Update versioning and dependencies in project files

- Incremented version of PraisonAI from 0.0.99 to 0.0.101 in pyproject.toml and uv.lock.
- Added 'litellm' dependency to memory requirements for enhanced functionality.
- Updated .gitignore to include 'CopilotKit*' for better file management.
- Optimised TaskOutput instantiation in agent.py for clarity.
- Refined memory handling in memory.py to utilise LiteLLM for consistency.
- Improved model extraction logic in task.py for better fallback handling.

Author: MervinPraison

.gitignore

@@ -74,4 +74,5 @@ agents/praisonaiagents/praisonaiagents.egg-info
 .praison
 # Local Netlify folder
 .netlify
-.qodo
+.qodo
+CopilotKit*

src/praisonai-agents/guardrail_agent_example.py

@@ -0,0 +1,14 @@
+from praisonaiagents import Agent
+
+def validate_content(data):
+    if len(str(data)) < 50:
+        return False, "Content too short"
+    return True, data
+
+agent = Agent(
+    instructions="You are a writer",
+    guardrail=validate_content,
+    max_guardrail_retries=1
+)
+
+agent.start("Write a welcome message with 5 words")

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

@@ -619,12 +619,9 @@ def _apply_guardrail_with_retry(self, response_text, prompt, temperature=0.2, to
         while retry_count <= self.max_guardrail_retries:
             # Create TaskOutput object
             task_output = TaskOutput(
+                description="Agent response output",
                 raw=current_response,
-                output=current_response,
-                pydantic=None,
-                json_dict=None,
-                name=f"{self.name}_output",
-                description="Agent response output"
+                agent=self.name
             )
 
             # Process guardrail

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

@@ -910,11 +910,14 @@ def calculate_quality_metrics(
         """
 
         try:
-            # Use OpenAI client from main.py
-            from ..main import client
+            # Use LiteLLM for consistency with the rest of the codebase
+            import litellm
 
-            response = client.chat.completions.create(
-                model=llm or "gpt-4o",
+            # Convert model name if it's in litellm format
+            model_name = llm or "gpt-4o-mini"
+            
+            response = litellm.completion(
+                model=model_name,
                 messages=[{
                     "role": "user", 
                     "content": custom_prompt or default_prompt

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

@@ -308,7 +308,11 @@ async def execute_callback(self, task_output: TaskOutput) -> None:
                 if self.agent:
                     if getattr(self.agent, '_using_custom_llm', False) and hasattr(self.agent, 'llm_instance'):
                         # For custom LLM instances (like Ollama)
-                        llm_model = self.agent.llm_instance
+                        # Extract the model name from the LLM instance
+                        if hasattr(self.agent.llm_instance, 'model'):
+                            llm_model = self.agent.llm_instance.model
+                        else:
+                            llm_model = "gpt-4o-mini"  # Default fallback
                     elif hasattr(self.agent, 'llm') and self.agent.llm:
                         # For standard model strings
                         llm_model = self.agent.llm

src/praisonai-agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.0.99"
+version = "0.0.101"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 requires-python = ">=3.10"
 authors = [
@@ -25,7 +25,8 @@ mcp = [
 ]
 
 memory = [
-    "chromadb>=1.0.0"
+    "chromadb>=1.0.0",
+    "litellm>=1.50.0",
 ]
 
 knowledge = [
@@ -63,5 +64,6 @@ all = [
     "praisonaiagents[api]"
 ]
 
-[tool.setuptools]
-packages = ["praisonaiagents"]
+[tool.setuptools.packages.find]
+where = ["."]
+include = ["praisonaiagents*"]

src/praisonai-agents/test.py

@@ -0,0 +1,33 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+llm_config = {
+    "model": "openai/gpt-4o-mini",
+    "api_key": os.getenv('OPENAI_API_KEY'),
+    "temperature": 0.7,
+    "max_tokens": 2000
+}
+
+blog_agent = Agent(
+    role="Blog Writer",
+    goal="Write a blog post about AI",
+    backstory="Expert at writing blog posts",
+    llm="gpt-4o-mini",
+)
+
+blog_task = Task(
+    description="Write a blog post about AI trends in 1 paragraph",
+    expected_output="Well-written blog post about AI trends",
+    agent=blog_agent
+)
+
+agents = PraisonAIAgents(
+    agents=[blog_agent],
+    tasks=[blog_task],
+    memory=True
+)
+
+result = agents.start()