Merge pull request #571 from MervinPraison/develop

chore: update PraisonAI version to 2.2.24 across Dockerfiles and docu…

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>=2.2.22" \
+    "praisonai>=2.2.24" \
     "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>=2.2.22" \
+    "praisonai>=2.2.24" \
     "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>=2.2.22" \
+    "praisonai>=2.2.24" \
     "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>=2.2.22" \
+    "praisonai>=2.2.24" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

docker/README.md

@@ -121,7 +121,7 @@ healthcheck:
 ## 📦 Package Versions
 
 All Docker images use consistent, up-to-date versions:
-- PraisonAI: `>=2.2.22`
+- PraisonAI: `>=2.2.24`
 - 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==2.2.22" "praisonaiagents==0.0.92"
+RUN pip install "praisonai==2.2.24" "praisonaiagents==0.0.92"
 ```
 
 ## 🌐 Production Deployment

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==2.2.22 gunicorn markdown\n&#34;)
+            file.write(&#34;RUN pip install flask praisonai==2.2.24 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;)
             

docs/developers/local-development.mdx

@@ -27,7 +27,7 @@ WORKDIR /app
 
 COPY . .
 
-RUN pip install flask praisonai==2.2.22 watchdog
+RUN pip install flask praisonai==2.2.24 watchdog
 
 EXPOSE 5555
 

docs/ui/chat.mdx

@@ -155,7 +155,7 @@ To facilitate local development with live reload, you can use Docker. Follow the
 
     COPY . .
 
-    RUN pip install flask praisonai==2.2.22 watchdog
+    RUN pip install flask praisonai==2.2.24 watchdog
 
     EXPOSE 5555
 

docs/ui/code.mdx

@@ -208,7 +208,7 @@ To facilitate local development with live reload, you can use Docker. Follow the
 
     COPY . .
 
-    RUN pip install flask praisonai==2.2.22 watchdog
+    RUN pip install flask praisonai==2.2.24 watchdog
 
     EXPOSE 5555
 

examples/python/concepts/knowledge-reranker-example.py

@@ -0,0 +1,159 @@
+"""
+Knowledge Agent with Reranker Example
+===================================
+
+This example demonstrates how to use Mem0's built-in reranking feature
+with the Knowledge Agent for improved search result relevance.
+"""
+
+import os
+from praisonaiagents import Agent
+from praisonaiagents.knowledge import Knowledge
+
+def main():
+    """
+    Demonstrates knowledge search with and without reranking.
+    """
+    
+    # Create sample content for knowledge base
+    sample_documents = [
+        "Artificial Intelligence is a field of computer science that aims to create machines that can perform tasks that typically require human intelligence.",
+        "Machine Learning is a subset of AI that enables computers to learn and improve from experience without being explicitly programmed.",
+        "Deep Learning is a type of machine learning that uses neural networks with multiple layers to model and understand complex patterns.",
+        "Natural Language Processing (NLP) is a branch of AI that helps computers understand, interpret and manipulate human language.",
+        "Computer Vision is an AI field that trains computers to interpret and understand the visual world from digital images and videos.",
+        "Reinforcement Learning is a type of machine learning where an agent learns to make decisions by taking actions in an environment.",
+        "Neural Networks are computing systems inspired by biological neural networks that constitute animal brains.",
+        "Data Science combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract insights from data."
+    ]
+    
+    print("=== Knowledge Agent Reranker Example ===\n")
+    
+    # Example 1: Basic Knowledge without reranking
+    print("1. Creating Knowledge base without reranking...")
+    basic_config = {
+        "vector_store": {
+            "provider": "chroma",
+            "config": {
+                "collection_name": "knowledge_basic",
+                "path": ".praison_basic"
+            }
+        },
+        "reranker": {
+            "enabled": True,
+            "default_rerank": False  # Disabled by default
+        }
+    }
+    
+    basic_knowledge = Knowledge(config=basic_config, verbose=1)
+    
+    # Add documents to knowledge base
+    for i, doc in enumerate(sample_documents):
+        basic_knowledge.store(doc, metadata={"doc_id": f"doc_{i}", "category": "AI"})
+    
+    # Example 2: Knowledge with reranking enabled by default
+    print("\n2. Creating Knowledge base with reranking enabled...")
