Merge pull request #339 from MervinPraison/develop

2.0.60

README.md

@@ -20,7 +20,7 @@
 
 </div>
 
-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.
+PraisonAI is a production-ready Multi-AI Agents framework with self-reflection, designed to create AI Agents to automate and solve problems ranging from simple tasks to complex challenges. By integrating PraisonAI Agents, AutoGen, and CrewAI into a low-code solution, it streamlines the building and management of multi-agent LLM systems, emphasising simplicity, customisation, and effective human-agent collaboration.
 
 <div align="center">
   <a href="https://docs.praison.ai">
@@ -37,35 +37,24 @@ PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application
 - 🧠 Reasoning AI Agents
 - 👁️ Multi Modal AI Agents
 - 🤝 Multi Agent Collaboration
-- ⚡ AI Agent Workflow
-- 🔄 Use CrewAI or AutoGen Framework
+- 🎭 AI Agent Workflow
+- 📚 Add Custom Knowledge
+- 🧠 Agents with Short and Long Term Memory
+- 📄 Chat with PDF Agents
+- 💻 Code Interpreter Agents
+- 📚 RAG Agents
+- 🤔 Async & Parallel Processing
+- 🔄 Auto Agents
+- 🔢 Math Agents
+- 🎯 Structured Output Agents
+- 🔗 LangChain Integrated Agents
+- 📞 Callback Agents
+- 🤏 Mini AI Agents
+- 🛠️ 100+ Custom Tools
+- 📄 YAML Configuration
 - 💯 100+ LLM Support
-- 💻 Chat with ENTIRE Codebase
-- 🎨 Interactive UIs
-- 📄 YAML-based Configuration
-- 🛠️ Custom Tool Integration
-- 🔍 Internet Search Capability (using Crawl4AI and Tavily)
-- 🖼️ Vision Language Model (VLM) Support
-- 🎙️ Real-time Voice Interaction
 
-## Using No Code
-
-### Auto Mode:
-```bash
-pip install praisonai
-export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
-praisonai --auto create a movie script about Robots in Mars
-```
-
-### Initialise Mode:
-```bash
-pip install praisonai
-export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
-praisonai --init create a movie script about Robots in Mars
-praisonai
-```
-
-## Using Coding
+## Using Code
 
 Light weight package dedicated for coding:
 ```bash
@@ -76,58 +65,13 @@ pip install praisonaiagents
 export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
 ```
 
+### 1. Single Agent
+
 Create app.py file and add the code below:
 ```python
-from praisonaiagents import Agent, Task, PraisonAIAgents
-
-# 1. Create agents
-researcher = Agent(
-    name="Researcher",
-    role="Senior Research Analyst",
-    goal="Uncover cutting-edge developments in AI and data science",
-    backstory="""You are an expert at a technology research group, 
-    skilled in identifying trends and analyzing complex data.""",
-    verbose=True,
-    llm="gpt-4o",
-    markdown=True
-)
-writer = Agent(
-    name="Writer",
-    role="Tech Content Strategist",
-    goal="Craft compelling content on tech advancements",
-    backstory="""You are a content strategist known for 
-    making complex tech topics interesting and easy to understand.""",
-    llm="gpt-4o",
-    markdown=True
-)
-
-# 2. Define Tasks
-task1 = Task(
-    name="research_task",
-    description="""Analyze 2024's AI advancements. 
-    Find major trends, new technologies, and their effects.""",
-    expected_output="""A detailed report on 2024 AI advancements""",
-    agent=researcher
-)
-
-task2 = Task(
-    name="writing_task",
-    description="""Create a blog post about major AI advancements using the insights you have.
-    Make it interesting, clear, and suited for tech enthusiasts. 
-    It should be at least 4 paragraphs long.""",
-    expected_output="A blog post of at least 4 paragraphs",
-    agent=writer,
-)
-
-agents = PraisonAIAgents(
-    agents=[researcher, writer],
-    tasks=[task1, task2],
-    verbose=False,
-    process="hierarchical",
-    manager_llm="gpt-4o"
-)
-
-result = agents.start()
+from praisonaiagents import Agent
+agent = Agent(instructions="Your are a helpful AI assistant")
+agent.start("Write a movie script about a robot in Mars")
 ```
 
 Run:
@@ -135,6 +79,381 @@ Run:
 python app.py
 ```
 
+### 2. Multi Agents
+
+Create app.py file and add the code below:
+```python
+from praisonaiagents import Agent, PraisonAIAgents
+
+research_agent = Agent(instructions="Research about AI")
+summarise_agent = Agent(instructions="Summarise research agent's findings")
+agents = PraisonAIAgents(agents=[research_agent, summarise_agent])
+agents.start()
+```
+
+Run:
+```bash
+python app.py
+```
+
+## Using No Code
+
+### Auto Mode:
+```bash
+pip install praisonai
+export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
+praisonai --auto create a movie script about Robots in Mars
+```
+
+## AI Agents Flow
+
+```mermaid
+graph LR
+    %% Define the main flow
+    Start([▶ Start]) --> Agent1
+    Agent1 --> Process[⚙ Process]
+    Process --> Agent2
+    Agent2 --> Output([✓ Output])
+    Process -.-> Agent1
+    
+    %% Define subgraphs for agents and their tasks
+    subgraph Agent1[ ]
+        Task1[📋 Task]
+        AgentIcon1[🤖 AI Agent]
+        Tools1[🔧 Tools]
+        
+        Task1 --- AgentIcon1
+        AgentIcon1 --- Tools1
+    end
+    
+    subgraph Agent2[ ]
+        Task2[📋 Task]
+        AgentIcon2[🤖 AI Agent]
+        Tools2[🔧 Tools]
+        
+        Task2 --- AgentIcon2
+        AgentIcon2 --- Tools2
+    end
+
+    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
+    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
+    classDef tools fill:#2E8B57,stroke:#7C90A0,color:#fff
+    classDef transparent fill:none,stroke:none
+
+    class Start,Output,Task1,Task2 input
+    class Process,AgentIcon1,AgentIcon2 process
+    class Tools1,Tools2 tools
+    class Agent1,Agent2 transparent
+```
+
+## AI Agents with Tools
+
+Create AI agents that can use tools to interact with external systems and perform actions.
+
+```mermaid
+flowchart TB
+    subgraph Tools
+        direction TB
+        T3[Internet Search]
+        T1[Code Execution]
+        T2[Formatting]
+    end
+
+    Input[Input] ---> Agents
+    subgraph Agents
+        direction LR
+        A1[Agent 1]
+        A2[Agent 2]
+        A3[Agent 3]
+    end
+    Agents ---> Output[Output]
+
+    T3 --> A1
+    T1 --> A2
+    T2 --> A3
+
+    style Tools fill:#189AB4,color:#fff
+    style Agents fill:#8B0000,color:#fff
+    style Input fill:#8B0000,color:#fff
+    style Output fill:#8B0000,color:#fff
+```
+
+## AI Agents with Memory
+
+Create AI agents with memory capabilities for maintaining context and information across tasks.
+
+```mermaid
+flowchart TB
+    subgraph Memory
+        direction TB
+        STM[Short Term]
+        LTM[Long Term]
+    end
+
+    subgraph Store
+        direction TB
+        DB[(Vector DB)]
+    end
+
+    Input[Input] ---> Agents
+    subgraph Agents
+        direction LR
+        A1[Agent 1]
+        A2[Agent 2]
+        A3[Agent 3]
+    end
+    Agents ---> Output[Output]
+
+    Memory <--> Store
+    Store <--> A1
+    Store <--> A2
+    Store <--> A3
+
+    style Memory fill:#189AB4,color:#fff
+    style Store fill:#2E8B57,color:#fff
+    style Agents fill:#8B0000,color:#fff
+    style Input fill:#8B0000,color:#fff
+    style Output fill:#8B0000,color:#fff
+```
+
+## AI Agents with Different Processes
+
+### Sequential Process
+
+The simplest form of task execution where tasks are performed one after another.
+
+```mermaid
+graph LR
+    Input[Input] --> A1
+    subgraph Agents
+        direction LR
+        A1[Agent 1] --> A2[Agent 2] --> A3[Agent 3]
+    end
+    A3 --> Output[Output]
+
+    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
+    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
+    classDef transparent fill:none,stroke:none
+
+    class Input,Output input
+    class A1,A2,A3 process
+    class Agents transparent
+```
+
+### Hierarchical Process
+
+Uses a manager agent to coordinate task execution and agent assignments.
+
+```mermaid
+graph TB
+    Input[Input] --> Manager
+    
+    subgraph Agents
+        Manager[Manager Agent]
+        
+        subgraph Workers
+            direction LR
+            W1[Worker 1]
+            W2[Worker 2]
+            W3[Worker 3]
+        end
+        
+        Manager --> W1
+        Manager --> W2
+        Manager --> W3
+    end
+    
+    W1 --> Manager
+    W2 --> Manager
+    W3 --> Manager
+    Manager --> Output[Output]
+
+    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
+    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
+    classDef transparent fill:none,stroke:none
+
+    class Input,Output input
+    class Manager,W1,W2,W3 process
+    class Agents,Workers transparent
+```
+
+### Workflow Process
+
+Advanced process type supporting complex task relationships and conditional execution.
+
+```mermaid
+graph LR
+    Input[Input] --> Start
+    
+    subgraph Workflow
+        direction LR
+        Start[Start] --> C1{Condition}
+        C1 --> |Yes| A1[Agent 1]
+        C1 --> |No| A2[Agent 2]
+        A1 --> Join
+        A2 --> Join
+        Join --> A3[Agent 3]
+    end
+    
+    A3 --> Output[Output]
+
+    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
+    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
+    classDef decision fill:#2E8B57,stroke:#7C90A0,color:#fff
+    classDef transparent fill:none,stroke:none
+
+    class Input,Output input
+    class Start,A1,A2,A3,Join process
+    class C1 decision
+    class Workflow transparent
+```
+
+#### Agentic Routing Workflow
+
+Create AI agents that can dynamically route tasks to specialized LLM instances.
+
+```mermaid
+flowchart LR
+    In[In] --> Router[LLM Call Router]
+    Router --> LLM1[LLM Call 1]
+    Router --> LLM2[LLM Call 2]
+    Router --> LLM3[LLM Call 3]
+    LLM1 --> Out[Out]
+    LLM2 --> Out
+    LLM3 --> Out
+    
+    style In fill:#8B0000,color:#fff
+    style Router fill:#2E8B57,color:#fff
+    style LLM1 fill:#2E8B57,color:#fff
+    style LLM2 fill:#2E8B57,color:#fff
+    style LLM3 fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+#### Agentic Orchestrator Worker
+
+Create AI agents that orchestrate and distribute tasks among specialized workers.
+
+```mermaid
+flowchart LR
+    In[In] --> Router[LLM Call Router]
+    Router --> LLM1[LLM Call 1]
+    Router --> LLM2[LLM Call 2]
+    Router --> LLM3[LLM Call 3]
+    LLM1 --> Synthesizer[Synthesizer]
+    LLM2 --> Synthesizer
+    LLM3 --> Synthesizer
+    Synthesizer --> Out[Out]
+    
+    style In fill:#8B0000,color:#fff
+    style Router fill:#2E8B57,color:#fff
+    style LLM1 fill:#2E8B57,color:#fff
+    style LLM2 fill:#2E8B57,color:#fff
+    style LLM3 fill:#2E8B57,color:#fff
+    style Synthesizer fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+#### Agentic Autonomous Workflow
+
+Create AI agents that can autonomously monitor, act, and adapt based on environment feedback.
+
+```mermaid
+flowchart LR
+    Human[Human] <--> LLM[LLM Call]
+    LLM -->|ACTION| Environment[Environment]
+    Environment -->|FEEDBACK| LLM
+    LLM --> Stop[Stop]
+    
+    style Human fill:#8B0000,color:#fff
+    style LLM fill:#2E8B57,color:#fff
+    style Environment fill:#8B0000,color:#fff
+    style Stop fill:#333,color:#fff
+```
+
+#### Agentic Parallelization
+
+Create AI agents that can execute tasks in parallel for improved performance.
+
+```mermaid
+flowchart LR
+    In[In] --> LLM2[LLM Call 2]
+    In --> LLM1[LLM Call 1]
+    In --> LLM3[LLM Call 3]
+    LLM1 --> Aggregator[Aggregator]
+    LLM2 --> Aggregator
+    LLM3 --> Aggregator
+    Aggregator --> Out[Out]
+    
+    style In fill:#8B0000,color:#fff
+    style LLM1 fill:#2E8B57,color:#fff
+    style LLM2 fill:#2E8B57,color:#fff
+    style LLM3 fill:#2E8B57,color:#fff
+    style Aggregator fill:#fff,color:#000
+    style Out fill:#8B0000,color:#fff
+```
+
+#### Agentic Prompt Chaining
+
+Create AI agents with sequential prompt chaining for complex workflows.
+
+```mermaid
+flowchart LR
+    In[In] --> LLM1[LLM Call 1] --> Gate{Gate}
+    Gate -->|Pass| LLM2[LLM Call 2] -->|Output 2| LLM3[LLM Call 3] --> Out[Out]
+    Gate -->|Fail| Exit[Exit]
+    
+    style In fill:#8B0000,color:#fff
+    style LLM1 fill:#2E8B57,color:#fff
+    style LLM2 fill:#2E8B57,color:#fff
+    style LLM3 fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+    style Exit fill:#8B0000,color:#fff
+```
+
+#### Agentic Evaluator Optimizer
+
+Create AI agents that can generate and optimize solutions through iterative feedback.
+
+```mermaid
+flowchart LR
+    In[In] --> Generator[LLM Call Generator] 
+    Generator -->|SOLUTION| Evaluator[LLM Call Evaluator] -->|ACCEPTED| Out[Out]
+    Evaluator -->|REJECTED + FEEDBACK| Generator
+    
+    style In fill:#8B0000,color:#fff
+    style Generator fill:#2E8B57,color:#fff
+    style Evaluator fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+#### Repetitive Agents
+
+Create AI agents that can efficiently handle repetitive tasks through automated loops.
+
+```mermaid
+flowchart LR
+    In[Input] --> LoopAgent[("Looping Agent")]
+    LoopAgent --> Task[Task]
+    Task --> |Next iteration| LoopAgent
+    Task --> |Done| Out[Output]
+    
+    style In fill:#8B0000,color:#fff
+    style LoopAgent fill:#2E8B57,color:#fff,shape:circle
+    style Task fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+## Adding Models
+
+<div align="center">
+  <a href="https://docs.praison.ai/models">
+    <p align="center">
+      <img src="https://img.shields.io/badge/%F0%9F%93%9A_Models-Visit_docs.praison.ai-blue?style=for-the-badge&logo=bookstack&logoColor=white" alt="Models" />
+    </p>
+  </a>
+</div>
+
 ## Ollama Integration
 ```bash
 export OPENAI_BASE_URL=http://localhost:11434/v1
@@ -147,267 +466,16 @@ export OPENAI_API_KEY=xxxxxxxxxxx
 export OPENAI_BASE_URL=https://api.groq.com/openai/v1
 ```
 
-## Logging
-```bash
-export LOGLEVEL=info
-```
-
-Advanced logging:
-```bash
-export LOGLEVEL=debug
-```
-
-<div align="center">
-  <picture>
-    <source media="(prefers-color-scheme: dark)" srcset="docs/images/architecture-dark.png" />
-    <source media="(prefers-color-scheme: light)" srcset="docs/images/architecture-light.png" />
-    <img alt="PraisonAI Architecture" src="docs/images/architecture-light.png" />
-  </picture>
-</div>
-
-## Different User Interfaces:
-
-| 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) |
-| **Realtime** | Real-time voice interaction with AI | [https://docs.praison.ai/ui/realtime](https://docs.praison.ai/ui/realtime) |
-
-| Other Features | Description | Docs |
-|---|---|---|
-| **Train** | Fine-tune LLMs using your custom data | [https://docs.praison.ai/train](https://docs.praison.ai/train) |
-
-
-## Google Colab Multi Agents
-
-|               | Cookbook        | Open in Colab                                                                                                                                                                                                                                  |
-| ------------- | --------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| Basic         | PraisonAI       | <a target="_blank" href="https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/cookbooks/praisonai-googlecolab.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" /></a>       |
-| Include Tools | PraisonAI Tools | <a target="_blank" href="https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/cookbooks/praisonai-tools-googlecolab.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" /></a> |
-
-## Installation Options
-
-### Using pip
-```bash
-pip install praisonai
-```
-
-### Framework-specific Installation
-```bash
-# Install with CrewAI support
-pip install "praisonai[crewai]"
-
-# Install with AutoGen support
-pip install "praisonai[autogen]"
-
-# Install with both frameworks
-pip install "praisonai[crewai,autogen]"
-```
-
-### UI and Additional Features
-```bash
-# Install UI support
-pip install "praisonai[ui]"
-
-# Install Chat interface
-pip install "praisonai[chat]"
-
-# Install Code interface
-pip install "praisonai[code]"
-
-# Install Realtime voice interaction
-pip install "praisonai[realtime]"
-
-# Install Call feature
-pip install "praisonai[call]"
-```
-
-## Quick Start
-
-```bash
-# Set your OpenAI API key
-export OPENAI_API_KEY="Enter your API key"
-
-# Initialize with CrewAI (default)
-praisonai --init "create a movie script about dog in moon"
-
-# Or initialize with AutoGen
-praisonai --framework autogen --init "create a movie script about dog in moon"
-
-# Run the agents
-praisonai
-```
-
-## Full Automatic Mode
-
-```bash
-# With CrewAI (default)
-praisonai --auto "create a movie script about Dog in Moon"
-
-# With AutoGen
-praisonai --framework autogen --auto "create a movie script about Dog in Moon"
-```
-
-## Framework-specific Features
-
-### CrewAI
-When installing with `pip install "praisonai[crewai]"`, you get:
-- CrewAI framework support
-- PraisonAI tools integration
-- Task delegation capabilities
-- Sequential and parallel task execution
-
-### AutoGen
-When installing with `pip install "praisonai[autogen]"`, you get:
-- AutoGen framework support
-- PraisonAI tools integration
-- Multi-agent conversation capabilities
-- Code execution environment
-
-
-
-## TL;DR Multi Agents
-
-```bash
-pip install praisonai
-export OPENAI_API_KEY="Enter your API key"
-praisonai --init create a movie script about dog in moon
-praisonai
-```
-
-## Table of Contents
-
-- [Installation](#installation)
-- [Initialise](#initialise)
-- [Run](#run)
-- [Full Automatic Mode](#full-automatic-mode)
-- [User Interface](#user-interface)
-- [Praison AI Chat](#praison-ai-chat)
-- [Create Custom Tools](#create-custom-tools)
-- [Agents Playbook](#agents-playbook)
-- [Include praisonai package in your project](#include-praisonai-package-in-your-project)
-- [Commands to Install Dev Dependencies](#commands-to-install-dependencies)
-- [Other Models](#other-models)
-- [Contributing](#contributing)
-- [Star History](#star-history)
-
-## Installation Multi Agents
-
-```bash
-pip install praisonai
-```
-
-## Initialise
-
-```bash
-export OPENAI_API_KEY="Enter your API key"
-```
-
-Generate your OPENAI API KEY from here: https://platform.openai.com/api-keys
-
-Note: You can use other providers such as Ollama, Mistral ... etc. Details are provided at the bottom.
-
-```bash
-praisonai --init create a movie script about dog in moon
-```
-
-This will automatically create agents.yaml file in the current directory.
-
-### To initialise with a specific agent framework (Optional):
-
-```bash
-praisonai --framework autogen --init create movie script about cat in mars
-```
-
-## Run
-
-```bash
-praisonai
-```
-
-or
-
-```bash
-python -m praisonai
-```
-
-### Specify the agent framework (Optional):
-
-```bash
-praisonai --framework autogen
-```
-
-### Full Automatic Mode
-
-```bash
-praisonai --auto create a movie script about Dog in Moon
-```
-
-## User Interface
-
-## PraisonAI User Interfaces:
-
-| Interface | Description                                | URL                                                                   |
-| --------- | ------------------------------------------ | --------------------------------------------------------------------- |
-| **UI**    | Multi Agents such as CrewAI or AutoGen     | [https://docs.praisonai.com/ui/ui](https://docs.praison.ai/ui/ui)     |
-| **Chat**  | Chat with 100+ LLMs, single AI Agent       | [https://docs.praisonai.com/ui/chat](https://docs.praison.ai/ui/chat) |
-| **Code**  | Chat with entire Codebase, single AI Agent | [https://docs.praisonai.com/ui/code](https://docs.praison.ai/ui/code) |
-
-```bash
-pip install -U "praisonai[ui]"
-export OPENAI_API_KEY="Enter your API key"
-chainlit create-secret
-export CHAINLIT_AUTH_SECRET=xxxxxxxx
-praisonai ui
-```
-
-or
-
-```
-python -m praisonai ui
-```
-
-## Praison AI Chat
-
-- https://docs.praison.ai/chat/
-
-```bash
-pip install "praisonai[chat]"
-export OPENAI_API_KEY="Enter your API key"
-praisonai chat
-```
-
-### Internet Search
-
-Praison AI Chat and Praison AI Code now includes internet search capabilities using Crawl4AI and Tavily, allowing you to retrieve up-to-date information during your conversations.
-
-### Vision Language Model Support
-
-You can now upload images and ask questions based on them using Vision Language Models. This feature enables visual understanding and analysis within your chat sessions.
-
-## Praison AI Code
-
-```bash
-pip install "praisonai[code]"
-export OPENAI_API_KEY="Enter your API key"
-praisonai code
-```
-
-### Internet Search
-
-Praison AI Code also includes internet search functionality, enabling you to find relevant code snippets and programming information online.
-
-## Create Custom Tools
-
-- https://docs.praison.ai/tools/custom/
+## No Code Options
 
 ## Agents Playbook
 
 ### Simple Playbook Example
 
+Create `agents.yaml` file and add the code below:
+
 ```yaml
-framework: crewai
+framework: praisonai
 topic: Artificial Intelligence
 roles:
   screenwriter:
@@ -420,62 +488,25 @@ roles:
         expected_output: "Complete script ready for production."
 ```
 
+*To run the playbook:*
+```bash
+praisonai agents.yaml
+```
+
 ## Use 100+ Models
 
 - https://docs.praison.ai/models/
+<div align="center">
+  <a href="https://docs.praison.ai">
+    <p align="center">
+      <img src="https://img.shields.io/badge/📚_Documentation-Visit_docs.praison.ai-blue?style=for-the-badge&logo=bookstack&logoColor=white" alt="Documentation" />
+    </p>
+  </a>
+</div>
 
-## Include praisonai package in your project
+## Development:
 
-- https://docs.praison.ai/developers/wrapper
-- https://docs.praison.ai/developers/wrapper-tools/
-
-## Option 1: Using RAW YAML
-
-```python
-from praisonai import PraisonAI
-
-# Example agent_yaml content
-agent_yaml = """
-framework: "crewai"
-topic: "Space Exploration"
-
-roles:
-  astronomer:
-    role: "Space Researcher"
-    goal: "Discover new insights about {topic}"
-    backstory: "You are a curious and dedicated astronomer with a passion for unraveling the mysteries of the cosmos."
-    tasks:
-      investigate_exoplanets:
-        description: "Research and compile information about exoplanets discovered in the last decade."
-        expected_output: "A summarized report on exoplanet discoveries, including their size, potential habitability, and distance from Earth."
-"""
-
-# Create a PraisonAI instance with the agent_yaml content
-praisonai = PraisonAI(agent_yaml=agent_yaml)
-
-# Run PraisonAI
-result = praisonai.run()
-
-# Print the result
-print(result)
-```
-
-## Option 2: Using separate agents.yaml file
-
-Note: Please create agents.yaml file before hand.
-
-```python
-from praisonai import PraisonAI
-
-def basic(): # Basic Mode
-    praisonai = PraisonAI(agent_file="agents.yaml")
-    praisonai.run()
-
-if __name__ == "__main__":
-    basic()
-```
-
-## Commands to Install Dependencies:
+Below is used for development only.
 
 ### Using uv
 ```bash
@@ -500,18 +531,33 @@ uv pip install -r pyproject.toml --extra "crewai,autogen"
 - Submit a pull request via GitHub's web interface.
 - Await feedback from project maintainers.
 
