Release v2.3.31

These tests make real API calls and should be excluded from CI.

.github/workflows/docker-publish.yml

@@ -1,45 +1,54 @@
-name: Daily Publish Docker
+name: Publish Docker
 
 on:
-  push:
-    branches:
-      - main
-  schedule:
-    - cron: '0 0 * * *'
+  release:
+    types: [published]
+  workflow_dispatch:
+    inputs:
+      tag:
+        description: 'Docker image tag (default: latest)'
+        required: false
+        default: 'latest'
 
 jobs:
-  check-and-build:
+  build-and-push:
     runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      packages: write
     
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v2
-
-      - name: Check last commit time
-        id: check_commit
-        run: |
-          last_commit_time=$(git log -1 --format=%ct)
-          current_time=$(date +%s)
-          time_diff=$((current_time - last_commit_time))
-          if [ $time_diff -ge 120 ] && [ $time_diff -lt 240 ]; then
-            echo "should_run=true" >> $GITHUB_OUTPUT
-          else
-            echo "should_run=false" >> $GITHUB_OUTPUT
-          fi
+        uses: actions/checkout@v4
 
       - name: Set up Docker Buildx
-        if: steps.check_commit.outputs.should_run == 'true'
-        uses: docker/setup-buildx-action@v1
+        uses: docker/setup-buildx-action@v3
 
-      - name: Log in to the GitHub Container Registry
-        if: steps.check_commit.outputs.should_run == 'true'
-        run: echo ${{ secrets.GH_TOKEN }} | docker login ghcr.io -u USERNAME --password-stdin
+      - name: Log in to GitHub Container Registry
+        uses: docker/login-action@v3
+        with:
+          registry: ghcr.io
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Extract metadata for Docker
+        id: meta
+        uses: docker/metadata-action@v5
+        with:
+          images: ghcr.io/mervinpraison/praisonai
+          tags: |
+            type=raw,value=latest,enable=${{ github.event_name == 'release' }}
+            type=semver,pattern={{version}},enable=${{ github.event_name == 'release' }}
+            type=semver,pattern={{major}}.{{minor}},enable=${{ github.event_name == 'release' }}
+            type=raw,value=${{ github.event.inputs.tag || 'latest' }},enable=${{ github.event_name == 'workflow_dispatch' }}
 
       - name: Build and push Docker image
-        if: steps.check_commit.outputs.should_run == 'true'
-        uses: docker/build-push-action@v2
+        uses: docker/build-push-action@v5
         with:
           context: .
           file: ./docker/Dockerfile
           push: true
-          tags: ghcr.io/mervinpraison/praisonai:latest
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}
+          cache-from: type=gha
+          cache-to: type=gha,mode=max

README.md

@@ -30,7 +30,113 @@ PraisonAI is a production-ready Multi-AI Agents framework with self-reflection,
   </a>
 </div>
 
-## Key Features
+---
+
+## 📑 Table of Contents
+
+- [🚀 Quick Start](#-quick-start)
+- [📦 Installation](#-installation)
+  - [Python SDK](#python-sdk)
+  - [JavaScript SDK](#javascript-sdk)
+- [💻 Usage](#-usage)
+  - [Python Code Examples](#python-code-examples)
+  - [CLI / No-Code Interface](#cli--no-code-interface)
+  - [JavaScript Code Examples](#javascript-code-examples)
+- [✨ Key Features](#-key-features)
+- [🌐 Supported Providers](#-supported-providers)
+- [🛠️ Custom Tools](#️-custom-tools)
+- [🔧 Configuration & Integration](#-configuration--integration)
+  - [Ollama Integration](#ollama-integration)
+  - [Groq Integration](#groq-integration)
+  - [100+ Models Support](#100-models-support)
+- [📋 Workflows](#-workflows)
+  - [Workflow Patterns](#workflow-patterns)
+  - [YAML Configuration](#yaml-configuration)
+  - [Agents Playbook](#agents-playbook)
+- [🧠 Memory & Context](#-memory--context)
+- [🔬 Advanced Features](#-advanced-features)
+  - [Research & Intelligence](#research--intelligence)
+  - [Planning & Workflows](#planning--workflows)
+  - [Specialised Agents](#specialised-agents)
+  - [MCP Protocol](#mcp-protocol)
+  - [Safety & Control](#safety--control)
+  - [Developer Tools](#developer-tools)
+- [📊 Process Types & Patterns](#-process-types--patterns)
+  - [Sequential Process](#sequential-process)
+  - [Hierarchical Process](#hierarchical-process)
+  - [Workflow Process](#workflow-process)
+  - [Agentic Patterns](#agentic-patterns)
+- [🎓 Video Tutorials](#-video-tutorials)
+- [⭐ Star History](#-star-history)
+- [👥 Contributing](#-contributing)
+- [🔧 Development](#-development)
+
+---
+
+## 🚀 Quick Start
+
+Get started with PraisonAI in under 1 minute:
+
+```bash
+# Install
+pip install praisonaiagents
+
+# Set API key
+export OPENAI_API_KEY=your_key_here
+
+# Create a simple agent
+python -c "from praisonaiagents import Agent; Agent(instructions='You are a helpful AI assistant').start('Write a haiku about AI')"
+```
+
+---
+
+## 📦 Installation
+
+### Python SDK
+
+Lightweight package dedicated for coding:
+
+```bash
+pip install praisonaiagents
+```
+
+For the full framework with CLI support:
+
+```bash
+pip install praisonai
+```
+
+### JavaScript SDK
+
+```bash
+npm install praisonai
+```
+
+---
+
+## 💻 Usage
+
+### Python Code Examples
+
+- [📘 Complete Python Examples](#using-python-code)
+- [🔧 Custom Tools](#️-custom-tools)
+- [🧠 Memory Configuration](#-memory--context)
+
+### CLI / No-Code Interface
+
+- [🎯 Auto Mode](#auto-mode)
+- [🔬 Deep Research](#deep-research-cli)
+- [📋 Planning Mode](#planning-mode-cli)
+- [💾 Memory Management](#memory-cli)
+- [🔄 Workflow Execution](#workflow-cli)
+
+### JavaScript Code Examples
+
+- [💻 JavaScript Usage](#using-javascript-code)
+
+---
+
+## ✨ Key Features
 
 | Feature | Code | Docs |
 |---------|:----:|:----:|
@@ -114,7 +220,11 @@ PraisonAI is a production-ready Multi-AI Agents framework with self-reflection,
 | 💾 Auto-Save Sessions | [Example](#session-management-python) | [📖](https://docs.praison.ai/cli/session) |
 | 📜 History in Context | [Example](#session-management-python) | [📖](https://docs.praison.ai/cli/session) |
 
-## Supported Providers
+---
+
+## 🌐 Supported Providers
+
+PraisonAI supports 100+ LLM providers through seamless integration:
 
 | Provider | Example |
 |----------|:-------:|
@@ -143,14 +253,14 @@ PraisonAI is a production-ready Multi-AI Agents framework with self-reflection,
 | Moonshot | [Example](examples/python/providers/moonshot/moonshot_example.py) |
 | vLLM | [Example](examples/python/providers/vllm/vllm_example.py) |
 
