Merge pull request #324 from MervinPraison/develop

Update PraisonAI to version 2.0.57 and enhance documentation

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.chat("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 +466,14 @@ 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
 
 ```yaml
-framework: crewai
+framework: praisonai
 topic: Artificial Intelligence
 roles:
   screenwriter:
@@ -420,62 +486,25 @@ 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
@@ -500,6 +529,17 @@ 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)
@@ -525,58 +565,3 @@ 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/.devcontainer/Dockerfile

@@ -0,0 +1,43 @@
+FROM mcr.microsoft.com/devcontainers/python:3.11
+
+# Prevent apt from showing prompts
+ENV DEBIAN_FRONTEND=noninteractive
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    git \
+    curl \
+    build-essential \
+    && rm -rf /var/lib/apt/lists/*
+
+# Create a non-root user
+ARG USERNAME=vscode
+ARG USER_UID=1000
+ARG USER_GID=$USER_UID
+
+# Create the user and group only if they don't exist
+RUN if ! getent group $USER_GID > /dev/null 2>&1; then \
+        groupadd --gid $USER_GID $USERNAME; \
+    fi && \
+    if ! getent passwd $USER_UID > /dev/null 2>&1; then \
+        useradd --uid $USER_UID --gid $USER_GID -m $USERNAME; \
+    fi && \
+    apt-get update && \
+    apt-get install -y sudo && \
+    echo $USERNAME ALL=\(root\) NOPASSWD:ALL > /etc/sudoers.d/$USERNAME && \
+    chmod 0440 /etc/sudoers.d/$USERNAME
+
+# Set Python environment variables
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1
+
+# Install Python dependencies
+COPY requirements.txt /tmp/pip-tmp/
+RUN pip3 --disable-pip-version-check --no-cache-dir install -r /tmp/pip-tmp/requirements.txt \
+    && rm -rf /tmp/pip-tmp
+
+# Switch to non-root user
+USER $USERNAME
+
+# Set the default shell to bash
+ENV SHELL=/bin/bash

agents/.devcontainer/devcontainer.json

@@ -0,0 +1,36 @@
+{
+    "name": "PraisonAI Development",
+    "dockerFile": "Dockerfile",
+    "context": "..",
+    "runArgs": [
+        "--env-file",
+        ".env",
+        "--network=host"
+    ],
+    "customizations": {
+        "vscode": {
+            "extensions": [
+                "ms-python.python",
+                "ms-python.vscode-pylance",
+                "ms-python.black-formatter",
+                "ms-toolsai.jupyter",
+                "yzhang.markdown-all-in-one",
+                "streetsidesoftware.code-spell-checker"
+            ],
+            "settings": {
+                "python.defaultInterpreterPath": "/usr/local/bin/python",
+                "python.linting.enabled": true,
+                "python.formatting.provider": "black",
+                "editor.formatOnSave": true,
+                "editor.rulers": [88],
+                "files.trimTrailingWhitespace": true
+            }
+        }
+    },
+    "remoteUser": "vscode",
+    "updateRemoteUserUID": false,
+    "forwardPorts": [],
+    "appPort": ["56040:56040"],
+    "overrideCommand": false,
+    "init": true
+}

agents/any-llm-agents-advanced.py

@@ -0,0 +1,45 @@
+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.")
+
+print(response)

agents/any-llm-agents-async.py

@@ -0,0 +1,18 @@
+import asyncio
+from praisonaiagents import Agent
+
+async def main():
+    agent = Agent(
+        instructions="You are a helpful assistant",
+        llm="gemini/gemini-1.5-flash-8b",
+        self_reflect=True,
+        verbose=True
+    )
+
+    # Use achat instead of start/chat for async operation
+    response = await agent.achat("Why sky is Blue in 1000 words?")
+    print(response)
+
+if __name__ == "__main__":
+    # Run the async main function
+    asyncio.run(main())

agents/any-llm-agents.py

@@ -0,0 +1,10 @@
+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?")

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/async_example.py

@@ -12,6 +12,14 @@ from praisonaiagents.main import (
 )
 from duckduckgo_search import DDGS
 from pydantic import BaseModel
+import logging
+
+# Configure logging with more detail
+logging.basicConfig(
+    level=logging.DEBUG,
+    format='%(asctime)s - %(levelname)s - %(name)s:%(lineno)d - %(message)s'
+)
+logger = logging.getLogger(__name__)
 
 console = Console()
 
@@ -48,7 +56,7 @@ def sync_search_tool(query: str) -> List[Dict]:
         error_logs.append(error_msg)
         return []
 
-async def async_search_tool(query: str) -> List[Dict]:
+async def async_search_tool(query: str) -> Dict:
     """
     Asynchronous search using DuckDuckGo.
     Args:
@@ -56,23 +64,36 @@ async def async_search_tool(query: str) -> List[Dict]:
     Returns:
         list: Search results
     """
+    logger.debug(f"Starting async_search_tool with query: {query}")
     display_tool_call(f"Running async search for: {query}", console)
     await asyncio.sleep(1)  # Simulate network delay
     try:
         results = []
         ddgs = DDGS()
+        logger.debug("Performing DuckDuckGo search")
         for result in ddgs.text(keywords=query, max_results=5):
             results.append({
                 "title": result.get("title", ""),
                 "url": result.get("href", ""),
                 "snippet": result.get("body", "")
             })
-        return results
+        
+        logger.debug(f"Search completed, found {len(results)} results")
+        return {
+            "query": query,
+            "results": results,
+            "total_results": len(results)
+        }
     except Exception as e:
         error_msg = f"Error during async search: {e}"
+        logger.error(f"Error in async_search_tool: {e}", exc_info=True)
         display_error(error_msg, console)
         error_logs.append(error_msg)
-        return []
+        return {
+            "query": query,
+            "results": [],
+            "total_results": 0
+        }
 
 # 3. Define both sync and async callbacks
 def sync_callback(output: TaskOutput):

agents/async_example_full.py

@@ -4,6 +4,13 @@ from typing import List, Dict
 from praisonaiagents import Agent, Task, PraisonAIAgents, TaskOutput
 from duckduckgo_search import DDGS
 from pydantic import BaseModel
+import logging
+
+# Configure logging
+logging.basicConfig(
+    level=logging.DEBUG,
+    format='%(asctime)s - %(levelname)s - %(message)s'
+)
 
 # 1. Define output model for structured results
 class SearchResult(BaseModel):

agents/autonomous-agent.py

@@ -0,0 +1,114 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import time
+
+def get_environment_state():
+    """Simulates getting current environment state"""
+    current_time = int(time.time())
+    states = ["normal", "critical", "optimal"]
+    state = states[current_time % 3]
+    print(f"Environment state: {state}")
+    return state
+
+def perform_action(state: str):
+    """Simulates performing an action based on state"""
+    actions = {
+        "normal": "maintain",
+        "critical": "fix",
+        "optimal": "enhance"
+    }
+    action = actions.get(state, "observe")
+    print(f"Performing action: {action} for state: {state}")
+    return action
+
+def get_feedback():
+    """Simulates environment feedback"""
+    current_time = int(time.time())
+    feedback = "positive" if current_time % 2 == 0 else "negative"
+    print(f"Feedback received: {feedback}")
+    return feedback
+
+# Create specialized agents
+llm_caller = Agent(
+    name="Environment Monitor",
+    role="State analyzer",
+    goal="Monitor environment and analyze state",
+    instructions="Check environment state and provide analysis",
+    tools=[get_environment_state]
+)
+
+action_agent = Agent(
+    name="Action Executor",
+    role="Action performer",
+    goal="Execute appropriate actions based on state",
+    instructions="Determine and perform actions based on environment state",
+    tools=[perform_action]
+)
+
+feedback_agent = Agent(
+    name="Feedback Processor",
+    role="Feedback analyzer",
+    goal="Process environment feedback and adapt strategy",
+    instructions="Analyze feedback and provide adaptation recommendations",
+    tools=[get_feedback]
+)
+
+# Create tasks for autonomous workflow
+monitor_task = Task(
+    name="monitor_environment",
+    description="Monitor and analyze environment state",
+    expected_output="Current environment state analysis",
+    agent=llm_caller,
+    is_start=True,
+    task_type="decision",
+    next_tasks=["execute_action"],
+    condition={
+        "normal": ["execute_action"],
+        "critical": ["execute_action"],
+        "optimal": "exit"
+    }
+)
+
+action_task = Task(
+    name="execute_action",
+    description="Execute appropriate action based on state",
+    expected_output="Action execution result",
+    agent=action_agent,
+    next_tasks=["process_feedback"]
+)
+
+feedback_task = Task(
+    name="process_feedback",
+    description="Process feedback and adapt strategy",
+    expected_output="Strategy adaptation based on feedback",
+    agent=feedback_agent,
+    next_tasks=["monitor_environment"],  # Create feedback loop
+    context=[monitor_task, action_task]  # Access to previous states and actions
+)
+
+# Create workflow manager
+workflow = PraisonAIAgents(
+    agents=[llm_caller, action_agent, feedback_agent],
+    tasks=[monitor_task, action_task, feedback_task],
+    process="workflow",
+    verbose=True
+)
+
+def main():
+    print("\nStarting Autonomous Agent Workflow...")
+    print("=" * 50)
+    
+    # Run autonomous workflow
+    results = workflow.start()
+    
+    # Print results
+    print("\nAutonomous Agent Results:")
+    print("=" * 50)
+    for task_id, result in results["task_results"].items():
+        if result:
+            task_name = result.description
+            print(f"\nTask: {task_name}")
+            print(f"Result: {result.raw}")
+            print("-" * 50)
+
+if __name__ == "__main__":
+    main()

agents/basic-agents.py

@@ -0,0 +1,8 @@
+from praisonaiagents import Agent
+
+agent = Agent(
+    instructions="You are a helpful assistant",
+    llm="gpt-4o-mini"
+)
+
+agent.start("Why sky is Blue?")

agents/chunking-direct-test.py

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

agents/code-analysis-agents.py

@@ -0,0 +1,97 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from pydantic import BaseModel
+from typing import List, Dict
+from gitingest import ingest
+
+class CodeMetrics(BaseModel):
+    category: str
+    score: int
+    findings: List[str]
+
+class CodeAnalysisReport(BaseModel):
+    overall_quality: int
+    code_metrics: List[CodeMetrics]
+    architecture_score: int
+    maintainability_score: int
+    performance_score: int
+    security_score: int
+    test_coverage: int
+    key_strengths: List[str]
+    improvement_areas: List[str]
+    tech_stack: List[str]
+    recommendations: List[str]
+    complexity_metrics: Dict[str, int]
+    best_practices: List[Dict[str, str]]
+    potential_risks: List[str]
+    documentation_quality: int
+
+code_analyzer = Agent(
+    role="Code Analysis Expert",
+    goal="Provide comprehensive code evaluation and recommendations",
+    backstory="""Expert code analyst specializing in architecture review, 
+    best practices, and technical debt assessment.""",
+    verbose=True
+)
+
+code_analysis_task = Task(
+    description="""Analyze code repository and provide structured evaluation:
+    
+    1. Overall Quality (0-100)
+    2. Core Metrics Analysis:
+       - Architecture and Design
+       - Code Maintainability
+       - Performance Optimization
+       - Security Practices
+       - Test Coverage
+    3. Technical Assessment:
+       - Technology Stack Review
+       - Code Complexity Analysis
+       - Best Practices Adherence
+       - Risk Assessment
+    4. Recommendations:
+       - Key Improvements
+       - Architecture Suggestions
+       - Security Enhancements""",
+    expected_output="Detailed code analysis report with metrics and recommendations",
+    agent=code_analyzer,
+    output_pydantic=CodeAnalysisReport
+)
+
+def analyze_code(code_source: str) -> CodeAnalysisReport:
+    """
+    Analyze code from directory path or GitHub URL
+    """
+    # Ingest code content
+    summary, tree, content = ingest(code_source)
+    
+    # Concatenate context into structured format
+    context_text = f"""
+    CODE REPOSITORY ANALYSIS
+    =======================
+    
+    SUMMARY
+    -------
+    {summary}
+    
+    REPOSITORY STRUCTURE
+    -------------------
+    {tree}
+    
+    SOURCE CODE
+    -----------
+    {content}
+    """
+    
+    # Initialize and run analysis
+    agents = PraisonAIAgents(
+        agents=[code_analyzer],
+        tasks=[code_analysis_task]
+    )
+    
+    return agents.start(context_text)
+
+if __name__ == "__main__":
+    # Example usage
+    code_source = "https://github.com/openai/openai-python/tree/main/src/openai/cli/_api/chat"  # GitHub URL or local directory
+    result = analyze_code(code_source)
+    print(result) 

agents/code-analysis-streamlit.py

@@ -0,0 +1,217 @@
+import streamlit as st
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from pydantic import BaseModel
+from typing import List, Dict, Any
+from gitingest import ingest
+
+# Import the same classes and code from code-analysis-agents.py
+class CodeMetrics(BaseModel):
+    category: str
+    score: int
+    findings: List[str]
+
+class CodeAnalysisReport(BaseModel):
+    overall_quality: int
+    code_metrics: List[CodeMetrics]
+    architecture_score: int
+    maintainability_score: int
+    performance_score: int
+    security_score: int
+    test_coverage: int
+    key_strengths: List[str]
+    improvement_areas: List[str]
+    tech_stack: List[str]
+    recommendations: List[str]
+    complexity_metrics: Dict[str, int]
+    best_practices: List[Dict[str, str]]
+    potential_risks: List[str]
+    documentation_quality: int
+
+def analyze_code(code_source: str) -> CodeAnalysisReport:
+    """
+    Analyze code from directory path or GitHub URL
+    """
+    # Create code analyzer agent
+    code_analyzer = Agent(
+        role="Code Analysis Expert",
+        goal="Provide comprehensive code evaluation and recommendations",
+        backstory="""Expert code analyst specializing in architecture review, 
+        best practices, and technical debt assessment.""",
+        verbose=True
+    )
+
+    # Create analysis task
+    code_analysis_task = Task(
+        description="""Analyze code repository and provide structured evaluation:
+        
+        1. Overall Quality (0-100)
+        2. Core Metrics Analysis:
+           - Architecture and Design
+           - Code Maintainability
+           - Performance Optimization
+           - Security Practices
+           - Test Coverage
+        3. Technical Assessment:
+           - Technology Stack Review
+           - Code Complexity Analysis
+           - Best Practices Adherence
+           - Risk Assessment
+        4. Recommendations:
+           - Key Improvements
+           - Architecture Suggestions
+           - Security Enhancements""",
+        expected_output="Detailed code analysis report with metrics and recommendations",
+        agent=code_analyzer,
+        output_pydantic=CodeAnalysisReport
+    )
+
+    # Ingest code content
+    summary, tree, content = ingest(code_source)
+    
+    # Concatenate context into structured format
+    context_text = f"""
+    CODE REPOSITORY ANALYSIS
+    =======================
+    
+    SUMMARY
+    -------
+    {summary}
+    
+    REPOSITORY STRUCTURE
+    -------------------
+    {tree}
+    
+    SOURCE CODE
+    -----------
+    {content}
+    """
+    
+    # Initialize and run analysis
+    agents = PraisonAIAgents(
+        agents=[code_analyzer],
+        tasks=[code_analysis_task]
+    )
+    
+    result = agents.start(context_text)
+    
+    # Extract the Pydantic model from the result
+    if isinstance(result, dict) and 'task_results' in result:
+        # Get the first task result's pydantic output
+        analysis_result = result['task_results'][0].pydantic
+        if isinstance(analysis_result, CodeAnalysisReport):
+            return analysis_result
+    
+    # If we can't get the Pydantic model, create one from the raw data
+    return CodeAnalysisReport(**result)
+
+def display_code_metrics(metrics, cols):
+    """Display code metrics in columns with color-coded scores"""
+    for i, metric in enumerate(metrics):
+        score = metric.score
+        color = "red" if score < 60 else "orange" if score < 75 else "green"
+        
+        # Use modulo to alternate between columns
+        with cols[i % len(cols)]:
+            st.markdown(f"### {metric.category}")
+            st.markdown(f"**Score:** :{color}[{score}%]")
+            for finding in metric.findings:
+                st.markdown(f"• {finding}")
+
+def display_section(title: str, items: list, icon: str = "•"):
+    """Display a section with items in a consistent format"""
+    st.markdown(f"### {title}")
+    for item in items:
+        if isinstance(item, dict):
+            for key, value in item.items():
+                st.markdown(f"{icon} **{key}**: {value}")
+        else:
+            st.markdown(f"{icon} {item}")
+
+def main():
+    st.set_page_config(
+        page_title="Code Analysis Agent",
+        layout="wide",
+        initial_sidebar_state="expanded"
+    )
+    
+    st.title("Code Analysis Agent")
+    
+    with st.sidebar:
+        st.header("Input")
+        code_source = st.text_input(
+            "GitHub URL or Local Path",
+            placeholder="https://github.com/username/repo or /path/to/directory"
+        )
+        analyze_button = st.button("Analyze Code", type="primary")
+    
+    if analyze_button and code_source:
+        try:
+            with st.spinner("Analyzing code..."):
+                result = analyze_code(code_source)
+                
+                # Overall Metrics
+                st.header("Overall Metrics")
+                cols = st.columns(6)
+                metrics = {
+                    "Overall Quality": result.overall_quality,
+                    "Architecture": result.architecture_score,
+                    "Maintainability": result.maintainability_score,
+                    "Performance": result.performance_score,
+                    "Security": result.security_score,
+                    "Test Coverage": result.test_coverage
+                }
+                
+                for (metric, value), col in zip(metrics.items(), cols):
+                    color = "red" if value < 60 else "orange" if value < 75 else "green"
+                    col.metric(metric, f"{value}%")
+                    col.markdown(f":{color}[{'●' * (value // 20)}]")
+                
+                # Detailed Analysis
+                st.header("Detailed Analysis")
+                metric_cols = st.columns(2)
+                display_code_metrics(result.code_metrics, metric_cols)
+                
+                # Technology Stack
+                col1, col2 = st.columns(2)
+                with col1:
+                    st.header("Technology Stack")
+                    for tech in result.tech_stack:
+                        st.markdown(f"🔧 {tech}")
+                
+                with col2:
+                    st.header("Complexity Metrics")
+                    for metric, value in result.complexity_metrics.items():
+                        st.metric(metric.replace('_', ' ').title(), value)
+                
+                # Key Findings
+                st.header("Key Findings")
+                cols = st.columns(2)
+                
+                with cols[0]:
+                    display_section("✅ Strengths", result.key_strengths)
+                    display_section("🔄 Best Practices", result.best_practices)
+                
+                with cols[1]:
+                    display_section("⚠️ Areas for Improvement", result.improvement_areas)
+                    display_section("❗ Potential Risks", result.potential_risks)
+                
+                # Recommendations
+                st.header("Recommendations")
+                for i, rec in enumerate(result.recommendations, 1):
+                    st.markdown(f"**{i}.** {rec}")
+                
+                # Documentation Quality
+                st.header("Documentation")
+                doc_score = result.documentation_quality
+                color = "red" if doc_score < 60 else "orange" if doc_score < 75 else "green"
+                st.markdown(f"Documentation Quality: :{color}[{doc_score}%]")
+                
+        except Exception as e:
+            st.error(f"Error analyzing code: {str(e)}")
+            st.exception(e)
+    
+    elif analyze_button:
+        st.warning("Please enter a GitHub URL or local path")
+
+if __name__ == "__main__":
+    main() 

agents/code_review.py

@@ -0,0 +1,108 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import time
+from typing import List, Dict
+
+def analyze_code_changes():
+    """Simulates code analysis"""
+    issues = [
+        {"type": "style", "severity": "low", "file": "main.py"},
+        {"type": "security", "severity": "high", "file": "auth.py"},
+        {"type": "performance", "severity": "medium", "file": "data.py"}
+    ]
+    return issues[int(time.time()) % 3]
+
+def suggest_fixes(issue: Dict):
+    """Simulates fix suggestions"""
+    fixes = {
+        "style": "Apply PEP 8 formatting",
+        "security": "Implement input validation",
+        "performance": "Use list comprehension"
+    }
+    return fixes.get(issue["type"], "Review manually")
+
+def apply_automated_fix(fix: str):
+    """Simulates applying automated fixes"""
+    success = int(time.time()) % 2 == 0
+    return "fixed" if success else "manual_review"
+
+# Create specialized agents
+analyzer = Agent(
+    name="Code Analyzer",
+    role="Code analysis",
+    goal="Analyze code changes and identify issues",
+    instructions="Review code changes and report issues",
+    tools=[analyze_code_changes]
+)
+
+fix_suggester = Agent(
+    name="Fix Suggester",
+    role="Solution provider",
+    goal="Suggest fixes for identified issues",
+    instructions="Provide appropriate fix suggestions",
+    tools=[suggest_fixes]
+)
+
+fix_applier = Agent(
+    name="Fix Applier",
+    role="Fix implementation",
+    goal="Apply suggested fixes automatically when possible",
+    instructions="Implement suggested fixes and report results",
+    tools=[apply_automated_fix]
+)
+
+# Create workflow tasks
+analysis_task = Task(
+    name="analyze_code",
+    description="Analyze code changes for issues",
+    expected_output="Identified code issues",
+    agent=analyzer,
+    is_start=True,
+    next_tasks=["suggest_fixes"]
+)
+
+suggestion_task = Task(
+    name="suggest_fixes",
+    description="Suggest fixes for identified issues",
+    expected_output="Fix suggestions",
+    agent=fix_suggester,
+    next_tasks=["apply_fixes"]
+)
+
+fix_task = Task(
+    name="apply_fixes",
+    description="Apply suggested fixes",
+    expected_output="Fix application status",
+    agent=fix_applier,
+    task_type="decision",
+    condition={
+        "fixed": "",
+        "manual_review": ["suggest_fixes"]  # Loop back for manual review
+    }
+)
+
+# Create workflow
+workflow = PraisonAIAgents(
+    agents=[analyzer, fix_suggester, fix_applier],
+    tasks=[analysis_task, suggestion_task, fix_task],
+    process="workflow",
+    verbose=True
+)
+
+def main():
+    print("\nStarting Code Review Workflow...")
