Merge pull request #262 from MervinPraison/develop

Develop

Dockerfile

@@ -1,6 +1,6 @@
 FROM python:3.11-slim
 WORKDIR /app
 COPY . .
-RUN pip install flask praisonai==2.0.25 gunicorn markdown
+RUN pip install flask praisonai==2.0.26 gunicorn markdown
 EXPOSE 8080
 CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]

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==2.0.25 gunicorn markdown\n&#34;)
+            file.write(&#34;RUN pip install flask praisonai==2.0.26 gunicorn markdown\n&#34;)
             file.write(&#34;EXPOSE 8080\n&#34;)
             file.write(&#39;CMD [&#34;gunicorn&#34;, &#34;-b&#34;, &#34;0.0.0.0:8080&#34;, &#34;api:app&#34;]\n&#39;)
             

praisonai.rb

@@ -3,7 +3,7 @@ class Praisonai < Formula
   
     desc "AI tools for various AI applications"
     homepage "https://github.com/MervinPraison/PraisonAI"
-    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/2.0.25.tar.gz"
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/2.0.26.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==2.0.25 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.0.26 gunicorn markdown\n")
             file.write("EXPOSE 8080\n")
             file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
             

praisonai/ui/together.py

@@ -0,0 +1,775 @@
+from chainlit.input_widget import Select, TextInput
+import os
+import sys
+import yaml
+import logging
+import inspect
+import chainlit as cl
+framework = "praisonai"
+config_list = [
+    {
+        'model': os.environ.get("OPENAI_MODEL_NAME", "gpt-4o"),
+        'base_url': os.environ.get("OPENAI_API_BASE", "https://api.openai.com/v1"),
+        'api_key': os.environ.get("OPENAI_API_KEY", "")
+    }
+]
+
+agent_file = "test.yaml"
+
+actions=[
+    cl.Action(name="run", value="run", label="✅ Run"),
+    cl.Action(name="modify", value="modify", label="🔧 Modify"),
+]
+import os
+import sys
+import yaml
+import logging
+import inspect
+import asyncio
+import importlib.util
+import sqlite3
+from queue import Queue
+from datetime import datetime
+from dotenv import load_dotenv
+
+# Chainlit imports
+import chainlit as cl
+from chainlit.types import ThreadDict
+import chainlit.data as cl_data
+
+# External imports from your local packages (adjust if needed)
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# -----------------------------------------------------------------------------
+# Global Setup
+# -----------------------------------------------------------------------------
+
+load_dotenv()
+log_level = os.getenv("LOGLEVEL", "INFO").upper()
+logger = logging.getLogger(__name__)
+logger.setLevel(log_level)
+
+message_queue = Queue()  # Queue to handle messages sent to Chainlit UI
+agent_file = "agents.yaml"
+
+# -----------------------------------------------------------------------------
+# Database and Settings Logic (untouched logic with minimal additions for Chainlit)
+# -----------------------------------------------------------------------------
+
+MAX_RETRIES = 3
+RETRY_DELAY = 1  # seconds
+
+# Original DatabaseManager from chainlit_ui.py, unmodified logic, plus minimal
+# placeholder methods for Chainlit's data layer calls (create_user, get_user, etc.)
+from db import DatabaseManager
+
+async def init_database_with_retry():
+    db = DatabaseManager()
+    for attempt in range(MAX_RETRIES):
+        try:
+            db.initialize()
+            return db
+        except sqlite3.OperationalError as e:
+            if "database is locked" in str(e) and attempt < MAX_RETRIES - 1:
+                await asyncio.sleep(RETRY_DELAY)
+                continue
+            raise
+
+db_manager = asyncio.run(init_database_with_retry())
+cl_data._data_layer = db_manager
+
+async def save_setting_with_retry(key: str, value: str):
+    for attempt in range(MAX_RETRIES):
+        try:
+            await db_manager.save_setting(key, value)
+            return
+        except sqlite3.OperationalError as e:
+            if "database is locked" in str(e) and attempt < MAX_RETRIES - 1:
+                await asyncio.sleep(RETRY_DELAY)
+                continue
+            raise
+
+async def load_setting_with_retry(key: str) -> str:
+    for attempt in range(MAX_RETRIES):
+        try:
+            return await db_manager.load_setting(key)
+        except sqlite3.OperationalError as e:
+            if "database is locked" in str(e) and attempt < MAX_RETRIES - 1:
+                await asyncio.sleep(RETRY_DELAY)
+                continue
+            raise
+    return ""
+
+def save_setting(key: str, value: str):
+    asyncio.run(save_setting_with_retry(key, value))
+
+def load_setting(key: str) -> str:
+    return asyncio.run(load_setting_with_retry(key))
+
+async def update_thread_metadata(thread_id: str, metadata: dict):
