fix: reset interaction_displayed flag to ensure final summary is shown in LLM responses

- Added execute_sync_callback call in custom LLM path to match OpenAI behavior
- Fixed issue where Gemini/custom LLM responses were not triggering callbacks
- Prevents duplicate display by relying on LLM class to handle verbose display
- Ensures both paths execute callbacks for consistent behavior

The issue was that custom LLM providers (like Gemini) were not calling
_execute_callback_and_display after guardrail validation, while OpenAI
path was. This caused missing Task/Response display boxes for Gemini.

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <[email protected]>

docker/Dockerfile

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     flask \
-    "praisonai>=2.2.74" \
+    "praisonai>=2.2.78" \
     "praisonai[api]" \
     gunicorn \
     markdown

docker/Dockerfile.chat

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=2.2.74" \
+    "praisonai>=2.2.78" \
     "praisonai[chat]" \
     "embedchain[github,youtube]"
 

docker/Dockerfile.dev

@@ -20,7 +20,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=2.2.74" \
+    "praisonai>=2.2.78" \
     "praisonai[ui]" \
     "praisonai[chat]" \
     "praisonai[realtime]" \

docker/Dockerfile.ui

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=2.2.74" \
+    "praisonai>=2.2.78" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

docker/README.md

@@ -121,7 +121,7 @@ healthcheck:
 ## 📦 Package Versions
 
 All Docker images use consistent, up-to-date versions:
-- PraisonAI: `>=2.2.74`
+- PraisonAI: `>=2.2.78`
 - PraisonAI Agents: `>=0.0.92`
 - Python: `3.11-slim`
 
@@ -218,7 +218,7 @@ docker-compose up -d
 ### Version Pinning
 To use specific versions, update the Dockerfile:
 ```dockerfile
-RUN pip install "praisonai==2.2.74" "praisonaiagents==0.0.92"
+RUN pip install "praisonai==2.2.78" "praisonaiagents==0.0.92"
 ```
 
 ## 🌐 Production Deployment

src/praisonai-agents/praisonaiagents/__init__.py

@@ -32,6 +32,12 @@ from .session import Session
 from .memory.memory import Memory
 from .guardrails import GuardrailResult, LLMGuardrail
 from .agent.handoff import Handoff, handoff, handoff_filters, RECOMMENDED_PROMPT_PREFIX, prompt_with_handoff_instructions
+# Flow display
+try:
+    from .flow_display import FlowDisplay, track_workflow
+except ImportError:
+    FlowDisplay = None
+    track_workflow = None
 from .main import (
     TaskOutput,
     ReflectionOutput,
@@ -135,4 +141,9 @@ __all__ = [
 
 # Add MCP to __all__ if available
 if _mcp_available:
-    __all__.append('MCP')
+    __all__.append('MCP')
+    
+# Add flow display if available
+if FlowDisplay is not None:
+    __all__.extend(['FlowDisplay', 'track_workflow'])
+

src/praisonai-agents/praisonaiagents/_logging.py

@@ -59,7 +59,7 @@ def _configure_loggers():
     
     # When DEBUG is set, allow some HTTP logging for API endpoints
     if loglevel == 'DEBUG':
-        allowed_debug_loggers = {"httpx._client", "openai._client"}
+        allowed_debug_loggers = {"httpx", "httpx._client", "openai._client"}
         
         for logger_name in _get_all_noisy_loggers():
             if logger_name not in allowed_debug_loggers:

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

@@ -331,8 +331,15 @@ class Agent:
 
         # Configure logging to suppress unwanted outputs
         logging.getLogger("litellm").setLevel(logging.WARNING)
-        logging.getLogger("httpx").setLevel(logging.WARNING)
-        logging.getLogger("httpcore").setLevel(logging.WARNING)
+        
+        # Allow httpx logging when LOGLEVEL=debug, otherwise suppress it
+        loglevel = os.environ.get('LOGLEVEL', 'INFO').upper()
+        if loglevel == 'DEBUG':
+            logging.getLogger("httpx").setLevel(logging.INFO)
+            logging.getLogger("httpcore").setLevel(logging.INFO)
+        else:
+            logging.getLogger("httpx").setLevel(logging.WARNING)
+            logging.getLogger("httpcore").setLevel(logging.WARNING)
 