-## Using Python Code
+---
+
+## 📘 Using Python Code
 
 Light weight package dedicated for coding:
-```bash
-pip install praisonaiagents
-```
 
 ```bash
+pip install praisonaiagents
 export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
 ```
 
@@ -605,6 +715,77 @@ result = manager.execute_yaml(
 )
 ```
 
+### Complete workflow.yaml Reference
+
+```yaml
+# workflow.yaml - Full feature reference
+name: Complete Workflow
+description: Demonstrates all workflow.yaml features
+framework: praisonai  # praisonai, crewai, autogen
+process: workflow     # sequential, hierarchical, workflow
+
+workflow:
+  planning: true
+  planning_llm: gpt-4o
+  reasoning: true
+  verbose: true
+  memory_config:
+    provider: chroma
+    persist: true
+
+variables:
+  topic: AI trends
+  items: [ML, NLP, Vision]
+
+agents:
+  researcher:
+    name: Researcher
+    role: Research Analyst
+    goal: Research topics thoroughly
+    instructions: "Provide detailed research findings"
+    backstory: "Expert researcher with 10 years experience"  # alias for instructions
+    llm: gpt-4o-mini
+    function_calling_llm: gpt-4o      # For tool calls
+    max_rpm: 10                        # Rate limiting
+    max_execution_time: 300            # Timeout in seconds
+    reflect_llm: gpt-4o               # For self-reflection
+    min_reflect: 1
+    max_reflect: 3
+    system_template: "You are a helpful assistant"
+    tools:
+      - tavily_search
+
+  writer:
+    name: Writer
+    role: Content Writer
+    goal: Write clear content
+    instructions: "Write engaging content"
+
+steps:
+  - name: research_step
+    agent: researcher
+    action: "Research {{topic}}"
+    expected_output: "Comprehensive research report"
+    output_file: "output/research.md"
+    create_directory: true
+    
+  - name: writing_step
+    agent: writer
+    action: "Write article based on research"
+    context:                          # Task dependencies
+      - research_step
+    output_json:                      # Structured output
+      type: object
+      properties:
+        title: { type: string }
+        content: { type: string }
+
+callbacks:
+  on_workflow_start: log_start
+  on_step_complete: log_step
+  on_workflow_complete: log_complete
+```
+
 ### 9. Hooks
 
 Configure in `.praison/hooks.json`:
@@ -621,9 +802,50 @@ hooks.register("pre_write_code", lambda ctx: print(f"Writing {ctx['file']}"))
 result = hooks.execute("pre_write_code", {"file": "main.py"})
 ```
 
-### 10. Extended agents.yaml with Workflow Patterns
+### 10. Field Names Reference (A-I-G-S)
 
-You can now use advanced workflow patterns directly in agents.yaml by setting `process: workflow`:
+PraisonAI accepts both old (agents.yaml) and new (workflow.yaml) field names. Use the **canonical names** for new projects:
+
+| Canonical (Recommended) | Alias (Also Works) | Purpose |
+|-------------------------|-------------------|---------|
+| `agents` | `roles` | Define agent personas |
+| `instructions` | `backstory` | Agent behavior/persona |
+| `action` | `description` | What the step does |
+| `steps` | `tasks` (nested) | Define work items |
+| `name` | `topic` | Workflow identifier |
+
+**A-I-G-S Mnemonic** - Easy to remember:
+- **A**gents - Who does the work
+- **I**nstructions - How they behave  
+- **G**oal - What they achieve
+- **S**teps - What they do
+
+```yaml
+# Quick Reference - Canonical Format
+name: My Workflow              # Workflow name (not 'topic')
+agents:                        # Define agents (not 'roles')
+  my_agent:
+    role: Job Title            # Agent's role
+    goal: What to achieve      # Agent's goal
+    instructions: How to act   # Agent's behavior (not 'backstory')
+    
+steps:                         # Define steps (not 'tasks')
+  - agent: my_agent
+    action: What to do         # Step action (not 'description')
+```
+
+> **Note:** The parser accepts both old and new names. Run `praisonai workflow validate <file.yaml>` to see suggestions for canonical names.
+
+### 11. Extended agents.yaml with Workflow Patterns
+
+**Feature Parity:** Both `agents.yaml` and `workflow.yaml` now support the same features:
+- All workflow patterns (route, parallel, loop, repeat)
+- All agent fields (function_calling_llm, max_rpm, max_execution_time, reflect_llm, templates)
+- All step fields (expected_output, context, output_json, create_directory, callback)
+- Framework support (praisonai, crewai, autogen)
+- Process types (sequential, hierarchical, workflow)
+
+You can use advanced workflow patterns directly in agents.yaml by setting `process: workflow`:
 
 ```yaml
 # agents.yaml with workflow patterns
@@ -639,15 +861,15 @@ workflow:
 variables:
   topic: AI trends
 
-roles:
+agents:  # Canonical: use 'agents' instead of 'roles'
   classifier:
     role: Request Classifier
-    backstory: "Classify requests into categories"
+    instructions: "Classify requests into categories"  # Canonical: use 'instructions' instead of 'backstory'
     goal: Classify requests
     
   researcher:
     role: Research Analyst
-    backstory: "Expert researcher"
+    instructions: "Expert researcher"  # Canonical: use 'instructions' instead of 'backstory'
     goal: Research topics
     tools:
       - tavily_search
@@ -690,9 +912,13 @@ Run with the same simple command:
 praisonai agents.yaml
 ```
 
-## Using No Code
+---
 
-### Auto Mode:
+## 🎯 CLI / No-Code Interface
+
+PraisonAI provides a powerful CLI for no-code automation and quick prototyping.
+
+### Auto Mode
 ```bash
 pip install praisonai
 export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
@@ -839,10 +1065,43 @@ praisonai "GPT-5" --workflow "Research:Search for info,Write:Write blog" --tools
 praisonai workflow help
 ```
 
