chore: bump praisonai to v2.2.70 and praisonaiagents to v0.0.142 across all files

fix: make TelemetryCollector.stop() consistent with shutdown behavior

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>=2.2.69" \
+    "praisonai>=2.2.70" \
     "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>=2.2.69" \
+    "praisonai>=2.2.70" \
     "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>=2.2.69" \
+    "praisonai>=2.2.70" \
     "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>=2.2.69" \
+    "praisonai>=2.2.70" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

docker/README.md

@@ -121,7 +121,7 @@ healthcheck:
 ## 📦 Package Versions
 
 All Docker images use consistent, up-to-date versions:
-- PraisonAI: `>=2.2.69`
+- PraisonAI: `>=2.2.70`
 - 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==2.2.69" "praisonaiagents==0.0.92"
+RUN pip install "praisonai==2.2.70" "praisonaiagents==0.0.92"
 ```
 
 ## 🌐 Production Deployment

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

@@ -354,6 +354,8 @@ class Agent:
         self.instructions = instructions
         # Check for model name in environment variable if not provided
         self._using_custom_llm = False
+        # Flag to track if final result has been displayed to prevent duplicates
+        self._final_display_shown = False
         
         # Store OpenAI client parameters for lazy initialization
         self._openai_api_key = api_key
@@ -1173,18 +1175,23 @@ Your Goal: {self.goal}"""
                 task_description=task_description, 
                 task_id=task_id
             )
-        # Only display interaction if not using custom LLM (to avoid double output) and verbose is True
-        if self.verbose and not self._using_custom_llm:
+        # Always display final interaction when verbose is True to ensure consistent formatting
+        # This ensures both OpenAI and custom LLM providers (like Gemini) show formatted output
+        if self.verbose and not self._final_display_shown:
             display_interaction(prompt, response, markdown=self.markdown, 
                               generation_time=generation_time, console=self.console,
                               agent_name=self.name,
                               agent_role=self.role,
                               agent_tools=[t.__name__ for t in self.tools] if self.tools else None,
-                              task_name=task_name,
-                              task_description=task_description,
-                              task_id=task_id)
+                              task_name=None,  # Not available in this context
+                              task_description=None,  # Not available in this context
+                              task_id=None)  # Not available in this context
+            self._final_display_shown = True
 
     def chat(self, prompt, temperature=0.2, tools=None, output_json=None, output_pydantic=None, reasoning_steps=False, stream=True, task_name=None, task_description=None, task_id=None):
+        # Reset the final display flag for each new conversation
+        self._final_display_shown = False
+        
         # Log all parameter values when in debug mode
         if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
             param_info = {
@@ -1533,6 +1540,9 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
 
     async def achat(self, prompt: str, temperature=0.2, tools=None, output_json=None, output_pydantic=None, reasoning_steps=False, task_name=None, task_description=None, task_id=None):
         """Async version of chat method with self-reflection support.""" 
+        # Reset the final display flag for each new conversation
+        self._final_display_shown = False
+        
         # Log all parameter values when in debug mode
         if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
             param_info = {

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

@@ -329,16 +329,29 @@ class LLM:
         # For Ollama, always generate summary when we have tool results
         # This prevents infinite loops caused by empty/minimal responses
             
-        # Build tool summary efficiently
-        summary_lines = ["Based on the tool execution results:"]
-        for i, result in enumerate(tool_results):
+        # Build tool summary more naturally to match OpenAI-style responses
+        if len(tool_results) == 1:
+            # Single tool result - create natural response
+            result = tool_results[0]
             if isinstance(result, dict) and 'result' in result:
-                function_name = result.get('function_name', 'Tool')
-                summary_lines.append(f"- {function_name}: {result['result']}")
+                return str(result['result'])
             else:
-                summary_lines.append(f"- Tool {i+1}: {result}")
-        
-        return "\n".join(summary_lines)
+                return str(result)
+        else:
+            # Multiple tool results - create coherent summary
+            summary_lines = []
+            for i, result in enumerate(tool_results):
+                if isinstance(result, dict) and 'result' in result:
+                    function_name = result.get('function_name', 'Tool')
+                    summary_lines.append(f"{function_name}: {result['result']}")
+                else:
+                    summary_lines.append(f"Tool {i+1}: {result}")
+            
+            # Create more natural summary text
+            if len(summary_lines) == 2:
+                return f"{summary_lines[0]}. {summary_lines[1]}."
+            else:
+                return "Based on the tool execution: " + ". ".join(summary_lines) + "."
 
     def _format_ollama_tool_result_message(self, function_name: str, tool_result: Any) -> Dict[str, str]:
         """
@@ -1173,6 +1186,7 @@ class LLM:
                             final_response_text = response_text.strip()
                             break
                         
+                        
                         # Special handling for Ollama to prevent infinite loops
                         # Only generate summary after multiple iterations to allow sequential execution
                         should_break, tool_summary_text, iteration_count = self._handle_ollama_sequential_logic(
@@ -1198,6 +1212,17 @@ class LLM:
                         continue
                     else:
                         # No tool calls, we're done with this iteration
+                        
+                        # Special early stopping logic for Ollama when tool results are available
+                        # Ollama often provides empty responses after successful tool execution
