style="class:status-bar",
+                ),
+                # Input area (fixed at bottom, 3 lines for visibility)
+                HSplit([
+                    Window(height=1),  # Spacer
+                    VSplit([
+                        Window(
+                            content=FormattedTextControl([("class:prompt", "› ")]),
+                            width=2,
+                        ),
+                        Window(
+                            content=BufferControl(buffer=input_buffer),
+                            wrap_lines=True,
+                        ),
+                    ]),
+                    Window(height=1),  # Spacer
+                ], height=3, style="class:input-area"),
+            ])
+        )
+        
+        # Create application
+        app = Application(
+            layout=layout,
+            key_bindings=kb,
+            style=style,
+            full_screen=True,
+            mouse_support=True,
+        )
+        
+        # Run
+        app.run()
+        
+        print("\n  Goodbye from Praison AI!")
+    
+    def run_single(self, prompt: str) -> Optional[str]:
+        """Run a single prompt (non-interactive)."""
+        return self._execute_prompt(prompt)
+
+
+def start_split_tui(
+    model: str = "gpt-4o-mini",
+    show_logo: bool = True,
+    **kwargs
+) -> None:
+    """Start the split-pane TUI application."""
+    config = SplitTUIConfig(
+        model=model,
+        show_logo=show_logo,
+    )
+    
+    tui = SplitTUI(config=config)
+    tui.run()

src/praisonai/praisonai/cli/interactive/tui_app.py

@@ -0,0 +1,510 @@
+"""
+PraisonAI Interactive TUI Application.
+
+Full-screen terminal UI with:
+- Sticky bottom input box (like Aider/Codex/Claude Code)
+- Scrollable output area above
+- ASCII art logo banner
+- Status bar with model/context info
+- Clean, spacious design inspired by Claude Code
+
+Uses prompt_toolkit for the full-screen application.
+"""
+
+import shutil
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Dict, List, Optional
+from datetime import datetime
+
+# Lazy imports for performance
+_PT_AVAILABLE = None
+_RICH_AVAILABLE = None
+
+
+def _check_prompt_toolkit():
+    global _PT_AVAILABLE
+    if _PT_AVAILABLE is None:
+        try:
+            import prompt_toolkit  # noqa: F401
+            _PT_AVAILABLE = True
+        except ImportError:
+            _PT_AVAILABLE = False
+    return _PT_AVAILABLE
+
+
+def _check_rich():
+    global _RICH_AVAILABLE
+    if _RICH_AVAILABLE is None:
+        try:
+            import rich  # noqa: F401
+            _RICH_AVAILABLE = True
+        except ImportError:
+            _RICH_AVAILABLE = False
+    return _RICH_AVAILABLE
+
+
+# ============================================================================
+# ASCII Art Logo
+# ============================================================================
+
+# PraisonAI ASCII art logo - unique gradient style
+LOGO_LARGE = r"""
+ ██████╗ ██████╗  █████╗ ██╗███████╗ ██████╗ ███╗   ██╗     █████╗ ██╗
+ ██╔══██╗██╔══██╗██╔══██╗██║██╔════╝██╔═══██╗████╗  ██║    ██╔══██╗██║
+ ██████╔╝██████╔╝███████║██║███████╗██║   ██║██╔██╗ ██║    ███████║██║
+ ██╔═══╝ ██╔══██╗██╔══██║██║╚════██║██║   ██║██║╚██╗██║    ██╔══██║██║
+ ██║     ██║  ██║██║  ██║██║███████║╚██████╔╝██║ ╚████║    ██║  ██║██║
+ ╚═╝     ╚═╝  ╚═╝╚═╝  ╚═╝╚═╝╚══════╝ ╚═════╝ ╚═╝  ╚═══╝    ╚═╝  ╚═╝╚═╝
+"""
+
+LOGO_MEDIUM = r"""
+ ████████╗ ████████╗  ██████╗ ██╗███████╗ ██████╗ ███╗   ██╗
+ ██╔═══██║ ██╔═══██║ ██╔══██╗██║██╔════╝██╔═══██╗████╗  ██║
+ ████████║ ████████║ ███████║██║███████╗██║   ██║██╔██╗ ██║
+ ██╔═════╝ ██╔═══██║ ██╔══██║██║╚════██║██║   ██║██║╚██╗██║
+ ██║       ██║   ██║ ██║  ██║██║███████║╚██████╔╝██║ ╚████║
+ ╚═╝       ╚═╝   ╚═╝ ╚═╝  ╚═╝╚═╝╚══════╝ ╚═════╝ ╚═╝  ╚═══╝
+"""
+
+LOGO_SMALL = r"""
+ ██████╗ ██████╗  █████╗ ██╗███████╗ ██████╗ ███╗   ██╗
+ ██╔══██╗██╔══██╗██╔══██╗██║██╔════╝██╔═══██╗████╗  ██║
+ ██████╔╝██████╔╝███████║██║███████╗██║   ██║██╔██╗ ██║
+ ██╔═══╝ ██╔══██╗██╔══██║██║╚════██║██║   ██║██║╚██╗██║
+ ██║     ██║  ██║██║  ██║██║███████║╚██████╔╝██║ ╚████║
+ ╚═╝     ╚═╝  ╚═╝╚═╝  ╚═╝╚═╝╚══════╝ ╚═════╝ ╚═╝  ╚═══╝
+"""
+
+LOGO_TINY = r"""
+ ╔═╗┬─┐┌─┐┬┌─┐┌─┐┌┐┌  ╔═╗╦
+ ╠═╝├┬┘├─┤│└─┐│ ││││  ╠═╣║
+ ╩  ┴└─┴ ┴┴└─┘└─┘┘└┘  ╩ ╩╩
+"""
+
+LOGO_MINIMAL = "▶ PraisonAI"
+
+
+def get_logo(width: int = 80) -> str:
+    """Get appropriate logo based on terminal width."""
+    if width >= 80:
+        return LOGO_SMALL
+    elif width >= 60:
+        return LOGO_TINY
+    else:
+        return LOGO_MINIMAL
+
+
+def get_version() -> str:
+    """Get PraisonAI version."""
+    try:
+        from praisonai import __version__
+        return __version__
+    except Exception:
+        return "1.0.0"
+
+
+# ============================================================================
+# Color Scheme
+# ============================================================================
+
+@dataclass
+class ColorScheme:
+    """Color scheme for the TUI."""
+    # Primary colors
+    primary: str = "#00aaff"      # Cyan-blue (PraisonAI brand)
+    secondary: str = "#888888"    # Gray
+    accent: str = "#00cc00"       # Green
+    
+    # Text colors
+    text: str = "#ffffff"         # White
+    text_dim: str = "#666666"     # Dim gray
+    text_muted: str = "#444444"   # Very dim
+    
+    # Status colors
+    success: str = "#00cc00"      # Green
+    warning: str = "#ffaa00"      # Orange
+    error: str = "#ff4444"        # Red
+    info: str = "#00aaff"         # Blue
+    
+    # UI elements
+    border: str = "#333333"       # Dark gray
+    input_bg: str = "#1a1a1a"     # Very dark
+    output_bg: str = "#0d0d0d"    # Almost black
+
+
+DEFAULT_COLORS = ColorScheme()
+
+
+# ============================================================================
+# TUI Configuration
+# ============================================================================
+
+@dataclass
+class TUIConfig:
+    """Configuration for the TUI application."""
+    # Model settings
+    model: str = "gpt-4o-mini"
+    
+    # Display settings
+    show_logo: bool = True
+    show_tips: bool = True
+    show_status_bar: bool = True
+    compact_mode: bool = False
+    
+    # Colors
+    colors: ColorScheme = field(default_factory=ColorScheme)
+    
+    # Input settings
+    multiline: bool = False
+    vi_mode: bool = False
+    
+    # History
+    history_file: Optional[str] = None
+    
+    # Session
+    session_id: Optional[str] = None
+    workspace: Optional[str] = None
+
+
+# ============================================================================
+# Message Types
+# ============================================================================
+
+@dataclass
+class Message:
+    """A chat message."""
+    role: str  # "user", "assistant", "system", "tool"
+    content: str
+    timestamp: datetime = field(default_factory=datetime.now)
+    metadata: Dict = field(default_factory=dict)
+
+
+# ============================================================================
+# TUI Application
+# ============================================================================
+
+class PraisonTUI:
+    """
+    Full-screen TUI application for PraisonAI.
+    
+    Features:
+    - Sticky bottom input box
+    - Scrollable output area
+    - ASCII art logo
+    - Status bar
+    - Clean, spacious design
+    """
+    
+    def __init__(self, config: Optional[TUIConfig] = None):
+        self.config = config or TUIConfig()
+        self.messages: List[Message] = []
+        self._running = False
+        self._agent = None
+        self._total_tokens = 0
+        self._total_cost = 0.0
+        
+        # Get terminal size
+        self.term_width, self.term_height = shutil.get_terminal_size((80, 24))
+        
+        # Generate session ID
+        import uuid
+        self.session_id = self.config.session_id or str(uuid.uuid4())[:8]
+    
+    def _get_agent(self):
+        """Lazy-load the agent."""
+        if self._agent is None:
+            try:
+                from praisonaiagents import Agent
+                
+                self._agent = Agent(
+                    name="PraisonAgent",
+                    role="Assistant",
+                    goal="Help the user with their requests",
+                    instructions="You are a helpful AI assistant. Be concise and helpful.",
+                    llm=self.config.model,
+                )
+            except ImportError as e:
+                raise RuntimeError(f"Failed to import praisonaiagents: {e}")
+        
+        return self._agent
+    
+    def _render_logo(self) -> str:
+        """Render the ASCII logo with colors."""
+        if not self.config.show_logo:
+            return ""
+        
+        logo = get_logo(self.term_width)
+        version = get_version()
+        
+        info_line = f"v{version} · Model: {self.config.model} · Session: {self.session_id}"
+        return logo.strip() + "\n" + info_line
+    
+    def _render_welcome(self) -> str:
+        """Render welcome message."""
+        lines = []
+        
+        # Logo
+        if self.config.show_logo:
+            lines.append(self._render_logo())
+            lines.append("")
+        
+        # Tips
+        if self.config.show_tips:
+            tips = [
+                "Type your message and press Enter to send",
+                "Use /help for commands, /exit to quit",
+                "Press ? for keyboard shortcuts",
+            ]
+            lines.append("  " + tips[0])
+        
+        lines.append("")
+        
+        return "\n".join(lines)
+    
+    def _render_status_bar(self) -> str:
+        """Render the status bar."""
+        if not self.config.show_status_bar:
+            return ""
+        
+        left = f"? for shortcuts"
+        center = f"{self.config.model}"
+        right = f"Session: {self.session_id}"
+        
+        # Calculate spacing
+        total_len = len(left) + len(center) + len(right)
+        available = self.term_width - total_len - 4
+        
+        if available > 0:
+            left_space = available // 2
+            right_space = available - left_space
+            return f" {left}{' ' * left_space}{center}{' ' * right_space}{right} "
+        else:
+            return f" {center} "
+    
+    def _format_message(self, msg: Message) -> str:
+        """Format a message for display."""
+        if msg.role == "user":
+            prefix = "› "
+            return f"{prefix}{msg.content}"
+        elif msg.role == "assistant":
+            prefix = "● "
+            return f"{prefix}{msg.content}"
+        elif msg.role == "tool":
+            prefix = "⚙ "
+            return f"{prefix}{msg.content}"
+        else:
+            return msg.content
+    
+    def _handle_command(self, command: str) -> bool:
+        """Handle slash commands. Returns True if handled."""
+        parts = command.split(maxsplit=1)
+        cmd = parts[0].lower().lstrip("/")
+        args = parts[1] if len(parts) > 1 else ""
+        
+        if cmd in ("exit", "quit", "q"):
+            self._running = False
+            return True
+        
+        elif cmd == "help":
+            help_text = """
+Available Commands:
+  /help          Show this help
+  /exit, /quit   Exit interactive mode
+  /clear         Clear conversation
+  /model [name]  Show or change model
+  /cost          Show token usage
+  /compact       Toggle compact mode
+  /multiline     Toggle multiline input
+"""
+            self.messages.append(Message(role="system", content=help_text.strip()))
+            return True
+        
+        elif cmd == "clear":
+            self.messages.clear()
+            return True
+        
+        elif cmd == "model":
+            if args:
+                self.config.model = args
+                self._agent = None
+                self.messages.append(Message(role="system", content=f"Model changed to: {args}"))
+            else:
+                self.messages.append(Message(role="system", content=f"Current model: {self.config.model}"))
+            return True
+        
+        elif cmd == "cost":
+            self.messages.append(Message(
+                role="system",
+                content=f"Tokens: {self._total_tokens} | Cost: ${self._total_cost:.4f}"
+            ))
+            return True
+        
+        elif cmd == "compact":
+            self.config.compact_mode = not self.config.compact_mode
+            mode = "enabled" if self.config.compact_mode else "disabled"
+            self.messages.append(Message(role="system", content=f"Compact mode {mode}"))
+            return True
+        
+        elif cmd == "multiline":
+            self.config.multiline = not self.config.multiline
+            mode = "enabled" if self.config.multiline else "disabled"
+            self.messages.append(Message(role="system", content=f"Multiline mode {mode}"))
+            return True
+        
+        else:
+            self.messages.append(Message(role="system", content=f"Unknown command: /{cmd}"))
+            return True
+    
+    def _execute_prompt(self, prompt: str) -> Optional[str]:
+        """Execute a prompt and return the response."""
+        try:
+            agent = self._get_agent()
+            response = agent.start(prompt)
+            return str(response) if response else None
+        except Exception as e:
+            return f"Error: {e}"
+    
+    def run(self) -> None:
+        """Run the TUI application."""
+        self._running = True
+        
+        # Print welcome
+        print(self._render_welcome())
+        
+        # Setup history
+        history_file = self.config.history_file or str(Path.home() / ".praison" / "history")
+        Path(history_file).parent.mkdir(parents=True, exist_ok=True)
+        
+        # Create prompt session
+        if _check_prompt_toolkit():
+            from prompt_toolkit import PromptSession
+            from prompt_toolkit.history import FileHistory
+            from prompt_toolkit.auto_suggest import AutoSuggestFromHistory
+            from prompt_toolkit.key_binding import KeyBindings
+            from prompt_toolkit.styles import Style
+            
+            # Key bindings
+            kb = KeyBindings()
+            
+            @kb.add("c-c")
+            def _(event):
+                event.current_buffer.reset()
+            
+            @kb.add("c-d")
+            def _(event):
+                if not event.current_buffer.text:
+                    event.app.exit()
+            
+            # Style
+            style = Style.from_dict({
+                "": self.config.colors.accent,
+                "prompt": self.config.colors.primary,
+            })
+            
+            session = PromptSession(
+                history=FileHistory(history_file),
+                auto_suggest=AutoSuggestFromHistory(),
+                key_bindings=kb,
+                style=style,
+                multiline=self.config.multiline,
+            )
+            
+            def get_input():
+                return session.prompt(
+                    [("class:prompt", "› ")],
+                    placeholder="Type your message or @path/to/file"
+                )
+        else:
+            def get_input():
+                return input("› ")
+        
+        # Main loop
+        while self._running:
+            try:
+                # Print status bar
+                if self.config.show_status_bar and not self.config.compact_mode:
+                    status = self._render_status_bar()
+                    if _check_rich():
+                        from rich.console import Console
+                        Console().print(f"[dim]{status}[/dim]")
+                    else:
+                        print(f"\033[90m{status}\033[0m")
+                
+                # Get input
+                user_input = get_input().strip()
+                
+                if not user_input:
+                    continue
+                
+                # Handle commands
+                if user_input.startswith("/"):
+                    self._handle_command(user_input)
+                    # Print system messages
+                    for msg in self.messages:
+                        if msg.role == "system":
+                            print(self._format_message(msg))
+                    self.messages = [m for m in self.messages if m.role != "system"]
+                    continue
+                
+                # Add user message
+                self.messages.append(Message(role="user", content=user_input))
+                
+                # Show thinking indicator
+                if _check_rich():
+                    from rich.console import Console
+                    Console().print("[dim]Thinking...[/dim]")
+                
+                # Execute
+                response = self._execute_prompt(user_input)
+                
+                if response:
+                    self.messages.append(Message(role="assistant", content=response))
+                    
+                    # Display response
+                    if _check_rich():
+                        from rich.console import Console
+                        from rich.markdown import Markdown
+                        from rich.panel import Panel
+                        
+                        console = Console()
+                        try:
+                            md = Markdown(response)
+                            console.print(Panel(md, border_style="cyan", padding=(0, 1)))
+                        except Exception:
+                            console.print(f"● {response}")
+                    else:
+                        print(f"● {response}")
+                
+                print()  # Spacing
+                
+            except KeyboardInterrupt:
+                print("\nUse /exit to quit")
+            except EOFError:
+                self._running = False
+                print("\nGoodbye!")
+            except Exception as e:
+                print(f"Error: {e}")
+        
+        print("Goodbye!")
+    
+    def run_single(self, prompt: str) -> Optional[str]:
+        """Run a single prompt."""
+        return self._execute_prompt(prompt)
+
+
+def start_tui(
+    model: str = "gpt-4o-mini",
+    show_logo: bool = True,
+    compact: bool = False,
+    **kwargs
+) -> None:
+    """Start the TUI application."""
+    config = TUIConfig(
+        model=model,
+        show_logo=show_logo,
+        compact_mode=compact,
+    )
+    
+    tui = PraisonTUI(config=config)
+    tui.run()

