v2.0.77

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==2.0.77 gunicorn markdown
 EXPOSE 8080
 CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]

docs/api/praisonai/deploy.html

@@ -110,7 +110,7 @@ <h2 id="raises">Raises</h2>
             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==2.0.77 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/2.0.77.tar.gz"
     sha256 "1828fb9227d10f991522c3f24f061943a254b667196b40b1a3e4a54a8d30ce32"  # Replace with actual SHA256 checksum
     license "MIT"
 

praisonai/deploy.py

@@ -56,7 +56,7 @@ def create_dockerfile(self):
             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==2.0.77 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 = "2.0.76"
+version = "2.0.77"
 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>=0.0.62",
     "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 = "2.0.77"
 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 = ">=0.0.62"
 python-dotenv = ">=0.19.0"
 instructor = ">=1.3.3"
 PyYAML = ">=6.0"

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 @@ def _chat_completion(self, messages, temperature=0.2, tools=None, stream=True, r
             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 @@ def chat(self, prompt, temperature=0.2, tools=None, output_json=None, output_pyd
                     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,
@@ -749,14 +765,19 @@ def chat(self, prompt, temperature=0.2, tools=None, output_json=None, output_pyd
                     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
                 )
 
                 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 @@ def chat(self, prompt, temperature=0.2, tools=None, output_json=None, output_pyd
                     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 @@ def clean_json_output(self, output: str) -> str:
 
     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 @@ async def achat(self, prompt: str, temperature=0.2, tools=None, output_json=None
                     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 @@ async def achat(self, prompt: str, temperature=0.2, tools=None, output_json=None
                             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,
@@ -1090,19 +1144,31 @@ async def achat(self, prompt: str, temperature=0.2, tools=None, output_json=None
                             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(
                             model=self.llm,
                             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):