+    for attempt in range(MAX_RETRIES):
+        try:
+            await cl_data.update_thread(thread_id, metadata=metadata)
+            return
+        except sqlite3.OperationalError as e:
+            if "database is locked" in str(e) and attempt < MAX_RETRIES - 1:
+                await asyncio.sleep(RETRY_DELAY)
+                continue
+            raise
+
+# -----------------------------------------------------------------------------
+# Callback Manager (untouched from colab_combined.py)
+# -----------------------------------------------------------------------------
+
+class CallbackManager:
+    def __init__(self):
+        self._callbacks = {}
+
+    def register(self, name: str, callback, is_async: bool = False) -> None:
+        self._callbacks[name] = {'func': callback, 'is_async': is_async}
+
+    async def call(self, name: str, **kwargs) -> None:
+        if name not in self._callbacks:
+            logger.warning(f"No callback registered for {name}")
+            return
+        callback_info = self._callbacks[name]
+        func = callback_info['func']
+        is_async = callback_info['is_async']
+        try:
+            if is_async:
+                await func(**kwargs)
+            else:
+                if asyncio.iscoroutinefunction(func):
+                    await func(**kwargs)
+                else:
+                    await asyncio.get_event_loop().run_in_executor(None, lambda: func(**kwargs))
+        except Exception as e:
+            logger.error(f"Error in callback {name}: {str(e)}")
+
+callback_manager = CallbackManager()
+
+def register_callback(name: str, callback, is_async: bool = False) -> None:
+    callback_manager.register(name, callback, is_async)
+
+async def trigger_callback(name: str, **kwargs) -> None:
+    await callback_manager.call(name, **kwargs)
+
+def callback(name: str, is_async: bool = False):
+    def decorator(func):
+        register_callback(name, func, is_async)
+        return func
+    return decorator
+
+# -----------------------------------------------------------------------------
+# Tools Loader (untouched logic but returning dict for tools)
+# -----------------------------------------------------------------------------
+
+def load_tools_from_tools_py():
+    """
+    Imports and returns all contents from tools.py file.
+    Also adds the tools to the global namespace.
+    """
+    tools_dict = {}
+    try:
+        spec = importlib.util.spec_from_file_location("tools", "tools.py")
+        logger.info(f"Spec: {spec}")
+        if spec is None:
+            logger.info("tools.py not found in current directory")
+            return tools_dict
+
+        module = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(module)
+
+        for name, obj in inspect.getmembers(module):
+            if not name.startswith('_') and callable(obj) and not inspect.isclass(obj):
+                globals()[name] = obj
+                tool_def = {
+                    "type": "function",
+                    "function": {
+                        "name": name,
+                        "description": obj.__doc__ or f"Function to {name.replace('_', ' ')}",
+                        "parameters": {
+                            "type": "object",
+                            "properties": {
+                                "query": {
+                                    "type": "string",
+                                    "description": "The search query to look up information about"
+                                }
+                            },
+                            "required": ["query"]
+                        }
+                    }
+                }
+                tools_dict[name] = tool_def
+                logger.info(f"Loaded and globalized tool function: {name}")
+
+        logger.info(f"Loaded {len(tools_dict)} tool functions from tools.py")
+    except Exception as e:
+        logger.warning(f"Error loading tools from tools.py: {e}")
+    return tools_dict
+
+# -----------------------------------------------------------------------------
+# Async Queue Processor (untouched logic)
+# -----------------------------------------------------------------------------
+
+async def process_message_queue():
+    while True:
+        try:
+            if not message_queue.empty():
+                msg_data = message_queue.get()
+                await cl.Message(**msg_data).send()
+            await asyncio.sleep(0.1)
+        except Exception as e:
+            logger.error(f"Error processing message queue: {e}")
+
+# -----------------------------------------------------------------------------
+# Step & Task Callbacks (Async & Sync) (untouched logic)
+# -----------------------------------------------------------------------------