+    rerank_config = {
+        "vector_store": {
+            "provider": "chroma", 
+            "config": {
+                "collection_name": "knowledge_rerank",
+                "path": ".praison_rerank"
+            }
+        },
+        "reranker": {
+            "enabled": True,
+            "default_rerank": True  # Enabled by default
+        }
+    }
+    
+    rerank_knowledge = Knowledge(config=rerank_config, verbose=1)
+    
+    # Add same documents to reranking knowledge base
+    for i, doc in enumerate(sample_documents):
+        rerank_knowledge.store(doc, metadata={"doc_id": f"doc_{i}", "category": "AI"})
+    
+    print("\n=== Search Results Comparison ===\n")
+    
+    query = "What is machine learning and how does it work?"
+    
+    # Search without reranking
+    print("3. Search WITHOUT reranking:")
+    basic_results = basic_knowledge.search(query, rerank=False)
+    print(f"Query: {query}")
+    print(f"Results (limit=3):")
+    for i, result in enumerate(basic_results[:3]):
+        text = result.get('memory', result.get('text', str(result)))
+        score = result.get('score', 'N/A')
+        print(f"  {i+1}. Score: {score}")
+        print(f"     Text: {text[:100]}...")
+        print()
+    
+    # Search with reranking explicitly enabled
+    print("4. Search WITH reranking:")
+    rerank_results = basic_knowledge.search(query, rerank=True)
+    print(f"Query: {query}")
+    print(f"Results (limit=3):")
+    for i, result in enumerate(rerank_results[:3]):
+        text = result.get('memory', result.get('text', str(result)))
+        score = result.get('score', 'N/A')
+        print(f"  {i+1}. Score: {score}")
+        print(f"     Text: {text[:100]}...")
+        print()
+    
+    # Search using config default (reranking enabled)
+    print("5. Search using config default (reranking enabled):")
+    default_results = rerank_knowledge.search(query)  # Will use default_rerank=True
+    print(f"Query: {query}")
+    print(f"Results (limit=3):")
+    for i, result in enumerate(default_results[:3]):
+        text = result.get('memory', result.get('text', str(result)))
+        score = result.get('score', 'N/A')
+        print(f"  {i+1}. Score: {score}")
+        print(f"     Text: {text[:100]}...")
+        print()
+    
+    print("=== Advanced Search Options ===\n")
+    
+    # Example with additional Mem0 advanced retrieval options
+    print("6. Search with keyword search + reranking:")
+    advanced_results = basic_knowledge.search(
+        query, 
+        rerank=True,
+        keyword_search=True,  # Enable keyword search for better recall
+        filter_memories=True   # Enable filtering for better precision
+    )
+    print(f"Query: {query}")
+    print(f"Advanced results with keyword_search=True, filter_memories=True:")
+    for i, result in enumerate(advanced_results[:3]):
+        text = result.get('memory', result.get('text', str(result)))
+        score = result.get('score', 'N/A')
+        print(f"  {i+1}. Score: {score}")
+        print(f"     Text: {text[:100]}...")
+        print()
+    
+    # Example with Agent using reranking knowledge
+    print("\n=== Agent with Reranking Knowledge ===\n")
+    
+    agent = Agent(
+        name="AI Research Assistant",
+        instructions="You are an AI research assistant. Use your knowledge base to answer questions about artificial intelligence topics.",
+        knowledge=sample_documents,  # Will use default knowledge config
+        knowledge_config=rerank_config,  # Use reranking config
+        llm="gpt-4o-mini"
+    )
+    
+    print("7. Agent with reranking knowledge:")
+    response = agent.run("Explain the difference between machine learning and deep learning, and how they relate to AI.")
