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==2.0.76 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==2.0.76 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/2.0.76.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==2.0.76 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')
             

praisonai/train_vision.py

@@ -240,6 +240,32 @@ class TrainVisionModel:
             quantization_method="q4_k_m"
         )
 
+    def prepare_modelfile_content(self):
+        output_model = self.config["hf_model_name"]
+        
+        template = '''{{- range $index, $_ := .Messages }}<|start_header_id|>{{ .Role }}<|end_header_id|>
+
+{{ .Content }}
+{{- if gt (len (slice $.Messages $index)) 1 }}<|eot_id|>
+{{- else if ne .Role "assistant" }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+
+{{ end }}
+{{- end }}'''
+        
+        return f"""FROM {output_model}
+TEMPLATE {template}
+PARAMETER temperature 0.6
+PARAMETER top_p 0.9
+"""
+
+    def create_and_push_ollama_model(self):
+        modelfile_content = self.prepare_modelfile_content()
+        with open("Modelfile", "w") as file:
+            file.write(modelfile_content)
+        subprocess.run(["ollama", "serve"])
+        subprocess.run(["ollama", "create", f"{self.config['ollama_model']}:{self.config['model_parameters']}", "-f", "Modelfile"])
+        subprocess.run(["ollama", "push", f"{self.config['ollama_model']}:{self.config['model_parameters']}"])
+
     def run(self):
         self.print_system_info()
         self.check_gpu()
@@ -247,6 +273,12 @@ class TrainVisionModel:
         if self.config.get("train", "true").lower() == "true":
             self.prepare_model()
             self.train_model()
+        if self.config.get("huggingface_save", "true").lower() == "true":
+            self.save_model_merged()
+        if self.config.get("huggingface_save_gguf", "true").lower() == "true":
+            self.push_model_gguf()
+        if self.config.get("ollama_save", "true").lower() == "true":
+            self.create_and_push_ollama_model()
 
 
 def main():

