Merge pull request #301 from MervinPraison/develop

v0.0.38

Dockerfile

@@ -1,6 +1,6 @@
 FROM python:3.11-slim
 WORKDIR /app
 COPY . .
-RUN pip install flask praisonai==2.0.46 gunicorn markdown
+RUN pip install flask praisonai==2.0.47 gunicorn markdown
 EXPOSE 8080
 CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]

agents/chunking-direct-test.py

@@ -0,0 +1,14 @@
+from praisonaiagents.knowledge import Chunking
+
+# No heavy imports or initialization happens here
+chunking = Chunking(chunker_type='token')
+
+# Chunker is only initialized when first used
+chunks = chunking.chunk("Your text here")
+
+# Embedding model is only loaded if using semantic chunking
+semantic_chunking = Chunking(chunker_type='recursive')
+# Embedding model and chunker are loaded here on first use
+semantic_chunks = semantic_chunking.chunk("Your text here")
+
+print(semantic_chunks)

agents/document.txt

@@ -0,0 +1,3 @@
+I am Mervin Praison, a software engineer and entrepreneur.
+
+I am the founder of Praison AI, a company that is building AI agents to help people with their daily tasks.

agents/gradio-agents.py

@@ -0,0 +1,34 @@
+import gradio as gr
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+def research(query):
+    agent = Agent(instructions="You are a Research Agent", tools=[duckduckgo])
+    result = agent.start(query)
+    # Format the result with enhanced markdown
+    formatted_result = f"""
+{result}
+----
+*Generated by PraisonAI Research Assistant*
+    """
+    return formatted_result
+
+# Create a simple Gradio interface
+demo = gr.Interface(
+    fn=research,
+    inputs=gr.Textbox(
+        label="Research Query",
+        placeholder="Enter your research topic...",
+        lines=2
+    ),
+    outputs=gr.Markdown(
+        show_copy_button=True,
+        height=500,
+        container=True
+    ),
+    title="AI Research Assistant",
+    description="Enter your research query below to get started!",
+)
+
+if __name__ == "__main__":
+    demo.launch()

agents/knowledge-agents-task.py

@@ -0,0 +1,48 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import logging
+import os
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+# Define the configuration for the Knowledge instance
+config = {
+    "vector_store": {
+        "provider": "chroma",
+        "config": {
+            "collection_name": "knowledge_test",
+            "path": ".praison",
+        }
+    }
+}
+
+# Create an agent with knowledge capabilities
+knowledge_agent = Agent(
+    name="KnowledgeAgent",
+    role="Information Specialist",
+    goal="Store and retrieve knowledge efficiently",
+    backstory="Expert in managing and utilizing stored knowledge",
+    knowledge=["sample.pdf"],
+    knowledge_config=config,
+    verbose=True
+)
+
+# Define a task for the agent
+knowledge_task = Task(
+    name="knowledge_task",
+    description="Who is Mervin Praison?",
+    expected_output="Answer to the question",
+    agent=knowledge_agent
+)
+
+# Create and start the agents
+agents = PraisonAIAgents(
+    agents=[knowledge_agent],
+    tasks=[knowledge_task],
+    process="sequential",
+    user_id="user1"
+)
+
+# Start execution
+result = agents.start()

agents/knowledge-agents.py

@@ -0,0 +1,20 @@
+from praisonaiagents import Agent
+
+config = {
+    "vector_store": {
+        "provider": "chroma",
+        "config": {
+            "collection_name": "custom_knowledge",
+            "path": ".praison",
+        }
+    }
+}
+
+agent = Agent(
+    name="Knowledge Agent",
+    instructions="You answer questions based on the provided knowledge.",
+    knowledge=["small.pdf"],
+    knowledge_config=config
+)
+
+agent.start("What is KAG in one line?")

agents/knowledge-direct-test.py

@@ -0,0 +1,171 @@
+import logging
+from praisonaiagents.knowledge import Knowledge
+import os
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+config = {
+    "vector_store": {
+        "provider": "chroma",
+        "config": {
+            "collection_name": "test",
+            "path": ".praison",
+        }
+    }
+}
+
+knowledge = Knowledge(config)
+
+# Store memories
+logger.info("Storing memories...")
+cricket_memory = knowledge.store("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
+logger.info(f"Stored cricket memory: {cricket_memory}")
+tennis_memory = knowledge.store("Enjoys playing tennis with friends", user_id="alice", metadata={"category": "hobbies"})
+logger.info(f"Stored tennis memory: {tennis_memory}")
+work_memory = knowledge.store("Works as a software engineer", user_id="alice", metadata={"category": "work"})
+logger.info(f"Stored work memory: {work_memory}")
+
+print("Stored memories:")
+print(cricket_memory)
+print(tennis_memory)
+print(work_memory)
+
+# Retrieve all memories for a user
+logger.info("Retrieving all memories for Alice...")
+alice_memories = knowledge.get_all(user_id="alice")
+logger.info(f"Retrieved memories for Alice: {alice_memories}")
+print("\nAll memories for Alice:")
+for memory in alice_memories:
+    print(memory)
+
+# Retrieve a specific memory by ID
+logger.info(f"Retrieving memory with ID: {cricket_memory['results'][0]['id']}")
+retrieved_memory = knowledge.get(cricket_memory['results'][0]["id"])
+logger.info(f"Retrieved memory: {retrieved_memory}")
+print("\nRetrieved memory:")
+print(retrieved_memory)
+
+# Search for memories based on a query
+query = "What are Alice's hobbies?"
+logger.info(f"Searching for memories with query: {query}")
+search_results = knowledge.search(query, user_id="alice")
+logger.info(f"Search results: {search_results}")
+print(f"\nSearch results for '{query}':")
+for memory in search_results:
+    print(memory)
+
+# Update a memory
+logger.info(f"Updating memory with ID: {tennis_memory['results'][0]['id']}")
+updated_memory = knowledge.update(memory_id=tennis_memory['results'][0]["id"], data="Loves playing tennis on weekends")
+logger.info(f"Updated memory: {updated_memory}")
+print("\nUpdated memory:")
+print(updated_memory)
+
+# Get memory history
+logger.info(f"Retrieving memory history for ID: {tennis_memory['results'][0]['id']}")
+memory_history = knowledge.history(memory_id=tennis_memory['results'][0]["id"])
+logger.info(f"Memory history: {memory_history}")
+print("\nMemory history:")
+for entry in memory_history:
+    print(entry)
+
+# Delete a memory
+logger.info(f"Deleting memory with ID: {work_memory['results'][0]['id']}")
+knowledge.delete(memory_id=work_memory['results'][0]["id"])
+logger.info("Memory deleted")
+print("\nDeleted memory")
+
+# Retrieve memories after deletion
+logger.info("Retrieving memories after deletion...")
+alice_memories = knowledge.get_all(user_id="alice")
+logger.info(f"Retrieved memories after deletion: {alice_memories}")
+print("\nMemories after deletion:")
+for memory in alice_memories:
+    print(memory)
+
+# Delete all memories for a user
+logger.info("Deleting all memories for Alice...")
+knowledge.delete_all(user_id="alice")
+logger.info("All memories for Alice deleted")
+print("\nAll memories for Alice deleted")
+
+# Retrieve memories after deleting all
+logger.info("Retrieving memories after deleting all...")
+alice_memories = knowledge.get_all(user_id="alice")
+logger.info(f"Retrieved memories after deleting all: {alice_memories}")
+print("\nMemories after deleting all:")
+print(alice_memories)
+
+# Reset all memories
+logger.info("Resetting all memories...")
+knowledge.reset()
+logger.info("All memories reset")
+print("\nAll memories reset")
+
+# Test file handling
+logger.info("Testing file handling...")
+
+try:
+    # Get absolute path to test files
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    pdf_file = os.path.join(current_dir, "small.pdf")
+    txt_file = os.path.join(current_dir, "sample.txt")
+
+    # Test text file
+    logger.info(f"Testing text file: {txt_file}")
+    # Read and display content
+    with open(txt_file, 'r') as file:
+        txt_content = file.read()
+        print("\nText file content:")
+        print(txt_content)
+    # Store the text file using add
+    txt_memory = knowledge.add(txt_file, user_id="bob", metadata={"type": "text"})
+    logger.info(f"Stored text file: {txt_memory}")
+    print("\nStored text content:")
+    if txt_memory and 'results' in txt_memory and txt_memory['results']:
+        for mem in txt_memory['results']:
+            print(mem)
+    else:
+        print("No valid memories were stored from the text file")
+
+    # Verify text memories are stored
+    if txt_memory and 'results' in txt_memory and txt_memory['results']:
+        logger.info("Verifying text memories...")
+        for mem in txt_memory['results']:
+            if isinstance(mem, dict) and 'id' in mem:
+                stored = knowledge.get(mem['id'])
+                logger.info(f"Verified text memory: {stored}")
+            else:
+                logger.warning(f"Unexpected memory format: {mem}")
+    else:
+        logger.warning("No valid memories were stored from the text file")
+
+    # Test pdf file
+    logger.info(f"Testing pdf file: {pdf_file}")
+    pdf_memory = knowledge.add(pdf_file, user_id="bob", metadata={"type": "pdf"})
+    logger.info(f"Stored pdf file: {pdf_memory}")
+    print("\nStored pdf content:")
+    if pdf_memory and 'results' in pdf_memory and pdf_memory['results']:
+        for mem in pdf_memory['results']:
+            print(mem)
+    else:
+        print("No valid memories were stored from the PDF file")
+
+    # Verify stored content
+    if pdf_memory and 'results' in pdf_memory and pdf_memory['results']:
+        for memory in pdf_memory['results']:
+            if isinstance(memory, dict) and 'id' in memory:
+                stored_memory = knowledge.get(memory['id'])
+                logger.info(f"Verified stored memory: {stored_memory}")
+                print("\nVerified content:")
+                print(stored_memory)
+            else:
+                logger.warning(f"Unexpected memory format: {memory}")
+    else:
+        logger.warning("No valid memories were stored from the PDF file")
+
+except Exception as e:
+    logger.error(f"Error during file handling test: {str(e)}")
+    raise
+

