chore: update PraisonAI and related package versions to 2.2.46 and 0.0.119

- Updated PraisonAI version in Dockerfiles and README to 2.2.46
- Updated version in pyproject.toml and uv.lock for PraisonAI and PraisonAI agents to 0.0.119
- Adjusted deployment script to reflect the new PraisonAI version

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>=2.2.45" \
+    "praisonai>=2.2.46" \
     "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>=2.2.45" \
+    "praisonai>=2.2.46" \
     "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>=2.2.45" \
+    "praisonai>=2.2.46" \
     "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>=2.2.45" \
+    "praisonai>=2.2.46" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

docker/README.md

@@ -121,7 +121,7 @@ healthcheck:
 ## 📦 Package Versions
 
 All Docker images use consistent, up-to-date versions:
-- PraisonAI: `>=2.2.45`
+- PraisonAI: `>=2.2.46`
 - 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==2.2.45" "praisonaiagents==0.0.92"
+RUN pip install "praisonai==2.2.46" "praisonaiagents==0.0.92"
 ```
 
 ## 🌐 Production Deployment

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

@@ -229,6 +229,39 @@ class LLM:
         
         return any(endpoint in base_url or endpoint in api_base for endpoint in ollama_endpoints)
 
+    def _process_stream_delta(self, delta, response_text: str, tool_calls: List[Dict], formatted_tools: Optional[List] = None) -> tuple:
+        """
+        Process a streaming delta chunk to extract content and tool calls.
+        
+        Args:
+            delta: The delta object from a streaming chunk
+            response_text: The accumulated response text so far
+            tool_calls: The accumulated tool calls list so far
+            formatted_tools: Optional list of formatted tools for tool call support check
+            
+        Returns:
+            tuple: (updated_response_text, updated_tool_calls)
+        """
+        # Process content
+        if delta.content:
+            response_text += delta.content
+        
+        # Capture tool calls from streaming chunks if provider supports it
+        if formatted_tools and self._supports_streaming_tools() and hasattr(delta, 'tool_calls') and delta.tool_calls:
+            for tc in delta.tool_calls:
+                if tc.index >= len(tool_calls):
+                    tool_calls.append({
+                        "id": tc.id,
+                        "type": "function",
+                        "function": {"name": "", "arguments": ""}
+                    })
+                if tc.function.name:
+                    tool_calls[tc.index]["function"]["name"] = tc.function.name
+                if tc.function.arguments:
+                    tool_calls[tc.index]["function"]["arguments"] += tc.function.arguments
+        
+        return response_text, tool_calls
+
     def _parse_tool_call_arguments(self, tool_call: Dict, is_ollama: bool = False) -> tuple:
         """
         Safely parse tool call arguments with proper error handling
@@ -651,23 +684,11 @@ class LLM:
                                     ):
                                         if chunk and chunk.choices and chunk.choices[0].delta:
                                             delta = chunk.choices[0].delta
+                                            response_text, tool_calls = self._process_stream_delta(
+                                                delta, response_text, tool_calls, formatted_tools
+                                            )
                                             if delta.content:
-                                                response_text += delta.content
                                                 live.update(display_generating(response_text, current_time))
-                                            
-                                            # Capture tool calls from streaming chunks if provider supports it
-                                            if formatted_tools and self._supports_streaming_tools() and hasattr(delta, 'tool_calls') and delta.tool_calls:
-                                                for tc in delta.tool_calls:
-                                                    if tc.index >= len(tool_calls):
-                                                        tool_calls.append({
-                                                            "id": tc.id,
-                                                            "type": "function",
-                                                            "function": {"name": "", "arguments": ""}
-                                                        })
-                                                    if tc.function.name:
-                                                        tool_calls[tc.index]["function"]["name"] = tc.function.name
-                                                    if tc.function.arguments:
-                                                        tool_calls[tc.index]["function"]["arguments"] += tc.function.arguments
                             else:
                                 # Non-verbose streaming
                                 for chunk in litellm.completion(
@@ -681,22 +702,9 @@ class LLM:
                                 ):
                                     if chunk and chunk.choices and chunk.choices[0].delta:
                                         delta = chunk.choices[0].delta
-                                        if delta.content:
-                                            response_text += delta.content
-                                        
-                                        # Capture tool calls from streaming chunks if provider supports it
-                                        if formatted_tools and self._supports_streaming_tools() and hasattr(delta, 'tool_calls') and delta.tool_calls:
-                                            for tc in delta.tool_calls:
-                                                if tc.index >= len(tool_calls):
-                                                    tool_calls.append({
-                                                        "id": tc.id,
-                                                        "type": "function",
-                                                        "function": {"name": "", "arguments": ""}
-                                                    })
-                                                if tc.function.name:
-                                                    tool_calls[tc.index]["function"]["name"] = tc.function.name
-                                                if tc.function.arguments:
-                                                    tool_calls[tc.index]["function"]["arguments"] += tc.function.arguments
+                                        response_text, tool_calls = self._process_stream_delta(
+                                            delta, response_text, tool_calls, formatted_tools
+                                        )
                             
                             response_text = response_text.strip()
                             
@@ -1297,24 +1305,12 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                         ):