+## Other Features
+
+- 🔄 Use CrewAI or AutoGen Framework
+- 💻 Chat with ENTIRE Codebase
+- 🎨 Interactive UIs
+- 📄 YAML-based Configuration
+- 🛠️ Custom Tool Integration
+- 🔍 Internet Search Capability (using Crawl4AI and Tavily)
+- 🖼️ Vision Language Model (VLM) Support
+- 🎙️ Real-time Voice Interaction
+
 ## Star History
 
 [![Star History Chart](https://api.star-history.com/svg?repos=MervinPraison/PraisonAI&type=Date)](https://docs.praison.ai)
 
-## License
-
-Praison AI is an open-sourced software licensed under the **[MIT license](https://opensource.org/licenses/MIT)**.
-
 ## Video Tutorials
 
 | Topic | Video |
 |-------|--------|
+| AI Agents with Self Reflection | [![Self Reflection](https://img.youtube.com/vi/vLXobEN2Vc8/0.jpg)](https://www.youtube.com/watch?v=vLXobEN2Vc8) |
+| AI Agents with Reasoning | [![Reasoning](https://img.youtube.com/vi/KNDVWGN3TpM/0.jpg)](https://www.youtube.com/watch?v=KNDVWGN3TpM) |
+| Multimodal AI Agents | [![Multimodal](https://img.youtube.com/vi/hjAWmUT1qqY/0.jpg)](https://www.youtube.com/watch?v=hjAWmUT1qqY) |
+| AI Agents Workflow | [![Workflow](https://img.youtube.com/vi/yWTH44QPl2A/0.jpg)](https://www.youtube.com/watch?v=yWTH44QPl2A) |
+| Async AI Agents | [![Async](https://img.youtube.com/vi/VhVQfgo00LE/0.jpg)](https://www.youtube.com/watch?v=VhVQfgo00LE) |
+| Mini AI Agents | [![Mini](https://img.youtube.com/vi/OkvYp5aAGSg/0.jpg)](https://www.youtube.com/watch?v=OkvYp5aAGSg) |
+| AI Agents with Memory | [![Memory](https://img.youtube.com/vi/1hVfVxvPnnQ/0.jpg)](https://www.youtube.com/watch?v=1hVfVxvPnnQ) |
+| Repetitive Agents | [![Repetitive](https://img.youtube.com/vi/dAYGxsjDOPg/0.jpg)](https://www.youtube.com/watch?v=dAYGxsjDOPg) |
 | Introduction | [![Introduction](https://img.youtube.com/vi/Fn1lQjC0GO0/0.jpg)](https://www.youtube.com/watch?v=Fn1lQjC0GO0) |
 | Tools Overview | [![Tools Overview](https://img.youtube.com/vi/XaQRgRpV7jo/0.jpg)](https://www.youtube.com/watch?v=XaQRgRpV7jo) |
 | Custom Tools | [![Custom Tools](https://img.youtube.com/vi/JSU2Rndh06c/0.jpg)](https://www.youtube.com/watch?v=JSU2Rndh06c) |
@@ -525,58 +571,3 @@ Praison AI is an open-sourced software licensed under the **[MIT license](https:
 | Realtime Voice Interface | [![Realtime](https://img.youtube.com/vi/frRHfevTCSw/0.jpg)](https://www.youtube.com/watch?v=frRHfevTCSw) |
 | Call Interface | [![Call](https://img.youtube.com/vi/m1cwrUG2iAk/0.jpg)](https://www.youtube.com/watch?v=m1cwrUG2iAk) |
 
-## License
-
-Praison AI is an open-sourced software licensed under the **[MIT license](https://opensource.org/licenses/MIT)**.
-
-## Local Docker Development with Live Reload
-
-To facilitate local development with live reload, you can use Docker. Follow the steps below:
-
-1. **Create a `Dockerfile.dev`**:
-    ```dockerfile
-    FROM python:3.11-slim
-
-    WORKDIR /app
-
-    COPY . .
-
-    RUN pip install flask praisonai==2.0.18 watchdog
-
-    EXPOSE 5555
-
-    ENV FLASK_ENV=development
-
-    CMD ["flask", "run", "--host=0.0.0.0"]
-    ```
-
-2. **Create a `docker-compose.yml`**:
-    ```yaml
-    version: '3.8'
-
-    services:
-      app:
-        build:
-          context: .
-          dockerfile: Dockerfile.dev
-        volumes:
-          - .:/app
-        ports:
-          - "5555:5555"
-        environment:
-          FLASK_ENV: development
-        command: flask run --host=0.0.0.0
-
-      watch:
-        image: alpine:latest
-        volumes:
-          - .:/app
-        command: sh -c "apk add --no-cache inotify-tools && while inotifywait -r -e modify,create,delete /app; do kill -HUP 1; done"
-    ```
-
-3. **Run Docker Compose**:
-    ```bash
-    docker-compose up
-    ```
-
-This setup will allow you to develop locally with live reload, making it easier to test and iterate on your code.

agents/.cursorrules

@@ -5,4 +5,146 @@
 5. Make it minimal change as possible
 6. Firstly try not to make any modification to the existing code as possible
 7. Only modify the existing code if its highly required, without that if it cant be done, then add new code section. 
-8. If you are adding new code, make sure to add it in a way that it can be easily integrated with the existing codebase. 
+8. If you are adding new code, make sure to add it in a way that it can be easily integrated with the existing codebase. 
+
+
+Below is a **detailed technical overview** of the issues that have been coming up in the workflow execution (specifically around loops, resetting tasks, and continuing on to subsequent tasks). It includes:
+
+1. **How the workflow currently flows**  
+2. **What logic marks a task as “completed”**  
+3. **What issues arose in `loop` tasks**  
+4. **Why the workflow can end up “stuck” or “looping indefinitely”**  
+
+---
+
+## 1. Overall Workflow Flow
+
+### a. Building relationships among tasks
+
+- At startup, the code iterates through all `tasks`.  
+- For each `task`:
+  - It looks at `task.next_tasks`, and for each `next_task_name`:
+    - Finds the corresponding `Task` object
+    - Appends the current task’s name to the found `next_task`'s `previous_tasks` list.  
+- This means if Task A has `next_tasks=["B"]`, then Task B’s `previous_tasks` will include `"A"`.
+
+### b. Finding and starting with a “start task”
+
+- The workflow code tries to locate a task with `is_start=True`.  
+- If no such task is found, it uses the first item in the tasks dictionary instead.  
+- That “start task” is what the workflow tries to run first.
+
+### c. Execution loop in the method (e.g., `workflow()` or `aworkflow()`)
+
+- There is a `while current_task:` loop that processes tasks in sequence, or conditionally, based on `task.condition`.
+- Each time a task runs (if non-loop), it yields the `task_id` or triggers an agent to run. Once the agent finishes (with or without a final result), the workflow picks up again to see what to do next:
+  - If the `task` is a `loop` type, the code tries to create or manage sub-tasks for each row/line in an “input_file.”
+  - If the `task` is a normal (“decision” or “task” or “some-other-type”), it just executes once, sets `status="completed"`, and the code moves on.
+
+### d. Condition-based branching
+
+- If a task has a result that includes a “decision” (like `{"decision":"more"}` or `"done"`), the code checks `task.condition`. For example:
+  ```python
+  condition = {
+    "more": "generate_task",
+    "done": "evaluate_total_questions"
+  }
+  ```
+- If the result’s decision is `"done"`, it jumps to the task named `"evaluate_total_questions"`.  
+- If the result’s decision is `"more"`, it jumps right back to `"generate_task"`.  
+- If no condition matches, it can fallback to the first item in `task.next_tasks`.
+
+### e. Marking tasks as “completed”
+
+- After a task’s execution (like a typical “non-loop” task), the code sets `task.status = "completed"`.
+- Then the code has a snippet that says:
+  ```python
+  if self.tasks[task_id].status == "completed":
+      # Possibly reset to "not started" so we can re-run if needed
+  ```
+- By default, the system tries to “reset” tasks to `"not started"`, **unless** it is a loop task or a subtask of a loop.  
+
+---
+
+## 2. How a “completed” task is marked
+
+Generally, tasks are marked `status="completed"` in two primary ways:
+
+1. **Non-Loop Execution**  
+   A normal task (like a “decision” or “task”) is executed once the code calls the agent, the agent returns a final result, and the system sets `task.status = "completed"`.
+2. **Loop Execution**  
+   A loop-type task is *programmatically* set to `status="completed"` when all of its sub-tasks have finished. That is:
+   - The code checks: “Have all child tasks of this loop finished?”  
+   - If `True`, the loop task is set to `completed`.
+
+---
+
+## 3. Issues Specifically in `loop` Tasks
+
+### a. Re-Entering the Loop
+
+- Before, the same snippet that “resets completed tasks to ‘not started’ so they can re-run if needed” **also** tried to reset loop tasks or their subtasks.  
+- If a loop task got reset to `"not started"`, the code would eventually pick it back up again, leading to repeated creation of sub-tasks (or repeated attempts to re-run them).
+- This caused an **infinite loop** or repeating the same steps in the workflow, never truly exiting the loop stage.
+
+### b. Subtasks Not Marked or Overwritten
+
+- Another tricky scenario: If sub-tasks themselves got reset, the parent loop would see them as “not started” again, and might wait for them to “complete,” or might re-run them. That can lead to indefinite re-running of sub-tasks.
+
+### c. Not Proceeding to Next (e.g., “upload_to_huggingface”)
+
+- If the loop kept “restarting,” the workflow never ended up hitting the next tasks. For example, if your workflow is:
+  1. `generate_task`  
+  2. `evaluate_total_questions`  
+  3. `generate_cot` (loop)  
+  4. `upload_to_huggingface`
+- The system might get stuck in step #3 indefinitely (the sub-tasks keep getting reset, so it never actually transitions to step #4).
+
+### d. Condition Logic vs. Next Tasks
+
+- Another subtlety: If the loop tasks had a `condition` that pointed them back to a prior step, it might cause unintentional re-entry. Typically, you only want loop tasks to proceed once, **unless** you explicitly want to re-visit. But if it’s a data ingestion process, you usually want to do it once, then move on to the next step.
+
+---
+
+## 4. Why the System Can End Up Stuck or “Looping Indefinitely”
+
+1. **Reset Mechanism**  
+   - The code tries to “reset tasks to ‘not started’ once they complete,” so they can be re-run in some dynamic multi-run scenario.  
+   - But that same logic can cause loop tasks to revert back to “not started” the moment they end. The system sees “Oh, a task is ‘not started’? Let’s run it!” and you’re in a cycle.
+
+2. **No Condition for Exit**  
+   - If the loop has a condition that leads back to a prior step (like `"more" -> generate_cot`), it can keep re-running.
+
+3. **Subtasks Not Marked**  
+   - If the subtask or the loop tries to “reactivate” each other, it never exits.
+
+---
+
+## Summary of the “Core Problem”
+
+1. We want to keep the resetting mechanism for **non-loop tasks** – because in some advanced workflows, we like re-running them from a different path or after some condition.  
+2. But we want **loop tasks** to remain `"completed"` once all sub-tasks are done, so the code can seamlessly proceed to the next major step.  
+3. Before the fix, loop tasks or their sub-tasks got reset. This triggered the system to re-enter the loop, re-run the sub-tasks, etc., causing an infinite loop and preventing the workflow from reaching tasks like “upload_to_huggingface.”
+
+---
+
+## Technical Highlights to Pass On
+
+- **In the reset snippet**:  
+  ```python
+  if self.tasks[task_id].status == "completed":
+      # never reset if loop or subtask-of-loop
+      # else reset to "not started"
+  ```
+  This is crucial to skipping re-runs on loop tasks.  
+- **Ensure** that once a loop’s sub-tasks are all “completed,” the loop’s status is set to “completed,” and it transitions to the next major tasks (like `upload_to_huggingface`).  
+- **Check** if the loop’s condition is correct. If you want a single pass, do not implement a condition that leads back to the same loop.  
+- Also, you can check you do not have “overlapping conditions” that cause re-entry.
+
+---
+
+### Conclusion
+
+**Hence,** the main challenge is that the reset logic (meant to let normal tasks be re-run) conflicts with a loop task’s one-pass usage. Once you avoid resetting the loop tasks or sub-tasks, you can finish them once, mark them “completed,” and properly proceed to the next stage.
+
+Dont remove any logging or debug statements, as it will help you to understand the flow of the code.

agents/async-tavily-agents-gemini.py

@@ -0,0 +1,127 @@
+import asyncio
+import logging
+from typing import List, Dict, Any
+from pydantic import BaseModel
+from praisonaiagents import Agent, Task, PraisonAIAgents, TaskOutput
+from tavily import AsyncTavilyClient
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+llm_config = {
+    "model": "gemini/gemini-1.5-flash-8b",
+    "response_format": {
+        "type": "text"
+    }
+}
+
+# Define output model
+class SearchResult(BaseModel):
+    query: str
+    results: List[Dict[str, Any]]
+    total_results: int
+
+# Define Tavily search tool
+async def tavily_search(query) -> Dict[str, Any]:
+    """Async Tavily search tool that returns structured results"""
+    client = AsyncTavilyClient()
+    try:
+        results = await client.search(query)
+        print(f"Tavily search results: {results}")
+        return {
+            "query": query,
+            "results": results.get("results", []),
+            "total_results": len(results.get("results", []))
+        }
+    except Exception as e:
+        logging.error(f"Tavily search error: {e}")
+        return {
+            "query": query,
+            "results": [],
+            "total_results": 0
+        }
+
+# Define async callback
+async def async_callback(output: TaskOutput):
+    if output.output_format == "JSON":
+        logging.info(f"Search completed: {output.json_dict.get('query', '')}")
+    elif output.output_format == "Pydantic":
+        logging.info(f"Search completed: {output.pydantic}")
+
+async def run_parallel_searches():
+    # Create search agent with direct tool function
+    search_agent = Agent(
+        name="TavilySearchAgent",
+        role="Search Specialist",
+        goal="Perform parallel web searches with high accuracy",
+        backstory="Expert in web search and information retrieval",
+        tools=[tavily_search],
+        llm=llm_config
+    )
+    
+    # Create summary agent
+    summary_agent = Agent(
+        name="ContentSynthesizer",
+        role="Research Analyst",
+        goal="Synthesize search results into coherent insights",
+        backstory="Expert in analyzing and combining multiple sources of information",
+        llm=llm_config
+    )
+    
+    # Define search topics
+    search_topics = [
+        "Latest AI breakthroughs 2024",
+        "Future of quantum computing",
+        "Advancements in robotics"
+    ]
+    
+    # Create parallel search tasks
+    search_tasks = [
+        Task(
+            name=f"search_{i}",
+            description=f"Search and analyze: {topic}",
+            expected_output="Comprehensive search results with analysis",
+            agent=search_agent,
+            async_execution=True,
+            callback=async_callback,
+        ) for i, topic in enumerate(search_topics)
+    ]
+    
+    # Create summary task
+    summary_task = Task(
+        name="synthesize_results",
+        description="Create a comprehensive summary of all search findings",
+        expected_output="Synthesized insights from all searches",
+        agent=summary_agent,
+        async_execution=False,
+        context=search_tasks
+    )
+    
+    # Initialize agents manager
+    agents = PraisonAIAgents(
+        agents=[search_agent, summary_agent],
+        tasks=search_tasks + [summary_task]
+    )
+    
+    # Execute tasks
+    results = await agents.astart()
+    return {
+        "search_results": [str(results["task_results"][i]) for i in range(len(search_tasks))],
+        "summary": str(results["task_results"][summary_task.id]),
+        "topics": search_topics
+    }
+
+async def main():
+    try:
+        logging.info("Starting parallel Tavily searches...")
+        results = await run_parallel_searches()
+        logging.info("Search process completed successfully")
+        print("\nResults Summary:")
+        for topic, result in zip(results["topics"], results["search_results"]):
+            print(f"\n{topic}:\n{result}")
+        print(f"\nFinal Summary:\n{results['summary']}")
+    except Exception as e:
+        logging.error(f"Error in execution: {e}")
+
+if __name__ == "__main__":
+    asyncio.run(main())

agents/async-tavily-agents.py

@@ -0,0 +1,119 @@
+import asyncio
+import logging
+from typing import List, Dict, Any
+from pydantic import BaseModel
+from praisonaiagents import Agent, Task, PraisonAIAgents, TaskOutput
+from tavily import AsyncTavilyClient
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+# Define output model
+class SearchResult(BaseModel):
+    query: str
+    results: List[Dict[str, Any]]
+    total_results: int
+
+# Define Tavily search tool
+async def tavily_search(query: str) -> Dict[str, Any]:
+    """Async Tavily search tool that returns structured results"""
+    client = AsyncTavilyClient()
+    try:
+        results = await client.search(query)
+        print(f"Tavily search results: {results}")
+        return {
+            "query": query,
+            "results": results.get("results", []),
+            "total_results": len(results.get("results", []))
+        }
+    except Exception as e:
+        logging.error(f"Tavily search error: {e}")
+        return {
+            "query": query,
+            "results": [],
+            "total_results": 0
+        }
+
+# Define async callback
+async def async_callback(output: TaskOutput):
+    if output.output_format == "JSON":
+        logging.info(f"Search completed: {output.json_dict.get('query', '')}")
+    elif output.output_format == "Pydantic":
+        logging.info(f"Search completed: {output.pydantic}")
+
+async def run_parallel_searches():
+    # Create search agent with direct tool function
+    search_agent = Agent(
+        name="TavilySearchAgent",
+        role="Search Specialist",
+        goal="Perform parallel web searches with high accuracy",
+        backstory="Expert in web search and information retrieval",
+        tools=[tavily_search]
+    )
+    
+    # Create summary agent
+    summary_agent = Agent(
+        name="ContentSynthesizer",
+        role="Research Analyst",
+        goal="Synthesize search results into coherent insights",
+        backstory="Expert in analyzing and combining multiple sources of information"
+    )
+    
+    # Define search topics
+    search_topics = [
+        "Latest AI breakthroughs 2024",
+        "Future of quantum computing",
+        "Advancements in robotics"
+    ]
+    
+    # Create parallel search tasks
+    search_tasks = [
+        Task(
+            name=f"search_{i}",
+            description=f"Search and analyze: {topic}",
+            expected_output="Comprehensive search results with analysis",
+            agent=search_agent,
+            async_execution=True,
+            callback=async_callback,
+            output_json=SearchResult
+        ) for i, topic in enumerate(search_topics)
+    ]
+    
+    # Create summary task
+    summary_task = Task(
+        name="synthesize_results",
+        description="Create a comprehensive summary of all search findings",
+        expected_output="Synthesized insights from all searches",
+        agent=summary_agent,
+        async_execution=False,
+        context=search_tasks
+    )
+    
+    # Initialize agents manager
+    agents = PraisonAIAgents(
+        agents=[search_agent, summary_agent],
+        tasks=search_tasks + [summary_task]
+    )
+    
+    # Execute tasks
+    results = await agents.astart()
+    return {
+        "search_results": [str(results["task_results"][i]) for i in range(len(search_tasks))],
+        "summary": str(results["task_results"][summary_task.id]),
+        "topics": search_topics
+    }
+
+async def main():
+    try:
+        logging.info("Starting parallel Tavily searches...")
+        results = await run_parallel_searches()
+        logging.info("Search process completed successfully")
+        print("\nResults Summary:")
+        for topic, result in zip(results["topics"], results["search_results"]):
+            print(f"\n{topic}:\n{result}")
+        print(f"\nFinal Summary:\n{results['summary']}")
+    except Exception as e:
+        logging.error(f"Error in execution: {e}")
+
+if __name__ == "__main__":
+    asyncio.run(main())

agents/cot-agents-batch.py

@@ -0,0 +1,73 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import cot_save, cot_upload_to_huggingface
+
+# Create a COT agent for generating and managing chain of thought solutions
+cot_agent = Agent(
+    name="COTGenerator",
+    role="Chain of Thought Specialist",
+    goal="Generate and manage chain of thought solutions for Q&A pairs",
+    backstory="Expert in breaking down problems and generating detailed solution steps",
+    tools=[cot_save],
+    llm="gpt-4o-mini",  # Using recommended model
+)
+
+save_to_huggingface_agent = Agent(
+    name="SaveToHuggingface",
+    role="Save to Huggingface",
+    goal="Save the generated chain of thought solutions to a Huggingface dataset",
+    backstory="Expert in saving data to Huggingface",
+    tools=[cot_upload_to_huggingface],
+    llm="gpt-4o-mini",  # Using recommended model
+)
+
+# Define question-answer pairs to process in batches
+qa_pairs = [
+    {
+        "question": "What is the sum of numbers from 1 to 10?",
+        "answer": "55"
+    },
+    {
+        "question": "Calculate the area of a circle with radius 5",
+        "answer": "78.54"
+    }
+]
+
+# Process qa_pairs in batches
+batch_size = 2
+for i in range(0, len(qa_pairs), batch_size):
+    batch_qa = qa_pairs[i:i + batch_size]
+    
+    # Create task for current batch
+    qa_text = "\n".join(
+        f"- Q: {qa['question']}\n  A: {qa['answer']}" 
+        for qa in batch_qa
+    )
+    
+    cot_task = Task(
+        description=(
+            "For each Q&A pair:\n"
+            "1. Use the tool cot_save to generate a chain of thought solution\n"
+            "2. Save to CSV file with filename 'cot_dataset.csv'\n\n"
+            f"Q&A Pairs:\n{qa_text}"
+        ),
+        expected_output="Chain of thought solutions saved to CSV",
+        agent=cot_agent,
+        name=f"generate_cot_solutions_batch_{i//batch_size + 1}"
+    )
+
+    save_to_huggingface_task = Task(
+        description="Save to Huggingface dataset at mervinpraison/cot-dataset , cot_dataset.csv",
+        expected_output="Chain of thought solutions saved to Huggingface",
+        agent=save_to_huggingface_agent,
+        name=f"save_to_huggingface_batch_{i//batch_size + 1}"
+    )
+
+    # Initialize and run the agent
+    agents = PraisonAIAgents(
+        agents=[cot_agent, save_to_huggingface_agent],
+        tasks=[cot_task, save_to_huggingface_task]
+    )
+
+    # Execute current batch
+    result = agents.start()
+    print(f"\nBatch {i//batch_size + 1} completed. Solutions appended to CSV file.")

agents/cot-agents.py

@@ -0,0 +1,142 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import cot_save, cot_upload_to_huggingface
+from pydantic import BaseModel
+import os
+
+# Define Pydantic model for structured output
+class DecisionModel(BaseModel):
+    response: str
+    decision: str
+
+def write_csv(file_path, data):
+    """Write data to CSV file."""
+    if not os.path.exists(file_path):
+        with open(file_path, 'w') as file:
+            file.write(data + '\n')
+    else:
+        with open(file_path, 'a') as file:
+            file.write(data + '\n')
+    return f"Data appended to {file_path}"
+
+def count_questions(file_path):
+    """Count lines in file."""
+    with open(file_path, 'r') as file:
+        return sum(1 for _ in file)
+
+# Keep existing agents with minimal changes
+qa_generator = Agent(
+    name="Generator",
+    role="Question Creator",
+    goal="Create challenging math and logic questions",
+    backstory="Expert in educational content creation",
+    llm="gpt-4o-mini",
+    tools=[write_csv, count_questions]
+)
+
+total_questions_evaluator = Agent(
+    name="TotalQuestionsEvaluator",
+    role="Total Questions Evaluator",
+    goal="Evaluate the total number of questions in qa_pairs.csv file",
+    backstory="Expert in evaluating the total number of questions in a file",
+    llm="gpt-4o-mini",
+    tools=[count_questions],
+    verbose=False
+)
+
+cot_generator = Agent(
+    name="COTGenerator",
+    role="Chain of Thought Specialist",
+    goal="Generate and manage chain of thought solutions for Q&A pairs",
+    backstory="Expert in breaking down problems and generating detailed solution steps",
+    tools=[cot_save],
+    llm="gpt-4o-mini",
+    verbose=False
+)
+
+upload_to_huggingface = Agent(
+    name="UploadToHuggingface",
+    role="Upload to Huggingface",
+    goal="Upload the generated chain of thought solutions to a Huggingface dataset",
+    backstory="Expert in saving data to Huggingface",
+    tools=[cot_upload_to_huggingface],
+    llm="gpt-4o-mini",
+    verbose=False
+)
+
+# Define tasks with workflow improvements
+generate_task = Task(
+    description="""Generate question and answer in csv format without headers: question, answer and append to qa_pairs.csv file
+generate 10 unique questions and answers and don't repeat on the same question and answer. Reponse with 'done' when done
+with append mode as 'a'
+Example question and answer:
+question, answer
+What is the sum of numbers from 1 to 10?, 55
+Number of r's in the word strawberry, 3
+""",
+    expected_output="append to qa_pairs.csv file with questions and answers and move to next task",
+    agent=qa_generator,
+    name="generate_task",
+    is_start=True,
+    next_tasks=["evaluate_total_questions"],
+    task_type="decision",
+    condition={
+        "more": "generate_task",
+        "done": "evaluate_total_questions"
+    }
+)
+
+evaluate_total_questions_task = Task(
+    description="Evaluate the total number of questions in qa_pairs.csv file is 1",
+    expected_output="Total number of questions in qa_pairs.csv file",
+    agent=total_questions_evaluator,
+    task_type="decision",
+    name="evaluate_total_questions",
+    condition={
+        "more": "generate_task",
+        "done": "generate_cot"
+    }
+)
+
+generate_cot_task = Task(
+    name="generate_cot",
+    description="""Generate chain of thought solutions for each question in the input file. 
+Save to cot_solutions.csv file
+Don't generate chain of thought solutions again after receiving the response from Tool Call
+After calling the tool, respond with a JSON object:
+{
+    "response": "done",
+    "decision": "done"
+}
+""",
+    expected_output="done",
+    agent=cot_generator,
+    input_file="qa_pairs.csv",
+    task_type="loop",
+    next_tasks=["upload_to_huggingface"],
+    condition={
+        "done": ["upload_to_huggingface"],
+        "exit": [],
+    },
+    output_pydantic=DecisionModel  # Use Pydantic model for output validation
+)
+
+upload_to_huggingface_task = Task(
+    name="upload_to_huggingface",
+    description="""Upload to Huggingface:
+    1. Save to cot_solutions.csv
+    2. Upload to mervinpraison/cot-dataset""",
+    expected_output="Dataset published successfully",
+    agent=upload_to_huggingface,
+    tools=[cot_upload_to_huggingface]
+)
+
+# Initialize workflow
+agents = PraisonAIAgents(
+    agents=[qa_generator, total_questions_evaluator, cot_generator, upload_to_huggingface],
+    tasks=[generate_task, evaluate_total_questions_task, generate_cot_task, upload_to_huggingface_task],
+    process="workflow",
+    max_iter=30,
+    verbose=False
+)
+
+agents.start()

agents/deepseek-reasoning-agents.py

@@ -0,0 +1,6 @@
+from praisonaiagents import Agent
+
+agent = Agent(instructions="You are helpful Assisant", llm="deepseek-reasoner", reasoning_steps=True)
+
+result = agent.start("Why sky is Blue?")
+print(result)

agents/deepseek-reasoning-multi-agents.py

@@ -0,0 +1,14 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+reasoning_agent = Agent(role="Helpful Assistant", reasoning_steps=True, llm="deepseek/deepseek-reasoner")
+small_agent = Agent(role="Helpful Assistant", llm="gpt-3.5-turbo")
+
+reasoning_task = Task(description="How many r's in the word 'Strawberry'?", agent=reasoning_agent)
+small_task = Task(description="With the provided reasoning tell me how many r's in the word 'Strawberry'?", agent=small_agent)
+
+agents = PraisonAIAgents(
+    agents=[reasoning_agent, small_agent],
+    tasks=[reasoning_task, small_task]
+)
+
+agents.start()

agents/deepseek-reasoning-output-tokens.py

@@ -0,0 +1,19 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+llm_config = {
+    "model": "deepseek/deepseek-reasoner",
+    "max_tokens": 1
+}
+
+reasoning_agent = Agent(role="Helpful Assistant", llm=llm_config, reasoning_steps=True)
+small_agent = Agent(role="Helpful Assistant", llm="openai/gpt-3.5-turbo")
+
+reasoning_task = Task(description="How many r's in the word 'Strawberry'?", agent=reasoning_agent)
+small_task = Task(description="With the provided reasoning tell me how many r's in the word 'Strawberry'?", agent=small_agent)
+
+agents = PraisonAIAgents(
+    agents=[reasoning_agent, small_agent],
+    tasks=[reasoning_task, small_task]
+)
+
+agents.start()

agents/deepseek-think.py

@@ -0,0 +1,18 @@
+from openai import OpenAI
+
+client = OpenAI()
+
+response = client.chat.completions.create(
+    model="deepseek-reasoner",
+    messages=[
+        {"role": "system", "content": "You are a helpful assistant"},
+        {"role": "user", "content": "Why sky is Blue?"},
+    ],
+    stream=True
+)
+
+for chunk in response:
+    if chunk.choices[0].delta.content is not None:
+        print(chunk.choices[0].delta.content, end="", flush=True)
+    if chunk.choices[0].delta.reasoning_content is not None:
+        print(chunk.choices[0].delta.reasoning_content, end="", flush=True)

agents/evaluator-optimiser.py

@@ -7,8 +7,8 @@ generator = Agent(
     goal="Generate initial solutions and incorporate feedback",
     instructions=(
         "1. Look at the context from previous tasks.\n"
-        "2. If you see that you have already produced 2 points, then add another 2 new points "
-        "   so that the total becomes 10.\n"
+        "2. If you see that you have already produced 2 points, then add another 2 new points starting with 3. "
+        "   so that the total becomes 4.\n"
         "3. Otherwise, just produce the first 2 points.\n"
         "4. Return only the final list of points, with no extra explanation."
     )
@@ -17,10 +17,10 @@ generator = Agent(
 evaluator = Agent(
     name="Evaluator",
     role="Solution evaluator",
-    goal="Evaluate solutions and provide improvement feedback",
+    goal="Evaluate solutions and respond with more or done",
     instructions=(
         "1. Count how many lines in the response start with a number and a period (like '1. ' or '2. ').\n"
-        "2. If there are 10 or more, respond with 'done'.\n"
+        "2. If there are 4 or more, respond with 'done'.\n"
         "3. Otherwise, respond with 'more'.\n"
         "4. Return only the single word: 'done' or 'more'."
     )
@@ -40,10 +40,9 @@ generate_task = Task(
 
 evaluate_task = Task(
     name="evaluate",
-    description="Check if there are 10 points about AI",
+    description="Check if there are 4 points about AI. ONLY RESPOND WITH 'more' or 'done' in your response field",
     expected_output="more or done",
     agent=evaluator,
-    next_tasks=["generate"],
     context=[generate_task],
     task_type="decision",
     condition={

agents/litellm-reasoning.py

@@ -0,0 +1,14 @@
+from litellm import completion
+
+messages = [{"role": "user", "content": "What is 1+1?"}]
+resp = completion(
+    model="deepseek/deepseek-reasoner",
+    messages=messages,
+    stream=False
+)
+
+reasoning_content = resp.choices[0].message.provider_specific_fields["reasoning_content"]
+content = resp.choices[0].message.content
+
+print(reasoning_content)
+print(content)

agents/llm-deepseek-agents.py

@@ -0,0 +1,9 @@
+from praisonaiagents import Agent
+
+agent = Agent(
+    instructions="You are helpful Assisant", 
+    llm="deepseek/deepseek-reasoner"
+)
+
+result = agent.start("Why sky is Blue?")
+print(result)

agents/llm-deepseek-reasoning-agents.py

@@ -0,0 +1,10 @@
+from praisonaiagents import Agent
+
+agent = Agent(
+    instructions="You are helpful Assisant", 
+    llm="deepseek/deepseek-reasoner", 
+    reasoning_steps=True
+)
+
+result = agent.start("Why sky is Blue?")
+print(result)

agents/llm-deepseek-reasoning-steps.py

@@ -0,0 +1,14 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+reasoning_agent = Agent(role="Helpful Assistant", reasoning_steps=True, llm="deepseek/deepseek-reasoner")
+small_agent = Agent(role="Helpful Assistant", llm="openai/gpt-3.5-turbo")
+
+reasoning_task = Task(description="How many r's in the word 'Strawberry'?", agent=reasoning_agent)
+small_task = Task(description="With the provided reasoning tell me how many r's in the word 'Strawberry'?", agent=small_agent)
+
+agents = PraisonAIAgents(
+    agents=[reasoning_agent, small_agent],
+    tasks=[reasoning_task, small_task]
+)
+
+agents.start()

agents/llm-langchain-toolcall.py

@@ -0,0 +1,30 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from langchain_community.tools import YouTubeSearchTool
+from langchain_community.utilities import WikipediaAPIWrapper
+
+# Create an agent with both tools
+agent = Agent(
+    name="SearchAgent",
+    role="Research Assistant",
+    goal="Search for information from multiple sources",
+    backstory="I am an AI assistant that can search YouTube and Wikipedia.",
+    tools=[YouTubeSearchTool, WikipediaAPIWrapper],
+    llm="openai/gpt-4o-mini"
+)
+
+# Create tasks to demonstrate both tools
+task = Task(
+    name="search_task",
+    description="Search for information about 'AI advancements' on both YouTube and Wikipedia",
+    expected_output="Combined information from YouTube videos and Wikipedia articles",
+    agent=agent
+)
+
+# Create and start the workflow
+agents = PraisonAIAgents(
+    agents=[agent],
+    tasks=[task],
+    verbose=True
+)
+
+agents.start()

agents/orchestrator-workers.py

@@ -97,7 +97,8 @@ workflow = PraisonAIAgents(
     agents=[router, worker1, worker2, worker3, synthesizer],
     tasks=[router_task, worker1_task, worker2_task, worker3_task, synthesize_task],
     process="workflow",
-    verbose=True
+    verbose=True,
+    max_iter=10
 )
 
