Merge pull request #609 from MervinPraison/develop

Develop

Author: MervinPraison

.gitignore

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

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>=2.2.28" \
+    "praisonai>=2.2.29" \
     "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>=2.2.28" \
+    "praisonai>=2.2.29" \
     "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>=2.2.28" \
+    "praisonai>=2.2.29" \
     "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>=2.2.28" \
+    "praisonai>=2.2.29" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

docker/README.md

@@ -121,7 +121,7 @@ healthcheck:
 ## 📦 Package Versions
 
 All Docker images use consistent, up-to-date versions:
-- PraisonAI: `>=2.2.28`
+- PraisonAI: `>=2.2.29`
 - 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==2.2.28" "praisonaiagents==0.0.92"
+RUN pip install "praisonai==2.2.29" "praisonaiagents==0.0.92"
 ```
 
 ## 🌐 Production Deployment

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