+    print("=" * 50)
+    
+    # Run workflow
+    results = workflow.start()
+    
+    # Print results
+    print("\nCode Review Results:")
+    print("=" * 50)
+    for task_id, result in results["task_results"].items():
+        if result:
+            print(f"\nTask: {task_id}")
+            print(f"Result: {result.raw}")
+            print("-" * 50)
+
+if __name__ == "__main__":
+    main()

agents/context-agents-code.py

@@ -0,0 +1,98 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from pydantic import BaseModel
+from typing import List, Dict
+
+class SkillMetrics(BaseModel):
+    name: str
+    score: int
+    comments: str
+
+class CVAnalysisReport(BaseModel):
+    overall_score: int
+    skill_metrics: List[SkillMetrics]
+    experience_score: int
+    education_score: int
+    presentation_score: int
+    achievement_impact_score: int
+    market_readiness: int
+    key_strengths: List[str]
+    areas_for_improvement: List[str]
+    market_fit: str
+    recommendations: List[str]
+    technical_skills: List[Dict[str, int]]
+    soft_skills: List[Dict[str, int]]
+    skill_gaps: List[str]
+    key_achievements: List[str]
+    impact_metrics: List[Dict[str, str]]
+    areas_of_expertise: List[str]
+
+# Create single CV analysis agent
+cv_analyzer = Agent(
+    role="CV Analysis Expert",
+    goal="Provide comprehensive CV evaluation and recommendations",
+    backstory="""You are an expert CV analyst with deep knowledge of industry requirements, 
+    market trends, and talent assessment. You excel at evaluating skills, experience, 
+    and providing actionable recommendations.""",
+    verbose=True
+)
+
+# Single comprehensive analysis task
+cv_analysis_task = Task(
+    description="""Perform a detailed CV analysis with scoring metrics and provide output in the following format:
+
+    1. Overall Score (0-100):
+       - Calculate based on weighted average of all metrics
+    
+    2. Individual Skill Metrics:
+       - For each major skill found in CV:
+         * Name of skill
+         * Score (0-100)
+         * Specific feedback/comments
+    
+    3. Core Metrics (0-100 each):
+       - Experience Score: Based on relevance and years
+       - Education Score: Based on relevance and level
+       - Presentation Score: CV format, clarity, structure
+       - Achievement Impact Score: Measurable results
+       - Market Readiness: Current market fit
+    
+    4. Technical Skills:
+       - List each skill with proficiency score (0-100)
+       - Example: {"Python": 85, "AWS": 70}
+    
+    5. Soft Skills:
+       - List each skill with proficiency score (0-100)
+       - Example: {"Communication": 90, "Leadership": 75}
+    
+    6. Detailed Analysis:
+       - Key Strengths: Top 3-5 standout points
+       - Areas for Improvement: 3-5 specific points
+       - Market Fit: Current industry alignment
+       - Skill Gaps: Missing critical skills for target role
+       - Key Achievements: Quantifiable accomplishments
+       - Impact Metrics: {"metric": "value"}
+       - Areas of Expertise: Primary domains of expertise
+    
+    7. Recommendations:
+       - 3-5 actionable steps for improvement
+       
+    Provide clear justification for all scores and ensure feedback is specific and actionable.""",
+    expected_output="Structured CV analysis report with detailed metrics and scoring justification",
+    agent=cv_analyzer,
+    output_pydantic=CVAnalysisReport
+)
+
+# Create and run the agent
+agents = PraisonAIAgents(
+    agents=[cv_analyzer],
+    tasks=[cv_analysis_task]
+)
+
+# Read and pass CV content through start()
+with open('cv.txt', 'r') as file:
+    cv_content = file.read()
+
+# Start the analysis by passing CV content
+result = agents.start(cv_content)
+
+print(result)

agents/context-agents.py

@@ -0,0 +1,202 @@
+import streamlit as st
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from pydantic import BaseModel
+from typing import List, Dict
+import json
+import asyncio
+import nest_asyncio
+from functools import partial
+
+# Enable nested event loops
+nest_asyncio.apply()
+
+class SkillMetrics(BaseModel):
+    name: str
+    score: int
+    comments: str
+
+class CVAnalysisReport(BaseModel):
+    overall_score: int
+    skill_metrics: List[SkillMetrics]
+    experience_score: int
+    education_score: int
+    presentation_score: int
+    achievement_impact_score: int
+    market_readiness: int
+    key_strengths: List[str]
+    areas_for_improvement: List[str]
+    market_fit: str
+    recommendations: List[str]
+    technical_skills: List[Dict[str, int]]
+    soft_skills: List[Dict[str, int]]
+    skill_gaps: List[str]
+    key_achievements: List[str]
+    impact_metrics: List[Dict[str, str]]
+    areas_of_expertise: List[str]
+
+async def analyze_cv_async(cv_content):
+    cv_analyzer = Agent(
+        role="CV Analysis Expert",
+        goal="Provide comprehensive CV evaluation and recommendations",
+        backstory="""You are an expert CV analyst with deep knowledge of industry requirements, 
+        market trends, and talent assessment. You excel at evaluating skills, experience, 
+        and providing actionable recommendations.""",
+        verbose=True
+    )
+
+    cv_analysis_task = Task(
+        description="""Perform a detailed CV analysis with scoring metrics and provide output in the following format:
+        1. Overall Score (0-100):
+           - Calculate based on weighted average of all metrics
+        
+        2. Individual Skill Metrics:
+           - For each major skill found in CV:
+             * Name of skill
+             * Score (0-100)
+             * Specific feedback/comments
+        
+        3. Core Metrics (0-100 each):
+           - Experience Score: Based on relevance and years
+           - Education Score: Based on relevance and level
+           - Presentation Score: CV format, clarity, structure
+           - Achievement Impact Score: Measurable results
+           - Market Readiness: Current market fit
+        
+        4. Technical Skills:
+           - List each skill with proficiency score (0-100)
+        
+        5. Soft Skills:
+           - List each skill with proficiency score (0-100)
+        
+        6. Detailed Analysis sections
+        
+        7. Recommendations:
+           - 3-5 actionable steps for improvement""",
+        expected_output="Structured CV analysis report with detailed metrics and scoring justification",
+        agent=cv_analyzer,
+        output_pydantic=CVAnalysisReport
+    )
+
+    agents = PraisonAIAgents(
+        agents=[cv_analyzer],
+        tasks=[cv_analysis_task]
+    )
+
+    try:
+        result = await agents.astart(cv_content)
+        return result['task_results'][0].pydantic
+    except Exception as e:
+        print(f"Error during analysis: {str(e)}")
+        raise e
+
+def analyze_cv(cv_content):
+    async def run_analysis():
+        return await analyze_cv_async(cv_content)
+    
+    try:
+        loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(loop)
+        result = loop.run_until_complete(run_analysis())
+        return result
+    except Exception as e:
+        raise e
+    finally:
+        loop.close()
+
+def main():
+    st.set_page_config(page_title="CV Analyzer", layout="wide")
+    st.title("Professional CV Analyzer")
+    
+    # File upload
+    cv_file = st.file_uploader("Upload your CV (TXT format)", type=['txt'])
+    
+    if cv_file:
+        cv_content = cv_file.read().decode()
+        
+        with st.spinner('Analyzing your CV...'):
+            try:
+                analysis = analyze_cv(cv_content)
+                
+                # Display Overall Score
+                col1, col2, col3 = st.columns(3)
+                with col1:
+                    st.metric("Overall Score", f"{analysis.overall_score}/100")
+                with col2:
+                    st.metric("Market Readiness", f"{analysis.market_readiness}/100")
+                with col3:
+                    st.metric("Experience Score", f"{analysis.experience_score}/100")
+
+                # Core Metrics
+                st.subheader("Core Metrics")
+                metrics_cols = st.columns(4)
+                with metrics_cols[0]:
+                    st.metric("Education", f"{analysis.education_score}/100")
+                with metrics_cols[1]:
+                    st.metric("Presentation", f"{analysis.presentation_score}/100")
+                with metrics_cols[2]:
+                    st.metric("Achievement Impact", f"{analysis.achievement_impact_score}/100")
+                with metrics_cols[3]:
+                    st.metric("Market Readiness", f"{analysis.market_readiness}/100")
+
+                # Skill Metrics
+                st.subheader("Detailed Skill Analysis")
+                for skill in analysis.skill_metrics:
+                    with st.expander(f"{skill.name} - Score: {skill.score}/100"):
+                        st.write(skill.comments)
+
+                # Technical and Soft Skills
+                col1, col2 = st.columns(2)
+                with col1:
+                    st.subheader("Technical Skills")
+                    for skill in analysis.technical_skills:
+                        for skill_name, score in skill.items():
+                            st.progress(score/100)
+                            st.caption(f"{skill_name}: {score}/100")
+                
+                with col2:
+                    st.subheader("Soft Skills")
+                    for skill in analysis.soft_skills:
+                        for skill_name, score in skill.items():
+                            st.progress(score/100)
+                            st.caption(f"{skill_name}: {score}/100")
+
+                # Key Information
+                st.subheader("Key Analysis")
+                col1, col2 = st.columns(2)
+                with col1:
+                    st.write("🎯 Key Strengths")
+                    for strength in analysis.key_strengths:
+                        st.write(f"• {strength}")
+                    
+                    st.write("🎮 Areas of Expertise")
+                    for expertise in analysis.areas_of_expertise:
+                        st.write(f"• {expertise}")
+
+                with col2:
+                    st.write("🔄 Areas for Improvement")
+                    for area in analysis.areas_for_improvement:
+                        st.write(f"• {area}")
+                    
+                    st.write("⚠️ Skill Gaps")
+                    for gap in analysis.skill_gaps:
+                        st.write(f"• {gap}")
+
+                # Impact Metrics
+                st.subheader("Impact Metrics")
+                for metric in analysis.impact_metrics:
+                    for key, value in metric.items():
+                        st.metric(key, value)
+
+                # Recommendations
+                st.subheader("📋 Recommendations")
+                for rec in analysis.recommendations:
+                    st.write(f"• {rec}")
+
+            except Exception as e:
+                st.error(f"An error occurred during analysis: {str(e)}")
+    
+    else:
+        st.info("Please upload your CV in TXT format to begin the analysis")
+
+if __name__ == "__main__":
+    main()

agents/context-multi-tasks.py

@@ -0,0 +1,61 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from pydantic import BaseModel
+from typing import List, Dict
+
+class CVAnalysisReport(BaseModel):
+    overall_score: int
+    key_strengths: List[str]
+    areas_for_improvement: List[str]
+    market_fit: str
+    recommendations: List[str]
+    technical_skills: List[str]
+    soft_skills: List[str]
+    skill_gaps: List[str]
+    key_achievements: List[str]
+    impact_metrics: List[Dict[str, str]]
+    areas_of_expertise: List[str]
+
+# Create single CV analysis agent
+cv_analyzer = Agent(
+    role="CV Analysis Expert",
+    goal="Provide comprehensive CV evaluation and recommendations",
+    backstory="""You are an expert CV analyst with deep knowledge of industry requirements, 
+    market trends, and talent assessment. You excel at evaluating skills, experience, 
+    and providing actionable recommendations.""",
+    verbose=True
+)
+
+# Read CV content first
+with open('cv.txt', 'r') as file:
+    cv_content = file.read()
+
+cv_key_strengths = Task(
+    description="List the key strengths to look",
+    expected_output="List of key strengths",
+    agent=cv_analyzer,
+)
+
+# Single comprehensive analysis task with CV content in context
+cv_analysis_task = Task(
+    description="""Analyze the CV comprehensively including:
+    1. Technical and soft skills assessment
+    2. Experience and achievements evaluation
+    3. Overall CV evaluation with recommendations
+    Provide detailed insights and actionable recommendations.""",
+    expected_output="Complete CV analysis report",
+    agent=cv_analyzer,
+    output_pydantic=CVAnalysisReport,
+    context=[cv_content, cv_key_strengths]  # Pass CV content directly in task context
+)
+
+# Create and run the agent
+agents = PraisonAIAgents(
+    agents=[cv_analyzer],
+    tasks=[cv_key_strengths,cv_analysis_task],
+    process="sequential"
+)
+
+# Start the analysis - no need to pass content again since it's in task context
+result = agents.start()
+
+print(result)

agents/crewai-tools-example.py

@@ -0,0 +1,27 @@
+# example tools.py
+from duckduckgo_search import DDGS
+from praisonai_tools import BaseTool
+
+class InternetSearchTool(BaseTool):
+    name: str = "InternetSearchTool"
+    description: str = "Search Internet for relevant information based on a query or latest news"
+
+    def _run(self, query: str):
+        ddgs = DDGS()
+        results = ddgs.text(keywords=query, region='wt-wt', safesearch='moderate', max_results=5)
+        return results
+
+# Example agent
+from praisonaiagents import Agent
+from praisonaiagents import PraisonAIAgents
+agent = Agent(
+    name="Internet Search Agent",
+    tools=[InternetSearchTool],
+    instructions="""
+    You are an agent that can search the internet for relevant information based on a query or latest news.
+    """
+)
+
+# Run the agent
+result = PraisonAIAgents(agents=[agent], verbose=10).start()
+print(result)

agents/cv.txt

@@ -0,0 +1,57 @@
+Name: Alex Johnson
+Location: London, UK
+
+Professional Summary
+Dynamic and results-oriented leader with a proven track record of driving innovation, scaling operations, and achieving transformational growth. Over 15 years of experience in engineering, strategic management, and executive leadership in the automotive and renewable energy sectors. Passionate about sustainable technologies and visionary leadership.
+
+Professional Experience
+Chief Operating Officer (COO)
+NextGen Mobility Ltd. | Palo Alto, CA | 2020 – Present
+
+Spearheaded global operations for an electric vehicle company, scaling production by 200% in three years.
+Introduced automation-driven supply chain strategies, reducing costs by 15%.
+Collaborated with R&D to launch three new EV models, driving 25% revenue growth annually.
+Vice President of Engineering
+ElectroDrive Systems | San Francisco, CA | 2015 – 2020
+
+Led a team of 500+ engineers in developing next-generation EV batteries, achieving a 30% increase in energy efficiency.
+Secured $50M in government funding for sustainable energy innovation.
+Drove cross-functional collaboration, aligning product development with market needs.
+Director of Renewable Energy Initiatives
+GreenTech Innovations | London, UK | 2011 – 2015
+
+Oversaw £100M projects in solar and wind energy deployment across Europe.
+Established partnerships with policymakers to influence renewable energy legislation.
+Pioneered a smart energy grid system, enhancing energy distribution efficiency by 40%.
+Senior Project Manager
+Innovate Auto Ltd. | Munich, Germany | 2008 – 2011
+
+Managed R&D projects for autonomous driving technologies.
+Delivered projects 20% under budget through strategic resource allocation.
+Filed 10+ patents in automotive AI and renewable integration.
+Mechanical Engineer
+AutoFusion Technologies | Berlin, Germany | 2005 – 2008
+
+Designed core systems for EV powertrains, optimising energy consumption by 15%.
+Collaborated with suppliers to improve material efficiency, cutting costs by £5M annually.
+Education
+MBA in Strategic Management
+Harvard Business School | Cambridge, MA | 2015
+
+MSc in Renewable Energy Engineering
+Imperial College London | London, UK | 2007
+
+BSc in Mechanical Engineering
+Technical University of Munich | Munich, Germany | 2005
+
+Skills
+Strategic Leadership & Visionary Thinking
+Electric Vehicle Engineering
+Renewable Energy Systems
+Global Operations Management
+Policy Advocacy & Government Relations
+Supply Chain Optimisation
+Achievements
+Featured in Forbes 30 Under 40 for innovations in EV technology.
+Author of The Future of Sustainable Mobility.
+Keynote speaker at World Energy Summit, 2023.

agents/data-analyst-agent.py

@@ -0,0 +1,10 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import read_csv, read_excel, write_csv, write_excel, filter_data, get_summary, group_by, pivot_table
+import os
+
+agent = Agent(instructions="You are a Data Analyst Agent", tools=[read_csv, read_excel, write_csv, write_excel, filter_data, get_summary, group_by, pivot_table])
+agent.start(f"""
+    Read the data from the csv file {os.path.join(os.path.dirname(__file__), "tesla-stock-price.csv")}
+    Analyse the data and give me the insights
+    read_csv to read the file
+""")

agents/deepseek-agents.py

@@ -0,0 +1,9 @@
+# pip install praisonaiagents
+# export OPENAI_BASE_URL=http://localhost:11434/v1
+# ollama pull deepseek-r1
+
+from praisonaiagents import Agent
+
+agent = Agent(instructions="You are helpful Assisant", llm="deepseek-r1")
+
+agent.start("Why sky is Blue?")

agents/deepseek-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}) 

agents/deepseek-rag-agents-task.py

@@ -0,0 +1,54 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+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",
+        },
+    },
+}
+
+# Create an agent
+rag_agent = Agent(
+    name="RAG Agent",
+    role="Information Specialist",
+    goal="Retrieve knowledge efficiently",
+    llm="deepseek-r1"
+)
+
+# Define a task for the agent
+rag_task = Task(
+    name="RAG Task",
+    description="What is KAG?",
+    expected_output="Answer to the question",
+    agent=rag_agent,
+    context=[config] # Vector Database provided as context
+)
+
+# Build Agents
+agents = PraisonAIAgents(
+    agents=[rag_agent],
+    tasks=[rag_task],
+    user_id="user1"
+)
+
+# Start Agents
+agents.start()

agents/deepseek-rag-agents-ui.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}) 

agents/deepseek-rag-agents.py

@@ -0,0 +1,39 @@
+from praisonaiagents import 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
+        },
+    },
+}
+
+agent = Agent(
+    name="Knowledge Agent",
+    instructions="You answer questions based on the provided knowledge.",
+    knowledge=["kag-research-paper.pdf"], # Indexing
+    knowledge_config=config,
+    user_id="user1",
+    llm="deepseek-r1"
+)
+
+agent.start("What is KAG in one line?") # Retrieval

agents/document.txt

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

agents/evaluator-optimiser.py

@@ -0,0 +1,70 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# Create generator and evaluator agents
+generator = Agent(
+    name="Generator",
+    role="Solution generator",
+    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"
+        "3. Otherwise, just produce the first 2 points.\n"
+        "4. Return only the final list of points, with no extra explanation."
+    )
+)
+
+evaluator = Agent(
+    name="Evaluator",
+    role="Solution evaluator",
+    goal="Evaluate solutions and provide improvement feedback",
+    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"
+        "3. Otherwise, respond with 'more'.\n"
+        "4. Return only the single word: 'done' or 'more'."
+    )
+)
+
+
+# Create tasks for the feedback loop
+generate_task = Task(
+    name="generate",
+    description="Write 2 points about AI incuding if anything exiting from previous points",
+    expected_output="2 points",
+    agent=generator,
+    is_start=True,
+    task_type="decision",
+    next_tasks=["evaluate"]
+)
+
+evaluate_task = Task(
+    name="evaluate",
+    description="Check if there are 10 points about AI",
+    expected_output="more or done",
+    agent=evaluator,
+    next_tasks=["generate"],
+    context=[generate_task],
+    task_type="decision",
+    condition={
+        "more": ["generate"],  # Continue to generate
+        "done": [""]  # Exit when optimization complete
+    }
+)
+
+# Create workflow manager
+workflow = PraisonAIAgents(
+    agents=[generator, evaluator],
+    tasks=[generate_task, evaluate_task],
+    process="workflow",
+    verbose=True
+)
+
+# Run optimization workflow
+results = workflow.start()
+
+# Print results
+print("\nEvaluator-Optimizer Results:")
+for task_id, result in results["task_results"].items():
+    if result:
+        print(f"Task {task_id}: {result.raw}")

agents/finance-agent.py

@@ -0,0 +1,5 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import get_stock_price, get_stock_info, get_historical_data
+
+agent = Agent(instructions="You are a Research Agent", tools=[get_stock_price, get_stock_info, get_historical_data])
+agent.start("Understand current stock price and historical data of Apple and Google. Tell me if I can invest in them")

agents/gradio-agents.py

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

agents/hackathon-judge-agent-code.py

@@ -0,0 +1,159 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from pydantic import BaseModel
+from typing import List, Dict
+import os
+import json
+
+class ProjectEvaluation(BaseModel):
+    innovation_score: int  # 0-100
+    technical_complexity: int # 0-100  
+    presentation_quality: int # 0-100
+    user_experience: int # 0-100
+    completeness: int # 0-100
+    overall_score: int # 0-100
+    key_strengths: List[str]
+    areas_for_improvement: List[str]
+    notable_features: List[str]
+    technical_highlights: List[str]
+    recommendations: List[str]
+    market_potential: str
+    scalability_assessment: str
+
+# Create Vision Analysis Agent
+hackathon_judge = Agent(
+    name="HackathonJudge",
+    role="Technical Project Evaluator",
+    goal="Evaluate hackathon projects through video demonstrations",
+    backstory="""You are an experienced hackathon judge and technical expert.
+    You excel at evaluating innovation, technical implementation, and presentation quality.
+    You provide constructive feedback and identify both strengths and areas for improvement.""",
+    llm="gpt-4o-mini",  # Using vision-capable model
+    self_reflect=False,
+    knowledge=""
+)
+
+def evaluate_project(video_path: str) -> ProjectEvaluation:
+    """
+    Evaluate a hackathon project based on its video demonstration
+    """
+    evaluation_task = Task(
+        name="project_evaluation",
+        description="""Analyze this hackathon project video demonstration and provide a comprehensive evaluation:
+        
+        1. Score the following aspects (0-100):
+           - Innovation and Creativity
+           - Technical Complexity
+           - Presentation Quality
+           - User Experience
+           - Project Completeness
+           
+        2. Identify:
+           - Key strengths and standout features
+           - Areas that could be improved
+           - Notable technical implementations
+           - Market potential and scalability
+           
+        3. Provide:
+           - Specific recommendations for improvement
+           - Technical suggestions
+           - Potential future enhancements""",
+        expected_output="Detailed project evaluation with scores and feedback",
+        agent=hackathon_judge,
+        output_pydantic=ProjectEvaluation,
+        images=[video_path]  # Video input for multimodal analysis
+    )
+
+    # Initialize and run evaluation
+    agents = PraisonAIAgents(
+        agents=[hackathon_judge],
+        tasks=[evaluation_task],
+        process="sequential",
+        verbose=True
+    )
+    
+    response = agents.start()
+    
+    try:
+        # If response contains task_results, extract the Pydantic model directly
+        if isinstance(response, dict) and 'task_results' in response:
+            task_output = response['task_results'][0]
+            if hasattr(task_output, 'pydantic'):
+                return task_output.pydantic
+            elif hasattr(task_output, 'raw'):
+                # Extract JSON from raw string if it's wrapped in ```json
+                raw_text = task_output.raw
+                if raw_text.startswith('```json'):
+                    raw_text = raw_text.split('\n', 1)[1].rsplit('\n', 1)[0]
+                evaluation_data = json.loads(raw_text)
+            else:
+                evaluation_data = json.loads(task_output) if isinstance(task_output, str) else task_output
+        # If response is a string, try to parse it as JSON
+        elif isinstance(response, str):
+            evaluation_data = json.loads(response)
+        # If response is a dict with task_status
+        elif isinstance(response, dict) and 'task_status' in response:
+            content = response['task_status']
+            if isinstance(content, dict):
+                evaluation_data = content
+            else:
+                evaluation_data = json.loads(content) if isinstance(content, str) else content
+        else:
+            evaluation_data = response
+            
+        print(f"Debug - Parsed evaluation_data: {evaluation_data}")
+        
+        # Create and return ProjectEvaluation instance
+        return ProjectEvaluation(
+            innovation_score=int(evaluation_data.get('innovation_score', 0)),
+            technical_complexity=int(evaluation_data.get('technical_complexity', 0)),
+            presentation_quality=int(evaluation_data.get('presentation_quality', 0)),
+            user_experience=int(evaluation_data.get('user_experience', 0)),
+            completeness=int(evaluation_data.get('completeness', 0)),
+            overall_score=int(evaluation_data.get('overall_score', 0)),
+            key_strengths=evaluation_data.get('key_strengths', []),
+            areas_for_improvement=evaluation_data.get('areas_for_improvement', []),
+            notable_features=evaluation_data.get('notable_features', []),
+            technical_highlights=evaluation_data.get('technical_highlights', []),
+            recommendations=evaluation_data.get('recommendations', []),
+            market_potential=str(evaluation_data.get('market_potential', '')),
+            scalability_assessment=str(evaluation_data.get('scalability_assessment', ''))
+        )
+    except Exception as e:
+        print(f"Debug - Raw response: {response}")
+        print(f"Error processing response: {e}")
+        raise
+
+if __name__ == "__main__":
+    # Example usage
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    video_path = os.path.join(current_dir, "presentation.mp4")
+    result = evaluate_project(video_path)  # Now returns ProjectEvaluation directly
+    
+    print("\nHackathon Project Evaluation")
+    print("===========================")
+    print(f"\nOverall Score: {result.overall_score}/100")
+    print("\nDetailed Scores:")
+    print(f"Innovation: {result.innovation_score}/100")
+    print(f"Technical Complexity: {result.technical_complexity}/100")
+    print(f"Presentation: {result.presentation_quality}/100")
+    print(f"User Experience: {result.user_experience}/100")
+    print(f"Completeness: {result.completeness}/100")
+    
+    print("\nKey Strengths:")
+    for strength in result.key_strengths:
+        print(f"- {strength}")
+        
+    print("\nAreas for Improvement:")
+    for area in result.areas_for_improvement:
+        print(f"- {area}")
+        
+    print("\nTechnical Highlights:")
+    for highlight in result.technical_highlights:
+        print(f"- {highlight}")
+        
+    print("\nRecommendations:")
+    for rec in result.recommendations:
+        print(f"- {rec}")
+        
+    print(f"\nMarket Potential: {result.market_potential}")
+    print(f"\nScalability Assessment: {result.scalability_assessment}") 

agents/hackathon-judge-agent.py

@@ -0,0 +1,159 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from pydantic import BaseModel
+from typing import List, Dict
+import os
+import json
+
+class ProjectEvaluation(BaseModel):
+    innovation_score: int  # 0-100
+    technical_complexity: int # 0-100  
+    presentation_quality: int # 0-100
+    user_experience: int # 0-100
+    completeness: int # 0-100
+    overall_score: int # 0-100
+    key_strengths: List[str]
+    areas_for_improvement: List[str]
+    notable_features: List[str]
+    technical_highlights: List[str]
+    recommendations: List[str]
+    market_potential: str
+    scalability_assessment: str
+
+# Create Vision Analysis Agent
+hackathon_judge = Agent(
+    name="HackathonJudge",
+    role="Technical Project Evaluator",
+    goal="Evaluate hackathon projects through video demonstrations",
+    backstory="""You are an experienced hackathon judge and technical expert.
+    You excel at evaluating innovation, technical implementation, and presentation quality.
+    You provide constructive feedback and identify both strengths and areas for improvement.""",
+    llm="gpt-4o-mini",  # Using vision-capable model
+    self_reflect=False,
+    knowledge=""
+)
+
+def evaluate_project(video_path: str) -> ProjectEvaluation:
+    """
+    Evaluate a hackathon project based on its video demonstration
+    """
+    evaluation_task = Task(
+        name="project_evaluation",
+        description="""Analyze this hackathon project video demonstration and provide a comprehensive evaluation:
+        
+        1. Score the following aspects (0-100):
+           - Innovation and Creativity
+           - Technical Complexity
+           - Presentation Quality
+           - User Experience
+           - Project Completeness
+           
+        2. Identify:
+           - Key strengths and standout features
+           - Areas that could be improved
+           - Notable technical implementations
+           - Market potential and scalability
+           
+        3. Provide:
+           - Specific recommendations for improvement
+           - Technical suggestions
+           - Potential future enhancements""",
+        expected_output="Detailed project evaluation with scores and feedback",
+        agent=hackathon_judge,
+        output_pydantic=ProjectEvaluation,
+        images=[video_path]  # Video input for multimodal analysis
+    )
+
+    # Initialize and run evaluation
+    agents = PraisonAIAgents(
+        agents=[hackathon_judge],
+        tasks=[evaluation_task],
+        process="sequential",
+        verbose=True
+    )
+    
+    response = agents.start()
+    
+    try:
+        # If response contains task_results, extract the Pydantic model directly
+        if isinstance(response, dict) and 'task_results' in response:
+            task_output = response['task_results'][0]
+            if hasattr(task_output, 'pydantic'):
+                return task_output.pydantic
+            elif hasattr(task_output, 'raw'):
+                # Extract JSON from raw string if it's wrapped in ```json
+                raw_text = task_output.raw
+                if raw_text.startswith('```json'):
+                    raw_text = raw_text.split('\n', 1)[1].rsplit('\n', 1)[0]
+                evaluation_data = json.loads(raw_text)
+            else:
+                evaluation_data = json.loads(task_output) if isinstance(task_output, str) else task_output
+        # If response is a string, try to parse it as JSON
+        elif isinstance(response, str):
+            evaluation_data = json.loads(response)
+        # If response is a dict with task_status
+        elif isinstance(response, dict) and 'task_status' in response:
+            content = response['task_status']
+            if isinstance(content, dict):
+                evaluation_data = content
+            else:
+                evaluation_data = json.loads(content) if isinstance(content, str) else content
+        else:
+            evaluation_data = response
+            
+        print(f"Debug - Parsed evaluation_data: {evaluation_data}")
+        
+        # Create and return ProjectEvaluation instance
+        return ProjectEvaluation(
+            innovation_score=int(evaluation_data.get('innovation_score', 0)),
+            technical_complexity=int(evaluation_data.get('technical_complexity', 0)),
+            presentation_quality=int(evaluation_data.get('presentation_quality', 0)),
+            user_experience=int(evaluation_data.get('user_experience', 0)),
+            completeness=int(evaluation_data.get('completeness', 0)),
+            overall_score=int(evaluation_data.get('overall_score', 0)),
+            key_strengths=evaluation_data.get('key_strengths', []),
+            areas_for_improvement=evaluation_data.get('areas_for_improvement', []),
+            notable_features=evaluation_data.get('notable_features', []),
+            technical_highlights=evaluation_data.get('technical_highlights', []),
+            recommendations=evaluation_data.get('recommendations', []),
+            market_potential=str(evaluation_data.get('market_potential', '')),
+            scalability_assessment=str(evaluation_data.get('scalability_assessment', ''))
+        )
+    except Exception as e:
+        print(f"Debug - Raw response: {response}")
+        print(f"Error processing response: {e}")
+        raise
+
+if __name__ == "__main__":
+    # Example usage
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    video_path = os.path.join(current_dir, "presentation.mp4")
+    result = evaluate_project(video_path)  # Now returns ProjectEvaluation directly
+    
+    print("\nHackathon Project Evaluation")
+    print("===========================")
+    print(f"\nOverall Score: {result.overall_score}/100")
+    print("\nDetailed Scores:")
+    print(f"Innovation: {result.innovation_score}/100")
+    print(f"Technical Complexity: {result.technical_complexity}/100")
+    print(f"Presentation: {result.presentation_quality}/100")
+    print(f"User Experience: {result.user_experience}/100")
+    print(f"Completeness: {result.completeness}/100")
+    
+    print("\nKey Strengths:")
+    for strength in result.key_strengths:
+        print(f"- {strength}")
+        
+    print("\nAreas for Improvement:")
+    for area in result.areas_for_improvement:
+        print(f"- {area}")
+        
+    print("\nTechnical Highlights:")
+    for highlight in result.technical_highlights:
+        print(f"- {highlight}")
+        
+    print("\nRecommendations:")
+    for rec in result.recommendations:
+        print(f"- {rec}")
+        
+    print(f"\nMarket Potential: {result.market_potential}")
+    print(f"\nScalability Assessment: {result.scalability_assessment}") 

agents/hackathon-judge-streamlit.py

@@ -0,0 +1,210 @@
+import streamlit as st
+import os
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from pydantic import BaseModel
+from typing import List, Dict
+import tempfile
+import json
+
+class ProjectEvaluation(BaseModel):
+    innovation_score: int  # 0-100
+    technical_complexity: int # 0-100  
+    presentation_quality: int # 0-100
+    user_experience: int # 0-100
+    completeness: int # 0-100
+    overall_score: int # 0-100
+    key_strengths: List[str]
+    areas_for_improvement: List[str]
+    notable_features: List[str]
+    technical_highlights: List[str]
+    recommendations: List[str]
+    market_potential: str
+    scalability_assessment: str
+
+# Set page config
+st.set_page_config(
+    page_title="Hackathon Project Evaluator",
+    page_icon="🏆",
+    layout="wide"
+)
+
+# Create Vision Analysis Agent
+@st.cache_resource
+def get_hackathon_judge():
+    return Agent(
+        name="HackathonJudge",
+        role="Technical Project Evaluator",
+        goal="Evaluate hackathon projects through video demonstrations",
+        backstory="""You are an experienced hackathon judge and technical expert.