 # Run orchestrated workflow

agents/praisonaiagents/agent/agent.py

@@ -21,10 +21,180 @@ from ..main import (
 )
 import inspect
 import uuid
+from dataclasses import dataclass
 
 if TYPE_CHECKING:
     from ..task.task import Task
 
+@dataclass
+class ChatCompletionMessage:
+    content: str
+    role: str = "assistant"
+    refusal: Optional[str] = None
+    audio: Optional[str] = None
+    function_call: Optional[dict] = None
+    tool_calls: Optional[List] = None
+    reasoning_content: Optional[str] = None
+
+@dataclass
+class Choice:
+    finish_reason: Optional[str]
+    index: int
+    message: ChatCompletionMessage
+    logprobs: Optional[dict] = None
+
+@dataclass
+class CompletionTokensDetails:
+    accepted_prediction_tokens: Optional[int] = None
+    audio_tokens: Optional[int] = None
+    reasoning_tokens: Optional[int] = None
+    rejected_prediction_tokens: Optional[int] = None
+
+@dataclass
+class PromptTokensDetails:
+    audio_tokens: Optional[int] = None
+    cached_tokens: int = 0
+
+@dataclass
+class CompletionUsage:
+    completion_tokens: int = 0
+    prompt_tokens: int = 0
+    total_tokens: int = 0
+    completion_tokens_details: Optional[CompletionTokensDetails] = None
+    prompt_tokens_details: Optional[PromptTokensDetails] = None
+    prompt_cache_hit_tokens: int = 0
+    prompt_cache_miss_tokens: int = 0
+
+@dataclass
+class ChatCompletion:
+    id: str
+    choices: List[Choice]
+    created: int
+    model: str
+    object: str = "chat.completion"
+    system_fingerprint: Optional[str] = None
+    service_tier: Optional[str] = None
+    usage: Optional[CompletionUsage] = None
+
+def process_stream_chunks(chunks):
+    """Process streaming chunks into combined response"""
+    if not chunks:
+        return None
+    
+    try:
+        first_chunk = chunks[0]
+        last_chunk = chunks[-1]
+        
+        # Basic metadata
+        id = getattr(first_chunk, "id", None) 
+        created = getattr(first_chunk, "created", None)
+        model = getattr(first_chunk, "model", None)
+        system_fingerprint = getattr(first_chunk, "system_fingerprint", None)
+        
+        # Track usage
+        completion_tokens = 0
+        prompt_tokens = 0
+        
+        content_list = []
+        reasoning_list = []
+        tool_calls = []
+        current_tool_call = None
+
+        # First pass: Get initial tool call data
+        for chunk in chunks:
+            if not hasattr(chunk, "choices") or not chunk.choices:
+                continue
+            
+            delta = getattr(chunk.choices[0], "delta", None)
+            if not delta:
+                continue
+
+            # Handle content and reasoning
+            if hasattr(delta, "content") and delta.content:
+                content_list.append(delta.content)
+            if hasattr(delta, "reasoning_content") and delta.reasoning_content:
+                reasoning_list.append(delta.reasoning_content)
+            
+            # Handle tool calls
+            if hasattr(delta, "tool_calls") and delta.tool_calls:
+                for tool_call_delta in delta.tool_calls:
+                    if tool_call_delta.index is not None and tool_call_delta.id:
+                        # Found the initial tool call
+                        current_tool_call = {
+                            "id": tool_call_delta.id,
+                            "type": "function",
+                            "function": {
+                                "name": tool_call_delta.function.name,
+                                "arguments": ""
+                            }
+                        }
+                        while len(tool_calls) <= tool_call_delta.index:
+                            tool_calls.append(None)
+                        tool_calls[tool_call_delta.index] = current_tool_call
+                        current_tool_call = tool_calls[tool_call_delta.index]
+                    elif current_tool_call is not None and hasattr(tool_call_delta.function, "arguments"):
+                        if tool_call_delta.function.arguments:
+                            current_tool_call["function"]["arguments"] += tool_call_delta.function.arguments
+
+        # Remove any None values and empty tool calls
+        tool_calls = [tc for tc in tool_calls if tc and tc["id"] and tc["function"]["name"]]
+
+        combined_content = "".join(content_list) if content_list else ""
+        combined_reasoning = "".join(reasoning_list) if reasoning_list else None
+        finish_reason = getattr(last_chunk.choices[0], "finish_reason", None) if hasattr(last_chunk, "choices") and last_chunk.choices else None
+
+        # Create ToolCall objects
+        processed_tool_calls = []
+        if tool_calls:
+            try:
+                from openai.types.chat import ChatCompletionMessageToolCall
+                for tc in tool_calls:
+                    tool_call = ChatCompletionMessageToolCall(
+                        id=tc["id"],
+                        type=tc["type"],
+                        function={
+                            "name": tc["function"]["name"],
+                            "arguments": tc["function"]["arguments"]
+                        }
+                    )
+                    processed_tool_calls.append(tool_call)
+            except Exception as e:
+                print(f"Error processing tool call: {e}")
+
+        message = ChatCompletionMessage(
+            content=combined_content,
+            role="assistant",
+            reasoning_content=combined_reasoning,
+            tool_calls=processed_tool_calls if processed_tool_calls else None
+        )
+        
+        choice = Choice(
+            finish_reason=finish_reason or "tool_calls" if processed_tool_calls else None,
+            index=0,
+            message=message
+        )
+
+        usage = CompletionUsage(
+            completion_tokens=completion_tokens,
+            prompt_tokens=prompt_tokens,
+            total_tokens=completion_tokens + prompt_tokens,
+            completion_tokens_details=CompletionTokensDetails(),
+            prompt_tokens_details=PromptTokensDetails()
+        )
+        
+        return ChatCompletion(
+            id=id,
+            choices=[choice],
+            created=created,
+            model=model,
+            system_fingerprint=system_fingerprint,
+            usage=usage
+        )
+        
+    except Exception as e:
+        print(f"Error processing chunks: {e}")
+        return None
+
 class Agent:
     def _generate_tool_definition(self, function_name):
         """
@@ -190,7 +360,8 @@ class Agent:
         max_reflect: int = 3,
         min_reflect: int = 1,
         reflect_llm: Optional[str] = None,
-        user_id: Optional[str] = None
+        user_id: Optional[str] = None,
+        reasoning_steps: bool = False
     ):
         # Add check at start if memory is requested
         if memory is not None:
@@ -297,7 +468,8 @@ Your Goal: {self.goal}
         self.agent_id = str(uuid.uuid4())
 
         # Store user_id
-        self.user_id = user_id
+        self.user_id = user_id or "praison"
+        self.reasoning_steps = reasoning_steps
 
         # Check if knowledge parameter has any values
         if not knowledge:
@@ -399,7 +571,54 @@ Your Goal: {self.goal}
     def __str__(self):
         return f"Agent(name='{self.name}', role='{self.role}', goal='{self.goal}')"
 
-    def _chat_completion(self, messages, temperature=0.2, tools=None, stream=True):
+    def _process_stream_response(self, messages, temperature, start_time, formatted_tools=None, reasoning_steps=False):
+        """Process streaming response and return final response"""
+        try:
+            # Create the response stream
+            response_stream = client.chat.completions.create(
+                model=self.llm,
+                messages=messages,
+                temperature=temperature,
+                tools=formatted_tools if formatted_tools else None,
+                stream=True
+            )
+            
+            full_response_text = ""
+            reasoning_content = ""
+            chunks = []
+            
+            # Create Live display with proper configuration
+            with Live(
+                display_generating("", start_time),
+                console=self.console,
+                refresh_per_second=4,
+                transient=True,
+                vertical_overflow="ellipsis",
+                auto_refresh=True
+            ) as live:
+                for chunk in response_stream:
+                    chunks.append(chunk)
+                    if chunk.choices[0].delta.content:
+                        full_response_text += chunk.choices[0].delta.content
+                        live.update(display_generating(full_response_text, start_time))
+                    
+                    # Update live display with reasoning content if enabled
+                    if reasoning_steps and hasattr(chunk.choices[0].delta, "reasoning_content"):
+                        rc = chunk.choices[0].delta.reasoning_content
+                        if rc:
+                            reasoning_content += rc
+                            live.update(display_generating(f"{full_response_text}\n[Reasoning: {reasoning_content}]", start_time))
+            
+            # Clear the last generating display with a blank line
+            self.console.print()
+            final_response = process_stream_chunks(chunks)
+            return final_response
+            
+        except Exception as e:
+            display_error(f"Error in stream processing: {e}")
+            return None
+
+    def _chat_completion(self, messages, temperature=0.2, tools=None, stream=True, reasoning_steps=False):
         start_time = time.time()
         logging.debug(f"{self.name} sending messages to LLM: {messages}")
 
@@ -425,20 +644,31 @@ Your Goal: {self.goal}
                     logging.warning(f"Tool {tool} not recognized")
 
         try:
-            initial_response = client.chat.completions.create(
-                model=self.llm,
-                messages=messages,
-                temperature=temperature,
-                tools=formatted_tools if formatted_tools else None,
-                stream=False
-            )
+            if stream:
+                # Process as streaming response with formatted tools
+                final_response = self._process_stream_response(
+                    messages, 
+                    temperature, 
+                    start_time, 
+                    formatted_tools=formatted_tools if formatted_tools else None,
+                    reasoning_steps=reasoning_steps
+                )
+            else:
+                # Process as regular non-streaming response
+                final_response = client.chat.completions.create(
+                    model=self.llm,
+                    messages=messages,
+                    temperature=temperature,
+                    tools=formatted_tools if formatted_tools else None,
+                    stream=False
+                )
 
-            tool_calls = getattr(initial_response.choices[0].message, 'tool_calls', None)
+            tool_calls = getattr(final_response.choices[0].message, 'tool_calls', None)
 
             if tool_calls:
                 messages.append({
-                    "role": "assistant",
-                    "content": initial_response.choices[0].message.content,
+                    "role": "assistant", 
+                    "content": final_response.choices[0].message.content,
                     "tool_calls": tool_calls
                 })
 
@@ -461,56 +691,31 @@ Your Goal: {self.goal}
                         "content": results_str
                     })
 
-            if stream:
-                response_stream = client.chat.completions.create(
-                    model=self.llm,
-                    messages=messages,
-                    temperature=temperature,
-                    stream=True
-                )
-                full_response_text = ""
-                
-                # Create Live display with proper configuration
-                with Live(
-                    display_generating("", start_time),
-                    console=self.console,
-                    refresh_per_second=4,
-                    transient=True,  # Changed to False to preserve output
-                    vertical_overflow="ellipsis",
-                    auto_refresh=True
-                ) as live:
-                    for chunk in response_stream:
-                        if chunk.choices[0].delta.content:
-                            full_response_text += chunk.choices[0].delta.content
-                            live.update(display_generating(full_response_text, start_time))
-                
-                # Clear the last generating display with a blank line
-                self.console.print()
-                
-                final_response = client.chat.completions.create(
-                    model=self.llm,
-                    messages=messages,
-                    temperature=temperature,
-                    stream=False
-                )
-                return final_response
-            else:
-                if tool_calls:
+                # Get final response after tool calls
+                if stream:
+                    final_response = self._process_stream_response(
+                        messages, 
+                        temperature, 
+                        start_time,
+                        formatted_tools=formatted_tools if formatted_tools else None,
+                        reasoning_steps=reasoning_steps
+                    )
+                else:
                     final_response = client.chat.completions.create(
                         model=self.llm,
                         messages=messages,
                         temperature=temperature,
                         stream=False
                     )
-                    return final_response
-                else:
-                    return initial_response
+
+            return final_response
 
         except Exception as e:
             display_error(f"Error in chat completion: {e}")
             return None
 
-    def chat(self, prompt, temperature=0.2, tools=None, output_json=None, output_pydantic=None):
+    def chat(self, prompt, temperature=0.2, tools=None, output_json=None, output_pydantic=None, reasoning_steps=False):
+        reasoning_steps = reasoning_steps or self.reasoning_steps
         # Search for existing knowledge if any knowledge is provided
         if self.knowledge:
             search_results = self.knowledge.search(prompt, agent_id=self.agent_id)
@@ -546,7 +751,8 @@ Your Goal: {self.goal}
                     agent_name=self.name,
                     agent_role=self.role,
                     agent_tools=[t.__name__ if hasattr(t, '__name__') else str(t) for t in self.tools],
-                    execute_tool_fn=self.execute_tool  # Pass tool execution function
+                    execute_tool_fn=self.execute_tool,  # Pass tool execution function
+                    reasoning_steps=reasoning_steps
                 )
 
                 self.chat_history.append({"role": "user", "content": prompt})
@@ -616,14 +822,13 @@ Your Goal: {self.goal}
                                 agent_tools=agent_tools
                             )
 
-                    response = self._chat_completion(messages, temperature=temperature, tools=tools if tools else None)
+                    response = self._chat_completion(messages, temperature=temperature, tools=tools if tools else None, reasoning_steps=reasoning_steps)
                     if not response:
                         return None
 
                     tool_calls = getattr(response.choices[0].message, 'tool_calls', None)
                     response_text = response.choices[0].message.content.strip()
-
-                    if tool_calls:
+                    if tool_calls: ## TODO: Most likely this tool call is already called in _chat_completion, so maybe we can remove this.
                         messages.append({
                             "role": "assistant",
                             "content": response_text,
@@ -675,6 +880,9 @@ Your Goal: {self.goal}
                         if self.verbose:
                             logging.debug(f"Agent {self.name} final response: {response_text}")
                         display_interaction(original_prompt, response_text, markdown=self.markdown, generation_time=time.time() - start_time, console=self.console)
+                        # Return only reasoning content if reasoning_steps is True
+                        if reasoning_steps and hasattr(response.choices[0].message, 'reasoning_content'):
+                            return response.choices[0].message.reasoning_content
                         return response_text
 
                     reflection_prompt = f"""
@@ -749,8 +957,9 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
             cleaned = cleaned[:-3].strip()
         return cleaned  
 
-    async def achat(self, prompt: str, temperature=0.2, tools=None, output_json=None, output_pydantic=None):
+    async def achat(self, prompt: str, temperature=0.2, tools=None, output_json=None, output_pydantic=None, reasoning_steps=False):
         """Async version of chat method. TODO: Requires Syncing with chat method.""" 
+        reasoning_steps = reasoning_steps or self.reasoning_steps
         try:
             # Search for existing knowledge if any knowledge is provided
             if self.knowledge:
@@ -781,7 +990,8 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                         agent_name=self.name,
                         agent_role=self.role,
                         agent_tools=[t.__name__ if hasattr(t, '__name__') else str(t) for t in self.tools],
-                        execute_tool_fn=self.execute_tool_async
+                        execute_tool_fn=self.execute_tool_async,
+                        reasoning_steps=reasoning_steps
                     )
 
                     self.chat_history.append({"role": "user", "content": prompt})
@@ -847,48 +1057,48 @@ Your Goal: {self.goal}
                             )
 
                     # Format tools if provided
-                        formatted_tools = []
-                        if tools:
-                            for tool in tools:
-                                if isinstance(tool, str):
-                                    tool_def = self._generate_tool_definition(tool)
-                                    if tool_def:
-                                        formatted_tools.append(tool_def)
-                                elif isinstance(tool, dict):
-                                    formatted_tools.append(tool)
-                                elif hasattr(tool, "to_openai_tool"):
-                                    formatted_tools.append(tool.to_openai_tool())
-                                elif callable(tool):
-                                    formatted_tools.append(self._generate_tool_definition(tool.__name__))
+                    formatted_tools = []
+                    if tools:
+                        for tool in tools:
+                            if isinstance(tool, str):
+                                tool_def = self._generate_tool_definition(tool)
+                                if tool_def:
+                                    formatted_tools.append(tool_def)
+                            elif isinstance(tool, dict):
+                                formatted_tools.append(tool)
+                            elif hasattr(tool, "to_openai_tool"):
+                                formatted_tools.append(tool.to_openai_tool())
+                            elif callable(tool):
+                                formatted_tools.append(self._generate_tool_definition(tool.__name__))
 
-                        # Create async OpenAI client
-                        async_client = AsyncOpenAI()
+                    # Create async OpenAI client
+                    async_client = AsyncOpenAI()
 
-                        # Make the API call based on the type of request
-                        if tools:
-                            response = await async_client.chat.completions.create(
-                                model=self.llm,
-                                messages=messages,
-                                temperature=temperature,
-                                tools=formatted_tools
-                            )
-                            return await self._achat_completion(response, tools)
-                        elif output_json or output_pydantic:
-                            response = await async_client.chat.completions.create(
-                                model=self.llm,
-                                messages=messages,
-                                temperature=temperature,
-                                response_format={"type": "json_object"}
-                            )
-                            # Return the raw response
-                            return response.choices[0].message.content
-                        else:
-                            response = await async_client.chat.completions.create(
-                                model=self.llm,
-                                messages=messages,
-                                temperature=temperature
-                            )
-                            return response.choices[0].message.content
+                    # Make the API call based on the type of request
+                    if tools:
+                        response = await async_client.chat.completions.create(
+                            model=self.llm,
+                            messages=messages,
+                            temperature=temperature,
+                            tools=formatted_tools
+                        )
+                        return await self._achat_completion(response, tools)
+                    elif output_json or output_pydantic:
+                        response = await async_client.chat.completions.create(
+                            model=self.llm,
+                            messages=messages,
+                            temperature=temperature,
+                            response_format={"type": "json_object"}
+                        )
+                        # Return the raw response
+                        return response.choices[0].message.content
+                    else:
+                        response = await async_client.chat.completions.create(
+                            model=self.llm,
+                            messages=messages,
+                            temperature=temperature
+                        )
+                        return response.choices[0].message.content
                 except Exception as e:
                     display_error(f"Error in chat completion: {e}")
                     return None
@@ -896,7 +1106,7 @@ Your Goal: {self.goal}
             display_error(f"Error in achat: {e}")
             return None
 
-    async def _achat_completion(self, response, tools):
+    async def _achat_completion(self, response, tools, reasoning_steps=False):
         """Async version of _chat_completion method"""
         try:
             message = response.choices[0].message
@@ -942,9 +1152,42 @@ Your Goal: {self.goal}
                         final_response = await async_client.chat.completions.create(
                             model=self.llm,
                             messages=messages,
-                            temperature=0.2
+                            temperature=0.2,
+                            stream=True
                         )
-                        return final_response.choices[0].message.content
+                        full_response_text = ""
+                        reasoning_content = ""
+                        chunks = []
+                        start_time = time.time()
+                        
+                        with Live(
+                            display_generating("", start_time),
+                            console=self.console,
+                            refresh_per_second=4,
+                            transient=True,
+                            vertical_overflow="ellipsis",
+                            auto_refresh=True
+                        ) as live:
+                            async for chunk in final_response:
+                                chunks.append(chunk)
+                                if chunk.choices[0].delta.content:
+                                    full_response_text += chunk.choices[0].delta.content
+                                    live.update(display_generating(full_response_text, start_time))
+                                
+                                if reasoning_steps and hasattr(chunk.choices[0].delta, "reasoning_content"):
+                                    rc = chunk.choices[0].delta.reasoning_content
+                                    if rc:
+                                        reasoning_content += rc
+                                        live.update(display_generating(f"{full_response_text}\n[Reasoning: {reasoning_content}]", start_time))
+                        
+                        self.console.print()
+                        
+                        final_response = process_stream_chunks(chunks)
+                        # Return only reasoning content if reasoning_steps is True
+                        if reasoning_steps and hasattr(final_response.choices[0].message, 'reasoning_content'):
+                            return final_response.choices[0].message.reasoning_content
+                        return final_response.choices[0].message.content if final_response else full_response_text
+
                     except Exception as e:
                         display_error(f"Error in final chat completion: {e}")
                         return formatted_results
@@ -952,7 +1195,7 @@ Your Goal: {self.goal}
             return None
         except Exception as e:
             display_error(f"Error in _achat_completion: {e}")
-            return None 
+            return None
 
     async def astart(self, prompt: str, **kwargs):
         """Async version of start method"""

agents/praisonaiagents/agents/agents.py

@@ -390,15 +390,8 @@ Context:
                     task.status = "completed"
                     # Run execute_callback for memory operations
                     try:
-                        try:
-                            # If a loop is already running, just create the task
-                            loop = asyncio.get_running_loop()
-                            loop.create_task(task.execute_callback(task_output))
-                        except RuntimeError:
-                            # Otherwise, create and set a new loop, and run the callback
-                            loop = asyncio.new_event_loop()
-                            asyncio.set_event_loop(loop)
-                            loop.create_task(task.execute_callback(task_output))
+                        # Use the new sync wrapper to avoid pending coroutine issues
+                        task.execute_callback_sync(task_output)
                     except Exception as e:
                         logger.error(f"Error executing memory callback for task {task_id}: {e}")
                         logger.exception(e)
@@ -720,15 +713,8 @@ Context:
                     task.status = "completed"
                     # Run execute_callback for memory operations
                     try:
-                        try:
-                            # If a loop is already running, just create the task
-                            loop = asyncio.get_running_loop()
-                            loop.create_task(task.execute_callback(task_output))
-                        except RuntimeError:
-                            # Otherwise, create and set a new loop, and run the callback
-                            loop = asyncio.new_event_loop()
-                            asyncio.set_event_loop(loop)
-                            loop.create_task(task.execute_callback(task_output))
+                        # Use the new sync wrapper to avoid pending coroutine issues
+                        task.execute_callback_sync(task_output)
                     except Exception as e:
                         logger.error(f"Error executing memory callback for task {task_id}: {e}")
                         logger.exception(e)

agents/praisonaiagents/llm/llm.py

@@ -17,6 +17,8 @@ from ..main import (
 from rich.console import Console
 from rich.live import Live
 
+# TODO: Include in-build tool calling in LLM class
+# TODO: Restructure so that duplicate calls are not made (Sync with agent.py)
 class LLMContextLengthExceededException(Exception):
     """Raised when LLM context length is exceeded"""
     def __init__(self, message: str):
@@ -111,10 +113,28 @@ class LLM:
             litellm.success_callback = []
             litellm._async_success_callback = []
             litellm.callbacks = []
-            # Additional logging suppression
-            litellm.suppress_debug_messages = True
-            logging.getLogger("litellm.utils").setLevel(logging.WARNING)
-            logging.getLogger("litellm.main").setLevel(logging.WARNING)
+            
+            verbose = extra_settings.get('verbose', True)
+            
+            # Only suppress logs if not in debug mode
+            if not isinstance(verbose, bool) and verbose >= 10:
+                # Enable detailed debug logging
+                logging.getLogger("asyncio").setLevel(logging.DEBUG)
+                logging.getLogger("selector_events").setLevel(logging.DEBUG)
+                logging.getLogger("litellm.utils").setLevel(logging.DEBUG)
+                logging.getLogger("litellm.main").setLevel(logging.DEBUG)
+                litellm.suppress_debug_messages = False
+                litellm.set_verbose = True
+            else:
+                # Suppress debug logging for normal operation
+                logging.getLogger("asyncio").setLevel(logging.WARNING)
+                logging.getLogger("selector_events").setLevel(logging.WARNING)
+                logging.getLogger("litellm.utils").setLevel(logging.WARNING)
+                logging.getLogger("litellm.main").setLevel(logging.WARNING)
+                litellm.suppress_debug_messages = True
+                litellm._logging._disable_debugging()
+                warnings.filterwarnings("ignore", category=RuntimeWarning)
+            
         except ImportError:
             raise ImportError(
                 "LiteLLM is required but not installed. "
@@ -142,11 +162,12 @@ class LLM:
         self.extra_settings = extra_settings
         self.console = Console()
         self.chat_history = []
-        self.verbose = extra_settings.get('verbose', True)
+        self.verbose = verbose
         self.markdown = extra_settings.get('markdown', True)
         self.self_reflect = extra_settings.get('self_reflect', False)
         self.max_reflect = extra_settings.get('max_reflect', 3)
         self.min_reflect = extra_settings.get('min_reflect', 1)
+        self.reasoning_steps = extra_settings.get('reasoning_steps', False)
         
         # Enable error dropping for cleaner output
         litellm.drop_params = True
@@ -176,7 +197,8 @@ class LLM:
         """Enhanced get_response with all OpenAI-like features"""
         try:
             import litellm
-            
+            # This below **kwargs** is passed to .completion() directly. so reasoning_steps has to be popped. OR find alternate best way of handling this.
+            reasoning_steps = kwargs.pop('reasoning_steps', self.reasoning_steps) 
             # Disable litellm debug messages
             litellm.set_verbose = False
             
@@ -230,8 +252,55 @@ class LLM:
 