src/praisonai/praisonai/cli/legacy.py

@@ -34,6 +34,8 @@ TYPER_COMMANDS = {
     'memory', 'workflow', 'tools', 'knowledge', 'deploy', 'agents', 'skills',
     'eval', 'templates', 'recipe', 'todo', 'docs', 'commit', 'hooks', 'rules',
     'registry', 'package', 'endpoints',
+    # Standardisation commands
+    'standardise', 'standardize',
 }
 
 

src/praisonai/praisonai/deploy.py

@@ -57,7 +57,7 @@ class CloudDeployer:
             file.write("FROM python:3.11-slim\n")
             file.write("WORKDIR /app\n")
             file.write("COPY . .\n")
-            file.write("RUN pip install flask praisonai==3.7.2 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==3.7.7 gunicorn markdown\n")
             file.write("EXPOSE 8080\n")
             file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
             

src/praisonai/praisonai/standardise/__init__.py

@@ -25,6 +25,9 @@ __all__ = [
     "ReportGenerator",
     # Config
     "StandardiseConfig",
+    # AI Generation
+    "AIGenerator",
+    "ExampleVerifier",
 ]
 
 
@@ -70,4 +73,12 @@ def __getattr__(name: str):
         from .config import StandardiseConfig
         return StandardiseConfig
     
+    if name == "AIGenerator":
+        from .ai_generator import AIGenerator
+        return AIGenerator
+    
+    if name == "ExampleVerifier":
+        from .example_verifier import ExampleVerifier
+        return ExampleVerifier
+    
     raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

src/praisonai/praisonai/standardise/config.py

@@ -117,25 +117,37 @@ class StandardiseConfig:
         
         root = self.project_root
         
-        # Docs root detection
+        # Docs root detection - default: ~/PraisonAIDocs/docs
         if self.docs_root is None:
-            for candidate in ["docs", "PraisonAIDocs/docs"]:
-                path = root / candidate
-                if path.exists():
-                    self.docs_root = path
-                    break
-            # Check parent for PraisonAIDocs
-            parent_docs = root.parent / "PraisonAIDocs"
-            if parent_docs.exists() and self.docs_root is None:
-                self.docs_root = parent_docs / "docs"
+            # First check home directory for PraisonAIDocs
+            home_docs = Path.home() / "PraisonAIDocs" / "docs"
+            if home_docs.exists():
+                self.docs_root = home_docs
+            else:
+                # Fallback to project-relative paths
+                for candidate in ["docs", "PraisonAIDocs/docs"]:
+                    path = root / candidate
+                    if path.exists():
+                        self.docs_root = path
+                        break
+                # Check parent for PraisonAIDocs
+                parent_docs = root.parent / "PraisonAIDocs"
+                if parent_docs.exists() and self.docs_root is None:
+                    self.docs_root = parent_docs / "docs"
         
-        # Examples root detection
+        # Examples root detection - default: ~/praisonai-package/examples
         if self.examples_root is None:
-            for candidate in ["examples/python", "examples"]:
-                path = root / candidate
-                if path.exists():
-                    self.examples_root = path
-                    break
+            # First check home directory for praisonai-package/examples
+            home_examples = Path.home() / "praisonai-package" / "examples"
+            if home_examples.exists():
+                self.examples_root = home_examples
+            else:
+                # Fallback to project-relative paths
+                for candidate in ["examples/python", "examples"]:
+                    path = root / candidate
+                    if path.exists():
+                        self.examples_root = path
+                        break
         
         # SDK root detection
         if self.sdk_root is None:

src/praisonai/praisonai/standardise/enhanced_templates.py

@@ -0,0 +1,674 @@
+"""
+Enhanced templates for the FDEP standardisation system.
+
+Based on analysis of existing PraisonAI documentation best practices:
+- Progressive disclosure (simple first, advanced later)
+- Mermaid diagrams for concepts
+- CodeGroup/Tabs for multiple examples
+- Consistent structure across page types
+"""
+
+from pathlib import Path
+from typing import Optional
+
+from .models import ArtifactType, FeatureSlug
+
+
+# Template constants based on existing doc analysis
+CONCEPT_TEMPLATE = '''---
+title: "{name}"
+sidebarTitle: "{name}"
+description: "{description}"
+icon: "{icon}"
+---
+
+## Overview
+
+{name} provides [brief description of what this feature does and why it exists].
+
+```mermaid
+flowchart LR
+    subgraph Input
+        A[User Request]
+    end
+    
+    subgraph {name}
+        B[Process] --> C[Validate]
+        C --> D[Execute]
+    end
+    
+    subgraph Output
+        E[Result]
+    end
+    
+    A --> B
+    D --> E
+    
+    style B fill:#189AB4,color:#fff
+    style C fill:#2E8B57,color:#fff
+    style D fill:#8B0000,color:#fff
+```
+
+## Types of {name}
+
+<CardGroup cols={{2}}>
+  <Card title="Type 1" icon="code">
+    Description of first type
+  </Card>
+  <Card title="Type 2" icon="brain">
+    Description of second type
+  </Card>
+</CardGroup>
+
+## Quick Start
+
+```python
+from praisonaiagents import Agent, Task
+
+# Basic {slug} usage
+agent = Agent(
+    instructions="You are a helpful assistant",
+    # Configure {slug} here
+)
+
+result = agent.start("Your prompt here")
+print(result)
+```
+
+## When to Use
+
+| Use Case | Recommendation |
+|----------|----------------|
+| Simple tasks | Use basic configuration |
+| Complex workflows | Use advanced patterns |
+| Production | Enable all safety features |
+
+## Related Features
+
+- [Related Feature 1](/docs/concepts/related1)
+- [Related Feature 2](/docs/concepts/related2)
+'''
+
+FEATURE_TEMPLATE = '''---
+title: "{name}"
+description: "How to use {slug} in PraisonAI"
+icon: "code"
+---
+
+## Quick Start
+
+<CodeGroup>
+```python Basic Usage
+from praisonaiagents import Agent, Task
+
+# Simple {slug} example
+agent = Agent(
+    instructions="You are a helpful assistant",
+    # {slug} configuration
+)
+
+result = agent.start("Hello!")
+print(result)
+```
+
+```python Advanced Usage
+from praisonaiagents import Agent, Task, Agents
+
+# Advanced {slug} with multiple agents
+agent1 = Agent(name="Agent1", instructions="First agent")
+agent2 = Agent(name="Agent2", instructions="Second agent")
+
+task = Task(
+    description="Complex task using {slug}",
+    agent=agent1,
+)
+
+agents = Agents(agents=[agent1, agent2], tasks=[task])
+result = agents.start()
+```
+</CodeGroup>
+
+## Configuration Options
+
+<Tabs>
+  <Tab title="Basic">
+    ```python
+    # Minimal configuration
+    agent = Agent(
+        instructions="Your instructions",
+        # {slug}=True,  # Enable feature
+    )
+    ```
+  </Tab>
+  <Tab title="Advanced">
+    ```python
+    # Full configuration
+    agent = Agent(
+        instructions="Your instructions",
+        # {slug}={{
+        #     "option1": "value1",
+        #     "option2": "value2",
+        # }},
+    )
+    ```
+  </Tab>
+</Tabs>
+
+## API Reference
+
+### Parameters
+
+<ParamField body="param1" type="string" required>
+  Description of the first parameter.
+</ParamField>
+
+<ParamField body="param2" type="boolean" default="false">
+  Description of the second parameter.
+</ParamField>
+
+<ParamField body="param3" type="object">
+  Advanced configuration object.
+</ParamField>
+
+## Examples
+
+### Example 1: Basic Usage
+
+```python
+from praisonaiagents import Agent
+
+agent = Agent(instructions="You are helpful")
+result = agent.start("Hello")
+```
+
+### Example 2: With Callbacks
+
+```python
+from praisonaiagents import Agent
+
+def on_complete(result):
+    print(f"Completed: {{result}}")
+
+agent = Agent(
+    instructions="You are helpful",
+    # callbacks=[on_complete],
+)
+```
+
+## Troubleshooting
+
+<Accordion title="Common Issue 1">
+  **Problem**: Description of the issue
+  
+  **Solution**: How to fix it
+</Accordion>
+
+<Accordion title="Common Issue 2">
+  **Problem**: Another common issue
+  
+  **Solution**: The fix for this issue
+</Accordion>
+
+## See Also
+
+- [Concept Guide](/docs/concepts/{slug})
+- [CLI Reference](/docs/cli/{slug})
+- [API Reference](/docs/sdk/praisonaiagents/{slug}/{slug})
+'''
+
+CLI_TEMPLATE = '''---
+title: "{name}"
+sidebarTitle: "{name}"
+description: "CLI commands for {slug}"
+icon: "terminal"
+---
+
+## Quick Start
+
+```bash
+praisonai {slug} [OPTIONS]
+```
+
+## Commands
+
+### Basic Command
+
+```bash
+praisonai {slug}
+```
+
+**Expected Output:**
+```
+[Output example here]
+```
+
+### With Options
+
+```bash
+praisonai {slug} --option value
+```
+
+## Options
+
+| Option | Type | Default | Description |
+|--------|------|---------|-------------|
+| `--help` | flag | - | Show help message |
+| `--verbose` | flag | false | Enable verbose output |
+| `--config` | string | - | Path to config file |
+
+## Environment Variables
+
+| Variable | Description | Default |
+|----------|-------------|---------|
+| `PRAISONAI_{slug_upper}` | Configure {slug} | None |
+
+## Examples
+
+### Example 1: Basic
+
+```bash
+praisonai {slug}
+```
+
+### Example 2: With Config
+
+```bash
+praisonai {slug} --config config.yaml
+```
+
+### Example 3: Verbose Mode
+
+```bash
+praisonai {slug} --verbose
+```
+
+## Integration with Python
+
+You can also use {slug} programmatically:
+
+```python
+from praisonaiagents import Agent
+
+# Equivalent to CLI command
+agent = Agent(instructions="Your instructions")
+result = agent.start("Your prompt")
+```
+
+## Troubleshooting
+
+<Accordion title="Command not found">
+  Ensure PraisonAI is installed: `pip install praisonai`
+</Accordion>
+
+<Accordion title="Permission denied">
+  Check file permissions and try with `sudo` if needed.
+</Accordion>
+'''
+
+SDK_TEMPLATE = '''---
+title: "{name} Module"
+description: "API reference for {slug}"
+icon: "code"
+---
+
+# {name}
+
+The {name} module provides [brief description].
+
+## Installation
+
+```bash
+pip install praisonaiagents
+```
+
+## Quick Start
+
+```python
+from praisonaiagents import {class_name}
+
+# Basic usage
+instance = {class_name}()
+result = instance.execute()
+```
+
+## Classes
+
+### `{class_name}`
+
+Main class for {slug} functionality.
+
+#### Constructor
+
+```python
+{class_name}(
+    param1: str,
+    param2: bool = False,
+    **kwargs
+)
+```
+
+#### Parameters
+
+<ParamField body="param1" type="string" required>
+  Primary configuration parameter.
+</ParamField>
+
+<ParamField body="param2" type="boolean" default="false">
+  Enable advanced features.
+</ParamField>
+
+#### Methods
+
+##### `execute()`
+
+Execute the main functionality.
+
+```python
+result = instance.execute(input_data)
+```
+
+**Parameters:**
+- `input_data` (str): The input to process
+
+**Returns:** `str` - The processed result
+
+##### `configure()`
+
+Update configuration.
+
+```python
+instance.configure(option="value")
+```
+
+## Type Definitions
+
+```python
+from typing import TypedDict, Optional
+
+class {class_name}Config(TypedDict):
+    param1: str
+    param2: Optional[bool]
+```
+
+## Examples
+
+### Basic Example
+
+```python
+from praisonaiagents import {class_name}
+
+instance = {class_name}(param1="value")
+result = instance.execute()
+print(result)
+```
+
+### Advanced Example
+
+```python
+from praisonaiagents import {class_name}
+
+# With full configuration
+instance = {class_name}(
+    param1="value",
+    param2=True,
+)
+
+# Custom processing
+result = instance.execute()
+```
+
+## See Also
+
+- [Concept Guide](/docs/concepts/{slug})
+- [Feature Guide](/docs/features/{slug})
+'''
+
+EXAMPLE_BASIC_TEMPLATE = '''"""
+{name} - Basic Example
+
+Demonstrates minimal usage of {slug} with a single agent.
+
+Features:
+- Simple configuration
+- Single agent setup
+- Basic execution
+
+Expected Output:
+    Agent response demonstrating {slug} functionality.
+
+Usage:
+    python {slug}-basic.py
+"""
+
+from praisonaiagents import Agent
+
+# ============================================================
+# Basic {name} Configuration
+# ============================================================
+
+# Create an agent with {slug} enabled
+agent = Agent(
+    name="{name}Agent",
+    role="{name} Specialist",
+    goal="Demonstrate basic {slug} functionality",
+    backstory="You are an expert in {slug} operations.",
+    instructions="Provide clear, helpful responses demonstrating {slug}.",
+    # {slug}=True,  # Enable {slug} feature
+)
+
+# ============================================================
+# Execute
+# ============================================================
+
+if __name__ == "__main__":
+    # Run the agent
+    result = agent.start("Demonstrate {slug} functionality")
+    print(result)
+'''
+
+EXAMPLE_ADVANCED_TEMPLATE = '''"""
+{name} - Advanced Example
+
+Demonstrates advanced {slug} patterns including:
+- Multi-agent configuration
+- Custom callbacks/handlers
+- Error handling
+- All available options
+
+Features:
+- Multiple agents working together
+- Custom configuration
+- Production-ready patterns
+
+Expected Output:
+    Comprehensive demonstration of {slug} with multiple agents.
+
+Usage:
+    python {slug}-advanced.py
+"""
+
+from praisonaiagents import Agent, Task, Agents
+from typing import Any
+
+# ============================================================
+# Section 1: Custom Configuration
+# ============================================================
+
+# Define custom {slug} configuration
+# config = {{
+#     "option1": "value1",
+#     "option2": True,
+# }}
+
+# ============================================================
+# Section 2: Custom Callbacks
+# ============================================================
+
+def on_task_complete(task_output: Any) -> None:
+    """Callback when a task completes."""
+    print(f"Task completed: {{task_output}}")
+
+def on_error(error: Exception) -> None:
+    """Callback when an error occurs."""
+    print(f"Error occurred: {{error}}")
+
+# ============================================================
+# Section 3: Multi-Agent Setup
+# ============================================================
+
+# Primary agent
+primary_agent = Agent(
+    name="Primary{name}Agent",
+    role="Primary {name} Handler",
+    goal="Handle primary {slug} operations",
+    backstory="You are the primary handler for {slug} tasks.",
+    instructions="Process {slug} requests thoroughly and accurately.",
+    # {slug}=config,
+)
+
+# Secondary agent
+secondary_agent = Agent(
+    name="Secondary{name}Agent",
+    role="Secondary {name} Handler",
+    goal="Support and verify {slug} operations",
+    backstory="You verify and enhance {slug} results.",
+    instructions="Review and improve {slug} outputs.",
+    # {slug}=config,
+)
+
+# ============================================================
+# Section 4: Task Definition
+# ============================================================
+
+primary_task = Task(
+    description="Demonstrate advanced {slug} with primary processing",
+    agent=primary_agent,
+    expected_output="Detailed {slug} demonstration",
+)
+
+verification_task = Task(
+    description="Verify and enhance the {slug} results",
+    agent=secondary_agent,
+    expected_output="Verified and enhanced {slug} output",
+)
+
+# ============================================================
+# Section 5: Execution
+# ============================================================
+
+if __name__ == "__main__":
+    # Create agents team
+    agents = Agents(
+        agents=[primary_agent, secondary_agent],
+        tasks=[primary_task, verification_task],
+        verbose=True,
+    )
+    
+    # Execute
+    result = agents.start()
+    print("\\n" + "=" * 60)
+    print("FINAL RESULT:")
+    print("=" * 60)
+    print(result)
+
+# ============================================================
+# Section 6: All Options Reference
+# ============================================================
+"""
+{name} Options:
+──────────────────────────────────────────────────────────────
+| Option          | Type     | Default | Description           |
+|-----------------|----------|---------|----------------------|
+| option1         | str      | None    | Primary option       |
+| option2         | bool     | False   | Enable advanced mode |
+| option3         | int      | 10      | Iteration count      |
+| option4         | list     | []      | Additional handlers  |
+──────────────────────────────────────────────────────────────
+"""
+'''
+
+
+class EnhancedTemplateGenerator:
+    """Enhanced template generator with best-practice templates."""
+    
+    # Icon mapping for features
+    ICON_MAP = {
+        "guardrails": "shield-halved",
+        "memory": "brain",
+        "knowledge": "book",
+        "tools": "wrench",
+        "agents": "robot",
+        "tasks": "list-check",
+        "workflows": "diagram-project",
+        "handoffs": "arrow-right-arrow-left",
+        "sessions": "clock",
+        "hooks": "plug",
+        "callbacks": "phone",
+        "telemetry": "chart-line",
+        "default": "code",
+    }
+    
+    def get_icon(self, slug: str) -> str:
+        """Get appropriate icon for a feature."""
+        return self.ICON_MAP.get(slug, self.ICON_MAP["default"])
+    
+    def generate(self, slug: FeatureSlug, artifact_type: ArtifactType) -> str:
+        """Generate template content for an artifact type."""
+        slug_str = slug.normalised
+        name = slug_str.replace("-", " ").title()
+        class_name = "".join(word.title() for word in slug_str.split("-"))
+        icon = self.get_icon(slug_str)
+        slug_upper = slug_str.upper().replace("-", "_")
+        
+        # Select template
+        if artifact_type == ArtifactType.DOCS_CONCEPT:
+            template = CONCEPT_TEMPLATE
+        elif artifact_type == ArtifactType.DOCS_FEATURE:
+            template = FEATURE_TEMPLATE
+        elif artifact_type == ArtifactType.DOCS_CLI:
+            template = CLI_TEMPLATE
+        elif artifact_type == ArtifactType.DOCS_SDK:
+            template = SDK_TEMPLATE
+        elif artifact_type == ArtifactType.EXAMPLE_BASIC:
+            template = EXAMPLE_BASIC_TEMPLATE
+        elif artifact_type == ArtifactType.EXAMPLE_ADVANCED:
+            template = EXAMPLE_ADVANCED_TEMPLATE
+        else:
+            return ""
+        
+        # Fill template
+        return template.format(
+            name=name,
+            slug=slug_str,
+            class_name=class_name,
+            icon=icon,
+            slug_upper=slug_upper,
+            description=f"{name} functionality for PraisonAI agents",
+        )
+    
+    def get_expected_path(self, slug: FeatureSlug, artifact_type: ArtifactType,
+                          docs_root: Optional[Path] = None,
+                          examples_root: Optional[Path] = None) -> Optional[Path]:
+        """Get the expected path for an artifact."""
+        slug_str = slug.normalised
+        
+        if artifact_type == ArtifactType.EXAMPLE_BASIC:
+            if examples_root:
+                return examples_root / slug_str / f"{slug_str}-basic.py"
+        elif artifact_type == ArtifactType.EXAMPLE_ADVANCED:
+            if examples_root:
+                return examples_root / slug_str / f"{slug_str}-advanced.py"
+        elif artifact_type == ArtifactType.DOCS_CONCEPT:
+            if docs_root:
+                return docs_root / "concepts" / f"{slug_str}.mdx"
+        elif artifact_type == ArtifactType.DOCS_FEATURE:
+            if docs_root:
+                return docs_root / "features" / f"{slug_str}.mdx"
+        elif artifact_type == ArtifactType.DOCS_CLI:
+            if docs_root:
+                return docs_root / "cli" / f"{slug_str}.mdx"
+        elif artifact_type == ArtifactType.DOCS_SDK:
+            if docs_root:
+                return docs_root / "sdk" / "praisonaiagents" / slug_str / f"{slug_str}.mdx"
+        
+        return None