+    print(f"Agent response: {response}")
+    
+    print("\n=== Performance Notes ===")
+    print("- Basic search: ~10ms latency")
+    print("- Reranking: 150-200ms additional latency")
+    print("- Reranking improves relevance ordering significantly")
+    print("- Use rerank=True for better results when query relevance is critical")
+    print("- Use keyword_search=True for better recall")
+    print("- Use filter_memories=True for better precision")
+
+if __name__ == "__main__":
+    main()

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

@@ -101,7 +101,11 @@ class Knowledge:
                 }
             },
             "version": "v1.1",
-            "custom_prompt": "Return {{\"facts\": [text]}} where text is the exact input provided and json response"
+            "custom_prompt": "Return {{\"facts\": [text]}} where text is the exact input provided and json response",
+            "reranker": {
+                "enabled": False,
+                "default_rerank": False
+            }
         }
 
         # If config is provided, merge it with base config
@@ -129,6 +133,10 @@ class Knowledge:
             # Merge llm config if provided
             if "llm" in self._config:
                 base_config["llm"] = self._config["llm"]
+            
+            # Merge reranker config if provided
+            if "reranker" in self._config:
+                base_config["reranker"].update(self._config["reranker"])
         return base_config
 
     @cached_property
@@ -191,9 +199,25 @@ class Knowledge:
         """Retrieve a specific memory by ID."""
         return self.memory.get(memory_id)
 
-    def search(self, query, user_id=None, agent_id=None, run_id=None):
-        """Search for memories related to a query."""
-        return self.memory.search(query, user_id=user_id, agent_id=agent_id, run_id=run_id)
+    def search(self, query, user_id=None, agent_id=None, run_id=None, rerank=None, **kwargs):
+        """Search for memories related to a query.
+        
+        Args:
+            query: The search query string
+            user_id: Optional user ID for user-specific search
+            agent_id: Optional agent ID for agent-specific search  
+            run_id: Optional run ID for run-specific search
+            rerank: Whether to use Mem0's advanced reranking. If None, uses config default
+            **kwargs: Additional search parameters to pass to Mem0 (keyword_search, filter_memories, etc.)
+        
+        Returns:
+            List of search results, reranked if rerank=True
+        """
+        # Use config default if rerank not explicitly specified
+        if rerank is None:
+            rerank = self.config.get("reranker", {}).get("default_rerank", False)
+        
+        return self.memory.search(query, user_id=user_id, agent_id=agent_id, run_id=run_id, rerank=rerank, **kwargs)
 
     def update(self, memory_id, data):
         """Update a memory."""

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

@@ -299,7 +299,11 @@ class MCP:
                     required_params.append(name)
         
         # Create the function signature
-        params = []
+        # Separate required and optional parameters to ensure proper ordering
+        # (required parameters must come before optional parameters)
+        required_param_objects = []
+        optional_param_objects = []
+        
         for name in param_names:
             is_required = name in required_params
             param = inspect.Parameter(
@@ -308,7 +312,14 @@ class MCP:
                 default=inspect.Parameter.empty if is_required else None,
                 annotation=param_annotations.get(name, Any)
             )
-            params.append(param)
+            
+            if is_required:
+                required_param_objects.append(param)
+            else:
+                optional_param_objects.append(param)
+        
+        # Combine parameters with required first, then optional
+        params = required_param_objects + optional_param_objects
         
         # Create function template to be properly decorated
         def template_function(*args, **kwargs):

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

@@ -262,13 +262,18 @@ class Memory:
         query: str, 
         limit: int = 5,
         min_quality: float = 0.0,
-        relevance_cutoff: float = 0.0
+        relevance_cutoff: float = 0.0,
+        rerank: bool = False,
+        **kwargs
     ) -> List[Dict[str, Any]]:
         """Search short-term memory with optional quality filter"""
         self._log_verbose(f"Searching short memory for: {query}")
         
         if self.use_mem0 and hasattr(self, "mem0_client"):
-            results = self.mem0_client.search(query=query, limit=limit)