agents/knowledge-large-pdf-test.py

@@ -0,0 +1,40 @@
+import logging
+from praisonaiagents.knowledge import Knowledge
+import os
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+knowledge = Knowledge()
+try:
+    # Get absolute path to test files
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    pdf_file = os.path.join(current_dir, "large.pdf")
+
+    # Test pdf file
+    logger.info(f"Testing pdf file: {pdf_file}")
+    pdf_memory = knowledge.add(pdf_file, user_id="bob", metadata={"type": "pdf"})
+    logger.info(f"Stored pdf file: {pdf_memory}")
+    print("\nStored pdf content:")
+    if pdf_memory and 'results' in pdf_memory and pdf_memory['results']:
+        for mem in pdf_memory['results']:
+            print(mem)
+    else:
+        print("No valid memories were stored from the PDF file")
+
+    # Verify stored content
+    if pdf_memory and 'results' in pdf_memory and pdf_memory['results']:
+        for memory in pdf_memory['results']:
+            if isinstance(memory, dict) and 'id' in memory:
+                stored_memory = knowledge.get(memory['id'])
+                logger.info(f"Verified stored memory: {stored_memory}")
+                print("\nVerified content:")
+                print(stored_memory)
+            else:
+                logger.warning(f"Unexpected memory format: {memory}")
+    else:
+        logger.warning("No valid memories were stored from the PDF file")
+
+except Exception as e:
+    logger.error(f"Error during file handling test: {str(e)}")
+    raise

agents/md.py

@@ -0,0 +1,5 @@
+from markitdown import MarkItDown
+
+md = MarkItDown()
+result = md.convert("sample.txt")
+print(result.text_content)

agents/praisonaiagents/__init__.py

@@ -7,6 +7,8 @@ from .agents.agents import PraisonAIAgents
 from .task.task import Task
 from .tools.tools import Tools
 from .agents.autoagents import AutoAgents
+from .knowledge.knowledge import Knowledge
+from .knowledge.chunking import Chunking
 from .memory.memory import Memory
 from .main import (
     TaskOutput,
@@ -48,4 +50,6 @@ __all__ = [
     'register_display_callback',
     'sync_display_callbacks',
     'async_display_callbacks',
+    'Knowledge',
+    'Chunking'
 ] 

agents/praisonaiagents/agent/agent.py

@@ -20,6 +20,7 @@ from ..main import (
     adisplay_instruction
 )
 import inspect
+import uuid
 
 if TYPE_CHECKING:
     from ..task.task import Task
@@ -176,13 +177,15 @@ class Agent:
         respect_context_window: bool = True,
         code_execution_mode: Literal["safe", "unsafe"] = "safe",
         embedder_config: Optional[Dict[str, Any]] = None,
-        knowledge_sources: Optional[List[Any]] = None,
+        knowledge: Optional[List[str]] = None,
+        knowledge_config: Optional[Dict[str, Any]] = None,
         use_system_prompt: Optional[bool] = True,
         markdown: bool = True,
         self_reflect: bool = False,
         max_reflect: int = 3,
         min_reflect: int = 1,
-        reflect_llm: Optional[str] = None
+        reflect_llm: Optional[str] = None,
+        user_id: Optional[str] = None
     ):
         # Handle backward compatibility for required fields
         if all(x is None for x in [name, role, goal, backstory, instructions]):
@@ -226,7 +229,7 @@ class Agent:
         self.respect_context_window = respect_context_window
         self.code_execution_mode = code_execution_mode
         self.embedder_config = embedder_config
-        self.knowledge_sources = knowledge_sources
+        self.knowledge = knowledge
         self.use_system_prompt = use_system_prompt
         self.chat_history = []
         self.markdown = markdown
@@ -242,6 +245,36 @@ Your Role: {self.role}\n
 Your Goal: {self.goal}
         """
 
+        # Generate unique IDs
+        self.agent_id = str(uuid.uuid4())
+
+        # Store user_id
+        self.user_id = user_id
+
+        # Initialize Knowledge with provided or default config
+        from praisonaiagents.knowledge import Knowledge
+        self.knowledge = Knowledge(knowledge_config or None)
+
+        # Handle knowledge
+        if knowledge:
+            for source in knowledge:
+                self._process_knowledge(source)
+
+    def _process_knowledge(self, knowledge_item):
+        """Process and store knowledge from a file path, URL, or string."""
+        try:
+            if os.path.exists(knowledge_item):
+                # It's a file path
+                self.knowledge.add(knowledge_item, user_id=self.user_id, agent_id=self.agent_id)
+            elif knowledge_item.startswith("http://") or knowledge_item.startswith("https://"):
+                # It's a URL
+                pass
+            else:
+                # It's a string content
+                self.knowledge.store(knowledge_item, user_id=self.user_id, agent_id=self.agent_id)
+        except Exception as e:
+            logging.error(f"Error processing knowledge item: {knowledge_item}, error: {e}")
+
     def generate_task(self) -> 'Task':
         """Generate a Task object from the agent's instructions"""
         from ..task.task import Task
@@ -418,6 +451,21 @@ Your Goal: {self.goal}
             return None
 
     def chat(self, prompt, temperature=0.2, tools=None, output_json=None, output_pydantic=None):
+        # Search for existing knowledge if any knowledge is provided
+        if self.knowledge:
+            search_results = self.knowledge.search(prompt, agent_id=self.agent_id)
+            if search_results:
+                # Check if search_results is a list of dictionaries or strings
+                if isinstance(search_results, dict) and 'results' in search_results:
+                    # Extract memory content from the results
+                    knowledge_content = "\n".join([result['memory'] for result in search_results['results']])
+                else:
+                    # If search_results is a list of strings, join them directly
+                    knowledge_content = "\n".join(search_results)
+                
+                # Append found knowledge to the prompt
+                prompt = f"{prompt}\n\nKnowledge: {knowledge_content}"
+
         if self.use_system_prompt:
             system_prompt = f"""{self.backstory}\n
 Your Role: {self.role}\n

agents/praisonaiagents/agents/agents.py

@@ -12,6 +12,7 @@ from ..agent.agent import Agent
 from ..task.task import Task
 from ..process.process import Process, LoopItems
 import asyncio
+import uuid
 
 # Set up logger
 logger = logging.getLogger(__name__)
@@ -44,10 +45,17 @@ def process_video(video_path: str, seconds_per_frame=2):
     return base64_frames
 
 class PraisonAIAgents:
-    def __init__(self, agents, tasks=None, verbose=0, completion_checker=None, max_retries=5, process="sequential", manager_llm=None, memory=False, memory_config=None, embedder=None):
+    def __init__(self, agents, tasks=None, verbose=0, completion_checker=None, max_retries=5, process="sequential", manager_llm=None, memory=False, memory_config=None, embedder=None, user_id=None):
         if not agents:
             raise ValueError("At least one agent must be provided")
-            
+        
+        self.run_id = str(uuid.uuid4())  # Auto-generate run_id
+        self.user_id = user_id  # Optional user_id
+
+        # Pass user_id to each agent
+        for agent in agents:
+            agent.user_id = self.user_id
+
         self.agents = agents
         self.tasks = {}
         if max_retries < 3:

agents/praisonaiagents/knowledge/__init__.py

@@ -0,0 +1,8 @@
+"""
+PraisonAI Knowledge - Advanced knowledge management system with configurable features
+"""
+
+from praisonaiagents.knowledge.knowledge import Knowledge
+from praisonaiagents.knowledge.chunking import Chunking
+
+__all__ = ["Knowledge", "Chunking"] 