src/praisonai/praisonai/standardise/example_verifier.py

@@ -0,0 +1,229 @@
+"""
+Example verification module for the FDEP standardisation system.
+
+Verifies that generated Python examples are:
+1. Syntactically correct (can be parsed)
+2. Runnable (can be executed without errors)
+3. Handle external library dependencies gracefully
+
+Features:
+- Syntax validation via ast.parse
+- Execution in subprocess with timeout
+- Detection of missing external libraries
+- Detailed error reporting
+"""
+
+import ast
+import subprocess
+import sys
+import tempfile
+from dataclasses import dataclass
+from pathlib import Path
+from typing import List, Tuple
+
+
+@dataclass
+class VerificationResult:
+    """Result of example verification."""
+    success: bool
+    syntax_valid: bool
+    execution_passed: bool
+    output: str
+    error: str
+    missing_libraries: List[str]
+    requires_external: bool
+    
+    @property
+    def can_write(self) -> bool:
+        """Whether the example is safe to write."""
+        return self.syntax_valid and (self.execution_passed or self.requires_external)
+
+
+class ExampleVerifier:
+    """Verifies that generated examples are runnable."""
+    
+    # Known external libraries that may not be installed
+    EXTERNAL_LIBRARIES = {
+        "requests", "httpx", "aiohttp",  # HTTP
+        "pandas", "numpy", "scipy",  # Data science
+        "torch", "tensorflow", "transformers",  # ML
+        "redis", "pymongo", "psycopg2",  # Databases
+        "boto3", "google-cloud",  # Cloud
+        "playwright", "selenium",  # Browser automation
+        "beautifulsoup4", "scrapy",  # Web scraping
+    }
+    
+    # Imports that indicate external dependencies
+    EXTERNAL_IMPORT_PATTERNS = [
+        "import requests",
+        "import httpx",
+        "import pandas",
+        "import numpy",
+        "import torch",
+        "import tensorflow",
+        "from transformers",
+        "import redis",
+        "import pymongo",
+        "import boto3",
+    ]
+    
+    # Patterns that indicate LLM API calls (will timeout during verification)
+    LLM_CALL_PATTERNS = [
+        "agent.start(",
+        "agents.start(",
+        ".chat(",
+        ".run(",
+    ]
+    
+    def __init__(self, timeout: int = 30):
+        self.timeout = timeout
+    
+    def verify(self, code: str) -> VerificationResult:
+        """
+        Verify that the code is valid and runnable.
+        
+        Args:
+            code: Python source code to verify
+            
+        Returns:
+            VerificationResult with details
+        """
+        # Step 1: Syntax validation
+        syntax_valid, syntax_error = self._check_syntax(code)
+        if not syntax_valid:
+            return VerificationResult(
+                success=False,
+                syntax_valid=False,
+                execution_passed=False,
+                output="",
+                error=f"Syntax error: {syntax_error}",
+                missing_libraries=[],
+                requires_external=False,
+            )
+        
+        # Step 2: Check for external dependencies
+        requires_external, external_libs = self._detect_external_deps(code)
+        
+        # Step 3: Skip execution for examples with LLM calls (they will timeout)
+        if "LLM_API_CALL" in external_libs:
+            return VerificationResult(
+                success=True,
+                syntax_valid=True,
+                execution_passed=False,
+                output="",
+                error="Skipped: Example makes LLM API calls",
+                missing_libraries=["praisonaiagents (LLM API)"],
+                requires_external=True,
+            )
+        
+        # Step 4: Execute the code
+        execution_passed, output, error, missing_libs = self._execute(code)
+        
+        # Combine missing libraries
+        all_missing = list(set(missing_libs + external_libs))
+        
+        return VerificationResult(
+            success=execution_passed or requires_external,
+            syntax_valid=True,
+            execution_passed=execution_passed,
+            output=output,
+            error=error,
+            missing_libraries=all_missing,
+            requires_external=requires_external and not execution_passed,
+        )
+    
+    def _check_syntax(self, code: str) -> Tuple[bool, str]:
+        """Check if code is syntactically valid."""
+        try:
+            ast.parse(code)
+            return True, ""
+        except SyntaxError as e:
+            return False, f"Line {e.lineno}: {e.msg}"
+    
+    def _detect_external_deps(self, code: str) -> Tuple[bool, List[str]]:
+        """Detect if code requires external libraries or makes LLM calls."""
+        external_libs = []
+        
+        for pattern in self.EXTERNAL_IMPORT_PATTERNS:
+            if pattern in code:
+                lib_name = pattern.split()[-1]
+                external_libs.append(lib_name)
+        
+        # Check for LLM API calls that will timeout during verification
+        for pattern in self.LLM_CALL_PATTERNS:
+            if pattern in code:
+                external_libs.append("LLM_API_CALL")
+                break
+        
+        return len(external_libs) > 0, external_libs
+    
+    def _execute(self, code: str) -> Tuple[bool, str, str, List[str]]:
+        """Execute code in a subprocess."""
+        missing_libs = []
+        
+        # Write code to temp file
+        with tempfile.NamedTemporaryFile(
+            mode="w", suffix=".py", delete=False, encoding="utf-8"
+        ) as f:
+            f.write(code)
+            temp_path = f.name
+        
+        try:
+            # Run with timeout
+            result = subprocess.run(
+                [sys.executable, temp_path],
+                capture_output=True,
+                text=True,
+                timeout=self.timeout,
+                env={**subprocess.os.environ, "PYTHONDONTWRITEBYTECODE": "1"},
+            )
+            
+            output = result.stdout
+            error = result.stderr
+            
+            # Check for import errors
+            if result.returncode != 0:
+                if "ModuleNotFoundError" in error or "ImportError" in error:
+                    # Extract module name
+                    for line in error.split("\n"):
+                        if "No module named" in line:
+                            # Extract module name from error
+                            parts = line.split("'")
+                            if len(parts) >= 2:
+                                missing_libs.append(parts[1].split(".")[0])
+                
+                return False, output, error, missing_libs
+            
+            return True, output, error, missing_libs
+            
+        except subprocess.TimeoutExpired:
+            return False, "", f"Execution timed out after {self.timeout}s", missing_libs
+        except Exception as e:
+            return False, "", str(e), missing_libs
+        finally:
+            # Clean up temp file
+            Path(temp_path).unlink(missing_ok=True)
+    
+    def format_result(self, result: VerificationResult) -> str:
+        """Format verification result for display."""
+        lines = []
+        
+        if result.success:
+            lines.append("✅ Verification PASSED")
+        else:
+            lines.append("❌ Verification FAILED")
+        
+        lines.append(f"  Syntax valid: {'✓' if result.syntax_valid else '✗'}")
+        lines.append(f"  Execution passed: {'✓' if result.execution_passed else '✗'}")
+        
+        if result.requires_external:
+            lines.append(f"  ⚠️  Requires external libraries: {', '.join(result.missing_libraries)}")
+            lines.append("     (Will be written with warning)")
+        
+        if result.error and not result.requires_external:
+            lines.append(f"  Error: {result.error[:200]}")
+        
+        if result.output:
+            lines.append(f"  Output preview: {result.output[:100]}...")
+        
+        return "\n".join(lines)