+            # Pass rerank and other kwargs to Mem0 search
+            search_params = {"query": query, "limit": limit, "rerank": rerank}
+            search_params.update(kwargs)
+            results = self.mem0_client.search(**search_params)
             filtered = [r for r in results if r.get("score", 1.0) >= relevance_cutoff]
             return filtered
             
@@ -439,7 +444,9 @@ class Memory:
         query: str, 
         limit: int = 5, 
         relevance_cutoff: float = 0.0,
-        min_quality: float = 0.0
+        min_quality: float = 0.0,
+        rerank: bool = False,
+        **kwargs
     ) -> List[Dict[str, Any]]:
         """Search long-term memory with optional quality filter"""
         self._log_verbose(f"Searching long memory for: {query}")
@@ -448,7 +455,10 @@ class Memory:
         found = []
 
         if self.use_mem0 and hasattr(self, "mem0_client"):
-            results = self.mem0_client.search(query=query, limit=limit)
+            # Pass rerank and other kwargs to Mem0 search
+            search_params = {"query": query, "limit": limit, "rerank": rerank}
+            search_params.update(kwargs)
+            results = self.mem0_client.search(**search_params)
             # Filter by quality
             filtered = [r for r in results if r.get("metadata", {}).get("quality", 0.0) >= min_quality]
             logger.info(f"Found {len(filtered)} results in Mem0")
@@ -595,12 +605,15 @@ class Memory:
         else:
             self.store_long_term(text, metadata=meta)
 
-    def search_user_memory(self, user_id: str, query: str, limit: int = 5) -> List[Dict[str, Any]]:
+    def search_user_memory(self, user_id: str, query: str, limit: int = 5, rerank: bool = False, **kwargs) -> List[Dict[str, Any]]:
         """
         If mem0 is used, pass user_id in. Otherwise fallback to local filter on user in metadata.
         """
         if self.use_mem0 and hasattr(self, "mem0_client"):
-            return self.mem0_client.search(query=query, limit=limit, user_id=user_id)
+            # Pass rerank and other kwargs to Mem0 search
+            search_params = {"query": query, "limit": limit, "user_id": user_id, "rerank": rerank}
+            search_params.update(kwargs)
+            return self.mem0_client.search(**search_params)
         else:
             hits = self.search_long_term(query, limit=20)
             filtered = []

src/praisonai-agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.0.93"
+version = "0.0.95"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 requires-python = ">=3.10"
 authors = [

src/praisonai-agents/uv.lock

@@ -2233,7 +2233,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.0.93"
+version = "0.0.95"
 source = { editable = "." }
 dependencies = [
     { name = "mcp" },

src/praisonai/praisonai.rb

@@ -3,8 +3,8 @@ 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/v2.2.22.tar.gz"
-    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.22.tar.gz | shasum -a 256`.split.first
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.24.tar.gz"
+    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.2.24.tar.gz | shasum -a 256`.split.first
     license "MIT"
   
     depends_on "[email protected]"

src/praisonai/praisonai/auto.py

@@ -121,12 +121,12 @@ Tools are not available for {framework}. To use tools, install:
             mode=instructor.Mode.JSON,
         )
 
-    def generate(self):
+    def generate(self, merge=False):
         """
         Generates a team structure for the specified topic.
 
         Args:
-            None
+            merge (bool): Whether to merge with existing agents.yaml file instead of overwriting.
 
         Returns:
             str: The full path of the YAML file containing the generated team structure.
@@ -149,46 +149,119 @@ Tools are not available for {framework}. To use tools, install:
             ]
         )
         json_data = json.loads(response.model_dump_json())
-        self.convert_and_save(json_data)
+        self.convert_and_save(json_data, merge=merge)
         full_path = os.path.abspath(self.agent_file)
         return full_path
 
-    def convert_and_save(self, json_data):
+    def convert_and_save(self, json_data, merge=False):
         """Converts the provided JSON data into the desired YAML format and saves it to a file.
 
         Args:
             json_data (dict): The JSON data representing the team structure.
-            topic (str, optional): The topic to be inserted into the YAML. Defaults to "Artificial Intelligence".
-            agent_file (str, optional): The name of the YAML file to save. Defaults to "test.yaml".
+            merge (bool): Whether to merge with existing agents.yaml file instead of overwriting.