agents/praisonaiagents/knowledge/chunking.py

@@ -0,0 +1,182 @@
+from typing import List, Union, Optional, Dict, Any
+from functools import cached_property
+import importlib
+
+class Chunking:
+    """A unified class for text chunking with various chunking strategies."""
+    
+    CHUNKER_PARAMS = {
+        'token': ['chunk_size', 'chunk_overlap', 'tokenizer'],
+        'word': ['chunk_size', 'chunk_overlap', 'tokenizer'],
+        'sentence': ['chunk_size', 'chunk_overlap', 'tokenizer'],
+        'semantic': ['chunk_size', 'embedding_model', 'tokenizer'],
+        'sdpm': ['chunk_size', 'embedding_model', 'tokenizer'],
+        'late': ['chunk_size', 'embedding_model', 'tokenizer'],
+        'recursive': ['chunk_size', 'tokenizer']
+    }
+    
+    @cached_property
+    def SUPPORTED_CHUNKERS(self) -> Dict[str, Any]:
+        """Lazy load chunker classes."""
+        try:
+            from chonkie.chunker import (
+                TokenChunker,
+                WordChunker,
+                SentenceChunker,
+                SemanticChunker,
+                SDPMChunker,
+                LateChunker,
+                RecursiveChunker
+            )
+        except ImportError:
+            raise ImportError(
+                "chonkie package not found. Please install it using: pip install 'praisonaiagents[knowledge]'"
+            )
+            
+        return {
+            'token': TokenChunker,
+            'word': WordChunker,
+            'sentence': SentenceChunker,
+            'semantic': SemanticChunker,
+            'sdpm': SDPMChunker,
+            'late': LateChunker,
+            'recursive': RecursiveChunker
+        }
+
+    def __init__(
+        self,
+        chunker_type: str = 'token',
+        chunk_size: int = 512,
+        chunk_overlap: int = 128,
+        tokenizer: str = "gpt2",
+        embedding_model: Optional[Union[str, Any]] = None,
+        **kwargs
+    ):
+        """Initialize the Chunking class."""
+        if chunker_type not in self.CHUNKER_PARAMS:
+            raise ValueError(
+                f"Unsupported chunker type: {chunker_type}. "
+                f"Must be one of: {list(self.CHUNKER_PARAMS.keys())}"
+            )
+        
+        self.chunker_type = chunker_type
+        self.chunk_size = chunk_size
+        self.chunk_overlap = chunk_overlap
+        self.tokenizer = tokenizer
+        self._embedding_model = embedding_model
+        self.kwargs = kwargs
+        
+        # Initialize these as None for lazy loading
+        self._chunker = None
+        self._embeddings = None
+        
+    @cached_property
+    def embedding_model(self):
+        """Lazy load the embedding model."""
+        if self._embedding_model is None and self.chunker_type in ['semantic', 'sdpm', 'late']:
+            from chonkie.embeddings import AutoEmbeddings
+            return AutoEmbeddings.get_embeddings("all-MiniLM-L6-v2")
+        elif isinstance(self._embedding_model, str):
+            from chonkie.embeddings import AutoEmbeddings
+            return AutoEmbeddings.get_embeddings(self._embedding_model)
+        return self._embedding_model
+
+    def _get_chunker_params(self) -> Dict[str, Any]:
+        """Get the appropriate parameters for the current chunker type."""
+        allowed_params = self.CHUNKER_PARAMS[self.chunker_type]
+        params = {'chunk_size': self.chunk_size}
+        
+        if 'chunk_overlap' in allowed_params:
+            params['chunk_overlap'] = self.chunk_overlap
+            
+        if 'tokenizer' in allowed_params:
+            if self.chunker_type in ['semantic', 'sdpm', 'late']:
+                params['tokenizer'] = self.embedding_model.get_tokenizer_or_token_counter()
+            else:
+                params['tokenizer'] = self.tokenizer
+                
+        if 'embedding_model' in allowed_params:
+            params['embedding_model'] = self.embedding_model
+            
+        # Add any additional kwargs that are in allowed_params
+        for key, value in self.kwargs.items():
+            if key in allowed_params:
+                params[key] = value
+                
+        return params
+
+    @cached_property
+    def chunker(self):
+        """Lazy load the chunker instance."""
+        if self._chunker is None:
+            chunker_cls = self.SUPPORTED_CHUNKERS[self.chunker_type]
+            common_params = self._get_chunker_params()
+            self._chunker = chunker_cls(**common_params)
+        
+        return self._chunker
+    
+    def _get_overlap_refinery(self, context_size: Optional[int] = None, **kwargs):
+        """Lazy load the overlap refinery."""
+        try:
+            from chonkie.refinery import OverlapRefinery
+        except ImportError:
+            raise ImportError("Failed to import OverlapRefinery from chonkie.refinery")
+            
+        if context_size is None:
+            context_size = self.chunk_overlap
+            
+        return OverlapRefinery(
+            context_size=context_size,
+            tokenizer=self.chunker.tokenizer,
+            **kwargs
+        )
+    
+    def add_overlap_context(
+        self,
+        chunks: List[Any],
+        context_size: int = None,
+        mode: str = "suffix",
+        merge_context: bool = True
+    ) -> List[Any]:
+        """Add overlap context to chunks using OverlapRefinery."""
+        refinery = self._get_overlap_refinery(
+            context_size=context_size,
+            mode=mode,
+            merge_context=merge_context
+        )
+        return refinery.refine(chunks)
+    
+    def chunk(
+        self,
+        text: Union[str, List[str]],
+        add_context: bool = False,
+        context_params: Optional[Dict[str, Any]] = None
+    ) -> Union[List[Any], List[List[Any]]]:
+        """Chunk text using the configured chunking strategy."""
+        chunks = self.chunker(text)
+        
+        if add_context:
+            context_params = context_params or {}
+            if isinstance(text, str):
+                chunks = self.add_overlap_context(chunks, **context_params)
+            else:
+                chunks = [self.add_overlap_context(c, **context_params) for c in chunks]
+                
+        return chunks
+    
+    def __call__(
+        self,
+        text: Union[str, List[str]],
+        add_context: bool = False,
+        context_params: Optional[Dict[str, Any]] = None
+    ) -> Union[List[Any], List[List[Any]]]:
+        """Make the Chunking instance callable."""
+        return self.chunk(text, add_context, context_params)
+    
+    def __repr__(self) -> str:
+        """String representation of the Chunking instance."""
+        return (
+            f"Chunking(chunker_type='{self.chunker_type}', "
+            f"chunk_size={self.chunk_size}, "
+            f"chunk_overlap={self.chunk_overlap})"
+        )