src/praisonai/praisonai/standardise/undo_redo.py

@@ -0,0 +1,232 @@
+"""
+Git-based undo/redo system for the FDEP standardisation system.
+
+Provides:
+- Checkpoint creation before changes
+- Undo to previous checkpoint
+- Redo to next checkpoint
+- List checkpoints
+"""
+
+import subprocess
+from datetime import datetime
+from pathlib import Path
+from typing import List, Optional, Tuple
+
+
+class UndoRedoManager:
+    """Git-based undo/redo for standardisation changes."""
+    
+    CHECKPOINT_PREFIX = "standardise-checkpoint-"
+    
+    def __init__(self, repo_path: Optional[Path] = None):
+        self.repo_path = repo_path or Path.cwd()
+        self._verify_git_repo()
+    
+    def _verify_git_repo(self) -> bool:
+        """Verify we're in a git repository."""
+        try:
+            result = subprocess.run(
+                ["git", "rev-parse", "--git-dir"],
+                cwd=self.repo_path,
+                capture_output=True,
+                text=True,
+            )
+            return result.returncode == 0
+        except Exception:
+            return False
+    
+    def _run_git(self, *args) -> Tuple[bool, str]:
+        """Run a git command."""
+        try:
+            result = subprocess.run(
+                ["git"] + list(args),
+                cwd=self.repo_path,
+                capture_output=True,
+                text=True,
+            )
+            return result.returncode == 0, result.stdout + result.stderr
+        except Exception as e:
+            return False, str(e)
+    
+    def create_checkpoint(self, message: Optional[str] = None) -> Tuple[bool, str]:
+        """
+        Create a checkpoint before making changes.
+        
+        Returns:
+            Tuple of (success, checkpoint_id or error message)
+        """
+        timestamp = datetime.now().strftime("%Y%m%d-%H%M%S")
+        checkpoint_id = f"{self.CHECKPOINT_PREFIX}{timestamp}"
+        
+        # Stage all changes
+        success, output = self._run_git("add", "-A")
+        if not success:
+            return False, f"Failed to stage changes: {output}"
+        
+        # Check if there are changes to commit
+        success, output = self._run_git("diff", "--cached", "--quiet")
+        if success:
+            # No changes to commit, just create a tag at current HEAD
+            pass
+        else:
+            # Commit changes
+            commit_msg = message or f"Standardise checkpoint: {timestamp}"
+            success, output = self._run_git("commit", "-m", commit_msg)
+            if not success and "nothing to commit" not in output:
+                return False, f"Failed to commit: {output}"
+        
+        # Create tag
+        tag_msg = message or f"Standardise checkpoint at {timestamp}"
+        success, output = self._run_git("tag", "-a", checkpoint_id, "-m", tag_msg)
+        if not success:
+            return False, f"Failed to create tag: {output}"
+        
+        return True, checkpoint_id
+    
+    def undo(self, checkpoint_id: Optional[str] = None) -> Tuple[bool, str]:
+        """
+        Undo to a previous checkpoint.
+        
+        Args:
+            checkpoint_id: Specific checkpoint to restore (default: previous)
+        
+        Returns:
+            Tuple of (success, message)
+        """
+        if checkpoint_id is None:
+            # Find the most recent checkpoint
+            checkpoints = self.list_checkpoints()
+            if len(checkpoints) < 2:
+                return False, "No previous checkpoint to undo to"
+            checkpoint_id = checkpoints[1][0]  # Second most recent
+        
+        # Verify checkpoint exists
+        success, output = self._run_git("rev-parse", checkpoint_id)
+        if not success:
+            return False, f"Checkpoint not found: {checkpoint_id}"
+        
+        # Create a safety checkpoint before undoing
+        self.create_checkpoint("Auto-checkpoint before undo")
+        
+        # Reset to checkpoint
+        success, output = self._run_git("reset", "--hard", checkpoint_id)
+        if not success:
+            return False, f"Failed to reset: {output}"
+        
+        return True, f"Restored to checkpoint: {checkpoint_id}"
+    
+    def redo(self) -> Tuple[bool, str]:
+        """
+        Redo to the next checkpoint (after an undo).
+        
+        Returns:
+            Tuple of (success, message)
+        """
+        # Get reflog to find the previous HEAD
+        success, output = self._run_git("reflog", "-n", "10", "--format=%H %s")
+        if not success:
+            return False, "Failed to read reflog"
+        
+        # Find the commit before the last reset
+        lines = output.strip().split("\n")
+        for i, line in enumerate(lines):
+            if "reset: moving to" in line and i > 0:
+                # The previous line has the commit we want to redo to
+                target_hash = lines[i - 1].split()[0]
+                success, output = self._run_git("reset", "--hard", target_hash)
+                if success:
+                    return True, "Redo successful"
+                return False, f"Failed to redo: {output}"
+        
+        return False, "No redo available"
+    
+    def list_checkpoints(self) -> List[Tuple[str, str, str]]:
+        """
+        List all standardise checkpoints.
+        
+        Returns:
+            List of (checkpoint_id, date, message) tuples
+        """
+        success, output = self._run_git(
+            "tag", "-l", f"{self.CHECKPOINT_PREFIX}*",
+            "--format=%(refname:short)|%(creatordate:short)|%(subject)"
+        )
+        
+        if not success:
+            return []
+        
+        checkpoints = []
+        for line in output.strip().split("\n"):
+            if line and "|" in line:
+                parts = line.split("|", 2)
+                if len(parts) >= 3:
+                    checkpoints.append((parts[0], parts[1], parts[2]))
+                elif len(parts) == 2:
+                    checkpoints.append((parts[0], parts[1], ""))
+        
+        # Sort by date descending
+        checkpoints.sort(key=lambda x: x[1], reverse=True)
+        return checkpoints
+    
+    def delete_checkpoint(self, checkpoint_id: str) -> Tuple[bool, str]:
+        """Delete a checkpoint."""
+        success, output = self._run_git("tag", "-d", checkpoint_id)
+        if success:
+            return True, f"Deleted checkpoint: {checkpoint_id}"
+        return False, f"Failed to delete: {output}"
+    
+    def get_changes_since(self, checkpoint_id: str) -> List[str]:
+        """Get list of files changed since a checkpoint."""
+        success, output = self._run_git(
+            "diff", "--name-only", checkpoint_id, "HEAD"
+        )
+        
+        if not success:
+            return []
+        
+        return [f for f in output.strip().split("\n") if f]
+    
+    def preview_undo(self, checkpoint_id: str) -> List[str]:
+        """Preview what files would change if we undo to a checkpoint."""
+        return self.get_changes_since(checkpoint_id)
+
+
+class FileBackupManager:
+    """Simple file-based backup for non-git scenarios."""
+    
+    def __init__(self, backup_dir: Optional[Path] = None):
+        self.backup_dir = backup_dir or Path(".praison/backups")
+        self.backup_dir.mkdir(parents=True, exist_ok=True)
+    
+    def backup_file(self, file_path: Path) -> Optional[Path]:
+        """Create a backup of a file."""
+        if not file_path.exists():
+            return None
+        
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        backup_name = f"{file_path.stem}_{timestamp}{file_path.suffix}"
+        backup_path = self.backup_dir / backup_name
+        
+        import shutil
+        shutil.copy2(file_path, backup_path)
+        return backup_path
+    
+    def restore_file(self, backup_path: Path, target_path: Path) -> bool:
+        """Restore a file from backup."""
+        if not backup_path.exists():
+            return False
+        
+        import shutil
+        shutil.copy2(backup_path, target_path)
+        return True
+    
+    def list_backups(self, file_stem: Optional[str] = None) -> List[Path]:
+        """List all backups, optionally filtered by file stem."""
+        backups = list(self.backup_dir.glob("*"))
+        
+        if file_stem:
+            backups = [b for b in backups if b.stem.startswith(file_stem)]
+        
+        backups.sort(key=lambda x: x.stat().st_mtime, reverse=True)
+        return backups

src/praisonai/praisonai/version.py

@@ -1 +1 @@
-__version__ = "3.7.2"
+__version__ = "3.7.7"

src/praisonai/pyproject.toml

@@ -12,7 +12,7 @@ dependencies = [
     "rich>=13.7",
     "markdown>=3.5",
     "pyparsing>=3.0.0",
-    "praisonaiagents>=0.11.1",
+    "praisonaiagents>=0.11.3",
     "python-dotenv>=0.19.0",
     "instructor>=1.3.3",
     "PyYAML>=6.0",