+
+async def step_callback(step_details):
+    logger.info(f"[CALLBACK DEBUG] step_callback: {step_details}")
+    agent_name = step_details.get("agent_name", "Agent")
+    try:
+        if step_details.get("response"):
+            message_queue.put({
+                "content": f"Agent Response: {step_details['response']}",
+                "author": agent_name
+            })
+        if step_details.get("tool_name"):
+            message_queue.put({
+                "content": f"🛠️ Using tool: {step_details['tool_name']}",
+                "author": "System"
+            })
+    except Exception as e:
+        logger.error(f"Error in step_callback: {e}", exc_info=True)
+
+async def task_callback(task_output):
+    logger.info(f"[CALLBACK DEBUG] task_callback: type={type(task_output)}")
+    try:
+        if hasattr(task_output, 'raw'):
+            content = task_output.raw
+        elif hasattr(task_output, 'content'):
+            content = task_output.content
+        else:
+            content = str(task_output)
+        message_queue.put({
+            "content": f"Task Output: {content}",
+            "author": "Task"
+        })
+    except Exception as e:
+        logger.error(f"Error in task_callback: {e}", exc_info=True)
+
+async def step_callback_wrapper(step_details):
+    logger.info(f"[CALLBACK DEBUG] step_callback_wrapper: {step_details}")
+    agent_name = step_details.get("agent_name", "Agent")
+    try:
+        if not cl.context.context_var.get():
+            logger.warning("[CALLBACK DEBUG] No Chainlit context in wrapper.")
+            return
+        if step_details.get("response"):
+            await cl.Message(
+                content=f"{agent_name}: {step_details['response']}",
+                author=agent_name,
+            ).send()
+        if step_details.get("tool_name"):
+            await cl.Message(
+                content=f"🛠️ {agent_name} is using tool: {step_details['tool_name']}",
+                author="System",
+            ).send()
+        if step_details.get("thought"):
+            await cl.Message(
+                content=f"💭 {agent_name}'s thought: {step_details['thought']}",
+                author=agent_name,
+            ).send()
+    except Exception as e:
+        logger.error(f"Error in step_callback_wrapper: {e}", exc_info=True)
+        try:
+            await cl.Message(content=f"Error in step callback: {e}", author="System").send()
+        except Exception as send_error:
+            logger.error(f"Error sending error message: {send_error}")
+
+async def task_callback_wrapper(task_output):
+    logger.info("[CALLBACK DEBUG] task_callback_wrapper triggered")
+    try:
+        if not cl.context.context_var.get():
+            logger.warning("[CALLBACK DEBUG] No Chainlit context in task wrapper.")
+            return
+        if hasattr(task_output, 'raw'):
+            content = task_output.raw
+        elif hasattr(task_output, 'content'):
+            content = task_output.content
+        else:
+            content = str(task_output)
+
+        await cl.Message(
+            content=f"✅ Agent completed task:\n{content}",
+            author="Agent",
+        ).send()
+
+        if hasattr(task_output, 'details'):
+            await cl.Message(
+                content=f"📝 Additional details:\n{task_output.details}",
+                author="Agent",
+            ).send()
+    except Exception as e:
+        logger.error(f"Error in task_callback_wrapper: {e}", exc_info=True)
+        try:
+            await cl.Message(content=f"Error in task callback: {e}", author="System").send()
+        except Exception as send_error:
+            logger.error(f"Error sending error message: {send_error}")
+
+def sync_task_callback_wrapper(task_output):
+    logger.info("[CALLBACK DEBUG] sync_task_callback_wrapper")
+    try:
+        try:
+            loop = asyncio.get_event_loop()
+        except RuntimeError:
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+
+        if loop.is_running():
+            asyncio.run_coroutine_threadsafe(task_callback_wrapper(task_output), loop)
+        else:
+            loop.run_until_complete(task_callback_wrapper(task_output))
+    except Exception as e:
+        logger.error(f"Error in sync_task_callback_wrapper: {e}", exc_info=True)
+
+def sync_step_callback_wrapper(step_details):
+    logger.info("[CALLBACK DEBUG] sync_step_callback_wrapper")
+    try:
+        try:
+            loop = asyncio.get_event_loop()
+        except RuntimeError:
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+
+        if loop.is_running():
+            asyncio.run_coroutine_threadsafe(step_callback_wrapper(step_details), loop)
+        else:
+            loop.run_until_complete(step_callback_wrapper(step_details))