+#### YAML Workflow Files:
+```bash
+# Run a YAML workflow file
+praisonai workflow run research.yaml
+
+# Run with variables
+praisonai workflow run research.yaml --var topic="AI trends"
+
+# Validate a YAML workflow
+praisonai workflow validate research.yaml
+
+# Create from template (simple, routing, parallel, loop, evaluator-optimizer)
+praisonai workflow template routing --output my_workflow.yaml
+```
+
+#### Auto-Generate Workflows:
+```bash
+# Auto-generate a sequential workflow from topic
+praisonai workflow auto "Research AI trends"
+
+# Generate parallel workflow (multiple agents work concurrently)
+praisonai workflow auto "Research AI trends" --pattern parallel
+
+# Generate routing workflow (classifier routes to specialists)
+praisonai workflow auto "Build a chatbot" --pattern routing
+
+# Specify output file
+praisonai workflow auto "Research AI" --pattern sequential --output my_workflow.yaml
+```
+
 **Workflow CLI Options:**
 | Flag | Description |
 |------|-------------|
 | `--workflow-var key=value` | Set workflow variable (can be repeated) |
+| `--var key=value` | Set variable for YAML workflows |
+| `--pattern <pattern>` | Pattern for auto-generation (sequential, parallel, routing, loop) |
+| `--output <file>` | Output file for auto-generation |
 | `--llm <model>` | LLM model (e.g., openai/gpt-4o-mini) |
 | `--tools <tools>` | Tools (comma-separated, e.g., tavily) |
 | `--planning` | Enable planning mode |
@@ -1264,6 +1523,21 @@ agent = Agent(
         env={"BRAVE_API_KEY": "your-key"}
     )
 )
+
+# Multiple MCP servers + regular functions
+def my_custom_tool(query: str) -> str:
+    """Custom tool function."""
+    return f"Result: {query}"
+
+agent = Agent(
+    name="MultiToolAgent",
+    instructions="Agent with multiple MCP servers",
+    tools=[
+        MCP("uvx mcp-server-time"),                    # Time tools
+        MCP("npx @modelcontextprotocol/server-memory"), # Memory tools
+        my_custom_tool                                  # Regular function
+    ]
+)
 ```
 
 ### MCP Server (Expose Tools as MCP Server)
@@ -1328,7 +1602,9 @@ server.run()  # stdio for Claude Desktop
 | 📥 Web Fetch - URL content retrieval | [📖](https://docs.praison.ai/cli/web-fetch) |
 | 💾 Prompt Caching - Cost reduction | [📖](https://docs.praison.ai/cli/prompt-caching) |
 
-## Using JavaScript Code
+---
+
+## 💻 Using JavaScript Code
 
 ```bash
 npm install praisonai
@@ -1343,11 +1619,17 @@ agent.start('Write a movie script about a robot in Mars');
 
 ![PraisonAI CLI Demo](docs/demo/praisonai-cli-demo.gif)
 
-## Star History
+---
+
+## ⭐ Star History
 
 [![Star History Chart](https://api.star-history.com/svg?repos=MervinPraison/PraisonAI&type=Date)](https://docs.praison.ai)
 
-## AI Agents Flow
+---
+
+## 📊 Process Types & Patterns
+
+### AI Agents Flow
 
 ```mermaid
 graph LR
@@ -1686,31 +1968,40 @@ flowchart LR
     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>
+## 🔧 Configuration & Integration
+
+### Ollama Integration
 
-## Ollama Integration
 ```bash
 export OPENAI_BASE_URL=http://localhost:11434/v1
 ```
 
-## Groq Integration
+### Groq Integration
+
 Replace xxxx with Groq API KEY:
+
 ```bash
 export OPENAI_API_KEY=xxxxxxxxxxx
 export OPENAI_BASE_URL=https://api.groq.com/openai/v1
 ```
 
-## No Code Options
+### 100+ Models Support
 
-## Agents Playbook
+PraisonAI supports 100+ LLM models from various providers. Visit our [models documentation](https://docs.praison.ai/models/) for the complete list.
+
+<div align="center">
+  <a href="https://docs.praison.ai/models">
+    <p align="center">
+      <img src="https://img.shields.io/badge/📚_Models_Documentation-Visit_docs.praison.ai-blue?style=for-the-badge&logo=bookstack&logoColor=white" alt="Models Documentation" />
+    </p>
+  </a>
+</div>
+
+---
+
+## 📋 Agents Playbook
 
 ### Simple Playbook Example
 
@@ -1719,9 +2010,9 @@ Create `agents.yaml` file and add the code below:
 ```yaml
 framework: praisonai
 topic: Artificial Intelligence
-roles:
+agents:  # Canonical: use 'agents' instead of 'roles'
   screenwriter:
-    backstory: "Skilled in crafting scripts with engaging dialogue about {topic}."
+    instructions: "Skilled in crafting scripts with engaging dialogue about {topic}."  # Canonical: use 'instructions' instead of 'backstory'
     goal: Create scripts from concepts.
     role: Screenwriter
     tasks:
@@ -1735,18 +2026,11 @@ roles:
 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>
+## 🛠️ Custom Tools / Create Plugins
 
-## Custom Tools
+PraisonAI supports multiple ways to create and integrate custom tools (plugins) into your agents.
 