src/praisonai/tests/unit/cli/test_async_tui.py

@@ -0,0 +1,600 @@
+"""
+Tests for PraisonAI Async TUI Application.
+"""
+
+
+class TestAsyncTUIConfig:
+    """Tests for AsyncTUIConfig dataclass."""
+    
+    def test_default_values(self):
+        """Test default configuration values."""
+        from praisonai.cli.interactive.async_tui import AsyncTUIConfig
+        
+        config = AsyncTUIConfig()
+        assert config.model == "gpt-4o-mini"
+        assert config.show_logo is True
+        assert config.show_status_bar is True
+        assert config.session_id is None
+        assert config.workspace is None
+    
+    def test_custom_values(self):
+        """Test custom configuration values."""
+        from praisonai.cli.interactive.async_tui import AsyncTUIConfig
+        
+        config = AsyncTUIConfig(
+            model="gpt-4o",
+            show_logo=False,
+            session_id="test123",
+        )
+        assert config.model == "gpt-4o"
+        assert config.show_logo is False
+        assert config.session_id == "test123"
+
+
+class TestChatMessage:
+    """Tests for ChatMessage dataclass."""
+    
+    def test_message_creation(self):
+        """Test message creation."""
+        from praisonai.cli.interactive.async_tui import ChatMessage
+        
+        msg = ChatMessage(role="user", content="Hello")
+        assert msg.role == "user"
+        assert msg.content == "Hello"
+        assert msg.timestamp is not None
+    
+    def test_message_roles(self):
+        """Test different message roles."""
+        from praisonai.cli.interactive.async_tui import ChatMessage
+        
+        roles = ["user", "assistant", "system", "status"]
+        for role in roles:
+            msg = ChatMessage(role=role, content="test")
+            assert msg.role == role
+
+
+class TestGetLogo:
+    """Tests for get_logo function."""
+    
+    def test_logo_wide_terminal(self):
+        """Test logo for wide terminal."""
+        from praisonai.cli.interactive.async_tui import get_logo, LOGO
+        
+        logo = get_logo(100)
+        assert logo == LOGO
+    
+    def test_logo_medium_terminal(self):
+        """Test logo for medium terminal."""
+        from praisonai.cli.interactive.async_tui import get_logo, LOGO_SMALL
+        
+        logo = get_logo(50)
+        assert logo == LOGO_SMALL
+    
+    def test_logo_narrow_terminal(self):
+        """Test logo for narrow terminal."""
+        from praisonai.cli.interactive.async_tui import get_logo, LOGO_MINIMAL
+        
+        logo = get_logo(30)
+        assert logo == LOGO_MINIMAL
+    
+    def test_logo_contains_praison_ai(self):
+        """Test that logo contains 'Praison AI' branding."""
+        from praisonai.cli.interactive.async_tui import LOGO_MINIMAL
+        
+        assert "Praison AI" in LOGO_MINIMAL
+
+
+class TestAsyncTUI:
+    """Tests for AsyncTUI class."""
+    
+    def test_init_default(self):
+        """Test default initialization."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI
+        
+        tui = AsyncTUI()
+        assert tui.config is not None
+        assert tui.messages == []
+        assert tui._running is False
+        assert tui._processing is False
+        assert tui.session_id is not None
+    
+    def test_init_with_config(self):
+        """Test initialization with config."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI, AsyncTUIConfig
+        
+        config = AsyncTUIConfig(model="gpt-4o", show_logo=False)
+        tui = AsyncTUI(config=config)
+        assert tui.config.model == "gpt-4o"
+        assert tui.config.show_logo is False
+    
+    def test_format_output_empty(self):
+        """Test format_output with no messages."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI, AsyncTUIConfig
+        
+        config = AsyncTUIConfig(show_logo=True)
+        tui = AsyncTUI(config=config)
+        
+        output = tui._format_output()
+        assert "Type your message" in output or "gpt-4o-mini" in output
+    
+    def test_format_output_with_messages(self):
+        """Test format_output with messages."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI, ChatMessage
+        
+        tui = AsyncTUI()
+        tui.messages = [
+            ChatMessage(role="user", content="Hello"),
+            ChatMessage(role="assistant", content="Hi there"),
+        ]
+        
+        output = tui._format_output()
+        assert "Hello" in output
+        assert "Hi there" in output
+    
+    def test_format_output_with_processing(self):
+        """Test format_output shows processing indicator."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI
+        
+        tui = AsyncTUI()
+        tui._processing = True
+        tui._status_text = "Thinking..."
+        
+        output = tui._format_output()
+        assert "Thinking..." in output
+    
+    def test_handle_command_exit(self):
+        """Test handling exit command."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI
+        
+        tui = AsyncTUI()
+        tui._running = True
+        
+        result = tui._handle_command("/exit")
+        assert result is True
+        assert tui._running is False
+    
+    def test_handle_command_quit(self):
+        """Test handling quit command."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI
+        
+        tui = AsyncTUI()
+        tui._running = True
+        
+        result = tui._handle_command("/quit")
+        assert result is True
+        assert tui._running is False
+    
+    def test_handle_command_help(self):
+        """Test handling help command."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI
+        
+        tui = AsyncTUI()
+        result = tui._handle_command("/help")
+        assert result is True
+        assert len(tui.messages) == 1
+        assert tui.messages[0].role == "system"
+        assert "PageUp" in tui.messages[0].content  # Scroll instructions
+    
+    def test_handle_command_clear(self):
+        """Test handling clear command."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI, ChatMessage
+        
+        tui = AsyncTUI()
+        tui.messages = [ChatMessage(role="user", content="test")]
+        
+        result = tui._handle_command("/clear")
+        assert result is True
+        # Clear now adds a confirmation message
+        assert len(tui.messages) == 1
+        assert "cleared" in tui.messages[0].content.lower()
+    
+    def test_handle_command_new(self):
+        """Test handling new command."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI, ChatMessage
+        
+        tui = AsyncTUI()
+        old_session = tui.session_id
+        tui.messages = [ChatMessage(role="user", content="test")]
+        
+        result = tui._handle_command("/new")
+        assert result is True
+        assert len(tui.messages) == 1
+        assert tui.session_id != old_session
+    
+    def test_handle_command_model_show(self):
+        """Test handling model command (show)."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI
+        
+        tui = AsyncTUI()
+        result = tui._handle_command("/model")
+        assert result is True
+        assert len(tui.messages) == 1
+        assert "gpt-4o-mini" in tui.messages[0].content
+    
+    def test_handle_command_model_change(self):
+        """Test handling model command (change)."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI
+        
+        tui = AsyncTUI()
+        result = tui._handle_command("/model gpt-4o")
+        assert result is True
+        assert tui.config.model == "gpt-4o"
+        assert tui._agent is None
+    
+    def test_handle_command_unknown(self):
+        """Test handling unknown command."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI
+        
+        tui = AsyncTUI()
+        result = tui._handle_command("/unknown")
+        assert result is True
+        assert len(tui.messages) == 1
+        assert "Unknown" in tui.messages[0].content
+
+
+class TestStartAsyncTUI:
+    """Tests for start_async_tui function."""
+    
+    def test_function_exists(self):
+        """Test that start_async_tui function exists."""
+        from praisonai.cli.interactive.async_tui import start_async_tui
+        assert callable(start_async_tui)
+
+
+class TestASCIILogos:
+    """Tests for ASCII logo constants."""
+    
+    def test_logo_exists(self):
+        """Test LOGO constant exists and has content."""
+        from praisonai.cli.interactive.async_tui import LOGO
+        assert len(LOGO) > 0
+        assert "██" in LOGO
+    
+    def test_logo_small_exists(self):
+        """Test LOGO_SMALL constant exists."""
+        from praisonai.cli.interactive.async_tui import LOGO_SMALL
+        assert len(LOGO_SMALL) > 0
+    
+    def test_logo_minimal_branding(self):
+        """Test LOGO_MINIMAL has correct branding."""
+        from praisonai.cli.interactive.async_tui import LOGO_MINIMAL
+        assert "Praison AI" in LOGO_MINIMAL
+
+
+class TestMessageFormatting:
+    """Tests for message formatting."""
+    
+    def test_user_message_prefix(self):
+        """Test user messages have correct prefix."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI, ChatMessage
+        
+        tui = AsyncTUI()
+        tui.messages = [ChatMessage(role="user", content="test")]
+        
+        output = tui._format_output()
+        assert "› test" in output
+    
+    def test_assistant_message_prefix(self):
+        """Test assistant messages have correct prefix."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI, ChatMessage
+        
+        tui = AsyncTUI()
+        tui.messages = [ChatMessage(role="assistant", content="response")]
+        
+        output = tui._format_output()
+        assert "● response" in output
+    
+    def test_status_message_prefix(self):
+        """Test status messages have correct prefix."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI, ChatMessage
+        
+        tui = AsyncTUI()
+        tui.messages = [ChatMessage(role="status", content="thinking")]
+        
+        output = tui._format_output()
+        assert "⏳ thinking" in output
+
+
+class TestNonBlockingExecution:
+    """Tests for non-blocking execution features."""
+    
+    def test_processing_flag(self):
+        """Test processing flag is used correctly."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI
+        
+        tui = AsyncTUI()
+        assert tui._processing is False
+        
+        tui._processing = True
+        tui._status_text = "Working..."
+        
+        output = tui._format_output()
+        assert "Working..." in output
+    
+    def test_status_text_updates(self):
+        """Test status text can be updated."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI
+        
+        tui = AsyncTUI()
+        tui._processing = True
+        
+        tui._status_text = "Step 1"
+        output1 = tui._format_output()
+        assert "Step 1" in output1
+        
+        tui._status_text = "Step 2"
+        output2 = tui._format_output()
+        assert "Step 2" in output2
+
+
+class TestNewSlashCommands:
+    """Tests for newly added slash commands."""
+    
+    def test_session_command(self):
+        """Test /session command shows session info."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI
+        
+        tui = AsyncTUI()
+        result = tui._handle_command("/session")
+        assert result is True
+        assert len(tui.messages) == 1
+        assert "Session:" in tui.messages[0].content
+        assert tui.session_id in tui.messages[0].content
+    
+    def test_history_command_empty(self):
+        """Test /history command with no history."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI
+        
+        tui = AsyncTUI()
+        result = tui._handle_command("/history")
+        assert result is True
+        assert "No conversation history" in tui.messages[0].content
+    
+    def test_history_command_with_history(self):
+        """Test /history command with conversation history."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI
+        
+        tui = AsyncTUI()
+        tui._conversation_history = [
+            {"role": "user", "content": "Hello"},
+            {"role": "assistant", "content": "Hi there"},
+        ]
+        result = tui._handle_command("/history")
+        assert result is True
+        assert "[user]" in tui.messages[0].content
+    
+    def test_cost_command(self):
+        """Test /cost command shows token usage."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI
+        
+        tui = AsyncTUI()
+        tui._total_tokens = 100
+        tui._total_cost = 0.001
+        result = tui._handle_command("/cost")
+        assert result is True
+        assert "100" in tui.messages[0].content
+    
+    def test_compact_command(self):
+        """Test /compact command toggles compact mode."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI
+        
+        tui = AsyncTUI()
+        assert tui.config.compact_mode is False
+        
+        result = tui._handle_command("/compact")
+        assert result is True
+        assert tui.config.compact_mode is True
+        
+        result = tui._handle_command("/compact")
+        assert tui.config.compact_mode is False
+    
+    def test_multiline_command(self):
+        """Test /multiline command toggles multiline mode."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI
+        
+        tui = AsyncTUI()
+        assert tui.config.multiline_mode is False
+        
+        result = tui._handle_command("/multiline")
+        assert result is True
+        assert tui.config.multiline_mode is True
+    
+    def test_files_command(self):
+        """Test /files command lists workspace files."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI
+        
+        tui = AsyncTUI()
+        tui._workspace_files = ["test.py", "main.py"]
+        result = tui._handle_command("/files")
+        assert result is True
+        assert "@test.py" in tui.messages[0].content
+    
+    def test_queue_command_empty(self):
+        """Test /queue command with empty queue."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI
+        
+        tui = AsyncTUI()
+        result = tui._handle_command("/queue")
+        assert result is True
+        assert "No prompts in queue" in tui.messages[0].content
+    
+    def test_queue_command_with_items(self):
+        """Test /queue command with queued prompts."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI
+        
+        tui = AsyncTUI()
+        tui._prompt_queue = ["First prompt", "Second prompt"]
+        result = tui._handle_command("/queue")
+        assert result is True
+        assert "First prompt" in tui.messages[0].content
+
+
+class TestQueueSystem:
+    """Tests for prompt queue system."""
+    
+    def test_queue_initialization(self):
+        """Test queue is initialized empty."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI
+        
+        tui = AsyncTUI()
+        assert tui._prompt_queue == []
+    
+    def test_queue_or_execute_when_not_processing(self):
+        """Test _queue_or_execute executes immediately when not processing."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI
+        
+        tui = AsyncTUI()
+        tui._processing = False
+        
+        # This would normally start background execution
+        # Just test that it adds user message
+        tui._queue_or_execute("test prompt")
+        
+        assert len(tui.messages) == 1
+        assert tui.messages[0].role == "user"
+        assert tui.messages[0].content == "test prompt"
+
+
+class TestFileProcessing:
+    """Tests for @file mention processing."""
+    
+    def test_process_file_mentions_no_mentions(self):
+        """Test processing prompt with no @file mentions."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI
+        
+        tui = AsyncTUI()
+        result = tui._process_file_mentions("Hello world")
+        assert result == "Hello world"
+    
+    def test_process_file_mentions_with_mention(self):
+        """Test processing prompt with @file mention."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI
+        import tempfile
+        import os
+        
+        tui = AsyncTUI()
+        
+        # Create a temp file
+        with tempfile.NamedTemporaryFile(mode='w', suffix='.txt', delete=False) as f:
+            f.write("test content")
+            temp_path = f.name
+        
+        try:
+            tui.config.workspace = os.path.dirname(temp_path)
+            filename = os.path.basename(temp_path)
+            result = tui._process_file_mentions(f"Check @{filename}")
+            assert "test content" in result
+        finally:
+            os.unlink(temp_path)
+
+
+class TestWorkspaceScanning:
+    """Tests for workspace file scanning."""
+    
+    def test_workspace_files_initialized(self):
+        """Test workspace files list is initialized."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI
+        
+        tui = AsyncTUI()
+        assert isinstance(tui._workspace_files, list)
+    
+    def test_scan_workspace_limits_files(self):
+        """Test workspace scanning limits files (may exceed slightly due to batch processing)."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI
+        
+        tui = AsyncTUI()
+        # Verify the limit is approximately enforced (may exceed slightly due to batch)
+        assert len(tui._workspace_files) <= 1100  # Allow some margin
+
+
+class TestToolLoading:
+    """Tests for tool loading functionality."""
+    
+    def test_load_tools_returns_list(self):
+        """Test _load_tools returns a list."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI
+        
+        tui = AsyncTUI()
+        tools = tui._load_tools()
+        assert isinstance(tools, list)
+    
+    def test_load_tools_method_exists(self):
+        """Test _load_tools method exists."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI
+        
+        tui = AsyncTUI()
+        assert hasattr(tui, '_load_tools')
+        assert callable(tui._load_tools)
+
+
+class TestNewSessionCommands:
+    """Tests for session-related commands."""
+    
+    def test_sessions_command(self):
+        """Test /sessions command."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI
+        
+        tui = AsyncTUI()
+        result = tui._handle_command("/sessions")
+        assert result is True
+        # Should have a message about sessions
+        assert len(tui.messages) >= 1
+    
+    def test_continue_command(self):
+        """Test /continue command."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI
+        
+        tui = AsyncTUI()
+        result = tui._handle_command("/continue")
+        assert result is True
+        assert len(tui.messages) >= 1
+    
+    def test_import_command_no_args(self):
+        """Test /import command without arguments."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI
+        
+        tui = AsyncTUI()
+        result = tui._handle_command("/import")
+        assert result is True
+        assert "Usage" in tui.messages[0].content
+    
+    def test_import_command_file_not_found(self):
+        """Test /import command with non-existent file."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI
+        
+        tui = AsyncTUI()
+        result = tui._handle_command("/import nonexistent_file.json")
+        assert result is True
+        assert "not found" in tui.messages[0].content.lower() or "failed" in tui.messages[0].content.lower()
+    
+    def test_status_command(self):
+        """Test /status command."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI
+        
+        tui = AsyncTUI()
+        result = tui._handle_command("/status")
+        assert result is True
+        # Should show runtime status or "not initialized"
+        assert len(tui.messages) >= 1
+
+
+class TestRuntimeIntegration:
+    """Tests for runtime integration."""
+    
+    def test_runtime_fields_initialized(self):
+        """Test runtime fields are initialized."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI
+        
+        tui = AsyncTUI()
+        assert tui._runtime is None
+        assert tui._runtime_started is False
+    
+    def test_start_runtime_method_exists(self):
+        """Test _start_runtime method exists."""
+        from praisonai.cli.interactive.async_tui import AsyncTUI
+        
+        tui = AsyncTUI()
+        assert hasattr(tui, '_start_runtime')
+        # It's an async method
+        import asyncio
+        assert asyncio.iscoroutinefunction(tui._start_runtime)