+        You excel at evaluating innovation, technical implementation, and presentation quality.
+        You provide constructive feedback and identify both strengths and areas for improvement.""",
+        llm="gpt-4o-mini",  # Using vision-capable model
+        self_reflect=False,
+        knowledge=""
+    )
+
+def evaluate_project(video_path: str) -> ProjectEvaluation:
+    """
+    Evaluate a hackathon project based on its video demonstration
+    """
+    hackathon_judge = get_hackathon_judge()
+    
+    evaluation_task = Task(
+        name="project_evaluation",
+        description="""Analyze this hackathon project video demonstration and provide a comprehensive evaluation:
+        
+        1. Score the following aspects (0-100):
+           - Innovation and Creativity
+           - Technical Complexity
+           - Presentation Quality
+           - User Experience
+           - Project Completeness
+           
+        2. Identify:
+           - Key strengths and standout features
+           - Areas that could be improved
+           - Notable technical implementations
+           - Market potential and scalability
+           
+        3. Provide:
+           - Specific recommendations for improvement
+           - Technical suggestions
+           - Potential future enhancements""",
+        expected_output="Detailed project evaluation with scores and feedback",
+        agent=hackathon_judge,
+        output_pydantic=ProjectEvaluation,
+        images=[video_path]  # Video input for multimodal analysis
+    )
+
+    # Initialize and run evaluation
+    agents = PraisonAIAgents(
+        agents=[hackathon_judge],
+        tasks=[evaluation_task],
+        process="sequential",
+        verbose=True
+    )
+    
+    response = agents.start()
+    
+    try:
+        # If response contains task_results, extract the Pydantic model directly
+        if isinstance(response, dict) and 'task_results' in response:
+            task_output = response['task_results'][0]
+            if hasattr(task_output, 'pydantic'):
+                return task_output.pydantic
+            elif hasattr(task_output, 'raw'):
+                # Extract JSON from raw string if it's wrapped in ```json
+                raw_text = task_output.raw
+                if raw_text.startswith('```json'):
+                    raw_text = raw_text.split('\n', 1)[1].rsplit('\n', 1)[0]
+                evaluation_data = json.loads(raw_text)
+            else:
+                evaluation_data = json.loads(task_output) if isinstance(task_output, str) else task_output
+        elif isinstance(response, str):
+            evaluation_data = json.loads(response)
+        elif isinstance(response, dict) and 'task_status' in response:
+            content = response['task_status']
+            if isinstance(content, dict):
+                evaluation_data = content
+            else:
+                evaluation_data = json.loads(content) if isinstance(content, str) else content
+        else:
+            evaluation_data = response
+            
+        # Create and return ProjectEvaluation instance
+        return ProjectEvaluation(
+            innovation_score=int(evaluation_data.get('innovation_score', 0)),
+            technical_complexity=int(evaluation_data.get('technical_complexity', 0)),
+            presentation_quality=int(evaluation_data.get('presentation_quality', 0)),
+            user_experience=int(evaluation_data.get('user_experience', 0)),
+            completeness=int(evaluation_data.get('completeness', 0)),
+            overall_score=int(evaluation_data.get('overall_score', 0)),
+            key_strengths=evaluation_data.get('key_strengths', []),
+            areas_for_improvement=evaluation_data.get('areas_for_improvement', []),
+            notable_features=evaluation_data.get('notable_features', []),
+            technical_highlights=evaluation_data.get('technical_highlights', []),
+            recommendations=evaluation_data.get('recommendations', []),
+            market_potential=str(evaluation_data.get('market_potential', '')),
+            scalability_assessment=str(evaluation_data.get('scalability_assessment', ''))
+        )
+    except Exception as e:
+        print(f"Debug - Raw response: {response}")
+        print(f"Error processing response: {e}")
+        raise
+
+# Title and description
+st.title("🏆 Hackathon Judge Agent")
+st.markdown("""
+Upload your hackathon project demonstration video for an AI-powered evaluation.
+Get comprehensive feedback on various aspects of your project.
+""")
+
+# File uploader
+uploaded_file = st.file_uploader("Choose a video file", type=['mp4', 'avi', 'mov', 'mkv'])
+
+if uploaded_file:
+    # Create a temporary file to store the video
+    with tempfile.NamedTemporaryFile(delete=False, suffix='.'+uploaded_file.name.split('.')[-1]) as tmp_file:
+        tmp_file.write(uploaded_file.getvalue())
+        video_path = tmp_file.name
+
+    with st.spinner("🤖 AI is evaluating your project..."):
+        try:
+            # Evaluate the project
+            result = evaluate_project(video_path)
+            
+            # Display results
+            st.header("Overall Score")
+            st.metric("Overall Score", f"{result.overall_score}/100")
+            
+            # Display detailed scores
+            st.header("Detailed Scores")
+            col1, col2, col3 = st.columns(3)
+            with col1:
+                st.metric("Innovation", f"{result.innovation_score}/100")
+                st.metric("Technical Complexity", f"{result.technical_complexity}/100")
+            with col2:
+                st.metric("Presentation", f"{result.presentation_quality}/100")
+                st.metric("User Experience", f"{result.user_experience}/100")
+            with col3:
+                st.metric("Completeness", f"{result.completeness}/100")
+
+            # Display qualitative feedback
+            st.header("Key Strengths")
+            for strength in result.key_strengths:
+                st.write(f"• {strength}")
+            
+            st.header("Areas for Improvement")
+            for area in result.areas_for_improvement:
+                st.write(f"• {area}")
+            
+            st.header("Technical Highlights")
+            for highlight in result.technical_highlights:
+                st.write(f"• {highlight}")
+            
+            st.header("Notable Features")
+            for feature in result.notable_features:
+                st.write(f"• {feature}")
+            
+            st.header("Recommendations")
+            for rec in result.recommendations:
+                st.write(f"• {rec}")
+            
+            # Market Analysis
+            st.header("Market Analysis")
+            col1, col2 = st.columns(2)
+            with col1:
+                st.subheader("Market Potential")
+                st.write(result.market_potential)
+            with col2:
+                st.subheader("Scalability Assessment")
+                st.write(result.scalability_assessment)
+
+        except Exception as e:
+            st.error(f"Error evaluating the project: {str(e)}")
+        finally:
+            # Clean up the temporary file
+            os.unlink(video_path)
+else:
+    # Display placeholder content
+    st.info("👆 Upload a video file to get started!") 

agents/image-agent.py

@@ -0,0 +1,47 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Image Analysis Agent", tools=[duckduckgo])
+agent.start("I want to go London next week, find me a good hotel and flight")
+
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# Create Image Analysis Agent
+image_agent = Agent(
+    name="ImageAnalyst",
+    role="Image Analysis Specialist",
+    goal="Analyze images and videos to extract meaningful information",
+    backstory="""You are an expert in computer vision and image analysis.
+    You excel at describing images, detecting objects, and understanding visual content.""",
+    llm="gpt-4o-mini",
+    self_reflect=False
+)
+
+# 1. Task with Image URL
+task1 = Task(
+    name="analyze_landmark",
+    description="Describe this famous landmark and its architectural features.",
+    expected_output="Detailed description of the landmark's architecture and significance",
+    agent=image_agent,
+    images=["https://upload.wikimedia.org/wikipedia/commons/b/bf/Krakow_-_Kosciol_Mariacki.jpg"]
+)
+
+# 2. Task with Local Image File
+task2 = Task(
+    name="analyze_local_image",
+    description="What objects can you see in this image? Describe their arrangement.",
+    expected_output="Detailed description of objects and their spatial relationships",
+    agent=image_agent,
+    images=["image.jpg"] 
+)
+
+# Create PraisonAIAgents instance
+agents = PraisonAIAgents(
+    agents=[image_agent],
+    tasks=[task1, task2],
+    process="sequential",
+    verbose=1
+)
+
+# Run all tasks
+agents.start()

agents/image-to-text-agent.py

@@ -0,0 +1,47 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Image Analysis Agent", tools=[duckduckgo])
+agent.start("I want to go London next week, find me a good hotel and flight")
+
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# Create Image Analysis Agent
+image_agent = Agent(
+    name="ImageAnalyst",
+    role="Image Analysis Specialist",
+    goal="Analyze images and videos to extract meaningful information",
+    backstory="""You are an expert in computer vision and image analysis.
+    You excel at describing images, detecting objects, and understanding visual content.""",
+    llm="gpt-4o-mini",
+    self_reflect=False
+)
+
+# 1. Task with Image URL
+task1 = Task(
+    name="analyze_landmark",
+    description="Describe this famous landmark and its architectural features.",
+    expected_output="Detailed description of the landmark's architecture and significance",
+    agent=image_agent,
+    images=["https://upload.wikimedia.org/wikipedia/commons/b/bf/Krakow_-_Kosciol_Mariacki.jpg"]
+)
+
+# 2. Task with Local Image File
+task2 = Task(
+    name="analyze_local_image",
+    description="What objects can you see in this image? Describe their arrangement.",
+    expected_output="Detailed description of objects and their spatial relationships",
+    agent=image_agent,
+    images=["image.jpg"] 
+)
+
+# Create PraisonAIAgents instance
+agents = PraisonAIAgents(
+    agents=[image_agent],
+    tasks=[task1, task2],
+    process="sequential",
+    verbose=1
+)
+
+# Run all tasks
+agents.start()

agents/knowledge-agents-task.py

@@ -0,0 +1,70 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import logging
+import os
+import json
+
+# 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 = {
+    "version": "v1.1",
+    "vector_store": {
+        "provider": "chroma",
+        "config": {
+            "collection_name": "praison",
+            "path": os.path.abspath("./.praison"),
+            "host": None,
+            "port": None
+        }
+    },
+    "embedder": {
+        "provider": "openai",
+        "config": {
+            "model": "text-embedding-3-small",
+            "embedding_dims": 1536
+        }
+    },
+    "llm": {
+        "provider": "openai",
+        "config": {
+            "model": "gpt-4o-mini",
+            "temperature": 0,
+            "max_tokens": 1000
+        }
+    }
+}
+
+# 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",
+    verbose=True
+)
+
+# Define a task for the agent
+knowledge_task = Task(
+    name="knowledge_task",
+    description="KAG",
+    expected_output="Answer to the question",
+    agent=knowledge_agent,
+    context=[
+        {
+            "embedding_db_config": json.dumps(config["vector_store"])
+        }
+    ]
+)
+
+# Create and start the agents
+agents = PraisonAIAgents(
+    agents=[knowledge_agent],
+    tasks=[knowledge_task],
+    process="sequential",
+    user_id="user1"
+)
+
+# Start execution
+result = agents.start()

agents/knowledge-agents.py

@@ -0,0 +1,21 @@
+from praisonaiagents import Agent
+
+config = {
+    "vector_store": {
+        "provider": "chroma",
+        "config": {
+            "collection_name": "praison",
+            "path": ".praison",
+        }
+    }
+}
+
+agent = Agent(
+    name="Knowledge Agent",
+    instructions="You answer questions based on the provided knowledge.",
+    knowledge=["large.pdf"],
+    knowledge_config=config,
+    user_id="user1"
+)
+
+agent.start("What is KAG in one line?")

agents/knowledge-direct-simple-test.py

@@ -0,0 +1,44 @@
+import logging
+from praisonaiagents.knowledge import Knowledge
+import os
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+config = {
+    "version": "v1.1",
+    "vector_store": {
+        "provider": "chroma",
+        "config": {
+            "collection_name": "praison",
+            "path": os.path.abspath("./.praison"),
+            "host": None,
+            "port": None
+        }
+    },
+    "embedder": {
+        "provider": "openai",
+        "config": {
+            "model": "text-embedding-3-small",
+            "embedding_dims": 1536
+        }
+    },
+    "llm": {
+        "provider": "openai",
+        "config": {
+            "model": "gpt-4o-mini",
+            "temperature": 0,
+            "max_tokens": 1000
+        }
+    }
+}
+
+knowledge = Knowledge(config)
+
+# Search for memories based on a query
+query = "KAG"
+logger.info(f"Searching for memories with query: {query}")
+search_results = knowledge.search(query, user_id="user1")
+logger.info(f"Search results: {search_results}")
+print(f"\nSearch results for '{query}':")
+print(search_results)

agents/knowledge-direct-test.py

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

agents/knowledge-large-pdf-test.py

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

agents/markdown-agent.py

@@ -0,0 +1,4 @@
+from praisonaiagents import Agent
+
+agent = Agent(instructions="You are a Markdown Agent, output in markdown format")
+agent.start("Write a blog post about AI")

agents/md.py

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

agents/memory-add-search.py

@@ -0,0 +1,220 @@
+from mem0 import Memory
+import logging
+import json
+import os
+import time
+from datetime import datetime
+from typing import Dict, Any, Optional
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+class MemoryManager:
+    def __init__(self):
+        """Initialize Mem0 Memory manager with configuration"""
+        # Define the complete configuration with only supported fields
+        config = {
+            "version": "v1.1",
+            "custom_prompt": None,
+            "vector_store": {
+                "provider": "chroma",
+                "config": {
+                    "collection_name": "praison",
+                    "path": os.path.abspath("./.praison/storage"),
+                    "host": None,
+                    "port": None
+                }
+            },
+            "embedder": {
+                "provider": "openai",
+                "config": {
+                    "model": "text-embedding-3-small",
+                    "embedding_dims": 1536
+                }
+            },
+            "llm": {
+                "provider": "openai",
+                "config": {
+                    "model": "gpt-4o-mini",
+                    "temperature": 0,
+                    "max_tokens": 1000
+                }
+            }
+        }
+        
+        # Ensure storage directory exists
+        storage_path = os.path.abspath("./.praison/storage")
+        if os.path.exists(storage_path):
+            logger.info(f"Clearing existing storage at {storage_path}")
+            import shutil
+            shutil.rmtree(storage_path)
+        os.makedirs(storage_path, exist_ok=True)
+        logger.info(f"Created storage path: {storage_path}")
+        
+        # Initialize memory
+        self.memory = Memory.from_config(config)
+        logger.info("Memory manager initialized")
+
+    def pretty_print(self, data: Any) -> None:
+        """Helper function to print memory data in a readable format"""
+        if isinstance(data, (dict, list)):
+            print(json.dumps(data, indent=2))
+        else:
+            print(data)
+            
+    def extract_memory_id(self, result: Any) -> Optional[str]:
+        """Extract memory ID from the result, handling different formats"""
+        try:
+            if isinstance(result, dict):
+                # v1.1 format
+                memories = result.get('results', [])
+                return memories[0]['id'] if memories else None
+            elif isinstance(result, list):
+                # v1.0 format
+                return result[0]['id'] if result else None
+            return None
+        except (KeyError, IndexError) as e:
+            logger.warning(f"Could not extract memory ID from result: {e}")
+            return None
+
+    def prepare_metadata(self, metadata: Optional[Dict]) -> Dict:
+        """Prepare metadata by converting values to acceptable types"""
+        if not metadata:
+            return {}
+            
+        clean_metadata = {}
+        for key, value in metadata.items():
+            if isinstance(value, (str, int, float, bool)):
+                clean_metadata[key] = value
+            elif isinstance(value, list):
+                clean_metadata[key] = ','.join(map(str, value))
+            elif isinstance(value, dict):
+                clean_metadata[key] = json.dumps(value)
+            else:
+                clean_metadata[key] = str(value)
+        return clean_metadata
+
+    def verify_memory_addition(self, user_id: str, max_retries: int = 3) -> bool:
+        """Verify that memory was added successfully"""
+        for attempt in range(max_retries):
+            try:
+                time.sleep(0.5)  # Brief pause to allow indexing
+                memories = self.memory.get_all(user_id=user_id)
+                if len(memories.get('results', [])) > 0:
+                    return True
+                logger.warning(f"Memory not found on attempt {attempt + 1}")
+            except Exception as e:
+                logger.warning(f"Verification attempt {attempt + 1} failed: {e}")
+            if attempt < max_retries - 1:
+                time.sleep(1)
+        return False
+
+    def add_memory(self, text: str, user_id: Optional[str] = None, metadata: Optional[Dict] = None) -> Dict:
+        """Add a new memory with error handling and verification"""
+        try:
+            logger.info(f"Adding memory for user {user_id}")
+            clean_metadata = self.prepare_metadata(metadata)
+            logger.debug(f"Prepared metadata: {clean_metadata}")
+            
+            messages = [{"role": "user", "content": text}]
+            result = self.memory.add(
+                messages=messages,
+                user_id=user_id,
+                metadata=clean_metadata
+            )
+            
+            if self.verify_memory_addition(user_id):
+                logger.info("Memory added and verified successfully")
+            else:
+                logger.warning("Memory addition could not be verified")
+                
+            return result
+            
+        except Exception as e:
+            logger.error(f"Failed to add memory: {str(e)}")
+            raise
+
+    def get_all_memories(self, user_id: Optional[str] = None) -> Dict:
+        """Retrieve all memories for a user"""
+        try:
+            logger.info(f"Retrieving all memories for user {user_id}")
+            memories = self.memory.get_all(user_id=user_id)
+            count = len(memories.get('results', []))
+            logger.info(f"Retrieved {count} memories")
+            return memories
+        except Exception as e:
+            logger.error(f"Failed to retrieve memories: {str(e)}")
+            raise
+
+    def search_memories(self, query: str, user_id: Optional[str] = None, limit: int = 5) -> Dict:
+        """Search memories with a query"""
+        try:
+            logger.info(f"Searching memories with query: {query}")
+            results = self.memory.search(
+                query=query,
+                user_id=user_id,
+                limit=limit
+            )
+            count = len(results.get('results', []))
+            logger.info(f"Found {count} matching memories")
+            return results
+        except Exception as e:
+            logger.error(f"Search failed: {str(e)}")
+            raise
+
+def main():
+    try:
+        # Initialize memory manager
+        manager = MemoryManager()
+        
+        # Add a test memory
+        print("\n1. Adding a new memory:")
+        metadata = {
+            "category": "hobbies",
+            "timestamp": datetime.now().isoformat(),
+            "tags": "tennis,learning",
+            "priority": 1,
+            "source": "user_input"
+        }
+        
+        result = manager.add_memory(
+            text="I am working on improving my tennis skills. Suggest some online courses.",
+            user_id="alice",
+            metadata=metadata
+        )
+        manager.pretty_print(result)
+        
+        # Wait briefly for indexing
+        time.sleep(1)
+        
+        print("\n2. Retrieving all memories:")
+        all_memories = manager.get_all_memories(user_id="alice")
+        manager.pretty_print(all_memories)
+        
+        print("\n3. Searching memories:")
+        search_results = manager.search_memories(
+            query="What are Alice's hobbies?",
+            user_id="alice",
+            limit=5
+        )
+        manager.pretty_print(search_results)
+        
+        # Print summary
+        print("\nMemory Store Summary:")
+        total_memories = len(all_memories.get('results', []))
+        print(f"Total memories stored: {total_memories}")
+        if total_memories > 0:
+            print("Available memories:")
+            for mem in all_memories['results']:
+                print(f"- {mem.get('memory', 'No content')}")
+
+    except Exception as e:
+        logger.error(f"Main process failed: {str(e)}", exc_info=True)
+        raise
+
+if __name__ == "__main__":
+    main()

agents/memory-search-advanced.py

@@ -0,0 +1,143 @@
+from mem0 import Memory
+import logging
+import json
+import os
+from typing import Dict, Any, Optional
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+class MemorySearcher:
+    def __init__(self):
+        """Initialize Mem0 Memory searcher with configuration"""
+        # Define the configuration
+        config = {
+            "version": "v1.1",
+            "vector_store": {
+                "provider": "chroma",
+                "config": {
+                    "collection_name": "praison",
+                    "path": os.path.abspath("./.praison/storage"),
+                    "host": None,
+                    "port": None
+                }
+            },
+            "embedder": {
+                "provider": "openai",
+                "config": {
+                    "model": "text-embedding-3-small",
+                    "embedding_dims": 1536
+                }
+            },
+            "llm": {
+                "provider": "openai",
+                "config": {
+                    "model": "gpt-4o-mini",
+                    "temperature": 0,
+                    "max_tokens": 1000
+                }
+            }
+        }
+        
+        # Initialize memory
+        self.memory = Memory.from_config(config)
+        logger.info("Memory searcher initialized")
+
+    def pretty_print(self, data: Any) -> None:
+        """Helper function to print data in a readable format"""
+        if isinstance(data, (dict, list)):
+            print(json.dumps(data, indent=2))
+        else:
+            print(data)
+
+    def get_all_memories(self, user_id: Optional[str] = None) -> Dict:
+        """Retrieve all memories for a user"""
+        try:
+            logger.info(f"Retrieving all memories for user {user_id}")
+            memories = self.memory.get_all(user_id=user_id)
+            count = len(memories.get('results', []))
+            logger.info(f"Retrieved {count} memories")
+            return memories
+        except Exception as e:
+            logger.error(f"Failed to retrieve memories: {str(e)}")
+            raise
+
+    def search_memories(self, query: str, user_id: Optional[str] = None, limit: int = 5) -> Dict:
+        """Search memories with a query"""
+        try:
+            logger.info(f"Searching memories with query: {query}")
+            results = self.memory.search(
+                query=query,
+                user_id=user_id,
+                limit=limit
+            )
+            count = len(results.get('results', []))
+            logger.info(f"Found {count} matching memories")
+            return results
+        except Exception as e:
+            logger.error(f"Search failed: {str(e)}")
+            raise
+
+    def list_unique_users(self) -> list:
+        """List all unique users in the memory store"""
+        try:
+            all_memories = self.memory.get_all()
+            users = set()
+            for memory in all_memories.get('results', []):
+                if 'user_id' in memory:
+                    users.add(memory['user_id'])
+            return sorted(list(users))
+        except Exception as e:
+            logger.error(f"Failed to list users: {str(e)}")
+            raise
+
+def main():
+    try:
+        # Initialize memory searcher
+        searcher = MemorySearcher()
+        
+        # Get list of users
+        print("\n1. Available users in the memory store:")
+        users = searcher.list_unique_users()
+        searcher.pretty_print(users)
+        
+        if not users:
+            print("No users found in the memory store. Please add some memories first.")