         # If instructions are provided, use them to set role, goal, and backstory
         if instructions:
@@ -1229,14 +1236,14 @@ Your Goal: {self.goal}"""
                 border_style="green",
                 expand=False
             )
-        else:
-            # No content yet: show generating message
-            return Panel(
-                f"[bold cyan]Generating response...[/bold cyan]",
-                title=f"[bold]{self.name}[/bold] - {elapsed:.1f}s",
-                border_style="cyan",
-                expand=False
-            )
+        # else:
+        #     # No content yet: show generating message
+        #     return Panel(
+        #         f"[bold cyan]Generating response...[/bold cyan]",
+        #         title=f"[bold]{self.name}[/bold] - {elapsed:.1f}s",
+        #         border_style="cyan",
+        #         expand=False
+        #     )
 
     def chat(self, prompt, temperature=0.2, tools=None, output_json=None, output_pydantic=None, reasoning_steps=False, stream=None, task_name=None, task_description=None, task_id=None):
         # Reset the final display flag for each new conversation
@@ -1353,6 +1360,8 @@ Your Goal: {self.goal}"""
                     # Apply guardrail validation for custom LLM response
                     try:
                         validated_response = self._apply_guardrail_with_retry(response_text, prompt, temperature, tools, task_name, task_description, task_id)
+                        # Execute callback and display after validation
+                        self._execute_callback_and_display(prompt, validated_response, time.time() - start_time, task_name, task_description, task_id)
                         return validated_response
                     except Exception as e:
                         logging.error(f"Agent {self.name}: Guardrail validation failed for custom LLM: {e}")