src/praisonai/tests/unit/cli/test_praison_io.py

@@ -0,0 +1,406 @@
+"""
+Tests for PraisonIO - Interactive CLI I/O handler.
+"""
+
+from unittest.mock import patch
+
+
+class TestIOConfig:
+    """Tests for IOConfig dataclass."""
+    
+    def test_default_values(self):
+        """Test default configuration values."""
+        from praisonai.cli.interactive.praison_io import IOConfig
+        
+        config = IOConfig()
+        assert config.pretty is True
+        assert config.user_input_color == "#00cc00"
+        assert config.multiline_mode is False
+        assert config.vi_mode is False
+        assert config.enable_completions is True
+    
+    def test_custom_values(self):
+        """Test custom configuration values."""
+        from praisonai.cli.interactive.praison_io import IOConfig
+        
+        config = IOConfig(
+            pretty=False,
+            user_input_color="#ff0000",
+            multiline_mode=True,
+            vi_mode=True,
+        )
+        assert config.pretty is False
+        assert config.user_input_color == "#ff0000"
+        assert config.multiline_mode is True
+        assert config.vi_mode is True
+
+
+class TestPraisonCompleter:
+    """Tests for PraisonCompleter."""
+    
+    def test_init_empty(self):
+        """Test initialization with no commands."""
+        from praisonai.cli.interactive.praison_io import PraisonCompleter
+        
+        completer = PraisonCompleter()
+        assert completer.commands == {}
+        assert len(completer.files) == 0
+        assert len(completer.symbols) == 0
+    
+    def test_init_with_commands(self):
+        """Test initialization with commands."""
+        from praisonai.cli.interactive.praison_io import PraisonCompleter
+        
+        commands = {"help": "Show help", "exit": "Exit"}
+        completer = PraisonCompleter(commands=commands)
+        assert completer.commands == commands
+    
+    def test_add_files(self):
+        """Test adding files."""
+        from praisonai.cli.interactive.praison_io import PraisonCompleter
+        
+        completer = PraisonCompleter()
+        completer.add_files(["file1.py", "file2.py"])
+        assert "file1.py" in completer.files
+        assert "file2.py" in completer.files
+    
+    def test_add_symbols(self):
+        """Test adding symbols."""
+        from praisonai.cli.interactive.praison_io import PraisonCompleter
+        
+        completer = PraisonCompleter()
+        completer.add_symbols(["MyClass", "my_function"])
+        assert "MyClass" in completer.symbols
+        assert "my_function" in completer.symbols
+    
+    def test_get_completions_slash_commands(self):
+        """Test completions for slash commands."""
+        from praisonai.cli.interactive.praison_io import PraisonCompleter
+        
+        commands = {"help": "Show help", "history": "Show history", "exit": "Exit"}
+        completer = PraisonCompleter(commands=commands)
+        
+        completions = completer.get_completions("/h", 2)
+        completion_texts = [c[0] for c in completions]
+        assert "/help" in completion_texts
+        assert "/history" in completion_texts
+        assert "/exit" not in completion_texts
+    
+    def test_get_completions_file_mentions(self):
+        """Test completions for @ file mentions."""
+        from praisonai.cli.interactive.praison_io import PraisonCompleter
+        
+        completer = PraisonCompleter()
+        completer.add_files(["main.py", "test.py", "README.md"])
+        
+        completions = completer.get_completions("@main", 5)
+        completion_texts = [c[0] for c in completions]
+        assert "@main.py" in completion_texts
+    
+    def test_get_completions_symbols(self):
+        """Test completions for symbols."""
+        from praisonai.cli.interactive.praison_io import PraisonCompleter
+        
+        completer = PraisonCompleter()
+        completer.add_symbols(["MyClass", "MyFunction", "other"])
+        
+        completions = completer.get_completions("My", 2)
+        completion_texts = [c[0] for c in completions]
+        assert "MyClass" in completion_texts
+        assert "MyFunction" in completion_texts
+        assert "other" not in completion_texts
+
+
+class TestMarkdownStream:
+    """Tests for MarkdownStream."""
+    
+    def test_init(self):
+        """Test initialization."""
+        from praisonai.cli.interactive.praison_io import MarkdownStream
+        
+        stream = MarkdownStream(live_window=8)
+        assert stream.live_window == 8
+        assert stream.printed == []
+        assert stream.live is None
+    
+    def test_render_to_lines_fallback(self):
+        """Test rendering without Rich."""
+        from praisonai.cli.interactive.praison_io import MarkdownStream
+        
+        stream = MarkdownStream()
+        # This tests the fallback path
+        text = "Hello\nWorld"
+        lines = stream._render_to_lines(text)
+        assert len(lines) >= 1
+
+
+class TestPraisonIO:
+    """Tests for PraisonIO."""
+    
+    def test_init_default(self):
+        """Test default initialization."""
+        from praisonai.cli.interactive.praison_io import PraisonIO
+        
+        io = PraisonIO()
+        assert io.config is not None
+        assert io.completer is not None
+    
+    def test_init_with_config(self):
+        """Test initialization with config."""
+        from praisonai.cli.interactive.praison_io import PraisonIO, IOConfig
+        
+        config = IOConfig(pretty=False)
+        io = PraisonIO(config=config)
+        assert io.config.pretty is False
+    
+    def test_add_commands(self):
+        """Test adding commands."""
+        from praisonai.cli.interactive.praison_io import PraisonIO
+        
+        io = PraisonIO()
+        io.add_commands({"test": "Test command"})
+        assert "test" in io.completer.commands
+    
+    def test_add_files(self):
+        """Test adding files."""
+        from praisonai.cli.interactive.praison_io import PraisonIO
+        
+        io = PraisonIO()
+        io.add_files(["file.py"])
+        assert "file.py" in io.completer.files
+    
+    def test_add_symbols(self):
+        """Test adding symbols."""
+        from praisonai.cli.interactive.praison_io import PraisonIO
+        
+        io = PraisonIO()
+        io.add_symbols(["MyClass"])
+        assert "MyClass" in io.completer.symbols
+    
+    @patch('builtins.print')
+    def test_rule_no_rich(self, mock_print):
+        """Test rule without Rich."""
+        from praisonai.cli.interactive.praison_io import PraisonIO, IOConfig
+        
+        config = IOConfig(pretty=False)
+        io = PraisonIO(config=config)
+        io.rule()
+        mock_print.assert_called()
+    
+    @patch('builtins.print')
+    def test_tool_output_no_rich(self, mock_print):
+        """Test tool output without Rich."""
+        from praisonai.cli.interactive.praison_io import PraisonIO, IOConfig
+        
+        config = IOConfig(pretty=False)
+        io = PraisonIO(config=config)
+        io.tool_output("Test message")
+        mock_print.assert_called_with("⚙ Test message")
+    
+    @patch('builtins.print')
+    def test_info_no_rich(self, mock_print):
+        """Test info without Rich."""
+        from praisonai.cli.interactive.praison_io import PraisonIO, IOConfig
+        
+        config = IOConfig(pretty=False)
+        io = PraisonIO(config=config)
+        io.info("Test info")
+        mock_print.assert_called_with("ℹ Test info")
+    
+    @patch('builtins.print')
+    def test_success_no_rich(self, mock_print):
+        """Test success without Rich."""
+        from praisonai.cli.interactive.praison_io import PraisonIO, IOConfig
+        
+        config = IOConfig(pretty=False)
+        io = PraisonIO(config=config)
+        io.success("Test success")
+        mock_print.assert_called_with("✓ Test success")
+
+
+class TestREPLConfig:
+    """Tests for REPLConfig."""
+    
+    def test_default_values(self):
+        """Test default configuration values."""
+        from praisonai.cli.interactive.repl import REPLConfig
+        
+        config = REPLConfig()
+        assert config.model == "gpt-4o-mini"
+        assert config.verbose is False
+        assert config.memory is False
+        assert config.tools is None
+        assert config.autonomy is True
+    
+    def test_custom_values(self):
+        """Test custom configuration values."""
+        from praisonai.cli.interactive.repl import REPLConfig
+        
+        config = REPLConfig(
+            model="gpt-4o",
+            verbose=True,
+            memory=True,
+        )
+        assert config.model == "gpt-4o"
+        assert config.verbose is True
+        assert config.memory is True
+
+
+class TestInteractiveREPL:
+    """Tests for InteractiveREPL."""
+    
+    def test_init_default(self):
+        """Test default initialization."""
+        from praisonai.cli.interactive.repl import InteractiveREPL
+        
+        repl = InteractiveREPL()
+        assert repl.config is not None
+        assert repl.io is not None
+        assert repl._running is False
+    
+    def test_init_with_config(self):
+        """Test initialization with config."""
+        from praisonai.cli.interactive.repl import InteractiveREPL, REPLConfig
+        
+        config = REPLConfig(model="gpt-4o")
+        repl = InteractiveREPL(config=config)
+        assert repl.config.model == "gpt-4o"
+    
+    def test_handle_command_exit(self):
+        """Test handling exit command."""
+        from praisonai.cli.interactive.repl import InteractiveREPL
+        
+        repl = InteractiveREPL()
+        repl._running = True
+        
+        result = repl._handle_command("/exit")
+        assert result is True
+        assert repl._running is False
+    
+    def test_handle_command_quit(self):
+        """Test handling quit command."""
+        from praisonai.cli.interactive.repl import InteractiveREPL
+        
+        repl = InteractiveREPL()
+        repl._running = True
+        
+        result = repl._handle_command("/quit")
+        assert result is True
+        assert repl._running is False
+    
+    def test_handle_command_help(self):
+        """Test handling help command."""
+        from praisonai.cli.interactive.repl import InteractiveREPL
+        
+        repl = InteractiveREPL()
+        result = repl._handle_command("/help")
+        assert result is True
+    
+    def test_handle_command_clear(self):
+        """Test handling clear command."""
+        from praisonai.cli.interactive.repl import InteractiveREPL
+        
+        repl = InteractiveREPL()
+        repl._conversation_history = [{"role": "user", "content": "test"}]
+        
+        result = repl._handle_command("/clear")
+        assert result is True
+        assert len(repl._conversation_history) == 0
+    
+    def test_handle_command_model_show(self):
+        """Test handling model command (show)."""
+        from praisonai.cli.interactive.repl import InteractiveREPL
+        
+        repl = InteractiveREPL()
+        result = repl._handle_command("/model")
+        assert result is True
+    
+    def test_handle_command_model_change(self):
+        """Test handling model command (change)."""
+        from praisonai.cli.interactive.repl import InteractiveREPL
+        
+        repl = InteractiveREPL()
+        result = repl._handle_command("/model gpt-4o")
+        assert result is True
+        assert repl.config.model == "gpt-4o"
+    
+    def test_handle_command_unknown(self):
+        """Test handling unknown command."""
+        from praisonai.cli.interactive.repl import InteractiveREPL
+        
+        repl = InteractiveREPL()
+        result = repl._handle_command("/unknown")
+        assert result is True  # Still handled (shows warning)
+    
+    def test_handle_command_session(self):
+        """Test handling session command."""
+        from praisonai.cli.interactive.repl import InteractiveREPL
+        
+        repl = InteractiveREPL()
+        repl._session_id = "test-session-123"
+        
+        result = repl._handle_command("/session")
+        assert result is True
+    
+    def test_handle_command_cost(self):
+        """Test handling cost command."""
+        from praisonai.cli.interactive.repl import InteractiveREPL
+        
+        repl = InteractiveREPL()
+        repl._total_tokens = 100
+        repl._total_cost = 0.01
+        
+        result = repl._handle_command("/cost")
+        assert result is True
+    
+    def test_handle_command_history_empty(self):
+        """Test handling history command with empty history."""
+        from praisonai.cli.interactive.repl import InteractiveREPL
+        
+        repl = InteractiveREPL()
+        result = repl._handle_command("/history")
+        assert result is True
+    
+    def test_handle_command_history_with_items(self):
+        """Test handling history command with items."""
+        from praisonai.cli.interactive.repl import InteractiveREPL
+        
+        repl = InteractiveREPL()
+        repl._conversation_history = [
+            {"role": "user", "content": "Hello"},
+            {"role": "assistant", "content": "Hi there!"},
+        ]
+        
+        result = repl._handle_command("/history")
+        assert result is True
+    
+    def test_handle_command_compact(self):
+        """Test handling compact command."""
+        from praisonai.cli.interactive.repl import InteractiveREPL
+        
+        repl = InteractiveREPL()
+        original = repl.io.config.pretty
+        
+        result = repl._handle_command("/compact")
+        assert result is True
+        assert repl.io.config.pretty != original
+    
+    def test_handle_command_multiline(self):
+        """Test handling multiline command."""
+        from praisonai.cli.interactive.repl import InteractiveREPL
+        
+        repl = InteractiveREPL()
+        original = repl.io.config.multiline_mode
+        
+        result = repl._handle_command("/multiline")
+        assert result is True
+        assert repl.io.config.multiline_mode != original
+
+
+class TestStartInteractive:
+    """Tests for start_interactive function."""
+    
+    def test_function_exists(self):
+        """Test that start_interactive function exists."""
+        from praisonai.cli.interactive.repl import start_interactive
+        assert callable(start_interactive)

src/praisonai/tests/unit/cli/test_split_tui.py

@@ -0,0 +1,282 @@
+"""
+Tests for PraisonAI Split-Pane TUI Application.
+"""
+
+
+class TestSplitTUIConfig:
+    """Tests for SplitTUIConfig dataclass."""
+    
+    def test_default_values(self):
+        """Test default configuration values."""
+        from praisonai.cli.interactive.split_tui import SplitTUIConfig
+        
+        config = SplitTUIConfig()
+        assert config.model == "gpt-4o-mini"
+        assert config.show_logo is True
+        assert config.show_status_bar is True
+        assert config.session_id is None
+        assert config.workspace is None
+    
+    def test_custom_values(self):
+        """Test custom configuration values."""
+        from praisonai.cli.interactive.split_tui import SplitTUIConfig
+        
+        config = SplitTUIConfig(
+            model="gpt-4o",
+            show_logo=False,
+            session_id="test123",
+        )
+        assert config.model == "gpt-4o"
+        assert config.show_logo is False
+        assert config.session_id == "test123"
+
+
+class TestChatMessage:
+    """Tests for ChatMessage dataclass."""
+    
+    def test_message_creation(self):
+        """Test message creation."""
+        from praisonai.cli.interactive.split_tui import ChatMessage
+        
+        msg = ChatMessage(role="user", content="Hello")
+        assert msg.role == "user"
+        assert msg.content == "Hello"
+        assert msg.timestamp is not None
+    
+    def test_message_roles(self):
+        """Test different message roles."""
+        from praisonai.cli.interactive.split_tui import ChatMessage
+        
+        roles = ["user", "assistant", "system", "status"]
+        for role in roles:
+            msg = ChatMessage(role=role, content="test")
+            assert msg.role == role
+
+
+class TestGetLogo:
+    """Tests for get_logo function."""
+    
+    def test_logo_wide_terminal(self):
+        """Test logo for wide terminal."""
+        from praisonai.cli.interactive.split_tui import get_logo, LOGO
+        
+        logo = get_logo(100)
+        assert logo == LOGO
+    
+    def test_logo_medium_terminal(self):
+        """Test logo for medium terminal."""
+        from praisonai.cli.interactive.split_tui import get_logo, LOGO_SMALL
+        
+        logo = get_logo(50)
+        assert logo == LOGO_SMALL
+    
+    def test_logo_narrow_terminal(self):
+        """Test logo for narrow terminal."""
+        from praisonai.cli.interactive.split_tui import get_logo, LOGO_MINIMAL
+        
+        logo = get_logo(30)
+        assert logo == LOGO_MINIMAL
+    
+    def test_logo_contains_praison_ai(self):
+        """Test that logo contains 'Praison AI' branding."""
+        from praisonai.cli.interactive.split_tui import LOGO_MINIMAL
+        
+        assert "Praison AI" in LOGO_MINIMAL
+
+
+class TestSplitTUI:
+    """Tests for SplitTUI class."""
+    
+    def test_init_default(self):
+        """Test default initialization."""
+        from praisonai.cli.interactive.split_tui import SplitTUI
+        
+        tui = SplitTUI()
+        assert tui.config is not None
+        assert tui.messages == []
+        assert tui._running is False
+        assert tui.session_id is not None
+    
+    def test_init_with_config(self):
+        """Test initialization with config."""
+        from praisonai.cli.interactive.split_tui import SplitTUI, SplitTUIConfig
+        
+        config = SplitTUIConfig(model="gpt-4o", show_logo=False)
+        tui = SplitTUI(config=config)
+        assert tui.config.model == "gpt-4o"
+        assert tui.config.show_logo is False
+    
+    def test_format_output_empty(self):
+        """Test format_output with no messages."""
+        from praisonai.cli.interactive.split_tui import SplitTUI, SplitTUIConfig
+        
+        config = SplitTUIConfig(show_logo=True)
+        tui = SplitTUI(config=config)
+        
+        output = tui._format_output()
+        # Should contain welcome message
+        assert "Type your message" in output or "gpt-4o-mini" in output
+    
+    def test_format_output_with_messages(self):
+        """Test format_output with messages."""
+        from praisonai.cli.interactive.split_tui import SplitTUI, ChatMessage
+        
+        tui = SplitTUI()
+        tui.messages = [
+            ChatMessage(role="user", content="Hello"),
+            ChatMessage(role="assistant", content="Hi there"),
+        ]
+        
+        output = tui._format_output()
+        assert "Hello" in output
+        assert "Hi there" in output
+    
+    def test_handle_command_exit(self):
+        """Test handling exit command."""
+        from praisonai.cli.interactive.split_tui import SplitTUI
+        
+        tui = SplitTUI()
+        tui._running = True
+        
+        result = tui._handle_command("/exit")
+        assert result is True
+        assert tui._running is False
+    
+    def test_handle_command_quit(self):
+        """Test handling quit command."""
+        from praisonai.cli.interactive.split_tui import SplitTUI
+        
+        tui = SplitTUI()
+        tui._running = True
+        
+        result = tui._handle_command("/quit")
+        assert result is True
+        assert tui._running is False
+    
+    def test_handle_command_help(self):
+        """Test handling help command."""
+        from praisonai.cli.interactive.split_tui import SplitTUI
+        
+        tui = SplitTUI()
+        result = tui._handle_command("/help")
+        assert result is True
+        assert len(tui.messages) == 1
+        assert tui.messages[0].role == "system"
+        assert "/help" in tui.messages[0].content
+    
+    def test_handle_command_clear(self):
+        """Test handling clear command."""
+        from praisonai.cli.interactive.split_tui import SplitTUI, ChatMessage
+        
+        tui = SplitTUI()
+        tui.messages = [ChatMessage(role="user", content="test")]
+        
+        result = tui._handle_command("/clear")
+        assert result is True
+        assert len(tui.messages) == 0
+    
+    def test_handle_command_new(self):
+        """Test handling new command."""
+        from praisonai.cli.interactive.split_tui import SplitTUI, ChatMessage
+        
+        tui = SplitTUI()
+        old_session = tui.session_id
+        tui.messages = [ChatMessage(role="user", content="test")]
+        
+        result = tui._handle_command("/new")
+        assert result is True
+        assert len(tui.messages) == 1  # New session message
+        assert tui.session_id != old_session
+    
+    def test_handle_command_model_show(self):
+        """Test handling model command (show)."""
+        from praisonai.cli.interactive.split_tui import SplitTUI
+        
+        tui = SplitTUI()
+        result = tui._handle_command("/model")
+        assert result is True
+        assert len(tui.messages) == 1
+        assert "gpt-4o-mini" in tui.messages[0].content
+    
+    def test_handle_command_model_change(self):
+        """Test handling model command (change)."""
+        from praisonai.cli.interactive.split_tui import SplitTUI
+        
+        tui = SplitTUI()
+        result = tui._handle_command("/model gpt-4o")
+        assert result is True
+        assert tui.config.model == "gpt-4o"
+        assert tui._agent is None  # Agent should be reset
+    
+    def test_handle_command_unknown(self):
+        """Test handling unknown command."""
+        from praisonai.cli.interactive.split_tui import SplitTUI
+        
+        tui = SplitTUI()
+        result = tui._handle_command("/unknown")
+        assert result is True
+        assert len(tui.messages) == 1
+        assert "Unknown" in tui.messages[0].content
+
+
+class TestStartSplitTUI:
+    """Tests for start_split_tui function."""
+    
+    def test_function_exists(self):
+        """Test that start_split_tui function exists."""
+        from praisonai.cli.interactive.split_tui import start_split_tui
+        assert callable(start_split_tui)
+
+
+class TestASCIILogos:
+    """Tests for ASCII logo constants."""
+    
+    def test_logo_exists(self):
+        """Test LOGO constant exists and has content."""
+        from praisonai.cli.interactive.split_tui import LOGO
+        assert len(LOGO) > 0
+        assert "██" in LOGO  # Contains block characters
+    
+    def test_logo_small_exists(self):
+        """Test LOGO_SMALL constant exists."""
+        from praisonai.cli.interactive.split_tui import LOGO_SMALL
+        assert len(LOGO_SMALL) > 0
+    
+    def test_logo_minimal_branding(self):
+        """Test LOGO_MINIMAL has correct branding."""
+        from praisonai.cli.interactive.split_tui import LOGO_MINIMAL
+        assert "Praison AI" in LOGO_MINIMAL
+
+
+class TestMessageFormatting:
+    """Tests for message formatting."""
+    
+    def test_user_message_prefix(self):
+        """Test user messages have correct prefix."""
+        from praisonai.cli.interactive.split_tui import SplitTUI, ChatMessage
+        
+        tui = SplitTUI()
+        tui.messages = [ChatMessage(role="user", content="test")]
+        
+        output = tui._format_output()
+        assert "› test" in output
+    
+    def test_assistant_message_prefix(self):
+        """Test assistant messages have correct prefix."""
+        from praisonai.cli.interactive.split_tui import SplitTUI, ChatMessage
+        
+        tui = SplitTUI()
+        tui.messages = [ChatMessage(role="assistant", content="response")]
+        
+        output = tui._format_output()
+        assert "● response" in output
+    
+    def test_status_message_prefix(self):
+        """Test status messages have correct prefix."""
+        from praisonai.cli.interactive.split_tui import SplitTUI, ChatMessage
+        
+        tui = SplitTUI()
+        tui.messages = [ChatMessage(role="status", content="thinking")]
+        
+        output = tui._format_output()
+        assert "⏳ thinking" in output