+            return
+            
+        # Example user (using first user found)
+        example_user = users[0]
+        
+        print(f"\n2. All memories for user '{example_user}':")
+        all_memories = searcher.get_all_memories(user_id=example_user)
+        searcher.pretty_print(all_memories)
+        
+        print(f"\n3. Searching memories for user '{example_user}':")
+        search_results = searcher.search_memories(
+            query="What are their hobbies?",
+            user_id=example_user,
+            limit=5
+        )
+        searcher.pretty_print(search_results)
+        
+        # Print summary
+        print("\nMemory Store Summary:")
+        total_memories = len(all_memories.get('results', []))
+        print(f"Total memories for {example_user}: {total_memories}")
+        if total_memories > 0:
+            print("Available memories:")
+            for mem in all_memories['results']:
+                print(f"- {mem.get('memory', 'No content')}")
+                if 'metadata' in mem:
+                    print(f"  Metadata: {mem['metadata']}")
+
+    except Exception as e:
+        logger.error(f"Main process failed: {str(e)}", exc_info=True)
+        raise
+
+if __name__ == "__main__":
+    main()

agents/memory-search-simple-test.py

@@ -0,0 +1,26 @@
+from mem0 import Memory
+import os
+import json
+
+# Basic configuration
+config = {
+    "vector_store": {
+        "provider": "chroma",
+        "config": {
+            "collection_name": "praison",
+            "path": ".praison",
+        }
+    }
+}
+
+# Initialize memory
+memory = Memory.from_config(config)
+
+# Search Alice's hobbies
+search_results = memory.search(
+    query="KAG",
+    user_id="user1",
+    limit=5
+)
+
+print(search_results)

agents/memory-search-simple.py

@@ -0,0 +1,46 @@
+from mem0 import Memory
+import os
+import json
+
+# Basic configuration
+config = {
+    "version": "v1.1",
+    "vector_store": {
+        "provider": "chroma",
+        "config": {
+            "collection_name": "praison",
+            "path": os.path.abspath("./.praison/storage")
+        }
+    },
+    "embedder": {
+        "provider": "openai",
+        "config": {
+            "model": "text-embedding-3-small"
+        }
+    },
+    "llm": {
+        "provider": "openai",
+        "config": {
+            "model": "gpt-4o-mini"
+        }
+    }
+}
+
+# Initialize memory
+memory = Memory.from_config(config)
+
+# Search Alice's hobbies
+search_results = memory.search(
+    query="What are Alice's hobbies?",
+    user_id="alice",
+    limit=5
+)
+
+# Print results in a readable format
+print("\nAlice's Memories:")
+for memory in search_results['results']:
+    print(f"\nMemory: {memory['memory']}")
+    print(f"Score: {memory['score']}")
+    if 'metadata' in memory:
+        print("Metadata:")
+        print(json.dumps(memory['metadata'], indent=2))

agents/multimodal.py

@@ -51,9 +51,4 @@ agents = PraisonAIAgents(
 )
 
 # Run all tasks
-result = agents.start()
-
-# Print results
-for task_id, task_result in result["task_results"].items():
-    print(f"\nTask {task_id} Result:")
-    print(task_result.raw)
+agents.start()

agents/orchestrator-workers.py

@@ -0,0 +1,110 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import time
+
+def get_time_check():
+    current_time = int(time.time())
+    if current_time % 3 == 0:
+        result = 1
+    elif current_time % 3 == 1:
+        result = 2
+    else:
+        result = 3
+    print(f"Time: {current_time}, Result: {result}")
+    return result
+
+# Create orchestrator and worker agents
+router = Agent(
+    name="Router",
+    role="Task router",
+    goal="Distribute tasks to based on response from get_time_check",
+    tools=[get_time_check]
+)
+
+worker1 = Agent(
+    name="Worker 1",
+    role="Specialized worker",
+    goal="Handle specific subtask type 1",
+)
+
+worker2 = Agent(
+    name="Worker 2",
+    role="Specialized worker",
+    goal="Handle specific subtask type 2",
+)
+
+worker3 = Agent(
+    name="Worker 3",
+    role="Specialized worker",
+    goal="Handle specific subtask type 3",
+)
+
+synthesizer = Agent(
+    name="Synthesizer",
+    role="Result synthesizer",
+    goal="Combine and synthesize worker outputs",
+)
+
+# Create orchestrated workflow tasks
+router_task = Task(
+    name="route_task",
+    description="Analyze input from get_time_check and route to appropriate workers",
+    expected_output="Task routing decision, 1 , 2 or 3",
+    agent=router,
+    is_start=True,
+    task_type="decision",
+    next_tasks=["worker1_task", "worker2_task", "worker3_task"],
+    condition={
+        "1": ["worker1_task"],
+        "2": ["worker2_task"],
+        "3": ["worker3_task"]
+    }
+)
+
+worker1_task = Task(
+    name="worker1_task",
+    description="Process type 1 operation",
+    expected_output="Worker 1 result",
+    agent=worker1,
+    next_tasks=["synthesize_task"]
+)
+
+worker2_task = Task(
+    name="worker2_task",
+    description="Process type 2 operation",
+    expected_output="Worker 2 result",
+    agent=worker2,
+    next_tasks=["synthesize_task"]
+)
+
+worker3_task = Task(
+    name="worker3_task",
+    description="Process type 3 operation",
+    expected_output="Worker 3 result",
+    agent=worker3,
+    next_tasks=["synthesize_task"]
+)
+
+synthesize_task = Task(
+    name="synthesize_task",
+    description="Synthesize worker results into final output",
+    expected_output="Final synthesized result",
+    agent=synthesizer,
+    context=[worker1_task, worker2_task, worker3_task]
+)
+
+# Create workflow manager
+workflow = PraisonAIAgents(
+    agents=[router, worker1, worker2, worker3, synthesizer],
+    tasks=[router_task, worker1_task, worker2_task, worker3_task, synthesize_task],
+    process="workflow",
+    verbose=True
+)
+
+# Run orchestrated workflow
+results = workflow.start()
+
+# Print results
+print("\nOrchestrator-Workers Results:")
+for task_id, result in results["task_results"].items():
+    if result:
+        print(f"Task {task_id}: {result.raw}")

agents/parallelisation.py

@@ -0,0 +1,95 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from datetime import datetime
+import asyncio
+
+def process_time():
+    """Simulate processing"""
+    current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    print(f"Processing at: {current_time}")
+    return f"Processed at {current_time}"
+
+# Create parallel processing agents
+agent1 = Agent(
+    name="Processor 1",
+    role="Time collector",
+    goal="Get the time and return it",
+    tools=[process_time]
+)
+
+agent2 = Agent(
+    name="Processor 2",
+    role="Time collector",
+    goal="Get the time and return it",
+    tools=[process_time]
+)
+
+agent3 = Agent(
+    name="Processor 3",
+    role="Time collector",
+    goal="Get the time and return it",
+    tools=[process_time]
+)
+
+aggregator = Agent(
+    name="Aggregator",
+    role="Result aggregator",
+    goal="Collect all the processed time from all tasks"
+)
+
+# Create parallel tasks with memory disabled
+task1 = Task(
+    name="process_1",
+    description="Use process_time tool to get the time",
+    expected_output="processed time",
+    agent=agent1,
+    is_start=True,
+    async_execution=True
+)
+
+task2 = Task(
+    name="process_2",
+    description="Use process_time tool to get the time",
+    expected_output="processed time",
+    agent=agent2,
+    is_start=True,
+    async_execution=True
+)
+
+task3 = Task(
+    name="process_3",
+    description="Use process_time tool to get the time",
+    expected_output="processed time",
+    agent=agent3,
+    is_start=True,
+    async_execution=True
+)
+
+aggregate_task = Task(
+    name="aggregate",
+    description="Collect all the processed time from all tasks",
+    expected_output="Output all the processed time from all tasks and just the time",
+    agent=aggregator,
+    context=[task1, task2, task3]
+)
+
+async def main():
+
+    # Create workflow manager
+    workflow = PraisonAIAgents(
+        agents=[agent1, agent2, agent3, aggregator],
+        tasks=[task1, task2, task3, aggregate_task],
+        process="workflow"
+    )
+
+    # Run parallel workflow
+    results = await workflow.astart()
+
+    # Print results
+    print("\nParallel Processing Results:")
+    for task_id, result in results["task_results"].items():
+        if result:
+            print(f"Task {task_id}: {result.raw}")
+
+# Run the async main function
+if __name__ == "__main__":
+    asyncio.run(main())

agents/pdf_agents_example.py

@@ -0,0 +1,41 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# Create PDF Analysis Agent
+pdf_agent = Agent(
+    name="PDFAnalyst",
+    role="PDF Document Specialist",
+    goal="Analyze PDF documents to extract meaningful information",
+    backstory="""You are an expert in PDF document analysis and text extraction.
+    You excel at understanding document structure, extracting content, and analyzing textual information.""",
+    llm="gpt-4o-mini",
+    self_reflect=False
+)
+
+# 1. Task with PDF URL
+task1 = Task(
+    name="analyze_pdf_url",
+    description="Extract and analyze content from this PDF document.",
+    expected_output="Detailed analysis of the PDF content and structure",
+    agent=pdf_agent,
+    input=["https://example.com/document.pdf"]
+)
+
+# 2. Task with Local PDF File
+task2 = Task(
+    name="analyze_local_pdf",
+    description="What information can you extract from this PDF? Analyze its content.",
+    expected_output="Detailed analysis of the PDF content and structure",
+    agent=pdf_agent,
+    input=["document.pdf"] 
+)
+
+# Create PraisonAIAgents instance
+agents = PraisonAIAgents(
+    agents=[pdf_agent],
+    tasks=[task1, task2],
+    process="sequential",
+    verbose=1
+)
+
+# Run all tasks
+agents.start()

agents/planning-agent.py

@@ -0,0 +1,5 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Planning Agent", tools=[duckduckgo])
+agent.start("I want to go London next week, find me a good hotel and flight")

agents/praisonaiagents/__init__.py

@@ -7,7 +7,8 @@ from .agents.agents import PraisonAIAgents
 from .task.task import Task
 from .tools.tools import Tools
 from .agents.autoagents import AutoAgents
-from .memory.memory import Memory
+from .knowledge.knowledge import Knowledge
+from .knowledge.chunking import Chunking
 from .main import (
     TaskOutput,
     ReflectionOutput,
@@ -36,7 +37,6 @@ __all__ = [
     'TaskOutput',
     'ReflectionOutput',
     'AutoAgents',
-    'Memory',
     'display_interaction',
     'display_self_reflection',
     'display_instruction',
@@ -48,4 +48,6 @@ __all__ = [
     'register_display_callback',
     'sync_display_callbacks',
     'async_display_callbacks',
+    'Knowledge',
+    'Chunking'
 ] 

agents/praisonaiagents/agent/agent.py

@@ -16,9 +16,11 @@ from ..main import (
     display_self_reflection,
     ReflectionOutput,
     client,
-    error_logs
+    error_logs,
+    adisplay_instruction
 )
 import inspect
+import uuid
 
 if TYPE_CHECKING:
     from ..task.task import Task
@@ -69,10 +71,15 @@ class Agent:
 
         import inspect
         # Langchain tools
-        if inspect.isclass(func) and hasattr(func, 'run'):
+        if inspect.isclass(func) and hasattr(func, 'run') and not hasattr(func, '_run'):
             original_func = func
             func = func.run
             function_name = original_func.__name__
+        # CrewAI tools
+        elif inspect.isclass(func) and hasattr(func, '_run'):
+            original_func = func
+            func = func._run
+            function_name = original_func.__name__
 
         sig = inspect.signature(func)
         logging.debug(f"Function signature: {sig}")
@@ -175,18 +182,36 @@ class Agent:
         respect_context_window: bool = True,
         code_execution_mode: Literal["safe", "unsafe"] = "safe",
         embedder_config: Optional[Dict[str, Any]] = None,
-        knowledge_sources: Optional[List[Any]] = None,
+        knowledge: Optional[List[str]] = None,
+        knowledge_config: Optional[Dict[str, Any]] = None,
         use_system_prompt: Optional[bool] = True,
         markdown: bool = True,
         self_reflect: bool = False,
         max_reflect: int = 3,
         min_reflect: int = 1,
-        reflect_llm: Optional[str] = None
+        reflect_llm: Optional[str] = None,
+        user_id: Optional[str] = None
     ):
+        # Add check at start if memory is requested
+        if memory is not None:
+            try:
+                from ..memory.memory import Memory
+                MEMORY_AVAILABLE = True
+            except ImportError:
+                raise ImportError(
+                    "Memory features requested in Agent but memory dependencies not installed. "
+                    "Please install with: pip install \"praisonaiagents[memory]\""
+                )
+
         # Handle backward compatibility for required fields
         if all(x is None for x in [name, role, goal, backstory, instructions]):
             raise ValueError("At least one of name, role, goal, backstory, or instructions must be provided")
 
+        # Configure logging to suppress unwanted outputs
+        logging.getLogger("litellm").setLevel(logging.WARNING)
+        logging.getLogger("httpx").setLevel(logging.WARNING)
+        logging.getLogger("httpcore").setLevel(logging.WARNING)
+
         # If instructions are provided, use them to set role, goal, and backstory
         if instructions:
             self.name = name or "Agent"
@@ -206,7 +231,34 @@ class Agent:
         
         self.instructions = instructions
         # Check for model name in environment variable if not provided
-        self.llm = llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-4o')
+        self._using_custom_llm = False
+
+        # If the user passes a dictionary (for advanced configuration)
+        if isinstance(llm, dict) and "model" in llm:
+            try:
+                from ..llm.llm import LLM
+                self.llm_instance = LLM(**llm)  # Pass all dict items as kwargs
+                self._using_custom_llm = True
+            except ImportError as e:
+                raise ImportError(
+                    "LLM features requested but dependencies not installed. "
+                    "Please install with: pip install \"praisonaiagents[llm]\""
+                ) from e
+        # If the user passes a string with a slash (provider/model)
+        elif isinstance(llm, str) and "/" in llm:
+            try:
+                from ..llm.llm import LLM
+                # Pass the entire string so LiteLLM can parse provider/model
+                self.llm_instance = LLM(model=llm)
+                self._using_custom_llm = True
+            except ImportError as e:
+                raise ImportError(
+                    "LLM features requested but dependencies not installed. "
+                    "Please install with: pip install \"praisonaiagents[llm]\""
+                ) from e
+        # Otherwise, fall back to OpenAI environment/name
+        else:
+            self.llm = llm or os.getenv('OPENAI_MODEL_NAME', 'gpt-4o')
         self.tools = tools if tools else []  # Store original tools
         self.function_calling_llm = function_calling_llm
         self.max_iter = max_iter
@@ -225,7 +277,7 @@ class Agent:
         self.respect_context_window = respect_context_window
         self.code_execution_mode = code_execution_mode
         self.embedder_config = embedder_config
-        self.knowledge_sources = knowledge_sources
+        self.knowledge = knowledge
         self.use_system_prompt = use_system_prompt
         self.chat_history = []
         self.markdown = markdown
@@ -241,6 +293,40 @@ Your Role: {self.role}\n
 Your Goal: {self.goal}
         """
 
+        # Generate unique IDs
+        self.agent_id = str(uuid.uuid4())
+
+        # Store user_id
+        self.user_id = user_id or "praison"
+
+        # Check if knowledge parameter has any values
+        if not knowledge:
+            self.knowledge = None
+        else:
+            # Initialize Knowledge with provided or default config
+            from praisonaiagents.knowledge import Knowledge
+            self.knowledge = Knowledge(knowledge_config or None)
+            
+            # Handle knowledge
+            if knowledge:
+                for source in knowledge:
+                    self._process_knowledge(source)
+
+    def _process_knowledge(self, knowledge_item):
+        """Process and store knowledge from a file path, URL, or string."""
+        try:
+            if os.path.exists(knowledge_item):
+                # It's a file path
+                self.knowledge.add(knowledge_item, user_id=self.user_id, agent_id=self.agent_id)
+            elif knowledge_item.startswith("http://") or knowledge_item.startswith("https://"):
+                # It's a URL
+                pass
+            else:
+                # It's a string content
+                self.knowledge.store(knowledge_item, user_id=self.user_id, agent_id=self.agent_id)
+        except Exception as e:
+            logging.error(f"Error processing knowledge item: {knowledge_item}, error: {e}")
+
     def generate_task(self) -> 'Task':
         """Generate a Task object from the agent's instructions"""
         from ..task.task import Task
@@ -279,14 +365,22 @@ Your Goal: {self.goal}
 
         if func:
             try:
-                # If it's a class with run method, instantiate and call run
-                if inspect.isclass(func) and hasattr(func, 'run'):
+                # Langchain: If it's a class with run but not _run, instantiate and call run
+                if inspect.isclass(func) and hasattr(func, 'run') and not hasattr(func, '_run'):
                     instance = func()
-                    # Extract only the parameters that run() expects
                     run_params = {k: v for k, v in arguments.items() 
-                                if k in inspect.signature(instance.run).parameters 
-                                and k != 'self'}
+                                  if k in inspect.signature(instance.run).parameters 
+                                  and k != 'self'}
                     return instance.run(**run_params)
+
+                # CrewAI: If it's a class with an _run method, instantiate and call _run
+                elif inspect.isclass(func) and hasattr(func, '_run'):
+                    instance = func()
+                    run_params = {k: v for k, v in arguments.items() 
+                                  if k in inspect.signature(instance._run).parameters 
+                                  and k != 'self'}
+                    return instance._run(**run_params)
+
                 # Otherwise treat as regular function
                 elif callable(func):
                     return func(**arguments)
@@ -417,185 +511,231 @@ Your Goal: {self.goal}
             return None
 
     def chat(self, prompt, temperature=0.2, tools=None, output_json=None, output_pydantic=None):
-        if self.use_system_prompt:
-            system_prompt = f"""{self.backstory}\n
+        # Search for existing knowledge if any knowledge is provided
+        if self.knowledge:
+            search_results = self.knowledge.search(prompt, agent_id=self.agent_id)
+            if search_results:
+                # Check if search_results is a list of dictionaries or strings
+                if isinstance(search_results, dict) and 'results' in search_results:
+                    # Extract memory content from the results
+                    knowledge_content = "\n".join([result['memory'] for result in search_results['results']])
+                else:
+                    # If search_results is a list of strings, join them directly
+                    knowledge_content = "\n".join(search_results)
+                
+                # Append found knowledge to the prompt
+                prompt = f"{prompt}\n\nKnowledge: {knowledge_content}"
+
+        if self._using_custom_llm:
+            try:
+                # Pass everything to LLM class
+                response_text = self.llm_instance.get_response(
+                    prompt=prompt,
+                    system_prompt=f"{self.backstory}\n\nYour Role: {self.role}\n\nYour Goal: {self.goal}" if self.use_system_prompt else None,
+                    chat_history=self.chat_history,
+                    temperature=temperature,
+                    tools=tools,
+                    output_json=output_json,
+                    output_pydantic=output_pydantic,
+                    verbose=self.verbose,
+                    markdown=self.markdown,
+                    self_reflect=self.self_reflect,
+                    max_reflect=self.max_reflect,
+                    min_reflect=self.min_reflect,
+                    console=self.console,
+                    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
+                )
+
+                self.chat_history.append({"role": "user", "content": prompt})
+                self.chat_history.append({"role": "assistant", "content": response_text})
+
+                return response_text
+            except Exception as e:
+                display_error(f"Error in LLM chat: {e}")
+                return None
+        else:
+            if self.use_system_prompt:
+                system_prompt = f"""{self.backstory}\n
 Your Role: {self.role}\n
 Your Goal: {self.goal}
-            """
-            if output_json:
-                system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {output_json.schema_json()}"
-            elif output_pydantic:
-                system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {output_pydantic.schema_json()}"
-        else:
-            system_prompt = None
+                """
+                if output_json:
+                    system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {json.dumps(output_json.model_json_schema())}"
+                elif output_pydantic:
+                    system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {json.dumps(output_pydantic.model_json_schema())}"
+            else:
+                system_prompt = None
 
-        messages = []
-        if system_prompt:
-            messages.append({"role": "system", "content": system_prompt})
-        messages.extend(self.chat_history)
+            messages = []
+            if system_prompt:
+                messages.append({"role": "system", "content": system_prompt})
+            messages.extend(self.chat_history)
 
-        # Modify prompt if output_json or output_pydantic is specified
-        original_prompt = prompt
-        if output_json or output_pydantic:
-            if isinstance(prompt, str):
-                prompt += "\nReturn ONLY a valid JSON object. No other text or explanation."
-            elif isinstance(prompt, list):
-                # For multimodal prompts, append to the text content
-                for item in prompt:
-                    if item["type"] == "text":
-                        item["text"] += "\nReturn ONLY a valid JSON object. No other text or explanation."
-                        break
+            # Modify prompt if output_json or output_pydantic is specified
+            original_prompt = prompt
+            if output_json or output_pydantic:
+                if isinstance(prompt, str):
+                    prompt += "\nReturn ONLY a valid JSON object. No other text or explanation."
+                elif isinstance(prompt, list):
+                    # For multimodal prompts, append to the text content
+                    for item in prompt:
+                        if item["type"] == "text":
+                            item["text"] += "\nReturn ONLY a valid JSON object. No other text or explanation."
+                            break
 
-        if isinstance(prompt, list):
-            # If we receive a multimodal prompt list, place it directly in the user message
-            messages.append({"role": "user", "content": prompt})
-        else:
-            messages.append({"role": "user", "content": prompt})
+            if isinstance(prompt, list):
+                # If we receive a multimodal prompt list, place it directly in the user message
+                messages.append({"role": "user", "content": prompt})
+            else:
+                messages.append({"role": "user", "content": prompt})
 
-        final_response_text = None
-        reflection_count = 0
-        start_time = time.time()
+            final_response_text = None
+            reflection_count = 0
+            start_time = time.time()
 
-        while True:
-            try:
-                if self.verbose:
-                    # Handle both string and list prompts for instruction display
-                    display_text = prompt
-                    if isinstance(prompt, list):
-                        # Extract text content from multimodal prompt
-                        display_text = next((item["text"] for item in prompt if item["type"] == "text"), "")
-                    
-                    if display_text and str(display_text).strip():
-                        # Pass agent information to display_instruction
-                        agent_tools = [t.__name__ if hasattr(t, '__name__') else str(t) for t in self.tools]
-                        display_instruction(
-                            f"Agent {self.name} is processing prompt: {display_text}", 
-                            console=self.console,
-                            agent_name=self.name,
-                            agent_role=self.role,
-                            agent_tools=agent_tools
-                        )
-
-                response = self._chat_completion(messages, temperature=temperature, tools=tools if tools else None)
-                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:
-                    messages.append({
-                        "role": "assistant",
-                        "content": response_text,
-                        "tool_calls": tool_calls
-                    })
-                    
-                    for tool_call in tool_calls:
-                        function_name = tool_call.function.name
-                        arguments = json.loads(tool_call.function.arguments)
-
-                        if self.verbose:
-                            display_tool_call(f"Agent {self.name} is calling function '{function_name}' with arguments: {arguments}", console=self.console)
-
-                        tool_result = self.execute_tool(function_name, arguments)
-
-                        if tool_result:
-                            if self.verbose:
-                                display_tool_call(f"Function '{function_name}' returned: {tool_result}", console=self.console)
-                            messages.append({
-                                "role": "tool",
-                                "tool_call_id": tool_call.id,
-                                "content": json.dumps(tool_result)
-                            })
-                        else:
-                            messages.append({
-                                "role": "tool",
-                                "tool_call_id": tool_call.id,
-                                "content": "Function returned an empty output"
-                            })
+            while True:
+                try:
+                    if self.verbose:
+                        # Handle both string and list prompts for instruction display
+                        display_text = prompt
+                        if isinstance(prompt, list):
+                            # Extract text content from multimodal prompt
+                            display_text = next((item["text"] for item in prompt if item["type"] == "text"), "")
                         
-                    response = self._chat_completion(messages, temperature=temperature)
+                        if display_text and str(display_text).strip():
+                            # Pass agent information to display_instruction
+                            agent_tools = [t.__name__ if hasattr(t, '__name__') else str(t) for t in self.tools]
+                            display_instruction(
+                                f"Agent {self.name} is processing prompt: {display_text}", 
+                                console=self.console,
+                                agent_name=self.name,
+                                agent_role=self.role,
+                                agent_tools=agent_tools
+                            )
+
+                    response = self._chat_completion(messages, temperature=temperature, tools=tools if tools else None)
                     if not response:
                         return None
+
+                    tool_calls = getattr(response.choices[0].message, 'tool_calls', None)
                     response_text = response.choices[0].message.content.strip()
 
-                # Handle output_json or output_pydantic if specified
-                if output_json or output_pydantic:
-                    # Add to chat history and return raw response
-                    self.chat_history.append({"role": "user", "content": original_prompt})
-                    self.chat_history.append({"role": "assistant", "content": response_text})
-                    if self.verbose:
-                        display_interaction(original_prompt, response_text, markdown=self.markdown, 
-                                         generation_time=time.time() - start_time, console=self.console)
-                    return response_text
+                    if tool_calls:
+                        messages.append({
+                            "role": "assistant",
+                            "content": response_text,
+                            "tool_calls": tool_calls
+                        })
+                        
+                        for tool_call in tool_calls:
+                            function_name = tool_call.function.name
+                            arguments = json.loads(tool_call.function.arguments)
 
-                if not self.self_reflect:
-                    self.chat_history.append({"role": "user", "content": original_prompt})
-                    self.chat_history.append({"role": "assistant", "content": response_text})
-                    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 response_text
+                            if self.verbose:
+                                display_tool_call(f"Agent {self.name} is calling function '{function_name}' with arguments: {arguments}", console=self.console)
 
-                reflection_prompt = f"""
+                            tool_result = self.execute_tool(function_name, arguments)
+
+                            if tool_result:
+                                if self.verbose:
+                                    display_tool_call(f"Function '{function_name}' returned: {tool_result}", console=self.console)
+                                messages.append({
+                                    "role": "tool",
+                                    "tool_call_id": tool_call.id,
+                                    "content": json.dumps(tool_result)
+                                })
+                            else:
+                                messages.append({
+                                    "role": "tool",
+                                    "tool_call_id": tool_call.id,
+                                    "content": "Function returned an empty output"
+                                })
+                            
+                        response = self._chat_completion(messages, temperature=temperature)
+                        if not response:
+                            return None
+                        response_text = response.choices[0].message.content.strip()
+
+                    # Handle output_json or output_pydantic if specified
+                    if output_json or output_pydantic:
+                        # Add to chat history and return raw response
+                        self.chat_history.append({"role": "user", "content": original_prompt})
+                        self.chat_history.append({"role": "assistant", "content": response_text})
+                        if self.verbose:
+                            display_interaction(original_prompt, response_text, markdown=self.markdown, 
+                                             generation_time=time.time() - start_time, console=self.console)
+                        return response_text
+
+                    if not self.self_reflect:
+                        self.chat_history.append({"role": "user", "content": original_prompt})
+                        self.chat_history.append({"role": "assistant", "content": response_text})
+                        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 response_text
+
+                    reflection_prompt = f"""
 Reflect on your previous response: '{response_text}'.
 Identify any flaws, improvements, or actions.
 Provide a "satisfactory" status ('yes' or 'no').
 Output MUST be JSON with 'reflection' and 'satisfactory'.
-                """
-                logging.debug(f"{self.name} reflection attempt {reflection_count+1}, sending prompt: {reflection_prompt}")
-                messages.append({"role": "user", "content": reflection_prompt})
+                    """
+                    logging.debug(f"{self.name} reflection attempt {reflection_count+1}, sending prompt: {reflection_prompt}")
+                    messages.append({"role": "user", "content": reflection_prompt})
 
-                try:
-                    reflection_response = client.beta.chat.completions.parse(
-                        model=self.reflect_llm if self.reflect_llm else self.llm,
-                        messages=messages,
-                        temperature=temperature,
-                        response_format=ReflectionOutput
-                    )
+                    try:
+                        reflection_response = client.beta.chat.completions.parse(
+                            model=self.reflect_llm if self.reflect_llm else self.llm,
+                            messages=messages,
+                            temperature=temperature,
+                            response_format=ReflectionOutput
+                        )
 
-                    reflection_output = reflection_response.choices[0].message.parsed
+                        reflection_output = reflection_response.choices[0].message.parsed
 
-                    if self.verbose:
-                        display_self_reflection(f"Agent {self.name} self reflection (using {self.reflect_llm if self.reflect_llm else self.llm}): reflection='{reflection_output.reflection}' satisfactory='{reflection_output.satisfactory}'", console=self.console)
-
-                    messages.append({"role": "assistant", "content": f"Self Reflection: {reflection_output.reflection} Satisfactory?: {reflection_output.satisfactory}"})
-
-                    # Only consider satisfactory after minimum reflections
-                    if reflection_output.satisfactory == "yes" and reflection_count >= self.min_reflect - 1:
                         if self.verbose:
-                            display_self_reflection("Agent marked the response as satisfactory after meeting minimum reflections", console=self.console)
-                        self.chat_history.append({"role": "user", "content": prompt})
-                        self.chat_history.append({"role": "assistant", "content": response_text})
-                        display_interaction(prompt, response_text, markdown=self.markdown, generation_time=time.time() - start_time, console=self.console)
-                        return response_text
+                            display_self_reflection(f"Agent {self.name} self reflection (using {self.reflect_llm if self.reflect_llm else self.llm}): reflection='{reflection_output.reflection}' satisfactory='{reflection_output.satisfactory}'", console=self.console)
 
-                    # Check if we've hit max reflections
-                    if reflection_count >= self.max_reflect - 1:
-                        if self.verbose:
-                            display_self_reflection("Maximum reflection count reached, returning current response", console=self.console)
-                        self.chat_history.append({"role": "user", "content": prompt})
-                        self.chat_history.append({"role": "assistant", "content": response_text})
-                        display_interaction(prompt, response_text, markdown=self.markdown, generation_time=time.time() - start_time, console=self.console)
-                        return response_text
+                        messages.append({"role": "assistant", "content": f"Self Reflection: {reflection_output.reflection} Satisfactory?: {reflection_output.satisfactory}"})
 
-                    logging.debug(f"{self.name} reflection count {reflection_count + 1}, continuing reflection process")
-                    messages.append({"role": "user", "content": "Now regenerate your response using the reflection you made"})
-                    response = self._chat_completion(messages, temperature=temperature, tools=None, stream=True)
-                    response_text = response.choices[0].message.content.strip()
-                    reflection_count += 1
-                    continue  # Continue the loop for more reflections
+                        # Only consider satisfactory after minimum reflections
+                        if reflection_output.satisfactory == "yes" and reflection_count >= self.min_reflect - 1:
+                            if self.verbose:
+                                display_self_reflection("Agent marked the response as satisfactory after meeting minimum reflections", console=self.console)
+                            self.chat_history.append({"role": "user", "content": prompt})
+                            self.chat_history.append({"role": "assistant", "content": response_text})
+                            display_interaction(prompt, response_text, markdown=self.markdown, generation_time=time.time() - start_time, console=self.console)
+                            return response_text
 
+                        # Check if we've hit max reflections
+                        if reflection_count >= self.max_reflect - 1:
+                            if self.verbose:
+                                display_self_reflection("Maximum reflection count reached, returning current response", console=self.console)
+                            self.chat_history.append({"role": "user", "content": prompt})
+                            self.chat_history.append({"role": "assistant", "content": response_text})
+                            display_interaction(prompt, response_text, markdown=self.markdown, generation_time=time.time() - start_time, console=self.console)
+                            return response_text
+
+                        logging.debug(f"{self.name} reflection count {reflection_count + 1}, continuing reflection process")
+                        messages.append({"role": "user", "content": "Now regenerate your response using the reflection you made"})
+                        response = self._chat_completion(messages, temperature=temperature, tools=None, stream=True)
+                        response_text = response.choices[0].message.content.strip()
+                        reflection_count += 1
+                        continue  # Continue the loop for more reflections
+
+                    except Exception as e:
+                        display_error(f"Error in parsing self-reflection json {e}. Retrying", console=self.console)
+                        logging.error("Reflection parsing failed.", exc_info=True)
+                        messages.append({"role": "assistant", "content": f"Self Reflection failed."})
+                        reflection_count += 1
+                        continue  # Continue even after error to try again
+                    
                 except Exception as e:
-                    display_error(f"Error in parsing self-reflection json {e}. Retrying", console=self.console)
-                    logging.error("Reflection parsing failed.", exc_info=True)
-                    messages.append({"role": "assistant", "content": f"Self Reflection failed."})
-                    reflection_count += 1
-                    continue  # Continue even after error to try again
-                
-            except Exception as e:
-                display_error(f"Error in chat: {e}", console=self.console)
-                return None 
+                    display_error(f"Error in chat: {e}", console=self.console)
+                    return None 
 
     def clean_json_output(self, output: str) -> str:
         """Clean and extract JSON from response text."""
