Merge pull request #399 from MervinPraison/develop

Bump version to 2.0.80 and update praisonaiagents dependency

docker/Dockerfile

@@ -1,6 +1,6 @@
 FROM python:3.11-slim
 WORKDIR /app
 COPY . .
-RUN pip install flask praisonai==2.0.77 gunicorn markdown
+RUN pip install flask praisonai==2.0.80 gunicorn markdown
 EXPOSE 8080
 CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]

docs/api/praisonai/deploy.html

@@ -110,7 +110,7 @@ Loads environment variables from .env file or system and sets them.</p>
             file.write(&#34;FROM python:3.11-slim\n&#34;)
             file.write(&#34;WORKDIR /app\n&#34;)
             file.write(&#34;COPY . .\n&#34;)
-            file.write(&#34;RUN pip install flask praisonai==2.0.77 gunicorn markdown\n&#34;)
+            file.write(&#34;RUN pip install flask praisonai==2.0.80 gunicorn markdown\n&#34;)
             file.write(&#34;EXPOSE 8080\n&#34;)
             file.write(&#39;CMD [&#34;gunicorn&#34;, &#34;-b&#34;, &#34;0.0.0.0:8080&#34;, &#34;api:app&#34;]\n&#39;)
             

praisonai.rb

@@ -3,7 +3,7 @@ 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/2.0.77.tar.gz"
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/2.0.80.tar.gz"
     sha256 "1828fb9227d10f991522c3f24f061943a254b667196b40b1a3e4a54a8d30ce32"  # Replace with actual SHA256 checksum
     license "MIT"
   

praisonai/deploy.py

@@ -56,7 +56,7 @@ 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==2.0.77 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.0.80 gunicorn markdown\n")
             file.write("EXPOSE 8080\n")
             file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
             

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "PraisonAI"
-version = "2.0.77"
+version = "2.0.80"
 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>=0.0.62",
+    "praisonaiagents>=0.0.65",
     "python-dotenv>=0.19.0",
     "instructor>=1.3.3",
     "PyYAML>=6.0",
@@ -84,7 +84,7 @@ autogen = ["pyautogen>=0.2.19", "praisonai-tools>=0.0.7", "crewai"]
 
 [tool.poetry]
 name = "PraisonAI"
-version = "2.0.77"
+version = "2.0.80"
 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 = ""
@@ -102,7 +102,7 @@ python = ">=3.10,<3.13"
 rich = ">=13.7"
 markdown = ">=3.5"
 pyparsing = ">=3.0.0"
-praisonaiagents = ">=0.0.62"
+praisonaiagents = ">=0.0.65"
 python-dotenv = ">=0.19.0"
 instructor = ">=1.3.3"
 PyYAML = ">=6.0"

src/praisonai-agents/llm-anthropic.py

@@ -0,0 +1,37 @@
+from praisonaiagents import Agent
+
+agent = Agent(
+    instructions="You are a Wikipedia Agent", 
+    llm="anthropic/claude-3-7-sonnet-20250219"
+)
+result = agent.start("Why Sky is Blue?")
+print(result)
+
+from praisonaiagents import Agent
+from praisonaiagents.tools import internet_search
+
+agent = Agent(
+    instructions="You are a Wikipedia Agent", 
+    tools=[internet_search],
+    llm="anthropic/claude-3-7-sonnet-20250219"
+)
+agent.start("What is Praison AI?")
+
+from praisonaiagents import Agent, PraisonAIAgents
+from praisonaiagents.tools import internet_search
+
+
+research_agent = Agent(
+    instructions="Search Information about Claude Sonnet 3.7", 
+    tools=[internet_search],
+    llm="anthropic/claude-3-7-sonnet-20250219"
+)
+
+editor_agent = Agent(
+    instructions="Write a Blog Post with the provided information about Claude Sonnet 3.7", 
+    llm="anthropic/claude-3-7-sonnet-20250219"
+)
+
+agents = PraisonAIAgents(agents=[research_agent, editor_agent])
+result = agents.start()
+print(result)

src/praisonai-agents/llm-gemini.py

@@ -1,10 +1,10 @@
 from praisonaiagents import Agent
+from praisonaiagents.tools import internet_search
 
 agent = Agent(
     instructions="You are a helpful assistant",
-    llm="gemini/gemini-1.5-flash-8b",
-    self_reflect=True,
-    verbose=True
+    tools=[internet_search],
+    llm="gemini/gemini-1.5-flash-8b"
 )
 
-agent.start("Why sky is Blue?")
+agent.start("What is Praison AI?")