src/praisonai/tests/unit/cli/test_tui_app.py

@@ -0,0 +1,344 @@
+"""
+Tests for PraisonAI TUI Application.
+"""
+
+
+
+class TestColorScheme:
+    """Tests for ColorScheme dataclass."""
+    
+    def test_default_values(self):
+        """Test default color scheme values."""
+        from praisonai.cli.interactive.tui_app import ColorScheme
+        
+        colors = ColorScheme()
+        assert colors.primary == "#00aaff"
+        assert colors.secondary == "#888888"
+        assert colors.accent == "#00cc00"
+        assert colors.text == "#ffffff"
+        assert colors.success == "#00cc00"
+        assert colors.error == "#ff4444"
+
+
+class TestTUIConfig:
+    """Tests for TUIConfig dataclass."""
+    
+    def test_default_values(self):
+        """Test default TUI configuration values."""
+        from praisonai.cli.interactive.tui_app import TUIConfig
+        
+        config = TUIConfig()
+        assert config.model == "gpt-4o-mini"
+        assert config.show_logo is True
+        assert config.show_tips is True
+        assert config.show_status_bar is True
+        assert config.compact_mode is False
+        assert config.multiline is False
+        assert config.vi_mode is False
+    
+    def test_custom_values(self):
+        """Test custom TUI configuration values."""
+        from praisonai.cli.interactive.tui_app import TUIConfig
+        
+        config = TUIConfig(
+            model="gpt-4o",
+            show_logo=False,
+            compact_mode=True,
+        )
+        assert config.model == "gpt-4o"
+        assert config.show_logo is False
+        assert config.compact_mode is True
+
+
+class TestMessage:
+    """Tests for Message dataclass."""
+    
+    def test_message_creation(self):
+        """Test message creation."""
+        from praisonai.cli.interactive.tui_app import Message
+        
+        msg = Message(role="user", content="Hello")
+        assert msg.role == "user"
+        assert msg.content == "Hello"
+        assert msg.timestamp is not None
+        assert msg.metadata == {}
+    
+    def test_message_with_metadata(self):
+        """Test message with metadata."""
+        from praisonai.cli.interactive.tui_app import Message
+        
+        msg = Message(role="assistant", content="Hi", metadata={"tokens": 10})
+        assert msg.metadata == {"tokens": 10}
+
+
+class TestGetLogo:
+    """Tests for get_logo function."""
+    
+    def test_logo_wide_terminal(self):
+        """Test logo for wide terminal."""
+        from praisonai.cli.interactive.tui_app import get_logo, LOGO_SMALL
+        
+        logo = get_logo(100)
+        assert logo == LOGO_SMALL
+    
+    def test_logo_medium_terminal(self):
+        """Test logo for medium terminal."""
+        from praisonai.cli.interactive.tui_app import get_logo, LOGO_TINY
+        
+        logo = get_logo(65)
+        assert logo == LOGO_TINY
+    
+    def test_logo_narrow_terminal(self):
+        """Test logo for narrow terminal."""
+        from praisonai.cli.interactive.tui_app import get_logo, LOGO_MINIMAL
+        
+        logo = get_logo(50)
+        assert logo == LOGO_MINIMAL
+
+
+class TestPraisonTUI:
+    """Tests for PraisonTUI class."""
+    
+    def test_init_default(self):
+        """Test default initialization."""
+        from praisonai.cli.interactive.tui_app import PraisonTUI
+        
+        tui = PraisonTUI()
+        assert tui.config is not None
+        assert tui.messages == []
+        assert tui._running is False
+        assert tui.session_id is not None
+    
+    def test_init_with_config(self):
+        """Test initialization with config."""
+        from praisonai.cli.interactive.tui_app import PraisonTUI, TUIConfig
+        
+        config = TUIConfig(model="gpt-4o", show_logo=False)
+        tui = PraisonTUI(config=config)
+        assert tui.config.model == "gpt-4o"
+        assert tui.config.show_logo is False
+    
+    def test_render_logo_disabled(self):
+        """Test render_logo when disabled."""
+        from praisonai.cli.interactive.tui_app import PraisonTUI, TUIConfig
+        
+        config = TUIConfig(show_logo=False)
+        tui = PraisonTUI(config=config)
+        
+        logo = tui._render_logo()
+        assert logo == ""
+    
+    def test_render_logo_enabled(self):
+        """Test render_logo when enabled."""
+        from praisonai.cli.interactive.tui_app import PraisonTUI, TUIConfig
+        
+        config = TUIConfig(show_logo=True, model="gpt-4o-mini")
+        tui = PraisonTUI(config=config)
+        
+        logo = tui._render_logo()
+        assert "PRAISON" in logo or "gpt-4o-mini" in logo
+    
+    def test_render_welcome(self):
+        """Test render_welcome."""
+        from praisonai.cli.interactive.tui_app import PraisonTUI, TUIConfig
+        
+        config = TUIConfig(show_logo=True, show_tips=True)
+        tui = PraisonTUI(config=config)
+        
+        welcome = tui._render_welcome()
+        assert "Type your message" in welcome or len(welcome) > 0
+    
+    def test_render_status_bar(self):
+        """Test render_status_bar."""
+        from praisonai.cli.interactive.tui_app import PraisonTUI, TUIConfig
+        
+        config = TUIConfig(show_status_bar=True, model="gpt-4o")
+        tui = PraisonTUI(config=config)
+        
+        status = tui._render_status_bar()
+        assert "gpt-4o" in status
+    
+    def test_render_status_bar_disabled(self):
+        """Test render_status_bar when disabled."""
+        from praisonai.cli.interactive.tui_app import PraisonTUI, TUIConfig
+        
+        config = TUIConfig(show_status_bar=False)
+        tui = PraisonTUI(config=config)
+        
+        status = tui._render_status_bar()
+        assert status == ""
+    
+    def test_format_message_user(self):
+        """Test format_message for user role."""
+        from praisonai.cli.interactive.tui_app import PraisonTUI, Message
+        
+        tui = PraisonTUI()
+        msg = Message(role="user", content="Hello")
+        
+        formatted = tui._format_message(msg)
+        assert "Hello" in formatted
+        assert "›" in formatted
+    
+    def test_format_message_assistant(self):
+        """Test format_message for assistant role."""
+        from praisonai.cli.interactive.tui_app import PraisonTUI, Message
+        
+        tui = PraisonTUI()
+        msg = Message(role="assistant", content="Hi there")
+        
+        formatted = tui._format_message(msg)
+        assert "Hi there" in formatted
+        assert "●" in formatted
+    
+    def test_format_message_tool(self):
+        """Test format_message for tool role."""
+        from praisonai.cli.interactive.tui_app import PraisonTUI, Message
+        
+        tui = PraisonTUI()
+        msg = Message(role="tool", content="Running command")
+        
+        formatted = tui._format_message(msg)
+        assert "Running command" in formatted
+        assert "⚙" in formatted
+    
+    def test_handle_command_exit(self):
+        """Test handling exit command."""
+        from praisonai.cli.interactive.tui_app import PraisonTUI
+        
+        tui = PraisonTUI()
+        tui._running = True
+        
+        result = tui._handle_command("/exit")
+        assert result is True
+        assert tui._running is False
+    
+    def test_handle_command_quit(self):
+        """Test handling quit command."""
+        from praisonai.cli.interactive.tui_app import PraisonTUI
+        
+        tui = PraisonTUI()
+        tui._running = True
+        
+        result = tui._handle_command("/quit")
+        assert result is True
+        assert tui._running is False
+    
+    def test_handle_command_help(self):
+        """Test handling help command."""
+        from praisonai.cli.interactive.tui_app import PraisonTUI
+        
+        tui = PraisonTUI()
+        result = tui._handle_command("/help")
+        assert result is True
+        # Help should add a system message
+        assert len(tui.messages) == 1
+        assert tui.messages[0].role == "system"
+    
+    def test_handle_command_clear(self):
+        """Test handling clear command."""
+        from praisonai.cli.interactive.tui_app import PraisonTUI, Message
+        
+        tui = PraisonTUI()
+        tui.messages = [Message(role="user", content="test")]
+        
+        result = tui._handle_command("/clear")
+        assert result is True
+        assert len(tui.messages) == 0
+    
+    def test_handle_command_model_show(self):
+        """Test handling model command (show)."""
+        from praisonai.cli.interactive.tui_app import PraisonTUI
+        
+        tui = PraisonTUI()
+        result = tui._handle_command("/model")
+        assert result is True
+        assert len(tui.messages) == 1
+        assert "gpt-4o-mini" in tui.messages[0].content
+    
+    def test_handle_command_model_change(self):
+        """Test handling model command (change)."""
+        from praisonai.cli.interactive.tui_app import PraisonTUI
+        
+        tui = PraisonTUI()
+        result = tui._handle_command("/model gpt-4o")
+        assert result is True
+        assert tui.config.model == "gpt-4o"
+    
+    def test_handle_command_cost(self):
+        """Test handling cost command."""
+        from praisonai.cli.interactive.tui_app import PraisonTUI
+        
+        tui = PraisonTUI()
+        tui._total_tokens = 100
+        tui._total_cost = 0.01
+        
+        result = tui._handle_command("/cost")
+        assert result is True
+        assert len(tui.messages) == 1
+        assert "100" in tui.messages[0].content
+    
+    def test_handle_command_compact(self):
+        """Test handling compact command."""
+        from praisonai.cli.interactive.tui_app import PraisonTUI
+        
+        tui = PraisonTUI()
+        original = tui.config.compact_mode
+        
+        result = tui._handle_command("/compact")
+        assert result is True
+        assert tui.config.compact_mode != original
+    
+    def test_handle_command_multiline(self):
+        """Test handling multiline command."""
+        from praisonai.cli.interactive.tui_app import PraisonTUI
+        
+        tui = PraisonTUI()
+        original = tui.config.multiline
+        
+        result = tui._handle_command("/multiline")
+        assert result is True
+        assert tui.config.multiline != original
+    
+    def test_handle_command_unknown(self):
+        """Test handling unknown command."""
+        from praisonai.cli.interactive.tui_app import PraisonTUI
+        
+        tui = PraisonTUI()
+        result = tui._handle_command("/unknown")
+        assert result is True
+        assert len(tui.messages) == 1
+        assert "Unknown" in tui.messages[0].content
+
+
+class TestStartTUI:
+    """Tests for start_tui function."""
+    
+    def test_function_exists(self):
+        """Test that start_tui function exists."""
+        from praisonai.cli.interactive.tui_app import start_tui
+        assert callable(start_tui)
+
+
+class TestASCIILogos:
+    """Tests for ASCII logo constants."""
+    
+    def test_logo_large_exists(self):
+        """Test LOGO_LARGE constant exists."""
+        from praisonai.cli.interactive.tui_app import LOGO_LARGE
+        assert len(LOGO_LARGE) > 0
+        assert "PRAISON" in LOGO_LARGE or "██" in LOGO_LARGE
+    
+    def test_logo_small_exists(self):
+        """Test LOGO_SMALL constant exists."""
+        from praisonai.cli.interactive.tui_app import LOGO_SMALL
+        assert len(LOGO_SMALL) > 0
+    
+    def test_logo_tiny_exists(self):
+        """Test LOGO_TINY constant exists."""
+        from praisonai.cli.interactive.tui_app import LOGO_TINY
+        assert len(LOGO_TINY) > 0
+    
+    def test_logo_minimal_exists(self):
+        """Test LOGO_MINIMAL constant exists."""
+        from praisonai.cli.interactive.tui_app import LOGO_MINIMAL
+        assert "PraisonAI" in LOGO_MINIMAL

src/praisonai/tests/unit/standardise/test_discovery.py

@@ -140,7 +140,11 @@ class TestFeatureDiscovery:
         cli_root.mkdir()
         (cli_root / "features").mkdir()
         