@@ -607,98 +747,153 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
             cleaned = cleaned[len("```"):].strip()
         if cleaned.endswith("```"):
             cleaned = cleaned[:-3].strip()
-        return cleaned 
+        return cleaned  
 
-    async def achat(self, prompt, temperature=0.2, tools=None, output_json=None, output_pydantic=None):
-        """Async version of chat method"""
+    async def achat(self, prompt: str, temperature=0.2, tools=None, output_json=None, output_pydantic=None):
+        """Async version of chat method. TODO: Requires Syncing with chat method.""" 
         try:
-            # Build system prompt
-            system_prompt = self.system_prompt
-            if output_json:
-                system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {output_json.schema_json()}"
-            elif output_pydantic:
-                system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {output_pydantic.schema_json()}"
+            # Search for existing knowledge if any knowledge is provided
+            if self.knowledge:
+                search_results = self.knowledge.search(prompt, agent_id=self.agent_id)
+                if search_results:
+                    if isinstance(search_results, dict) and 'results' in search_results:
+                        knowledge_content = "\n".join([result['memory'] for result in search_results['results']])
+                    else:
+                        knowledge_content = "\n".join(search_results)
+                    prompt = f"{prompt}\n\nKnowledge: {knowledge_content}"
 
-            # Build messages
-            if isinstance(prompt, str):
-                messages = [
-                    {"role": "system", "content": system_prompt},
-                    {"role": "user", "content": prompt + ("\nReturn ONLY a valid JSON object. No other text or explanation." if (output_json or output_pydantic) else "")}
-                ]
+            if self._using_custom_llm:
+                try:
+                    response_text = await self.llm_instance.get_response_async(
+                        prompt=prompt,
+                        system_prompt=f"{self.backstory}\n\nYour Role: {self.role}\n\nYour Goal: {self.goal}" if self.use_system_prompt else None,
+                        chat_history=self.chat_history,
+                        temperature=temperature,
+                        tools=tools,
+                        output_json=output_json,
+                        output_pydantic=output_pydantic,
+                        verbose=self.verbose,
+                        markdown=self.markdown,
+                        self_reflect=self.self_reflect,
+                        max_reflect=self.max_reflect,
+                        min_reflect=self.min_reflect,
+                        console=self.console,
+                        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
+                    )
+
+                    self.chat_history.append({"role": "user", "content": prompt})
+                    self.chat_history.append({"role": "assistant", "content": response_text})
+
+                    return response_text
+                except Exception as e:
+                    display_error(f"Error in LLM chat: {e}")
+                    return None
+
+            # For OpenAI client
+            if self.use_system_prompt:
+                system_prompt = f"""{self.backstory}\n
+Your Role: {self.role}\n
+Your Goal: {self.goal}
+                """
+                if output_json:
+                    system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {json.dumps(output_json.model_json_schema())}"
+                elif output_pydantic:
+                    system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {json.dumps(output_pydantic.model_json_schema())}"
             else:
-                # For multimodal prompts
-                messages = [
-                    {"role": "system", "content": system_prompt},
-                    {"role": "user", "content": prompt}
-                ]
-                if output_json or output_pydantic:
-                    # Add JSON instruction to text content
-                    for item in messages[-1]["content"]:
+                system_prompt = None
+
+            messages = []
+            if system_prompt:
+                messages.append({"role": "system", "content": system_prompt})
+            messages.extend(self.chat_history)
+
+            # Modify prompt if output_json or output_pydantic is specified
+            original_prompt = prompt
+            if output_json or output_pydantic:
+                if isinstance(prompt, str):
+                    prompt += "\nReturn ONLY a valid JSON object. No other text or explanation."
+                elif isinstance(prompt, list):
+                    for item in prompt:
                         if item["type"] == "text":
                             item["text"] += "\nReturn ONLY a valid JSON object. No other text or explanation."
                             break
 
-            # Display instruction with agent info if verbose
-            if self.verbose:
-                display_text = prompt
-                if isinstance(prompt, list):
-                    display_text = next((item["text"] for item in prompt if item["type"] == "text"), "")
-                
-                if display_text and str(display_text).strip():
-                    agent_tools = [t.__name__ if hasattr(t, '__name__') else str(t) for t in self.tools]
-                    await adisplay_instruction(
-                        f"Agent {self.name} is processing prompt: {display_text}",
-                        console=self.console,
-                        agent_name=self.name,
-                        agent_role=self.role,
-                        agent_tools=agent_tools
-                    )
-
-            # 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__))
-
-            # 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
+            if isinstance(prompt, list):
+                messages.append({"role": "user", "content": prompt})
             else:
-                response = await async_client.chat.completions.create(
-                    model=self.llm,
-                    messages=messages,
-                    temperature=temperature
-                )
-                return response.choices[0].message.content
+                messages.append({"role": "user", "content": prompt})
+
+            reflection_count = 0
+            start_time = time.time()
+
+            while True:
+                try:
+                    if self.verbose:
+                        display_text = prompt
+                        if isinstance(prompt, list):
+                            display_text = next((item["text"] for item in prompt if item["type"] == "text"), "")
+                        
+                        if display_text and str(display_text).strip():
+                            agent_tools = [t.__name__ if hasattr(t, '__name__') else str(t) for t in self.tools]
+                            await adisplay_instruction(
+                                f"Agent {self.name} is processing prompt: {display_text}",
+                                console=self.console,
+                                agent_name=self.name,
+                                agent_role=self.role,
+                                agent_tools=agent_tools
+                            )
+
+                    # 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__))
+
+                        # 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
+                except Exception as e:
+                    display_error(f"Error in chat completion: {e}")
+                    return None
         except Exception as e:
-            display_error(f"Error in chat completion: {e}")
+            display_error(f"Error in achat: {e}")
             return None
 
     async def _achat_completion(self, response, tools):
@@ -759,6 +954,57 @@ Output MUST be JSON with 'reflection' and 'satisfactory'.
             display_error(f"Error in _achat_completion: {e}")
             return None 
 
+    async def astart(self, prompt: str, **kwargs):
+        """Async version of start method"""
+        return await self.achat(prompt, **kwargs)
+
     def run(self):
         """Alias for start() method"""
-        return self.start() 
+        return self.start() 
+
+    def start(self, prompt: str, **kwargs):
+        """Start the agent with a prompt. This is a convenience method that wraps chat()."""
+        return self.chat(prompt, **kwargs) 
+
+    async def execute_tool_async(self, function_name: str, arguments: Dict[str, Any]) -> Any:
+        """Async version of execute_tool"""
+        try:
+            logging.info(f"Executing async tool: {function_name} with arguments: {arguments}")
+            # Try to find the function in the agent's tools list first
+            func = None
+            for tool in self.tools:
+                if (callable(tool) and getattr(tool, '__name__', '') == function_name):
+                    func = tool
+                    break
+            
+            if func is None:
+                logging.error(f"Function {function_name} not found in tools")
+                return {"error": f"Function {function_name} not found in tools"}
+
+            try:
+                if inspect.iscoroutinefunction(func):
+                    logging.debug(f"Executing async function: {function_name}")
+                    result = await func(**arguments)
+                else:
+                    logging.debug(f"Executing sync function in executor: {function_name}")
+                    loop = asyncio.get_event_loop()
+                    result = await loop.run_in_executor(None, lambda: func(**arguments))
+                
+                # Ensure result is JSON serializable
+                logging.debug(f"Raw result from tool: {result}")
+                if result is None:
+                    return {"result": None}
+                try:
+                    json.dumps(result)  # Test serialization
+                    return result
+                except TypeError:
+                    logging.warning(f"Result not JSON serializable, converting to string: {result}")
+                    return {"result": str(result)}
+
+            except Exception as e:
+                logging.error(f"Error executing {function_name}: {str(e)}", exc_info=True)
+                return {"error": f"Error executing {function_name}: {str(e)}"}
+
+        except Exception as e:
+            logging.error(f"Error in execute_tool_async: {str(e)}", exc_info=True)
+            return {"error": f"Error in execute_tool_async: {str(e)}"} 

agents/praisonaiagents/agents/agents.py

@@ -12,6 +12,7 @@ from ..agent.agent import Agent
 from ..task.task import Task
 from ..process.process import Process, LoopItems
 import asyncio
+import uuid
 
 # Set up logger
 logger = logging.getLogger(__name__)
@@ -44,10 +45,29 @@ def process_video(video_path: str, seconds_per_frame=2):
     return base64_frames
 
 class PraisonAIAgents:
-    def __init__(self, agents, tasks=None, verbose=0, completion_checker=None, max_retries=5, process="sequential", manager_llm=None, memory=False, memory_config=None, embedder=None):
+    def __init__(self, agents, tasks=None, verbose=0, completion_checker=None, max_retries=5, process="sequential", manager_llm=None, memory=False, memory_config=None, embedder=None, user_id=None, max_iter=10):
+        # Add check at the start if memory is requested
+        if memory:
+            try:
+                from ..memory.memory import Memory
+                MEMORY_AVAILABLE = True
+            except ImportError:
+                raise ImportError(
+                    "Memory features requested but memory dependencies not installed. "
+                    "Please install with: pip install \"praisonaiagents[memory]\""
+                )
+
         if not agents:
             raise ValueError("At least one agent must be provided")
-            
+        
+        self.run_id = str(uuid.uuid4())  # Auto-generate run_id
+        self.user_id = user_id or "praison"  # Optional user_id
+        self.max_iter = max_iter  # Add max_iter parameter
+
+        # Pass user_id to each agent
+        for agent in agents:
+            agent.user_id = self.user_id
+
         self.agents = agents
         self.tasks = {}
         if max_retries < 3:
@@ -211,20 +231,56 @@ class PraisonAIAgents:
 
         executor_agent = task.agent
 
+        # Ensure tools are available from both task and agent
+        tools = task.tools or []
+        if executor_agent and executor_agent.tools:
+            tools.extend(executor_agent.tools)
+
         task_prompt = f"""
 You need to do the following task: {task.description}.
 Expected Output: {task.expected_output}.
 """
         if task.context:
-            context_results = ""
-            for context_task in task.context:
-                if context_task.result:
-                    context_results += f"Result of previous task {context_task.name if context_task.name else context_task.description}: {context_task.result.raw}\n"
-                else:
-                    context_results += f"Previous task {context_task.name if context_task.name else context_task.description} had no result.\n"
+            context_results = []  # Use list to avoid duplicates
+            for context_item in task.context:
+                if isinstance(context_item, str):
+                    context_results.append(f"Input Content:\n{context_item}")
+                elif isinstance(context_item, list):
+                    context_results.append(f"Input Content: {' '.join(str(x) for x in context_item)}")
+                elif hasattr(context_item, 'result'):  # Task object
+                    if context_item.result:
+                        context_results.append(
+                            f"Result of previous task {context_item.name if context_item.name else context_item.description}:\n{context_item.result.raw}"
+                        )
+                    else:
+                        context_results.append(
+                            f"Previous task {context_item.name if context_item.name else context_item.description} has no result yet."
+                        )
+                elif isinstance(context_item, dict) and "vector_store" in context_item:
+                    from ..knowledge.knowledge import Knowledge
+                    try:
+                        # Handle both string and dict configs
+                        cfg = context_item["vector_store"]
+                        if isinstance(cfg, str):
+                            cfg = json.loads(cfg)
+                        
+                        knowledge = Knowledge(config={"vector_store": cfg}, verbose=self.verbose)
+                        
+                        # Only use user_id as filter
+                        db_results = knowledge.search(
+                            task.description,
+                            user_id=self.user_id if self.user_id else None
+                        )
+                        context_results.append(f"[DB Context]: {str(db_results)}")
+                    except Exception as e:
+                        context_results.append(f"[Vector DB Error]: {e}")
+            
+            # Join unique context results
+            unique_contexts = list(dict.fromkeys(context_results))  # Remove duplicates
             task_prompt += f"""
-Here are the results of previous tasks that might be useful:\n
-{context_results}
+Context:
+
+{'  '.join(unique_contexts)}
 """
         task_prompt += "Please provide only the final result of your work. Do not add any conversation or extra explanation."
 
@@ -267,14 +323,14 @@ Here are the results of previous tasks that might be useful:\n
 
             agent_output = await executor_agent.achat(
                 _get_multimodal_message(task_prompt, task.images),
-                tools=task.tools,
+                tools=tools,
                 output_json=task.output_json,
                 output_pydantic=task.output_pydantic
             )
         else:
             agent_output = await executor_agent.achat(
                 task_prompt,
-                tools=task.tools,
+                tools=tools,
                 output_json=task.output_json,
                 output_pydantic=task.output_pydantic
             )
@@ -334,7 +390,15 @@ Here are the results of previous tasks that might be useful:\n
                     task.status = "completed"
                     # Run execute_callback for memory operations
                     try:
-                        await task.execute_callback(task_output)
+                        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))
                     except Exception as e:
                         logger.error(f"Error executing memory callback for task {task_id}: {e}")
                         logger.exception(e)
@@ -343,7 +407,12 @@ Here are the results of previous tasks that might be useful:\n
                     if task.callback:
                         try:
                             if asyncio.iscoroutinefunction(task.callback):
-                                await task.callback(task_output)
+                                if asyncio.get_event_loop().is_running():
+                                    asyncio.create_task(task.callback(task_output))
+                                else:
+                                    loop = asyncio.new_event_loop()
+                                    asyncio.set_event_loop(loop)
+                                    loop.run_until_complete(task.callback(task_output))
                             else:
                                 task.callback(task_output)
                         except Exception as e:
@@ -376,15 +445,30 @@ Here are the results of previous tasks that might be useful:\n
             tasks=self.tasks,
             agents=self.agents,
             manager_llm=self.manager_llm,
-            verbose=self.verbose
+            verbose=self.verbose,
+            max_iter=self.max_iter
         )
         
         if self.process == "workflow":
+            # Collect all tasks that should run in parallel
+            parallel_tasks = []
             async for task_id in process.aworkflow():
-                if self.tasks[task_id].async_execution:
-                    await self.arun_task(task_id)
-                else:
-                    self.run_task(task_id)
+                if self.tasks[task_id].async_execution and self.tasks[task_id].is_start:
+                    parallel_tasks.append(task_id)
+                elif parallel_tasks:
+                    # Execute collected parallel tasks
+                    await asyncio.gather(*[self.arun_task(t) for t in parallel_tasks])
+                    parallel_tasks = []
+                    # Run the current non-parallel task
+                    if self.tasks[task_id].async_execution:
+                        await self.arun_task(task_id)
+                    else:
+                        self.run_task(task_id)
+            
+            # Execute any remaining parallel tasks
+            if parallel_tasks:
+                await asyncio.gather(*[self.arun_task(t) for t in parallel_tasks])
+                
         elif self.process == "sequential":
             async for task_id in process.asequential():
                 if self.tasks[task_id].async_execution:
@@ -400,8 +484,16 @@ Here are the results of previous tasks that might be useful:\n
                 else:
                     self.run_task(task_id)
 
-    async def astart(self):
+    async def astart(self, content=None, **kwargs):
         """Async version of start method"""
+        if content:
+            # Add content to context of all tasks
+            for task in self.tasks.values():
+                if isinstance(content, (str, list)):
+                    if not task.context:
+                        task.context = []
+                    task.context.append(content)
+
         await self.arun_all_tasks()
         return {
             "task_status": self.get_all_tasks_status(),
@@ -458,16 +550,48 @@ You need to do the following task: {task.description}.
 Expected Output: {task.expected_output}.
 """
         if task.context:
-            context_results = ""
-            for context_task in task.context:
-                if context_task.result:
-                    context_results += f"Result of previous task {context_task.name if context_task.name else context_task.description}: {context_task.result.raw}\n"
-                else:
-                    context_results += f"Previous task {context_task.name if context_task.name else context_task.description} had no result.\n"
+            context_results = []  # Use list to avoid duplicates
+            for context_item in task.context:
+                if isinstance(context_item, str):
+                    context_results.append(f"Input Content:\n{context_item}")
+                elif isinstance(context_item, list):
+                    context_results.append(f"Input Content: {' '.join(str(x) for x in context_item)}")
+                elif hasattr(context_item, 'result'):  # Task object
+                    if context_item.result:
+                        context_results.append(
+                            f"Result of previous task {context_item.name if context_item.name else context_item.description}:\n{context_item.result.raw}"
+                        )
+                    else:
+                        context_results.append(
+                            f"Previous task {context_item.name if context_item.name else context_item.description} has no result yet."
+                        )
+                elif isinstance(context_item, dict) and "vector_store" in context_item:
+                    from ..knowledge.knowledge import Knowledge
+                    try:
+                        # Handle both string and dict configs
+                        cfg = context_item["vector_store"]
+                        if isinstance(cfg, str):
+                            cfg = json.loads(cfg)
+                        
+                        knowledge = Knowledge(config={"vector_store": cfg}, verbose=self.verbose)
+                        
+                        # Only use user_id as filter
+                        db_results = knowledge.search(
+                            task.description,
+                            user_id=self.user_id if self.user_id else None
+                        )
+                        context_results.append(f"[DB Context]: {str(db_results)}")
+                    except Exception as e:
+                        context_results.append(f"[Vector DB Error]: {e}")
+            
+            # Join unique context results
+            unique_contexts = list(dict.fromkeys(context_results))  # Remove duplicates
             task_prompt += f"""
-Here are the results of previous tasks that might be useful:\n
-{context_results}
+Context:
+
+{'  '.join(unique_contexts)}
 """
+
         # Add memory context if available
         if task.memory:
             try:
@@ -596,8 +720,15 @@ Here are the results of previous tasks that might be useful:\n
                     task.status = "completed"
                     # Run execute_callback for memory operations
                     try:
-                        loop = asyncio.get_event_loop()
-                        loop.run_until_complete(task.execute_callback(task_output))
+                        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))
                     except Exception as e:
                         logger.error(f"Error executing memory callback for task {task_id}: {e}")
                         logger.exception(e)
@@ -606,7 +737,12 @@ Here are the results of previous tasks that might be useful:\n
                     if task.callback:
                         try:
                             if asyncio.iscoroutinefunction(task.callback):
-                                loop.run_until_complete(task.callback(task_output))
+                                if asyncio.get_event_loop().is_running():
+                                    asyncio.create_task(task.callback(task_output))
+                                else:
+                                    loop = asyncio.new_event_loop()
+                                    asyncio.set_event_loop(loop)
+                                    loop.run_until_complete(task.callback(task_output))
                             else:
                                 task.callback(task_output)
                         except Exception as e:
@@ -639,7 +775,8 @@ Here are the results of previous tasks that might be useful:\n
             tasks=self.tasks,
             agents=self.agents,
             manager_llm=self.manager_llm,
-            verbose=self.verbose
+            verbose=self.verbose,
+            max_iter=self.max_iter
         )
         
         if self.process == "workflow":
@@ -678,12 +815,24 @@ Here are the results of previous tasks that might be useful:\n
             return str(agent[0])
         return None
 
-    def start(self):
+    def start(self, content=None, **kwargs):
+        """Start agent execution with optional content and config"""
+        if content:
+            # Add content to context of all tasks
+            for task in self.tasks.values():
+                if isinstance(content, (str, list)):
+                    # If context is empty, initialize it
+                    if not task.context:
+                        task.context = []
+                    # Add content to context
+                    task.context.append(content)
+                
+        # Run tasks as before
         self.run_all_tasks()
         return {
             "task_status": self.get_all_tasks_status(),
             "task_results": {task_id: self.get_task_result(task_id) for task_id in self.tasks}
-        } 
+        }
 
     def set_state(self, key: str, value: Any) -> None:
         """Set a state value"""

agents/praisonaiagents/knowledge/__init__.py

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

agents/praisonaiagents/knowledge/chunking.py

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

agents/praisonaiagents/knowledge/knowledge.py

@@ -0,0 +1,321 @@
+import os
+import logging
+import uuid
+import time
+from .chunking import Chunking
+from functools import cached_property
+from rich.progress import Progress, SpinnerColumn, TextColumn, BarColumn, TaskProgressColumn
+
+logger = logging.getLogger(__name__)
+
+class CustomMemory:
+    @classmethod
+    def from_config(cls, config):
+        from mem0 import Memory
+        return type('CustomMemory', (Memory,), {
+            '_add_to_vector_store': cls._add_to_vector_store
+        }).from_config(config)
+
+    @staticmethod
+    def _add_to_vector_store(self, messages, metadata, filters):
+        # Custom implementation that doesn't use LLM
+        parsed_messages = "\n".join([msg["content"] for msg in messages])
+        
+        # Create a simple fact without using LLM
+        new_retrieved_facts = [parsed_messages]
+        
+        # Process embeddings and continue with vector store operations
+        new_message_embeddings = {}
+        for new_mem in new_retrieved_facts:
+            messages_embeddings = self.embedding_model.embed(new_mem)
+            new_message_embeddings[new_mem] = messages_embeddings
+            
+        # Create the memory
+        memory_id = self._create_memory(
+            data=parsed_messages,
+            existing_embeddings=new_message_embeddings,
+            metadata=metadata
+        )
+        
+        return [{
+            "id": memory_id,
+            "memory": parsed_messages,
+            "event": "ADD"
+        }]
+
+class Knowledge:
+    def __init__(self, config=None, verbose=None):
+        self._config = config
+        self._verbose = verbose or 0
+        os.environ['ANONYMIZED_TELEMETRY'] = 'False'  # Chromadb
+        
+        # Configure logging levels based on verbose setting
+        if not self._verbose:
+            # Suppress logs from all relevant dependencies
+            for logger_name in [
+                'mem0', 
+                'chromadb', 
+                'local_persistent_hnsw',
+                '_client',
+                'main'
+            ]:
+                logging.getLogger(logger_name).setLevel(logging.WARNING)
+            
+            # Disable OpenAI API request logging
+            logging.getLogger('openai').setLevel(logging.WARNING)
+            
+            # Set root logger to warning to catch any uncategorized logs
+            logging.getLogger().setLevel(logging.WARNING)
+
+    @cached_property
+    def _deps(self):
+        try:
+            from markitdown import MarkItDown
+            import chromadb
+            return {
+                'chromadb': chromadb,
+                'markdown': MarkItDown()
+            }
+        except ImportError:
+            raise ImportError(
+                "Required packages not installed. Please install using: "
+                'pip install "praisonaiagents[knowledge]"'
+            )
+
+    @cached_property
+    def config(self):
+        # Generate unique collection name for each instance (only if not provided in config)
+        default_collection = f"test_{int(time.time())}_{str(uuid.uuid4())[:8]}"
+        persist_dir = ".praison"
+
+        # Create persistent client config
+        base_config = {
+            "vector_store": {
+                "provider": "chroma",
+                "config": {
+                    "collection_name": default_collection,
+                    "path": persist_dir,
+                    "client": self._deps['chromadb'].PersistentClient(path=persist_dir),
+                    "host": None,
+                    "port": None
+                }
+            },
+            "version": "v1.1",
+            "custom_prompt": "Return {{\"facts\": [text]}} where text is the exact input provided and json response"
+        }
+
+        # If config is provided, merge it with base config
+        if self._config:
+            # Merge version if provided
+            if "version" in self._config:
+                base_config["version"] = self._config["version"]
+            
+            # Merge vector_store config
+            if "vector_store" in self._config:
+                if "provider" in self._config["vector_store"]:
+                    base_config["vector_store"]["provider"] = self._config["vector_store"]["provider"]
+            
+                if "config" in self._config["vector_store"]:
+                    config_copy = self._config["vector_store"]["config"].copy()
+                    # Only exclude client as it's managed internally
+                    if "client" in config_copy:
+                        del config_copy["client"]
+                    base_config["vector_store"]["config"].update(config_copy)
+            
+            # Merge embedder config if provided
+            if "embedder" in self._config:
+                base_config["embedder"] = self._config["embedder"]
+            
+            # Merge llm config if provided
+            if "llm" in self._config:
+                base_config["llm"] = self._config["llm"]
+        return base_config
+
+    @cached_property
+    def memory(self):
+        try:
+            return CustomMemory.from_config(self.config)
+        except (NotImplementedError, ValueError) as e:
+            if "list_collections" in str(e) or "Extra fields not allowed" in str(e):
+                # Keep only allowed fields
+                vector_store_config = {
+                    "collection_name": self.config["vector_store"]["config"]["collection_name"],
+                    "path": self.config["vector_store"]["config"]["path"]
+                }
+                self.config["vector_store"]["config"] = vector_store_config
+                from mem0 import Memory
+                return Memory.from_config(self.config)
+            raise
+
+    @cached_property
+    def markdown(self):
+        return self._deps['markdown']
+
+    @cached_property
+    def chunker(self):
+        return Chunking(
+            chunker_type='recursive',
+            chunk_size=512,
+            chunk_overlap=50
+        )
+
+    def _log(self, message, level=2):
+        """Internal logging helper"""
+        if self._verbose and self._verbose >= level:
+            logger.info(message)
+
+    def store(self, content, user_id=None, agent_id=None, run_id=None, metadata=None):
+        """Store a memory."""