 ### Using `@tool` Decorator
 
@@ -1819,276 +2103,96 @@ After `pip install`, tools are auto-discovered:
 agent = Agent(tools=["my_tool"])  # Works automatically!
 ```
 
+---
 
-## Prompt Expansion
+## 🧠 Memory & Context
 
-Expand short prompts into detailed, actionable prompts:
+PraisonAI provides zero-dependency persistent memory for agents. For detailed examples, see [section 6. Agent Memory](#6-agent-memory-zero-dependencies) in the Python Code Examples.
 
-### CLI Usage
+---
 
-```bash
-# Expand a short prompt into detailed prompt
-praisonai "write a movie script in 3 lines" --expand-prompt
+## 🔬 Advanced Features
 
-# With verbose output
-praisonai "blog about AI" --expand-prompt -v
+### Research & Intelligence
 
-# With tools for context gathering
-praisonai "latest AI trends" --expand-prompt --expand-tools tools.py
+- 🔬 **Deep Research Agents** - OpenAI & Gemini support for automated research
+- 🔄 **Query Rewriter Agent** - HyDE, Step-back, Multi-query strategies for RAG optimization
+- 🌐 **Native Web Search** - Real-time search via OpenAI, Gemini, Anthropic, xAI, Perplexity
+- 📥 **Web Fetch** - Retrieve full content from URLs (Anthropic)
+- 📝 **Prompt Expander Agent** - Expand short prompts into detailed instructions
 
-# Combine with query rewrite
-praisonai "AI news" --query-rewrite --expand-prompt
-```
+### Memory & Caching
 
-### Programmatic Usage
+- 💾 **Prompt Caching** - Reduce costs & latency (OpenAI, Anthropic, Bedrock, Deepseek)
+- 🧠 **Claude Memory Tool** - Persistent cross-conversation memory (Anthropic Beta)
+- 💾 **File-Based Memory** - Zero-dependency persistent memory for all agents
+- 🔍 **Built-in Search Tools** - Tavily, You.com, Exa for web search, news, content extraction
 
-```python
-from praisonaiagents import PromptExpanderAgent, ExpandStrategy
+### Planning & Workflows
 
-# Basic usage
-agent = PromptExpanderAgent()
-result = agent.expand("write a movie script in 3 lines")
-print(result.expanded_prompt)
+- 📋 **Planning Mode** - Plan before execution for agents & multi-agent systems
+- 🔧 **Planning Tools** - Research with tools during planning phase
+- 🧠 **Planning Reasoning** - Chain-of-thought planning for complex tasks
+- ⛓️ **Prompt Chaining** - Sequential prompt workflows with conditional gates
+- 🔍 **Evaluator Optimiser** - Generate and optimize through iterative feedback
+- 👷 **Orchestrator Workers** - Distribute tasks among specialised workers
+- ⚡ **Parallelisation** - Execute tasks in parallel for improved performance
+- 🔁 **Repetitive Agents** - Handle repetitive tasks through automated loops
+- 🤖 **Autonomous Workflow** - Monitor, act, adapt based on environment feedback
 
-# With specific strategy
-result = agent.expand("blog about AI", strategy=ExpandStrategy.DETAILED)
+### Specialised Agents
 
-# Available strategies: BASIC, DETAILED, STRUCTURED, CREATIVE, AUTO
-```
+- 🖼️ **Image Generation Agent** - Create images from text descriptions
+- 📷 **Image to Text Agent** - Extract text and descriptions from images
+- 🎬 **Video Agent** - Analyse and process video content
+- 📊 **Data Analyst Agent** - Analyse data and generate insights
+- 💰 **Finance Agent** - Financial analysis and recommendations
+- 🛒 **Shopping Agent** - Price comparison and shopping assistance
+- ⭐ **Recommendation Agent** - Personalised recommendations
+- 📖 **Wikipedia Agent** - Search and extract Wikipedia information
+- 💻 **Programming Agent** - Code development and analysis
+- 📝 **Markdown Agent** - Generate and format Markdown content
+- 🔀 **Router Agent** - Dynamic task routing with cost optimisation
 
-**Key Difference:**
-- `--query-rewrite`: Optimizes queries for **search/retrieval** (RAG)
-- `--expand-prompt`: Expands prompts for **detailed task execution**
+### MCP Protocol
 
+- 🔌 **MCP Transports** - stdio, Streamable HTTP, WebSocket, SSE (Protocol 2025-11-25)
+- 🌐 **WebSocket MCP** - Real-time bidirectional connections with auto-reconnect
+- 🔐 **MCP Security** - Origin validation, DNS rebinding prevention, secure sessions
+- 🔄 **MCP Resumability** - SSE stream recovery via Last-Event-ID
 
-## Web Search, Web Fetch & Prompt Caching
+### Safety & Control
 
-### CLI Usage
+- 🤝 **Agent Handoffs** - Transfer context between specialised agents
+- 🛡️ **Guardrails** - Input/output validation and safety checks
+- ✅ **Human Approval** - Require human confirmation for critical actions
+- 💬 **Sessions Management** - Isolated conversation contexts
+- 🔄 **Stateful Agents** - Maintain state across interactions
 
-```bash
-# Web Search - Get real-time information
-praisonai "What are the latest AI news today?" --web-search --llm openai/gpt-4o-search-preview
+### Developer Tools
 
-# Web Fetch - Retrieve and analyze URL content (Anthropic only)
-praisonai "Summarize https://docs.praison.ai" --web-fetch --llm anthropic/claude-sonnet-4-20250514
+- ⚡ **Fast Context** - Rapid parallel code search (10-20x faster)
+- 📜 **Rules & Instructions** - Auto-discover CLAUDE.md, AGENTS.md, GEMINI.md
+- 🪝 **Hooks** - Pre/post operation hooks for custom logic
+- 📈 **Telemetry** - Track agent performance and usage
+- 📹 **Camera Integration** - Capture and analyse camera input
 
-# Prompt Caching - Reduce costs for repeated prompts
-praisonai "Analyze this document..." --prompt-caching --llm anthropic/claude-sonnet-4-20250514
-```
+### Other Features
 
-### Programmatic Usage
+- 🔄 **CrewAI & AG2 Integration** - Use CrewAI or AG2 (Formerly AutoGen) Framework
+- 💻 **Codebase Chat** - Chat with entire codebase
+- 🎨 **Interactive UIs** - Multiple interactive interfaces
+- 📄 **YAML Configuration** - YAML-based agent and workflow configuration
+- 🛠️ **Custom Tools** - Easy custom tool integration
+- 🔍 **Internet Search** - Multiple providers (Tavily, You.com, Exa, DuckDuckGo, Crawl4AI)
+- 🖼️ **VLM Support** - Vision Language Model support
+- 🎙️ **Voice Interaction** - Real-time voice interaction
 
-```python
-from praisonaiagents import Agent
+---
 
-# Web Search
-agent = Agent(
-    instructions="You are a research assistant",
-    llm="openai/gpt-4o-search-preview",
-    web_search=True
-)
+## 🎓 Video Tutorials
 
-# Web Fetch (Anthropic only)
-agent = Agent(
-    instructions="You are a content analyzer",
-    llm="anthropic/claude-sonnet-4-20250514",
-    web_fetch=True
-)
-
-# Prompt Caching
-agent = Agent(
-    instructions="You are an AI assistant..." * 50,  # Long system prompt
-    llm="anthropic/claude-sonnet-4-20250514",
-    prompt_caching=True
-)
-```
-
-**Supported Providers:**
-| Feature | Providers |
-|---------|-----------|
-| Web Search | OpenAI, Gemini, Anthropic, xAI, Perplexity |
-| Web Fetch | Anthropic |
-| Prompt Caching | OpenAI (auto), Anthropic, Bedrock, Deepseek |
-
-
-
-
-
-## MCP (Model Context Protocol)
-
-PraisonAI supports MCP Protocol Revision 2025-11-25 with multiple transports.
-
-### MCP Client (Consume MCP Servers)
-
-```python
-from praisonaiagents import Agent, MCP
-
-# stdio - Local NPX/Python servers
-agent = Agent(tools=MCP("npx @modelcontextprotocol/server-memory"))
-
-# Streamable HTTP - Production servers
-agent = Agent(tools=MCP("https://api.example.com/mcp"))
-