-        config = StandardiseConfig(sdk_root=sdk_root, docs_root=docs_root, cli_root=cli_root)
+        # Create empty examples root to ensure no examples found
+        examples_root = tmp_path / "examples"
+        examples_root.mkdir()
+        
+        config = StandardiseConfig(sdk_root=sdk_root, docs_root=docs_root, cli_root=cli_root, examples_root=examples_root)
         discovery = FeatureDiscovery(config)
         
         slug = FeatureSlug.from_string("guardrails")

src/praisonai/tests/unit/standardise/test_example_verifier.py

@@ -0,0 +1,121 @@
+"""
+Unit tests for the ExampleVerifier.
+"""
+
+from praisonai.standardise.example_verifier import ExampleVerifier, VerificationResult
+
+
+class TestExampleVerifier:
+    """Tests for ExampleVerifier."""
+    
+    def test_valid_simple_code(self):
+        """Test verification of valid simple code."""
+        verifier = ExampleVerifier(timeout=10)
+        code = '''
+print("Hello, World!")
+'''
+        result = verifier.verify(code)
+        
+        assert result.syntax_valid
+        assert result.execution_passed
+        assert result.success
+        assert result.can_write
+    
+    def test_syntax_error(self):
+        """Test detection of syntax errors."""
+        verifier = ExampleVerifier(timeout=10)
+        code = '''
+def broken(
+    print("missing closing paren"
+'''
+        result = verifier.verify(code)
+        
+        assert not result.syntax_valid
+        assert not result.success
+        assert not result.can_write
+        assert "Syntax error" in result.error
+    
+    def test_runtime_error(self):
+        """Test detection of runtime errors."""
+        verifier = ExampleVerifier(timeout=10)
+        code = '''
+x = 1 / 0  # Division by zero
+'''
+        result = verifier.verify(code)
+        
+        assert result.syntax_valid
+        assert not result.execution_passed
+        assert not result.success
+        assert "ZeroDivisionError" in result.error
+    
+    def test_missing_import(self):
+        """Test detection of missing imports."""
+        verifier = ExampleVerifier(timeout=10)
+        code = '''
+import nonexistent_module_xyz
+'''
+        result = verifier.verify(code)
+        
+        assert result.syntax_valid
+        assert not result.execution_passed
+        assert "nonexistent_module_xyz" in result.missing_libraries
+    
+    def test_external_library_detection(self):
+        """Test detection of external library requirements."""
+        verifier = ExampleVerifier(timeout=10)
+        code = '''
+import pandas
+df = pandas.DataFrame()
+'''
+        result = verifier.verify(code)
+        
+        assert result.syntax_valid
+        # May or may not pass depending on if pandas is installed
+        # But should detect pandas as external
+        assert "pandas" in result.missing_libraries or result.execution_passed
+    
+    def test_can_write_with_external_libs(self):
+        """Test that code requiring external libs can still be written."""
+        # Create a result that simulates external lib requirement
+        result = VerificationResult(
+            success=True,
+            syntax_valid=True,
+            execution_passed=False,
+            output="",
+            error="ModuleNotFoundError: No module named 'requests'",
+            missing_libraries=["requests"],
+            requires_external=True,
+        )
+        
+        assert result.can_write  # Should allow writing despite execution failure
+    
+    def test_timeout_handling(self):
+        """Test that infinite loops are handled."""
+        verifier = ExampleVerifier(timeout=2)
+        code = '''
+import time
+time.sleep(10)  # Will timeout
+'''
+        result = verifier.verify(code)
+        
+        assert result.syntax_valid
+        assert not result.execution_passed
+        assert "timed out" in result.error.lower()
+    
+    def test_format_result(self):
+        """Test result formatting."""
+        verifier = ExampleVerifier()
+        
+        result = VerificationResult(
+            success=True,
+            syntax_valid=True,
+            execution_passed=True,
+            output="Hello",
+            error="",
+            missing_libraries=[],
+            requires_external=False,
+        )
+        
+        formatted = verifier.format_result(result)
+        assert "PASSED" in formatted
+        assert "Syntax valid: ✓" in formatted

src/praisonai/tests/unit/standardise/test_undo_redo.py

@@ -0,0 +1,207 @@
+"""
+Unit tests for the undo/redo system.
+"""
+
+from unittest.mock import patch
+
+from praisonai.standardise.undo_redo import UndoRedoManager, FileBackupManager
+
+
+class TestUndoRedoManager:
+    """Tests for UndoRedoManager."""
+    
+    def test_init_with_path(self, tmp_path):
+        """Test initialization with a path."""
+        manager = UndoRedoManager(tmp_path)
+        assert manager.repo_path == tmp_path
+    
+    def test_checkpoint_prefix(self):
+        """Test checkpoint prefix constant."""
+        assert UndoRedoManager.CHECKPOINT_PREFIX == "standardise-checkpoint-"
+    
+    @patch.object(UndoRedoManager, '_run_git')
+    def test_create_checkpoint_success(self, mock_git, tmp_path):
+        """Test successful checkpoint creation."""
+        mock_git.side_effect = [
+            (True, ""),  # git add
+            (False, "changes"),  # git diff (has changes)
+            (True, ""),  # git commit
+            (True, ""),  # git tag
+        ]
+        
+        manager = UndoRedoManager(tmp_path)
+        success, result = manager.create_checkpoint("Test checkpoint")
+        
+        assert success
+        assert result.startswith("standardise-checkpoint-")
+    
+    @patch.object(UndoRedoManager, '_run_git')
+    def test_create_checkpoint_no_changes(self, mock_git, tmp_path):
+        """Test checkpoint creation with no changes."""
+        mock_git.side_effect = [
+            (True, ""),  # git add
+            (True, ""),  # git diff (no changes)
+            (True, ""),  # git tag
+        ]
+        
+        manager = UndoRedoManager(tmp_path)
+        success, result = manager.create_checkpoint()
+        
+        assert success
+    
+    @patch.object(UndoRedoManager, '_run_git')
+    def test_list_checkpoints(self, mock_git, tmp_path):
+        """Test listing checkpoints."""
+        mock_git.return_value = (True, "standardise-checkpoint-20240101|2024-01-01|Test message\n")
+        
+        manager = UndoRedoManager(tmp_path)
+        checkpoints = manager.list_checkpoints()
+        
+        assert len(checkpoints) == 1
+        assert checkpoints[0][0] == "standardise-checkpoint-20240101"
+        assert checkpoints[0][1] == "2024-01-01"
+        assert checkpoints[0][2] == "Test message"
+    
+    @patch.object(UndoRedoManager, '_run_git')
+    def test_list_checkpoints_empty(self, mock_git, tmp_path):
+        """Test listing checkpoints when none exist."""
+        mock_git.return_value = (True, "")
+        
+        manager = UndoRedoManager(tmp_path)
+        checkpoints = manager.list_checkpoints()
+        
+        assert checkpoints == []
+    
+    @patch.object(UndoRedoManager, '_run_git')
+    def test_undo_success(self, mock_git, tmp_path):
+        """Test successful undo."""
+        mock_git.side_effect = [
+            (True, ""),  # rev-parse (checkpoint exists)
+            (True, ""),  # git add (safety checkpoint)
+            (True, ""),  # git diff
+            (True, ""),  # git tag
+            (True, ""),  # git reset
+        ]
+        
+        manager = UndoRedoManager(tmp_path)
+        success, result = manager.undo("standardise-checkpoint-test")
+        
+        assert success
+        assert "Restored" in result
+    
+    @patch.object(UndoRedoManager, '_run_git')
+    def test_undo_checkpoint_not_found(self, mock_git, tmp_path):
+        """Test undo with non-existent checkpoint."""
+        mock_git.return_value = (False, "not found")
+        
+        manager = UndoRedoManager(tmp_path)
+        success, result = manager.undo("nonexistent")
+        
+        assert not success
+        assert "not found" in result
+    
+    @patch.object(UndoRedoManager, '_run_git')
+    def test_redo_no_redo_available(self, mock_git, tmp_path):
+        """Test redo when no redo is available."""
+        mock_git.return_value = (True, "abc123 HEAD@{0}: commit: test\n")
+        
+        manager = UndoRedoManager(tmp_path)
+        success, result = manager.redo()
+        
+        assert not success
+        assert "No redo available" in result
+    
+    @patch.object(UndoRedoManager, '_run_git')
+    def test_delete_checkpoint(self, mock_git, tmp_path):
+        """Test deleting a checkpoint."""
+        mock_git.return_value = (True, "")
+        
+        manager = UndoRedoManager(tmp_path)
+        success, result = manager.delete_checkpoint("test-checkpoint")
+        
+        assert success
+        assert "Deleted" in result
+    
+    @patch.object(UndoRedoManager, '_run_git')
+    def test_get_changes_since(self, mock_git, tmp_path):
+        """Test getting changes since a checkpoint."""
+        mock_git.return_value = (True, "file1.py\nfile2.py\n")
+        
+        manager = UndoRedoManager(tmp_path)
+        changes = manager.get_changes_since("test-checkpoint")
+        
+        assert len(changes) == 2
+        assert "file1.py" in changes
+        assert "file2.py" in changes
+
+
+class TestFileBackupManager:
+    """Tests for FileBackupManager."""
+    
+    def test_init_creates_backup_dir(self, tmp_path):
+        """Test that init creates backup directory."""
+        backup_dir = tmp_path / "backups"
+        _ = FileBackupManager(backup_dir)  # Creates dir on init
+        
+        assert backup_dir.exists()
+    
+    def test_backup_file(self, tmp_path):
+        """Test backing up a file."""
+        # Create a test file
+        test_file = tmp_path / "test.txt"
+        test_file.write_text("test content")
+        
+        backup_dir = tmp_path / "backups"
+        manager = FileBackupManager(backup_dir)
+        
+        backup_path = manager.backup_file(test_file)
+        
+        assert backup_path is not None
+        assert backup_path.exists()
+        assert backup_path.read_text() == "test content"
+    
+    def test_backup_nonexistent_file(self, tmp_path):
+        """Test backing up a non-existent file."""
+        manager = FileBackupManager(tmp_path / "backups")
+        
+        result = manager.backup_file(tmp_path / "nonexistent.txt")
+        
+        assert result is None
+    
+    def test_restore_file(self, tmp_path):
+        """Test restoring a file from backup."""
+        # Create backup
+        backup_dir = tmp_path / "backups"
+        backup_dir.mkdir()
+        backup_file = backup_dir / "backup.txt"
+        backup_file.write_text("backup content")
+        
+        # Target file
+        target_file = tmp_path / "restored.txt"
+        
+        manager = FileBackupManager(backup_dir)
+        result = manager.restore_file(backup_file, target_file)
+        
+        assert result
+        assert target_file.exists()
+        assert target_file.read_text() == "backup content"
+    
+    def test_list_backups(self, tmp_path):
+        """Test listing backups."""
+        backup_dir = tmp_path / "backups"
+        backup_dir.mkdir()
+        
+        # Create some backup files
+        (backup_dir / "file1_20240101.txt").write_text("1")
+        (backup_dir / "file1_20240102.txt").write_text("2")
+        (backup_dir / "file2_20240101.txt").write_text("3")
+        
+        backup_manager = FileBackupManager(backup_dir)
+        
+        # List all
+        all_backups = backup_manager.list_backups()
+        assert len(all_backups) == 3
+        
+        # List filtered
+        file1_backups = backup_manager.list_backups("file1")
+        assert len(file1_backups) == 2

src/praisonai/uv.lock

@@ -4276,7 +4276,7 @@ requires-dist = [
     { name = "praisonai-tools", marker = "extra == 'autogen'", specifier = ">=0.1.0" },
     { name = "praisonai-tools", marker = "extra == 'autogen-v4'", specifier = ">=0.1.0" },
     { name = "praisonai-tools", marker = "extra == 'crewai'", specifier = ">=0.1.0" },
-    { name = "praisonaiagents", specifier = ">=0.11.1" },
+    { name = "praisonaiagents", specifier = ">=0.11.3" },
     { name = "pyautogen", marker = "extra == 'autogen'", specifier = "==0.2.29" },
     { name = "pydantic", marker = "extra == 'chat'", specifier = "<=2.10.1" },
     { name = "pydantic", marker = "extra == 'code'", specifier = "<=2.10.1" },
@@ -4324,7 +4324,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.11.1"
+version = "0.11.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohttp" },
@@ -4333,9 +4333,9 @@ dependencies = [
     { name = "pydantic" },
     { name = "rich" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/40/42/8fe814f73b19a7decd12bca9e3c720a97906658bf993fb4c3063505531fb/praisonaiagents-0.11.1.tar.gz", hash = "sha256:b0f3720d7d5334fb9aaeb8b8e62cc1a20b9ce9c1cad489031414326479a3c23f", size = 963761, upload-time = "2026-01-10T14:23:57.185Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/96/4a/ab73d7381b0e9139f722dad51f9cb63575032c3d11aa83bc281f76bfd13a/praisonaiagents-0.11.3.tar.gz", hash = "sha256:5aa024935184eaa66578ff4c888e06b052d55568c68bfb702dd35c8a7951e10b", size = 973113, upload-time = "2026-01-11T07:29:29.515Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/0f/97/d7d465eec9443b80fd79daa57bbac7292b680a8702b5d6ef3fb82899bf7d/praisonaiagents-0.11.1-py3-none-any.whl", hash = "sha256:3792747bd2f449995bb4f77eb9086c1b9a88bc2f89414dd447e3d687449c389b", size = 943019, upload-time = "2026-01-10T14:23:54.691Z" },
+    { url = "https://files.pythonhosted.org/packages/a2/05/f142e42a88cd61d0af4391be3e97c5ecfe660deeed96ebcdc31f95180977/praisonaiagents-0.11.3-py3-none-any.whl", hash = "sha256:a78dcbaa3eb66cfab52a4c9789654c58eb71b0ea34b2f0dcca54baff91917e7d", size = 954407, upload-time = "2026-01-11T07:29:26.456Z" },
 ]
 
 [[package]]