+        try:
+            if isinstance(content, str):
+                if any(content.lower().endswith(ext) for ext in ['.pdf', '.doc', '.docx', '.txt']):
+                    self._log(f"Content appears to be a file path, processing file: {content}")
+                    return self.add(content, user_id=user_id, agent_id=agent_id, run_id=run_id, metadata=metadata)
+                
+                content = content.strip()
+                if not content:
+                    return []
+                
+            result = self.memory.add(content, user_id=user_id, agent_id=agent_id, run_id=run_id, metadata=metadata)
+            self._log(f"Store operation result: {result}")
+            return result
+        except Exception as e:
+            logger.error(f"Error storing content: {str(e)}")
+            return []
+
+    def get_all(self, user_id=None, agent_id=None, run_id=None):
+        """Retrieve all memories."""
+        return self.memory.get_all(user_id=user_id, agent_id=agent_id, run_id=run_id)
+
+    def get(self, memory_id):
+        """Retrieve a specific memory by ID."""
+        return self.memory.get(memory_id)
+
+    def search(self, query, user_id=None, agent_id=None, run_id=None):
+        """Search for memories related to a query."""
+        return self.memory.search(query, user_id=user_id, agent_id=agent_id, run_id=run_id)
+
+    def update(self, memory_id, data):
+        """Update a memory."""
+        return self.memory.update(memory_id, data)
+
+    def history(self, memory_id):
+        """Get the history of changes for a memory."""
+        return self.memory.history(memory_id)
+
+    def delete(self, memory_id):
+        """Delete a memory."""
+        self.memory.delete(memory_id)
+
+    def delete_all(self, user_id=None, agent_id=None, run_id=None):
+        """Delete all memories."""
+        self.memory.delete_all(user_id=user_id, agent_id=agent_id, run_id=run_id)
+
+    def reset(self):
+        """Reset all memories."""
+        self.memory.reset()
+
+    def normalize_content(self, content):
+        """Normalize content for consistent storage."""
+        # Example normalization: strip whitespace, convert to lowercase
+        return content.strip().lower()
+
+    def add(self, file_path, user_id=None, agent_id=None, run_id=None, metadata=None):
+        """Read file content and store it in memory.
+        
+        Args:
+            file_path: Can be:
+                - A string path to local file
+                - A URL string
+                - A list containing file paths and/or URLs
+        """
+        if isinstance(file_path, (list, tuple)):
+            results = []
+            for path in file_path:
+                result = self._process_single_input(path, user_id, agent_id, run_id, metadata)
+                results.extend(result.get('results', []))
+            return {'results': results, 'relations': []}
+        
+        return self._process_single_input(file_path, user_id, agent_id, run_id, metadata)
+
+    def _process_single_input(self, input_path, user_id=None, agent_id=None, run_id=None, metadata=None):
+        """Process a single input which can be a file path or URL."""
+        try:
+            # Define supported file extensions
+            DOCUMENT_EXTENSIONS = {
+                'document': ('.pdf', '.ppt', '.pptx', '.doc', '.docx', '.xls', '.xlsx'),
+                'media': ('.jpg', '.jpeg', '.png', '.gif', '.bmp', '.tiff', '.mp3', '.wav', '.ogg', '.m4a'),
+                'text': ('.txt', '.csv', '.json', '.xml', '.md', '.html', '.htm'),
+                'archive': '.zip'
+            }
+
+            # Check if input is URL
+            if isinstance(input_path, str) and (input_path.startswith('http://') or input_path.startswith('https://')):
+                self._log(f"Processing URL: {input_path}")
+                raise NotImplementedError("URL processing not yet implemented")
+
+            # Check if input ends with any supported extension
+            is_supported_file = any(input_path.lower().endswith(ext) 
+                                  for exts in DOCUMENT_EXTENSIONS.values()
+                                  for ext in (exts if isinstance(exts, tuple) else (exts,)))
+            
+            if is_supported_file:
+                self._log(f"Processing as file path: {input_path}")
+                if not os.path.exists(input_path):
+                    logger.error(f"File not found: {input_path}")
+                    raise FileNotFoundError(f"File not found: {input_path}")
+                
+                file_ext = '.' + input_path.lower().split('.')[-1]  # Get extension reliably
+                
+                # Process file based on type
+                if file_ext in DOCUMENT_EXTENSIONS['text']:
+                    with open(input_path, 'r', encoding='utf-8') as file:
+                        content = file.read().strip()
+                    if not content:
+                        raise ValueError("Empty text file")
+                    memories = [self.normalize_content(content)]
+                else:
+                    # Use MarkItDown for documents and media
+                    result = self.markdown.convert(input_path)
+                    content = result.text_content
+                    if not content:
+                        raise ValueError("No content could be extracted from file")
+                    chunks = self.chunker.chunk(content)
+                    memories = [chunk.text.strip() if hasattr(chunk, 'text') else str(chunk).strip() 
+                              for chunk in chunks if chunk]
+
+                # Set metadata for file
+                if not metadata:
+                    metadata = {}
+                metadata['file_type'] = file_ext.lstrip('.')
+                metadata['filename'] = os.path.basename(input_path)
+            else:
+                # Treat as raw text content only if no file extension
+                memories = [self.normalize_content(input_path)]
+
+            # Create progress display
+            progress = Progress(
+                SpinnerColumn(),
+                TextColumn("[progress.description]{task.description}"),
+                BarColumn(),
+                TaskProgressColumn(),
+                transient=True
+            )
+
+            # Store memories with progress bar
+            all_results = []
+            with progress:
+                store_task = progress.add_task(f"Adding to Knowledge from {os.path.basename(input_path)}", total=len(memories))
+                for memory in memories:
+                    if memory:
+                        memory_result = self.store(memory, user_id=user_id, agent_id=agent_id, 
+                                                 run_id=run_id, metadata=metadata)
+                        if memory_result:
+                            all_results.extend(memory_result.get('results', []))
+                        progress.advance(store_task)
+
+            return {'results': all_results, 'relations': []}
+
+        except Exception as e:
+            logger.error(f"Error processing input {input_path}: {str(e)}", exc_info=True)
+            raise

agents/praisonaiagents/llm/__init__.py

@@ -0,0 +1,20 @@
+import logging
+import warnings
+
+# Suppress all relevant logs at module level
+logging.getLogger("litellm").setLevel(logging.ERROR)
+logging.getLogger("openai").setLevel(logging.ERROR)
+logging.getLogger("httpx").setLevel(logging.ERROR)
+logging.getLogger("httpcore").setLevel(logging.ERROR)
+logging.getLogger("pydantic").setLevel(logging.ERROR)
+
+# Suppress pydantic warnings
+warnings.filterwarnings("ignore", category=UserWarning, module="pydantic")
+
+# Configure logging to suppress all INFO messages
+logging.basicConfig(level=logging.WARNING)
+
+# Import after suppressing warnings
+from .llm import LLM, LLMContextLengthExceededException
+
+__all__ = ["LLM", "LLMContextLengthExceededException"]

agents/praisonaiagents/llm/llm.py

@@ -0,0 +1,823 @@
+import logging
+import os
+import warnings
+from typing import Any, Dict, List, Optional, Union, Literal, Callable
+from pydantic import BaseModel
+import time
+import json
+from ..main import (
+    display_error,
+    display_tool_call,
+    display_instruction,
+    display_interaction,
+    display_generating,
+    display_self_reflection,
+    ReflectionOutput,
+)
+from rich.console import Console
+from rich.live import Live
+
+class LLMContextLengthExceededException(Exception):
+    """Raised when LLM context length is exceeded"""
+    def __init__(self, message: str):
+        self.message = message
+        super().__init__(self.message)
+
+    def _is_context_limit_error(self, error_message: str) -> bool:
+        """Check if error is related to context length"""
+        context_limit_phrases = [
+            "maximum context length",
+            "context window is too long",
+            "context length exceeded",
+            "context_length_exceeded"
+        ]
+        return any(phrase in error_message.lower() for phrase in context_limit_phrases)
+
+class LLM:
+    """
+    Easy to use wrapper for language models. Supports multiple providers like OpenAI, 
+    Anthropic, and others through LiteLLM.
+    """
+    
+    # Default window sizes for different models (75% of actual to be safe)
+    MODEL_WINDOWS = {
+        # OpenAI
+        "gpt-4": 6144,                    # 8,192 actual
+        "gpt-4o": 96000,                  # 128,000 actual
+        "gpt-4o-mini": 96000,            # 128,000 actual
+        "gpt-4-turbo": 96000,            # 128,000 actual
+        "o1-preview": 96000,             # 128,000 actual
+        "o1-mini": 96000,                # 128,000 actual
+        
+        # Anthropic
+        "claude-3-5-sonnet": 12288,       # 16,384 actual
+        "claude-3-sonnet": 12288,         # 16,384 actual
+        "claude-3-opus": 96000,           # 128,000 actual
+        "claude-3-haiku": 96000,          # 128,000 actual
+        
+        # Gemini
+        "gemini-2.0-flash": 786432,       # 1,048,576 actual
+        "gemini-1.5-pro": 1572864,        # 2,097,152 actual
+        "gemini-1.5-flash": 786432,       # 1,048,576 actual
+        "gemini-1.5-flash-8b": 786432,    # 1,048,576 actual
+        
+        # Deepseek
+        "deepseek-chat": 96000,           # 128,000 actual
+        
+        # Groq
+        "gemma2-9b-it": 6144,            # 8,192 actual
+        "gemma-7b-it": 6144,             # 8,192 actual
+        "llama3-70b-8192": 6144,         # 8,192 actual
+        "llama3-8b-8192": 6144,          # 8,192 actual
+        "mixtral-8x7b-32768": 24576,     # 32,768 actual
+        "llama-3.3-70b-versatile": 96000, # 128,000 actual
+        "llama-3.3-70b-instruct": 96000,  # 128,000 actual
+        
+        # Other llama models
+        "llama-3.1-70b-versatile": 98304, # 131,072 actual
+        "llama-3.1-8b-instant": 98304,    # 131,072 actual
+        "llama-3.2-1b-preview": 6144,     # 8,192 actual
+        "llama-3.2-3b-preview": 6144,     # 8,192 actual
+        "llama-3.2-11b-text-preview": 6144,  # 8,192 actual
+        "llama-3.2-90b-text-preview": 6144   # 8,192 actual
+    }
+
+    def __init__(
+        self,
+        model: str,
+        timeout: Optional[int] = None,
+        temperature: Optional[float] = None,
+        top_p: Optional[float] = None,
+        n: Optional[int] = None,
+        max_tokens: Optional[int] = None,
+        presence_penalty: Optional[float] = None,
+        frequency_penalty: Optional[float] = None,
+        logit_bias: Optional[Dict[int, float]] = None,
+        response_format: Optional[Dict[str, Any]] = None,
+        seed: Optional[int] = None,
+        logprobs: Optional[bool] = None,
+        top_logprobs: Optional[int] = None,
+        api_version: Optional[str] = None,
+        stop_phrases: Optional[Union[str, List[str]]] = None,
+        api_key: Optional[str] = None,
+        base_url: Optional[str] = None,
+        events: List[Any] = [],
+        **extra_settings
+    ):
+        try:
+            import litellm
+            # Set litellm options globally
+            litellm.set_verbose = False
+            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)
+        except ImportError:
+            raise ImportError(
+                "LiteLLM is required but not installed. "
+                "Please install with: pip install 'praisonaiagents[llm]'"
+            )
+
+        self.model = model
+        self.timeout = timeout
+        self.temperature = temperature
+        self.top_p = top_p
+        self.n = n
+        self.max_tokens = max_tokens
+        self.presence_penalty = presence_penalty
+        self.frequency_penalty = frequency_penalty
+        self.logit_bias = logit_bias
+        self.response_format = response_format
+        self.seed = seed
+        self.logprobs = logprobs
+        self.top_logprobs = top_logprobs
+        self.api_version = api_version
+        self.stop_phrases = stop_phrases
+        self.api_key = api_key
+        self.base_url = base_url
+        self.events = events
+        self.extra_settings = extra_settings
+        self.console = Console()
+        self.chat_history = []
+        self.verbose = extra_settings.get('verbose', True)
+        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)
+        
+        # Enable error dropping for cleaner output
+        litellm.drop_params = True
+        self._setup_event_tracking(events)
+
+    def get_response(
+        self,
+        prompt: Union[str, List[Dict]],
+        system_prompt: Optional[str] = None,
+        chat_history: Optional[List[Dict]] = None,
+        temperature: float = 0.2,
+        tools: Optional[List[Any]] = None,
+        output_json: Optional[BaseModel] = None,
+        output_pydantic: Optional[BaseModel] = None,
+        verbose: bool = True,
+        markdown: bool = True,
+        self_reflect: bool = False,
+        max_reflect: int = 3,
+        min_reflect: int = 1,
+        console: Optional[Console] = None,
+        agent_name: Optional[str] = None,
+        agent_role: Optional[str] = None,
+        agent_tools: Optional[List[str]] = None,
+        execute_tool_fn: Optional[Callable] = None,
+        **kwargs
+    ) -> str:
+        """Enhanced get_response with all OpenAI-like features"""
+        try:
+            import litellm
+            
+            # Disable litellm debug messages
+            litellm.set_verbose = False
+            
+            # Build messages list
+            messages = []
+            if system_prompt:
+                if output_json:
+                    system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {json.dumps(output_json.model_json_schema())}"
+                elif output_pydantic:
+                    system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {json.dumps(output_pydantic.model_json_schema())}"
+                messages.append({"role": "system", "content": system_prompt})
+            
+            if chat_history:
+                messages.extend(chat_history)
+
+            # Handle prompt modifications for JSON output
+            original_prompt = prompt
+            if output_json or output_pydantic:
+                if isinstance(prompt, str):
+                    prompt += "\nReturn ONLY a valid JSON object. No other text or explanation."
+                elif isinstance(prompt, list):
+                    for item in prompt:
+                        if item["type"] == "text":
+                            item["text"] += "\nReturn ONLY a valid JSON object. No other text or explanation."
+                            break
+
+            # Add prompt to messages
+            if isinstance(prompt, list):
+                messages.append({"role": "user", "content": prompt})
+            else:
+                messages.append({"role": "user", "content": prompt})
+
+            start_time = time.time()
+            reflection_count = 0
+
+            while True:
+                try:
+                    if verbose:
+                        display_text = prompt
+                        if isinstance(prompt, list):
+                            display_text = next((item["text"] for item in prompt if item["type"] == "text"), "")
+                        
+                        if display_text and str(display_text).strip():
+                            display_instruction(
+                                f"Agent {agent_name} is processing prompt: {display_text}",
+                                console=console,
+                                agent_name=agent_name,
+                                agent_role=agent_role,
+                                agent_tools=agent_tools
+                            )
+
+                    # Get response from LiteLLM
+                    start_time = time.time()
+                    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,
+                            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()
+
+                    # Get final completion to check for tool calls
+                    final_response = litellm.completion(
+                        model=self.model,
+                        messages=messages,
+                        temperature=temperature,
+                        stream=False,  # No streaming for tool call check
+                        **kwargs
+                    )
+                    
+                    tool_calls = final_response["choices"][0]["message"].get("tool_calls")
+                    
+                    # Handle tool calls
+                    if tool_calls and execute_tool_fn:
+                        messages.append({
+                            "role": "assistant",
+                            "content": response_text,
+                            "tool_calls": tool_calls
+                        })
+                        
+                        for tool_call in tool_calls:
+                            function_name = tool_call["function"]["name"]
+                            arguments = json.loads(tool_call["function"]["arguments"])
+
+                            if verbose:
+                                display_tool_call(f"Agent {agent_name} is calling function '{function_name}' with arguments: {arguments}", console=console)
+
+                            tool_result = execute_tool_fn(function_name, arguments)
+
+                            if tool_result:
+                                if verbose:
+                                    display_tool_call(f"Function '{function_name}' returned: {tool_result}", console=console)
+                                messages.append({
+                                    "role": "tool",
+                                    "tool_call_id": tool_call["id"],
+                                    "content": json.dumps(tool_result)
+                                })
+                            else:
+                                messages.append({
+                                    "role": "tool",
+                                    "tool_call_id": tool_call["id"],
+                                    "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:
+                                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,
+                                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 = response_text.strip()
+
+                    # Handle output formatting
+                    if output_json or output_pydantic:
+                        self.chat_history.append({"role": "user", "content": original_prompt})
+                        self.chat_history.append({"role": "assistant", "content": response_text})
+                        if verbose:
+                            display_interaction(original_prompt, response_text, markdown=markdown,
+                                             generation_time=time.time() - start_time, console=console)
+                        return response_text
+
+                    if not self_reflect:
+                        if verbose:
+                            display_interaction(original_prompt, response_text, markdown=markdown,
+                                             generation_time=time.time() - start_time, console=console)
+                        return response_text
+
+                    # Handle self-reflection
+                    reflection_prompt = f"""
+Reflect on your previous response: '{response_text}'.
+Identify any flaws, improvements, or actions.
+Provide a "satisfactory" status ('yes' or 'no').
+Output MUST be JSON with 'reflection' and 'satisfactory'.
+                    """
+                    
+                    reflection_messages = messages + [
+                        {"role": "assistant", "content": response_text},
+                        {"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:
+                            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:
+                                    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
+
+                    try:
+                        reflection_data = json.loads(reflection_text)
+                        satisfactory = reflection_data.get("satisfactory", "no").lower() == "yes"
+
+                        if verbose:
+                            display_self_reflection(
+                                f"Agent {agent_name} self reflection: reflection='{reflection_data['reflection']}' satisfactory='{reflection_data['satisfactory']}'",
+                                console=console
+                            )
+
+                        if satisfactory and reflection_count >= min_reflect - 1:
+                            if verbose:
+                                display_interaction(prompt, response_text, markdown=markdown,
+                                                 generation_time=time.time() - start_time, console=console)
+                            return response_text
+
+                        if reflection_count >= max_reflect - 1:
+                            if verbose:
+                                display_interaction(prompt, response_text, markdown=markdown,
+                                                 generation_time=time.time() - start_time, console=console)
+                            return response_text
+
+                        reflection_count += 1
+                        messages.extend([
+                            {"role": "assistant", "content": response_text},
+                            {"role": "user", "content": reflection_prompt},
+                            {"role": "assistant", "content": reflection_text},
+                            {"role": "user", "content": "Now regenerate your response using the reflection you made"}
+                        ])
+                        continue
+
+                    except json.JSONDecodeError:
+                        reflection_count += 1
+                        if reflection_count >= max_reflect:
+                            return response_text
+                        continue
+
+                except Exception as e:
+                    display_error(f"Error in LLM response: {str(e)}")
+                    return None
+
+        except Exception as error:
+            display_error(f"Error in get_response: {str(error)}")
+            raise
+
+    async def get_response_async(
+        self,
+        prompt: Union[str, List[Dict]],
+        system_prompt: Optional[str] = None,
+        chat_history: Optional[List[Dict]] = None,
+        temperature: float = 0.2,
+        tools: Optional[List[Any]] = None,
+        output_json: Optional[BaseModel] = None,
+        output_pydantic: Optional[BaseModel] = None,
+        verbose: bool = True,
+        markdown: bool = True,
+        self_reflect: bool = False,
+        max_reflect: int = 3,
+        min_reflect: int = 1,
+        console: Optional[Console] = None,
+        agent_name: Optional[str] = None,
+        agent_role: Optional[str] = None,
+        agent_tools: Optional[List[str]] = None,
+        execute_tool_fn: Optional[Callable] = None,
+        **kwargs
+    ) -> str:
+        """Async version of get_response with identical functionality."""
+        try:
+            import litellm
+            litellm.set_verbose = False
+
+            # Build messages list
+            messages = []
+            if system_prompt:
+                if output_json:
+                    system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {json.dumps(output_json.model_json_schema())}"
+                elif output_pydantic:
+                    system_prompt += f"\nReturn ONLY a JSON object that matches this Pydantic model: {json.dumps(output_pydantic.model_json_schema())}"
+                messages.append({"role": "system", "content": system_prompt})
+            
+            if chat_history:
+                messages.extend(chat_history)
+
+            # Handle prompt modifications for JSON output
+            original_prompt = prompt
+            if output_json or output_pydantic:
+                if isinstance(prompt, str):
+                    prompt += "\nReturn ONLY a valid JSON object. No other text or explanation."
+                elif isinstance(prompt, list):
+                    for item in prompt:
+                        if item["type"] == "text":
+                            item["text"] += "\nReturn ONLY a valid JSON object. No other text or explanation."
+                            break
+
+            # Add prompt to messages
+            if isinstance(prompt, list):
+                messages.append({"role": "user", "content": prompt})
+            else:
+                messages.append({"role": "user", "content": prompt})
+
+            start_time = time.time()
+            reflection_count = 0
+
+            # Format tools for LiteLLM
+            formatted_tools = None
+            if tools:
+                logging.info(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)}")
+                    if hasattr(tool, '__name__'):
+                        tool_name = tool.__name__
+                        tool_doc = tool.__doc__ or "No description available"
+                        # Get function signature
+                        import inspect
+                        sig = inspect.signature(tool)
+                        logging.debug(f"Tool signature: {sig}")
+                        params = {}
+                        required = []
+                        for name, param in sig.parameters.items():
+                            logging.debug(f"Processing parameter: {name} with annotation: {param.annotation}")
+                            param_type = "string"
+                            if param.annotation != inspect.Parameter.empty:
+                                if param.annotation == int:
+                                    param_type = "integer"
+                                elif param.annotation == float:
+                                    param_type = "number"
+                                elif param.annotation == bool:
+                                    param_type = "boolean"
+                                elif param.annotation == Dict:
+                                    param_type = "object"
+                                elif param.annotation == List:
+                                    param_type = "array"
+                                elif hasattr(param.annotation, "__name__"):
+                                    param_type = param.annotation.__name__.lower()
+                            params[name] = {"type": param_type}
+                            if param.default == inspect.Parameter.empty:
+                                required.append(name)
+                        
+                        logging.debug(f"Generated parameters: {params}")
+                        logging.debug(f"Required parameters: {required}")
+                        
+                        tool_def = {
+                            "type": "function",
+                            "function": {
+                                "name": tool_name,
+                                "description": tool_doc,
+                                "parameters": {
+                                    "type": "object",
+                                    "properties": params,
+                                    "required": required
+                                }
+                            }
+                        }
+                        # 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}")
+                            formatted_tools.append(tool_def)
+                        except TypeError as e:
+                            logging.error(f"Tool definition not JSON serializable: {e}")
+                            continue
+
+            # Validate final tools list
+            if formatted_tools:
+                try:
+                    json.dumps(formatted_tools)  # Final serialization check
+                    logging.info(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(
+                    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()
+
+            # ----------------------------------------------------
+            # 2) If tool calls are needed, do a non-streaming call
+            # ----------------------------------------------------
+            if tools and execute_tool_fn:
+                # Next call with tools if needed
+                tool_response = await litellm.acompletion(
+                    model=self.model,
+                    messages=messages,
+                    temperature=temperature,
+                    stream=False,
+                    tools=formatted_tools,  # We safely pass tools here
+                    **kwargs
+                )
+                # handle tool_calls from tool_response as usual...
+                tool_calls = tool_response.choices[0].message.get("tool_calls")
+                
+                if tool_calls:
+                    messages.append({
+                        "role": "assistant",
+                        "content": response_text,
+                        "tool_calls": tool_calls
+                    })
+                    
+                    for tool_call in tool_calls:
+                        function_name = tool_call.function.name
+                        arguments = json.loads(tool_call.function.arguments)
+
+                        if verbose:
+                            display_tool_call(f"Agent {agent_name} is calling function '{function_name}' with arguments: {arguments}", console=console)
+
+                        tool_result = await execute_tool_fn(function_name, arguments)
+
+                        if tool_result:
+                            if verbose:
+                                display_tool_call(f"Function '{function_name}' returned: {tool_result}", console=console)
+                            messages.append({
+                                "role": "tool",
+                                "tool_call_id": tool_call.id,
+                                "content": json.dumps(tool_result)
+                            })
+                        else:
+                            messages.append({
+                                "role": "tool",
+                                "tool_call_id": tool_call.id,
+                                "content": "Function returned an empty output"
+                            })
+
+                    # Get response after tool calls with streaming
+                    response_text = ""
+                    if verbose:
+                        async for chunk in 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
+
+            response_text = response_text.strip()
+
+            # Handle output formatting
+            if output_json or output_pydantic:
+                self.chat_history.append({"role": "user", "content": original_prompt})
+                self.chat_history.append({"role": "assistant", "content": response_text})
+                if verbose:
+                    display_interaction(original_prompt, response_text, markdown=markdown,
+                                     generation_time=time.time() - start_time, console=console)
+                return response_text
+
+            if not self_reflect:
+                if verbose:
+                    display_interaction(original_prompt, response_text, markdown=markdown,
+                                     generation_time=time.time() - start_time, console=console)
+                return response_text
+
+            # Handle self-reflection
+            reflection_prompt = f"""
+Reflect on your previous response: '{response_text}'.
+Identify any flaws, improvements, or actions.
+Provide a "satisfactory" status ('yes' or 'no').
+Output MUST be JSON with 'reflection' and 'satisfactory'.