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

@@ -477,8 +477,14 @@ Context:
                 else:
                     self.run_task(task_id)
 
-    async def astart(self, content=None, **kwargs):
-        """Async version of start method"""
+    async def astart(self, content=None, return_dict=False, **kwargs):
+        """Async version of start method
+        
+        Args:
+            content: Optional content to add to all tasks' context
+            return_dict: If True, returns the full results dictionary instead of only the final response
+            **kwargs: Additional arguments
+        """
         if content:
             # Add content to context of all tasks
             for task in self.tasks.values():
@@ -488,10 +494,25 @@ Context:
                     task.context.append(content)
 
         await self.arun_all_tasks()
-        return {
+        
+        # Get results
+        results = {
             "task_status": self.get_all_tasks_status(),
             "task_results": {task_id: self.get_task_result(task_id) for task_id in self.tasks}
         }
+        
+        # By default, return only the final agent's response
+        if not return_dict:
+            # Get the last task (assuming sequential processing)
+            task_ids = list(self.tasks.keys())
+            if task_ids:
+                last_task_id = task_ids[-1]
+                last_result = self.get_task_result(last_task_id)
+                if last_result:
+                    return last_result.raw
+                    
+        # Return full results dict if return_dict is True or if no final result was found
+        return results
 
     def save_output_to_file(self, task, task_output):
         if task.output_file:
@@ -801,8 +822,14 @@ Context:
             return str(agent[0])
         return None
 
-    def start(self, content=None, **kwargs):
-        """Start agent execution with optional content and config"""
+    def start(self, content=None, return_dict=False, **kwargs):
+        """Start agent execution with optional content and config
+        
+        Args:
+            content: Optional content to add to all tasks' context
+            return_dict: If True, returns the full results dictionary instead of only the final response
+            **kwargs: Additional arguments
+        """
         if content:
             # Add content to context of all tasks
             for task in self.tasks.values():
@@ -815,10 +842,25 @@ Context:
                 
         # Run tasks as before
         self.run_all_tasks()
-        return {
+        
+        # Get results
+        results = {
             "task_status": self.get_all_tasks_status(),
             "task_results": {task_id: self.get_task_result(task_id) for task_id in self.tasks}
         }
+        
+        # By default, return only the final agent's response
+        if not return_dict:
+            # Get the last task (assuming sequential processing)
+            task_ids = list(self.tasks.keys())
+            if task_ids:
+                last_task_id = task_ids[-1]
+                last_result = self.get_task_result(last_task_id)
+                if last_result:
+                    return last_result.raw
+                    
+        # Return full results dict if return_dict is True or if no final result was found
+        return results
 
     def set_state(self, key: str, value: Any) -> None:
         """Set a state value"""

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

@@ -171,6 +171,8 @@ class LLM:
         
         # Enable error dropping for cleaner output
         litellm.drop_params = True
+        # Enable parameter modification for providers like Anthropic
+        litellm.modify_params = True
         self._setup_event_tracking(events)
         
         # Log all initialization parameters when in debug mode
@@ -438,20 +440,29 @@ class LLM:
                             function_name = tool_call["function"]["name"]
                             arguments = json.loads(tool_call["function"]["arguments"])
 
-                            if verbose:
-                                display_tool_call(f"Agent {agent_name} is calling function '{function_name}' with arguments: {arguments}", console=console)
-
+                            logging.debug(f"[TOOL_EXEC_DEBUG] About to execute tool {function_name} with args: {arguments}")
                             tool_result = execute_tool_fn(function_name, arguments)
+                            logging.debug(f"[TOOL_EXEC_DEBUG] Tool execution result: {tool_result}")
 
-                            if tool_result:
-                                if verbose:
-                                    display_tool_call(f"Function '{function_name}' returned: {tool_result}", console=console)
+                            if verbose:
+                                display_message = f"Agent {agent_name} called function '{function_name}' with arguments: {arguments}\n"
+                                if tool_result:
+                                    display_message += f"Function returned: {tool_result}"
+                                    logging.debug(f"[TOOL_EXEC_DEBUG] Display message with result: {display_message}")
+                                else:
+                                    display_message += "Function returned no output"
+                                    logging.debug("[TOOL_EXEC_DEBUG] Tool returned no output")
+                                
+                                logging.debug(f"[TOOL_EXEC_DEBUG] About to display tool call with message: {display_message}")
+                                display_tool_call(display_message, console=console)
+                                
                                 messages.append({
                                     "role": "tool",
                                     "tool_call_id": tool_call["id"],
                                     "content": json.dumps(tool_result)
                                 })
                             else:
+                                logging.debug("[TOOL_EXEC_DEBUG] Verbose mode off, not displaying tool call")
                                 messages.append({
                                     "role": "tool",
                                     "tool_call_id": tool_call["id"],
@@ -923,14 +934,15 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                         function_name = tool_call.function.name
                         arguments = json.loads(tool_call.function.arguments)
 
-                        if verbose:
-                            display_tool_call(f"Agent {agent_name} is calling function '{function_name}' with arguments: {arguments}", console=console)
-
                         tool_result = await execute_tool_fn(function_name, arguments)
 