agents/praisonaiagents/knowledge/knowledge.py

@@ -0,0 +1,290 @@
+import os
+import logging
+import uuid
+import time
+from .chunking import Chunking
+from functools import cached_property
+
+logger = logging.getLogger(__name__)
+
+class CustomMemory:
+    @classmethod
+    def from_config(cls, config):
+        from mem0 import Memory
+        return type('CustomMemory', (Memory,), {
+            '_add_to_vector_store': cls._add_to_vector_store
+        }).from_config(config)
+
+    @staticmethod
+    def _add_to_vector_store(self, messages, metadata, filters):
+        # Custom implementation that doesn't use LLM
+        parsed_messages = "\n".join([msg["content"] for msg in messages])
+        
+        # Create a simple fact without using LLM
+        new_retrieved_facts = [parsed_messages]
+        
+        # Process embeddings and continue with vector store operations
+        new_message_embeddings = {}
+        for new_mem in new_retrieved_facts:
+            messages_embeddings = self.embedding_model.embed(new_mem)
+            new_message_embeddings[new_mem] = messages_embeddings
+            
+        # Create the memory
+        memory_id = self._create_memory(
+            data=parsed_messages,
+            existing_embeddings=new_message_embeddings,
+            metadata=metadata
+        )
+        
+        return [{
+            "id": memory_id,
+            "memory": parsed_messages,
+            "event": "ADD"
+        }]
+
+class Knowledge:
+    def __init__(self, config=None, verbose=None):
+        self._config = config
+        self._verbose = verbose or 0
+        os.environ['ANONYMIZED_TELEMETRY'] = 'False'  # Chromadb
+        
+        # Configure logging levels based on verbose setting
+        if not self._verbose:
+            # Suppress logs from all relevant dependencies
+            for logger_name in [
+                'mem0', 
+                'chromadb', 
+                'local_persistent_hnsw',
+                '_client',
+                'main'
+            ]:
+                logging.getLogger(logger_name).setLevel(logging.WARNING)
+            
+            # Disable OpenAI API request logging
+            logging.getLogger('openai').setLevel(logging.WARNING)
+            
+            # Set root logger to warning to catch any uncategorized logs
+            logging.getLogger().setLevel(logging.WARNING)
+
+    @cached_property
+    def _deps(self):
+        try:
+            from markitdown import MarkItDown
+            import chromadb
+            return {
+                'chromadb': chromadb,
+                'markdown': MarkItDown()
+            }
+        except ImportError:
+            raise ImportError(
+                "Required packages not installed. Please install using: "
+                'pip install "praisonaiagents[knowledge]"'
+            )
+
+    @cached_property
+    def config(self):
+        # Generate unique collection name for each instance
+        collection_name = f"test_{int(time.time())}_{str(uuid.uuid4())[:8]}"
+        persist_dir = ".praison"
+
+        # Create persistent client config
+        base_config = {
+            "vector_store": {
+                "provider": "chroma",
+                "config": {
+                    "collection_name": collection_name,
+                    "path": persist_dir,
+                    "client": self._deps['chromadb'].PersistentClient(path=persist_dir)
+                }
+            },
+            "version": "v1.1",
+            "custom_prompt": "Return {{\"facts\": [text]}} where text is the exact input provided and json response"
+        }
+
+        # If config is provided, merge it with base config
+        if self._config:
+            if "vector_store" in self._config and "config" in self._config["vector_store"]:
+                config_copy = self._config["vector_store"]["config"].copy()
+                for key in ["collection_name", "client"]:
+                    if key in config_copy:
+                        del config_copy[key]
+                base_config["vector_store"]["config"].update(config_copy)
+
+        return base_config
+
+    @cached_property
+    def memory(self):
+        try:
+            return CustomMemory.from_config(self.config)
+        except (NotImplementedError, ValueError) as e:
+            if "list_collections" in str(e) or "Extra fields not allowed" in str(e):
+                # Keep only allowed fields
+                vector_store_config = {
+                    "collection_name": self.config["vector_store"]["config"]["collection_name"],
+                    "path": self.config["vector_store"]["config"]["path"]
+                }
+                self.config["vector_store"]["config"] = vector_store_config
+                from mem0 import Memory
+                return Memory.from_config(self.config)
+            raise
+
+    @cached_property
+    def markdown(self):
+        return self._deps['markdown']
+
+    @cached_property
+    def chunker(self):
+        return Chunking(
+            chunker_type='recursive',
+            chunk_size=512,
+            chunk_overlap=50
+        )
+
+    def _log(self, message, level=2):
+        """Internal logging helper"""
+        if self._verbose and self._verbose >= level:
+            logger.info(message)
+
+    def store(self, content, user_id=None, agent_id=None, run_id=None, metadata=None):
+        """Store a memory."""
+        try:
+            if isinstance(content, str):
+                if any(content.lower().endswith(ext) for ext in ['.pdf', '.doc', '.docx', '.txt']):
+                    self._log(f"Content appears to be a file path, processing file: {content}")
+                    return self.add(content, user_id=user_id, agent_id=agent_id, run_id=run_id, metadata=metadata)
+                
+                content = content.strip()
+                if not content:
+                    return []
+                
+            result = self.memory.add(content, user_id=user_id, agent_id=agent_id, run_id=run_id, metadata=metadata)
+            self._log(f"Store operation result: {result}")
+            return result
+        except Exception as e:
+            logger.error(f"Error storing content: {str(e)}")
+            return []
+
+    def get_all(self, user_id=None, agent_id=None, run_id=None):
+        """Retrieve all memories."""
+        return self.memory.get_all(user_id=user_id, agent_id=agent_id, run_id=run_id)
+
+    def get(self, memory_id):
+        """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 update(self, memory_id, data):
+        """Update a memory."""
+        return self.memory.update(memory_id, data)
+
+    def history(self, memory_id):
+        """Get the history of changes for a memory."""
+        return self.memory.history(memory_id)
+
+    def delete(self, memory_id):
+        """Delete a memory."""
+        self.memory.delete(memory_id)
+
+    def delete_all(self, user_id=None, agent_id=None, run_id=None):
+        """Delete all memories."""
+        self.memory.delete_all(user_id=user_id, agent_id=agent_id, run_id=run_id)
+
+    def reset(self):
+        """Reset all memories."""
+        self.memory.reset()
+
+    def normalize_content(self, content):
+        """Normalize content for consistent storage."""
+        # Example normalization: strip whitespace, convert to lowercase
+        return content.strip().lower()
+
+    def add(self, file_path, user_id=None, agent_id=None, run_id=None, metadata=None):
+        """Read file content and store it in memory.
+        
+        Args:
+            file_path: Can be:
+                - A string path to local file
+                - A URL string
+                - A list containing file paths and/or URLs
+        """
+        if isinstance(file_path, (list, tuple)):
+            results = []
+            for path in file_path:
+                result = self._process_single_input(path, user_id, agent_id, run_id, metadata)
+                results.extend(result.get('results', []))
+            return {'results': results, 'relations': []}
+        
+        return self._process_single_input(file_path, user_id, agent_id, run_id, metadata)
+
+    def _process_single_input(self, input_path, user_id=None, agent_id=None, run_id=None, metadata=None):
+        """Process a single input which can be a file path or URL."""
+        try:
+            # Define supported file extensions
+            DOCUMENT_EXTENSIONS = {
+                'document': ('.pdf', '.ppt', '.pptx', '.doc', '.docx', '.xls', '.xlsx'),
+                'media': ('.jpg', '.jpeg', '.png', '.gif', '.bmp', '.tiff', '.mp3', '.wav', '.ogg', '.m4a'),
+                'text': ('.txt', '.csv', '.json', '.xml', '.md', '.html', '.htm'),
+                'archive': '.zip'
+            }
+
+            # Check if input is URL
+            if isinstance(input_path, str) and (input_path.startswith('http://') or input_path.startswith('https://')):
+                self._log(f"Processing URL: {input_path}")
+                raise NotImplementedError("URL processing not yet implemented")
+
+            # Check if input ends with any supported extension
+            is_supported_file = any(input_path.lower().endswith(ext) 
+                                  for exts in DOCUMENT_EXTENSIONS.values()
+                                  for ext in (exts if isinstance(exts, tuple) else (exts,)))
+            
+            if is_supported_file:
+                self._log(f"Processing as file path: {input_path}")
+                if not os.path.exists(input_path):
+                    logger.error(f"File not found: {input_path}")
+                    raise FileNotFoundError(f"File not found: {input_path}")
+                
+                file_ext = '.' + input_path.lower().split('.')[-1]  # Get extension reliably
+                
+                # Process file based on type
+                if file_ext in DOCUMENT_EXTENSIONS['text']:
+                    with open(input_path, 'r', encoding='utf-8') as file:
+                        content = file.read().strip()
+                    if not content:
+                        raise ValueError("Empty text file")
+                    memories = [self.normalize_content(content)]
+                else:
+                    # Use MarkItDown for documents and media
+                    result = self.markdown.convert(input_path)
+                    content = result.text_content
+                    if not content:
+                        raise ValueError("No content could be extracted from file")
+                    chunks = self.chunker.chunk(content)
+                    memories = [chunk.text.strip() if hasattr(chunk, 'text') else str(chunk).strip() 
+                              for chunk in chunks if chunk]
+
+                # Set metadata for file
+                if not metadata:
+                    metadata = {}
+                metadata['file_type'] = file_ext.lstrip('.')
+                metadata['filename'] = os.path.basename(input_path)
+            else:
+                # Treat as raw text content only if no file extension
+                memories = [self.normalize_content(input_path)]
+
+            # Store memories
+            all_results = []
+            for memory in memories:
+                if memory:
+                    memory_result = self.store(memory, user_id=user_id, agent_id=agent_id, 
+                                             run_id=run_id, metadata=metadata)
+                    if memory_result:
+                        all_results.extend(memory_result.get('results', []))
+
+            return {'results': all_results, 'relations': []}
+
+        except Exception as e:
+            logger.error(f"Error processing input {input_path}: {str(e)}", exc_info=True)
+            raise

agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.0.36"
+version = "0.0.38"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 authors = [
     { name="Mervin Praison" }
@@ -17,5 +17,18 @@ dependencies = [
 
 [project.optional-dependencies]
 memory = [
-    "chromadb>=0.6.0"
-] 
+    "chromadb>=0.5.23"
+]
+
+knowledge = [
+    "mem0ai>=0.1.0",
+    "chromadb==0.5.23",
+    "markitdown",
+    "chonkie"
+]
+
+# Combined features
+all = [
+    "praisonaiagents[memory]",
+    "praisonaiagents[knowledge]"
+]

agents/sample.md

@@ -0,0 +1,5 @@
+# Sample Markdown
+## Section 1
+This is a test markdown file.
+## Section 2
+It contains some formatted text and sections.

agents/sample.txt

@@ -0,0 +1,3 @@
+This is a sample text file.
+It contains multiple lines.
+This is for testing purposes.

agents/streamlit-agents.py

@@ -0,0 +1,21 @@
+import streamlit as st
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+st.title("AI Research Assistant")
+st.write("Enter your research query below to get started!")
+
+# Initialize the research agent
+agent = Agent(instructions="You are a Research Agent", tools=[duckduckgo])
+
+# Create the input field
+query = st.text_input("Research Query", placeholder="Enter your research topic...")
+
+# Add a search button
+if st.button("Search"):
+    if query:
+        with st.spinner("Researching..."):
+            result = agent.start(query)
+            st.write(result)
+    else:
+        st.warning("Please enter a research query")