src/praisonai-agents/praisonaiagents/flow_display.py

@@ -0,0 +1,194 @@
+"""
+Flow Display for PraisonAI Agents
+
+Visual display with agents in center and tools on sides.
+"""
+
+from typing import Dict, List, Set, Tuple
+from collections import defaultdict
+import threading
+from rich.console import Console
+from rich.panel import Panel
+from rich.text import Text
+from rich.align import Align
+from rich.columns import Columns
+from rich.table import Table
+from rich import box
+
+class FlowDisplay:
+    """Displays agent workflow with agents centered and tools on sides."""
+    
+    def __init__(self):
+        self.console = Console()
+        self.agents = []  # List of agents in order
+        self.agent_tools = defaultdict(list)  # agent -> [tools]
+        self.tracking = False
+        self.lock = threading.Lock()
+        
+    def start(self):
+        """Start tracking workflow."""
+        self.tracking = True
+        self.agents.clear()
+        self.agent_tools.clear()
+        
+        # Register callbacks
+        try:
+            from praisonaiagents.main import register_display_callback
+            
+            def on_interaction(**kwargs):
+                if self.tracking:
+                    agent = kwargs.get('agent_name', 'Unknown')
+                    self._add_agent(agent)
+                    
+            def on_tool_call(message, **kwargs):
+                if self.tracking and "called function" in message:
+                    parts = message.split("'")
+                    if len(parts) > 1:
+                        tool_name = parts[1]
+                        agent = kwargs.get('agent_name', 'Unknown')
+                        self._add_tool(agent, tool_name)
+                        
+            register_display_callback('interaction', on_interaction)
+            register_display_callback('tool_call', on_tool_call)
+            
+        except ImportError:
+            pass
+            
+    def stop(self):
+        """Stop tracking and display the flow."""
+        self.tracking = False
+        self.display()
+        
+    def _add_agent(self, name: str):
+        """Add an agent if not already present."""
+        with self.lock:
+            if name not in self.agents:
+                self.agents.append(name)
+                
+    def _add_tool(self, agent_name: str, tool_name: str):
+        """Add a tool to an agent."""
+        with self.lock:
+            if agent_name not in self.agents:
+                self.agents.append(agent_name)
+            if tool_name not in self.agent_tools[agent_name]:
+                self.agent_tools[agent_name].append(tool_name)
+                
+    def display(self):
+        """Display the flow chart with agents in center and tools on sides."""
+        if not self.agents:
+            return
+            
+        self.console.print("\n[bold cyan]🔄 Agent Workflow Flow[/bold cyan]\n")
+        
+        # Display start
+        self._display_centered_node("── start ──", "grey35")
+        self._display_arrow_down()
+        
+        # Display each agent with their tools
+        for i, agent in enumerate(self.agents):
+            self._display_agent_with_tools(agent)
+            
+            # Add arrow to next agent or end
+            if i < len(self.agents) - 1:
+                self._display_arrow_down()
+        
+        # Display end
+        self._display_arrow_down()
+        self._display_centered_node("── end ──", "grey35")
+        
+    def _display_agent_with_tools(self, agent: str):
+        """Display agent with tools on the sides."""
+        tools = self.agent_tools.get(agent, [])
+        
+        if not tools:
+            # No tools - just agent
+            self._display_centered_node(agent, "purple")
+        else:
+            # Split tools between left and right
+            left_tools = tools[::2]  # Even indices
+            right_tools = tools[1::2]  # Odd indices
+            
+            # Create the layout
+            table = Table(show_header=False, show_edge=False, box=None, padding=0)
+            table.add_column(justify="center", min_width=20)  # Left tools
+            table.add_column(justify="center", min_width=5)   # Space
+            table.add_column(justify="center", min_width=20)  # Agent
+            table.add_column(justify="center", min_width=5)   # Space
+            table.add_column(justify="center", min_width=20)  # Right tools
+            
+            # Create panels
+            left_panel = self._create_tools_panel(left_tools) if left_tools else ""
+            agent_panel = Panel(
+                Text(agent, style="white on purple", justify="center"),
+                style="white on purple",
+                box=box.ROUNDED,
+                padding=(0, 2)
+            )
+            right_panel = self._create_tools_panel(right_tools) if right_tools else ""
+            
+            # Add row
+            table.add_row(left_panel, "", agent_panel, "", right_panel)
+            
+            # Display centered
+            self.console.print(Align.center(table))
+            
+            # Show arrows
+            if left_tools or right_tools:
+                arrow_parts = []
+                if left_tools:
+                    arrow_parts.append("←→")
+                else:
+                    arrow_parts.append("  ")
+                    
+                arrow_parts.append("        ")  # Center space
+                
+                if right_tools:
+                    arrow_parts.append("←→")
+                else:
+                    arrow_parts.append("  ")
+                    
+                self.console.print(Align.center(Text("".join(arrow_parts))))
+                
+    def _create_tools_panel(self, tools: List[str]) -> Panel:
+        """Create a panel for tools."""
+        if not tools:
+            return ""
+            
+        if len(tools) == 1:
+            return Panel(
+                Text(tools[0], style="black on yellow", justify="center"),
+                style="black on yellow",
+                box=box.ROUNDED,
+                padding=(0, 1)
+            )
+        else:
+            # Multiple tools
+            content = "\n".join(tools)
+            return Panel(
+                Text(content, style="black on yellow", justify="center"),
+                style="black on yellow",
+                box=box.ROUNDED,
+                padding=(0, 1)
+            )
+            
+    def _display_centered_node(self, label: str, color: str):
+        """Display a centered node."""
+        panel = Panel(
+            Text(label, style=f"white on {color}", justify="center"),
+            style=f"white on {color}",
+            box=box.ROUNDED,
+            padding=(0, 2)
+        )
+        self.console.print(Align.center(panel))
+        
+    def _display_arrow_down(self):
+        """Display a downward arrow."""
+        self.console.print()
+        self.console.print(Align.center("↓"))
+        self.console.print()
+
+
+# Simple function to create and use
+def track_workflow():
+    """Create a flow display tracker."""
+    return FlowDisplay()

src/praisonai-agents/praisonaiagents/llm/OLLAMA_HANDLING.md

@@ -1,25 +0,0 @@
-# Ollama Handling in LLM.py
-
-The `ollama_handled` flag is used in `llm.py` (not in `agent.py`) to track whether special Ollama model handling has been applied for tool results. Here's what I found:
-
-## Where it's used in llm.py:
-- **Line 863**: `ollama_handled = False` - initialized at the beginning of the tool execution loop
-- **Line 864**: Special handling logic via `_handle_ollama_model()` method
-- **Line 907**: `ollama_handled = True` - set when Ollama special handling is applied
-- **Line 928 & 959**: Checks `if not ollama_handled` to avoid duplicate processing
-
-## Why it's needed:
-The flag prevents duplicate processing when Ollama models require special handling. Some Ollama models return only the tool call JSON without processing the results, requiring a follow-up prompt to get the final answer. The `ollama_handled` flag ensures this special handling happens only once.
-
-## Does this contradict the requirement?
-**No, it doesn't.** The issue states:
-
-> "ollama/xxxx model is already handled via llm.py via litellm, so no special condition or handling required for ollama inside agent.py"
-
-✅ **Current implementation is correct:**
-
-- `agent.py` has NO ollama-specific conditions (only one documentation mention as an example)
-- All ollama handling is contained within `llm.py` as intended
-- The `ollama_handled` flag is an internal implementation detail within `llm.py`
-
-The codebase already follows the desired pattern where ollama models are handled through the generic LLM interface via litellm without special conditions in agent.py.