praisonai/upload_vision.py

@@ -0,0 +1,140 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+This script handles uploading trained vision models to Hugging Face and Ollama.
+It reads configuration from config.yaml and provides options to upload in different formats.
+"""
+
+import os
+import yaml
+import torch
+import shutil
+import subprocess
+from unsloth import FastVisionModel
+
+class UploadVisionModel:
+    def __init__(self, config_path="config.yaml"):
+        self.load_config(config_path)
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.model = None
+        self.hf_tokenizer = None
+
+    def load_config(self, path):
+        """Load configuration from yaml file."""
+        with open(path, "r") as file:
+            self.config = yaml.safe_load(file)
+        print("DEBUG: Loaded config:", self.config)
+
+    def prepare_model(self):
+        """Load the trained model for uploading."""
+        print("DEBUG: Loading trained model and tokenizer...")
+        self.model, original_tokenizer = FastVisionModel.from_pretrained(
+            model_name=self.config.get("output_dir", "lora_model"),
+            load_in_4bit=self.config.get("load_in_4bit", True)
+        )
+        self.hf_tokenizer = original_tokenizer
+        print("DEBUG: Model and tokenizer loaded successfully.")
+
+    def save_model_merged(self):
+        """Save merged model to Hugging Face Hub."""
+        print(f"DEBUG: Saving merged model to Hugging Face Hub: {self.config['hf_model_name']}")
+        if os.path.exists(self.config["hf_model_name"]):
+            shutil.rmtree(self.config["hf_model_name"])
+        self.model.push_to_hub_merged(
+            self.config["hf_model_name"],
+            self.hf_tokenizer,
+            save_method="merged_16bit",
+            token=os.getenv("HF_TOKEN")
+        )
+        print("DEBUG: Model saved to Hugging Face Hub successfully.")
+
+    def push_model_gguf(self):
+        """Push model in GGUF format to Hugging Face Hub."""
+        print(f"DEBUG: Pushing GGUF model to Hugging Face Hub: {self.config['hf_model_name']}")
+        self.model.push_to_hub_gguf(
+            self.config["hf_model_name"],
+            self.hf_tokenizer,
+            quantization_method=self.config.get("quantization_method", "q4_k_m"),
+            token=os.getenv("HF_TOKEN")
+        )
+        print("DEBUG: GGUF model pushed to Hugging Face Hub successfully.")
+
+    def prepare_modelfile_content(self):
+        """Prepare Ollama modelfile content using Llama 3.2 vision template."""
+        output_model = self.config["hf_model_name"]
+        
+        # Using Llama 3.2 vision template format
+        template = """{{- range $index, $_ := .Messages }}<|start_header_id|>{{ .Role }}<|end_header_id|>
+
+{{ .Content }}
+{{- if gt (len (slice $.Messages $index)) 1 }}<|eot_id|>
+{{- else if ne .Role "assistant" }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+
+{{ end }}
+{{- end }}"""
+        
+        # Assemble the modelfile content with Llama 3.2 vision parameters
+        modelfile = f"FROM {output_model}\n"
+        modelfile += "TEMPLATE \"""" + template + "\"""\n"
+        modelfile += "PARAMETER temperature 0.6\n"
+        modelfile += "PARAMETER top_p 0.9\n"
+        return modelfile
+
+    def create_and_push_ollama_model(self):
+        """Create and push model to Ollama."""
+        print(f"DEBUG: Creating Ollama model: {self.config['ollama_model']}:{self.config['model_parameters']}")
+        modelfile_content = self.prepare_modelfile_content()
+        with open("Modelfile", "w") as file:
+            file.write(modelfile_content)
+        
+        print("DEBUG: Starting Ollama server...")
+        subprocess.run(["ollama", "serve"])
+        
+        print("DEBUG: Creating Ollama model...")
+        subprocess.run([
+            "ollama", "create", 
+            f"{self.config['ollama_model']}:{self.config['model_parameters']}", 
+            "-f", "Modelfile"
+        ])
+        
+        print("DEBUG: Pushing model to Ollama...")
+        subprocess.run([
+            "ollama", "push", 
+            f"{self.config['ollama_model']}:{self.config['model_parameters']}"
+        ])
+        print("DEBUG: Model pushed to Ollama successfully.")
+
+    def upload(self, target="all"):
+        """
+        Upload the model to specified targets.
+        Args:
+            target (str): One of 'all', 'huggingface', 'huggingface_gguf', or 'ollama'
+        """
+        self.prepare_model()
+        
+        if target in ["all", "huggingface"]:
+            self.save_model_merged()
+        
+        if target in ["all", "huggingface_gguf"]:
+            self.push_model_gguf()
+            
+        if target in ["all", "ollama"]:
+            self.create_and_push_ollama_model()
+
+def main():
+    import argparse
+    parser = argparse.ArgumentParser(description="Upload Vision Model to Various Platforms")
+    parser.add_argument("--config", default="config.yaml", help="Path to configuration file")
+    parser.add_argument(
+        "--target", 
+        choices=["all", "huggingface", "huggingface_gguf", "ollama"],
+        default="all",
+        help="Target platform to upload to"
+    )
+    args = parser.parse_args()
+
+    uploader = UploadVisionModel(config_path=args.config)
+    uploader.upload(target=args.target)
+
+if __name__ == "__main__":
+    main()

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "PraisonAI"
-version = "2.0.76"
+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>=0.0.61",
+    "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 = "2.0.76"
+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 = ">=0.0.61"
+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/llm-tool-call.py