docs/agents/agents.mdx

@@ -0,0 +1,85 @@
+---
+title: "AI Agents"
+description: "Overview of all available PraisonAI agents and their capabilities"
+icon: "robot"
+---
+
+PraisonAI provides a diverse set of specialized agents for various tasks. Each agent is designed with specific capabilities and tools to handle different types of tasks effectively.
+
+## Data & Analysis
+
+<CardGroup cols={2}>
+  <Card title="Data Analyst" icon="chart-line" href="/agents/data-analyst">
+    Analyze data from various sources, create visualizations, and generate insights.
+  </Card>
+  <Card title="Finance" icon="money-bill-trend-up" href="/agents/finance">
+    Track stocks, analyze financial data, and provide investment recommendations.
+  </Card>
+  <Card title="Research" icon="magnifying-glass-chart" href="/agents/research">
+    Conduct comprehensive research and analysis across various topics.
+  </Card>
+  <Card title="Wikipedia" icon="book" href="/agents/wikipedia">
+    Search and extract information from Wikipedia articles.
+  </Card>
+</CardGroup>
+
+## Media & Content
+
+<CardGroup cols={2}>
+  <Card title="Image Analysis" icon="image" href="/agents/image">
+    Analyze and understand visual content from images.
+  </Card>
+  <Card title="Image to Text" icon="text" href="/agents/image-to-text">
+    Convert images to textual descriptions and extract text content.
+  </Card>
+  <Card title="Video" icon="video" href="/agents/video">
+    Analyze video content and extract meaningful information.
+  </Card>
+  <Card title="Markdown" icon="markdown" href="/agents/markdown">
+    Generate and format content in Markdown syntax.
+  </Card>
+</CardGroup>
+
+## Search & Recommendations
+
+<CardGroup cols={2}>
+  <Card title="Web Search" icon="globe" href="/agents/websearch">
+    Perform intelligent web searches and gather information.
+  </Card>
+  <Card title="Recommendation" icon="thumbs-up" href="/agents/recommendation">
+    Generate personalized recommendations based on preferences.
+  </Card>
+  <Card title="Shopping" icon="shop" href="/agents/shopping">
+    Compare prices and find the best deals across stores.
+  </Card>
+  <Card title="Planning" icon="calendar" href="/agents/planning">
+    Create travel plans and detailed itineraries.
+  </Card>
+</CardGroup>
+
+## Development
+
+<CardGroup cols={2}>
+  <Card title="Programming" icon="code" href="/agents/programming">
+    Write, analyze, and debug code across multiple languages.
+  </Card>
+  <Card title="Single Agent" icon="circle-1" href="/agents/single">
+    Simple, focused agent for basic tasks without external tools.
+  </Card>
+</CardGroup>
+
+## Getting Started
+
+Each agent can be easily initialized and customized for your specific needs. Here's a basic example:
+
+```python
+from praisonaiagents import Agent
+
+# Create an agent with specific instructions
+agent = Agent(instructions="Your task-specific instructions")
+
+# Start the agent with a task
+response = agent.start("Your task description")
+```
+
+For more detailed information about each agent, click on the respective cards above.

docs/agents/image-to-text.mdx

@@ -2,7 +2,7 @@
 title: "Image to Text Agent"
 sidebarTitle: "Image to Text"
 description: "Learn how to create AI agents for converting images to textual descriptions and extracting text from images."
-icon: "image-to-text"
+icon: "text"
 ---
 
 ```mermaid

docs/agents/shopping.mdx

@@ -2,7 +2,7 @@
 title: "Shopping Agent"
 sidebarTitle: "Shopping"
 description: "Learn how to create AI agents for price comparison and shopping assistance."
-icon: "shopping-cart"
+icon: "shop"
 ---
 
 ```mermaid

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==2.0.46 gunicorn markdown\n&#34;)
+            file.write(&#34;RUN pip install flask praisonai==2.0.47 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/concepts/knowledge.mdx

@@ -0,0 +1,232 @@
+---
+title: "Knowledge"
+description: "Learn how to create AI agents with custom knowledge bases"
+icon: "book"
+---
+
+```mermaid
+flowchart LR
+    subgraph In[Input]
+        PDF[PDF]
+        TXT[TXT]
+        MD[MD]
+    end
+
+    subgraph Router[Vector Store]
+        DB[(Chroma)]
+    end
+    
+    subgraph Out[Agents]
+        A1[Agent 1]
+        A2[Agent 2]
+        A3[Agent 3]
+    end
+
+    In --> Router
+    Router --> A1
+    Router --> A2
+    Router --> A3
+
+    style In fill:#8B0000,color:#fff
+    style Router fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Quick Start
+
+<Steps>
+  <Step title="Install Package">
+    Install PraisonAI Agents with knowledge support:
+    ```bash
+    pip install "praisonaiagents[knowledge]"
+    ```
+  </Step>
+
+  <Step title="Set API Key">
+    Set your OpenAI API key:
+    ```bash
+    export OPENAI_API_KEY=xxxxx
+    ```
+  </Step>
+
+  <Step title="Create Script">
+    Create a new file `app.py`:
+    ```python
+    from praisonaiagents import Agent
+
+    agent = Agent(
+        name="Knowledge Agent",
+        instructions="You answer questions based on the provided knowledge.",
+        knowledge=["small.pdf"]
+    )
+
+    agent.start("What is KAG in one line?")
+    ```
+  </Step>
+</Steps>
+
+## Basic Usage
+
+The simplest way to create a knowledge-based agent is without any configuration:
+
+```python
+from praisonaiagents import Agent
+
+agent = Agent(
+    name="Knowledge Agent",
+    instructions="You answer questions based on the provided knowledge.",
+    knowledge=["small.pdf"]
+)
+
+agent.start("What is KAG in one line?")
+```
+
+## Advanced Configuration
+
+For more control over the knowledge base, you can specify a configuration:
+
+```python
+from praisonaiagents import Agent
+
+config = {
+    "vector_store": {
+        "provider": "chroma",
+        "config": {
+            "collection_name": "custom_knowledge",
+            "path": ".praison",
+        }
+    }
+}
+
+agent = Agent(
+    name="Knowledge Agent",
+    instructions="You answer questions based on the provided knowledge.",
+    knowledge=["small.pdf"],
+    knowledge_config=config
+)
+
+agent.start("What is KAG in one line?")
+```
+
+## Multi-Agent Knowledge System
+
+For more complex scenarios, you can create a knowledge-based system with multiple agents:
+
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import logging
+import os
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+# Define the configuration for the Knowledge instance
+config = {
+    "vector_store": {
+        "provider": "chroma",
+        "config": {
+            "collection_name": "knowledge_test",
+            "path": ".praison",
+        }
+    }
+}
+
+# Create an agent with knowledge capabilities
+knowledge_agent = Agent(
+    name="KnowledgeAgent",
+    role="Information Specialist",
+    goal="Store and retrieve knowledge efficiently",
+    backstory="Expert in managing and utilizing stored knowledge",
+    knowledge=["sample.pdf"],
+    knowledge_config=config,
+    verbose=True
+)
+
+# Define a task for the agent
+knowledge_task = Task(
+    name="knowledge_task",
+    description="Who is Mervin Praison?",
+    expected_output="Answer to the question",
+    agent=knowledge_agent
+)
+
+# Create and start the agents
+agents = PraisonAIAgents(
+    agents=[knowledge_agent],
+    tasks=[knowledge_task],
+    process="sequential",
+    user_id="user1"
+)
+
+# Start execution
+result = agents.start()
+```
+
+## Understanding Knowledge Configuration
+
+<AccordionGroup>
+  <Accordion title="Vector Store Options">
+    - **Provider**: Choose between different vector store backends (e.g., "chroma")
+    - **Collection Name**: Name for your knowledge collection
+    - **Path**: Location to store the vector database
+  </Accordion>
+  
+  <Accordion title="Supported File Types">
+    - PDF documents (*.pdf)
+    - Text files (*.txt)
+    - Markdown files (*.md, *.mdx)
+    - And more...
+  </Accordion>
+
+  <Accordion title="Multi-Agent Setup">
+    - **Role**: Define specialized roles for knowledge agents
+    - **Goal**: Set specific knowledge management objectives
+    - **Process**: Choose between sequential or parallel execution
+  </Accordion>
+</AccordionGroup>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Custom Knowledge" icon="book">
+    Import your own documents and data as knowledge sources
+  </Card>
+  <Card title="Vector Storage" icon="database">
+    Efficient storage and retrieval of knowledge embeddings
+  </Card>
+  <Card title="Multiple Sources" icon="layer-group">
+    Combine multiple documents and file types
+  </Card>
+  <Card title="Persistent Storage" icon="hard-drive">
+    Save and reuse knowledge bases across sessions
+  </Card>
+</CardGroup>
+
+## Best Practices
+
+1. **Document Preparation**
+   - Clean and well-formatted documents work best
+   - Break large documents into smaller chunks
+   - Use consistent formatting
+
+2. **Knowledge Organization**
+   - Group related documents together
+   - Use meaningful file names
+   - Keep knowledge bases focused and relevant
+
+3. **Performance Optimization**
+   - Monitor vector store size
+   - Clean up unused collections
+   - Use appropriate chunk sizes
+
+4. **Multi-Agent Coordination**
+   - Define clear roles and responsibilities
+   - Set appropriate logging levels for debugging
+   - Use unique collection names for different agent groups
+
+## Next Steps
+
+- Learn about [Memory Management](/concepts/memory) for long-term recall
+- Explore [Tool Integration](/concepts/tools) for enhanced capabilities
+- Check out [Examples](/examples) for implementation ideas