+    except Exception as e:
+        logger.error(f"Error in sync_step_callback_wrapper: {e}", exc_info=True)
+
+# -----------------------------------------------------------------------------
+# Main PraisonAI Runner (untouched logic)
+# -----------------------------------------------------------------------------
+
+async def ui_run_praisonai(config, topic, tools_dict):
+    logger.info("Starting ui_run_praisonai")
+    agents_map = {}
+    tasks = []
+    tasks_dict = {}
+
+    try:
+        queue_processor = asyncio.create_task(process_message_queue())
+
+        # Create agents
+        for role, details in config['roles'].items():
+            role_name = details.get('name', role).format(topic=topic)
+            role_filled = details.get('role', role).format(topic=topic)
+            goal_filled = details['goal'].format(topic=topic)
+            backstory_filled = details['backstory'].format(topic=topic)
+
+            await cl.Message(content=f"[DEBUG] Creating agent: {role_name}", author="System").send()
+
+            def step_callback_sync(step_details):
+                step_details["agent_name"] = role_name
+                try:
+                    loop_ = asyncio.new_event_loop()
+                    asyncio.set_event_loop(loop_)
+                    loop_.run_until_complete(step_callback(step_details))
+                    loop_.close()
+                except Exception as e:
+                    logger.error(f"Error in step_callback_sync: {e}", exc_info=True)
+
+            agent = Agent(
+                name=role_name,
+                role=role_filled,
+                goal=goal_filled,
+                backstory=backstory_filled,
+                llm=details.get('llm', 'gpt-4o'),
+                verbose=True,
+                allow_delegation=details.get('allow_delegation', False),
+                max_iter=details.get('max_iter', 15),
+                max_rpm=details.get('max_rpm'),
+                max_execution_time=details.get('max_execution_time'),
+                cache=details.get('cache', True),
+                step_callback=step_callback_sync
+            )
+            agents_map[role] = agent
+
+        # Create tasks
+        for role, details in config['roles'].items():
+            agent = agents_map[role]
+            role_name = agent.name
+            role_tools = []
+            for tool_name in details.get('tools', []):
+                if tool_name in tools_dict:
+                    role_tools.append(tools_dict[tool_name])
+
+            for tname, tdetails in details.get('tasks', {}).items():
+                description_filled = tdetails['description'].format(topic=topic)
+                expected_output_filled = tdetails['expected_output'].format(topic=topic)
+
+                await cl.Message(content=f"[DEBUG] Created task: {tname} for {role_name}", author="System").send()
+
+                def task_callback_sync(task_output):
+                    try:
+                        loop_ = asyncio.new_event_loop()
+                        asyncio.set_event_loop(loop_)
+                        loop_.run_until_complete(task_callback(task_output))
+                        loop_.close()
+                    except Exception as e:
+                        logger.error(f"Error in task_callback_sync: {e}", exc_info=True)
+
+                task = Task(
+                    description=description_filled,
+                    expected_output=expected_output_filled,
+                    agent=agent,
+                    tools=role_tools,
+                    async_execution=True,
+                    context=[],
+                    config=tdetails.get('config', {}),
+                    output_json=tdetails.get('output_json'),
+                    output_pydantic=tdetails.get('output_pydantic'),
+                    output_file=tdetails.get('output_file', ""),
+                    callback=task_callback_sync,
+                    create_directory=tdetails.get('create_directory', False)
+                )
+                tasks.append(task)
+                tasks_dict[tname] = task
+
+        # Build context links
+        for role, details in config['roles'].items():
+            for tname, tdetails in details.get('tasks', {}).items():
+                if tname not in tasks_dict:
+                    continue
+                task = tasks_dict[tname]
+                context_tasks = [
+                    tasks_dict[ctx]
+                    for ctx in tdetails.get('context', [])
+                    if ctx in tasks_dict
+                ]
+                task.context = context_tasks
+
+        await cl.Message(content="Starting PraisonAI agents execution...", author="System").send()
+
+        if config.get('process') == 'hierarchical':