-# WebSocket - Real-time bidirectional
-agent = Agent(tools=MCP("wss://api.example.com/mcp", auth_token="token"))
-
-# SSE (Legacy) - Backward compatibility
-agent = Agent(tools=MCP("http://localhost:8080/sse"))
-
-# With environment variables
-agent = Agent(
-    tools=MCP(
-        command="npx",
-        args=["-y", "@modelcontextprotocol/server-brave-search"],
-        env={"BRAVE_API_KEY": "your-key"}
-    )
-)
-```
-
-### MCP Server (Expose Tools as MCP Server)
-
-Expose your Python functions as MCP tools for Claude Desktop, Cursor, and other MCP clients:
-
-```python
-from praisonaiagents.mcp import ToolsMCPServer
-
-def search_web(query: str, max_results: int = 5) -> dict:
-    """Search the web for information."""
-    return {"results": [f"Result for {query}"]}
-
-def calculate(expression: str) -> dict:
-    """Evaluate a mathematical expression."""
-    return {"result": eval(expression)}
-
-# Create and run MCP server
-server = ToolsMCPServer(name="my-tools")
-server.register_tools([search_web, calculate])
-server.run()  # stdio for Claude Desktop
-# server.run_sse(host="0.0.0.0", port=8080)  # SSE for web clients
-```
-
-### MCP Features
-
-| Feature | Description |
-|---------|-------------|
-| **Session Management** | Automatic `Mcp-Session-Id` handling |
-| **Protocol Versioning** | `Mcp-Protocol-Version` header |
-| **Resumability** | SSE stream recovery via `Last-Event-ID` |
-| **Security** | Origin validation, DNS rebinding prevention |
-| **WebSocket** | Auto-reconnect with exponential backoff |
-
-
-## Development:
-
-Below is used for development only.
-
-### Using uv
-```bash
-# Install uv if you haven't already
-pip install uv
-
-# Install from requirements
-uv pip install -r pyproject.toml
-
-# Install with extras
-uv pip install -r pyproject.toml --extra code
-uv pip install -r pyproject.toml --extra "crewai,autogen"
-```
-
-### Bump and Release
-
-```bash
-# From project root - bumps version and releases in one command
-python src/praisonai/scripts/bump_and_release.py 2.2.99
-
-# With praisonaiagents dependency
-python src/praisonai/scripts/bump_and_release.py 2.2.99 --agents 0.0.169
-
-# Then publish
-cd src/praisonai && uv publish
-```
-
-## Contributing
-
-- Fork on GitHub: Use the "Fork" button on the repository page.
-- Clone your fork: `git clone https://github.com/yourusername/praisonAI.git`
-- Create a branch: `git checkout -b new-feature`
-- Make changes and commit: `git commit -am "Add some feature"`
-- Push to your fork: `git push origin new-feature`
-- Submit a pull request via GitHub's web interface.
-- Await feedback from project maintainers.
-
-## Advanced Features
-
-**Research & Intelligence:**
-- 🔬 **Deep Research Agents** (OpenAI & Gemini)
-- 🔄 **Query Rewriter Agent** (HyDE, Step-back, Multi-query)
-- 🌐 **Native Web Search** (OpenAI, Gemini, Anthropic, xAI, Perplexity)
-- 📥 **Web Fetch** (Retrieve full content from URLs - Anthropic)
-- 📝 **Prompt Expander Agent** (Expand short prompts into detailed instructions)
-
-**Memory & Caching:**
-- 💾 **Prompt Caching** (Reduce costs & latency - OpenAI, Anthropic, Bedrock, Deepseek)
-- 🧠 **Claude Memory Tool** (Persistent cross-conversation memory - Anthropic Beta)
-- 💾 **File-Based Memory** (Zero-dependency persistent memory for all agents)
-- 🔍 **Built-in Search Tools** (Tavily, You.com, Exa - web search, news, content extraction)
-
-**Planning & Workflows:**
-- 📋 **Planning Mode** (Plan before execution - Agent & Multi-Agent)
-- 🔧 **Planning Tools** (Research with tools during planning)
-- 🧠 **Planning Reasoning** (Chain-of-thought planning)
-- ⛓️ **Prompt Chaining** (Sequential prompt workflows with gates)
-- 🔍 **Evaluator Optimiser** (Generate and optimize through iterative feedback)
-- 👷 **Orchestrator Workers** (Distribute tasks among specialized workers)
-- ⚡ **Parallelisation** (Execute tasks in parallel for improved performance)
-- 🔁 **Repetitive Agents** (Handle repetitive tasks through automated loops)
-- 🤖 **Autonomous Workflow** (Monitor, act, adapt based on environment feedback)
-
-**Agent Types:**
-- 🖼️ **Image Generation Agent** (Create images from text descriptions)
-- 📷 **Image to Text Agent** (Extract text and descriptions from images)
-- 🎬 **Video Agent** (Analyze and process video content)
-- 📊 **Data Analyst Agent** (Analyze data and generate insights)
-- 💰 **Finance Agent** (Financial analysis and recommendations)
-- 🛒 **Shopping Agent** (Price comparison and shopping assistance)
-- ⭐ **Recommendation Agent** (Personalized recommendations)
-- 📖 **Wikipedia Agent** (Search and extract Wikipedia information)
-- 💻 **Programming Agent** (Code development and analysis)
-- 📝 **Markdown Agent** (Generate and format Markdown content)
-- 🔀 **Router Agent** (Dynamic task routing with cost optimization)
-
-**MCP Protocol:**
-- 🔌 **MCP Transports** (stdio, Streamable HTTP, WebSocket, SSE - Protocol 2025-11-25)
-- 🌐 **WebSocket MCP** (Real-time bidirectional connections with auto-reconnect)
-- 🔐 **MCP Security** (Origin validation, DNS rebinding prevention, secure sessions)
-- 🔄 **MCP Resumability** (SSE stream recovery via Last-Event-ID)
-
-**Safety & Control:**
-- 🤝 **Agent Handoffs** (Transfer context between specialized agents)
-- 🛡️ **Guardrails** (Input/output validation and safety checks)
-- ✅ **Human Approval** (Require human confirmation for critical actions)
-- 💬 **Sessions Management** (Isolated conversation contexts)
-- 🔄 **Stateful Agents** (Maintain state across interactions)
-
-**Developer Tools:**
-- ⚡ **Fast Context** (Rapid parallel code search - 10-20x faster than traditional methods)
-- 📜 **Rules & Instructions** (Auto-discover CLAUDE.md, AGENTS.md, GEMINI.md)
-- 🪝 **Hooks** (Pre/post operation hooks for custom logic)
-- 📈 **Telemetry** (Track agent performance and usage)
-- 📹 **Camera Integration** (Capture and analyze camera input)
-
-## Other Features
-
-- 🔄 Use CrewAI or AG2 (Formerly AutoGen) Framework
-- 💻 Chat with ENTIRE Codebase
-- 🎨 Interactive UIs
-- 📄 YAML-based Configuration
-- 🛠️ Custom Tool Integration
-- 🔍 Internet Search Capability (Tavily, You.com, Exa, DuckDuckGo, Crawl4AI)
-- 🖼️ Vision Language Model (VLM) Support
-- 🎙️ Real-time Voice Interaction
-
-## Video Tutorials
+Learn PraisonAI through our comprehensive video series:
 
 | Topic | Video |
 |-------|--------|
@@ -2115,3 +2219,58 @@ cd src/praisonai && uv publish
 | Call Interface | [![Call](https://img.youtube.com/vi/m1cwrUG2iAk/0.jpg)](https://www.youtube.com/watch?v=m1cwrUG2iAk) |
 | Reasoning Extract Agents | [![Reasoning Extract](https://img.youtube.com/vi/2PPamsADjJA/0.jpg)](https://www.youtube.com/watch?v=2PPamsADjJA) |
 
+---
+
+## 👥 Contributing
+
+We welcome contributions from the community! Here's how you can contribute:
+
+1. **Fork on GitHub** - Use the "Fork" button on the [repository page](https://github.com/MervinPraison/PraisonAI)
+2. **Clone your fork** - `git clone https://github.com/yourusername/praisonAI.git`
+3. **Create a branch** - `git checkout -b new-feature`
+4. **Make changes and commit** - `git commit -am "Add some feature"`
+5. **Push to your fork** - `git push origin new-feature`