docs/examples/examples.mdx

@@ -11,7 +11,7 @@ icon: "code"
   <Card 
     title="Predictive Maintenance" 
     icon="wrench" 
-    href="/usecases/predictive-maintenance"
+    href="/examples/predictive-maintenance"
   >
     Learn how to create AI agents for predictive maintenance and equipment monitoring.
   </Card>
@@ -19,7 +19,7 @@ icon: "code"
   <Card 
     title="Emergency Response" 
     icon="truck-medical" 
-    href="/usecases/emergency-response"
+    href="/examples/emergency-response"
   >
     Learn how to create AI agents for coordinated emergency response and resource management.
   </Card>
@@ -27,7 +27,7 @@ icon: "code"
   <Card 
     title="Code Review" 
     icon="code" 
-    href="/usecases/code-review"
+    href="/examples/code-review"
   >
     Learn how to create AI agents for automated code review and issue resolution.
   </Card>
@@ -35,7 +35,7 @@ icon: "code"
   <Card 
     title="Adaptive Learning" 
     icon="graduation-cap" 
-    href="/usecases/adaptive-learning"
+    href="/examples/adaptive-learning"
   >
     Learn how to create AI agents for personalized adaptive learning experiences.
   </Card>
@@ -43,7 +43,7 @@ icon: "code"
   <Card 
     title="Supply Chain" 
     icon="truck" 
-    href="/usecases/supply-chain"
+    href="/examples/supply-chain"
   >
     Learn how to create AI agents for supply chain risk management and mitigation.
   </Card>
@@ -51,7 +51,7 @@ icon: "code"
   <Card 
     title="Customer Service" 
     icon="headset" 
-    href="/usecases/customer-service"
+    href="/examples/customer-service"
   >
     Learn how to create AI agents for automated customer service and support.
   </Card>
@@ -59,7 +59,7 @@ icon: "code"
   <Card 
     title="Fraud Detection" 
     icon="shield-check" 
-    href="/usecases/fraud-detection"
+    href="/examples/fraud-detection"
   >
     Learn how to create AI agents for real-time fraud detection and alert management.
   </Card>
@@ -67,7 +67,7 @@ icon: "code"
   <Card 
     title="Healthcare Diagnosis" 
     icon="stethoscope" 
-    href="/usecases/healthcare-diagnosis"
+    href="/examples/healthcare-diagnosis"
   >
     Learn how to create AI agents for medical diagnosis and treatment recommendations.
   </Card>
@@ -75,7 +75,7 @@ icon: "code"
   <Card 
     title="Smart City" 
     icon="city" 
-    href="/usecases/smart-city"
+    href="/examples/smart-city"
   >
     Learn how to create AI agents for smart city resource optimization and management.
   </Card>
@@ -83,7 +83,7 @@ icon: "code"
   <Card 
     title="Multilingual Content" 
     icon="language" 
-    href="/usecases/multilingual-content"
+    href="/examples/multilingual-content"
   >
     Learn how to create AI agents for multilingual content generation and cultural adaptation.
   </Card>

docs/mint.json

@@ -60,12 +60,16 @@
         "url": "/index"
     },
     {
-        "name": "Use Cases",
-        "url": "/usecases"
+        "name": "Examples",
+        "url": "/examples"
     },
     {
         "name": "Agents",
         "url": "/agents"
+    },
+    {
+        "name": "UI",
+        "url": "/ui"
     }
   ],
   "navigation": [
@@ -88,7 +92,8 @@
         "concepts/tasks",
         "concepts/process",
         "concepts/tools",
-        "concepts/memory"
+        "concepts/memory",
+        "concepts/knowledge"
       ]
     },
     {
@@ -134,23 +139,25 @@
       ]
     },
     {
-      "group": "Usecases",
+      "group": "Examples",
       "pages": [
-        "usecases/predictive-maintenance",
-        "usecases/emergency-response",
-        "usecases/code-review",
-        "usecases/fraud-detection",
-        "usecases/supply-chain",
-        "usecases/healthcare-diagnosis",
-        "usecases/customer-service",
-        "usecases/smart-city",
-        "usecases/multilingual-content",
-        "usecases/adaptive-learning"
+        "examples/examples",
+        "examples/predictive-maintenance",
+        "examples/emergency-response",
+        "examples/code-review",
+        "examples/fraud-detection",
+        "examples/supply-chain",
+        "examples/healthcare-diagnosis",
+        "examples/customer-service",
+        "examples/smart-city",
+        "examples/multilingual-content",
+        "examples/adaptive-learning"
       ]
     },
     {
       "group": "Agents",
       "pages": [
+        "agents/agents",
         "agents/data-analyst",
         "agents/finance",
         "agents/image",
@@ -217,10 +224,12 @@
     {
       "group": "User Interface",
       "pages": [
+        "ui/overview",
+        "ui/gradio",
+        "ui/streamlit",
         "ui/ui",
         "ui/chat",
-        "ui/code",
-        "ui/realtime"
+        "ui/code"
       ]
     },
     {

docs/ui/gradio.mdx

@@ -0,0 +1,147 @@
+---
+title: "Gradio Agent"
+sidebarTitle: "Gradio"
+description: "Learn how to create web interfaces for your AI agents using Gradio"
+icon: "browser"
+---
+
+```mermaid
+flowchart LR
+    In[User Input] --> UI[Gradio UI]
+    UI --> Agent[AI Agent]
+    Agent --> Out[Results Display]
+    
+    style In fill:#8B0000,color:#fff
+    style UI fill:#2E8B57,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Dependencies">
+        First, install the required packages:
+        ```bash
+        pip install praisonaiagents gradio
+        ```
+    </Step>
+
+    <Step title="Create Script">
+        Create a new file `app.py`:
+        ```python
+        import gradio as gr
+        from praisonaiagents import Agent, Tools
+        from praisonaiagents.tools import duckduckgo
+
+        def research(query):
+            agent = Agent(instructions="You are a Research Agent", tools=[duckduckgo])
+            result = agent.start(query)
+            # Format the result with enhanced markdown
+            formatted_result = f"""
+{result}
+----
+*Generated by PraisonAI Research Assistant*
+            """
+            return formatted_result
+
+        # Create a simple Gradio interface
+        demo = gr.Interface(
+            fn=research,
+            inputs=gr.Textbox(
+                label="Research Query",
+                placeholder="Enter your research topic...",
+                lines=2
+            ),
+            outputs=gr.Markdown(
+                show_copy_button=True,
+                height=500,
+                container=True
+            ),
+            title="AI Research Assistant",
+            description="Enter your research query below to get started!",
+        )
+
+        if __name__ == "__main__":
+            demo.launch()
+        ```
+    </Step>
+
+    <Step title="Run Application">
+        Run your Gradio app:
+        ```bash
+        python app.py
+        ```
+    </Step>
+</Steps>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Simple Interface" icon="wand-magic-sparkles">
+    Create beautiful UIs with minimal code.
+  </Card>
+  <Card title="Markdown Support" icon="markdown">
+    Rich text output with built-in markdown rendering.
+  </Card>
+  <Card title="Copy Button" icon="copy">
+    One-click copying of results.
+  </Card>
+  <Card title="Responsive Design" icon="mobile">
+    Mobile-friendly interface out of the box.
+  </Card>
+</CardGroup>
+
+## Understanding the Code
+
+The example demonstrates a simple research assistant with these key components:
+
+1. **Function Definition**:
+   - `research()` function that processes user input
+   - Agent initialization and execution
+   - Result formatting with markdown
+
+2. **Interface Setup**:
+   - Input textbox configuration
+   - Markdown output with copy button
+   - Title and description settings
+
+3. **Launch Configuration**:
+   - Main entry point check
+   - Server launch with default settings
+
+## Customization
+
+You can enhance the UI with additional Gradio components:
+
+```python
+# Add multiple input types
+demo = gr.Interface(
+    fn=process_inputs,
+    inputs=[
+        gr.Textbox(label="Query"),
+        gr.File(label="Upload Document"),
+        gr.Dropdown(choices=["Option 1", "Option 2"])
+    ],
+    outputs=[
+        gr.Markdown(label="Results"),
+        gr.Plot(label="Visualization")
+    ]
+)
+
+# Add themes and styling
+demo = gr.Interface(
+    ...
+    theme="default",
+    css=".gradio-container {background-color: #f0f0f0}"
+)
+
+# Add authentication
+demo.launch(auth=("username", "password"))
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex UI workflows
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving responses
+- Check out [Streamlit Integration](/ui/streamlit) for an alternative UI framework

docs/ui/overview.mdx

@@ -0,0 +1,63 @@
+---
+title: "User Interfaces"
+sidebarTitle: "Overview"
+description: "Overview of available user interfaces for PraisonAI agents"
+icon: "browser"
+---
+
+## Available Interfaces
+
+| Interface | Description | URL |
+|---|---|---|
+| **Streamlit** | Create interactive web apps with Python | [https://docs.praison.ai/ui/streamlit](/ui/streamlit) |
+| **Gradio** | Build machine learning web apps rapidly | [https://docs.praison.ai/ui/gradio](/ui/gradio) |
+| **UI** | Multi Agents such as CrewAI or AutoGen | [https://docs.praison.ai/ui/ui](/ui/ui) |
+| **Chat** | Chat with 100+ LLMs, single AI Agent | [https://docs.praison.ai/ui/chat](/ui/chat) |
+| **Code** | Chat with entire Codebase, single AI Agent | [https://docs.praison.ai/ui/code](/ui/code) |
+
+## Interface Types
+
+<CardGroup cols={2}>
+  <Card title="Web Applications" icon="browser">
+    Create full-featured web interfaces with Streamlit and Gradio.
+  </Card>
+  <Card title="Chat Interfaces" icon="messages">
+    Build conversational experiences with Chat and Chainlit.
+  </Card>
+  <Card title="Code Interaction" icon="code">
+    Interact with codebases using the Code interface.
+  </Card>
+  <Card title="Multi-Agent Systems" icon="users">
+    Orchestrate multiple agents with UI framework.
+  </Card>
+</CardGroup>
+
+## Features by Interface
+
+### Web Frameworks
+- **Streamlit**: Interactive components, real-time updates, data visualization
+- **Gradio**: Rapid prototyping, markdown support, file handling
+
+### Chat Systems
+- **Chat**: Multiple LLM support, conversation history, context management
+
+### Specialized Interfaces
+- **UI**: Agent orchestration, workflow management, inter-agent communication
+- **Code**: Codebase navigation, syntax highlighting, version control integration
+
+## Getting Started
+
+<Steps>
+  <Step title="Choose Interface">
+    Select the interface that best matches your use case from the table above.
+  </Step>
+  <Step title="Install Dependencies">
+    Each interface has its own installation requirements. Follow the specific guide.
+  </Step>
+  <Step title="Create Application">
+    Use the provided examples and templates to build your application.
+  </Step>
+  <Step title="Customize">
+    Add features and styling to match your needs using the interface's components.
+  </Step>
+</Steps>