@@ -0,0 +1,18 @@
+from praisonaiagents import Agent
+from praisonaiagents.tools import wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language
+
+agent1 = Agent(
+    instructions="You are a Wikipedia Agent", 
+    tools=[wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language],
+    llm="openai/gpt-4o-mini",
+    verbose=10
+)
+agent1.start("history of AI in 1 line")
+
+agent2 = Agent(
+    instructions="You are a Wikipedia Agent", 
+    tools=[wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language],
+    llm="gpt-4o-mini",
+    verbose=10
+)
+agent2.start("history of AI in 1 line")

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

@@ -714,6 +714,22 @@ Your Goal: {self.goal}
             return None
 
     def chat(self, prompt, temperature=0.2, tools=None, output_json=None, output_pydantic=None, reasoning_steps=False):
+        # Log all parameter values when in debug mode
+        if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+            param_info = {
+                "prompt": str(prompt)[:100] + "..." if isinstance(prompt, str) and len(str(prompt)) > 100 else str(prompt),
+                "temperature": temperature,
+                "tools": [t.__name__ if hasattr(t, "__name__") else str(t) for t in tools] if tools else None,
+                "output_json": str(output_json.__class__.__name__) if output_json else None,
+                "output_pydantic": str(output_pydantic.__class__.__name__) if output_pydantic else None,
+                "reasoning_steps": reasoning_steps,
+                "agent_name": self.name,
+                "agent_role": self.role,
+                "agent_goal": self.goal
+            }
+            logging.debug(f"Agent.chat parameters: {json.dumps(param_info, indent=2, default=str)}")
+        
+        start_time = time.time()
         reasoning_steps = reasoning_steps or self.reasoning_steps
         # Search for existing knowledge if any knowledge is provided
         if self.knowledge:
@@ -738,7 +754,7 @@ Your Goal: {self.goal}
                     system_prompt=f"{self.backstory}\n\nYour Role: {self.role}\n\nYour Goal: {self.goal}" if self.use_system_prompt else None,
                     chat_history=self.chat_history,
                     temperature=temperature,
-                    tools=tools,
+                    tools=self.tools if tools is None else tools,
                     output_json=output_json,
                     output_pydantic=output_pydantic,
                     verbose=self.verbose,
@@ -749,7 +765,7 @@ Your Goal: {self.goal}
                     console=self.console,
                     agent_name=self.name,
                     agent_role=self.role,
-                    agent_tools=[t.__name__ if hasattr(t, '__name__') else str(t) for t in self.tools],
+                    agent_tools=[t.__name__ if hasattr(t, '__name__') else str(t) for t in (tools if tools is not None else self.tools)],
                     execute_tool_fn=self.execute_tool,  # Pass tool execution function
                     reasoning_steps=reasoning_steps
                 )
@@ -757,6 +773,11 @@ Your Goal: {self.goal}
                 self.chat_history.append({"role": "user", "content": prompt})
                 self.chat_history.append({"role": "assistant", "content": response_text})
 
+                # Log completion time if in debug mode
+                if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+                    total_time = time.time() - start_time
+                    logging.debug(f"Agent.chat completed in {total_time:.2f} seconds")
+
                 return response_text
             except Exception as e:
                 display_error(f"Error in LLM chat: {e}")
@@ -944,6 +965,13 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                     display_error(f"Error in chat: {e}", console=self.console)
                     return None 
 