                     # Get response from LiteLLM
                     start_time = time.time()
-                    if verbose:
-                        with Live(display_generating("", start_time), console=console, refresh_per_second=4) as live:
+
+                    # If reasoning_steps is True, do a single non-streaming call
+                    if reasoning_steps:
+                        resp = litellm.completion(
+                            model=self.model,
+                            messages=messages,
+                            temperature=temperature,
+                            stream=False,  # force non-streaming
+                            **{k:v for k,v in kwargs.items() if k != 'reasoning_steps'}
+                        )
+                        reasoning_content = resp["choices"][0]["message"].get("provider_specific_fields", {}).get("reasoning_content")
+                        response_text = resp["choices"][0]["message"]["content"]
+                        
+                        # Optionally display reasoning if present
+                        if verbose and reasoning_content:
+                            display_interaction(
+                                original_prompt,
+                                f"Reasoning:\n{reasoning_content}\n\nAnswer:\n{response_text}",
+                                markdown=markdown,
+                                generation_time=time.time() - start_time,
+                                console=console
+                            )
+                        else:
+                            display_interaction(
+                                original_prompt,
+                                response_text,
+                                markdown=markdown,
+                                generation_time=time.time() - start_time,
+                                console=console
+                            )
+                    
+                    # Otherwise do the existing streaming approach
+                    else:
+                        if verbose:
+                            with Live(display_generating("", start_time), console=console, refresh_per_second=4) as live:
+                                response_text = ""
+                                for chunk in litellm.completion(
+                                    model=self.model,
+                                    messages=messages,
+                                    temperature=temperature,
+                                    stream=True,
+                                    **kwargs
+                                ):
+                                    if chunk and chunk.choices and chunk.choices[0].delta.content:
+                                        content = chunk.choices[0].delta.content
+                                        response_text += content
+                                        live.update(display_generating(response_text, start_time))
+                        else:
+                            # Non-verbose mode, just collect the response
                             response_text = ""
                             for chunk in litellm.completion(
                                 model=self.model,
@@ -241,23 +310,9 @@ class LLM:
                                 **kwargs
                             ):
                                 if chunk and chunk.choices and chunk.choices[0].delta.content:
-                                    content = chunk.choices[0].delta.content
-                                    response_text += content
-                                    live.update(display_generating(response_text, start_time))
-                    else:
-                        # Non-verbose mode, just collect the response
-                        response_text = ""
-                        for chunk in litellm.completion(
-                            model=self.model,
-                            messages=messages,
-                            temperature=temperature,
-                            stream=True,
-                            **kwargs
-                        ):
-                            if chunk and chunk.choices and chunk.choices[0].delta.content:
-                                response_text += chunk.choices[0].delta.content
+                                    response_text += chunk.choices[0].delta.content
 
-                    response_text = response_text.strip()
+                        response_text = response_text.strip()
 
                     # Get final completion to check for tool calls
                     final_response = litellm.completion(
@@ -302,9 +357,53 @@ class LLM:
                                     "content": "Function returned an empty output"
                                 })
 
-                        # Get response after tool calls with streaming
-                        if verbose:
-                            with Live(display_generating("", start_time), console=console, refresh_per_second=4) as live:
+                        # If reasoning_steps is True, do a single non-streaming call
+                        if reasoning_steps:
+                            resp = litellm.completion(
+                                model=self.model,
+                                messages=messages,
+                                temperature=temperature,
+                                stream=False,  # force non-streaming
+                                **{k:v for k,v in kwargs.items() if k != 'reasoning_steps'}
+                            )
+                            reasoning_content = resp["choices"][0]["message"].get("provider_specific_fields", {}).get("reasoning_content")
+                            response_text = resp["choices"][0]["message"]["content"]
+                            
+                            # Optionally display reasoning if present
+                            if verbose and reasoning_content:
+                                display_interaction(
+                                    original_prompt,
+                                    f"Reasoning:\n{reasoning_content}\n\nAnswer:\n{response_text}",
+                                    markdown=markdown,
+                                    generation_time=time.time() - start_time,
+                                    console=console
+                                )
+                            else:
+                                display_interaction(
+                                    original_prompt,
+                                    response_text,
+                                    markdown=markdown,
+                                    generation_time=time.time() - start_time,
+                                    console=console
+                                )
+                        
+                        # Otherwise do the existing streaming approach
+                        else:
+                            # Get response after tool calls with streaming
+                            if verbose:
+                                with Live(display_generating("", start_time), console=console, refresh_per_second=4) as live:
+                                    response_text = ""
+                                    for chunk in litellm.completion(
+                                        model=self.model,
+                                        messages=messages,
+                                        temperature=temperature,
+                                        stream=True
+                                    ):
+                                        if chunk and chunk.choices and chunk.choices[0].delta.content:
+                                            content = chunk.choices[0].delta.content
+                                            response_text += content
+                                            live.update(display_generating(response_text, start_time))
+                            else:
                                 response_text = ""
                                 for chunk in litellm.completion(
                                     model=self.model,
@@ -313,21 +412,9 @@ class LLM:
                                     stream=True
                                 ):
                                     if chunk and chunk.choices and chunk.choices[0].delta.content:
-                                        content = chunk.choices[0].delta.content
-                                        response_text += content
-                                        live.update(display_generating(response_text, start_time))
-                        else:
-                            response_text = ""
-                            for chunk in litellm.completion(
-                                model=self.model,
-                                messages=messages,
-                                temperature=temperature,
-                                stream=True
-                            ):
-                                if chunk and chunk.choices and chunk.choices[0].delta.content:
-                                    response_text += chunk.choices[0].delta.content
+                                        response_text += chunk.choices[0].delta.content
 
-                        response_text = response_text.strip()
+                            response_text = response_text.strip()
 
                     # Handle output formatting
                     if output_json or output_pydantic:
@@ -342,6 +429,9 @@ class LLM:
                         if verbose:
                             display_interaction(original_prompt, response_text, markdown=markdown,
                                              generation_time=time.time() - start_time, console=console)
+                        # Return reasoning content if reasoning_steps is True
+                        if reasoning_steps and reasoning_content:
+                            return reasoning_content
                         return response_text
 
                     # Handle self-reflection
@@ -357,32 +447,66 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                         {"role": "user", "content": reflection_prompt}
                     ]
 
-                    # Get reflection response with streaming
-                    if verbose:
-                        with Live(display_generating("", start_time), console=console, refresh_per_second=4) as live:
+                    # If reasoning_steps is True, do a single non-streaming call to capture reasoning
+                    if reasoning_steps:
+                        reflection_resp = litellm.completion(
+                            model=self.model,
+                            messages=reflection_messages,
+                            temperature=temperature,
+                            stream=False,  # Force non-streaming
+                            response_format={"type": "json_object"},
+                            **{k:v for k,v in kwargs.items() if k != 'reasoning_steps'}
+                        )
+                        # Grab reflection text and optional reasoning
+                        reasoning_content = reflection_resp["choices"][0]["message"].get("provider_specific_fields", {}).get("reasoning_content")
+                        reflection_text = reflection_resp["choices"][0]["message"]["content"]
+
+                        # Optionally display reasoning if present
+                        if verbose and reasoning_content:
+                            display_interaction(
+                                "Reflection reasoning:",
+                                f"{reasoning_content}\n\nReflection result:\n{reflection_text}",
+                                markdown=markdown,
+                                generation_time=time.time() - start_time,
+                                console=console
+                            )
+                        elif verbose:
+                            display_interaction(
+                                "Self-reflection (non-streaming):",
+                                reflection_text,
+                                markdown=markdown,
+                                generation_time=time.time() - start_time,
+                                console=console
+                            )
+                    else:
+                        # Existing streaming approach
+                        if verbose:
+                            with Live(display_generating("", start_time), console=console, refresh_per_second=4) as live:
+                                reflection_text = ""
+                                for chunk in litellm.completion(
+                                    model=self.model,
+                                    messages=reflection_messages,
+                                    temperature=temperature,
+                                    stream=True,
+                                    response_format={"type": "json_object"},
+                                    **{k:v for k,v in kwargs.items() if k != 'reasoning_steps'}
+                                ):
+                                    if chunk and chunk.choices and chunk.choices[0].delta.content:
+                                        content = chunk.choices[0].delta.content
+                                        reflection_text += content
+                                        live.update(display_generating(reflection_text, start_time))
+                        else:
                             reflection_text = ""
                             for chunk in litellm.completion(
                                 model=self.model,
                                 messages=reflection_messages,
                                 temperature=temperature,
                                 stream=True,
-                                response_format={"type": "json_object"}
+                                response_format={"type": "json_object"},
+                                **{k:v for k,v in kwargs.items() if k != 'reasoning_steps'}
                             ):
                                 if chunk and chunk.choices and chunk.choices[0].delta.content:
-                                    content = chunk.choices[0].delta.content
-                                    reflection_text += content
-                                    live.update(display_generating(reflection_text, start_time))
-                    else:
-                        reflection_text = ""
-                        for chunk in litellm.completion(
-                            model=self.model,
-                            messages=reflection_messages,
-                            temperature=temperature,
-                            stream=True,
-                            response_format={"type": "json_object"}
-                        ):
-                            if chunk and chunk.choices and chunk.choices[0].delta.content:
-                                reflection_text += chunk.choices[0].delta.content
+                                    reflection_text += chunk.choices[0].delta.content
 
                     try:
                         reflection_data = json.loads(reflection_text)
@@ -453,6 +577,7 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
         """Async version of get_response with identical functionality."""
         try:
             import litellm
+            reasoning_steps = kwargs.pop('reasoning_steps', self.reasoning_steps)
             litellm.set_verbose = False
 
             # Build messages list
@@ -490,10 +615,10 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
             # Format tools for LiteLLM
             formatted_tools = None
             if tools:
-                logging.info(f"Starting tool formatting for {len(tools)} tools")
+                logging.debug(f"Starting tool formatting for {len(tools)} tools")
                 formatted_tools = []
                 for tool in tools:
-                    logging.info(f"Processing tool: {tool.__name__ if hasattr(tool, '__name__') else str(tool)}")
+                    logging.debug(f"Processing tool: {tool.__name__ if hasattr(tool, '__name__') else str(tool)}")
                     if hasattr(tool, '__name__'):
                         tool_name = tool.__name__
                         tool_doc = tool.__doc__ or "No description available"
@@ -539,10 +664,9 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                             }
                         }
                         # Ensure tool definition is JSON serializable
-                        print(f"Generated tool definition: {tool_def}")
                         try:
                             json.dumps(tool_def)  # Test serialization
-                            logging.info(f"Generated tool definition: {tool_def}")
+                            logging.debug(f"Generated tool definition: {tool_def}")
                             formatted_tools.append(tool_def)
                         except TypeError as e:
                             logging.error(f"Tool definition not JSON serializable: {e}")
@@ -552,38 +676,67 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
             if formatted_tools:
                 try:
                     json.dumps(formatted_tools)  # Final serialization check
-                    logging.info(f"Final formatted tools: {json.dumps(formatted_tools, indent=2)}")
+                    logging.debug(f"Final formatted tools: {json.dumps(formatted_tools, indent=2)}")
                 except TypeError as e:
                     logging.error(f"Final tools list not JSON serializable: {e}")
                     formatted_tools = None
 
             response_text = ""
-            if verbose:
-                # ----------------------------------------------------
-                # 1) Make the streaming call WITHOUT tools
-                # ----------------------------------------------------
-                async for chunk in await litellm.acompletion(
+            if reasoning_steps:
+                # Non-streaming call to capture reasoning
+                resp = await litellm.acompletion(
                     model=self.model,
                     messages=messages,
                     temperature=temperature,
-                    stream=True,
-                    **kwargs
-                ):
-                    if chunk and chunk.choices and chunk.choices[0].delta.content:
-                        response_text += chunk.choices[0].delta.content
-                        print("\033[K", end="\r")  
-                        print(f"Generating... {time.time() - start_time:.1f}s", end="\r")
+                    stream=False,  # force non-streaming
+                    **{k:v for k,v in kwargs.items() if k != 'reasoning_steps'}
+                )
+                reasoning_content = resp["choices"][0]["message"].get("provider_specific_fields", {}).get("reasoning_content")
+                response_text = resp["choices"][0]["message"]["content"]
+                
+                if verbose and reasoning_content:
+                    display_interaction(
+                        "Initial reasoning:",
+                        f"Reasoning:\n{reasoning_content}\n\nAnswer:\n{response_text}",
+                        markdown=markdown,
+                        generation_time=time.time() - start_time,
+                        console=console
+                    )
+                elif verbose:
+                    display_interaction(
+                        "Initial response:",
+                        response_text,
+                        markdown=markdown,
+                        generation_time=time.time() - start_time,
+                        console=console
+                    )
             else:
-                # Non-verbose streaming call, still no tools
-                async for chunk in await litellm.acompletion(
-                    model=self.model,
-                    messages=messages,
-                    temperature=temperature,
-                    stream=True,
-                    **kwargs
-                ):
-                    if chunk and chunk.choices and chunk.choices[0].delta.content:
-                        response_text += chunk.choices[0].delta.content
+                if verbose:
+                    # ----------------------------------------------------
+                    # 1) Make the streaming call WITHOUT tools
+                    # ----------------------------------------------------
+                    async for chunk in await litellm.acompletion(
+                        model=self.model,
+                        messages=messages,
+                        temperature=temperature,
+                        stream=True,
+                        **kwargs
+                    ):
+                        if chunk and chunk.choices and chunk.choices[0].delta.content:
+                            response_text += chunk.choices[0].delta.content
+                            print("\033[K", end="\r")  
+                            print(f"Generating... {time.time() - start_time:.1f}s", end="\r")
+                else:
+                    # Non-verbose streaming call, still no tools
+                    async for chunk in await litellm.acompletion(
+                        model=self.model,
+                        messages=messages,
+                        temperature=temperature,
+                        stream=True,
+                        **kwargs
+                    ):
+                        if chunk and chunk.choices and chunk.choices[0].delta.content:
+                            response_text += chunk.choices[0].delta.content
 
             response_text = response_text.strip()
 
@@ -598,7 +751,7 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                     temperature=temperature,
                     stream=False,
                     tools=formatted_tools,  # We safely pass tools here
-                    **kwargs
+                    **{k:v for k,v in kwargs.items() if k != 'reasoning_steps'}
                 )
                 # handle tool_calls from tool_response as usual...
                 tool_calls = tool_response.choices[0].message.get("tool_calls")
@@ -634,35 +787,66 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                                 "content": "Function returned an empty output"
                             })
 
-                    # Get response after tool calls with streaming
+                    # Get response after tool calls
                     response_text = ""
-                    if verbose:
-                        async for chunk in await litellm.acompletion(
+                    if reasoning_steps:
+                        # Non-streaming call to capture reasoning
+                        resp = await litellm.acompletion(
                             model=self.model,
                             messages=messages,
                             temperature=temperature,
-                            stream=True,
-                            tools=formatted_tools,
-                            **kwargs
-                        ):
-                            if chunk and chunk.choices and chunk.choices[0].delta.content:
-                                content = chunk.choices[0].delta.content
-                                response_text += content
-                                print("\033[K", end="\r")
-                                print(f"Reflecting... {time.time() - start_time:.1f}s", end="\r")
-            else:
-                response_text = ""
-                for chunk in litellm.completion(
-                    model=self.model,
-                    messages=messages,
-                    temperature=temperature,
-                    stream=True,
-                    **kwargs
-                ):
-                    if chunk and chunk.choices and chunk.choices[0].delta.content:
-                        response_text += chunk.choices[0].delta.content
+                            stream=False,  # force non-streaming
+                            tools=formatted_tools,  # Include tools
+                            **{k:v for k,v in kwargs.items() if k != 'reasoning_steps'}
+                        )
+                        reasoning_content = resp["choices"][0]["message"].get("provider_specific_fields", {}).get("reasoning_content")
+                        response_text = resp["choices"][0]["message"]["content"]
+                        
+                        if verbose and reasoning_content:
+                            display_interaction(
+                                "Tool response reasoning:",
+                                f"Reasoning:\n{reasoning_content}\n\nAnswer:\n{response_text}",
+                                markdown=markdown,
+                                generation_time=time.time() - start_time,
+                                console=console
+                            )
+                        elif verbose:
+                            display_interaction(
+                                "Tool response:",
+                                response_text,
+                                markdown=markdown,
+                                generation_time=time.time() - start_time,
+                                console=console
+                            )
+                    else:
+                        # Get response after tool calls with streaming
+                        if verbose:
+                            async for chunk in await litellm.acompletion(
+                                model=self.model,
+                                messages=messages,
+                                temperature=temperature,
+                                stream=True,
+                                tools=formatted_tools,
+                                **{k:v for k,v in kwargs.items() if k != 'reasoning_steps'}
+                            ):
+                                if chunk and chunk.choices and chunk.choices[0].delta.content:
+                                    content = chunk.choices[0].delta.content
+                                    response_text += content
+                                    print("\033[K", end="\r")
+                                    print(f"Reflecting... {time.time() - start_time:.1f}s", end="\r")
+                        else:
+                            response_text = ""
+                            for chunk in litellm.completion(
+                                model=self.model,
+                                messages=messages,
+                                temperature=temperature,
+                                stream=True,
+                                **{k:v for k,v in kwargs.items() if k != 'reasoning_steps'}
+                            ):
+                                if chunk and chunk.choices and chunk.choices[0].delta.content:
+                                    response_text += chunk.choices[0].delta.content
 
-            response_text = response_text.strip()
+                    response_text = response_text.strip()
 
             # Handle output formatting
             if output_json or output_pydantic:
@@ -677,6 +861,9 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                 if verbose:
                     display_interaction(original_prompt, response_text, markdown=markdown,
                                      generation_time=time.time() - start_time, console=console)
+                # Return reasoning content if reasoning_steps is True
+                if reasoning_steps and reasoning_content:
+                    return reasoning_content
                 return response_text
 
             # Handle self-reflection
@@ -692,33 +879,66 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
                 {"role": "user", "content": reflection_prompt}
             ]
 
-            # Get reflection response
-            reflection_text = ""
-            if verbose:
-                async for chunk in await litellm.acompletion(
+            # If reasoning_steps is True, do a single non-streaming call to capture reasoning
+            if reasoning_steps:
+                reflection_resp = litellm.completion(
                     model=self.model,
                     messages=reflection_messages,
                     temperature=temperature,
-                    stream=True,
+                    stream=False,  # Force non-streaming
                     response_format={"type": "json_object"},
-                    **kwargs
-                ):
-                    if chunk and chunk.choices and chunk.choices[0].delta.content:
-                        content = chunk.choices[0].delta.content
-                        reflection_text += content
-                        print("\033[K", end="\r")
-                        print(f"Reflecting... {time.time() - start_time:.1f}s", end="\r")
+                    **{k:v for k,v in kwargs.items() if k != 'reasoning_steps'}
+                )
+                # Grab reflection text and optional reasoning
+                reasoning_content = reflection_resp["choices"][0]["message"].get("provider_specific_fields", {}).get("reasoning_content")
+                reflection_text = reflection_resp["choices"][0]["message"]["content"]
+
+                # Optionally display reasoning if present
+                if verbose and reasoning_content:
+                    display_interaction(
+                        "Reflection reasoning:",
+                        f"{reasoning_content}\n\nReflection result:\n{reflection_text}",
+                        markdown=markdown,
+                        generation_time=time.time() - start_time,
+                        console=console
+                    )
+                elif verbose:
+                    display_interaction(
+                        "Self-reflection (non-streaming):",
+                        reflection_text,
+                        markdown=markdown,
+                        generation_time=time.time() - start_time,
+                        console=console
+                    )
             else:
-                async for chunk in await litellm.acompletion(
-                    model=self.model,
-                    messages=reflection_messages,
-                    temperature=temperature,
-                    stream=True,
-                    response_format={"type": "json_object"},
-                    **kwargs
-                ):
-                    if chunk and chunk.choices and chunk.choices[0].delta.content:
-                        reflection_text += chunk.choices[0].delta.content
+                # Existing streaming approach
+                if verbose:
+                    with Live(display_generating("", start_time), console=console, refresh_per_second=4) as live:
+                        reflection_text = ""
+                        for chunk in litellm.completion(
+                            model=self.model,
+                            messages=reflection_messages,
+                            temperature=temperature,
+                            stream=True,
+                            response_format={"type": "json_object"},
+                            **{k:v for k,v in kwargs.items() if k != 'reasoning_steps'}
+                        ):
+                            if chunk and chunk.choices and chunk.choices[0].delta.content:
+                                content = chunk.choices[0].delta.content
+                                reflection_text += content
+                                live.update(display_generating(reflection_text, start_time))
+                else:
+                    reflection_text = ""
+                    for chunk in litellm.completion(
+                        model=self.model,
+                        messages=reflection_messages,
+                        temperature=temperature,
+                        stream=True,
+                        response_format={"type": "json_object"},
+                        **{k:v for k,v in kwargs.items() if k != 'reasoning_steps'}
+                    ):
+                        if chunk and chunk.choices and chunk.choices[0].delta.content:
+                            reflection_text += chunk.choices[0].delta.content
 
             while True:  # Add loop for reflection handling
                 try:
@@ -820,4 +1040,174 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
             if type(event) in event_types:
                 litellm._async_success_callback.remove(event)
                 
-        litellm.callbacks = events
+        litellm.callbacks = events
+
+    # Response without tool calls
+    def response(
+        self,
+        prompt: Union[str, List[Dict]],
+        system_prompt: Optional[str] = None,
+        temperature: float = 0.2,
+        stream: bool = True,
+        verbose: bool = True,
+        markdown: bool = True,
+        console: Optional[Console] = None,
+        **kwargs
+    ) -> str:
+        """Simple function to get model response without tool calls or complex features"""
+        try:
+            import litellm
+            import logging
+            logger = logging.getLogger(__name__)
+            
+            litellm.set_verbose = False
+            start_time = time.time()
+            
+            logger.debug("Using synchronous response function")
+            
+            # Build messages list
+            messages = []
+            if system_prompt:
+                messages.append({"role": "system", "content": system_prompt})
+            
+            # Add prompt to messages
+            if isinstance(prompt, list):
+                messages.append({"role": "user", "content": prompt})
+            else:
+                messages.append({"role": "user", "content": prompt})
+
+            # Get response from LiteLLM
+            if stream:
+                response_text = ""
+                if verbose:
+                    with Live(display_generating("", start_time), console=console or self.console, refresh_per_second=4) as live:
+                        for chunk in litellm.completion(
+                            model=self.model,
+                            messages=messages,
+                            temperature=temperature,
+                            stream=True,
+                            **kwargs
+                        ):
+                            if chunk and chunk.choices and chunk.choices[0].delta.content:
+                                content = chunk.choices[0].delta.content
+                                response_text += content
+                                live.update(display_generating(response_text, start_time))
+                else:
+                    for chunk in litellm.completion(
+                        model=self.model,
+                        messages=messages,
+                        temperature=temperature,
+                        stream=True,
+                        **kwargs
+                    ):
+                        if chunk and chunk.choices and chunk.choices[0].delta.content:
+                            response_text += chunk.choices[0].delta.content
+            else:
+                response = litellm.completion(
+                    model=self.model,
+                    messages=messages,
+                    temperature=temperature,
+                    stream=False,
+                    **kwargs
+                )
+                response_text = response.choices[0].message.content.strip()
+
+            if verbose:
+                display_interaction(
+                    prompt if isinstance(prompt, str) else prompt[0].get("text", ""),
+                    response_text,
+                    markdown=markdown,
+                    generation_time=time.time() - start_time,
+                    console=console or self.console
+                )
+            
+            return response_text.strip()
+
+        except Exception as error:
+            display_error(f"Error in response: {str(error)}")
+            raise
+
+    # Async version of response function. Response without tool calls
+    async def aresponse(
+        self,
+        prompt: Union[str, List[Dict]],
+        system_prompt: Optional[str] = None,
+        temperature: float = 0.2,
+        stream: bool = True,
+        verbose: bool = True,
+        markdown: bool = True,
+        console: Optional[Console] = None,
+        **kwargs
+    ) -> str:
+        """Async version of response function"""
+        try:
+            import litellm
+            import logging
+            logger = logging.getLogger(__name__)
+            
+            litellm.set_verbose = False
+            start_time = time.time()
+            
+            logger.debug("Using asynchronous response function")
+            
+            # Build messages list
+            messages = []
+            if system_prompt:
+                messages.append({"role": "system", "content": system_prompt})
+            
+            # Add prompt to messages
+            if isinstance(prompt, list):
+                messages.append({"role": "user", "content": prompt})
+            else:
+                messages.append({"role": "user", "content": prompt})
+
+            # Get response from LiteLLM
+            if stream:
+                response_text = ""
+                if verbose:
+                    with Live(display_generating("", start_time), console=console or self.console, refresh_per_second=4) as live:
+                        async for chunk in await litellm.acompletion(
+                            model=self.model,
+                            messages=messages,
+                            temperature=temperature,
+                            stream=True,
+                            **kwargs
+                        ):
+                            if chunk and chunk.choices and chunk.choices[0].delta.content:
+                                content = chunk.choices[0].delta.content
+                                response_text += content
+                                live.update(display_generating(response_text, start_time))
+                else:
+                    async for chunk in await litellm.acompletion(
+                        model=self.model,
+                        messages=messages,
+                        temperature=temperature,
+                        stream=True,
+                        **kwargs
+                    ):
+                        if chunk and chunk.choices and chunk.choices[0].delta.content:
+                            response_text += chunk.choices[0].delta.content
+            else:
+                response = await litellm.acompletion(
+                    model=self.model,
+                    messages=messages,
+                    temperature=temperature,
+                    stream=False,
+                    **kwargs
+                )
+                response_text = response.choices[0].message.content.strip()
+
+            if verbose:
+                display_interaction(
+                    prompt if isinstance(prompt, str) else prompt[0].get("text", ""),
+                    response_text,
+                    markdown=markdown,
+                    generation_time=time.time() - start_time,
+                    console=console or self.console
+                )
+            
+            return response_text.strip()
+
+        except Exception as error:
+            display_error(f"Error in response_async: {str(error)}")
+            raise

agents/praisonaiagents/main.py

@@ -353,7 +353,7 @@ class ReflectionOutput(BaseModel):
     reflection: str
     satisfactory: Literal["yes", "no"]
 
-client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
+client = OpenAI(api_key=(os.environ["OPENAI_API_KEY"] if os.environ.get("OPENAI_API_KEY") else "xxxx"))
 
 class TaskOutput(BaseModel):
     description: str

agents/praisonaiagents/task/task.py

@@ -38,7 +38,8 @@ class Task:
         loop_state: Optional[Dict[str, Union[str, int]]] = None,
         memory=None,
         quality_check=True,
-        input_file: Optional[str] = None
+        input_file: Optional[str] = None,
+        rerun: bool = False # Renamed from can_rerun and logic inverted, default True for backward compatibility
     ):
         # Add check if memory config is provided
         if memory is not None or (config and config.get('memory_config')):
@@ -76,6 +77,7 @@ class Task:
         self.loop_state = loop_state if loop_state else {}
         self.memory = memory
         self.quality_check = quality_check
+        self.rerun = rerun # Assigning the rerun parameter
 