         """
 
-        yaml_data = {
-            "framework": self.framework,
-            "topic": self.topic,
-            "roles": {},
-            "dependencies": []
-        }
-
-        for role_id, role_details in json_data['roles'].items():
-            yaml_data['roles'][role_id] = {
-                "backstory": "" + role_details['backstory'],
-                "goal": role_details['goal'],
-                "role": role_details['role'],
-                "tasks": {},
-                # "tools": role_details.get('tools', []),
-                "tools": ['']
+        # Handle merge functionality
+        if merge and os.path.exists(self.agent_file):
+            yaml_data = self.merge_with_existing_agents(json_data)
+        else:
+            # Original behavior: create new yaml_data structure
+            yaml_data = {
+                "framework": self.framework,
+                "topic": self.topic,
+                "roles": {},
+                "dependencies": []
             }
 
-            for task_id, task_details in role_details['tasks'].items():
-                yaml_data['roles'][role_id]['tasks'][task_id] = {
-                    "description": "" + task_details['description'],
-                    "expected_output": "" + task_details['expected_output']
+            for role_id, role_details in json_data['roles'].items():
+                yaml_data['roles'][role_id] = {
+                    "backstory": "" + role_details['backstory'],
+                    "goal": role_details['goal'],
+                    "role": role_details['role'],
+                    "tasks": {},
+                    # "tools": role_details.get('tools', []),
+                    "tools": ['']
                 }
 
+                for task_id, task_details in role_details['tasks'].items():
+                    yaml_data['roles'][role_id]['tasks'][task_id] = {
+                        "description": "" + task_details['description'],
+                        "expected_output": "" + task_details['expected_output']
+                    }
+
         # Save to YAML file, maintaining the order
         with open(self.agent_file, 'w') as f:
             yaml.dump(yaml_data, f, allow_unicode=True, sort_keys=False)
 
+    def merge_with_existing_agents(self, new_json_data):
+        """
+        Merge existing agents.yaml with new auto-generated agents.
+        
+        Args:
+            new_json_data (dict): The JSON data representing the new team structure.
+            
+        Returns:
+            dict: The merged YAML data structure.
+        """
+        try:
+            # Load existing agents.yaml
+            with open(self.agent_file, 'r') as f:
+                existing_data = yaml.safe_load(f)
+            
+            if not existing_data:
+                # If existing file is empty, treat as new file
+                existing_data = {"roles": {}, "dependencies": []}
+        except (yaml.YAMLError, FileNotFoundError) as e:
+            logging.warning(f"Could not load existing agents file {self.agent_file}: {e}")
+            logging.warning("Creating new file instead of merging")
+            existing_data = {"roles": {}, "dependencies": []}
+        
+        # Start with existing data structure
+        merged_data = existing_data.copy()
+        
+        # Ensure required fields exist
+        if 'roles' not in merged_data:
+            merged_data['roles'] = {}
+        if 'dependencies' not in merged_data:
+            merged_data['dependencies'] = []
+        if 'framework' not in merged_data:
+            merged_data['framework'] = self.framework
+        
+        # Handle topic merging
+        existing_topic = merged_data.get('topic', '')
+        new_topic = self.topic
+        if existing_topic and existing_topic != new_topic:
+            merged_data['topic'] = f"{existing_topic} + {new_topic}"
+        else:
+            merged_data['topic'] = new_topic
+        
+        # Merge new roles with existing ones
+        for role_id, role_details in new_json_data['roles'].items():
+            # Check for conflicts and rename if necessary
+            final_role_id = role_id
+            counter = 1
+            while final_role_id in merged_data['roles']:
+                final_role_id = f"{role_id}_auto_{counter}"
+                counter += 1
+            
+            # Add the new role
+            merged_data['roles'][final_role_id] = {
+                "backstory": "" + role_details['backstory'],
+                "goal": role_details['goal'],
+                "role": role_details['role'],
+                "tasks": {},
+                "tools": ['']
+            }
+            
+            # Add tasks for this role
+            for task_id, task_details in role_details['tasks'].items():
+                merged_data['roles'][final_role_id]['tasks'][task_id] = {
+                    "description": "" + task_details['description'],
+                    "expected_output": "" + task_details['expected_output']
+                }
+        
+        return merged_data
+
     def get_user_content(self):
         """
         Generates a prompt for the OpenAI API to generate a team structure.