+        # Log completion time if in debug mode
+        if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+            total_time = time.time() - start_time
+            logging.debug(f"Agent.chat completed in {total_time:.2f} seconds")
+            
+        return response_text
+
     def clean_json_output(self, output: str) -> str:
         """Clean and extract JSON from response text."""
         cleaned = output.strip()
@@ -958,6 +986,22 @@ 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):
         """Async version of chat method. TODO: Requires Syncing with chat method.""" 
+        # Log all parameter values when in debug mode
+        if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+            param_info = {
+                "prompt": str(prompt)[:100] + "..." if isinstance(prompt, str) and len(str(prompt)) > 100 else str(prompt),
+                "temperature": temperature,
+                "tools": [t.__name__ if hasattr(t, "__name__") else str(t) for t in tools] if tools else None,
+                "output_json": str(output_json.__class__.__name__) if output_json else None,
+                "output_pydantic": str(output_pydantic.__class__.__name__) if output_pydantic else None,
+                "reasoning_steps": reasoning_steps,
+                "agent_name": self.name,
+                "agent_role": self.role,
+                "agent_goal": self.goal
+            }
+            logging.debug(f"Agent.achat parameters: {json.dumps(param_info, indent=2, default=str)}")
+        
+        start_time = time.time()
         reasoning_steps = reasoning_steps or self.reasoning_steps
         try:
             # Search for existing knowledge if any knowledge is provided
@@ -996,9 +1040,15 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                     self.chat_history.append({"role": "user", "content": prompt})
                     self.chat_history.append({"role": "assistant", "content": response_text})
 
+                    if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+                        total_time = time.time() - start_time
+                        logging.debug(f"Agent.achat completed in {total_time:.2f} seconds")
                     return response_text
                 except Exception as e:
                     display_error(f"Error in LLM chat: {e}")
+                    if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+                        total_time = time.time() - start_time
+                        logging.debug(f"Agent.achat failed in {total_time:.2f} seconds: {str(e)}")
                     return None
 
             # For OpenAI client
@@ -1081,7 +1131,11 @@ Your Goal: {self.goal}
                             temperature=temperature,
                             tools=formatted_tools
                         )
-                        return await self._achat_completion(response, tools)
+                        result = await self._achat_completion(response, tools)
+                        if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+                            total_time = time.time() - start_time
+                            logging.debug(f"Agent.achat completed in {total_time:.2f} seconds")
+                        return result
                     elif output_json or output_pydantic:
                         response = await async_client.chat.completions.create(
                             model=self.llm,
@@ -1090,6 +1144,9 @@ Your Goal: {self.goal}
                             response_format={"type": "json_object"}
                         )
                         # Return the raw response
+                        if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+                            total_time = time.time() - start_time
+                            logging.debug(f"Agent.achat completed in {total_time:.2f} seconds")
                         return response.choices[0].message.content
                     else:
                         response = await async_client.chat.completions.create(
@@ -1097,12 +1154,21 @@ Your Goal: {self.goal}
                             messages=messages,
                             temperature=temperature
                         )
+                        if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+                            total_time = time.time() - start_time
+                            logging.debug(f"Agent.achat completed in {total_time:.2f} seconds")
                         return response.choices[0].message.content
                 except Exception as e:
                     display_error(f"Error in chat completion: {e}")
+                    if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+                        total_time = time.time() - start_time
+                        logging.debug(f"Agent.achat failed in {total_time:.2f} seconds: {str(e)}")
                     return None
         except Exception as e:
             display_error(f"Error in achat: {e}")
+            if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+                total_time = time.time() - start_time
+                logging.debug(f"Agent.achat failed in {total_time:.2f} seconds: {str(e)}")
             return None
 
     async def _achat_completion(self, response, tools, reasoning_steps=False):

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

@@ -171,7 +171,39 @@ 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
+        if not isinstance(verbose, bool) and verbose >= 10:
+            debug_info = {
+                "model": self.model,
+                "timeout": self.timeout,
+                "temperature": self.temperature,
+                "top_p": self.top_p,
+                "n": self.n,
+                "max_tokens": self.max_tokens,
+                "presence_penalty": self.presence_penalty,
+                "frequency_penalty": self.frequency_penalty,
+                "logit_bias": self.logit_bias,
+                "response_format": self.response_format,
+                "seed": self.seed,
+                "logprobs": self.logprobs,
+                "top_logprobs": self.top_logprobs,
+                "api_version": self.api_version,
+                "stop_phrases": self.stop_phrases,
+                "api_key": "***" if self.api_key else None,  # Mask API key for security
+                "base_url": self.base_url,
+                "verbose": self.verbose,
+                "markdown": self.markdown,
+                "self_reflect": self.self_reflect,
+                "max_reflect": self.max_reflect,
+                "min_reflect": self.min_reflect,
+                "reasoning_steps": self.reasoning_steps,
+                "extra_settings": {k: v for k, v in self.extra_settings.items() if k not in ["api_key"]}
+            }
+            logging.debug(f"LLM instance initialized with: {json.dumps(debug_info, indent=2, default=str)}")
 