         # Set logger level based on config verbose level
         verbose = self.config.get("verbose", 0)
@@ -83,7 +85,7 @@ class Task:
             logger.setLevel(logging.INFO)
         else:
             logger.setLevel(logging.WARNING)
-            
+
         # Also set third-party loggers to WARNING
         logging.getLogger('chromadb').setLevel(logging.WARNING)
         logging.getLogger('openai').setLevel(logging.WARNING)
@@ -148,7 +150,7 @@ class Task:
                 logger.info(f"Task {self.id}: Initializing memory from config: {self.config['memory_config']}")
                 self.memory = Memory(config=self.config['memory_config'])
                 logger.info(f"Task {self.id}: Memory initialized successfully")
-                
+
                 # Verify database was created
                 if os.path.exists(self.config['memory_config']['storage']['path']):
                     logger.info(f"Task {self.id}: Memory database exists after initialization")
@@ -182,11 +184,11 @@ class Task:
         """Execute callback and store quality metrics if enabled"""
         logger.info(f"Task {self.id}: execute_callback called")
         logger.info(f"Quality check enabled: {self.quality_check}")
-        
+
         # Initialize memory if not already initialized
         if not self.memory:
             self.memory = self.initialize_memory()
-        
+
         logger.info(f"Memory object exists: {self.memory is not None}")
         if self.memory:
             logger.info(f"Memory config: {self.memory.cfg}")
@@ -202,24 +204,24 @@ class Task:
             except Exception as e:
                 logger.error(f"Task {self.id}: Failed to store task output in memory: {e}")
                 logger.exception(e)
-        
+
         logger.info(f"Task output: {task_output.raw[:100]}...")
-        
+
         if self.quality_check and self.memory:
             try:
                 logger.info(f"Task {self.id}: Starting memory operations")
                 logger.info(f"Task {self.id}: Calculating quality metrics for output: {task_output.raw[:100]}...")
-                
+
                 # Get quality metrics from LLM
                 metrics = self.memory.calculate_quality_metrics(
                     task_output.raw,
                     self.expected_output
                 )
                 logger.info(f"Task {self.id}: Quality metrics calculated: {metrics}")
-                
+
                 quality_score = metrics.get("accuracy", 0.0)
                 logger.info(f"Task {self.id}: Quality score: {quality_score}")
-                
+
                 # Store in both short and long-term memory with higher threshold
                 logger.info(f"Task {self.id}: Finalizing task output in memory...")
                 self.memory.finalize_task_output(
@@ -231,7 +233,7 @@ class Task:
                     task_id=self.id
                 )
                 logger.info(f"Task {self.id}: Finalized task output in memory")
-                
+
                 # Store quality metrics separately
                 logger.info(f"Task {self.id}: Storing quality metrics...")
                 self.memory.store_quality(
@@ -240,7 +242,7 @@ class Task:
                     task_id=self.id,
                     metrics=metrics
                 )
-                
+
                 # Store in both short and long-term memory with higher threshold
                 self.memory.finalize_task_output(
                     content=task_output.raw,
@@ -248,7 +250,7 @@ class Task:
                     quality_score=quality_score,
                     threshold=0.7  # Only high quality outputs in long-term memory
                 )
-                
+
                 # Build context for next tasks
                 if self.next_tasks:
                     logger.info(f"Task {self.id}: Building context for next tasks...")
@@ -257,7 +259,7 @@ class Task:
                         max_items=5
                     )
                     logger.info(f"Task {self.id}: Built context for next tasks: {len(context)} items")
-                
+
                 logger.info(f"Task {self.id}: Memory operations complete")
             except Exception as e:
                 logger.error(f"Task {self.id}: Failed to process memory operations: {e}")
@@ -295,11 +297,28 @@ Expected Output: {self.expected_output}.
                         context_results.append(
                             f"Previous task {context_item.name if context_item.name else context_item.description} has no result yet."
                         )
-            
+
             # Join unique context results
             unique_contexts = list(dict.fromkeys(context_results))  # Remove duplicates
             task_prompt += f"""
 Context:
 
 {'  '.join(unique_contexts)}
-"""
+"""
+
+    def execute_callback_sync(self, task_output: TaskOutput) -> None:
+        """
+        Synchronous wrapper to ensure that execute_callback is awaited,
+        preventing 'Task was destroyed but pending!' warnings if called
+        from non-async code.
+        """
+        import asyncio
+        try:
+            loop = asyncio.get_running_loop()
+            if loop.is_running():
+                loop.create_task(self.execute_callback(task_output))
+            else:
+                loop.run_until_complete(self.execute_callback(task_output))
+        except RuntimeError:
+            # If no loop is running in this context
+            asyncio.run(self.execute_callback(task_output))

agents/praisonaiagents/tools/__init__.py

@@ -135,6 +135,27 @@ TOOL_MAPPINGS = {
     'group_by': ('.pandas_tools', 'PandasTools'),
     'pivot_table': ('.pandas_tools', 'PandasTools'),
     'pandas_tools': ('.pandas_tools', 'PandasTools'),
+
+    # Chain of Thought Training Tools
+    'cot_run': ('.train.data.generatecot', 'GenerateCOT'),           # Orchestrates text solution
+    'cot_run_dict': ('.train.data.generatecot', 'GenerateCOT'),      # Orchestrates dict-based solution
+    'cot_generate': ('.train.data.generatecot', 'GenerateCOT'),      # Generate text solution
+    'cot_generate_dict': ('.train.data.generatecot', 'GenerateCOT'), # Generate structured solution
+    'cot_improve': ('.train.data.generatecot', 'GenerateCOT'),       # Improve text solution
+    'cot_improve_dict': ('.train.data.generatecot', 'GenerateCOT'),  # Improve dict-based solution
+    'cot_check': ('.train.data.generatecot', 'GenerateCOT'),         # Check correctness
+    'cot_find_error': ('.train.data.generatecot', 'GenerateCOT'),    # Locate error in solution
+    'cot_load_answers': ('.train.data.generatecot', 'GenerateCOT'),  # Load QA pairs
+    
+    # COT Save/Export with QA Pairs
+    'cot_save_solutions_with_qa_pairs': ('.train.data.generatecot', 'GenerateCOT'),    # Save with QA pairs
+    'cot_append_solutions_with_qa_pairs': ('.train.data.generatecot', 'GenerateCOT'),  # Append with QA pairs
+    'cot_export_json_with_qa_pairs': ('.train.data.generatecot', 'GenerateCOT'),       # Export JSON with QA pairs
+    'cot_export_csv_with_qa_pairs': ('.train.data.generatecot', 'GenerateCOT'),        # Export CSV with QA pairs
+    'cot_append_csv_with_qa_pairs': ('.train.data.generatecot', 'GenerateCOT'),        # Append CSV with QA pairs
+    'cot_save': ('.train.data.generatecot', 'GenerateCOT'),                           # Save single QA to file
+    'cot_upload_to_huggingface': ('.train.data.generatecot', 'GenerateCOT'),           # Upload dataset to HuggingFace
+    'cot_tools': ('.train.data.generatecot', 'GenerateCOT'),                           # Full toolkit access
 }
 
 _instances = {}  # Cache for class instances
@@ -161,7 +182,7 @@ def __getattr__(name: str) -> Any:
         ]:
             return getattr(module, name)
         if name in ['file_tools', 'pandas_tools', 'wikipedia_tools',
-                   'newspaper_tools', 'arxiv_tools', 'spider_tools', 'duckdb_tools', 'csv_tools', 'json_tools', 'excel_tools', 'xml_tools', 'yaml_tools', 'calculator_tools', 'python_tools', 'shell_tools']:
+                   'newspaper_tools', 'arxiv_tools', 'spider_tools', 'duckdb_tools', 'csv_tools', 'json_tools', 'excel_tools', 'xml_tools', 'yaml_tools', 'calculator_tools', 'python_tools', 'shell_tools', 'cot_tools']:
             return module  # Returns the callable module
         return getattr(module, name)
     else:

agents/praisonaiagents/tools/csv_tools.py

@@ -89,7 +89,7 @@ class CSVTools:
     def write_csv(
         self,
         filepath: str,
-        data: List[Dict[str, Any]],
+        data: Union[List[Dict[str, Any]], str],
         encoding: str = 'utf-8',
         delimiter: str = ',',
         index: bool = False,
@@ -102,35 +102,66 @@ class CSVTools:
         
         Args:
             filepath: Path to CSV file
-            data: List of row dicts to write
-            encoding: File encoding
-            delimiter: Column delimiter
-            index: Whether to write row indices
-            header: Whether to write column headers
-            float_format: Format string for float values
-            date_format: Format string for date values
-            mode: Write mode ('w' for write, 'a' for append)
-            
+            data: Either a list of dictionaries or a string containing CSV data
+                  If string, each line should be comma-separated values
+            encoding: File encoding (default: 'utf-8')
+            delimiter: Column delimiter (default: ',')
+            index: Whether to write row indices (default: False)
+            header: Whether to write column headers (default: True)
+            float_format: Format string for float values (default: None)
+            date_format: Format string for date values (default: None)
+            mode: Write mode - 'w' for write, 'a' for append (default: 'w')
+        
         Returns:
-            bool: Success status
+            bool: True if successful, False otherwise
         """
         try:
             pd = self._get_pandas()
             if pd is None:
                 return False
 
-            df = pd.DataFrame(data)
-            df.to_csv(
-                filepath,
-                encoding=encoding,
-                sep=delimiter,
-                index=index,
-                header=header,
-                float_format=float_format,
-                date_format=date_format,
-                mode=mode
-            )
-            return True
+            # Handle string input
+            if isinstance(data, str):
+                # Convert string to list of dicts
+                rows = []
+                if delimiter in data:
+                    # Get existing columns if file exists and in append mode
+                    existing_cols = []
+                    if mode == 'a' and Path(filepath).exists():
+                        try:
+                            existing_df = pd.read_csv(filepath, nrows=1)
+                            existing_cols = existing_df.columns.tolist()
+                        except:
+                            pass
+
+                    values = [v.strip() for v in data.split(delimiter)]
+                    
+                    if existing_cols:
+                        # Use existing column names
+                        row_dict = dict(zip(existing_cols, values))
+                    else:
+                        # Create generic column names
+                        row_dict = {f'col{i}': val for i, val in enumerate(values)}
+                    
+                    rows.append(row_dict)
+                    data = rows
+
+                df = pd.DataFrame(data)
+                
+                # Handle append mode properly
+                write_header = header if mode == 'w' else (header and not Path(filepath).exists())
+                
+                df.to_csv(
+                    filepath,
+                    encoding=encoding,
+                    sep=delimiter,
+                    index=index,
+                    header=write_header,
+                    float_format=float_format,
+                    date_format=date_format,
+                    mode=mode
+                )
+                return True
             
         except Exception as e:
             error_msg = f"Error writing CSV file {filepath}: {str(e)}"

agents/praisonaiagents/tools/train/data/generatecot.py

@@ -0,0 +1,531 @@
+from typing import Dict, Optional, Union, Any
+import json
+from datetime import datetime
+import os
+import logging
+
+# Setup logging based on environment variable
+log_level = os.getenv('LOGLEVEL', 'INFO').upper()
+logging.basicConfig(
+    level=log_level,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+# Lazy loader for LLM
+def get_llm():
+    try:
+        from praisonaiagents.llm.llm import LLM
+        return LLM
+    except ImportError:
+        raise ImportError(
+            "LLM is required for this toolbut not installed. "
+            "Please install with: pip install 'praisonaiagents[llm]' datasets huggingface-hub pandas"
+        )
+
+class GenerateCOT:
+    def __init__(
+        self,
+        qa_pairs: Optional[Dict[str, str]] = None,
+        model: str = "gpt-4o-mini",
+        api_key: Optional[str] = None,
+        max_attempts: int = 3,
+        verbose: bool = True,
+        temperature: float = 0.5
+    ):
+        self.qa_pairs = qa_pairs or {}
+        self.max_attempts = max_attempts
+        self.solutions = {}
+        self.llm = get_llm()(model=model)  # Get LLM class and instantiate
+        self.model = model
+        self.temperature = temperature
+        self.verbose = verbose
+        logger.debug(f"Initialized GenerateCOT with model={model}, max_attempts={max_attempts}")
+        
+    def _ask_ai(self, prompt: str) -> str:
+        logger.debug(f"Sending prompt to LLM: {prompt[:100]}...")
+        response = self.llm.response(prompt, temperature=self.temperature)
+        logger.debug(f"Received response: {response[:100]}...")
+        return response
+        
+    def _build_solution_prompt(self, question: str, context: str) -> str:
+        return f"""
+        Solve this problem step by step: {question}
+        Context: {context}
+        Steps needed:
+        1. Break down the problem
+        2. Show your work
+        3. Explain each step
+        4. Give final answer
+        """
+
+    def cot_generate(self, question: str, context: str = "") -> str:
+        prompt = self._build_solution_prompt(question, context)
+        return self._ask_ai(prompt)
+        
+    def cot_check(self, question: str, answer: str) -> bool:
+        if question not in self.qa_pairs:
+            raise ValueError(f"No correct answer found for: {question}")
+            
+        prompt = f"""
+        Question: {question}
+        Given Answer: {answer}
+        Correct Answer: {self.qa_pairs[question]}
+        Is the given answer correct? Reply only with 'true' or 'false'.
+        """
+        return self._ask_ai(prompt).lower().strip() == "true"
+        
+    def cot_find_error(self, question: str, solution: str) -> int:
+        if self.cot_check(question, solution):
+            return -1
+            
+        sentences = [s.strip() for s in solution.replace('。', '.').split('.') if s.strip()]
+        left, right = 0, len(sentences)
+        
+        while left < right:
+            mid = (left + right) // 2
+            partial = '. '.join(sentences[:mid]) + '.'
+            if self.cot_check(question, partial):
+                left = mid + 1
+            else:
+                right = mid
+                
+        return left
+        
+    def cot_improve(self, question: str, current: str) -> str:
+        best_solution = current
+        best_score = self._rate_solution(question, current)
+        attempts = 0
+        
+        while attempts < self.max_attempts:
+            attempts += 1
+            new_solution = self.cot_generate(question, current)
+            new_score = self._rate_solution(question, new_solution)
+            
+            if new_score > best_score:
+                best_solution = new_solution
+                best_score = new_score
+                
+            if best_score > 0.8:
+                break
+                
+        return best_solution
+        
+    def _rate_solution(self, question: str, solution: str) -> float:
+        prompt = f"""
+        Rate this solution from 0 to 1:
+        Question: {question}
+        Solution: {solution}
+        Correct Answer: {self.qa_pairs.get(question, '')}
+        Return only a number between 0 and 1.
+        """
+        try:
+            score = float(self._ask_ai(prompt))
+            return min(max(score, 0), 1)
+        except ValueError:
+            return 0.0
+            
+    def cot_run(self, question: str) -> str:
+        """Run COT generation for a single question."""
+        solution = self.cot_generate(question)
+        if self.cot_check(question, solution):
+            return solution
+
+        solution = self.cot_improve(question, solution)
+        
+        error_pos = self.cot_find_error(question, solution)
+        if error_pos != -1:
+            correct_part = '. '.join(solution.split('. ')[:error_pos]) + '.'
+            solution = self.cot_generate(question, correct_part)
+
+        self.solutions[question] = {
+            "solution": solution,
+            "error_position": error_pos,
+        }
+        return solution
+        
+    def cot_load_answers(self, filepath: str) -> bool:
+        try:
+            with open(filepath, 'r', encoding='utf-8') as f:
+                data = json.load(f)
+            self.qa_pairs.update(data)
+            return True
+        except Exception as e:
+            print(f"Error loading answers: {e}")
+            return False
+            
+    def _is_qa_pairs(self, qa_pairs: Any) -> bool:
+        """Validate if input is a proper QA pairs dictionary."""
+        if not qa_pairs:
+            return True  # None or empty is valid
+        if not isinstance(qa_pairs, dict):
+            raise ValueError("qa_pairs must be a dictionary with questions as keys and answers as values")
+        return True
+
+    def cot_append_solutions_with_qa_pairs(
+        self,
+        filepath: str = 'solutions.json',
+        qa_pairs: Optional[Dict[str, str]] = None
+    ) -> None:
+        """Appends current solutions to existing file or creates a new one."""
+        try:
+            self._is_qa_pairs(qa_pairs)  # Validate format
+            if qa_pairs:
+                self.qa_pairs.update(qa_pairs)
+
+            data = {
+                "solutions": self.solutions,
+                "qa_pairs": self.qa_pairs,
+                "saved_at": datetime.now().isoformat()
+            }
+
+            existing_data = {}
+            try:
+                with open(filepath, 'r', encoding='utf-8') as f:
+                    existing_data = json.load(f)
+            except (FileNotFoundError, json.JSONDecodeError):
+                pass
+
+            if existing_data:
+                existing_data["solutions"].update(data["solutions"])
+                existing_data["qa_pairs"].update(data["qa_pairs"])
+                existing_data["saved_at"] = data["saved_at"]
+                data = existing_data
+
+            with open(filepath, 'w', encoding='utf-8') as f:
+                json.dump(data, f, ensure_ascii=False, indent=2)
+        except Exception as e:
+            print(f"Error appending solutions: {e}")
+
+    def cot_save_solutions_with_qa_pairs(
+        self,
+        filepath: str = 'solutions.json',
+        append: bool = False,
+        qa_pairs: Optional[Dict[str, str]] = None
+    ) -> None:
+        try:
+            self._is_qa_pairs(qa_pairs)  # Validate format
+            if qa_pairs:
+                self.qa_pairs.update(qa_pairs)
+
+            if append:
+                self.cot_append_solutions_with_qa_pairs(filepath)
+                return
+
+            data = {
+                "solutions": self.solutions,
+                "qa_pairs": self.qa_pairs,
+                "saved_at": datetime.now().isoformat()
+            }
+            with open(filepath, 'w', encoding='utf-8') as f:
+                json.dump(data, f, ensure_ascii=False, indent=2)
+        except Exception as e:
+            print(f"Error saving solutions: {e}")
+
+    def cot_generate_dict(self, question: str, context: str = "") -> dict:
+        prompt = self._build_solution_prompt(question, context)
+        thought_process = self._ask_ai(prompt)
+        
+        final_answer_prompt = f"""
+        Based on this solution, what is the final answer only:
+        {thought_process}
+        Give only the final answer, no explanation.
+        """
+        final_answer = self._ask_ai(final_answer_prompt)
+        return {
+            "thought_process": thought_process,
+            "final_answer": final_answer
+        }
+
+    def cot_improve_dict(self, question: str, current_solution: str) -> dict:
+        """
+        Improves the existing solution (text form), returning the best dictionary-based version.
+        """
+        best_solution = {
+            "thought_process": current_solution,
+            "final_answer": current_solution
+        }
+        best_score = self._rate_solution(question, current_solution)
+        attempts = 0
+
+        while attempts < self.max_attempts:
+            attempts += 1
+            new_solution = self.cot_generate_dict(question, current_solution)
+            new_score = self._rate_solution(question, new_solution["thought_process"])
+            if new_score > best_score:
+                best_solution = new_solution
+                best_score = new_score
+            if best_score > 0.8:
+                break
+        return best_solution
+
+    def cot_run_dict(self, question: str) -> dict:
+        """Uses the dictionary-based solution approach, storing the final solution in self.solutions."""
+        logger.debug(f"Starting cot_run_dict for question: {question}")
+        
+        solution = self.cot_generate_dict(question)
+        logger.debug(f"Initial solution generated: {str(solution)[:100]}...")
+        
+        if self.cot_check(question, solution["final_answer"]):
+            logger.debug("Initial solution passed verification")
+            self.solutions[question] = solution
+            return solution
+
+        logger.debug("Initial solution failed verification, attempting improvement")
+        improved = self.cot_improve_dict(question, solution["thought_process"])
+        if self.cot_check(question, improved["final_answer"]):
+            logger.debug("Improved solution passed verification")
+            self.solutions[question] = improved
+            return improved
+
+        logger.debug("Checking for errors in improved solution")
+        error_pos = self.cot_find_error(question, improved["thought_process"])
+        if error_pos != -1:
+            logger.debug(f"Found error at position {error_pos}, generating final solution")
+            partial_solution = '. '.join(improved["thought_process"].split('. ')[:error_pos]) + '.'
+            final = self.cot_generate_dict(question, partial_solution)
+            self.solutions[question] = final
+            return final
+
+        logger.debug("Using improved solution as final result")
+        self.solutions[question] = improved
+        return improved
+
+    def cot_export_json_with_qa_pairs(
+        self,
+        filepath: str = 'dataset.json',
+        save_to_file: bool = True,
+        qa_pairs: Optional[Dict[str, str]] = None
+    ) -> Union[str, list]:
+        """
+        Export solutions in Alpaca training format with their full thought process.
+        """
+        try:
+            self._is_qa_pairs(qa_pairs)  # Validate format
+            if qa_pairs:
+                self.qa_pairs.update(qa_pairs)
+                # Generate solutions if empty
+                if not self.solutions:
+                    for question in qa_pairs:
+                        self.cot_run_dict(question)
+
+            alpaca_data = []
+            for question, sol in self.solutions.items():
+                alpaca_data.append({
+                    "instruction": question,
+                    "input": "",
+                    "output": sol.get("thought_process", "")
+                })
+
+            if not save_to_file:
+                return alpaca_data
+
+            with open(filepath, 'w', encoding='utf-8') as f:
+                json.dump(alpaca_data, f, ensure_ascii=False, indent=2)
+            return filepath
+        except Exception as e:
+            print(f"Error exporting to Alpaca format: {e}")
+            return None
+
+    def cot_export_csv_with_qa_pairs(
+        self,
+        filepath: str = 'dataset.csv',
+        qa_pairs: Optional[Dict[str, str]] = None
+    ) -> Optional[str]:
+        """Export solutions in CSV format."""
+        try:
+            import csv
+            self._is_qa_pairs(qa_pairs)  # Validate format
+            if qa_pairs:
+                self.qa_pairs.update(qa_pairs)
+                # Generate solutions if empty
+                if not self.solutions:
+                    for question in qa_pairs:
+                        self.cot_run_dict(question)
+
+            with open(filepath, 'w', newline='', encoding='utf-8') as f:
+                writer = csv.writer(f)
+                writer.writerow(['instruction', 'input', 'output'])
+                for question, sol in self.solutions.items():
+                    writer.writerow([question, '', sol.get("thought_process", "")])
+            return filepath
+        except Exception as e:
+            print(f"Error exporting to CSV format: {e}")
+            return None
+
+    def cot_save(
+        self,
+        question: str,
+        answer: str,
+        filepath: str = 'dataset.csv'
+    ) -> Optional[str]:
+        """Save a single question-answer pair with chain of thought to CSV file."""
+        logger.debug(f"Saving QA pair to {filepath}")
+        try:
+            self.qa_pairs[question] = answer
+            logger.debug("Added QA pair to internal dictionary")
+            
+            solution = self.cot_run_dict(question)
+            logger.debug("Generated solution for question")
+            
+            import csv
+            import os
+            file_exists = os.path.exists(filepath)
+            logger.debug(f"File exists: {file_exists}")
+
+            with open(filepath, 'a', newline='', encoding='utf-8') as f:
+                writer = csv.writer(f)
+                if not file_exists:
+                    logger.debug("Creating new file with headers")
+                    writer.writerow(['instruction', 'input', 'output'])
+                writer.writerow([question, '', solution.get("thought_process", "")])
+                logger.debug("Wrote solution to file")
+            return filepath
+        except Exception as e:
+            logger.error(f"Error saving to CSV: {str(e)}")
+            return None
+
+    # Rename existing function to indicate it handles qa_pairs dictionary
+    def cot_append_csv_with_qa_pairs(
+        self,
+        filepath: str = 'dataset.csv',
+        qa_pairs: Optional[Dict[str, str]] = None
+    ) -> Optional[str]:
+        """Append solutions to CSV file using qa_pairs dictionary."""
+        try:
+            self._is_qa_pairs(qa_pairs)  # Validate format
+            if qa_pairs:
+                self.qa_pairs.update(qa_pairs)
+            
+            import csv
+            import os
+            file_exists = os.path.exists(filepath)
+
+            with open(filepath, 'a', newline='', encoding='utf-8') as f:
+                writer = csv.writer(f)
+                if not file_exists:
+                    writer.writerow(['instruction', 'input', 'output'])
+
+                for question, sol in self.solutions.items():
+                    writer.writerow([question, '', sol.get("thought_process", "")])
+            return filepath
+        except Exception as e:
+            print(f"Error appending to CSV: {e}")
+            return None
+
+    def cot_upload_to_huggingface(
+        self,
+        huggingface_username: str,
+        dataset_name: str,
+        filepath: str,
+        private: bool = False
+    ) -> str:
+        """Upload generated solutions to HuggingFace datasets."""
+        logger.debug(f"Attempting to upload {filepath} to HuggingFace as {dataset_name}")
+        try:
+            from datasets import Dataset
+            import pandas as pd
+            
+            logger.debug(f"Loading data from {filepath}")
+            if filepath.endswith('.csv'):
+                data = pd.read_csv(filepath)
+                logger.debug(f"Loaded CSV with {len(data)} rows")
+            elif filepath.endswith('.json'):
+                data = pd.read_json(filepath)
+                logger.debug(f"Loaded JSON with {len(data)} records")
+            else:
+                raise ValueError("Only CSV and JSON files are supported")
+                
+            logger.debug("Converting to HuggingFace dataset")
+            dataset = Dataset.from_pandas(data)
+            
+            repo_id = f"{huggingface_username}/{dataset_name}"
+            logger.debug(f"Pushing to hub: {repo_id}")
+            dataset.push_to_hub(
+                repo_id,
+                private=private
+            )
+            
+            logger.debug("Upload completed successfully")
+            return f"Dataset uploaded successfully to {repo_id}"
+            
+        except Exception as e:
+            logger.error(f"Error uploading to HuggingFace: {str(e)}")
+            return None
+
+# Usage example:
+if __name__ == "__main__":
+    # Direct QA Pairs Export Example
+    print("\n=== Direct QA Pairs Export Example ===")
+    direct_qa_data = {
+        "Number of r's in the word strawberry": "3"
+    }
+    
+    direct_generator = GenerateCOT()
+
+    # Export with qa_pairs passed directly to functions
+    direct_generator.cot_export_csv_with_qa_pairs(
+        filepath='direct_solutions.csv',
+        qa_pairs=direct_qa_data
+    )
+    
+    # Example of using cot_save for a single QA pair
+    direct_generator.cot_save(
+        question="What is the capital of France?",
+        answer="Paris",
+        filepath="single_qa.csv"
+    )
+    
+
+    
+    # Upload to HuggingFace
+    direct_generator.cot_upload_to_huggingface(
+        huggingface_username="mervinpraison",
+        dataset_name="cot-test",
+        filepath="single_qa.csv"
+    )
+    
+    # direct_generator.cot_export_json_with_qa_pairs(
+    #     filepath='direct_solutions.json',
+    #     qa_pairs=direct_qa_data
+    # )
+
+    # # Rest of the original examples...
+    # qa_data = {
+    #     "What is 2+2?": "4",
+    #     "How many letters in 'hello'?": "5"
+    # }
+    
+    # generator = GenerateCOT(qa_pairs=qa_data)
+    # for question in qa_data:
+    #     solution = generator.cot_run(question)
+    #     print(f"Question: {question}")
+    #     print(f"Solution: {solution}\n")
+    # answer = generator.cot_run("What is 2+2?")
+    # print(answer)
+
+    # # Additional QA data processing example
+    # print("\n=== Processing Additional QA Data ===")
+    # extra_qa_data = {
+
+    #     "What is 5 * 3?": "15"
+    # }
+    
+    # # Create separate generator for additional data
+    # extra_generator = GenerateCOT(qa_pairs=extra_qa_data)
+    
+    # # Process and save solutions
+    # for question in extra_qa_data:
+    #     solution = extra_generator.cot_run_dict(question)
+    #     print(f"Processing extra question: {question}")
+    
+    # # Save solutions separately
+    # extra_generator.cot_save_solutions_with_qa_pairs('extra_qa_solutions.json')
+    
+    # # Export in Alpaca format
+    # extra_generator.cot_export_json_with_qa_pairs(filepath='extra_qa_alpaca.json', save_to_file=True)
+    
+    # # Demonstrate loading saved data
+    # loaded_generator = GenerateCOT(qa_pairs={})
+    # loaded_generator.cot_load_answers('extra_qa_solutions.json')
+    # print("\nLoaded extra QA pairs:", loaded_generator.qa_pairs)

agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.0.49"
+version = "0.0.60"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 authors = [
     { name="Mervin Praison" }

agents/repetitive-agents.py

@@ -6,7 +6,8 @@ agent = Agent(
 )
 
 task = Task(
-    description="Create the list of tasks to be looped through.",
+    description="complete the task",
+    expected_output="task completed",
     agent=agent,
     task_type="loop",
     input_file="tasks.csv"

agents/uv.lock

@@ -1868,7 +1868,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.0.49"
+version = "0.0.60"
 source = { editable = "." }
 dependencies = [
     { name = "openai" },

agents/wikipedia-agent.py

@@ -7,6 +7,7 @@ agent = Agent(
     self_reflect=True,
     min_reflect=3,
     max_reflect=5,
+    llm="gpt-4o-mini"
 )
 agent.start(
     "What is the history of AI?"