+6. **Submit a pull request** - Via GitHub's web interface
+7. **Await feedback** - From project maintainers
+
+---
+
+## 🔧 Development
+
+### Using uv
+
+```bash
+# Install uv if you haven't already
+pip install uv
+
+# Install from requirements
+uv pip install -r pyproject.toml
+
+# Install with extras
+uv pip install -r pyproject.toml --extra code
+uv pip install -r pyproject.toml --extra "crewai,autogen"
+```
+
+### Bump and Release
+
+```bash
+# From project root - bumps version and releases in one command
+python src/praisonai/scripts/bump_and_release.py 2.2.99
+
+# With praisonaiagents dependency
+python src/praisonai/scripts/bump_and_release.py 2.2.99 --agents 0.0.169
+
+# Then publish
+cd src/praisonai && uv publish
+```
+
+---
+
+<div align="center">
+  <p><strong>Made with ❤️ by the PraisonAI Team</strong></p>
+  <p>
+    <a href="https://docs.praison.ai">Documentation</a> •
+    <a href="https://github.com/MervinPraison/PraisonAI">GitHub</a> •
+    <a href="https://github.com/MervinPraison/PraisonAI/issues">Issues</a>
+  </p>
+</div>

docker/Dockerfile

@@ -14,9 +14,10 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
 RUN mkdir -p /root/.praison
 
 # Install Python packages (using latest versions)
+# Note: praisonai is installed from PyPI - remove version pin to use latest available
 RUN pip install --no-cache-dir \
     flask \
-    "praisonai>=2.3.26" \
+    praisonai \
     "praisonai[api]" \
     gunicorn \
     markdown

docker/Dockerfile.chat

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=2.3.26" \
+    "praisonai>=2.3.31" \
     "praisonai[chat]" \
     "embedchain[github,youtube]"
 

docker/Dockerfile.dev

@@ -20,7 +20,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=2.3.26" \
+    "praisonai>=2.3.31" \
     "praisonai[ui]" \
     "praisonai[chat]" \
     "praisonai[realtime]" \

docker/Dockerfile.ui

@@ -16,7 +16,7 @@ RUN mkdir -p /root/.praison
 # Install Python packages (using latest versions)
 RUN pip install --no-cache-dir \
     praisonai_tools \
-    "praisonai>=2.3.26" \
+    "praisonai>=2.3.31" \
     "praisonai[ui]" \
     "praisonai[crewai]"
 

examples/cookbooks/yaml/aws_bedrock_agents.yaml

@@ -1,10 +1,10 @@
 framework: crewai
 topic: "Technology Research and Content Creation using AWS Bedrock"
 
-roles:
+agents:  # Canonical: use 'agents' instead of 'roles'
   research_specialist:
     role: "Research Specialist"
-    backstory: |
+    instructions:  # Canonical |
       You are an experienced technology researcher with deep expertise in analyzing 
       emerging technologies and market trends. You excel at gathering comprehensive 
       information from various sources and synthesizing it into actionable insights.
@@ -35,7 +35,7 @@ roles:
 
   content_creator:
     role: "Technical Content Creator"
-    backstory: |
+    instructions:  # Canonical |
       You are a skilled technical writer and content creator who specializes in 
       translating complex technology concepts into engaging, accessible content 
       for various audiences including developers, business leaders, and end users.
@@ -64,7 +64,7 @@ roles:
 
   quality_reviewer:
     role: "Content Quality Reviewer"
-    backstory: |
+    instructions:  # Canonical |
       You are a meticulous editor and quality assurance specialist with extensive 
       experience in technical content review. You ensure accuracy, clarity, and 
       consistency while maintaining the intended tone and message.

examples/cookbooks/yaml/secondary_market_research_agents.yaml

@@ -8,10 +8,10 @@ variables:
   industry: "Electric Vehicles"
   research_focus: "comprehensive market analysis"
 
-roles:
+agents:  # Canonical: use 'agents' instead of 'roles'
   market_overview_specialist:
     role: "Market Overview Specialist" 
-    backstory: |
+    instructions:  # Canonical |
       You are a seasoned market research analyst with 10+ years of experience 
       in analyzing market dynamics, trends, and growth patterns across various 
       industries. You excel at synthesizing complex market data into clear, 
@@ -64,7 +64,7 @@ roles:
 
   competitive_intelligence_analyst:
     role: "Competitive Intelligence Analyst"
-    backstory: |
+    instructions:  # Canonical |
       You are a competitive intelligence expert specializing in analyzing 
       competitive landscapes, market positioning, and strategic intelligence. 
       You have deep expertise in competitor analysis, market share assessment, 
@@ -119,7 +119,7 @@ roles:
 
   financial_performance_analyst:
     role: "Financial Performance Analyst"
-    backstory: |
+    instructions:  # Canonical |
       You are a financial analyst with expertise in corporate finance, 
       financial statement analysis, and industry benchmarking. You specialize 
       in analyzing financial performance metrics, trends, and comparative 
@@ -173,7 +173,7 @@ roles:
 
   growth_opportunities_researcher:
     role: "Growth Opportunities Researcher"
-    backstory: |
+    instructions:  # Canonical |
       You are a strategic business analyst specializing in identifying growth 
       opportunities, market expansion strategies, and emerging business models. 
       You excel at spotting trends, gaps, and untapped potential in markets 
@@ -228,7 +228,7 @@ roles:
 
   risk_assessment_specialist:
     role: "Risk Assessment Specialist"
-    backstory: |
+    instructions:  # Canonical |
       You are a risk management expert with extensive experience in identifying, 
       analyzing, and assessing business risks across various industries. You 
       specialize in market risks, competitive threats, regulatory challenges, 
@@ -287,7 +287,7 @@ roles:
 
   research_report_synthesizer:
     role: "Research Report Synthesizer"
-    backstory: |
+    instructions:  # Canonical |
       You are an expert business report writer and analyst with extensive 
       experience in synthesizing complex research findings into clear, 
       actionable business intelligence reports. You excel at creating 

examples/python/mcp/multiple-mcp-servers.py

@@ -0,0 +1,108 @@
+"""
+Multiple MCP Servers Example
+
+This example demonstrates how to use multiple MCP servers with a single agent.
+You can combine different MCP servers (time, memory, filesystem, etc.) along
+with regular Python functions as tools.
+
+Requirements:
+- pip install praisonaiagents
+- uvx (for mcp-server-time)
+- npx (for @modelcontextprotocol servers)
+"""
+
+from praisonaiagents import Agent
+from praisonaiagents.mcp import MCP
+
+
+def my_custom_tool(query: str) -> str:
+    """
+    A custom Python function that can be used alongside MCP tools.
+    
+    Args:
+        query: The search query
+        
+    Returns:
+        A formatted result string
+    """
+    return f"Custom tool result for: {query}"
+
+
+def main():
+    # Example 1: Single MCP server (basic usage)
+    print("=" * 60)
+    print("Example 1: Single MCP Server")
+    print("=" * 60)
+    
+    agent_single = Agent(
+        name="TimeAgent",
+        instructions="You are a helpful assistant that can tell time.",
+        tools=MCP("uvx mcp-server-time")