                             if chunk and chunk.choices and chunk.choices[0].delta:
                                 delta = chunk.choices[0].delta
+                                response_text, tool_calls = self._process_stream_delta(
+                                    delta, response_text, tool_calls, formatted_tools
+                                )
                                 if delta.content:
-                                    response_text += delta.content
                                     print("\033[K", end="\r")  
                                     print(f"Generating... {time.time() - start_time:.1f}s", end="\r")
-                                
-                                # Capture tool calls from streaming chunks if provider supports it
-                                if formatted_tools and self._supports_streaming_tools() and hasattr(delta, 'tool_calls') and delta.tool_calls:
-                                    for tc in delta.tool_calls:
-                                        if tc.index >= len(tool_calls):
-                                            tool_calls.append({
-                                                "id": tc.id,
-                                                "type": "function",
-                                                "function": {"name": "", "arguments": ""}
-                                            })
-                                        if tc.function.name:
-                                            tool_calls[tc.index]["function"]["name"] = tc.function.name
-                                        if tc.function.arguments:
-                                            tool_calls[tc.index]["function"]["arguments"] += tc.function.arguments
                     else:
                         # Non-verbose streaming
                         async for chunk in await litellm.acompletion(
@@ -1328,22 +1324,9 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                         ):
                             if chunk and chunk.choices and chunk.choices[0].delta:
                                 delta = chunk.choices[0].delta
-                                if delta.content:
-                                    response_text += delta.content
-                                
-                                # Capture tool calls from streaming chunks if provider supports it
-                                if formatted_tools and self._supports_streaming_tools() and hasattr(delta, 'tool_calls') and delta.tool_calls:
-                                    for tc in delta.tool_calls:
-                                        if tc.index >= len(tool_calls):
-                                            tool_calls.append({
-                                                "id": tc.id,
-                                                "type": "function",
-                                                "function": {"name": "", "arguments": ""}
-                                            })
-                                        if tc.function.name:
-                                            tool_calls[tc.index]["function"]["name"] = tc.function.name
-                                        if tc.function.arguments:
-                                            tool_calls[tc.index]["function"]["arguments"] += tc.function.arguments
+                                response_text, tool_calls = self._process_stream_delta(
+                                    delta, response_text, tool_calls, formatted_tools
+                                )
                     
                     response_text = response_text.strip()
                     

src/praisonai-agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.0.118"
+version = "0.0.119"
 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 = "0.0.118"
+version = "0.0.119"
 source = { editable = "." }
 dependencies = [
     { name = "mcp" },

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/v2.2.45.tar.gz"
-    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.45.tar.gz | shasum -a 256`.split.first
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.46.tar.gz"
+    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.46.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==2.2.45 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.2.46 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 = "2.2.45"
+version = "2.2.46"
 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>=0.0.118",
+    "praisonaiagents>=0.0.119",
     "python-dotenv>=0.19.0",
     "instructor>=1.3.3",
     "PyYAML>=6.0",
@@ -95,7 +95,7 @@ autogen = ["pyautogen>=0.2.19", "praisonai-tools>=0.0.15", "crewai"]
 
 [tool.poetry]
 name = "PraisonAI"
-version = "2.2.45"
+version = "2.2.46"
 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 = ""
@@ -113,7 +113,7 @@ python = ">=3.10,<3.13"
 rich = ">=13.7"
 markdown = ">=3.5"
 pyparsing = ">=3.0.0"
-praisonaiagents = ">=0.0.118"
+praisonaiagents = ">=0.0.119"
 python-dotenv = ">=0.19.0"
 instructor = ">=1.3.3"
 PyYAML = ">=6.0"

src/praisonai/uv.lock

@@ -3931,7 +3931,7 @@ wheels = [
 
 [[package]]
 name = "praisonai"
-version = "2.2.45"
+version = "2.2.46"
 source = { editable = "." }
 dependencies = [
     { name = "instructor" },
@@ -4073,7 +4073,7 @@ requires-dist = [
     { name = "plotly", marker = "extra == 'realtime'", specifier = ">=5.24.0" },
     { name = "praisonai-tools", marker = "extra == 'autogen'", specifier = ">=0.0.15" },
     { name = "praisonai-tools", marker = "extra == 'crewai'", specifier = ">=0.0.15" },
-    { name = "praisonaiagents", specifier = ">=0.0.118" },
+    { name = "praisonaiagents", specifier = ">=0.0.119" },
     { name = "pyautogen", marker = "extra == 'autogen'", specifier = ">=0.2.19" },
     { name = "pydantic", marker = "extra == 'chat'", specifier = "<=2.10.1" },
     { name = "pydantic", marker = "extra == 'code'", specifier = "<=2.10.1" },
@@ -4130,7 +4130,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.0.118"
+version = "0.0.119"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "mcp" },
@@ -4139,9 +4139,9 @@ dependencies = [
     { name = "pydantic" },
     { name = "rich" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/ba/53/0363b4e986b3916a246af2798cecaba08d47d46acc5faf313789d8de07cf/praisonaiagents-0.0.118.tar.gz", hash = "sha256:0e7cb593fc1733e33c6f17d55e4670ca973cfe4233c86747c7d97be41347a3da", size = 172108 }
+sdist = { url = "https://files.pythonhosted.org/packages/5f/ea/56832de856714fa3bb29da1577dcdfa7aa488b15035341329888a4e28c47/praisonaiagents-0.0.119.tar.gz", hash = "sha256:2e16aab996669ff2b0051cf9a688d6482e5560457422506d991336e7fcc357da", size = 171774 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/9a/a1/605677f442a983259be11079b492fff2a1cca78686c8a6385f8adcdea285/praisonaiagents-0.0.118-py3-none-any.whl", hash = "sha256:5e3f27ffc9af64c2db1726dc1174f348b5bb0dea07b110651fcde6f480ca36a1", size = 192626 },
+    { url = "https://files.pythonhosted.org/packages/c1/bf/ea47cf5843d128ab24973a7a5260e72503be00707435d1fca79e6acc7a19/praisonaiagents-0.0.119-py3-none-any.whl", hash = "sha256:e4ddb1da0c2fdf38c25e2f918fc59f686ac5d142884c34506fd53550813edd51", size = 192290 },
 ]
 
 [[package]]