agents/workflow-test-agents.py

@@ -0,0 +1,124 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import cot_save, cot_upload_to_huggingface
+from pydantic import BaseModel
+import os
+
+def print_data(data):
+    print(data)
+    return "done"
+
+def upload_to_huggingface(data):
+    print(data)
+    return "done"
+
+# Define Pydantic model for structured output
+class DecisionModel(BaseModel):
+    response: str
+    decision: str
+
+def write_csv(file_path, data):
+    """Write data to CSV file."""
+    # delete file if exists
+    if os.path.exists(file_path):
+        os.remove(file_path)
+    with open(file_path, 'w') as file:
+        file.write(data + '\n')
+    return f"Data appended to {file_path}"
+
+def count_questions(file_path):
+    """Count lines in file."""
+    with open(file_path, 'r') as file:
+        return sum(1 for _ in file)
+
+# Keep existing agents with minimal changes
+qa_generator = Agent(
+    name="Generator",
+    role="Question Creator",
+    goal="Create challenging math and logic questions",
+    backstory="Expert in educational content creation",
+    llm="gpt-4o-mini",
+    tools=[write_csv, count_questions]
+)
+
+cot_generator = Agent(
+    name="COTGenerator",
+    role="Chain of Thought Specialist",
+    goal="Generate and manage chain of thought solutions for Q&A pairs",
+    backstory="Expert in breaking down problems and generating detailed solution steps",
+    tools=[print_data],
+    llm="gpt-4o-mini",
+    verbose=False
+)
+
+upload_to_huggingface = Agent(
+    name="UploadToHuggingface",
+    role="Upload to Huggingface",
+    goal="Upload the generated chain of thought solutions to a Huggingface dataset",
+    backstory="Expert in saving data to Huggingface",
+    tools=[upload_to_huggingface],
+    llm="gpt-4o-mini",
+    verbose=False
+)
+
+# Define tasks with workflow improvements
+generate_task = Task(
+    description="""Generate question and answer in csv format without headers: question, answer and append to qa_pairs.csv file
+generate 1 unique questions and answers and don't repeat on the same question and answer. Reponse with 'done' when done
+with append mode as 'a'
+Example question and answer:
+question, answer
+What is the sum of numbers from 1 to 10?, 55
+Number of r's in the word strawberry, 3
+""",
+    expected_output="append to qa_pairs.csv file with questions and answers and move to next task",
+    agent=qa_generator,
+    name="generate_task",
+    is_start=True,
+    next_tasks=["evaluate_total_questions"],
+    task_type="decision",
+    condition={
+        "more": "generate_task",
+        "done": "generate_cot"
+    }
+)
+
+generate_cot_task = Task(
+    name="generate_cot",
+    description="""Generate chain of thought solutions for each question in the input file. 
+After processing all questions, respond with a JSON object:
+{
+    "response": "done",
+    "decision": "done"
+}
+""",
+    expected_output="done",
+    agent=cot_generator,
+    input_file="qa_pairs.csv",
+    task_type="loop",
+    next_tasks=["upload_to_huggingface"],
+    condition={
+        "done": ["upload_to_huggingface"],
+        "exit": [],
+    },
+    output_pydantic=DecisionModel  # Use Pydantic model for output validation
+)
+
+upload_to_huggingface_task = Task(
+    name="upload_to_huggingface",
+    description="""Upload to Huggingface:
+    1. Save to cot_solutions.csv
+    2. Upload to mervinpraison/cot-dataset""",
+    expected_output="Dataset published successfully",
+    agent=upload_to_huggingface
+)
+
+# Initialize workflow
+agents = PraisonAIAgents(
+    agents=[qa_generator, cot_generator, upload_to_huggingface],
+    tasks=[generate_task, generate_cot_task, upload_to_huggingface_task],
+    process="workflow",
+    max_iter=10,
+    verbose=False
+)
+
+agents.start()

docker/Dockerfile

@@ -1,6 +1,6 @@
 FROM python:3.11-slim
 WORKDIR /app
 COPY . .
-RUN pip install flask praisonai==2.0.56 gunicorn markdown
+RUN pip install flask praisonai==2.0.60 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==2.0.56 gunicorn markdown\n&#34;)
+            file.write(&#34;RUN pip install flask praisonai==2.0.60 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/agents.mdx

@@ -112,86 +112,22 @@ graph LR
   <Step title="Create Agent">
     Create `app.py`:
 <CodeGroup>
-  ```python Single Agent
-  from praisonaiagents import Agent, Task, PraisonAIAgents
+```python Single Agent
+from praisonaiagents import Agent
 
-  # Create an agent
-  researcher = Agent(
-      name="Researcher",
-      role="Senior Research Analyst",
-      goal="Uncover cutting-edge developments in AI",
-      backstory="You are an expert at a technology research group",
-      verbose=True,
-      llm="gpt-4o"
-  )
+agent = Agent(instructions="Your are a helpful AI assistant")
+agent.start("Write a movie script about a robot in Mars")
+```
 
-  # Define a task
-  task = Task(
-      name="research_task",
-      description="Analyze 2024's AI advancements",
-      expected_output="A detailed report",
-      agent=researcher
-  )
+```python Multi Agents
+from praisonaiagents import Agent, PraisonAIAgents
 
-  # Run the agents
-  agents = PraisonAIAgents(
-      agents=[researcher],
-      tasks=[task],
-      verbose=False
-  )
+research_agent = Agent(instructions="Research about AI")
+summarise_agent = Agent(instructions="Summarise research agent's findings")
 
-  result = agents.start()
-  ```
-
-  ```python Multiple Agents
-  from praisonaiagents import Agent, Task, PraisonAIAgents
-
-  # Create multiple agents
-  researcher = Agent(
-      name="Researcher",
-      role="Senior Research Analyst",
-      goal="Uncover cutting-edge developments in AI",
-      backstory="You are an expert at a technology research group",
-      verbose=True,
-      llm="gpt-4o",
-      markdown=True
-  )
-
-  writer = Agent(
-      name="Writer",
-      role="Tech Content Strategist",
-      goal="Craft compelling content on tech advancements",
-      backstory="You are a content strategist",
-      llm="gpt-4o",
-      markdown=True
-  )
-
-  # Define multiple tasks
-  task1 = Task(
-      name="research_task",
-      description="Analyze 2024's AI advancements",
-      expected_output="A detailed report",
-      agent=researcher
-  )
-
-  task2 = Task(
-      name="writing_task",
-      description="Create a blog post about AI advancements",
-      expected_output="A blog post",
-      agent=writer
-  )
-
-  # Run with hierarchical process
-  agents = PraisonAIAgents(
-      agents=[researcher, writer],
-      tasks=[task1, task2],
-      verbose=False,
-      process="hierarchical",
-      manager_llm="gpt-4o"
-  )
-
-  result = agents.start()
-  ```
+agents = PraisonAIAgents(agents=[research_agent, summarise_agent])
+agents.start()
+```
 </CodeGroup>
   </Step>
 

docs/developers/local-development.mdx

@@ -0,0 +1,75 @@
+---
+title: 'Local Development'
+description: 'Set up local development environment with Docker and live reload'
+---
+
+## Logging
+```bash
+export LOGLEVEL=info
+```
+
+Advanced logging:
+```bash
+export LOGLEVEL=debug
+```
+
+## Local Docker Development with Live Reload
+
+To facilitate local development with live reload, you can use Docker. Follow the steps below:
+
+### 1. Create Dockerfile.dev
+
+<CodeGroup>
+```dockerfile Dockerfile.dev
+FROM python:3.11-slim
+
+WORKDIR /app
+
+COPY . .
+
+RUN pip install flask praisonai==2.0.18 watchdog
+
+EXPOSE 5555
+
+ENV FLASK_ENV=development
+
+CMD ["flask", "run", "--host=0.0.0.0"]
+```
+</CodeGroup>
+
+### 2. Create docker-compose.yml
+
+<CodeGroup>
+```yaml docker-compose.yml
+version: '3.8'
+
+services:
+  app:
+    build:
+      context: .
+      dockerfile: Dockerfile.dev
+    volumes:
+      - .:/app
+    ports:
+      - "5555:5555"
+    environment:
+      FLASK_ENV: development
+    command: flask run --host=0.0.0.0
+
+  watch:
+    image: alpine:latest
+    volumes:
+      - .:/app
+    command: sh -c "apk add --no-cache inotify-tools && while inotifywait -r -e modify,create,delete /app; do kill -HUP 1; done"
+```
+</CodeGroup>
+
+### 3. Run Docker Compose
+
+<CodeGroup>
+```bash Terminal
+docker-compose up
+```
+</CodeGroup>
+
+This setup will allow you to develop locally with live reload, making it easier to test and iterate on your code.

docs/features/chat-with-pdf.mdx

@@ -0,0 +1,244 @@
+---
+title: "Chat with PDF Agents"
+sidebarTitle: "Chat with PDF"
+description: "Learn how to create AI agents that can intelligently chat with PDF documents using vector databases for efficient information retrieval."
+icon: "file-pdf"
+---
+
+```mermaid
+flowchart LR
+    PDF[PDF Document] --> PDFAgent[("PDF Chat Agent")]
+    VDB[(Vector DB)] --> PDFAgent
+    PDFAgent --> Chat[Chat Interface]
+    Chat --> |Query| VDB
+    Chat --> Out[Response]
+    
+    style PDF fill:#8B0000,color:#fff
+    style PDFAgent fill:#2E8B57,color:#fff,shape:circle
+    style VDB fill:#4169E1,color:#fff,shape:cylinder
+    style Chat fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+A PDF-centric workflow where Chat agents interact with vector databases to store and retrieve information from PDF documents, enabling natural conversations and intelligent question-answering capabilities.
+
+## Quick Start
+
+<Steps>
+  <Step title="Install Package">
+    Install PraisonAI Agents with PDF chat 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 `chat_with_pdf.py`:
+```python
+from praisonaiagents import Agent
+
+agent = Agent(
+    name="PDF Chat Agent",
+    instructions="You answer questions based on the provided PDF document.",
+    knowledge=["document.pdf"], # PDF Indexing
+)
+
+agent.start("What is the main topic of this PDF?") # Chat Query
+```
+  </Step>
+</Steps>
+
+## PDF Processing and Chat Agents 
+
+<Note>
+  PDF processing involves indexing the document content for efficient retrieval during chat.
+</Note>
+
+```mermaid
+flowchart LR
+    subgraph In[Input]
+        PDF[PDF Documents]
+    end
+
+    subgraph Router[Vector Store]
+        DB[(Vector DB)]
+    end
+    
+    subgraph Out[Chat Agents]
+        A1[Chat Agent 1]
+        A2[Chat Agent 2]
+        A3[Chat 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
+```
+
+The simplest way to create a PDF chat agent is without any configuration:
+
+```python
+from praisonaiagents import Agent
+
+agent = Agent(
+    name="PDF Chat Agent",
+    instructions="You answer questions based on the provided PDF document.",
+    knowledge=["document.pdf"] # PDF Indexing
+)
+
+agent.start("What are the key points in this document?") # Chat Query
+```
+
+### 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": "praison",
+            "path": ".praison",
+        }
+    }
+}
+
+agent = Agent(
+    name="PDF Chat Agent",
+    instructions="You answer questions based on the provided PDF document.",
+    knowledge=["document.pdf"], # PDF Indexing
+    knowledge_config=config # Configuration
+)
+
+agent.start("What is the main topic of this PDF?") # Chat Query
+```
+
+### 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": "praison",
+            "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"], # Indexing
+    knowledge_config=config, # Configuration
+    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 # Agent
+)
+
+# Create and start the agents
+agents = PraisonAIAgents(
+    agents=[knowledge_agent],
+    tasks=[knowledge_task],
+    process="sequential",
+    user_id="user1" # User ID
+)
+
+# Start execution
+result = agents.start() # Retrieval
+```
+
+## Understanding PDF Chat Agents
+
+<Card title="What are PDF Chat Agents?" icon="question">
+  PDF Chat agents enable:
+  - Natural conversation with PDF documents
+  - Intelligent information extraction
+  - Context-aware document understanding
+  - Quick answers to document-specific questions
+</Card>
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="PDF Processing" icon="file-pdf">
+    Process and index PDF documents efficiently.
+  </Card>
+  <Card title="Natural Chat" icon="comments">
+    Have natural conversations about PDF content.
+  </Card>
+  <Card title="Smart Retrieval" icon="brain">
+    Intelligently retrieve relevant information from PDFs.
+  </Card>
+  <Card title="Context Awareness" icon="layer-group">
+    Maintain context throughout the conversation.
+  </Card>
+</CardGroup>
+
+## Troubleshooting
+
+<CardGroup cols={2}>
+  <Card title="PDF Issues" icon="triangle-exclamation">
+    If PDF processing isn't working:
+    - Check PDF file format and encoding
+    - Verify document accessibility
+    - Enable verbose mode for debugging
+  </Card>
+
+  <Card title="Chat Issues" icon="gauge-high">
+    If chat responses aren't accurate:
+    - Check PDF indexing quality
+    - Verify question clarity
+    - Monitor context retention
+  </Card>
+</CardGroup>
+
+## Next Steps
+
+<CardGroup cols={2}>
+  <Card title="AutoAgents" icon="robot" href="./autoagents">
+    Learn about automatically created and managed AI agents
+  </Card>
+  <Card title="Mini Agents" icon="microchip" href="./mini">
+    Explore lightweight, focused AI agents
+  </Card>
+</CardGroup>
+
+<Note>
+  For optimal chat experience, ensure your PDFs are properly formatted and text-searchable.
+</Note>

docs/features/generate-reasoning.mdx

@@ -0,0 +1,289 @@
+---
+title: "Generate Synthetic Reasoning Data Agents"
+sidebarTitle: "Generate Reasoning Data"
+description: "Learn how to generate chain-of-thought reasoning data using PraisonAI Agents."
+icon: "brain"
+---
+
+```mermaid
+flowchart TD
+    Start[Start] --> Generator[Question Answer Generator Agent]
+    Generator --> Evaluator[Evaluator Agent]
+    Evaluator --> COT[Reasoning Steps / COT Generator Agent]
+    COT --> COT
+    COT --> Upload[HuggingFace Uploader Agent]
+    Upload --> End[End]
+    
+    style Start fill:#8B0000,color:#fff
+    style Generator fill:#2E8B57,color:#fff
+    style Evaluator fill:#2E8B57,color:#fff
+    style COT fill:#2E8B57,color:#fff
+    style Upload fill:#2E8B57,color:#fff
+    style End fill:#8B0000,color:#fff
+```
+
+## What is Chain-of-Thought Generation?
+
+Chain-of-Thought (CoT) Generation is a process where AI agents create detailed, step-by-step reasoning paths for solving problems. This involves generating questions, evaluating them, producing detailed solution steps, and making the data available for training and analysis.
+
+## Quick Start
+
+<Steps>
+    <Step title="Install Package">
+        First, install the PraisonAI Agents package:
+        ```bash
+        pip install "praisonaiagents[llm]" datasets huggingface-hub pandas
+        ```
+    </Step>
+
+    <Step title="Set API Key">
+        Set your OpenAI API key as an environment variable in your terminal:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create a file">
+        Create a new file `app.py` with the basic setup:
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import cot_save, cot_upload_to_huggingface
+from pydantic import BaseModel
+import os
+
+# Define Pydantic model for structured output
+class DecisionModel(BaseModel):
+    response: str
+    decision: str
+
+def write_csv(file_path, data):
+    """Write data to CSV file."""
+    if not os.path.exists(file_path):
+        with open(file_path, 'w') as file:
+            file.write(data + '\n')
+    else:
+        with open(file_path, 'a') as file:
+            file.write(data + '\n')
+    return f"Data appended to {file_path}"
+
+def count_questions(file_path):
+    """Count lines in file."""
+    with open(file_path, 'r') as file:
+        return sum(1 for _ in file)
+
+# Create specialized agents
+qa_generator = Agent(
+    name="Generator",
+    role="Question Creator",
+    goal="Create challenging math and logic questions",
+    backstory="Expert in educational content creation",
+    llm="gpt-4o-mini",
+    tools=[write_csv, count_questions]
+)
+
+total_questions_evaluator = Agent(
+    name="TotalQuestionsEvaluator",
+    role="Total Questions Evaluator",
+    goal="Evaluate the total number of questions in qa_pairs.csv file",
+    backstory="Expert in evaluating the total number of questions in a file",
+    llm="gpt-4o-mini",
+    tools=[count_questions],
+    verbose=False
+)
+
+cot_generator = Agent(
+    name="COTGenerator",
+    role="Chain of Thought Specialist",
+    goal="Generate and manage chain of thought solutions for Q&A pairs",
+    backstory="Expert in breaking down problems and generating detailed solution steps",
+    tools=[cot_save],
+    llm="gpt-4o-mini",
+    verbose=False
+)
+
+upload_to_huggingface = Agent(
+    name="UploadToHuggingface",
+    role="Upload to Huggingface",
+    goal="Upload the generated chain of thought solutions to a Huggingface dataset",
+    backstory="Expert in saving data to Huggingface",
+    tools=[cot_upload_to_huggingface],
+    llm="gpt-4o-mini",
+    verbose=False
+)
+
+# Define tasks with workflow
+generate_task = Task(
+    description="""Generate question and answer in csv format without headers: question, answer and append to qa_pairs.csv file
+generate 10 unique questions and answers and don't repeat on the same question and answer. Reponse with 'done' when done
+with append mode as 'a'
+Example question and answer:
+question, answer
+What is the sum of numbers from 1 to 10?, 55
+Number of r's in the word strawberry, 3
+""",
+    expected_output="append to qa_pairs.csv file with questions and answers and move to next task",
+    agent=qa_generator,
+    name="generate_task",
+    is_start=True,
+    next_tasks=["evaluate_total_questions"],
+    task_type="decision",
+    condition={
+        "more": "generate_task",
+        "done": "evaluate_total_questions"
+    }
+)
+
+evaluate_total_questions_task = Task(
+    description="Evaluate the total number of questions in qa_pairs.csv file is 1",
+    expected_output="Total number of questions in qa_pairs.csv file",
+    agent=total_questions_evaluator,
+    task_type="decision",
+    name="evaluate_total_questions",
+    condition={
+        "more": "generate_task",
+        "done": "generate_cot"
+    }
+)
+
+generate_cot_task = Task(
+    name="generate_cot",
+    description="""Generate chain of thought solutions for each question in the input file. 
+Save to cot_solutions.csv file
+Don't generate chain of thought solutions again after receiving the response from Tool Call
+After calling the tool, respond with a JSON object:
+{
+    "response": "done",
+    "decision": "done"
+}
+""",
+    expected_output="done",
+    agent=cot_generator,
+    input_file="qa_pairs.csv",
+    task_type="loop",
+    next_tasks=["upload_to_huggingface"],
+    condition={
+        "done": ["upload_to_huggingface"],
+        "exit": [],
+    },
+    output_pydantic=DecisionModel  # Use Pydantic model for output validation
+)
+
+upload_to_huggingface_task = Task(
+    name="upload_to_huggingface",
+    description="""Upload to Huggingface:
+    1. Save to cot_solutions.csv
+    2. Upload to mervinpraison/cot-dataset""",
+    expected_output="Dataset published successfully",
+    agent=upload_to_huggingface,
+    tools=[cot_upload_to_huggingface]
+)
+
+# Initialize workflow
+agents = PraisonAIAgents(
+    agents=[qa_generator, total_questions_evaluator, cot_generator, upload_to_huggingface],
+    tasks=[generate_task, evaluate_total_questions_task, generate_cot_task, upload_to_huggingface_task],
+    process="workflow",
+    max_iter=30,
+    verbose=False
+)
+
+agents.start()
+```
+    </Step>
+</Steps>
+
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Question Generation" icon="question">
+    Create challenging math and logic questions with answers.
+  </Card>
+  <Card title="Question Evaluation" icon="check-double">
+    Evaluate and validate generated questions for quality.
+  </Card>
+  <Card title="CoT Solutions" icon="diagram-project">
+    Generate detailed chain-of-thought solutions for each question.
+  </Card>
+  <Card title="Data Management" icon="database">
+    Save and manage generated data in structured formats.
+  </Card>
+  <Card title="HuggingFace Integration" icon="cloud-arrow-up">
+    Upload datasets directly to HuggingFace for sharing.
+  </Card>
+</CardGroup>
+
+## Understanding the Workflow
+
+<AccordionGroup>
+  <Accordion title="Key Components">
+    <CardGroup cols={2}>
+      <Card title="Question Generator" icon="robot">
+        Creates unique math and logic questions with answers. Uses `write_csv` and `count_questions` tools.
+      </Card>
+      <Card title="Questions Evaluator" icon="magnifying-glass-chart">
+        Validates the total number of generated questions. Uses `count_questions` tool.
+      </Card>
+      <Card title="CoT Generator" icon="diagram-project">
+        Produces detailed step-by-step solutions. Uses `cot_save` tool for solution management.
+      </Card>
+      <Card title="HuggingFace Uploader" icon="cloud-arrow-up">
+        Publishes datasets to HuggingFace. Uses `cot_upload_to_huggingface` tool.
+      </Card>
+    </CardGroup>
+  </Accordion>
+
+  <Accordion title="Task Types and Flow Control">
+    <Tabs>
+      <Tab title="Decision Tasks">
+        Used in question generation and evaluation phases.
+        
+        ```python Decision Task Example
+        generate_task = Task(
+            task_type="decision",
+            condition={
+                "more": "generate_task",
+                "done": "evaluate_total_questions"
+            }
+        )
+        ```
+
+        <Note>
+          Conditions determine whether to continue generating or move forward. The task can loop back to itself or proceed to the next task.
+        </Note>
+      </Tab>
+      <Tab title="Loop Tasks">
+        Used in Chain-of-Thought generation phase.
+        
+        ```python Loop Task Example
+        generate_cot_task = Task(
+            task_type="loop",
+            input_file="qa_pairs.csv",
+            output_pydantic=DecisionModel
+        )
+        ```
+
+        <Warning>
+          Always use Pydantic models for output validation in loop tasks to ensure data consistency.
+        </Warning>
+      </Tab>
+    </Tabs>
+
+    <Info>
+      Each task type serves a specific purpose in the workflow:
+      - **Decision Tasks**: Control flow and branching logic
+      - **Loop Tasks**: Process data iteratively with validation
+    </Info>
+  </Accordion>
+</AccordionGroup>
+
+## Next Steps
+
+<CardGroup>
+  <Card title="Introduction" icon="book" href="/introduction">
+    Learn more about PraisonAI and its core concepts
+  </Card>
+  <Card title="Quick Start" icon="bolt" href="/quickstart">
+    Get started with the basics of PraisonAI
+  </Card>
+</CardGroup>

docs/features/reasoning-extract.mdx

@@ -0,0 +1,137 @@
+---
+title: "Reasoning Extract Agents"
+description: "Learn how to create AI agents that can perform step-by-step reasoning and extract information"
+icon: "brain-circuit"
+---
+
+```mermaid
+flowchart LR
+    In[Input] --> RA[("Reasoning Agent")]
+    RA --> Steps[Reasoning Steps]
+    Steps --> SA[("Small Agent")]
+    SA --> Out[Output]
+    
+    style In fill:#8B0000,color:#fff
+    style RA fill:#2E8B57,color:#fff,shape:circle
+    style Steps fill:#4169E1,color:#fff
+    style SA fill:#2E8B57,color:#fff,shape:circle
+    style Out fill:#8B0000,color:#fff
+```
+
+A workflow where a reasoning agent breaks down complex problems into steps, followed by a smaller agent that processes these steps to provide concise answers.
+
+## Prerequisites
+
+<Steps>
+    <Step title="Install Package">
+        Install required packages:
+        ```bash
+        pip install "praisonaiagents[llm]"
+        ```
+
+        <Note>
+        praisonaiagents[llm] includes all necessary dependencies for reasoning agents
+        </Note>
+    </Step>
+
+    <Step title="Set API Key">
+        Configure your API key:
+        ```bash
+        export OPENAI_API_KEY=your_api_key_here
+        ```
+    </Step>
+
+    <Step title="Create File">
+        Create a new file called `app.py` and add the following code:
+    </Step>
+
+    <Step title="Run Application">
+        Execute the script:
+        ```bash
+        python app.py
+        ```
+    </Step>
+</Steps>
+
+## Code
+
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+reasoning_agent = Agent(
+    role="Helpful Assistant", 
+    reasoning_steps=True, 
+    llm="deepseek/deepseek-reasoner"
+)
+
+small_agent = Agent(
+    role="Helpful Assistant", 
+    llm="gpt-3.5-turbo"
+)
+
+reasoning_task = Task(
+    description="How many r's in the word 'Strawberry'?", 
+    agent=reasoning_agent
+)
+
+small_task = Task(
+    description="With the provided reasoning tell me how many r's in the word 'Strawberry'?", 
+    agent=small_agent
+)
+
+agents = PraisonAIAgents(
+    agents=[reasoning_agent, small_agent],
+    tasks=[reasoning_task, small_task]
+)
+
+agents.start()
+```
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Step-by-Step Reasoning" icon="list-check">
+    Break down complex problems into logical steps.
+  </Card>
+  <Card title="Multi-Agent Collaboration" icon="users">
+    Combine reasoning and processing agents.
+  </Card>
+  <Card title="Flexible Models" icon="arrows-split-up-and-left">
+    Use different models for reasoning and processing.
+  </Card>
+  <Card title="Task Chaining" icon="link">
+    Connect reasoning output to processing tasks.
+  </Card>
+</CardGroup>
+
+## Understanding Reasoning Agents
+
+<Card title="What are Reasoning Agents?" icon="question">
+  Reasoning agents enable:
+  - Detailed step-by-step analysis
+  - Logical problem decomposition
+  - Clear reasoning paths
+  - Enhanced decision making
+</Card>
+
+## Troubleshooting
+
+<CardGroup cols={2}>
+  <Card title="Reasoning Issues" icon="triangle-exclamation">
+    If reasoning steps aren't clear:
+    - Check reasoning_steps parameter
+    - Verify model compatibility
+    - Review task descriptions
+  </Card>
+
+  <Card title="Agent Communication" icon="messages">
+    If agents aren't coordinating:
+    - Ensure task order is correct
+    - Check task dependencies
+    - Verify agent configurations
+  </Card>
+</CardGroup>
+
+<Note>
+  The reasoning agent uses a specialized model (deepseek-reasoner) optimized for step-by-step analysis, while the small agent can use a more general-purpose model for processing the reasoning output.
+</Note>

docs/index.mdx

@@ -1,7 +1,7 @@
 ---
 title: "Praison AI"
 sidebarTitle: "Home"
-description: "Comprehensive guide to PraisonAI - an AI Agents Framework with Self Reflection, combining PraisonAI Agents, AutoGen, and CrewAI for building multi-agent LLM systems"
+description: "PraisonAI is a production-ready Multi-AI Agents framework with self-reflection, designed to create AI Agents to automate and solve problems ranging from simple tasks to complex challenges. By integrating PraisonAI Agents, AutoGen, and CrewAI into a low-code solution, it streamlines the building and management of multi-agent LLM systems, emphasising simplicity, customisation, and effective human-agent collaboration."
 icon: "house"
 openGraph:
   url: "/"
@@ -66,24 +66,53 @@ seo:
     Internet search using Crawl4AI and Tavily
   </Card>
 </CardGroup>
-<br />
-<div className="relative w-full aspect-video overflow-hidden rounded-lg shadow-lg mb-8">
-  <iframe
-    className="absolute top-0 left-0 w-full h-full border-0"
-    src="https://www.youtube.com/embed/Fn1lQjC0GO0"
-    title="YouTube video player"
-    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
-    allowFullScreen
-    style={{
-      maxWidth: '100vw',
-      margin: '0 auto'
-    }}
-  ></iframe>
-</div>
 