+    )
+    
+    response = agent_single.start("What time is it in Tokyo?")
+    print(f"Response: {response}\n")
+    
+    # Example 2: Multiple MCP servers
+    print("=" * 60)
+    print("Example 2: Multiple MCP Servers")
+    print("=" * 60)
+    
+    agent_multi = Agent(
+        name="MultiMCPAgent",
+        instructions="""You are a helpful assistant with access to multiple tools:
+        - Time tools for getting current time and converting timezones
+        - Memory tools for storing and retrieving information
+        Use the appropriate tool based on the user's request.""",
+        tools=[
+            MCP("uvx mcp-server-time"),                     # Time tools
+            MCP("npx @modelcontextprotocol/server-memory"), # Memory tools
+        ]
+    )
+    
+    response = agent_multi.start("What time is it in New York and London?")
+    print(f"Response: {response}\n")
+    
+    # Example 3: Multiple MCP servers + custom functions
+    print("=" * 60)
+    print("Example 3: MCP Servers + Custom Functions")
+    print("=" * 60)
+    
+    agent_mixed = Agent(
+        name="MixedToolsAgent",
+        instructions="""You are a helpful assistant with access to:
+        - Time tools for timezone operations
+        - A custom search tool for general queries
+        Choose the right tool for each task.""",
+        tools=[
+            MCP("uvx mcp-server-time"),  # MCP server
+            my_custom_tool                # Regular Python function
+        ]
+    )
+    
+    response = agent_mixed.start("What time is it in Paris?")
+    print(f"Response: {response}\n")
+    
+    # Example 4: MCP with environment variables
+    print("=" * 60)
+    print("Example 4: MCP with Environment Variables")
+    print("=" * 60)
+    
+    # Note: Replace with your actual API key
+    # agent_with_env = Agent(
+    #     name="SearchAgent",
+    #     instructions="You can search the web for information.",
+    #     tools=[
+    #         MCP(
+    #             command="npx",
+    #             args=["-y", "@modelcontextprotocol/server-brave-search"],
+    #             env={"BRAVE_API_KEY": "your-api-key-here"}
+    #         ),
+    #         MCP("uvx mcp-server-time")
+    #     ]
+    # )
+    print("(Skipped - requires BRAVE_API_KEY)")
+
+
+if __name__ == "__main__":
+    main()

examples/python/tools/e2b/agents.yaml

@@ -1,8 +1,8 @@
 framework: praisonai
 topic: crawl and extract all data from each url from this page https://news.ycombinator.com/
-roles:
+agents:  # Canonical: use 'agents' instead of 'roles'
   web_scraper:
-    backstory: An expert in data extraction from websites, adept at navigating and
+    instructions:  # Canonical An expert in data extraction from websites, adept at navigating and
       retrieving detailed information.
     goal: Extract URLs from the source page
     role: Web Scraper
@@ -15,7 +15,7 @@ roles:
     tools:
     - 'code_interpret'
   url_data_extractor:
-    backstory: Specializes in crawling websites to gather comprehensive data, ensuring
+    instructions:  # Canonical Specializes in crawling websites to gather comprehensive data, ensuring
       nothing is missed from each link.
     goal: Crawl each URL for data extraction
     role: URL Data Extractor
@@ -29,7 +29,7 @@ roles:
     tools:
     - 'code_interpret'
   data_organizer:
-    backstory: Known for keen attention to detail, capable of structuring vast amounts
+    instructions:  # Canonical Known for keen attention to detail, capable of structuring vast amounts
       of information into usable formats efficiently.
     goal: Organize extracted data into a structured format
     role: Data Organizer

examples/yaml/agents_workflow.yaml

@@ -11,7 +11,7 @@
 
 framework: praisonai
 process: workflow
-topic: "Research AI trends and create a comprehensive report"
+input: "Research AI trends and create a comprehensive report"  # The input passed to {{input}} in steps
 
 # Workflow configuration (new)
 workflow:
@@ -31,17 +31,17 @@ variables:
     - Natural Language Processing
 
 # Agent definitions using familiar 'roles' format
-roles:
+agents:  # Canonical: use 'agents' instead of 'roles'
   classifier:
     role: Request Classifier
-    backstory: |
+    instructions:  # Canonical |
       You are an expert at classifying requests into categories.
       Respond with ONLY one word: 'technical', 'creative', or 'research'.
     goal: Classify incoming requests accurately
     
   researcher:
     role: Research Analyst
-    backstory: |
+    instructions:  # Canonical |
       You are a skilled research analyst with expertise in technology trends.
       You provide concise, factual research findings with citations.
     goal: Research topics thoroughly and provide insights
@@ -52,28 +52,28 @@ roles:
       
   tech_expert:
     role: Technical Expert
-    backstory: |
+    instructions:  # Canonical |
       You are a technical expert with deep knowledge in AI and ML.
       You provide detailed technical explanations.
     goal: Handle technical questions with expertise
     
   creative_writer:
     role: Creative Writer
-    backstory: |
+    instructions:  # Canonical |
       You are a creative writer who can make complex topics engaging.
       You write in a clear, accessible style.
     goal: Create engaging content from research
     
   market_analyst:
     role: Market Analyst
-    backstory: |
+    instructions:  # Canonical |
       You analyze market trends and competitive landscapes.
       You provide actionable business insights.
     goal: Analyze market trends and opportunities
     
   aggregator:
     role: Synthesizer
-    backstory: |
+    instructions:  # Canonical |
       You combine findings from multiple sources into coherent reports.
       You identify patterns and key insights.
     goal: Synthesize all findings into comprehensive reports

examples/yaml/workflows/loop_workflow.yaml

@@ -39,4 +39,4 @@ steps:
       over: topics
       
   - agent: summarizer
-    action: "Create a comprehensive summary of all the analyzed topics"
+    action: "Create a comprehensive summary of all the analyzed topics based on these results: {{previous_output}}"

examples/yaml/workflows/parallel_workflow.yaml

@@ -3,6 +3,7 @@
 
 name: Parallel Research Workflow
 description: Multiple agents research different aspects concurrently, then an aggregator combines results
+input: "AI-powered customer service solutions"  # The input passed to {{input}} in steps
 
 workflow:
   verbose: true
@@ -43,4 +44,4 @@ steps:
         action: "Research customer needs for: {{input}}"
         
   - agent: aggregator
-    action: "Synthesize all the research findings into a comprehensive report"
+    action: "Synthesize all the research findings into a comprehensive report: {{previous_output}}"

examples/yaml/workflows/routing_workflow.yaml

@@ -3,6 +3,7 @@
 
 name: Agentic Routing Workflow
 description: A classifier agent routes requests to specialized handler agents
+input: "Write a poem about the ocean"  # The input passed to {{input}} in steps
 
 workflow:
   verbose: true