     def get_response(
         self,
@@ -195,6 +227,56 @@ class LLM:
         **kwargs
     ) -> str:
         """Enhanced get_response with all OpenAI-like features"""
+        logging.info(f"Getting response from {self.model}")
+        # Log all self values when in debug mode
+        if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+            debug_info = {
+                "model": self.model,
+                "timeout": self.timeout,
+                "temperature": self.temperature,
+                "top_p": self.top_p,
+                "n": self.n,
+                "max_tokens": self.max_tokens,
+                "presence_penalty": self.presence_penalty,
+                "frequency_penalty": self.frequency_penalty,
+                "logit_bias": self.logit_bias,
+                "response_format": self.response_format,
+                "seed": self.seed,
+                "logprobs": self.logprobs,
+                "top_logprobs": self.top_logprobs,
+                "api_version": self.api_version,
+                "stop_phrases": self.stop_phrases,
+                "api_key": "***" if self.api_key else None,  # Mask API key for security
+                "base_url": self.base_url,
+                "verbose": self.verbose,
+                "markdown": self.markdown,
+                "self_reflect": self.self_reflect,
+                "max_reflect": self.max_reflect,
+                "min_reflect": self.min_reflect,
+                "reasoning_steps": self.reasoning_steps
+            }
+            logging.debug(f"LLM instance configuration: {json.dumps(debug_info, indent=2, default=str)}")
+            
+            # Log the parameter values passed to get_response
+            param_info = {
+                "prompt": str(prompt)[:100] + "..." if isinstance(prompt, str) and len(str(prompt)) > 100 else str(prompt),
+                "system_prompt": system_prompt[:100] + "..." if system_prompt and len(system_prompt) > 100 else system_prompt,
+                "chat_history": f"[{len(chat_history)} messages]" if chat_history else None,
+                "temperature": temperature,
+                "tools": [t.__name__ if hasattr(t, "__name__") else str(t) for t in tools] if tools else None,
+                "output_json": str(output_json.__class__.__name__) if output_json else None,
+                "output_pydantic": str(output_pydantic.__class__.__name__) if output_pydantic else None,
+                "verbose": verbose,
+                "markdown": markdown,
+                "self_reflect": self_reflect,
+                "max_reflect": max_reflect,
+                "min_reflect": min_reflect,
+                "agent_name": agent_name,
+                "agent_role": agent_role,
+                "agent_tools": agent_tools,
+                "kwargs": str(kwargs)
+            }
+            logging.debug(f"get_response parameters: {json.dumps(param_info, indent=2, default=str)}")
         try:
             import litellm
             # This below **kwargs** is passed to .completion() directly. so reasoning_steps has to be popped. OR find alternate best way of handling this.
@@ -202,6 +284,23 @@ class LLM:
             # Disable litellm debug messages
             litellm.set_verbose = False
             