+            prai_agents = PraisonAIAgents(
+                agents=list(agents_map.values()),
+                tasks=tasks,
+                verbose=True,
+                process="hierarchical",
+                manager_llm=config.get('manager_llm', 'gpt-4o')
+            )
+        else:
+            prai_agents = PraisonAIAgents(
+                agents=list(agents_map.values()),
+                tasks=tasks,
+                verbose=2
+            )
+
+        cl.user_session.set("agents", prai_agents)
+
+        loop = asyncio.get_event_loop()
+        response = await loop.run_in_executor(None, prai_agents.start)
+
+        if hasattr(response, 'raw'):
+            result = response.raw
+        elif hasattr(response, 'content'):
+            result = response.content
+        else:
+            result = str(response)
+
+        await cl.Message(content="PraisonAI agents execution completed.", author="System").send()
+        await asyncio.sleep(1)
+        queue_processor.cancel()
+        return result
+
+    except Exception as e:
+        error_msg = f"Error in ui_run_praisonai: {str(e)}"
+        logger.error(error_msg, exc_info=True)
+        await cl.Message(content=error_msg, author="System").send()
+        raise
+
+# -----------------------------------------------------------------------------
+# Chainlit Handlers + logic from chainlit_ui.py (no changes in logic)
+# -----------------------------------------------------------------------------
+
+tools_dict = load_tools_from_tools_py()
+
+# Load agent config (default) from 'agents.yaml'
+with open(agent_file, 'r') as f:
+    config = yaml.safe_load(f)
+
+AUTH_PASSWORD_ENABLED = os.getenv("AUTH_PASSWORD_ENABLED", "true").lower() == "true"
+CHAINLIT_AUTH_SECRET = os.getenv("CHAINLIT_AUTH_SECRET")
+if not CHAINLIT_AUTH_SECRET:
+    os.environ["CHAINLIT_AUTH_SECRET"] = "p8BPhQChpg@J>jBz$wGxqLX2V>yTVgP*7Ky9H$aV:axW~ANNX-7_T:o@lnyCBu^U"
+
+username_env = os.getenv("CHAINLIT_USERNAME", "admin")
+password_env = os.getenv("CHAINLIT_PASSWORD", "admin")
+
+def simple_auth_callback(u: str, p: str):
+    if (u, p) == (username_env, password_env):
+        return cl.User(identifier=u, metadata={"role": "ADMIN", "provider": "credentials"})
+    return None
+
+if AUTH_PASSWORD_ENABLED:
+    auth_callback = cl.password_auth_callback(simple_auth_callback)
+
+@cl.set_chat_profiles
+async def set_profiles(current_user: cl.User):
+    """
+    Keep all the same starter logic from chainlit_ui.py.
+    """
+    return [
+        cl.ChatProfile(
+            name="Auto",
+            markdown_description=(
+                "Automatically generate agents and tasks based on your input."
+            ),
+            starters=[
+                cl.Starter(
+                    label="Create a movie script",
+                    message=(
+                        "Create a movie script about a futuristic society where AI "
+                        "and humans coexist, focusing on the conflict and resolution "
+                        "between them. Start with an intriguing opening scene."
+                    ),
+                    icon="/public/movie.svg",
+                ),
+                cl.Starter(
+                    label="Design a fantasy world",
+                    message=(
+                        "Design a detailed fantasy world with unique geography, "
+                        "cultures, and magical systems. Start by describing the main "
+                        "continent and its inhabitants."
+                    ),
+                    icon="/public/fantasy.svg",
+                ),
+                cl.Starter(
+                    label="Write a futuristic political thriller",
+                    message=(
+                        "Write a futuristic political thriller involving a conspiracy "
+                        "within a global government. Start with a high-stakes meeting "
+                        "that sets the plot in motion."
+                    ),
+                    icon="/public/thriller.svg",
+                ),
+                cl.Starter(
+                    label="Develop a new board game",
+                    message=(
+                        "Develop a new, innovative board game. Describe the game's "
+                        "objective, rules, and unique mechanics. Create a scenario to "
+                        "illustrate gameplay."
+                    ),
+                    icon="/public/game.svg",
+                ),
+            ],
+        ),
+        cl.ChatProfile(
+            name="Manual",
+            markdown_description="Manually define your agents and tasks using a YAML file.",
+        ),
+    ]
+
+@cl.on_chat_start
+async def start_chat():
+    try:
+        # Load model name from database