+            """
+            
+            reflection_messages = messages + [
+                {"role": "assistant", "content": response_text},
+                {"role": "user", "content": reflection_prompt}
+            ]
+
+            # Get reflection response
+            reflection_text = ""
+            if verbose:
+                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:
+                        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")
+            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
+
+            while True:  # Add loop for reflection handling
+                try:
+                    reflection_data = json.loads(reflection_text)
+                    satisfactory = reflection_data.get("satisfactory", "no").lower() == "yes"
+
+                    if verbose:
+                        display_self_reflection(
+                            f"Agent {agent_name} self reflection: reflection='{reflection_data['reflection']}' satisfactory='{reflection_data['satisfactory']}'",
+                            console=console
+                        )
+
+                    if satisfactory and reflection_count >= min_reflect - 1:
+                        if verbose:
+                            display_interaction(prompt, response_text, markdown=markdown,
+                                             generation_time=time.time() - start_time, console=console)
+                        return response_text
+
+                    if reflection_count >= max_reflect - 1:
+                        if verbose:
+                            display_interaction(prompt, response_text, markdown=markdown,
+                                             generation_time=time.time() - start_time, console=console)
+                        return response_text
+
+                    reflection_count += 1
+                    messages.extend([
+                        {"role": "assistant", "content": response_text},
+                        {"role": "user", "content": reflection_prompt},
+                        {"role": "assistant", "content": reflection_text},
+                        {"role": "user", "content": "Now regenerate your response using the reflection you made"}
+                    ])
+                    continue  # Now properly in a loop
+
+                except json.JSONDecodeError:
+                    reflection_count += 1
+                    if reflection_count >= max_reflect:
+                        return response_text
+                    continue  # Now properly in a loop
+            
+        except Exception as error:
+            if LLMContextLengthExceededException(str(error))._is_context_limit_error(str(error)):
+                raise LLMContextLengthExceededException(str(error))
+            display_error(f"Error in get_response_async: {str(error)}")
+            raise
+
+    def can_use_tools(self) -> bool:
+        """Check if this model can use tool functions"""
+        try:
+            import litellm
+            allowed_params = litellm.get_supported_openai_params(model=self.model)
+            return "response_format" in allowed_params
+        except ImportError:
+            raise ImportError(
+                "LiteLLM is required but not installed. "
+                "Please install it with: pip install 'praisonaiagents[llm]'"
+            )
+        except:
+            return False
+
+    def can_use_stop_words(self) -> bool:
+        """Check if this model supports stop words"""
+        try:
+            import litellm
+            allowed_params = litellm.get_supported_openai_params(model=self.model)
+            return "stop" in allowed_params
+        except ImportError:
+            raise ImportError(
+                "LiteLLM is required but not installed. "
+                "Please install it with: pip install 'praisonaiagents[llm]'"
+            )
+        except:
+            return False
+
+    def get_context_size(self) -> int:
+        """Get safe input size limit for this model"""
+        for model_prefix, size in self.MODEL_WINDOWS.items():
+            if self.model.startswith(model_prefix):
+                return size
+        return 4000  # Safe default
+
+    def _setup_event_tracking(self, events: List[Any]) -> None:
+        """Setup callback functions for tracking model usage"""
+        try:
+            import litellm
+        except ImportError:
+            raise ImportError(
+                "LiteLLM is required but not installed. "
+                "Please install it with: pip install 'praisonaiagents[llm]'"
+            )
+
+        event_types = [type(event) for event in events]
+        
+        # Remove old events of same type
+        for event in litellm.success_callback[:]:
+            if type(event) in event_types:
+                litellm.success_callback.remove(event)
+                
+        for event in litellm._async_success_callback[:]:
+            if type(event) in event_types:
+                litellm._async_success_callback.remove(event)
+                
+        litellm.callbacks = events

agents/praisonaiagents/main.py

@@ -14,6 +14,15 @@ from rich.logging import RichHandler
 from rich.live import Live
 import asyncio
 
+# # Configure root logger
+# logging.basicConfig(level=logging.WARNING)
+
+# Suppress litellm logs
+logging.getLogger("litellm").handlers = []
+logging.getLogger("litellm.utils").handlers = []
+logging.getLogger("litellm").propagate = False
+logging.getLogger("litellm.utils").propagate = False
+
 LOGLEVEL = os.environ.get('LOGLEVEL', 'INFO').upper()
 
 logging.basicConfig(
@@ -23,6 +32,10 @@ logging.basicConfig(
     handlers=[RichHandler(rich_tracebacks=True)]
 )
 
+# Add these lines to suppress markdown parser debug logs
+logging.getLogger('markdown_it').setLevel(logging.WARNING)
+logging.getLogger('rich.markdown').setLevel(logging.WARNING)
+
 # Global list to store error logs
 error_logs = []
 
@@ -340,7 +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/memory/memory.py

@@ -15,7 +15,7 @@ try:
     CHROMADB_AVAILABLE = True
 except ImportError:
     CHROMADB_AVAILABLE = False
-    logger.warning("To use memory features, please run: pip install \"praisonaiagents[memory]\"")
+    pass
 
 try:
     import mem0

agents/praisonaiagents/process/process.py

@@ -5,19 +5,23 @@ from pydantic import BaseModel
 from ..agent.agent import Agent
 from ..task.task import Task
 from ..main import display_error, client
+import csv
+import os
 
 class LoopItems(BaseModel):
     items: List[Any]
 
 class Process:
-    def __init__(self, tasks: Dict[str, Task], agents: List[Agent], manager_llm: Optional[str] = None, verbose: bool = False):
+    def __init__(self, tasks: Dict[str, Task], agents: List[Agent], manager_llm: Optional[str] = None, verbose: bool = False, max_iter: int = 10):
         self.tasks = tasks
         self.agents = agents
         self.manager_llm = manager_llm
         self.verbose = verbose
+        self.max_iter = max_iter
 
     async def aworkflow(self) -> AsyncGenerator[str, None]:
         """Async version of workflow method"""
+        current_iter = 0  # Track how many times we've looped
         # Build workflow relationships first
         for task in self.tasks.values():
             if task.next_tasks:
@@ -41,7 +45,12 @@ class Process:
         visited_tasks = set()
         loop_data = {}  # Store loop-specific data
         
-        while current_task and current_task.id not in visited_tasks:
+        while current_task:
+            current_iter += 1
+            if current_iter > self.max_iter:
+                logging.info(f"Max iteration limit {self.max_iter} reached, ending workflow.")
+                break
+
             task_id = current_task.id
             logging.info(f"Executing workflow task: {current_task.name if current_task.name else task_id}")
             
@@ -55,46 +64,47 @@ class Process:
                     if prev_task and prev_task.result:
                         # Handle loop data
                         if current_task.task_type == "loop":
-                            # create a loop manager Agent
-                            loop_manager = Agent(
-                                name="Loop Manager",
-                                role="Loop data processor",
-                                goal="Process loop data and convert it to list format",
-                                backstory="Expert at handling loop data and converting it to proper format",
-                                llm=self.manager_llm,
-                                verbose=self.verbose,
-                                markdown=True
-                            )
+#                             # create a loop manager Agent
+#                             loop_manager = Agent(
+#                                 name="Loop Manager",
+#                                 role="Loop data processor",
+#                                 goal="Process loop data and convert it to list format",
+#                                 backstory="Expert at handling loop data and converting it to proper format",
+#                                 llm=self.manager_llm,
+#                                 verbose=self.verbose,
+#                                 markdown=True
+#                             )
 
-                            # get the loop data convert it to list using calling Agent class chat
-                            loop_prompt = f"""
-Process this data into a list format:
-{prev_task.result.raw}
+#                             # get the loop data convert it to list using calling Agent class chat
+#                             loop_prompt = f"""
+# Process this data into a list format:
+# {prev_task.result.raw}
 
-Return a JSON object with an 'items' array containing the items to process.
-"""
-                            if current_task.async_execution:
-                                loop_data_str = await loop_manager.achat(
-                                    prompt=loop_prompt,
-                                    output_json=LoopItems
-                                )
-                            else:
-                                loop_data_str = loop_manager.chat(
-                                    prompt=loop_prompt,
-                                    output_json=LoopItems
-                                )
+# Return a JSON object with an 'items' array containing the items to process.
+# """
+#                             if current_task.async_execution:
+#                                 loop_data_str = await loop_manager.achat(
+#                                     prompt=loop_prompt,
+#                                     output_json=LoopItems
+#                                 )
+#                             else:
+#                                 loop_data_str = loop_manager.chat(
+#                                     prompt=loop_prompt,
+#                                     output_json=LoopItems
+#                                 )
                             
-                            try:
-                                # The response will already be parsed into LoopItems model
-                                loop_data[f"loop_{current_task.name}"] = {
-                                    "items": loop_data_str.items,
-                                    "index": 0,
-                                    "remaining": len(loop_data_str.items)
-                                }
-                                context += f"\nCurrent loop item: {loop_data_str.items[0]}"
-                            except Exception as e:
-                                display_error(f"Failed to process loop data: {e}")
-                                context += f"\n{prev_name}: {prev_task.result.raw}"
+#                             try:
+#                                 # The response will already be parsed into LoopItems model
+#                                 loop_data[f"loop_{current_task.name}"] = {
+#                                     "items": loop_data_str.items,
+#                                     "index": 0,
+#                                     "remaining": len(loop_data_str.items)
+#                                 }
+#                                 context += f"\nCurrent loop item: {loop_data_str.items[0]}"
+#                             except Exception as e:
+#                                 display_error(f"Failed to process loop data: {e}")
+#                                 context += f"\n{prev_name}: {prev_task.result.raw}"
+                            context += f"\n{prev_name}: {prev_task.result.raw}"
                         else:
                             context += f"\n{prev_name}: {prev_task.result.raw}"
                 
@@ -111,6 +121,11 @@ Return a JSON object with an 'items' array containing the items to process.
             yield task_id
             visited_tasks.add(task_id)
             
+            # Reset completed task to "not started" so it can run again
+            if self.tasks[task_id].status == "completed":
+                logging.debug(f"Task {task_id} was completed, resetting to 'not started' for next iteration.")
+                self.tasks[task_id].status = "not started"
+            
             # Handle loop progression
             if current_task.task_type == "loop":
                 loop_key = f"loop_{current_task.name}"
@@ -130,22 +145,32 @@ Return a JSON object with an 'items' array containing the items to process.
                     
             # Determine next task based on result
             next_task = None
-            if current_task.result:
+            if current_task and current_task.result:
                 if current_task.task_type in ["decision", "loop"]:
-                    result = current_task.result.raw.lower()
+                    # MINIMAL CHANGE: use pydantic decision if present
+                    decision_str = current_task.result.raw.lower()
+                    if current_task.result.pydantic and hasattr(current_task.result.pydantic, "decision"):
+                        decision_str = current_task.result.pydantic.decision.lower()
+
                     # Check conditions
                     for condition, tasks in current_task.condition.items():
-                        if condition.lower() in result and tasks:
-                            next_task_name = tasks[0]
+                        if condition.lower() == decision_str:
+                            # Handle both list and direct string values
+                            task_value = tasks[0] if isinstance(tasks, list) else tasks
+                            if not task_value or task_value == "exit":
+                                logging.info("Workflow exit condition met, ending workflow")
+                                current_task = None
+                                break
+                            next_task_name = task_value
                             next_task = next((t for t in self.tasks.values() if t.name == next_task_name), None)
                             # For loops, allow revisiting the same task
                             if next_task and next_task.id == current_task.id:
                                 visited_tasks.discard(current_task.id)
                             break
             
-                if not next_task and current_task.next_tasks:
-                    next_task_name = current_task.next_tasks[0]
-                    next_task = next((t for t in self.tasks.values() if t.name == next_task_name), None)
+            if not next_task and current_task and current_task.next_tasks:
+                next_task_name = current_task.next_tasks[0]
+                next_task = next((t for t in self.tasks.values() if t.name == next_task_name), None)
             
             current_task = next_task
             if not current_task:
@@ -285,6 +310,7 @@ Provide a JSON with the structure:
 
     def workflow(self):
         """Synchronous version of workflow method"""
+        current_iter = 0  # Track how many times we've looped
         # Build workflow relationships first
         for task in self.tasks.values():
             if task.next_tasks:
@@ -303,12 +329,86 @@ Provide a JSON with the structure:
         if not start_task:
             start_task = list(self.tasks.values())[0]
             logging.info("No start task marked, using first task")
-        
+
+        # If loop type and no input_file, default to tasks.csv 
+        if start_task and start_task.task_type == "loop" and not start_task.input_file:
+            start_task.input_file = "tasks.csv"
+
+        # --- If loop + input_file, read file & create tasks
+        if start_task and start_task.task_type == "loop" and getattr(start_task, "input_file", None):
+            try:
+                file_ext = os.path.splitext(start_task.input_file)[1].lower()
+                new_tasks = []
+                
+                if file_ext == ".csv":
+                    # existing CSV reading logic
+                    with open(start_task.input_file, "r", encoding="utf-8") as f:
+                        # Try as simple CSV first
+                        reader = csv.reader(f)
+                        previous_task = None
+                        for i, row in enumerate(reader):
+                            if row:  # Skip empty rows
+                                task_desc = row[0]  # Take first column
+                                row_task = Task(
+                                    description=task_desc,  # Keep full row as description
+                                    agent=start_task.agent,
+                                    name=task_desc,       # Use first column as name
+                                    is_start=(i == 0),
+                                    task_type="task",
+                                    condition={
+                                        "complete": ["next"],
+                                        "retry": ["current"]
+                                    }
+                                )
+                                self.tasks[row_task.id] = row_task
+                                new_tasks.append(row_task)
+                                
+                                if previous_task:
+                                    previous_task.next_tasks = [row_task.name]
+                                    previous_task.condition["complete"] = [row_task.name]
+                                previous_task = row_task
+                else:
+                    # If not CSV, read lines
+                    with open(start_task.input_file, "r", encoding="utf-8") as f:
+                        lines = f.read().splitlines()
+                        previous_task = None
+                        for i, line in enumerate(lines):
+                            row_task = Task(
+                                description=line.strip(),
+                                agent=start_task.agent,
+                                name=line.strip(),
+                                is_start=(i == 0),
+                                task_type="task",
+                                condition={
+                                    "complete": ["next"],
+                                    "retry": ["current"]
+                                }
+                            )
+                            self.tasks[row_task.id] = row_task
+                            new_tasks.append(row_task)
+                            
+                            if previous_task:
+                                previous_task.next_tasks = [row_task.name]
+                                previous_task.condition["complete"] = [row_task.name]
+                            previous_task = row_task
+
+                if new_tasks:
+                    start_task = new_tasks[0]
+                    logging.info(f"Created {len(new_tasks)} tasks from: {start_task.input_file}")
+            except Exception as e:
+                logging.error(f"Failed to read file tasks: {e}")
+
+        # end of the new block
         current_task = start_task
         visited_tasks = set()
         loop_data = {}  # Store loop-specific data
         
-        while current_task and current_task.id not in visited_tasks:
+        while current_task:
+            current_iter += 1
+            if current_iter > self.max_iter:
+                logging.info(f"Max iteration limit {self.max_iter} reached, ending workflow.")
+                break
+
             task_id = current_task.id
             logging.info(f"Executing workflow task: {current_task.name if current_task.name else task_id}")
             
@@ -322,40 +422,41 @@ Provide a JSON with the structure:
                     if prev_task and prev_task.result:
                         # Handle loop data
                         if current_task.task_type == "loop":
-                            # create a loop manager Agent
-                            loop_manager = Agent(
-                                name="Loop Manager",
-                                role="Loop data processor",
-                                goal="Process loop data and convert it to list format",
-                                backstory="Expert at handling loop data and converting it to proper format",
-                                llm=self.manager_llm,
-                                verbose=self.verbose,
-                                markdown=True
-                            )
+#                             # create a loop manager Agent
+#                             loop_manager = Agent(
+#                                 name="Loop Manager",
+#                                 role="Loop data processor",
+#                                 goal="Process loop data and convert it to list format",
+#                                 backstory="Expert at handling loop data and converting it to proper format",
+#                                 llm=self.manager_llm,
+#                                 verbose=self.verbose,
+#                                 markdown=True
+#                             )
 
-                            # get the loop data convert it to list using calling Agent class chat
-                            loop_prompt = f"""
-Process this data into a list format:
-{prev_task.result.raw}
+#                             # get the loop data convert it to list using calling Agent class chat
+#                             loop_prompt = f"""
+# Process this data into a list format:
+# {prev_task.result.raw}
 
-Return a JSON object with an 'items' array containing the items to process.
-"""
-                            loop_data_str = loop_manager.chat(
-                                prompt=loop_prompt,
-                                output_json=LoopItems
-                            )
+# Return a JSON object with an 'items' array containing the items to process.
+# """
+#                             loop_data_str = loop_manager.chat(
+#                                 prompt=loop_prompt,
+#                                 output_json=LoopItems
+#                             )
                             
-                            try:
-                                # The response will already be parsed into LoopItems model
-                                loop_data[f"loop_{current_task.name}"] = {
-                                    "items": loop_data_str.items,
-                                    "index": 0,
-                                    "remaining": len(loop_data_str.items)
-                                }
-                                context += f"\nCurrent loop item: {loop_data_str.items[0]}"
-                            except Exception as e:
-                                display_error(f"Failed to process loop data: {e}")
-                                context += f"\n{prev_name}: {prev_task.result.raw}"
+#                             try:
+#                                 # The response will already be parsed into LoopItems model
+#                                 loop_data[f"loop_{current_task.name}"] = {
+#                                     "items": loop_data_str.items,
+#                                     "index": 0,
+#                                     "remaining": len(loop_data_str.items)
+#                                 }
+#                                 context += f"\nCurrent loop item: {loop_data_str.items[0]}"
+#                             except Exception as e:
+#                                 display_error(f"Failed to process loop data: {e}")
+#                                 context += f"\n{prev_name}: {prev_task.result.raw}"
+                            context += f"\n{prev_name}: {prev_task.result.raw}"
                         else:
                             context += f"\n{prev_name}: {prev_task.result.raw}"
                 
@@ -372,6 +473,11 @@ Return a JSON object with an 'items' array containing the items to process.
             yield task_id
             visited_tasks.add(task_id)
             
+            # Reset completed task to "not started" so it can run again: Only for workflow because some tasks may be revisited
+            if self.tasks[task_id].status == "completed":
+                logging.debug(f"Task {task_id} was completed, resetting to 'not started' for next iteration.")
+                self.tasks[task_id].status = "not started"
+            
             # Handle loop progression
             if current_task.task_type == "loop":
                 loop_key = f"loop_{current_task.name}"
@@ -391,22 +497,32 @@ Return a JSON object with an 'items' array containing the items to process.
                     
             # Determine next task based on result
             next_task = None
-            if current_task.result:
+            if current_task and current_task.result:
                 if current_task.task_type in ["decision", "loop"]:
-                    result = current_task.result.raw.lower()
+                    # MINIMAL CHANGE: use pydantic decision if present
+                    decision_str = current_task.result.raw.lower()
+                    if current_task.result.pydantic and hasattr(current_task.result.pydantic, "decision"):
+                        decision_str = current_task.result.pydantic.decision.lower()
+
                     # Check conditions
                     for condition, tasks in current_task.condition.items():
-                        if condition.lower() in result and tasks:
-                            next_task_name = tasks[0]
+                        if condition.lower() == decision_str:
+                            # Handle both list and direct string values
+                            task_value = tasks[0] if isinstance(tasks, list) else tasks
+                            if not task_value or task_value == "exit":
+                                logging.info("Workflow exit condition met, ending workflow")
+                                current_task = None
+                                break
+                            next_task_name = task_value
                             next_task = next((t for t in self.tasks.values() if t.name == next_task_name), None)
                             # For loops, allow revisiting the same task
                             if next_task and next_task.id == current_task.id:
                                 visited_tasks.discard(current_task.id)
                             break
             
-                if not next_task and current_task.next_tasks:
-                    next_task_name = current_task.next_tasks[0]
-                    next_task = next((t for t in self.tasks.values() if t.name == next_task_name), None)
+            if not next_task and current_task and current_task.next_tasks:
+                next_task_name = current_task.next_tasks[0]
+                next_task = next((t for t in self.tasks.values() if t.name == next_task_name), None)
             
             current_task = next_task
             if not current_task:

agents/praisonaiagents/task/task.py

@@ -1,6 +1,6 @@
 import logging
 import asyncio
-from typing import List, Optional, Dict, Any, Type, Callable, Union, Coroutine
+from typing import List, Optional, Dict, Any, Type, Callable, Union, Coroutine, Literal
 from pydantic import BaseModel
 from ..main import TaskOutput
 from ..agent.agent import Agent
@@ -19,7 +19,7 @@ class Task:
         agent: Optional[Agent] = None,
         name: Optional[str] = None,
         tools: Optional[List[Any]] = None,
-        context: Optional[List["Task"]] = None,
+        context: Optional[List[Union[str, List, 'Task']]] = None,
         async_execution: Optional[bool] = False,
         config: Optional[Dict[str, Any]] = None,
         output_file: Optional[str] = None,
@@ -37,8 +37,21 @@ class Task:
         is_start: bool = False,
         loop_state: Optional[Dict[str, Union[str, int]]] = None,
         memory=None,
-        quality_check=True
+        quality_check=True,
+        input_file: Optional[str] = None
     ):
+        # Add check if memory config is provided
+        if memory is not None or (config and config.get('memory_config')):
+            try:
+                from ..memory.memory import Memory
+                MEMORY_AVAILABLE = True
+            except ImportError:
+                raise ImportError(
+                    "Memory features requested in Task but memory dependencies not installed. "
+                    "Please install with: pip install \"praisonaiagents[memory]\""
+                )
+
+        self.input_file = input_file
         self.id = str(uuid.uuid4()) if id is None else str(id)
         self.name = name
         self.description = description
@@ -83,6 +96,47 @@ class Task:
         # Track previous tasks based on next_tasks relationships
         self.previous_tasks = []
 
+        # If task_type="decision" and output_pydantic is not set
+        if self.task_type == "decision" and not self.output_pydantic:
+            from pydantic import BaseModel
+            from typing import Literal
+
+            # Gather condition keys for the "decision" field
+            condition_keys = list(self.condition.keys())
+            if not condition_keys:
+                # Fall back to placeholders if nothing is specified
+                condition_keys = ["next_task", "exit"]
+
+            # Create a dynamic literal type from condition keys
+            DecisionLiteral = Literal.__getitem__(tuple(condition_keys))
+
+            class DecisionModel(BaseModel):
+                response: str
+                decision: DecisionLiteral
+
+            self.output_pydantic = DecisionModel
+
+        # If task_type="loop" and output_pydantic is not set
+        if self.task_type == "loop" and not self.output_pydantic:
+            from pydantic import BaseModel
+            from typing import Literal
+
+            # Gather condition keys for the "decision" field
+            condition_keys = list(self.condition.keys())
+            if not condition_keys:
+                # Fall back to placeholders if nothing is specified
+                condition_keys = ["next_item", "exit"]
+
+            # Create a dynamic literal type
+            LoopLiteral = Literal.__getitem__(tuple(condition_keys))
+
+            class LoopModel(BaseModel):
+                response: str
+                decision: LoopLiteral
+                loop_id: str  # Additional field for loop
+
+            self.output_pydantic = LoopModel
+
     def __str__(self):
         return f"Task(name='{self.name if self.name else 'None'}', description='{self.description}', agent='{self.agent.name if self.agent else 'None'}', status='{self.status}')"
 
@@ -219,4 +273,33 @@ class Task:
                     self.callback(task_output)
             except Exception as e:
                 logger.error(f"Task {self.id}: Failed to execute callback: {e}")
-                logger.exception(e)
+                logger.exception(e)
+
+        task_prompt = f"""
+You need to do the following task: {self.description}.
+Expected Output: {self.expected_output}.