docs/ui/streamlit.mdx

@@ -0,0 +1,124 @@
+---
+title: "Streamlit Agent"
+sidebarTitle: "Streamlit"
+description: "Learn how to create web interfaces for your AI agents using Streamlit"
+icon: "browser"
+---
+
+```mermaid
+flowchart LR
+    In[User Input] --> UI[Streamlit UI]
+    UI --> Agent[AI Agent]
+    Agent --> Out[Results Display]
+    
+    style In fill:#8B0000,color:#fff
+    style UI fill:#2E8B57,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Dependencies">
+        First, install the required packages:
+        ```bash
+        pip install praisonaiagents streamlit
+        ```
+    </Step>
+
+    <Step title="Create Script">
+        Create a new file `app.py`:
+        ```python
+        import streamlit as st
+        from praisonaiagents import Agent, Tools
+        from praisonaiagents.tools import duckduckgo
+
+        st.title("AI Research Assistant")
+        st.write("Enter your research query below to get started!")
+
+        # Initialize the research agent
+        agent = Agent(instructions="You are a Research Agent", tools=[duckduckgo])
+
+        # Create the input field
+        query = st.text_input("Research Query", placeholder="Enter your research topic...")
+
+        # Add a search button
+        if st.button("Search"):
+            if query:
+                with st.spinner("Researching..."):
+                    result = agent.start(query)
+                    st.write(result)
+            else:
+                st.warning("Please enter a research query")
+        ```
+    </Step>
+
+    <Step title="Run Application">
+        Run your Streamlit app:
+        ```bash
+        streamlit run app.py
+        ```
+    </Step>
+</Steps>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Easy Integration" icon="puzzle-piece">
+    Seamlessly integrate AI agents with Streamlit's UI components.
+  </Card>
+  <Card title="Interactive UI" icon="hand-pointer">
+    Create responsive interfaces with real-time updates.
+  </Card>
+  <Card title="Progress Indicators" icon="spinner">
+    Built-in loading states and progress indicators.
+  </Card>
+  <Card title="Rich Output" icon="text-size">
+    Display formatted text, markdown, and other rich content.
+  </Card>
+</CardGroup>
+
+## Understanding the Code
+
+The example demonstrates a simple research assistant with these key components:
+
+1. **UI Setup**:
+   - Title and description using `st.title()` and `st.write()`
+   - Input field with `st.text_input()`
+   - Search button with `st.button()`
+
+2. **Agent Integration**:
+   - Initialize the AI agent with specific instructions
+   - Connect the agent to the UI components
+   - Handle user input and display results
+
+3. **User Experience**:
+   - Loading spinner during processing
+   - Input validation and error messages
+   - Clean result display
+
+## Customization
+
+You can enhance the UI with additional Streamlit components:
+
+```python
+# Add sidebar options
+st.sidebar.title("Settings")
+model = st.sidebar.selectbox("Select Model", ["GPT-3", "GPT-4"])
+
+# Add multiple input types
+text_input = st.text_area("Long Query", height=100)
+file_input = st.file_uploader("Upload File")
+
+# Display results with formatting
+st.markdown("### Results")
+st.json(structured_data)
+st.dataframe(tabular_data)
+```
+
+## Next Steps
+
+- Learn about [Prompt Chaining](/features/promptchaining) for complex UI workflows
+- Explore [Evaluator Optimizer](/features/evaluator-optimiser) for improving responses
+- Check out [Gradio Integration](/ui/gradio) for an alternative UI framework

docs/ui/ui.mdx

@@ -1,30 +1,44 @@
 ---
-title: "UI"
-description: "Comprehensive guide to PraisonAI's user interfaces, including Chainlit, Gradio, and Streamlit integrations with step-by-step setup instructions"
-icon: "desktop"
+title: "User Interface"
+sidebarTitle: "UI"
+description: "Interface to Build multi-agent systems with PraisonAI Agent's framework"
+icon: "users"
 ---
 
-<div className="relative w-full aspect-video">
-  <iframe
-    className="absolute top-0 left-0 w-full h-full"
-    src="https://www.youtube.com/embed/tg-ZjNl3OCg"
-    title="YouTube video player"
-    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
-    allowFullScreen
-  ></iframe>
-</div>
+## Installation
 
-## Different User Interfaces:
+```bash
+pip install -U "praisonai[ui]"
+```
 
-| Interface | Description | URL |
-|---|---|---|
-| **UI** | Multi Agents such as CrewAI or AutoGen | [https://docs.praison.ai/ui/ui](https://docs.praison.ai/ui/ui) |
-| **Chat** | Chat with 100+ LLMs, single AI Agent | [https://docs.praison.ai/ui/chat](https://docs.praison.ai/ui/chat) |
-| **Code** | Chat with entire Codebase, single AI Agent | [https://docs.praison.ai/ui/code](https://docs.praison.ai/ui/code) |
+## Interface Types
+
+<CardGroup cols={2}>
+  <Card title="Multi-Agent Systems" icon="users">
+    Orchestrate multiple agents with UI framework.
+  </Card>
+</CardGroup>
+
+## Features
+
+Agent orchestration, workflow management, inter-agent communication
+
+## Getting Started
+
+<Steps>
+  <Step title="Install Dependencies">
+    Each interface has its own installation requirements. Follow the specific guide.
+  </Step>
+  <Step title="Create Application">
+    Use the provided examples and templates to build your application.
+  </Step>
+  <Step title="Customize">
+    Add features and styling to match your needs using the interface's components.
+  </Step>
+</Steps>
 
 ## Chainlit
 ```bash
-pip install -U "praisonai[ui]"
 export OPENAI_API_KEY="Enter your API key"
 chainlit create-secret
 export CHAINLIT_AUTH_SECRET=xxxxxxxx
@@ -49,22 +63,6 @@ CHAINLIT_USERNAME=admin
 CHAINLIT_USERNAME=admin
 ```
 
-## Gradio
-```bash
-pip install "praisonai[gradio]"
-export OPENAI_API_KEY="Enter your API key"
-praisonai --ui gradio
-```
-
-## Streamlit
-
-```bash
-git clone https://github.com/leporejoseph/PraisonAi-Streamlit
-cd PraisonAi-Streamlit
-pip install -r requirements.txt
-streamlit run app.py
-```
-
 ## Using Chainlit (with Pictures)
 
 ## Run Automatically
@@ -103,3 +101,13 @@ streamlit run app.py
 
 ### Manual Model Output
 ![Manual Model Output](../images/ui-step-11.png)
+
+<div className="relative w-full aspect-video">
+  <iframe
+    className="absolute top-0 left-0 w-full h-full"
+    src="https://www.youtube.com/embed/tg-ZjNl3OCg"
+    title="YouTube video player"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
+    allowFullScreen
+  ></iframe>
+</div>

examples/concepts/knowledge-agents-task.py

@@ -0,0 +1,48 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import logging
+import os
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+# Define the configuration for the Knowledge instance
+config = {
+    "vector_store": {
+        "provider": "chroma",
+        "config": {
+            "collection_name": "knowledge_test",
+            "path": ".praison",
+        }
+    }
+}
+
+# Create an agent with knowledge capabilities
+knowledge_agent = Agent(
+    name="KnowledgeAgent",
+    role="Information Specialist",
+    goal="Store and retrieve knowledge efficiently",
+    backstory="Expert in managing and utilizing stored knowledge",
+    knowledge=["sample.pdf"],
+    knowledge_config=config,
+    verbose=True
+)
+
+# Define a task for the agent
+knowledge_task = Task(
+    name="knowledge_task",
+    description="Who is Mervin Praison?",
+    expected_output="Answer to the question",
+    agent=knowledge_agent
+)
+
+# Create and start the agents
+agents = PraisonAIAgents(
+    agents=[knowledge_agent],
+    tasks=[knowledge_task],
+    process="sequential",
+    user_id="user1"
+)
+
+# Start execution
+result = agents.start()

examples/concepts/knowledge-agents.py

@@ -0,0 +1,20 @@
+from praisonaiagents import Agent
+
+config = {
+    "vector_store": {
+        "provider": "chroma",
+        "config": {
+            "collection_name": "custom_knowledge",
+            "path": ".praison",
+        }
+    }
+}
+
+agent = Agent(
+    name="Knowledge Agent",
+    instructions="You answer questions based on the provided knowledge.",
+    knowledge=["small.pdf"],
+    knowledge_config=config
+)
+
+agent.start("What is KAG in one line?")