+        model_name = load_setting("model_name") or os.getenv("MODEL_NAME", "gpt-4o-mini")
+        cl.user_session.set("model_name", model_name)
+        logger.debug(f"Model name: {model_name}")
+
+        cl.user_session.set(
+            "message_history",
+            [{"role": "system", "content": "You are a helpful assistant."}],
+        )
+        
+        # Create tools.py if it doesn't exist
+        if not os.path.exists("tools.py"):
+            with open("tools.py", "w") as f:
+                f.write("# Add your custom tools here\n")
+        
+        settings = await cl.ChatSettings(
+            [
+                TextInput(id="Model", label="OpenAI - Model", initial=model_name),
+                TextInput(id="BaseUrl", label="OpenAI - Base URL", initial=config_list[0]['base_url']),
+                TextInput(id="ApiKey", label="OpenAI - API Key", initial=config_list[0]['api_key']), 
+                Select(
+                    id="Framework",
+                    label="Framework",
+                    values=["praisonai", "crewai", "autogen"],
+                    initial_index=0,
+                ),
+            ]
+        ).send()
+        cl.user_session.set("settings", settings)
+        chat_profile = cl.user_session.get("chat_profile")
+
+        if chat_profile=="Manual":
+            agent_file = "agents.yaml"
+            full_agent_file_path = os.path.abspath(agent_file)
+            if os.path.exists(full_agent_file_path):
+                with open(full_agent_file_path, 'r') as f:
+                    yaml_content = f.read()
+                msg = cl.Message(content=yaml_content, language="yaml")
+                await msg.send()
+                
+            full_tools_file_path = os.path.abspath("tools.py")
+            if os.path.exists(full_tools_file_path):
+                with open(full_tools_file_path, 'r') as f:
+                    tools_content = f.read()
+                msg = cl.Message(content=tools_content, language="python")
+                await msg.send()
+
+            settings = await cl.ChatSettings(
+                [
+                    TextInput(id="Model", label="OpenAI - Model", initial=model_name),
+                    TextInput(id="BaseUrl", label="OpenAI - Base URL", initial=config_list[0]['base_url']),
+                    TextInput(id="ApiKey", label="OpenAI - API Key", initial=config_list[0]['api_key']), 
+                    Select(
+                        id="Framework",
+                        label="Framework",
+                        values=["praisonai", "crewai", "autogen"],
+                        initial_index=0,
+                    ),
+                    TextInput(id="agents", label="agents.yaml", initial=yaml_content, multiline=True),
+                    TextInput(id="tools", label="tools.py", initial=tools_content, multiline=True),
+                ]
+            ).send()
+            cl.user_session.set("settings", settings)
+            
+            res = await cl.AskActionMessage(
+                content="Pick an action!",
+                actions=actions,
+            ).send()
+            if res and res.get("value") == "modify":
+                await cl.Message(content="Modify the agents and tools from below settings", actions=actions).send()
+            elif res and res.get("value") == "run":
+                await main(cl.Message(content="", actions=actions))
+
+        await on_settings_update(settings)
+    except Exception as e:
+        logger.error(f"Error in start_chat: {str(e)}")
+        await cl.Message(content=f"An error occurred while starting the chat: {str(e)}").send()
+
+
+@cl.on_chat_resume
+async def on_chat_resume(thread: ThreadDict):
+    """
+    Logic from chainlit_ui.py for chat resume:
+    - Restore message history from thread steps
+    """
+    try:
+        message_history = cl.user_session.get("message_history", [])
+        root_messages = [m for m in thread["steps"] if m["parentId"] is None]
+        for message in root_messages:
+            if message["type"] == "user_message":
+                message_history.append({"role": "user", "content": message["output"]})
+            elif message["type"] == "ai_message":
+                message_history.append({"role": "assistant", "content": message["content"]})
+        cl.user_session.set("message_history", message_history)
+    except Exception as e:
+        logger.error(f"Error in on_chat_resume: {str(e)}")
+
+@cl.on_message
+async def main(message: cl.Message):
+    """
+    Merged logic from colab_combined.py and chainlit_ui.py for main message:
+    - Use existing ui_run_praisonai to process user message with PraisonAI
+    - Keep message history, no changes to logic
+    """
+    try:
+        logger.info(f"User message: {message.content}")