-## Installation Options
+## Install
 
 <Tabs>
+  <Tab title="Code">
+    <Steps>
+      <Step title="Install Package">
+        ```bash
+        pip install praisonaiagents
+        ```
+      </Step>
+      <Step title="Set API Key">
+        ```bash
+        export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
+        ```
+      </Step>
+      <Step title="Create File">
+        Create `app.py` file
+
+        ## Code Example
+
+<CodeGroup>
+```python Single Agent
+from praisonaiagents import Agent
+
+agent = Agent(instructions="Your are a helpful AI assistant")
+agent.start("Write a movie script about a robot in Mars")
+```
+
+```python Multi Agents
+from praisonaiagents import Agent, PraisonAIAgents
+
+research_agent = Agent(instructions="Research about AI")
+summarise_agent = Agent(instructions="Summarise research agent's findings")
+
+agents = PraisonAIAgents(agents=[research_agent, summarise_agent])
+agents.start()
+```
+</CodeGroup>
+      </Step>
+      <Step title="Run Script">
+        ```bash
+        python app.py
+        ```
+      </Step>
+    </Steps>
+  </Tab>
   <Tab title="No Code">
     <Tip>
         PraisonAI combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customization, and efficient human-agent collaboration.
@@ -109,115 +138,361 @@ seo:
       </Step>
     </Steps>
   </Tab>
-  <Tab title="Code">
-    <Steps>
-      <Step title="Install Package">
-        ```bash
-        pip install praisonaiagents
-        ```
-      </Step>
-      <Step title="Set API Key">
-        ```bash
-        export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
-        ```
-      </Step>
-      <Step title="Create File">
-        Create `app.py` file
-
-        ## Code Example
-
-<CodeGroup>
-  ```python Basic
-  from praisonaiagents import Agent, Task, PraisonAIAgents
-
-  # Create an agent
-  researcher = Agent(
-      name="Researcher",
-      role="Senior Research Analyst",
-      goal="Uncover cutting-edge developments in AI",
-      backstory="You are an expert at a technology research group",
-      verbose=True,
-      llm="gpt-4o"
-  )
-
-  # Define a task
-  task = Task(
-      name="research_task",
-      description="Analyze 2024's AI advancements",
-      expected_output="A detailed report",
-      agent=researcher
-  )
-
-  # Run the agents
-  agents = PraisonAIAgents(
-      agents=[researcher],
-      tasks=[task],
-      verbose=False
-  )
-
-  result = agents.start()
-  ```
-
-  ```python Advanced
-  from praisonaiagents import Agent, Task, PraisonAIAgents
-
-  # Create multiple agents
-  researcher = Agent(
-      name="Researcher",
-      role="Senior Research Analyst",
-      goal="Uncover cutting-edge developments in AI",
-      backstory="You are an expert at a technology research group",
-      verbose=True,
-      llm="gpt-4o",
-      markdown=True
-  )
-
-  writer = Agent(
-      name="Writer",
-      role="Tech Content Strategist",
-      goal="Craft compelling content on tech advancements",
-      backstory="You are a content strategist",
-      llm="gpt-4o",
-      markdown=True
-  )
-
-  # Define multiple tasks
-  task1 = Task(
-      name="research_task",
-      description="Analyze 2024's AI advancements",
-      expected_output="A detailed report",
-      agent=researcher
-  )
-
-  task2 = Task(
-      name="writing_task",
-      description="Create a blog post about AI advancements",
-      expected_output="A blog post",
-      agent=writer
-  )
-
-  # Run with hierarchical process
-  agents = PraisonAIAgents(
-      agents=[researcher, writer],
-      tasks=[task1, task2],
-      verbose=False,
-      process="hierarchical",
-      manager_llm="gpt-4o"
-  )
-
-  result = agents.start()
-  ```
-</CodeGroup>
-      </Step>
-      <Step title="Run Script">
-        ```bash
-        python app.py
-        ```
-      </Step>
-    </Steps>
-  </Tab>
 </Tabs>
 
+## AI Agents Flow
+
+```mermaid
+graph LR
+    %% Define the main flow
+    Start([▶ Start]) --> Agent1
+    Agent1 --> Process[⚙ Process]
+    Process --> Agent2
+    Agent2 --> Output([✓ Output])
+    Process -.-> Agent1
+    
+    %% Define subgraphs for agents and their tasks
+    subgraph Agent1[ ]
+        Task1[📋 Task]
+        AgentIcon1[🤖 AI Agent]
+        Tools1[🔧 Tools]
+        
+        Task1 --- AgentIcon1
+        AgentIcon1 --- Tools1
+    end
+    
+    subgraph Agent2[ ]
+        Task2[📋 Task]
+        AgentIcon2[🤖 AI Agent]
+        Tools2[🔧 Tools]
+        
+        Task2 --- AgentIcon2
+        AgentIcon2 --- Tools2
+    end
+
+    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
+    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
+    classDef tools fill:#2E8B57,stroke:#7C90A0,color:#fff
+    classDef transparent fill:none,stroke:none
+
+    class Start,Output,Task1,Task2 input
+    class Process,AgentIcon1,AgentIcon2 process
+    class Tools1,Tools2 tools
+    class Agent1,Agent2 transparent
+```
+
+## AI Agents with Tools
+
+Create AI agents that can use tools to interact with external systems and perform actions.
+
+```mermaid
+flowchart TB
+    subgraph Tools
+        direction TB
+        T3[Internet Search]
+        T1[Code Execution]
+        T2[Formatting]
+    end
+
+    Input[Input] ---> Agents
+    subgraph Agents
+        direction LR
+        A1[Agent 1]
+        A2[Agent 2]
+        A3[Agent 3]
+    end
+    Agents ---> Output[Output]
+
+    T3 --> A1
+    T1 --> A2
+    T2 --> A3
+
+    style Tools fill:#189AB4,color:#fff
+    style Agents fill:#8B0000,color:#fff
+    style Input fill:#8B0000,color:#fff
+    style Output fill:#8B0000,color:#fff
+```
+
+## AI Agents with Memory
+
+Create AI agents with memory capabilities for maintaining context and information across tasks.
+
+```mermaid
+flowchart TB
+    subgraph Memory
+        direction TB
+        STM[Short Term]
+        LTM[Long Term]
+    end
+
+    subgraph Store
+        direction TB
+        DB[(Vector DB)]
+    end
+
+    Input[Input] ---> Agents
+    subgraph Agents
+        direction LR
+        A1[Agent 1]
+        A2[Agent 2]
+        A3[Agent 3]
+    end
+    Agents ---> Output[Output]
+
+    Memory <--> Store
+    Store <--> A1
+    Store <--> A2
+    Store <--> A3
+
+    style Memory fill:#189AB4,color:#fff
+    style Store fill:#2E8B57,color:#fff
+    style Agents fill:#8B0000,color:#fff
+    style Input fill:#8B0000,color:#fff
+    style Output fill:#8B0000,color:#fff
+```
+
+## AI Agents with Different Processes
+
+### Sequential Process
+
+The simplest form of task execution where tasks are performed one after another.
+
+```mermaid
+graph LR
+    Input[Input] --> A1
+    subgraph Agents
+        direction LR
+        A1[Agent 1] --> A2[Agent 2] --> A3[Agent 3]
+    end
+    A3 --> Output[Output]
+
+    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
+    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
+    classDef transparent fill:none,stroke:none
+
+    class Input,Output input
+    class A1,A2,A3 process
+    class Agents transparent
+```
+
+### Hierarchical Process
+
+Uses a manager agent to coordinate task execution and agent assignments.
+
+```mermaid
+graph TB
+    Input[Input] --> Manager
+    
+    subgraph Agents
+        Manager[Manager Agent]
+        
+        subgraph Workers
+            direction LR
+            W1[Worker 1]
+            W2[Worker 2]
+            W3[Worker 3]
+        end
+        
+        Manager --> W1
+        Manager --> W2
+        Manager --> W3
+    end
+    
+    W1 --> Manager
+    W2 --> Manager
+    W3 --> Manager
+    Manager --> Output[Output]
+
+    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
+    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
+    classDef transparent fill:none,stroke:none
+
+    class Input,Output input
+    class Manager,W1,W2,W3 process
+    class Agents,Workers transparent
+```
+
+### Workflow Process
+
+Advanced process type supporting complex task relationships and conditional execution.
+
+```mermaid
+graph LR
+    Input[Input] --> Start
+    
+    subgraph Workflow
+        direction LR
+        Start[Start] --> C1{Condition}
+        C1 --> |Yes| A1[Agent 1]
+        C1 --> |No| A2[Agent 2]
+        A1 --> Join
+        A2 --> Join
+        Join --> A3[Agent 3]
+    end
+    
+    A3 --> Output[Output]
+
+    classDef input fill:#8B0000,stroke:#7C90A0,color:#fff
+    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
+    classDef decision fill:#2E8B57,stroke:#7C90A0,color:#fff
+    classDef transparent fill:none,stroke:none
+
+    class Input,Output input
+    class Start,A1,A2,A3,Join process
+    class C1 decision
+    class Workflow transparent
+```
+
+#### Agentic Routing Workflow
+
+Create AI agents that can dynamically route tasks to specialized LLM instances.
+
+```mermaid
+flowchart LR
+    In[In] --> Router[LLM Call Router]
+    Router --> LLM1[LLM Call 1]
+    Router --> LLM2[LLM Call 2]
+    Router --> LLM3[LLM Call 3]
+    LLM1 --> Out[Out]
+    LLM2 --> Out
+    LLM3 --> Out
+    
+    style In fill:#8B0000,color:#fff
+    style Router fill:#2E8B57,color:#fff
+    style LLM1 fill:#2E8B57,color:#fff
+    style LLM2 fill:#2E8B57,color:#fff
+    style LLM3 fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+#### Agentic Orchestrator Worker
+
+Create AI agents that orchestrate and distribute tasks among specialized workers.
+
+```mermaid
+flowchart LR
+    In[In] --> Router[LLM Call Router]
+    Router --> LLM1[LLM Call 1]
+    Router --> LLM2[LLM Call 2]
+    Router --> LLM3[LLM Call 3]
+    LLM1 --> Synthesizer[Synthesizer]
+    LLM2 --> Synthesizer
+    LLM3 --> Synthesizer
+    Synthesizer --> Out[Out]
+    
+    style In fill:#8B0000,color:#fff
+    style Router fill:#2E8B57,color:#fff
+    style LLM1 fill:#2E8B57,color:#fff
+    style LLM2 fill:#2E8B57,color:#fff
+    style LLM3 fill:#2E8B57,color:#fff
+    style Synthesizer fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+#### Agentic Autonomous Workflow
+
+Create AI agents that can autonomously monitor, act, and adapt based on environment feedback.
+
+```mermaid
+flowchart LR
+    Human[Human] <--> LLM[LLM Call]
+    LLM -->|ACTION| Environment[Environment]
+    Environment -->|FEEDBACK| LLM
+    LLM --> Stop[Stop]
+    
+    style Human fill:#8B0000,color:#fff
+    style LLM fill:#2E8B57,color:#fff
+    style Environment fill:#8B0000,color:#fff
+    style Stop fill:#333,color:#fff
+```
+
+#### Agentic Parallelization
+
+Create AI agents that can execute tasks in parallel for improved performance.
+
+```mermaid
+flowchart LR
+    In[In] --> LLM2[LLM Call 2]
+    In --> LLM1[LLM Call 1]
+    In --> LLM3[LLM Call 3]
+    LLM1 --> Aggregator[Aggregator]
+    LLM2 --> Aggregator
+    LLM3 --> Aggregator
+    Aggregator --> Out[Out]
+    
+    style In fill:#8B0000,color:#fff
+    style LLM1 fill:#2E8B57,color:#fff
+    style LLM2 fill:#2E8B57,color:#fff
+    style LLM3 fill:#2E8B57,color:#fff
+    style Aggregator fill:#fff,color:#000
+    style Out fill:#8B0000,color:#fff
+```
+
+#### Agentic Prompt Chaining
+
+Create AI agents with sequential prompt chaining for complex workflows.
+
+```mermaid
+flowchart LR
+    In[In] --> LLM1[LLM Call 1] --> Gate{Gate}
+    Gate -->|Pass| LLM2[LLM Call 2] -->|Output 2| LLM3[LLM Call 3] --> Out[Out]
+    Gate -->|Fail| Exit[Exit]
+    
+    style In fill:#8B0000,color:#fff
+    style LLM1 fill:#2E8B57,color:#fff
+    style LLM2 fill:#2E8B57,color:#fff
+    style LLM3 fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+    style Exit fill:#8B0000,color:#fff
+```
+
+#### Agentic Evaluator Optimizer
+
+Create AI agents that can generate and optimize solutions through iterative feedback.
+
+```mermaid
+flowchart LR
+    In[In] --> Generator[LLM Call Generator] 
+    Generator -->|SOLUTION| Evaluator[LLM Call Evaluator] -->|ACCEPTED| Out[Out]
+    Evaluator -->|REJECTED + FEEDBACK| Generator
+    
+    style In fill:#8B0000,color:#fff
+    style Generator fill:#2E8B57,color:#fff
+    style Evaluator fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+#### Repetitive Agents
+
+Create AI agents that can efficiently handle repetitive tasks through automated loops.
+
+```mermaid
+flowchart LR
+    In[Input] --> LoopAgent[("Looping Agent")]
+    LoopAgent --> Task[Task]
+    Task --> |Next iteration| LoopAgent
+    Task --> |Done| Out[Output]
+    
+    style In fill:#8B0000,color:#fff
+    style LoopAgent fill:#2E8B57,color:#fff,shape:circle
+    style Task fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+<br />
+<div className="relative w-full aspect-video overflow-hidden rounded-lg shadow-lg mb-8">
+  <iframe
+    className="absolute top-0 left-0 w-full h-full border-0"
+    src="https://www.youtube.com/embed/Fn1lQjC0GO0"
+    title="YouTube video player"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
+    allowFullScreen
+    style={{
+      maxWidth: '100vw',
+      margin: '0 auto'
+    }}
+  ></iframe>
+</div>
+
 ## Integration Options
 
 <AccordionGroup>
@@ -338,4 +613,29 @@ seo:
   <div className="hover:opacity-80 transition-opacity">
     <img src="https://img.shields.io/github/forks/MervinPraison/PraisonAI?style=social" alt="GitHub Forks" />
   </div>
-</div>
+</div>
+
+## Video Tutorials
+
+| Topic | Video |
+|-------|--------|
+| AI Agents with Self Reflection | [![Self Reflection](https://img.youtube.com/vi/vLXobEN2Vc8/0.jpg)](https://www.youtube.com/watch?v=vLXobEN2Vc8) |
+| AI Agents with Reasoning | [![Reasoning](https://img.youtube.com/vi/KNDVWGN3TpM/0.jpg)](https://www.youtube.com/watch?v=KNDVWGN3TpM) |
+| Multimodal AI Agents | [![Multimodal](https://img.youtube.com/vi/hjAWmUT1qqY/0.jpg)](https://www.youtube.com/watch?v=hjAWmUT1qqY) |
+| AI Agents Workflow | [![Workflow](https://img.youtube.com/vi/yWTH44QPl2A/0.jpg)](https://www.youtube.com/watch?v=yWTH44QPl2A) |
+| Async AI Agents | [![Async](https://img.youtube.com/vi/VhVQfgo00LE/0.jpg)](https://www.youtube.com/watch?v=VhVQfgo00LE) |
+| Mini AI Agents | [![Mini](https://img.youtube.com/vi/OkvYp5aAGSg/0.jpg)](https://www.youtube.com/watch?v=OkvYp5aAGSg) |
+| AI Agents with Memory | [![Memory](https://img.youtube.com/vi/1hVfVxvPnnQ/0.jpg)](https://www.youtube.com/watch?v=1hVfVxvPnnQ) |
+| Repetitive Agents | [![Repetitive](https://img.youtube.com/vi/dAYGxsjDOPg/0.jpg)](https://www.youtube.com/watch?v=dAYGxsjDOPg) |
+| Introduction | [![Introduction](https://img.youtube.com/vi/Fn1lQjC0GO0/0.jpg)](https://www.youtube.com/watch?v=Fn1lQjC0GO0) |
+| Tools Overview | [![Tools Overview](https://img.youtube.com/vi/XaQRgRpV7jo/0.jpg)](https://www.youtube.com/watch?v=XaQRgRpV7jo) |
+| Custom Tools | [![Custom Tools](https://img.youtube.com/vi/JSU2Rndh06c/0.jpg)](https://www.youtube.com/watch?v=JSU2Rndh06c) |
+| Firecrawl Integration | [![Firecrawl](https://img.youtube.com/vi/UoqUDcLcOYo/0.jpg)](https://www.youtube.com/watch?v=UoqUDcLcOYo) |
+| User Interface | [![UI](https://img.youtube.com/vi/tg-ZjNl3OCg/0.jpg)](https://www.youtube.com/watch?v=tg-ZjNl3OCg) |
+| Crawl4AI Integration | [![Crawl4AI](https://img.youtube.com/vi/KAvuVUh0XU8/0.jpg)](https://www.youtube.com/watch?v=KAvuVUh0XU8) |
+| Chat Interface | [![Chat](https://img.youtube.com/vi/sw3uDqn2h1Y/0.jpg)](https://www.youtube.com/watch?v=sw3uDqn2h1Y) |
+| Code Interface | [![Code](https://img.youtube.com/vi/_5jQayO-MQY/0.jpg)](https://www.youtube.com/watch?v=_5jQayO-MQY) |
+| Mem0 Integration | [![Mem0](https://img.youtube.com/vi/KIGSgRxf1cY/0.jpg)](https://www.youtube.com/watch?v=KIGSgRxf1cY) |
+| Training | [![Training](https://img.youtube.com/vi/aLawE8kwCrI/0.jpg)](https://www.youtube.com/watch?v=aLawE8kwCrI) |
+| Realtime Voice Interface | [![Realtime](https://img.youtube.com/vi/frRHfevTCSw/0.jpg)](https://www.youtube.com/watch?v=frRHfevTCSw) |
+| Call Interface | [![Call](https://img.youtube.com/vi/m1cwrUG2iAk/0.jpg)](https://www.youtube.com/watch?v=m1cwrUG2iAk) |

docs/introduction.mdx

@@ -89,143 +89,121 @@ graph LR
 ## Getting Started is Easy
 <Tabs>
   <Tab title="Code">
-<Steps>
+    <Steps>
+      <Step title="Install Package">
+        Install the PraisonAI Agents package:
+        ```bash
+        pip install praisonaiagents
+        ```
+      </Step>
 
-  <Step title="Install PraisonAI">
-    Install the core package:
-    ```bash Terminal
-    pip install praisonaiagents
-    ```
-  </Step>
+      <Step title="Set API Key">
+        ```bash
+        export OPENAI_API_KEY=your_openai_key
+        ```
+        Generate your OpenAI API key from [OpenAI](https://platform.openai.com/api-keys).
+        Use other LLM providers like Ollama, Anthropic, Groq, Google, etc. Please refer to the [Models](/models) for more information.
+      </Step>
 
-  <Step title="Configure Environment">
-    ```bash Terminal
-    export OPENAI_API_KEY=your_openai_key
-    ```
-    Generate your OpenAI API key from [OpenAI](https://platform.openai.com/api-keys)
-    Use other LLM providers like Ollama, Anthropic, Groq, Google, etc. Please refer to the [Models](/models) for more information.
-  </Step>
+      <Step title="Create Agents">
+        Create `app.py`:
+    <CodeGroup>
+      ```python Single Agent
+      from praisonaiagents import Agent, PraisonAIAgents
 
-  <Step title="Create Agent">
-    Create `app.py`:
-<CodeGroup>
-  ```python Single Agent
-  from praisonaiagents import Agent, Task, PraisonAIAgents
+      # Create a simple agent
+      summarise_agent = Agent(instructions="Summarise Photosynthesis")
 
-  # Create an agent
-  researcher = Agent(
-      name="Researcher",
-      role="Senior Research Analyst",
-      goal="Uncover cutting-edge developments in AI",
-      backstory="You are an expert at a technology research group",
-      verbose=True,
-      llm="gpt-4o"
-  )
+      # Run the agent
+      agents = PraisonAIAgents(agents=[summarise_agent])
+      agents.start()
+      ```
 
-  # Define a task
-  task = Task(
-      name="research_task",
-      description="Analyze 2024's AI advancements",
-      expected_output="A detailed report",
-      agent=researcher
-  )
+      ```python Multiple Agents
+      from praisonaiagents import Agent, PraisonAIAgents
 
-  # Run the agents
-  agents = PraisonAIAgents(
-      agents=[researcher],
-      tasks=[task],
-      verbose=False
-  )
+      # Create agents with specific roles
+      diet_agent = Agent(
+          instructions="Give me 5 healthy food recipes",
+      )
 
-  result = agents.start()
-  ```
+      blog_agent = Agent(
+          instructions="Write a blog post about the food recipes",
+      )
 
-  ```python Multiple Agents
-  from praisonaiagents import Agent, Task, PraisonAIAgents
+      # Run multiple agents
+      agents = PraisonAIAgents(agents=[diet_agent, blog_agent])
+      agents.start()
+      ```
+    </CodeGroup>
+      </Step>
 
-  # Create multiple agents
-  researcher = Agent(
-      name="Researcher",
-      role="Senior Research Analyst",
-      goal="Uncover cutting-edge developments in AI",
-      backstory="You are an expert at a technology research group",
-      verbose=True,
-      llm="gpt-4o",
-      markdown=True
-  )
+      <Step title="Run Agents">
+        Execute your script:
+        ```bash
+        python app.py
+        ```
 
-  writer = Agent(
-      name="Writer",
-      role="Tech Content Strategist",
-      goal="Craft compelling content on tech advancements",
-      backstory="You are a content strategist",
-      llm="gpt-4o",
-      markdown=True
-  )
-
-  # Define multiple tasks
-  task1 = Task(
-      name="research_task",
-      description="Analyze 2024's AI advancements",
-      expected_output="A detailed report",
-      agent=researcher
-  )
-
-  task2 = Task(
-      name="writing_task",
-      description="Create a blog post about AI advancements",
-      expected_output="A blog post",
-      agent=writer
-  )
-
-  # Run with hierarchical process
-  agents = PraisonAIAgents(
-      agents=[researcher, writer],
-      tasks=[task1, task2],
-      verbose=False,
-      process="hierarchical",
-      manager_llm="gpt-4o"
-  )
-
-  result = agents.start()
-  ```
-</CodeGroup>
-  </Step>
-
-  <Step title="Start Agents">
-    Execute your script:
-    ```bash Terminal
-    python app.py
-    ```
-  </Step>
-</Steps>
-</Tab>
+        You'll see:
+        - Agent initialization
+        - Task execution progress
+        - Final results
 
+        <Tip>
+        You have successfully CreatedAI Agents and made them work for you!
+        </Tip>
+      </Step>
+    </Steps>
+  </Tab>
   <Tab title="No Code">
     <Steps>
+      <Step title="Install Package">
+        Install the No Code PraisonAI Package:
+        ```bash
+        pip install praisonaiagents
+        ```
+      </Step>
 
-        <Step title="Install PraisonAI">
-            Install the PraisonAI package:
-            ```bash
-            pip install praisonai
-            ```
-        </Step>
+      <Step title="Set API Key">
+        ```bash
+        export OPENAI_API_KEY=your_openai_key
+        ```
+      </Step>
 
-        <Step title="Set API Key">
-            Set your OpenAI API key as an environment variable in your terminal:
-            ```bash Terminal
-            export OPENAI_API_KEY=your_openai_key
-            ```
-            Generate your OpenAI API key from [OpenAI](https://platform.openai.com/api-keys)
-            Use other LLM providers like Ollama, Anthropic, Groq, Google, etc. Please refer to the [Models](/models) for more information.
-        </Step>
+      <Step title="Create Config">
+        Create `agents.yaml`:
 
-        <Step title="Start AutoAgents">    
-            Run your AutoAgents:
-            ```bash
-            praisonai --auto "Create a movie script about a robot in Mars"
-            ```
-        </Step>
+    <CodeGroup>
+      ```yaml Single Agent
+      roles:
+        summarise_agent:
+          instructions: Summarise Photosynthesis
+      ```
+
+      ```yaml Multiple Agents
+      roles:
+        diet_agent:
+          instructions: Give me 5 healthy food recipes
+        blog_agent:
+          instructions: Write a blog post about the food recipes
+      ```
+    </CodeGroup>
+
+<Note>
+You can automatically create `agents.yaml` using:
+```bash
+praisonai --init "your task description"
+```
+</Note>
+
+      </Step>
+
+      <Step title="Run Agents">
+        Execute your config:
+        ```bash
+        praisonai agents.yaml
+        ```
+      </Step>
     </Steps>
   </Tab>
 </Tabs>

docs/mint.json

@@ -118,15 +118,18 @@
         "features/autoagents",
         "features/selfreflection",
         "features/rag",
+        "features/reasoning-extract",
         "features/reasoning",
         "features/multimodal",
         "features/langchain",
         "features/async",
         "features/mini",
+        "features/generate-reasoning",
         "features/codeagent",
         "features/mathagent",
         "features/structured",
-        "features/callbacks"
+        "features/callbacks",
+        "features/chat-with-pdf"
       ]
     },
     {
@@ -251,7 +254,10 @@
           "pages": [
             "ui/streamlit",
             "ui/streamlit/code-analysis-streamlit",
-            "ui/streamlit/hackathon-judge-streamlit"
+            "ui/streamlit/hackathon-judge-streamlit",
+            "ui/streamlit/ollama-streamlit",
+            "ui/streamlit/deepseek-streamlit",
+            "ui/streamlit/gemini-streamlit"
           ]
         },
         "ui/ui",
@@ -272,6 +278,7 @@
         "developers/wrapper-tools",
         "developers/googlecolab",
         "developers/googlecolab-tools",
+        "developers/local-development",
         "deploy"
       ]
     },

docs/models.mdx

@@ -4,7 +4,184 @@ description: "Overview of supported language models in PraisonAI, including Open
 icon: "brain"
 ---
 