src/praisonai/praisonai/cli.py

@@ -417,7 +417,7 @@ class PraisonAI:
 
             self.agent_file = "test.yaml"
             generator = AutoGenerator(topic=self.topic, framework=self.framework, agent_file=self.agent_file)
-            self.agent_file = generator.generate()
+            self.agent_file = generator.generate(merge=getattr(args, 'merge', False))
             agents_generator = AgentsGenerator(self.agent_file, self.framework, self.config_list)
             result = agents_generator.generate_crew_and_kickoff()
             print(result)
@@ -430,7 +430,7 @@ class PraisonAI:
 
             self.agent_file = "agents.yaml"
             generator = AutoGenerator(topic=self.topic, framework=self.framework, agent_file=self.agent_file)
-            self.agent_file = generator.generate()
+            self.agent_file = generator.generate(merge=getattr(args, 'merge', False))
             print(f"File {self.agent_file} created successfully")
             return f"File {self.agent_file} created successfully"
 
@@ -476,6 +476,36 @@ class PraisonAI:
             'unittest' in sys.modules
         )
         
+        # Check if we're being used as a library (not from praisonai CLI)
+        # Skip CLI parsing to avoid conflicts with applications like Fabric
+        is_library_usage = (
+            'praisonai' not in sys.argv[0] and
+            not in_test_env
+        )
+        
+        if is_library_usage:
+            # Return default args when used as library to prevent CLI conflicts
+            default_args = argparse.Namespace()
+            default_args.framework = None
+            default_args.ui = None
+            default_args.auto = None
+            default_args.init = None
+            default_args.command = None
+            default_args.deploy = False
+            default_args.schedule = None
+            default_args.schedule_config = None
+            default_args.provider = "gcp"
+            default_args.max_retries = 3
+            default_args.model = None
+            default_args.llm = None
+            default_args.hf = None
+            default_args.ollama = None
+            default_args.dataset = "yahma/alpaca-cleaned"
+            default_args.realtime = False
+            default_args.call = False
+            default_args.public = False
+            return default_args
+        
         # Define special commands
         special_commands = ['chat', 'code', 'call', 'realtime', 'train', 'ui']
         
@@ -498,6 +528,7 @@ class PraisonAI:
         parser.add_argument("--realtime", action="store_true", help="Start the realtime voice interaction interface")
         parser.add_argument("--call", action="store_true", help="Start the PraisonAI Call server")
         parser.add_argument("--public", action="store_true", help="Use ngrok to expose the server publicly (only with --call)")
+        parser.add_argument("--merge", action="store_true", help="Merge existing agents.yaml with auto-generated agents instead of overwriting")
         
         # If we're in a test environment, parse with empty args to avoid pytest interference
         if in_test_env:

src/praisonai/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==2.2.22 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.2.24 gunicorn markdown\n")
             file.write("EXPOSE 8080\n")
             file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
             

src/praisonai/praisonai/ui/code.py

@@ -20,6 +20,7 @@ from chainlit.input_widget import TextInput
 from chainlit.types import ThreadDict
 import chainlit.data as cl_data
 from litellm import acompletion
+import litellm
 from db import DatabaseManager
 
 # Load environment variables
@@ -40,6 +41,15 @@ logger.addHandler(console_handler)
 # Set the logging level for the logger
 logger.setLevel(log_level)
 
+# Configure litellm same as in llm.py
+litellm.set_verbose = False
+litellm.success_callback = []
+litellm._async_success_callback = []
+litellm.callbacks = []
+litellm.drop_params = True
+litellm.modify_params = True
+litellm.suppress_debug_messages = True
+
 CHAINLIT_AUTH_SECRET = os.getenv("CHAINLIT_AUTH_SECRET")
 
 if not CHAINLIT_AUTH_SECRET:
@@ -55,6 +65,17 @@ db_manager.initialize()
 
 deleted_thread_ids = []  # type: List[str]
 
+def _build_completion_params(model_name, **override_params):
+    """Build parameters for litellm completion calls with proper model handling"""
+    params = {
+        "model": model_name,
+    }
+    
+    # Override with any provided parameters
+    params.update(override_params)
+    
+    return params
+
 def save_setting(key: str, value: str):
     """Saves a setting to the database.