+            # Format tools if provided
+            formatted_tools = None
+            if tools:
+                formatted_tools = []
+                for tool in tools:
+                    if callable(tool):
+                        tool_def = self._generate_tool_definition(tool.__name__)
+                    elif isinstance(tool, str):
+                        tool_def = self._generate_tool_definition(tool)
+                    else:
+                        continue
+                        
+                    if tool_def:
+                        formatted_tools.append(tool_def)
+                if not formatted_tools:
+                    formatted_tools = None
+            
             # Build messages list
             messages = []
             if system_prompt:
@@ -260,6 +359,7 @@ class LLM:
                             messages=messages,
                             temperature=temperature,
                             stream=False,  # force non-streaming
+                            tools=formatted_tools,
                             **{k:v for k,v in kwargs.items() if k != 'reasoning_steps'}
                         )
                         reasoning_content = resp["choices"][0]["message"].get("provider_specific_fields", {}).get("reasoning_content")
@@ -291,6 +391,7 @@ class LLM:
                                 for chunk in litellm.completion(
                                     model=self.model,
                                     messages=messages,
+                                    tools=formatted_tools,
                                     temperature=temperature,
                                     stream=True,
                                     **kwargs
@@ -305,6 +406,7 @@ class LLM:
                             for chunk in litellm.completion(
                                 model=self.model,
                                 messages=messages,
+                                tools=formatted_tools,
                                 temperature=temperature,
                                 stream=True,
                                 **kwargs
@@ -318,6 +420,7 @@ class LLM:
                     final_response = litellm.completion(
                         model=self.model,
                         messages=messages,
+                        tools=formatted_tools,
                         temperature=temperature,
                         stream=False,  # No streaming for tool call check
                         **kwargs
@@ -337,20 +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"],
@@ -552,6 +664,11 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
         except Exception as error:
             display_error(f"Error in get_response: {str(error)}")
             raise
+        
+        # Log completion time if in debug mode
+        if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+            total_time = time.time() - start_time
+            logging.debug(f"get_response completed in {total_time:.2f} seconds")
 
     async def get_response_async(
         self,
@@ -577,6 +694,56 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
         """Async version of get_response with identical functionality."""
         try:
             import litellm
+            logging.info(f"Getting async response from {self.model}")
+            # Log all self values when in debug mode
+            if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+                debug_info = {
+                    "model": self.model,
+                    "timeout": self.timeout,
+                    "temperature": self.temperature,
+                    "top_p": self.top_p,
+                    "n": self.n,
+                    "max_tokens": self.max_tokens,
+                    "presence_penalty": self.presence_penalty,
+                    "frequency_penalty": self.frequency_penalty,
+                    "logit_bias": self.logit_bias,
+                    "response_format": self.response_format,
+                    "seed": self.seed,
+                    "logprobs": self.logprobs,
+                    "top_logprobs": self.top_logprobs,
+                    "api_version": self.api_version,
+                    "stop_phrases": self.stop_phrases,
+                    "api_key": "***" if self.api_key else None,  # Mask API key for security
+                    "base_url": self.base_url,
+                    "verbose": self.verbose,
+                    "markdown": self.markdown,
+                    "self_reflect": self.self_reflect,
+                    "max_reflect": self.max_reflect,
+                    "min_reflect": self.min_reflect,
+                    "reasoning_steps": self.reasoning_steps
+                }
+                logging.debug(f"LLM async instance configuration: {json.dumps(debug_info, indent=2, default=str)}")
+                
+                # Log the parameter values passed to get_response_async
+                param_info = {
+                    "prompt": str(prompt)[:100] + "..." if isinstance(prompt, str) and len(str(prompt)) > 100 else str(prompt),
+                    "system_prompt": system_prompt[:100] + "..." if system_prompt and len(system_prompt) > 100 else system_prompt,
+                    "chat_history": f"[{len(chat_history)} messages]" if chat_history else None,
+                    "temperature": temperature,
+                    "tools": [t.__name__ if hasattr(t, "__name__") else str(t) for t in tools] if tools else None,
+                    "output_json": str(output_json.__class__.__name__) if output_json else None,
+                    "output_pydantic": str(output_pydantic.__class__.__name__) if output_pydantic else None,
+                    "verbose": verbose,
+                    "markdown": markdown,
+                    "self_reflect": self_reflect,
+                    "max_reflect": max_reflect,
+                    "min_reflect": min_reflect,
+                    "agent_name": agent_name,
+                    "agent_role": agent_role,
+                    "agent_tools": agent_tools,
+                    "kwargs": str(kwargs)
+                }
+                logging.debug(f"get_response_async parameters: {json.dumps(param_info, indent=2, default=str)}")
             reasoning_steps = kwargs.pop('reasoning_steps', self.reasoning_steps)
             litellm.set_verbose = False
 