+                        if (self._is_ollama_provider() and accumulated_tool_results and iteration_count >= 1 and 
+                            (not response_text or response_text.strip() == "")):
+                            # Generate coherent response from tool results
+                            tool_summary = self._generate_ollama_tool_summary(accumulated_tool_results, response_text)
+                            if tool_summary:
+                                final_response_text = tool_summary
+                                break
+                        
                         # If we've executed tools in previous iterations, this response contains the final answer
                         if iteration_count > 0 and not final_response_text:
                             final_response_text = response_text.strip() if response_text else ""
@@ -1956,6 +1981,7 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                         final_response_text = response_text.strip()
                         break
                     
+                    
                     # Special handling for Ollama to prevent infinite loops
                     # Only generate summary after multiple iterations to allow sequential execution
                     should_break, tool_summary_text, iteration_count = self._handle_ollama_sequential_logic(
@@ -1981,6 +2007,17 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                     continue
                 else:
                     # No tool calls, we're done with this iteration
+                    
+                    # Special early stopping logic for Ollama when tool results are available
+                    # Ollama often provides empty responses after successful tool execution
+                    if (self._is_ollama_provider() and accumulated_tool_results and iteration_count >= 1 and 
+                        (not response_text or response_text.strip() == "")):
+                        # Generate coherent response from tool results
+                        tool_summary = self._generate_ollama_tool_summary(accumulated_tool_results, response_text)
+                        if tool_summary:
+                            final_response_text = tool_summary
+                            break
+                    
                     # If we've executed tools in previous iterations, this response contains the final answer
                     if iteration_count > 0 and not final_response_text:
                         final_response_text = response_text.strip()

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

@@ -65,8 +65,8 @@ def _ensure_atexit():
     ])
     
     if not telemetry_disabled:
-        # Register atexit handler to flush telemetry on exit
-        atexit.register(lambda: get_telemetry().flush())
+        # Register atexit handler to properly shutdown telemetry on exit
+        atexit.register(lambda: get_telemetry().shutdown())
         _atexit_registered = True
 
 def _initialize_telemetry():

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

@@ -354,8 +354,8 @@ class TelemetryCollector:
         pass
         
     def stop(self):
-        """Stop telemetry collection and flush data."""
-        self.telemetry.flush()
+        """Stop telemetry collection and properly shutdown."""
+        self.telemetry.shutdown()
     
     def trace_agent_execution(self, agent_name: str, **attributes):
         """Compatibility method for agent execution tracking."""

src/praisonai-agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.0.141"
+version = "0.0.142"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 requires-python = ">=3.10"
 authors = [

src/praisonai-agents/uv.lock

@@ -2375,7 +2375,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.0.141"
+version = "0.0.142"
 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/v2.2.69.tar.gz"
-    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.69.tar.gz | shasum -a 256`.split.first
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.70.tar.gz"
+    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.70.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==2.2.69 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.2.70 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 = "2.2.69"
+version = "2.2.70"
 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>=0.0.141",
+    "praisonaiagents>=0.0.142",
     "python-dotenv>=0.19.0",
     "instructor>=1.3.3",
     "PyYAML>=6.0",
@@ -102,7 +102,7 @@ autogen-v4 = [
 
 [tool.poetry]
 name = "PraisonAI"
-version = "2.2.69"
+version = "2.2.70"
 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 = ">=0.0.141"
+praisonaiagents = ">=0.0.142"
 python-dotenv = ">=0.19.0"
 instructor = ">=1.3.3"
 PyYAML = ">=6.0"

src/praisonai/uv.lock

@@ -4048,7 +4048,7 @@ wheels = [
 
 [[package]]
 name = "praisonai"
-version = "2.2.68"
+version = "2.2.70"
 source = { editable = "." }
 dependencies = [
     { name = "instructor" },
@@ -4202,7 +4202,7 @@ 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 = ">=0.0.140" },
+    { name = "praisonaiagents", specifier = ">=0.0.142" },
     { 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" },
@@ -4257,7 +4257,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.0.140"
+version = "0.0.142"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohttp" },
@@ -4266,9 +4266,9 @@ dependencies = [
     { name = "pydantic" },
     { name = "rich" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/65/33/1390fa209dada4871cbd6295a29e539765b52955054c458483f39d016eb2/praisonaiagents-0.0.140.tar.gz", hash = "sha256:b45e492cdc11593fddb5df8f3bbc7f558e9ec10dc025858f2c9d5e4178832b56", size = 204508, upload-time = "2025-07-16T23:18:29.748Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/51/2b/d341e8f774adc84c273e57f7bb6c4ec8af5f974b4d5ec0a1811ab96ef5c5/praisonaiagents-0.0.142.tar.gz", hash = "sha256:9f6dfd26d5c334eda3f0ba86c821616cb673ef230c15d57e8230e4f9bb7108a8", size = 205694, upload-time = "2025-07-17T00:31:09.554Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/7a/78/92f61b70bfda2ffabeababf956e0742d952ae4728d39aaaa3f071f36714f/praisonaiagents-0.0.140-py3-none-any.whl", hash = "sha256:793a06363c1f7dfb83690a85879f307c3fc5f32e77ab1bd5f3ee2295c3fbc603", size = 223605, upload-time = "2025-07-16T23:18:28.415Z" },
+    { url = "https://files.pythonhosted.org/packages/71/c9/79f2124287cc99c0d50f23b20ff2958e08eb8b0b496198607f343dfc2245/praisonaiagents-0.0.142-py3-none-any.whl", hash = "sha256:0887cf92acda4be650dd8e7a110a16e4e7624eae12a7c10e8ca8d32f1ac282cf", size = 224783, upload-time = "2025-07-17T00:31:07.946Z" },
 ]
 
 [[package]]