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

@@ -201,7 +201,14 @@ class LLM:
             # These are from external libraries and not useful for debugging user code
             logging.getLogger("litellm.utils").setLevel(logging.WARNING)
             logging.getLogger("litellm.main").setLevel(logging.WARNING)
-            logging.getLogger("litellm.llms.custom_httpx.http_handler").setLevel(logging.WARNING)
+            
+            # Allow httpx logging when LOGLEVEL=debug, otherwise suppress it
+            loglevel = os.environ.get('LOGLEVEL', 'INFO').upper()
+            if loglevel == 'DEBUG':
+                logging.getLogger("litellm.llms.custom_httpx.http_handler").setLevel(logging.INFO)
+            else:
+                logging.getLogger("litellm.llms.custom_httpx.http_handler").setLevel(logging.WARNING)
+            
             logging.getLogger("litellm.litellm_logging").setLevel(logging.WARNING)
             logging.getLogger("litellm.transformation").setLevel(logging.WARNING)
             litellm.suppress_debug_messages = True
@@ -1449,6 +1456,8 @@ class LLM:
                         )
                         if should_break:
                             final_response_text = tool_summary_text
+                            # Reset interaction_displayed to ensure final summary is shown
+                            interaction_displayed = False
                             break
                         elif tool_summary_text is None and iteration_count > self.OLLAMA_SUMMARY_ITERATION_THRESHOLD:
                             # Continue iteration after adding final answer prompt
@@ -1478,6 +1487,8 @@ class LLM:
                             tool_summary = self._generate_ollama_tool_summary(accumulated_tool_results, response_text)
                             if tool_summary:
                                 final_response_text = tool_summary
+                                # Reset interaction_displayed to ensure final summary is shown
+                                interaction_displayed = False
                                 break
                         
                         # If we've executed tools in previous iterations, this response contains the final answer
@@ -2560,6 +2571,8 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                     )
                     if should_break:
                         final_response_text = tool_summary_text
+                        # Reset interaction_displayed to ensure final summary is shown
+                        interaction_displayed = False
                         break
                     elif tool_summary_text is None and iteration_count > self.OLLAMA_SUMMARY_ITERATION_THRESHOLD:
                         # Continue iteration after adding final answer prompt
@@ -2587,6 +2600,8 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                         tool_summary = self._generate_ollama_tool_summary(accumulated_tool_results, response_text)
                         if tool_summary:
                             final_response_text = tool_summary
+                            # Reset interaction_displayed to ensure final summary is shown
+                            interaction_displayed = False
                             break
                     
                     # If we've executed tools in previous iterations, this response contains the final answer

src/praisonai-agents/praisonaiagents/main.py

@@ -154,6 +154,10 @@ def display_interaction(message, response, markdown=True, generation_time=None,
 
     message = _clean_display_content(str(message))
     response = _clean_display_content(str(response))
+    
+    # Skip display if response is empty (common with Gemini tool calls)
+    if not response or not response.strip():
+        return
 
     # Execute synchronous callbacks
     execute_sync_callback(

src/praisonai-agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.0.147"
+version = "0.0.152"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 requires-python = ">=3.10"
 authors = [

src/praisonai-agents/test_flow_display.py

@@ -0,0 +1,35 @@
+#!/usr/bin/env python
+"""
+Test the flow display with agents centered and tools on sides.
+"""
+
+from praisonaiagents.flow_display import track_workflow
+
+# Create flow tracker
+flow = track_workflow()
+flow.tracking = True
+
+# Simulate a workflow
+# Agent 1 with multiple tools
+flow._add_agent("Researcher")
+flow._add_tool("Researcher", "web_search")
+flow._add_tool("Researcher", "fetch_data")
+flow._add_tool("Researcher", "parse_html")
+
+# Agent 2 with one tool
+flow._add_agent("Analyst")
+flow._add_tool("Analyst", "analyze_data")
+
+# Agent 3 with multiple tools
+flow._add_agent("Reporter")
+flow._add_tool("Reporter", "format_report")
+flow._add_tool("Reporter", "send_email")
+flow._add_tool("Reporter", "save_file")
+flow._add_tool("Reporter", "upload_cloud")
+
+# Agent 4 with no tools
+flow._add_agent("Reviewer")
+
+# Display the flow
+flow.tracking = False
+flow.display()