-                        if tool_result:
-                            if verbose:
-                                display_tool_call(f"Function '{function_name}' returned: {tool_result}", console=console)
+                        if verbose:
+                            display_message = f"Agent {agent_name} called function '{function_name}' with arguments: {arguments}\n"
+                            if tool_result:
+                                display_message += f"Function returned: {tool_result}"
+                            else:
+                                display_message += "Function returned no output"
+                            display_tool_call(display_message, console=console)
                             messages.append({
                                 "role": "tool",
                                 "tool_call_id": tool_call.id,

src/praisonai-agents/praisonaiagents/main.py

@@ -86,6 +86,7 @@ async def execute_callback(display_type: str, **kwargs):
 def _clean_display_content(content: str, max_length: int = 20000) -> str:
     """Helper function to clean and truncate content for display."""
     if not content or not str(content).strip():
+        logging.debug(f"Empty content received in _clean_display_content: {repr(content)}")
         return ""
         
     content = str(content)
@@ -174,11 +175,14 @@ def display_instruction(message: str, console=None, agent_name: str = None, agen
         console.print(Panel.fit(Text(message, style="bold blue"), title="Instruction", border_style="cyan"))
 
 def display_tool_call(message: str, console=None):
+    logging.debug(f"display_tool_call called with message: {repr(message)}")
     if not message or not message.strip():
+        logging.debug("Empty message in display_tool_call, returning early")
         return
     if console is None:
         console = Console()
     message = _clean_display_content(str(message))
+    logging.debug(f"Cleaned message in display_tool_call: {repr(message)}")
     
     # Execute callback if registered
     if 'tool_call' in sync_display_callbacks:
@@ -202,7 +206,8 @@ def display_error(message: str, console=None):
 
 def display_generating(content: str = "", start_time: Optional[float] = None):
     if not content or not str(content).strip():
-        return Panel("", title="", border_style="green")
+        logging.debug("Empty content in display_generating, returning early")
+        return None
     
     elapsed_str = ""
     if start_time is not None:
@@ -293,11 +298,14 @@ async def adisplay_instruction(message: str, console=None, agent_name: str = Non
 
 async def adisplay_tool_call(message: str, console=None):
     """Async version of display_tool_call."""
+    logging.debug(f"adisplay_tool_call called with message: {repr(message)}")
     if not message or not message.strip():
+        logging.debug("Empty message in adisplay_tool_call, returning early")
         return
     if console is None:
         console = Console()
     message = _clean_display_content(str(message))
+    logging.debug(f"Cleaned message in adisplay_tool_call: {repr(message)}")
     
     if 'tool_call' in async_display_callbacks:
         await async_display_callbacks['tool_call'](message=message)
@@ -321,7 +329,8 @@ async def adisplay_error(message: str, console=None):
 async def adisplay_generating(content: str = "", start_time: Optional[float] = None):
     """Async version of display_generating."""
     if not content or not str(content).strip():
-        return Panel("", title="", border_style="green")
+        logging.debug("Empty content in adisplay_generating, returning early")
+        return None
     
     elapsed_str = ""
     if start_time is not None:

src/praisonai-agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.0.62"
+version = "0.0.65"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 authors = [
     { name="Mervin Praison" }

src/praisonai-agents/uv.lock

@@ -1868,7 +1868,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.0.62"
+version = "0.0.65"
 source = { editable = "." }
 dependencies = [
     { name = "openai" },

uv.lock

@@ -3060,7 +3060,7 @@ wheels = [
 
 [[package]]
 name = "praisonai"
-version = "2.0.77"
+version = "2.0.80"
 source = { editable = "." }
 dependencies = [
     { name = "instructor" },
@@ -3197,7 +3197,7 @@ requires-dist = [
     { name = "plotly", marker = "extra == 'realtime'", specifier = ">=5.24.0" },
     { name = "praisonai-tools", marker = "extra == 'autogen'", specifier = ">=0.0.7" },
     { name = "praisonai-tools", marker = "extra == 'crewai'", specifier = ">=0.0.7" },
-    { name = "praisonaiagents", specifier = ">=0.0.62" },
+    { name = "praisonaiagents", specifier = ">=0.0.65" },
     { 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" },
@@ -3250,16 +3250,16 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.0.62"
+version = "0.0.65"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "openai" },
     { name = "pydantic" },
     { name = "rich" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/26/44/528286f187d8be35839382251407a3b5ec7ecf5fc9b1fafd7e4197b436cb/praisonaiagents-0.0.62.tar.gz", hash = "sha256:d461553d7dc64441544a6ad92e6e446531f1be8e5cbb95261407540756b2cb8a", size = 104636 }
+sdist = { url = "https://files.pythonhosted.org/packages/9e/58/6e07c4abf57d78be642f1517fe0692a003dfc253ad91a03c70521d246b5e/praisonaiagents-0.0.65.tar.gz", hash = "sha256:16627166f1b42bd8d21759fbd67015fff0b4730ca224b4a566cffe2b5bb770e8", size = 105128 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/42/34/c389ba1a9a45b0f7637b073e25717d2f9e0e6e727cc4946182fcf86e8c12/praisonaiagents-0.0.62-py3-none-any.whl", hash = "sha256:f6883ba12642bb3a7af277bfc1c0020276c3ac31bc251ed381f0388b3a61e73b", size = 123702 },
+    { url = "https://files.pythonhosted.org/packages/04/02/e4f2ef3c008a407d3b945256d1513bf0159989055e9462c691265be3d294/praisonaiagents-0.0.65-py3-none-any.whl", hash = "sha256:f53e9c3ddf31c575dd5f65315db70b35487adb0f9ae908596e74ed315c12afc5", size = 124306 },
 ]
 
 [[package]]