-## Supported Models for Code
+# Code
+
+## Set model by 3 ways
+
+### 1. OpenAI Compatible Endpoints
+
+<Note>By Default it uses OPENAI_BASE_URL https://api.openai.com/v1 </Note>
+Example Groq Implementation:
+
+```bash
+export OPENAI_API_KEY=groq-api-key
+export OPENAI_BASE_URL=https://api.groq.com/openai/v1
+```
+
+```python
+from praisonaiagents import Agent
+
+agent = Agent(
+    instructions="You are a helpful assistant",
+    llm="llama-3.1-8b-instant",
+)
+
+agent.start("Why sky is Blue?")
+```
+
+### 2. Litellm Compatible model names (eg: gemini/gemini-1.5-flash-8b)
+
+```bash
+pip install "praisonaiagents[llm]"
+```
+
+```python
+from praisonaiagents import Agent
+
+agent = Agent(
+    instructions="You are a helpful assistant",
+    llm="gemini/gemini-1.5-flash-8b",
+    self_reflect=True,
+    verbose=True
+)
+
+agent.start("Why sky is Blue?")
+```
+
+### 3. Litellm Compatible Configuration
+
+```bash
+pip install "praisonaiagents[llm]"
+```
+
+```python 
+from praisonaiagents import Agent
+
+llm_config = {
+    "model": "gemini/gemini-1.5-flash-latest",  # Model name without provider prefix
+    
+    # Core settings
+    "temperature": 0.7,                # Controls randomness (like temperature)
+    "timeout": 30,                 # Timeout in seconds
+    "top_p": 0.9,                    # Nucleus sampling parameter
+    "max_tokens": 1000,               # Max tokens in response
+    
+    # Advanced parameters
+    "presence_penalty": 0.1,         # Penalize repetition of topics (-2.0 to 2.0)
+    "frequency_penalty": 0.1,        # Penalize token repetition (-2.0 to 2.0)
+    
+    # API settings (optional)
+    "api_key": None,                 # Your API key (or use environment variable)
+    "base_url": None,                # Custom API endpoint if needed
+    
+    # Response formatting
+    "response_format": {             # Force specific response format
+        "type": "text"               # Options: "text", "json_object"
+    },
+    
+    # Additional controls
+    "seed": 42,                      # For reproducible responses
+    "stop_phrases": ["##", "END"],   # Custom stop sequences
+}
+
+agent = Agent(
+    instructions="You are a helpful Assistant."
+    llm=llm_config
+)
+agent.start()
+```
+
+## Advanced Configuration (Litellm Support)
+
+<Note>This uses Litellm</Note>
+<Steps>
+  <Step title="Install Package">
+    Install required packages:
+    ```bash
+    pip install "praisonaiagents[llm]"
+    ```
+  </Step>
+
+  <Step title="Setup Environment">
+    Configure environment:
+    ```bash
+    export GOOGLE_API_KEY=your-api-key
+    ```
+    
+    <Note>
+    Get your API key from [Google AI Studio](https://makersuite.google.com/app/apikey)
+    </Note>
+  </Step>
+
+  <Step title="Create Agent">
+    Create `app.py`:
+
+<CodeGroup>
+
+```python Basic
+# if json_object is supported by the model
+from praisonaiagents import Agent
+
+agent = Agent(
+    instructions="You are a helpful assistant",
+    llm="gemini/gemini-1.5-flash-8b",
+    self_reflect=True,
+    verbose=True
+)
+
+agent.start("Why sky is Blue?")
+```
+
+```python Advanced 
+# if json_object is not supported by the model
+from praisonaiagents import Agent
+
+# Detailed LLM configuration
+llm_config = {
+    "model": "gemini/gemini-1.5-flash-latest",  # Model name without provider prefix
+    
+    # Core settings
+    "temperature": 0.7,                # Controls randomness (like temperature)
+    "timeout": 30,                 # Timeout in seconds
+    "top_p": 0.9,                    # Nucleus sampling parameter
+    "max_tokens": 1000,               # Max tokens in response
+    
+    # Advanced parameters
+    "presence_penalty": 0.1,         # Penalize repetition of topics (-2.0 to 2.0)
+    "frequency_penalty": 0.1,        # Penalize token repetition (-2.0 to 2.0)
+    
+    # API settings (optional)
+    "api_key": None,                 # Your API key (or use environment variable)
+    "base_url": None,                # Custom API endpoint if needed
+    
+    # Response formatting
+    "response_format": {             # Force specific response format
+        "type": "text"               # Options: "text", "json_object"
+    },
+    
+    # Additional controls
+    "seed": 42,                      # For reproducible responses
+    "stop_phrases": ["##", "END"],   # Custom stop sequences
+}
+
+agent = Agent(
+    instructions="You are a helpful Assistant specialized in scientific explanations. "
+                "Provide clear, accurate, and engaging responses.",
+    llm=llm_config,                  # Pass the detailed configuration
+    verbose=True,                    # Enable detailed output
+    markdown=True,                   # Format responses in markdown
+    self_reflect=True,              # Enable self-reflection
+    max_reflect=3,                  # Maximum reflection iterations
+    min_reflect=1                   # Minimum reflection iterations
+)
+
+# Test the agent
+response = agent.start("Why is the sky blue? Please explain in simple terms.")
+
+```
+</CodeGroup>
+</Step>
+</Steps>
 
 <AccordionGroup>
   <Accordion title="Ollama Integration" defaultOpen>

docs/models/google.mdx

@@ -115,6 +115,49 @@ agents.start()
   This uses Litellm to connect to Google Gemini.
 </Note>
 
+## Gemini 2.0 Flash Thinking
+
+### LLM Configuration
+```python
+llm_config = {
+  "model": "gemini/gemini-2.0-flash-thinking-exp-01-21",
+  "response_format": {"type": "text"} # type is text, because json_object is not supported
+}
+```
+
+<Steps>
+    <Step title="Install Package">
+        Install required packages:
+        ```bash
+        pip install "praisonaiagents[llm]"
+        ```
+    </Step>
+
+    <Step title="Set API Key">
+        Set your Gemini API key:
+        ```bash
+        export GEMINI_API_KEY=xxxxxxxxxxx
+        ```
+    </Step>
+
+    <Step title="Code">
+
+```python
+from praisonaiagents import Agent
+
+llm_config = {"model": "gemini/gemini-2.0-flash-thinking-exp-01-21","response_format": {"type": "text"}}
+
+agent = Agent(
+    instructions="You are a helpful assistant",
+    llm=llm_config
+)
+
+result = agent.start("Why sky is Blue?")
+print(result)
+```
+    </Step>
+</Steps>
+
 ## Alternative Setup
 
 ### Using Environment Variables

docs/ui/streamlit/deepseek-streamlit.mdx

@@ -0,0 +1,158 @@
+---
+title: "Deepseek Streamlit UI"
+description: "Create interactive chat interfaces with Deepseek models using Streamlit"
+icon: "window"
+---
+
+```mermaid
+flowchart LR
+    In[Input] --> UI[("Streamlit UI")]
+    VDB[(Vector DB)] --> Agent
+    DS[("Deepseek")] --> Agent
+    UI --> Agent[("Knowledge Agent")]
+    Agent --> |Query| VDB
+    Agent --> Out[Output]
+    Out --> UI
+    
+    style In fill:#8B0000,color:#fff
+    style UI fill:#FF4B4B,color:#fff,shape:circle
+    style DS fill:#4169E1,color:#fff,shape:circle
+    style Agent fill:#2E8B57,color:#fff,shape:circle
+    style VDB fill:#4169E1,color:#fff,shape:cylinder
+    style Out fill:#8B0000,color:#fff
+```
+
+## Prerequisites
+
+<Steps>
+    <Step title="Install Package">
+        Install required packages:
+        ```bash
+        pip install "praisonaiagents[knowledge]" streamlit ollama
+        ```
+
+        <Note>
+        streamlit for UI
+        ollama for Deepseek model hosting
+        praisonaiagents[knowledge] for RAG capabilities
+        </Note>
+    </Step>
+
+    <Step title="Setup Model">
+        Pull Deepseek model:
+        ```bash
+        # Large Language Model
+        ollama pull deepseek-r1
+        
+        # Embedding Model
+        ollama pull nomic-embed-text
+        ```
+    </Step>
+
+    <Step title="Setup Environment">
+        Configure environment:
+        ```bash
+        export OPENAI_BASE_URL=http://localhost:11434/v1
+        export OPENAI_API_KEY=fake-key
+        ```
+    </Step>
+
+    <Step title="Create File">
+        Create a new file called `app.py` and add the following code:
+    </Step>
+
+    <Step title="Run Application">
+        Start the Streamlit application:
+        ```bash
+        streamlit run app.py
+        ```
+    </Step>
+</Steps>
+
+## Code
+
+```python
+import streamlit as st
+from praisonaiagents import Agent
+
+def init_agent():
+    config = {
+        "vector_store": {
+            "provider": "chroma",
+            "config": {
+                "collection_name": "praison",
+                "path": ".praison"
+            }
+        },
+        "llm": {
+            "provider": "ollama",
+            "config": {
+                "model": "deepseek-r1:latest",
+                "temperature": 0,
+                "max_tokens": 8000,
+                "ollama_base_url": "http://localhost:11434",
+            },
+        },
+        "embedder": {
+            "provider": "ollama",
+            "config": {
+                "model": "nomic-embed-text:latest",
+                "ollama_base_url": "http://localhost:11434",
+                "embedding_dims": 1536
+            },
+        },
+    }
+    
+    return Agent(
+        name="Knowledge Agent",
+        instructions="You answer questions based on the provided knowledge.",
+        knowledge=["kag-research-paper.pdf"],
+        knowledge_config=config,
+        user_id="user1",
+        llm="deepseek-r1"
+    )
+
+st.title("Knowledge Agent Chat")
+
+if "agent" not in st.session_state:
+    st.session_state.agent = init_agent()
+    st.session_state.messages = []
+
+if "messages" in st.session_state:
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+
+prompt = st.chat_input("Ask a question...")
+
+if prompt:
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    with st.chat_message("assistant"):
+        response = st.session_state.agent.start(prompt)
+        st.markdown(response)
+        st.session_state.messages.append({"role": "assistant", "content": response}) 
+```
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Interactive Chat" icon="comments">
+    Real-time chat interface with message history.
+  </Card>
+  <Card title="Knowledge Base" icon="database">
+    RAG capabilities with ChromaDB integration.
+  </Card>
+  <Card title="Model Integration" icon="server">
+    Uses Deepseek through Ollama.
+  </Card>
+  <Card title="Session Management" icon="clock-rotate-left">
+    Maintains chat history in session state.
+  </Card>
+</CardGroup>
+
+<Note>
+  Make sure your system meets the requirements for running Deepseek models locally through Ollama.
+</Note>

docs/ui/streamlit/gemini-streamlit.mdx

@@ -0,0 +1,119 @@
+---
+title: "Gemini Streamlit UI"
+description: "Create interactive chat interfaces with Google's Gemini models using Streamlit"
+icon: "window"
+---
+
+```mermaid
+flowchart LR
+    In[Input] --> UI[("Streamlit UI")]
+    VDB[(Vector DB)] --> Agent
+    Gemini[("Gemini")] --> Agent
+    UI --> Agent[("Knowledge Agent")]
+    Agent --> |Query| VDB
+    Agent --> Out[Output]
+    Out --> UI
+    
+    style In fill:#8B0000,color:#fff
+    style UI fill:#FF4B4B,color:#fff,shape:circle
+    style Gemini fill:#4169E1,color:#fff,shape:circle
+    style Agent fill:#2E8B57,color:#fff,shape:circle
+    style VDB fill:#4169E1,color:#fff,shape:cylinder
+    style Out fill:#8B0000,color:#fff
+```
+
+## Prerequisites
+
+<Steps>
+    <Step title="Install Package">
+        Install required packages:
+        ```bash
+        pip install "praisonaiagents[llm]" streamlit
+        ```
+
+        <Note>
+        streamlit for UI<br />
+        praisonaiagents[llm] for Gemini model access (It uses Litellm)
+        </Note>
+    </Step>
+
+    <Step title="Setup Environment">
+        Configure environment:
+        ```bash
+        export GOOGLE_API_KEY=your-api-key
+        ```
+        
+        <Note>
+        Get your API key from [Google AI Studio](https://makersuite.google.com/app/apikey)
+        </Note>
+    </Step>
+
+    <Step title="Create File">
+        Create a new file called `app.py` and add the following code:
+    </Step>
+
+    <Step title="Run Application">
+        Start the Streamlit application:
+        ```bash
+        streamlit run app.py
+        ```
+    </Step>
+</Steps>
+
+## Code
+
+```python
+import streamlit as st
+from praisonaiagents import Agent
+
+st.title("Gemini 2.0 Thinking AI Agent")
+
+# Initialize the agent
+@st.cache_resource
+def get_agent():
+    llm_config = {
+        "model": "gemini/gemini-2.0-flash-thinking-exp-01-21",
+        "response_format": {"type": "text"}
+    }
+    
+    return Agent(
+        instructions="You are a helpful assistant",
+        llm=llm_config
+    )
+
+agent = get_agent()
+
+# Create text area input field
+user_question = st.text_area("Ask your question:", height=150)
+
+# Add ask button
+if st.button("Ask"):
+    if user_question:
+        with st.spinner('Thinking...'):
+            result = agent.start(user_question)
+            st.write("### Answer")
+            st.write(result)
+    else:
+        st.warning("Please enter a question") 
+```
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Interactive Chat" icon="comments">
+    Real-time chat interface with message history.
+  </Card>
+  <Card title="Knowledge Base" icon="database">
+    RAG capabilities with ChromaDB integration.
+  </Card>
+  <Card title="Model Integration" icon="server">
+    Uses Google's Gemini Pro model.
+  </Card>
+  <Card title="Session Management" icon="clock-rotate-left">
+    Maintains chat history in session state.
+  </Card>
+</CardGroup>
+
+<Note>
+  Make sure you have a valid Google API key and sufficient quota for using Gemini models.
+</Note>

docs/ui/streamlit/ollama-streamlit.mdx

@@ -0,0 +1,158 @@
+---
+title: "Ollama Streamlit UI"
+description: "Create interactive chat interfaces with Ollama models using Streamlit"
+icon: "window"
+---
+
+```mermaid
+flowchart LR
+    In[Input] --> UI[("Streamlit UI")]
+    VDB[(Vector DB)] --> Agent
+    Ollama[("Ollama")] --> Agent
+    UI --> Agent[("Knowledge Agent")]
+    Agent --> |Query| VDB
+    Agent --> Out[Output]
+    Out --> UI
+    
+    style In fill:#8B0000,color:#fff
+    style UI fill:#FF4B4B,color:#fff,shape:circle
+    style Ollama fill:#4169E1,color:#fff,shape:circle
+    style Agent fill:#2E8B57,color:#fff,shape:circle
+    style VDB fill:#4169E1,color:#fff,shape:cylinder
+    style Out fill:#8B0000,color:#fff
+```
+
+## Prerequisites
+
+<Steps>
+    <Step title="Install Package">
+        Install required packages:
+        ```bash
+        pip install "praisonaiagents[knowledge]" streamlit ollama
+        ```
+
+        <Note>
+        streamlit for UI
+        ollama for model hosting
+        praisonaiagents[knowledge] for RAG capabilities
+        </Note>
+    </Step>
+
+    <Step title="Setup Model">
+        Pull Ollama models:
+        ```bash
+        # Large Language Model
+        ollama pull deepseek-r1
+        
+        # Embedding Model
+        ollama pull nomic-embed-text
+        ```
+    </Step>
+
+    <Step title="Setup Environment">
+        Configure environment:
+        ```bash
+        export OPENAI_BASE_URL=http://localhost:11434/v1
+        export OPENAI_API_KEY=fake-key
+        ```
+    </Step>
+
+    <Step title="Create File">
+        Create a new file called `app.py` and add the following code:
+    </Step>
+
+    <Step title="Run Application">
+        Start the Streamlit application:
+        ```bash
+        streamlit run app.py
+        ```
+    </Step>
+</Steps>
+
+## Code
+
+```python
+import streamlit as st
+from praisonaiagents import Agent
+
+def init_agent():
+    config = {
+        "vector_store": {
+            "provider": "chroma",
+            "config": {
+                "collection_name": "praison",
+                "path": ".praison"
+            }
+        },
+        "llm": {
+            "provider": "ollama",
+            "config": {
+                "model": "deepseek-r1:latest",
+                "temperature": 0,
+                "max_tokens": 8000,
+                "ollama_base_url": "http://localhost:11434",
+            },
+        },
+        "embedder": {
+            "provider": "ollama",
+            "config": {
+                "model": "nomic-embed-text:latest",
+                "ollama_base_url": "http://localhost:11434",
+                "embedding_dims": 1536
+            },
+        },
+    }
+    
+    return Agent(
+        name="Knowledge Agent",
+        instructions="You answer questions based on the provided knowledge.",
+        knowledge=["kag-research-paper.pdf"],
+        knowledge_config=config,
+        user_id="user1",
+        llm="deepseek-r1"
+    )
+
+st.title("Knowledge Agent Chat")
+
+if "agent" not in st.session_state:
+    st.session_state.agent = init_agent()
+    st.session_state.messages = []
+
+if "messages" in st.session_state:
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+
+prompt = st.chat_input("Ask a question...")
+
+if prompt:
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    with st.chat_message("assistant"):
+        response = st.session_state.agent.start(prompt)
+        st.markdown(response)
+        st.session_state.messages.append({"role": "assistant", "content": response}) 
+```
+
+## Features
+
+<CardGroup cols={2}>
+  <Card title="Interactive Chat" icon="comments">
+    Real-time chat interface with message history.
+  </Card>
+  <Card title="Knowledge Base" icon="database">
+    RAG capabilities with ChromaDB integration.
+  </Card>
+  <Card title="Model Integration" icon="server">
+    Uses Ollama for local model hosting.
+  </Card>
+  <Card title="Session Management" icon="clock-rotate-left">
+    Maintains chat history in session state.
+  </Card>
+</CardGroup>
+
+<Note>
+  Make sure your system meets the requirements for running models locally through Ollama.
+</Note>

examples/data/cot-agents.py

@@ -0,0 +1,142 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import cot_save, cot_upload_to_huggingface
+from pydantic import BaseModel
+import os
+
+# Define Pydantic model for structured output
+class DecisionModel(BaseModel):
+    response: str
+    decision: str
+
+def write_csv(file_path, data):
+    """Write data to CSV file."""
+    if not os.path.exists(file_path):
+        with open(file_path, 'w') as file:
+            file.write(data + '\n')
+    else:
+        with open(file_path, 'a') as file:
+            file.write(data + '\n')
+    return f"Data appended to {file_path}"
+
+def count_questions(file_path):
+    """Count lines in file."""
+    with open(file_path, 'r') as file:
+        return sum(1 for _ in file)
+
+# Keep existing agents with minimal changes
+qa_generator = Agent(
+    name="Generator",
+    role="Question Creator",
+    goal="Create challenging math and logic questions",
+    backstory="Expert in educational content creation",
+    llm="gpt-4o-mini",
+    tools=[write_csv, count_questions]
+)
+
+total_questions_evaluator = Agent(
+    name="TotalQuestionsEvaluator",
+    role="Total Questions Evaluator",
+    goal="Evaluate the total number of questions in qa_pairs.csv file",
+    backstory="Expert in evaluating the total number of questions in a file",
+    llm="gpt-4o-mini",
+    tools=[count_questions],
+    verbose=False
+)
+
+cot_generator = Agent(
+    name="COTGenerator",
+    role="Chain of Thought Specialist",
+    goal="Generate and manage chain of thought solutions for Q&A pairs",
+    backstory="Expert in breaking down problems and generating detailed solution steps",
+    tools=[cot_save],
+    llm="gpt-4o-mini",
+    verbose=False
+)
+
+upload_to_huggingface = Agent(
+    name="UploadToHuggingface",
+    role="Upload to Huggingface",
+    goal="Upload the generated chain of thought solutions to a Huggingface dataset",
+    backstory="Expert in saving data to Huggingface",
+    tools=[cot_upload_to_huggingface],
+    llm="gpt-4o-mini",
+    verbose=False
+)
+
+# Define tasks with workflow improvements
+generate_task = Task(
+    description="""Generate question and answer in csv format without headers: question, answer and append to qa_pairs.csv file
+generate 10 unique questions and answers and don't repeat on the same question and answer. Reponse with 'done' when done
+with append mode as 'a'
+Example question and answer:
+question, answer
+What is the sum of numbers from 1 to 10?, 55
+Number of r's in the word strawberry, 3
+""",
+    expected_output="append to qa_pairs.csv file with questions and answers and move to next task",
+    agent=qa_generator,
+    name="generate_task",
+    is_start=True,
+    next_tasks=["evaluate_total_questions"],
+    task_type="decision",
+    condition={
+        "more": "generate_task",
+        "done": "evaluate_total_questions"
+    }
+)
+
+evaluate_total_questions_task = Task(
+    description="Evaluate the total number of questions in qa_pairs.csv file is 1",
+    expected_output="Total number of questions in qa_pairs.csv file",
+    agent=total_questions_evaluator,
+    task_type="decision",
+    name="evaluate_total_questions",
+    condition={
+        "more": "generate_task",
+        "done": "generate_cot"
+    }
+)
+
+generate_cot_task = Task(
+    name="generate_cot",
+    description="""Generate chain of thought solutions for each question in the input file. 
+Save to cot_solutions.csv file
+Don't generate chain of thought solutions again after receiving the response from Tool Call
+After calling the tool, respond with a JSON object:
+{
+    "response": "done",
+    "decision": "done"
+}
+""",
+    expected_output="done",
+    agent=cot_generator,
+    input_file="qa_pairs.csv",
+    task_type="loop",
+    next_tasks=["upload_to_huggingface"],
+    condition={
+        "done": ["upload_to_huggingface"],
+        "exit": [],
+    },
+    output_pydantic=DecisionModel  # Use Pydantic model for output validation
+)
+
+upload_to_huggingface_task = Task(
+    name="upload_to_huggingface",
+    description="""Upload to Huggingface:
+    1. Save to cot_solutions.csv
+    2. Upload to mervinpraison/cot-dataset""",
+    expected_output="Dataset published successfully",
+    agent=upload_to_huggingface,
+    tools=[cot_upload_to_huggingface]
+)
+
+# Initialize workflow
+agents = PraisonAIAgents(
+    agents=[qa_generator, total_questions_evaluator, cot_generator, upload_to_huggingface],
+    tasks=[generate_task, evaluate_total_questions_task, generate_cot_task, upload_to_huggingface_task],
+    process="workflow",
+    max_iter=30,
+    verbose=False
+)
+
+agents.start()

examples/ui/gemini-streamlit.py

@@ -0,0 +1,32 @@
+import streamlit as st
+from praisonaiagents import Agent
+
+st.title("Gemini 2.0 Thinking AI Agent")
+
+# Initialize the agent
+@st.cache_resource
+def get_agent():
+    llm_config = {
+        "model": "gemini/gemini-2.0-flash-thinking-exp-01-21",
+        "response_format": {"type": "text"}
+    }
+    
+    return Agent(
+        instructions="You are a helpful assistant",
+        llm=llm_config
+    )
+
+agent = get_agent()
+
+# Create text area input field
+user_question = st.text_area("Ask your question:", height=150)
+
+# Add ask button
+if st.button("Ask"):
+    if user_question:
+        with st.spinner('Thinking...'):
+            result = agent.start(user_question)
+            st.write("### Answer")
+            st.write(result)
+    else:
+        st.warning("Please enter a question") 

examples/ui/ollama-rag-agents-streamlit.py

@@ -0,0 +1,62 @@
+import streamlit as st
+from praisonaiagents import Agent
+
+def init_agent():
+    config = {
+        "vector_store": {
+            "provider": "chroma",
+            "config": {
+                "collection_name": "praison",
+                "path": ".praison"
+            }
+        },
+        "llm": {
+            "provider": "ollama",
+            "config": {
+                "model": "deepseek-r1:latest",
+                "temperature": 0,
+                "max_tokens": 8000,
+                "ollama_base_url": "http://localhost:11434",
+            },
+        },
+        "embedder": {
+            "provider": "ollama",
+            "config": {
+                "model": "nomic-embed-text:latest",
+                "ollama_base_url": "http://localhost:11434",
+                "embedding_dims": 1536
+            },
+        },
+    }
+    
+    return Agent(
+        name="Knowledge Agent",
+        instructions="You answer questions based on the provided knowledge.",
+        knowledge=["kag-research-paper.pdf"],
+        knowledge_config=config,
+        user_id="user1",
+        llm="deepseek-r1"
+    )
+
+st.title("Knowledge Agent Chat")
+
+if "agent" not in st.session_state:
+    st.session_state.agent = init_agent()
+    st.session_state.messages = []
+
+if "messages" in st.session_state:
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+
+prompt = st.chat_input("Ask a question...")
+
+if prompt:
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    with st.chat_message("assistant"):
+        response = st.session_state.agent.start(prompt)
+        st.markdown(response)
+        st.session_state.messages.append({"role": "assistant", "content": response}) 

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/2.0.56.tar.gz"
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/2.0.60.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==2.0.56 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.0.60 gunicorn markdown\n")
             file.write("EXPOSE 8080\n")
             file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
             

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "PraisonAI"
-version = "2.0.56"
+version = "2.0.60"
 description = "PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human-agent collaboration."
 readme = "README.md"
 license = ""
@@ -12,7 +12,7 @@ dependencies = [
     "rich>=13.7",
     "markdown>=3.5",
     "pyparsing>=3.0.0",
-    "praisonaiagents>=0.0.49",
+    "praisonaiagents>=0.0.60",
     "python-dotenv>=0.19.0",
     "instructor>=1.3.3",
     "PyYAML>=6.0",
@@ -84,7 +84,7 @@ autogen = ["pyautogen>=0.2.19", "praisonai-tools>=0.0.7", "crewai"]
 
 [tool.poetry]
 name = "PraisonAI"
-version = "2.0.56"
+version = "2.0.60"
 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 = ""
@@ -102,7 +102,7 @@ python = ">=3.10,<3.13"
 rich = ">=13.7"
 markdown = ">=3.5"
 pyparsing = ">=3.0.0"
-praisonaiagents = ">=0.0.49"
+praisonaiagents = ">=0.0.60"
 python-dotenv = ">=0.19.0"
 instructor = ">=1.3.3"
 PyYAML = ">=6.0"

uv.lock

@@ -3060,7 +3060,7 @@ wheels = [
 
 [[package]]
 name = "praisonai"
-version = "2.0.56"
+version = "2.0.60"
 source = { editable = "." }
 dependencies = [
     { name = "instructor" },
@@ -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 = ">=0.0.49" },
+    { name = "praisonaiagents", specifier = ">=0.0.60" },
     { 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 = "0.0.49"
+version = "0.0.60"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "openai" },
     { name = "pydantic" },
     { name = "rich" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/3c/8a/521d13124d338c5b9d397214656b7f76377ef65c885bcb3859a1484997c5/praisonaiagents-0.0.49.tar.gz", hash = "sha256:257e8572866edb989ed8c3808afec66f65ccdd47f47b5e8916a319863a01cc28", size = 85081 }
+sdist = { url = "https://files.pythonhosted.org/packages/ba/31/19a0226dda6c6a367294b613296852e89294f5b839482157e8e7b8b6985a/praisonaiagents-0.0.60.tar.gz", hash = "sha256:38404fc976c086bde71ad51fff7b417a105574ba7877f71403625cef90494fc0", size = 100110 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/59/64/a80c2cbb760d4fa73383e661301cfacacb49598686fe0da79da3deed9d05/praisonaiagents-0.0.49-py3-none-any.whl", hash = "sha256:b5b1abb9dc668e0f238282c546b76ddbfc51a6d463c6cbc8cf8da4b829a14ff1", size = 103198 },
+    { url = "https://files.pythonhosted.org/packages/13/b0/ae8e2b0b3d0dc1e259e670506530db46cc18ab8300e98323788299e62a95/praisonaiagents-0.0.60-py3-none-any.whl", hash = "sha256:a3a73b60bb8fd760bdeeeed3a7dded49223dc3a29044e73d2af174a0179f710d", size = 118558 },
 ]
 
 [[package]]