src/praisonai-agents/uv.lock

@@ -2422,7 +2422,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.0.147"
+version = "0.0.152"
 source = { editable = "." }
 dependencies = [
     { name = "aiohttp" },

src/praisonai/praisonai.rb

@@ -3,8 +3,8 @@ class Praisonai < Formula
   
     desc "AI tools for various AI applications"
     homepage "https://github.com/MervinPraison/PraisonAI"
-    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.74.tar.gz"
-    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.74.tar.gz | shasum -a 256`.split.first
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.78.tar.gz"
+    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.78.tar.gz | shasum -a 256`.split.first
     license "MIT"
   
     depends_on "[email protected]"

src/praisonai/praisonai/deploy.py

@@ -57,7 +57,7 @@ class CloudDeployer:
             file.write("FROM python:3.11-slim\n")
             file.write("WORKDIR /app\n")
             file.write("COPY . .\n")
-            file.write("RUN pip install flask praisonai==2.2.74 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.2.78 gunicorn markdown\n")
             file.write("EXPOSE 8080\n")
             file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
             

src/praisonai/pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "PraisonAI"
-version = "2.2.74"
+version = "2.2.78"
 description = "PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human-agent collaboration."
 readme = "README.md"
 license = ""
@@ -12,7 +12,7 @@ dependencies = [
     "rich>=13.7",
     "markdown>=3.5",
     "pyparsing>=3.0.0",
-    "praisonaiagents>=0.0.147",
+    "praisonaiagents>=0.0.152",
     "python-dotenv>=0.19.0",
     "instructor>=1.3.3",
     "PyYAML>=6.0",
@@ -102,7 +102,7 @@ autogen-v4 = [
 
 [tool.poetry]
 name = "PraisonAI"
-version = "2.2.74"
+version = "2.2.78"
 description = "PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human-agent collaboration."
 authors = ["Mervin Praison"]
 license = ""
@@ -120,7 +120,7 @@ python = ">=3.10,<3.13"
 rich = ">=13.7"
 markdown = ">=3.5"
 pyparsing = ">=3.0.0"
-praisonaiagents = ">=0.0.147"
+praisonaiagents = ">=0.0.152"
 python-dotenv = ">=0.19.0"
 instructor = ">=1.3.3"
 PyYAML = ">=6.0"

src/praisonai/uv.lock

@@ -4031,7 +4031,7 @@ wheels = [
 
 [[package]]
 name = "praisonai"
-version = "2.2.74"
+version = "2.2.78"
 source = { editable = "." }
 dependencies = [
     { name = "instructor" },
@@ -4185,7 +4185,7 @@ requires-dist = [
     { name = "praisonai-tools", marker = "extra == 'autogen'", specifier = ">=0.0.22" },
     { name = "praisonai-tools", marker = "extra == 'autogen-v4'", specifier = ">=0.0.22" },
     { name = "praisonai-tools", marker = "extra == 'crewai'", specifier = ">=0.0.22" },
-    { name = "praisonaiagents", specifier = ">=0.0.147" },
+    { name = "praisonaiagents", specifier = ">=0.0.152" },
     { 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" },
@@ -4240,7 +4240,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.0.147"
+version = "0.0.152"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohttp" },
@@ -4249,9 +4249,9 @@ dependencies = [
     { name = "pydantic" },
     { name = "rich" },
 ]
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+sdist = { url = "https://files.pythonhosted.org/packages/91/cf/22a86b5d45f926914b14b8fe23d377c3a1c5809d653f51ced87de0faf8ad/praisonaiagents-0.0.152.tar.gz", hash = "sha256:52f8bb281187c047ffbdb6d8f8282c8e26b53bd89fd47ea7b507470c9e152827", size = 272639, upload-time = "2025-07-25T17:08:34.332Z" }
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