+        await cl.Message(
+            content=f"🔄 Processing your request: {message.content}...",
+            author="System"
+        ).send()
+
+        # Run PraisonAI
+        result = await ui_run_praisonai(config, message.content, tools_dict)
+
+        # Update message history
+        message_history = cl.user_session.get("message_history", [])
+        message_history.append({"role": "user", "content": message.content})
+        message_history.append({"role": "assistant", "content": str(result)})
+        cl.user_session.set("message_history", message_history)
+
+    except Exception as e:
+        error_msg = f"Error running PraisonAI agents: {str(e)}"
+        logger.error(error_msg, exc_info=True)
+        await cl.Message(content=error_msg, author="System").send()
+
+@cl.on_settings_update
+async def on_settings_update(settings):
+    """Handle updates to the ChatSettings form."""
+    try:
+        global config_list, framework
+        config_list[0]['model'] = settings["Model"]
+        config_list[0]['base_url'] = settings["BaseUrl"]
+        config_list[0]['api_key'] = settings["ApiKey"]
+        
+        # Save settings to database with retry
+        for attempt in range(MAX_RETRIES):
+            try:
+                await save_setting_with_retry("model_name", config_list[0]['model'])
+                await save_setting_with_retry("base_url", config_list[0]['base_url'])
+                await save_setting_with_retry("api_key", config_list[0]['api_key'])
+                break
+            except sqlite3.OperationalError as e:
+                if "database is locked" in str(e) and attempt < MAX_RETRIES - 1:
+                    await asyncio.sleep(RETRY_DELAY)
+                    continue
+                raise
+        
+        # Save to environment variables for compatibility
+        os.environ["OPENAI_API_KEY"] = config_list[0]['api_key']
+        os.environ["OPENAI_MODEL_NAME"] = config_list[0]['model']
+        os.environ["OPENAI_API_BASE"] = config_list[0]['base_url']
+        os.environ["MODEL_NAME"] = config_list[0]['model']
+        framework = settings["Framework"]
+        os.environ["FRAMEWORK"] = framework
+        
+        if "agents" in settings:
+            with open("agents.yaml", "w") as f:
+                f.write(settings["agents"])
+        if "tools" in settings:
+            with open("tools.py", "w") as f:
+                f.write(settings["tools"])
+        
+        # Update thread metadata if exists with retry
+        thread_id = cl.user_session.get("thread_id")
+        if thread_id:
+            for attempt in range(MAX_RETRIES):
+                try:
+                    thread = await cl_data.get_thread(thread_id)
+                    if thread:
+                        metadata = thread.get("metadata", {})
+                        if isinstance(metadata, str):
+                            try:
+                                metadata = json.loads(metadata)
+                            except json.JSONDecodeError:
+                                metadata = {}
+                        metadata["model_name"] = config_list[0]['model']
+                        await cl_data.update_thread(thread_id, metadata=metadata)
+                        cl.user_session.set("metadata", metadata)
+                    break
+                except sqlite3.OperationalError as e:
+                    if "database is locked" in str(e) and attempt < MAX_RETRIES - 1:
+                        await asyncio.sleep(RETRY_DELAY)
+                        continue
+                    raise
+        
+        logger.info("Settings updated successfully")
+    except Exception as e:
+        logger.error(f"Error updating settings: {str(e)}")
+        await cl.Message(content=f"An error occurred while updating settings: {str(e)}. Retrying...").send()
+        # One final retry after a longer delay
+        try:
+            await asyncio.sleep(RETRY_DELAY * 2)
+            await on_settings_update(settings)
+        except Exception as e:
+            logger.error(f"Final retry failed: {str(e)}")
+            await cl.Message(content=f"Failed to update settings after retries: {str(e)}").send()

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "PraisonAI"
-version = "2.0.25"
+version = "2.0.26"
 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 = ""
@@ -84,7 +84,7 @@ autogen = ["pyautogen>=0.2.19", "praisonai-tools>=0.0.7", "crewai"]
 
 [tool.poetry]
 name = "PraisonAI"
-version = "2.0.25"
+version = "2.0.26"
 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 = ""

uv.lock

@@ -3061,7 +3061,7 @@ wheels = [
 
 [[package]]
 name = "praisonai"
-version = "2.0.25"
+version = "2.0.26"
 source = { editable = "." }
 dependencies = [
     { name = "instructor" },