+"""
+        if self.context:
+            context_results = []  # Use list to avoid duplicates
+            for context_item in self.context:
+                if isinstance(context_item, str):
+                    context_results.append(f"Input Content:\n{context_item}")
+                elif isinstance(context_item, list):
+                    context_results.append(f"Input Content: {' '.join(str(x) for x in context_item)}")
+                elif hasattr(context_item, 'result'):  # Task object
+                    if context_item.result:
+                        context_results.append(
+                            f"Result of previous task {context_item.name if context_item.name else context_item.description}:\n{context_item.result.raw}"
+                        )
+                    else:
+                        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)}
+"""

agents/praisonaiagents/tools/pandas_tools.py

@@ -160,6 +160,9 @@ class PandasTools:
             Dict: Summary statistics and information
         """
         try:
+            if not isinstance(df, pd.DataFrame):
+                raise TypeError(f"Expected pandas DataFrame, got {type(df).__name__}")
+                
             numeric_summary = df.describe().to_dict()
             # Convert numpy types to native Python types
             for col in numeric_summary:

agents/praisonaiagents/tools/yfinance_tools.py

@@ -145,7 +145,15 @@ class YFinanceTools:
                 return [{"error": "yfinance package not available"}]
 
             hist = ticker.history(period=period, interval=interval, start=start, end=end)
-            return hist.reset_index().to_dict('records')
+            data = hist.reset_index().to_dict('records')
+            
+            # Convert timestamps to ISO format strings
+            for record in data:
+                if 'Date' in record and hasattr(record['Date'], 'isoformat'):
+                    record['Date'] = record['Date'].isoformat()
+                if 'Datetime' in record and hasattr(record['Datetime'], 'isoformat'):
+                    record['Datetime'] = record['Datetime'].isoformat()
+            return data
         except Exception as e:
             error_msg = f"Error fetching historical data: {str(e)}"
             logging.error(error_msg)

agents/programming-agent.py

@@ -0,0 +1,19 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import execute_code, analyze_code, format_code, lint_code, disassemble_code # Code Tools
+from praisonaiagents.tools import execute_command, list_processes, kill_process, get_system_info # Shell Tools
+from praisonaiagents.tools import duckduckgo # Web Search Tool
+
+agent = Agent(
+    instructions="You are a Programming Agent", self_reflect=True, min_reflect=5, max_reflect=10, 
+    tools=[execute_code, analyze_code, format_code, lint_code, disassemble_code, execute_command, list_processes, kill_process, get_system_info, duckduckgo]
+)
+agent.start(
+    "Write a python script using yfinance to find the stock price of Tesla"
+    "First check if required packages are installed"
+    "Run it using execute_code"
+    "execute_command if you want to run any terminal command"
+    "search internet using duckduckgo if you want to know update python package information"
+    "Analyse the output using analyze_code and fix error if required"
+    "if no package is installed, install it"
+    "then run the code"
+)

agents/prompt_chaining copy.py

@@ -0,0 +1,91 @@
+from praisonaiagents.agent import Agent
+from praisonaiagents.task import Task
+from praisonaiagents.agents import PraisonAIAgents
+from typing import List, Dict
+import time
+
+def get_time_check():
+    current_time = int(time.time())
+    result = "even" if current_time % 2 == 0 else "odd"
+    print(f"Time check: {current_time} is {result}")
+    return result
+
+def create_prompt_chain():
+    # Create agents for each step in the chain
+    agent1 = Agent(
+        name="Time Checker",
+        role="Time checker",
+        goal="Check if the time is even or odd",
+        instructions="Check if the time is even or odd",
+        tools=[get_time_check]
+    )
+
+    agent2 = Agent(
+        name="Advanced Analyzer",
+        role="Advanced data analyzer",
+        goal="Perform in-depth analysis of processed data",
+        instructions="Analyze the processed data in detail"
+    )
+
+    agent3 = Agent(
+        name="Final Processor",
+        role="Final data processor",
+        goal="Generate final output based on analysis",
+        instructions="Create final output based on analyzed data"
+    )
+
+    # Create tasks for each step
+    initial_task = Task(
+        name="time_check",
+        description="Getting time check and checking if it is even or odd",
+        expected_output="Getting time check and checking if it is even or odd",
+        agent=agent1,
+        is_start=True,  # Mark as the starting task
+        task_type="decision",  # This task will make a decision
+        next_tasks=["advanced_analysis"],  # Next task if condition passes
+        condition={
+            "even": ["advanced_analysis"],  # If passes, go to advanced analysis
+            "odd": ["final_processing"]  # If fails, exit the chain
+        }
+    )
+
+    analysis_task = Task(
+        name="advanced_analysis",
+        description="Perform advanced analysis on the processed data",
+        expected_output="Analyzed data ready for final processing",
+        agent=agent2,
+        next_tasks=["final_processing"]
+    )
+
+    final_task = Task(
+        name="final_processing",
+        description="Generate final output",
+        expected_output="Final processed result",
+        agent=agent3
+    )
+
+    # Create the workflow manager
+    workflow = PraisonAIAgents(
+        agents=[agent1, agent2, agent3],
+        tasks=[initial_task, analysis_task, final_task],
+        process="workflow",  # Use workflow process type
+        verbose=True
+    )
+
+    return workflow
+
+def main():
+    # Create and run the prompt chain
+    workflow = create_prompt_chain()
+    
+    # Run the workflow
+    results = workflow.start()
+    
+    # Print results
+    print("\nWorkflow Results:")
+    for task_id, result in results["task_results"].items():
+        if result:
+            print(f"Task {task_id}: {result.raw}")
+
+if __name__ == "__main__":
+    main()

agents/prompt_chaining.py

@@ -0,0 +1,78 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import time
+
+def get_time_check():
+    current_time = int(time.time())
+    result = "even" if current_time % 2 == 0 else "odd"
+    print(f"Time check: {current_time} is {result}")
+    return result
+
+# Create agents for each step in the chain
+agent1 = Agent(
+    name="Time Checker",
+    role="Time checker",
+    goal="Check if the time is even or odd",
+    instructions="Check if the time is even or odd",
+    tools=[get_time_check]
+)
+
+agent2 = Agent(
+    name="Advanced Analyzer",
+    role="Advanced data analyzer",
+    goal="Perform in-depth analysis of processed data",
+    instructions="Analyze the processed data in detail"
+)
+
+agent3 = Agent(
+    name="Final Processor",
+    role="Final data processor",
+    goal="Generate final output based on analysis",
+    instructions="Create final output based on analyzed data"
+)
+
+# Create tasks for each step
+initial_task = Task(
+    name="time_check",
+    description="Getting time check and checking if it is even or odd",
+    expected_output="Getting time check and checking if it is even or odd",
+    agent=agent1,
+    is_start=True,  # Mark as the starting task
+    task_type="decision",  # This task will make a decision
+    next_tasks=["advanced_analysis"],  # Next task if condition passes
+    condition={
+        "even": ["advanced_analysis"],  # If passes, go to advanced analysis
+        "odd": "exit"  # If fails, exit the chain
+    }
+)
+
+analysis_task = Task(
+    name="advanced_analysis",
+    description="Perform advanced analysis on the processed data",
+    expected_output="Analyzed data ready for final processing",
+    agent=agent2,
+    next_tasks=["final_processing"]
+)
+
+final_task = Task(
+    name="final_processing",
+    description="Generate final output",
+    expected_output="Final processed result",
+    agent=agent3
+)
+
+# Create the workflow manager
+workflow = PraisonAIAgents(
+    agents=[agent1, agent2, agent3],
+    tasks=[initial_task, analysis_task, final_task],
+    process="workflow",  # Use workflow process type
+    verbose=True
+)
+
+# Run the workflow
+results = workflow.start()
+
+# Print results
+print("\nWorkflow Results:")
+for task_id, result in results["task_results"].items():
+    if result:
+        print(f"Task {task_id}: {result.raw}")

agents/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.0.31"
+version = "0.0.50"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
 authors = [
     { name="Mervin Praison" }
@@ -17,5 +17,25 @@ dependencies = [
 
 [project.optional-dependencies]
 memory = [
-    "chromadb>=0.6.0"
-] 
+    "chromadb>=0.5.23"
+]
+
+knowledge = [
+    "mem0ai>=0.1.0",
+    "chromadb==0.5.23",
+    "markitdown",
+    "chonkie"
+]
+
+# Add LLM dependencies
+llm = [
+    "litellm>=1.50.0",
+    "pydantic>=2.4.2"
+]
+
+# Combined features
+all = [
+    "praisonaiagents[memory]",
+    "praisonaiagents[knowledge]",
+    "praisonaiagents[llm]"
+]

agents/recommendation-agent.py

@@ -0,0 +1,5 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Recommendation Agent", tools=[duckduckgo])
+agent.start("Recommend me a good movie to watch in 2025")

agents/repetitive-agents.py

@@ -0,0 +1,22 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+agent = Agent(
+    instructions="You are a loop agent that creating a loop of tasks.",
+    llm="gpt-4o-mini"
+)
+
+task = Task(
+    description="Create the list of tasks to be looped through.",
+    agent=agent,
+    task_type="loop",
+    input_file="tasks.csv"
+)
+
+agents = PraisonAIAgents(
+    agents=[agent],
+    tasks=[task],
+    process="workflow",
+    max_iter=30
+)
+
+agents.start()

agents/repetitive-manual-agents.py

@@ -0,0 +1,139 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import read_csv, write_csv
+import pandas as pd
+
+def read_csv_smart(csv_path: str) -> pd.DataFrame:
+    """Read CSV and use first column as task, handling both header/no-header cases"""
+    # Try reading with headers first
+    df = pd.read_csv(csv_path)
+    
+    # Check if first row looks like data
+    if not any(col.lower() in ['task', 'description', 'status', 'state'] for col in df.columns):
+        # If no header-like names found, read without headers
+        df = pd.read_csv(csv_path, header=None)
+        
+    # Rename columns consistently
+    df.columns = [*['task'], *[f'col_{i}' for i in range(1, len(df.columns))]]
+    if 'status' not in df.columns:
+        df['status'] = 'pending'
+    if 'loop_id' not in df.columns:
+        # Start loop_id from 1 instead of 0
+        df['loop_id'] = (df.index + 1).astype(str)
+    return df[['task', 'status', 'loop_id']]
+
+def has_pending_tasks(df: pd.DataFrame) -> bool:
+    """Check if there are any pending tasks in the DataFrame"""
+    return not df[df['status'] == 'pending'].empty
+
+def check_task_status(csv_path: str) -> dict:
+    """Read task status from CSV file"""
+    try:
+        # Always read directly from CSV file
+        df = pd.read_csv(csv_path, keep_default_na=False)
+        
+        # Get only pending tasks from original status
+        pending_tasks = df[df['status'].str.lower() == 'pending']
+        
+        if not pending_tasks.empty:
+            task_data = pending_tasks.iloc[0].to_dict()
+            return {
+                'has_pending': True,
+                'next_task': task_data
+            }
+        return {'has_pending': False, 'next_task': None}
+    except Exception as e:
+        print(f"Error reading CSV: {e}")
+        return {'has_pending': False, 'next_task': None, 'error': str(e)}
+
+def get_next_task() -> dict:
+    """Get the next pending task from CSV"""
+    status = check_task_status("tasks.csv")
+    if status['has_pending'] and status['next_task']:
+        task = status['next_task']
+        if task['status'].lower() == 'pending':
+            return {
+                'loop_data': {
+                    'task': task['task'],
+                    'loop_id': task['loop_id'],
+                    'status': task['status']
+                }
+            }
+    return None
+
+def update_task_status(loop_id: str, new_status: str) -> bool:
+    """Update task status in CSV file using loop_id as identifier"""
+    try:
+        # Read directly from CSV file
+        df = pd.read_csv("tasks.csv", keep_default_na=False)
+        df['loop_id'] = df['loop_id'].astype(str)
+        mask = df['loop_id'] == str(loop_id)
+        if mask.any():
+            df.loc[mask, 'status'] = new_status
+            df.to_csv("tasks.csv", index=False)
+            return True
+        print(f"No task found with loop_id: {loop_id}")
+        return False
+    except Exception as e:
+        print(f"Error updating status: {e}")
+        return False
+
+identify_agent = Agent(
+    name="Identify Agent",
+    role="Pending Task Identifier",
+    goal="Identify the next pending task",
+    tools=[get_next_task],
+    llm="gpt-4o-mini"
+)
+
+# Create the repetitive agent
+repetitive_agent = Agent(
+    name="TaskProcessor",
+    role="Task Processing Specialist",
+    goal="Complete the task response",
+    llm="gpt-4o-mini"
+)
+
+update_status_agent = Agent(
+    name="UpdateStatusAgent",
+    role="Status Updater",
+    goal="Update the status of the task in CSV",
+    tools=[update_task_status],
+    llm="gpt-4o-mini"
+)
+
+# Define the repetitive task
+identify_task = Task(
+    name="identify_task",
+    description="Identify the next pending task. If there are no pending tasks decision is completed.",
+    agent=identify_agent,
+    task_type="loop",
+    condition={
+        "pending": ["process_task"],
+        "completed": "exit"
+    }
+)
+
+process_task = Task(
+    name="process_task",
+    description="Complete the task mentioned in the response",
+    agent=repetitive_agent,
+    context=[identify_task],
+    task_type="loop",
+    next_tasks=["update_task"]
+)
+
+update_task = Task(
+    name="update_task",
+    description="Update the task status",
+    agent=update_status_agent,
+    next_tasks= ["identify_task"]
+)
+
+# Create and run agents manager
+agents = PraisonAIAgents(
+    agents=[identify_agent, repetitive_agent],
+    tasks=[identify_task, process_task, update_task],
+    process="workflow"
+)
+
+agents.start()

agents/requirements.txt

@@ -0,0 +1,15 @@
+# Core dependencies
+pydantic
+rich
+openai>=1.30.0
+
+# Memory dependencies
+chromadb>=0.5.23
+
+# Knowledge dependencies
+mem0ai>=0.1.0
+markitdown
+chonkie
+
+# Development dependencies
+python-dotenv>=0.19.0

agents/research-agent.py

@@ -0,0 +1,5 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Research Agent", tools=[duckduckgo])
+agent.start("Research about AI 2024")

agents/routing copy.py

@@ -0,0 +1,82 @@
+from praisonaiagents.agent import Agent
+from praisonaiagents.task import Task
+from praisonaiagents.agents import PraisonAIAgents
+import time
+
+current_time = int(time.time())
+result = "even" if current_time % 2 == 0 else "odd"
+print(f"Time check: {current_time} is {result}")
+
+# Create specialized agents
+router = Agent(
+    name="Router",
+    role="Input Router",
+    goal="Evaluate input and determine routing path",
+    instructions="Analyze input and decide whether to proceed or exit",
+    tools=[get_time_check]
+)
+
+processor1 = Agent(
+    name="Processor 1",
+    role="Secondary Processor",
+    goal="Process valid inputs that passed initial check",
+    instructions="Process data that passed the routing check"
+)
+
+processor2 = Agent(
+    name="Processor 2",
+    role="Final Processor",
+    goal="Perform final processing on validated data",
+    instructions="Generate final output for processed data"
+)
+
+# Create tasks with routing logic
+routing_task = Task(
+    name="initial_routing",
+    description="check the time and return according to what is returned",
+    expected_output="pass or fail based on what is returned",
+    agent=router,
+    is_start=True,
+    task_type="decision",
+    condition={
+        "pass": ["process_valid"],
+        "fail": "exit"
+    }
+)
+
+processing_task = Task(
+    name="process_valid",
+    description="Process validated input",
+    expected_output="Processed data ready for final step",
+    agent=processor1,
+    next_tasks=["final_process"]
+)
+
+final_task = Task(
+    name="final_process",
+    description="Generate final output",
+    expected_output="Final processed result",
+    agent=processor2
+)
+
+# Create and run workflow
+workflow = PraisonAIAgents(
+    agents=[router, processor1, processor2],
+    tasks=[routing_task, processing_task, final_task],
+    process="workflow",
+    verbose=True
+)
+
+print("\nStarting Routing Workflow...")
+print("=" * 50)
+
+results = workflow.start()
+
+print("\nWorkflow Results:")
+print("=" * 50)
+for task_id, result in results["task_results"].items():
+    if result:
+        task_name = result.description
+        print(f"\nTask: {task_name}")
+        print(f"Result: {result.raw}")
+        print("-" * 50)

agents/routing.py

@@ -0,0 +1,83 @@
+from praisonaiagents.agent import Agent
+from praisonaiagents.task import Task
+from praisonaiagents.agents import PraisonAIAgents
+import time
+
+def get_time_check():
+    current_time = int(time.time())
+    result = "even" if current_time % 2 == 0 else "odd"
+    print(f"Time check: {current_time} is {result}")
+    return result
+
+# Create specialized agents
+router = Agent(
+    name="Router",
+    role="Input Router",
+    goal="Evaluate input and determine routing path",
+    instructions="Analyze input and decide whether to proceed or exit",
+    tools=[get_time_check]
+)
+
+processor1 = Agent(
+    name="Processor 1",
+    role="Secondary Processor",
+    goal="Process valid inputs that passed initial check",
+    instructions="Process data that passed the routing check"
+)
+
+processor2 = Agent(
+    name="Processor 2",
+    role="Final Processor",
+    goal="Perform final processing on validated data",
+    instructions="Generate final output for processed data"
+)
+
+# Create tasks with routing logic
+routing_task = Task(
+    name="initial_routing",
+    description="check the time and return according to what is returned",
+    expected_output="pass or fail based on what is returned",
+    agent=router,
+    is_start=True,
+    task_type="decision",
+    condition={
+        "pass": ["process_valid"],
+        "fail": ["process_invalid"]
+    }
+)
+
+processing_task = Task(
+    name="process_valid",
+    description="Process validated input",
+    expected_output="Processed data ready for final step",
+    agent=processor1,
+)
+
+final_task = Task(
+    name="process_invalid",
+    description="Generate final output",
+    expected_output="Final processed result",
+    agent=processor2
+)
+
+# Create and run workflow
+workflow = PraisonAIAgents(
+    agents=[router, processor1, processor2],
+    tasks=[routing_task, processing_task, final_task],
+    process="workflow",
+    verbose=True
+)
+
+print("\nStarting Routing Workflow...")
+print("=" * 50)
+
+results = workflow.start()
+
+print("\nWorkflow Results:")
+print("=" * 50)
+for task_id, result in results["task_results"].items():
+    if result:
+        task_name = result.description
+        print(f"\nTask: {task_name}")
+        print(f"Result: {result.raw}")
+        print("-" * 50)

agents/sample.md

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

agents/sample.txt

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

agents/shopping-agent.py

@@ -0,0 +1,5 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Shopping Agent", tools=[duckduckgo])
+agent.start("I want to buy iPhone 16 Pro Max, check 5 stores and give me price in table")

agents/single-agent.py

@@ -0,0 +1,4 @@
+from praisonaiagents import Agent
+
+agent = Agent(instructions="You are a Markdown Agent, output in markdown format")
+agent.start("Write a blog post about AI")

agents/streamlit-agents.py

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

agents/tasks.csv

@@ -0,0 +1,100 @@
+Write a product description for a smartwatch in 2 lines.
+Create a user manual for a blender in 2 lines.
+Draft an apology email for delayed delivery in 2 lines.
+Write a blog post on "Benefits of Meditation" in 2 lines.
+Create a privacy policy for a website in 2 lines.
+Write instructions for setting up a Wi-Fi router in 2 lines.
+Draft an email to follow up after a job interview in 2 lines.
+Create a FAQ page for an e-commerce store in 2 lines.
+Write an invitation email for a webinar in 2 lines.
+Create a terms and conditions page for an app in 2 lines.
+Write a press release for a product launch in 2 lines.
+Create a checklist for house cleaning tasks in 2 lines.
+Draft a resignation letter template in 2 lines.
+Write a cover letter for a software developer role in 2 lines.
+Create onboarding instructions for new employees in 2 lines.
+Write a memo announcing a new policy in a company in 2 lines.
+Create a safety guidelines document for a workplace in 2 lines.
+Write a pitch email to potential investors in 2 lines.
+Draft a thank-you email for attending a conference in 2 lines.
+Create a refund policy template for an online store in 2 lines.
+Write a blog on "How to Improve Time Management" in 2 lines.
+Create step-by-step instructions for changing a car tyre in 2 lines.
+Write a welcome email for new subscribers in 2 lines.
+Draft an event agenda template in 2 lines.
+Create a code of conduct for an online forum in 2 lines.
+Write a guide for beginners to start a blog in 2 lines.
+Create a checklist for organising a party in 2 lines.
+Draft an email to request a product review in 2 lines.
+Write a policy for remote work in a company in 2 lines.
+Create a simple tutorial for using Excel formulas in 2 lines.
+Write a thank-you letter for a donation in 2 lines.
+Draft a meeting minutes template in 2 lines.
+Create an FAQ for a gym membership in 2 lines.
+Write a blog post about "Healthy Snacks for Kids" in 2 lines.
+Create a guide for securing your social media accounts in 2 lines.
+Draft an email to request a testimonial from a client in 2 lines.
+Write a safety checklist for camping trips in 2 lines.
+Create a script for a customer service call in 2 lines.
+Write a tutorial for creating a simple PowerPoint presentation in 2 lines.
+Create a packing list for a beach vacation in 2 lines.
+Draft an email to cancel a service subscription in 2 lines.
+Write a personal statement for university admission in 2 lines.
+Create a template for tracking monthly expenses in 2 lines.
+Write a guide for planting and caring for succulents in 2 lines.
+Create a simple user guide for a mobile app in 2 lines.
+Draft an email to announce a team-building activity in 2 lines.
+Write a step-by-step guide for baking a cake in 2 lines.
+Create a list of house maintenance tasks in 2 lines.
+Draft a partnership proposal email in 2 lines.
+Write a guide for basic photography tips in 2 lines.
+Create a feedback form template for an online course in 2 lines.
+Write a blog on "Top 5 Budget Travel Destinations" in 2 lines.
+Draft a letter of recommendation for a colleague in 2 lines.
+Create a workout plan for beginners in 2 lines.
+Write a guide for troubleshooting common tech issues in 2 lines.
+Draft a reminder email for an upcoming appointment in 2 lines.
+Create a guide for setting personal goals in 2 lines.
+Write a blog post about "How to Start Freelancing" in 2 lines.
+Create a template for event planning in 2 lines.
+Draft an email to notify users about system maintenance in 2 lines.
+Write a guide for choosing the right laptop in 2 lines.
+Create a checklist for moving to a new house in 2 lines.
+Draft an email to update a client on project status in 2 lines.
+Write a guide for reducing screen time in 2 lines.
+Create a shopping list template for weekly groceries in 2 lines.
+Draft a thank-you email for completing a survey in 2 lines.
+Write a blog on "Simple DIY Crafts for Beginners" in 2 lines.
+Create a checklist for starting a small business in 2 lines.
+Draft an email to apologise for a customer complaint in 2 lines.
+Write a guide for setting up a personal budget in 2 lines.
+Create a policy for using company-provided equipment in 2 lines.
+Draft an email to notify about a price increase in 2 lines.
+Write a tutorial for creating a resume using Word in 2 lines.
+Create a simple guide for organising a charity event in 2 lines.
+Draft a formal invitation letter for a conference in 2 lines.
+Write a blog post about "Top Productivity Apps" in 2 lines.
+Create a checklist for first-time pet owners in 2 lines.
+Draft an email to confirm a service appointment in 2 lines.
+Write a beginner’s guide for learning to code in 2 lines.
+Create a guide for assembling a basic toolkit in 2 lines.
+Draft a welcome email for a new team member in 2 lines.
+Write a guide for preparing a home for winter in 2 lines.
+Create a schedule template for meal planning in 2 lines.
+Draft an email to request feedback from a customer in 2 lines.
+Write a guide on "How to Build a Morning Routine" in 2 lines.
+Create a checklist for hosting a virtual meeting in 2 lines.
+Draft a newsletter for a non-profit organisation in 2 lines.
+Write a guide for beginners to use Canva in 2 lines.
+Create a guide for organising your workspace in 2 lines.
+Draft an email to invite users to a beta program in 2 lines.
+Write a step-by-step guide for creating a podcast in 2 lines.
+Create a simple guide for buying your first car in 2 lines.
+Draft an email to announce a new team member in 2 lines.
+Write a tutorial on creating basic charts in Excel in 2 lines.
+Create a guide for teaching basic first aid in 2 lines.
+Draft an email to follow up after a sales call in 2 lines.
+Write a blog post on "How to Save Money on Utilities" in 2 lines.
+Create a guide for designing a basic website in 2 lines.
+Draft a thank-you email for a business referral in 2 lines.
+Write a guide for setting up a new smartphone in 2 lines.

agents/tesla-stock-price.csv

@@ -0,0 +1,758 @@
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+"2016/01/14","206.1800","6490579.0000","202.2100","210.0000","193.3800"
+"2016/01/13","200.3100","4120993.0000","212.0100","212.6500","200.0000"
+"2016/01/12","209.9700","3079051.0000","211.6000","213.7395","205.3100"
+"2016/01/11","207.8500","4090843.0000","214.0100","214.4500","203.0000"
+"2016/01/08","211.0000","3572360.0000","217.8600","220.4400","210.7700"
+"2016/01/07","215.6500","3550704.0000","214.1900","218.4400","213.6700"
+"2016/01/06","219.0400","3774353.0000","220.0000","220.0500","215.9800"
+"2016/01/05","223.4300","3185525.0000","226.3600","226.8900","220.0000"
+"2016/01/04","223.4100","6823510.0000","230.7200","231.3800","219.0000"
+"2015/12/31","240.0100","2713817.0000","238.5100","243.4500","238.3700"
+"2015/12/30","238.0900","3694448.0000","236.6000","243.6340","235.6707"
+"2015/12/29","237.1900","2402438.0000","230.0600","237.7200","229.5470"
+"2015/12/28","228.9500","1900171.0000","231.4900","231.9800","225.5400"
+"2015/12/24","230.5700","710277.0000","230.5600","231.8800","228.2800"
+"2015/12/23","229.7000","1551031.0000","232.1800","233.4500","228.1300"
+"2015/12/22","229.9500","1958836.0000","234.9900","236.5500","229.6300"
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+"2015/12/11","217.0200","3259114.0000","225.2400","225.7500","216.6400"
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+"2015/11/20","220.0100","4394199.0000","223.4900","225.0000","213.5800"
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agents/tools-class.py

@@ -0,0 +1,115 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+import os
+import requests
+from typing import Any, Dict, List, Optional
+from pydantic import BaseModel, Field
+
+class EXASearchTool(BaseModel):
+    """Wrapper for EXA Search API."""
+    search_url: str = "https://api.exa.ai/search"
+    headers: Dict = {
+        "accept": "application/json",
+        "content-type": "application/json",
+    }
+    max_results: Optional[int] = None
+
+    def run(self, query: str) -> str:
+        """Run query through EXA and return concatenated results.
+        
+        Args:
+            query (str): The search query to use
+            
+        Returns:
+            str: The concatenated search results
+        """
+        payload = {
+            "query": query,
+            "type": "magic",
+        }
+
+        headers = self.headers.copy()
+        headers["x-api-key"] = os.environ['EXA_API_KEY']
+
+        response = requests.post(self.search_url, json=payload, headers=headers)
+        results = response.json()
+        
+        if 'results' in results:
+            return self._parse_results(results['results'])
+        return ""
+
+    def results(self, query: str, max_results: Optional[int] = None) -> List[Dict[str, Any]]:
+        """Run query through EXA and return metadata.
+        
+        Args:
+            query (str): The search query to use
+            max_results (Optional[int]): Maximum number of results to return
+            
+        Returns:
+            List[Dict[str, Any]]: List of result dictionaries
+        """
+        payload = {
+            "query": query,
+            "type": "magic",
+        }
+
+        headers = self.headers.copy()
+        headers["x-api-key"] = os.environ['EXA_API_KEY']
+
+        response = requests.post(self.search_url, json=payload, headers=headers)
+        results = response.json()
+        
+        if 'results' in results:
+            return results['results'][:max_results] if max_results else results['results']
+        return []
+
+    def _parse_results(self, results: List[Dict[str, Any]]) -> str:
+        """Parse results into a readable string format.
+        
+        Args:
+            results (List[Dict[str, Any]]): List of result dictionaries
+            
+        Returns:
+            str: Formatted string of results
+        """
+        strings = []
+        for result in results:
+            try:
+                strings.append('\n'.join([
+                    f"Title: {result['title']}",
+                    f"Score: {result['score']}",
+                    f"Url: {result['url']}",
+                    f"ID: {result['id']}",
+                    "---"
+                ]))
+            except KeyError:
+                continue
+
+        content = '\n'.join(strings)
+        return f"\nSearch results: {content}\n"
+
+# Create an agent with the tool
+agent = Agent(
+    name="SearchAgent",
+    role="Research Assistant",
+    goal="Search for information about 'AI Agents Framework'",
+    backstory="I am an AI assistant that can search GitHub.",
+    tools=[EXASearchTool],
+    self_reflect=False
+)
+
+# Create tasks to demonstrate both tools
+task = Task(
+    name="search_task",
+    description="Search for information about 'AI Agents Framework'",
+    expected_output="Information about AI Agents Framework",
+    agent=agent
+)
+
+# Create and start the workflow
+agents = PraisonAIAgents(
+    agents=[agent],
+    tasks=[task],
+    verbose=True
+)
+
+agents.start()

agents/vector-db-agents.py

@@ -0,0 +1,39 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# Define the configuration for the Knowledge instance
+config = {
+    "vector_store": {
+        "provider": "chroma",
+        "config": {
+            "collection_name": "praison",
+            "path": ".praison"
+        }
+    }
+}
+
+# Create an agent
+rag_agent = Agent(
+    name="RAG Agent",
+    role="Information Specialist",
+    goal="Retrieve knowledge efficiently",
+    llm="gpt-4o-mini"
+)
+
+# Define a task for the agent
+rag_task = Task(
+    name="RAG Task",
+    description="What is KAG?",
+    expected_output="Answer to the question",
+    agent=rag_agent,
+    context=[config] # Vector Database provided as context
+)
+
+# Build Agents
+agents = PraisonAIAgents(
+    agents=[rag_agent],
+    tasks=[rag_task],
+    user_id="user1"
+)
+
+# Start Agents
+agents.start()

agents/video-agent.py

@@ -0,0 +1,36 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# Create Video Analysis Agent
+video_agent = Agent(
+    name="VideoAnalyst",
+    role="Video Analysis Specialist",
+    goal="Analyze images and videos to extract meaningful information",
+    backstory="""You are an expert in computer vision and image analysis.
+    You excel at describing images, detecting objects, and understanding visual content.""",
+    llm="gpt-4o-mini",
+    self_reflect=False
+)
+
+# Task with Video File
+task1 = Task(
+    name="analyze_video",
+    description="""Watch this video and provide:
+    1. A summary of the main events
+    2. Key objects and people visible
+    3. Any text or important information shown
+    4. The overall context and setting""",
+    expected_output="Comprehensive analysis of the video content",
+    agent=video_agent,
+    images=["video.mp4"]  
+)
+
+# Create PraisonAIAgents instance
+agents = PraisonAIAgents(
+    agents=[video_agent],
+    tasks=[task1],
+    process="sequential",
+    verbose=1
+)
+
+# Run all tasks
+agents.start()

agents/websearch-agent.py

@@ -0,0 +1,5 @@
+from praisonaiagents import Agent, Tools
+from praisonaiagents.tools import duckduckgo
+
+agent = Agent(instructions="You are a Web Search Agent", tools=[duckduckgo])
+agent.start("Search about AI 2024")

agents/wikipedia-agent.py

@@ -0,0 +1,16 @@
+from praisonaiagents import Agent, Task, PraisonAIAgents
+from praisonaiagents.tools import wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language
+
+agent = Agent(
+    instructions="You are a Wikipedia Agent", 
+    tools=[wiki_search, wiki_summary, wiki_page, wiki_random, wiki_language],
+    self_reflect=True,
+    min_reflect=3,
+    max_reflect=5,
+)
+agent.start(
+    "What is the history of AI?"
+    "First search the history of AI"
+    "Read the page of the history of AI"
+    "Get the summary of the page"
+)

docker/Dockerfile

@@ -1,6 +1,6 @@
 FROM python:3.11-slim
 WORKDIR /app
 COPY . .
-RUN pip install flask praisonai==2.0.40 gunicorn markdown
+RUN pip install flask praisonai==2.0.57 gunicorn markdown
 EXPOSE 8080
 CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]