     
@@ -237,12 +258,12 @@ Context:
     msg = cl.Message(content="")
     await msg.send()
 
-    # Prepare the completion parameters
-    completion_params = {
-        "model": model_name,
-        "messages": message_history,
-        "stream": True,
-    }
+    # Prepare the completion parameters using the helper function
+    completion_params = _build_completion_params(
+        model_name,
+        messages=message_history,
+        stream=True,
+    )
 
     # If an image is uploaded, include it in the message
     if image:
@@ -344,9 +365,11 @@ Context:
                         logger.error(f"Failed to parse function arguments: {function_args}")
 
         second_response = await acompletion(
-            model=model_name,
-            stream=True,
-            messages=messages,
+            **_build_completion_params(
+                model_name,
+                stream=True,
+                messages=messages,
+            )
         )
         logger.debug(f"Second LLM response: {second_response}")
 

src/praisonai/pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "PraisonAI"
-version = "2.2.22"
+version = "2.2.24"
 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>=0.0.91",
+    "praisonaiagents>=0.0.95",
     "python-dotenv>=0.19.0",
     "instructor>=1.3.3",
     "PyYAML>=6.0",
@@ -95,7 +95,7 @@ autogen = ["pyautogen>=0.2.19", "praisonai-tools>=0.0.15", "crewai"]
 
 [tool.poetry]
 name = "PraisonAI"
-version = "2.2.22"
+version = "2.2.24"
 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 = ""
@@ -113,7 +113,7 @@ python = ">=3.10,<3.13"
 rich = ">=13.7"
 markdown = ">=3.5"
 pyparsing = ">=3.0.0"
-praisonaiagents = ">=0.0.91"
+praisonaiagents = ">=0.0.95"
 python-dotenv = ">=0.19.0"
 instructor = ">=1.3.3"
 PyYAML = ">=6.0"

src/praisonai/uv.lock

@@ -3931,7 +3931,7 @@ wheels = [
 
 [[package]]
 name = "praisonai"
-version = "2.2.22"
+version = "2.2.24"
 source = { editable = "." }
 dependencies = [
     { name = "instructor" },
@@ -4073,7 +4073,7 @@ requires-dist = [
     { name = "plotly", marker = "extra == 'realtime'", specifier = ">=5.24.0" },
     { name = "praisonai-tools", marker = "extra == 'autogen'", specifier = ">=0.0.15" },
     { name = "praisonai-tools", marker = "extra == 'crewai'", specifier = ">=0.0.15" },
-    { name = "praisonaiagents", specifier = ">=0.0.91" },
+    { name = "praisonaiagents", specifier = ">=0.0.95" },
     { 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" },
@@ -4130,7 +4130,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.0.91"
+version = "0.0.95"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "mcp" },
@@ -4138,9 +4138,9 @@ dependencies = [
     { name = "pydantic" },
     { name = "rich" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/9f/63/a26fe7ae31191ed5998fabe6cb13f779728caf3123ac8c8e8f4707a99e58/praisonaiagents-0.0.91.tar.gz", hash = "sha256:ec0409591f9278b766c4c6b3876ed142cfc217b1545c1b9809494f8a826b55fa", size = 125710 }
+sdist = { url = "https://files.pythonhosted.org/packages/6a/7c/c6ae58b98d652f3c986683e72883eda5a3a4c2de054c01f6674ebdb0ae26/praisonaiagents-0.0.95.tar.gz", hash = "sha256:3b71869c3c73241e7dee109232d492545ea2a397fbfd9c922d4c9cfd41d93094", size = 127328 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/8c/2b/2e2d29ceb21a8e3af15beaf60955b85a9373ee6164d2d76f649a3e533786/praisonaiagents-0.0.91-py3-none-any.whl", hash = "sha256:aaaa51370a81aae29500168678d4b7208845890485f0a13da5367f1210dfaa3c", size = 143898 },
+    { url = "https://files.pythonhosted.org/packages/00/d2/63c8725b03d4f4bb1387594ed2cf93d1d544a6216ec7c374536ebfe6bf2a/praisonaiagents-0.0.95-py3-none-any.whl", hash = "sha256:04528314fd23240b4cb388149d8f181714753e6a3b68d429de46f66fc2c94857", size = 145811 },
 ]
 
 [[package]]