@@ -767,14 +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,
@@ -983,6 +1151,11 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                 raise LLMContextLengthExceededException(str(error))
             display_error(f"Error in get_response_async: {str(error)}")
             raise
+            
+        # Log completion time if in debug mode
+        if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+            total_time = time.time() - start_time
+            logging.debug(f"get_response_async completed in {total_time:.2f} seconds")
 
     def can_use_tools(self) -> bool:
         """Check if this model can use tool functions"""
@@ -1065,6 +1238,24 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
             
             logger.debug("Using synchronous response function")
             
+            # Log all self values when in debug mode
+            if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+                debug_info = {
+                    "model": self.model,
+                    "timeout": self.timeout,
+                    "temperature": temperature,
+                    "top_p": self.top_p,
+                    "n": self.n,
+                    "max_tokens": self.max_tokens,
+                    "presence_penalty": self.presence_penalty,
+                    "frequency_penalty": self.frequency_penalty,
+                    "stream": stream,
+                    "verbose": verbose,
+                    "markdown": markdown,
+                    "kwargs": str(kwargs)
+                }
+                logger.debug(f"Response method configuration: {json.dumps(debug_info, indent=2, default=str)}")
+            
             # Build messages list
             messages = []
             if system_prompt:
@@ -1150,6 +1341,24 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
             
             logger.debug("Using asynchronous response function")
             
+            # Log all self values when in debug mode
+            if logging.getLogger().getEffectiveLevel() == logging.DEBUG:
+                debug_info = {
+                    "model": self.model,
+                    "timeout": self.timeout,
+                    "temperature": temperature,
+                    "top_p": self.top_p,
+                    "n": self.n,
+                    "max_tokens": self.max_tokens,
+                    "presence_penalty": self.presence_penalty,
+                    "frequency_penalty": self.frequency_penalty,
+                    "stream": stream,
+                    "verbose": verbose,
+                    "markdown": markdown,
+                    "kwargs": str(kwargs)
+                }
+                logger.debug(f"Async response method configuration: {json.dumps(debug_info, indent=2, default=str)}")
+            
             # Build messages list
             messages = []
             if system_prompt:
@@ -1210,4 +1419,117 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
 