examples/ui/gradio-agents.py

@@ -0,0 +1,34 @@
+import gradio as gr
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+def research(query):
+    agent = Agent(instructions="You are a Research Agent", tools=[duckduckgo])
+    result = agent.start(query)
+    # Format the result with enhanced markdown
+    formatted_result = f"""
+{result}
+----
+*Generated by PraisonAI Research Assistant*
+    """
+    return formatted_result
+
+# Create a simple Gradio interface
+demo = gr.Interface(
+    fn=research,
+    inputs=gr.Textbox(
+        label="Research Query",
+        placeholder="Enter your research topic...",
+        lines=2
+    ),
+    outputs=gr.Markdown(
+        show_copy_button=True,
+        height=500,
+        container=True
+    ),
+    title="AI Research Assistant",
+    description="Enter your research query below to get started!",
+)
+
+if __name__ == "__main__":
+    demo.launch()

examples/ui/streamlit-agents.py

@@ -0,0 +1,21 @@
+import streamlit as st
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+st.title("AI Research Assistant")
+st.write("Enter your research query below to get started!")
+
+# Initialize the research agent
+agent = Agent(instructions="You are a Research Agent", tools=[duckduckgo])
+
+# Create the input field
+query = st.text_input("Research Query", placeholder="Enter your research topic...")
+
+# Add a search button
+if st.button("Search"):
+    if query:
+        with st.spinner("Researching..."):
+            result = agent.start(query)
+            st.write(result)
+    else:
+        st.warning("Please enter a research query")

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/2.0.46.tar.gz"
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/2.0.47.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==2.0.46 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.0.47 gunicorn markdown\n")
             file.write("EXPOSE 8080\n")
             file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
             

praisonai/ui/realtime.py

@@ -229,7 +229,7 @@ except Exception as e:
 @cl.on_chat_start
 async def start():
     initialize_db()
-    model_name = load_setting("model_name") or os.getenv("MODEL_NAME", "gpt-4o-mini")
+    model_name = load_setting("model_name") or os.getenv("MODEL_NAME", "gpt-4o-mini-realtime-preview")
     cl.user_session.set("model_name", model_name)
     cl.user_session.set("message_history", [])  # Initialize message history
     logger.debug(f"Model name: {model_name}")
@@ -238,7 +238,7 @@ async def start():
     #         TextInput(
     #             id="model_name",
     #             label="Enter the Model Name",
-    #             placeholder="e.g., gpt-4o-mini",
+    #             placeholder="e.g., gpt-4o-mini-realtime-preview",
     #             initial=model_name
     #         )
     #     ]
@@ -423,14 +423,14 @@ def auth_callback(username: str, password: str):
 @cl.on_chat_resume
 async def on_chat_resume(thread: ThreadDict):
     logger.info(f"Resuming chat: {thread['id']}")
-    model_name = load_setting("model_name") or os.getenv("MODEL_NAME") or "gpt-4o-mini"
+    model_name = load_setting("model_name") or os.getenv("MODEL_NAME") or "gpt-4o-mini-realtime-preview"
     logger.debug(f"Model name: {model_name}")
     settings = cl.ChatSettings(
         [
             TextInput(
                 id="model_name",
                 label="Enter the Model Name",
-                placeholder="e.g., gpt-4o-mini",
+                placeholder="e.g., gpt-4o-mini-realtime-preview",
                 initial=model_name
             )
         ]

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "PraisonAI"
-version = "2.0.46"
-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."
+version = "2.0.47"
+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 = ""
 requires-python = ">=3.10,<3.13"
@@ -12,7 +12,7 @@ dependencies = [
     "rich>=13.7",
     "markdown>=3.5",
     "pyparsing>=3.0.0",
-    "praisonaiagents>=0.0.36",
+    "praisonaiagents>=0.0.38",
     "python-dotenv>=0.19.0",
     "instructor>=1.3.3",
     "PyYAML>=6.0",
@@ -20,7 +20,7 @@ dependencies = [
 
 [project.optional-dependencies]
 ui = [
-    "chainlit==2.0.1",
+    "chainlit==2.0.3",
     "sqlalchemy>=2.0.36",
     "aiosqlite>=0.20.0",
     "greenlet>=3.0.3",
@@ -34,7 +34,7 @@ openai = ["langchain-openai>=0.1.7"]
 anthropic = ["langchain-anthropic>=0.1.13"]
 cohere = ["langchain-cohere>=0.1.4"]
 chat = [
-    "chainlit==2.0.1",
+    "chainlit==2.0.3",
     "litellm>=1.41.8",
     "aiosqlite>=0.20.0",
     "greenlet>=3.0.3",
@@ -46,7 +46,7 @@ chat = [
     "pydantic<=2.10.1"
 ]
 code = [
-    "chainlit==2.0.1",
+    "chainlit==2.0.3",
     "litellm>=1.41.8",
     "aiosqlite>=0.20.0",
     "greenlet>=3.0.3",
@@ -57,7 +57,7 @@ code = [
     "pydantic<=2.10.1"
 ]
 realtime = [
-    "chainlit==2.0.1",
+    "chainlit==2.0.3",
     "litellm>=1.41.8",
     "aiosqlite>=0.20.0",
     "greenlet>=3.0.3",
@@ -84,7 +84,7 @@ autogen = ["pyautogen>=0.2.19", "praisonai-tools>=0.0.7", "crewai"]
 
 [tool.poetry]
 name = "PraisonAI"
-version = "2.0.46"
+version = "2.0.47"
 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 = ""
@@ -102,14 +102,14 @@ python = ">=3.10,<3.13"
 rich = ">=13.7"
 markdown = ">=3.5"
 pyparsing = ">=3.0.0"
-praisonaiagents = ">=0.0.36"
+praisonaiagents = ">=0.0.38"
 python-dotenv = ">=0.19.0"
 instructor = ">=1.3.3"
 PyYAML = ">=6.0"
 pyautogen = {version = ">=0.2.19", optional = true}
 crewai = {version = ">=0.32.0", optional = true}
 praisonai-tools = {version = ">=0.0.7", optional = true}
-chainlit = {version = "==2.0rc1", optional = true}
+chainlit = {version = "==2.0.3", optional = true}
 gradio = {version = ">=4.26.0", optional = true}
 flask = {version = ">=3.0.0", optional = true}
 agentops = {version = ">=0.3.12", optional = true}
@@ -173,7 +173,7 @@ mkdocstrings-python = "*"
 pdoc3 = "*"
 xmlrunner = "*"
 unittest2 = "*"
-chainlit = "==2.0rc1"
+chainlit = "==2.0.3"
 gradio = ">=4.26.0"
 flask = ">=3.0.0"
 agentops = ">=0.3.12"

uv.lock

@@ -482,7 +482,7 @@ wheels = [
 
 [[package]]
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-version = "2.0.1"
+version = "2.0.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
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@@ -508,9 +508,9 @@ dependencies = [
     { name = "uvicorn" },
     { name = "watchfiles" },
 ]
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 [[package]]
@@ -2199,7 +2199,7 @@ wheels = [
 
 [[package]]
 name = "literalai"
-version = "0.0.623"
+version = "0.1.103"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
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@@ -2207,7 +2207,7 @@ dependencies = [
     { name = "packaging" },
     { name = "pydantic" },
 ]
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 [[package]]
 name = "lxml"
@@ -3060,7 +3060,7 @@ wheels = [
 
 [[package]]
 name = "praisonai"
-version = "2.0.46"
+version = "2.0.47"
 source = { editable = "." }
 dependencies = [
     { name = "instructor" },
@@ -3164,10 +3164,10 @@ requires-dist = [
     { name = "aiosqlite", marker = "extra == 'code'", specifier = ">=0.20.0" },
     { name = "aiosqlite", marker = "extra == 'realtime'", specifier = ">=0.20.0" },
     { name = "aiosqlite", marker = "extra == 'ui'", specifier = ">=0.20.0" },
-    { name = "chainlit", marker = "extra == 'chat'", specifier = "==2.0.1" },
-    { name = "chainlit", marker = "extra == 'code'", specifier = "==2.0.1" },
-    { name = "chainlit", marker = "extra == 'realtime'", specifier = "==2.0.1" },
-    { name = "chainlit", marker = "extra == 'ui'", specifier = "==2.0.1" },
+    { name = "chainlit", marker = "extra == 'chat'", specifier = "==2.0.3" },
+    { name = "chainlit", marker = "extra == 'code'", specifier = "==2.0.3" },
+    { name = "chainlit", marker = "extra == 'realtime'", specifier = "==2.0.3" },
+    { name = "chainlit", marker = "extra == 'ui'", specifier = "==2.0.3" },
     { name = "crawl4ai", marker = "extra == 'chat'", specifier = "==0.3.4" },
     { name = "crawl4ai", marker = "extra == 'code'", specifier = "==0.3.4" },
     { name = "crawl4ai", marker = "extra == 'realtime'", specifier = "==0.3.4" },
@@ -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 = ">=0.0.36" },
+    { name = "praisonaiagents", specifier = ">=0.0.38" },
     { 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 = "0.0.36"
+version = "0.0.38"
 source = { registry = "https://pypi.org/simple" }
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     { name = "pydantic" },
     { name = "rich" },
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 [[package]]