         except Exception as error:
             display_error(f"Error in response_async: {str(error)}")
-            raise
+            raise
+
+    def _generate_tool_definition(self, function_name: str) -> Optional[Dict]:
+        """Generate a tool definition from a function name."""
+        logging.debug(f"Attempting to generate tool definition for: {function_name}")
+        
+        # First try to get the tool definition if it exists
+        tool_def_name = f"{function_name}_definition"
+        tool_def = globals().get(tool_def_name)
+        logging.debug(f"Looking for {tool_def_name} in globals: {tool_def is not None}")
+        
+        if not tool_def:
+            import __main__
+            tool_def = getattr(__main__, tool_def_name, None)
+            logging.debug(f"Looking for {tool_def_name} in __main__: {tool_def is not None}")
+        
+        if tool_def:
+            logging.debug(f"Found tool definition: {tool_def}")
+            return tool_def
+
+        # Try to find the function
+        func = globals().get(function_name)
+        logging.debug(f"Looking for {function_name} in globals: {func is not None}")
+        
+        if not func:
+            import __main__
+            func = getattr(__main__, function_name, None)
+            logging.debug(f"Looking for {function_name} in __main__: {func is not None}")
+        
+        if not func or not callable(func):
+            logging.debug(f"Function {function_name} not found or not callable")
+            return None
+
+        import inspect
+        # Handle Langchain and CrewAI tools
+        if inspect.isclass(func) and hasattr(func, 'run') and not hasattr(func, '_run'):
+            original_func = func
+            func = func.run
+            function_name = original_func.__name__
+        elif inspect.isclass(func) and hasattr(func, '_run'):
+            original_func = func
+            func = func._run
+            function_name = original_func.__name__
+
+        sig = inspect.signature(func)
+        logging.debug(f"Function signature: {sig}")
+        
+        # Skip self, *args, **kwargs
+        parameters_list = []
+        for name, param in sig.parameters.items():
+            if name == "self":
+                continue
+            if param.kind in (inspect.Parameter.VAR_POSITIONAL, inspect.Parameter.VAR_KEYWORD):
+                continue
+            parameters_list.append((name, param))
+
+        parameters = {
+            "type": "object",
+            "properties": {},
+            "required": []
+        }
+        
+        # Parse docstring for parameter descriptions
+        docstring = inspect.getdoc(func)
+        logging.debug(f"Function docstring: {docstring}")
+        
+        param_descriptions = {}
+        if docstring:
+            import re
+            param_section = re.split(r'\s*Args:\s*', docstring)
+            logging.debug(f"Param section split: {param_section}")
+            if len(param_section) > 1:
+                param_lines = param_section[1].split('\n')
+                for line in param_lines:
+                    line = line.strip()
+                    if line and ':' in line:
+                        param_name, param_desc = line.split(':', 1)
+                        param_descriptions[param_name.strip()] = param_desc.strip()
+        
+        logging.debug(f"Parameter descriptions: {param_descriptions}")
+
+        for name, param in parameters_list:
+            param_type = "string"  # Default type
+            if param.annotation != inspect.Parameter.empty:
+                if param.annotation == int:
+                    param_type = "integer"
+                elif param.annotation == float:
+                    param_type = "number"
+                elif param.annotation == bool:
+                    param_type = "boolean"
+                elif param.annotation == list:
+                    param_type = "array"
+                elif param.annotation == dict:
+                    param_type = "object"
+            
+            parameters["properties"][name] = {
+                "type": param_type,
+                "description": param_descriptions.get(name, "Parameter description not available")
+            }
+            
+            if param.default == inspect.Parameter.empty:
+                parameters["required"].append(name)
+        
+        logging.debug(f"Generated parameters: {parameters}")
+        tool_def = {
+            "type": "function",
+            "function": {
+                "name": function_name,
+                "description": docstring.split('\n\n')[0] if docstring else "No description available",
+                "parameters": parameters
+            }
+        }
+        logging.debug(f"Generated tool definition: {tool_def}")
+        return tool_def

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 = "0.0.61"
+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 = "0.0.61"
+version = "0.0.65"
 source = { editable = "." }
 dependencies = [
     { name = "openai" },

uv.lock

@@ -3060,7 +3060,7 @@ wheels = [
 
 [[package]]
 name = "praisonai"
-version = "2.0.76"
+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 = ">=0.0.61" },
+    { 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 = "0.0.61"
+version = "0.0.65"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "openai" },
     { name = "pydantic" },
     { name = "rich" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/50/be/09af17ea9eee5135c7b091bca77e1fc7a8ff732a6148497276ff5a06bd2e/praisonaiagents-0.0.61.tar.gz", hash = "sha256:9e178b99a1d2ffe23acd8413810e5d937d910a72fbd851e04d10b0bcb129b48a", size = 102057 }
+sdist = { url = "https://files.pythonhosted.org/packages/9e/58/6e07c4abf57d78be642f1517fe0692a003dfc253ad91a03c70521d246b5e/praisonaiagents-0.0.65.tar.gz", hash = "sha256:16627166f1b42bd8d21759fbd67015fff0b4730ca224b4a566cffe2b5bb770e8", size = 105128 }
 wheels = [
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