examples/yaml/workflows/simple_workflow.yaml

@@ -3,6 +3,7 @@
 
 name: Simple Research Workflow
 description: A simple workflow where a researcher gathers info and a writer summarizes it
+input: "The benefits of renewable energy"  # The input passed to {{input}} in steps
 
 workflow:
   verbose: true

src/praisonai-agents/.gitignore

@@ -87,3 +87,4 @@ graph.py
 chroma_db/
 .qodo
 *_COMPARISON_ANALYSIS.md
+*_REPORT.md

src/praisonai-agents/README.md

@@ -0,0 +1 @@
+../../README.md

src/praisonai-agents/benchmarks/deep_profiling.py

@@ -0,0 +1,363 @@
+#!/usr/bin/env python3
+"""
+PraisonAI Agents - Deep Profiling Benchmark
+
+Profiles each step of agent initialization to identify performance bottlenecks.
+
+Usage:
+    python benchmarks/deep_profiling.py
+"""
+
+import time
+import tracemalloc
+import cProfile
+import pstats
+import io
+from typing import Literal, Callable, Dict, List
+from dataclasses import dataclass
+
+# ============================================================================
+# Timing Utilities
+# ============================================================================
+
+@dataclass
+class StepTiming:
+    name: str
+    duration_us: float  # microseconds
+    memory_kb: float    # kilobytes
+
+
+class StepProfiler:
+    """Profile individual steps during initialization"""
+    
+    def __init__(self):
+        self.steps: List[StepTiming] = []
+        self._start_time = None
+        self._start_memory = None
+    
+    def start_step(self, name: str):
+        tracemalloc.start()
+        self._start_time = time.perf_counter()
+        self._step_name = name
+    
+    def end_step(self):
+        end_time = time.perf_counter()
+        current, peak = tracemalloc.get_traced_memory()
+        tracemalloc.stop()
+        
+        duration_us = (end_time - self._start_time) * 1_000_000
+        memory_kb = peak / 1024
+        
+        self.steps.append(StepTiming(
+            name=self._step_name,
+            duration_us=duration_us,
+            memory_kb=memory_kb
+        ))
+    
+    def print_report(self, title: str):
+        print(f"\n{'='*70}")
+        print(f"STEP-BY-STEP PROFILING: {title}")
+        print(f"{'='*70}")
+        
+        total_time = sum(s.duration_us for s in self.steps)
+        total_memory = sum(s.memory_kb for s in self.steps)
+        
+        print(f"\n{'Step':<40} {'Time (μs)':<15} {'Memory (KB)':<15} {'% Time':<10}")
+        print("-" * 80)
+        
+        for step in sorted(self.steps, key=lambda x: x.duration_us, reverse=True):
+            pct = (step.duration_us / total_time * 100) if total_time > 0 else 0
+            print(f"{step.name:<40} {step.duration_us:<15.2f} {step.memory_kb:<15.2f} {pct:<10.1f}%")
+        
+        print("-" * 80)
+        print(f"{'TOTAL':<40} {total_time:<15.2f} {total_memory:<15.2f} {'100.0':<10}%")
+
+
+def time_function(func: Callable, iterations: int = 100) -> Dict:
+    """Time a function with detailed statistics"""
+    times = []
+    
+    # Warmup
+    for _ in range(10):
+        func()
+    
+    # Measure
+    for _ in range(iterations):
+        start = time.perf_counter()
+        func()
+        end = time.perf_counter()
+        times.append((end - start) * 1_000_000)  # Convert to microseconds
+    
+    return {
+        'avg': sum(times) / len(times),
+        'min': min(times),
+        'max': max(times),
+        'median': sorted(times)[len(times)//2]
+    }
+
+
+# ============================================================================
+# Profile PraisonAI Agent Initialization - Step by Step
+# ============================================================================
+
+def profile_praisonai_detailed():
+    """Profile each step of PraisonAI Agent initialization"""
+    print("\n" + "="*70)
+    print("DETAILED PROFILING: PraisonAI Agent")
+    print("="*70)
+    
+    profiler = StepProfiler()
+    
+    # Step 1: Import
+    profiler.start_step("1. Import praisonaiagents")
+    from praisonaiagents import Agent as PraisonAgent
+    profiler.end_step()
+    
+    # Step 2: Define tool
+    profiler.start_step("2. Define tool function")
+    def get_weather(city: Literal["nyc", "sf"]):
+        """Use this to get weather information."""
+        if city == "nyc":
+            return "It might be cloudy in nyc"
+        elif city == "sf":
+            return "It's always sunny in sf"
+    tools = [get_weather]
+    profiler.end_step()
+    
+    # Step 3: Full instantiation
+    profiler.start_step("3. Full Agent instantiation")
+    agent = PraisonAgent(
+        name="Test Agent",
+        instructions="Be concise, reply with one sentence.",
+        llm="gpt-4o-mini",
+        tools=tools,
+        verbose=False
+    )
+    profiler.end_step()
+    
+    profiler.print_report("PraisonAI Agent")
+    
+    # Now profile the __init__ internals using cProfile
+    print("\n" + "="*70)
+    print("cProfile ANALYSIS: PraisonAI Agent.__init__")
+    print("="*70)
+    
+    pr = cProfile.Profile()
+    pr.enable()
+    
+    for _ in range(100):
+        agent = PraisonAgent(
+            name="Test Agent",
+            instructions="Be concise, reply with one sentence.",
+            llm="gpt-4o-mini",
+            tools=tools,
+            verbose=False
+        )
+    
+    pr.disable()
+    
+    # Print top 30 functions by cumulative time
+    s = io.StringIO()
+    ps = pstats.Stats(pr, stream=s).sort_stats('cumulative')
+    ps.print_stats(30)
+    print(s.getvalue())
+    
+    return agent
+
+
+# ============================================================================
+# Profile Agno Agent Initialization - Step by Step
+# ============================================================================
+
+def profile_agno_detailed():
+    """Profile each step of Agno Agent initialization"""
+    print("\n" + "="*70)
+    print("DETAILED PROFILING: Agno Agent")
+    print("="*70)
+    
+    profiler = StepProfiler()
+    
+    # Step 1: Import
+    profiler.start_step("1. Import agno")
+    from agno.agent import Agent as AgnoAgent
+    from agno.models.openai import OpenAIChat
+    profiler.end_step()
+    
+    # Step 2: Define tool
+    profiler.start_step("2. Define tool function")
+    def get_weather(city: Literal["nyc", "sf"]):
+        """Use this to get weather information."""
+        if city == "nyc":
+            return "It might be cloudy in nyc"
+        elif city == "sf":
+            return "It's always sunny in sf"
+    tools = [get_weather]
+    profiler.end_step()
+    
+    # Step 3: Full instantiation
+    profiler.start_step("3. Full Agent instantiation")
+    agent = AgnoAgent(model=OpenAIChat(id="gpt-4o-mini"), tools=tools)
+    profiler.end_step()
+    
+    profiler.print_report("Agno Agent")
+    
+    # Now profile the __init__ internals using cProfile
+    print("\n" + "="*70)
+    print("cProfile ANALYSIS: Agno Agent.__init__")
+    print("="*70)
+    
+    pr = cProfile.Profile()
+    pr.enable()
+    
+    for _ in range(100):
+        agent = AgnoAgent(model=OpenAIChat(id="gpt-4o-mini"), tools=tools)
+    
+    pr.disable()
+    
+    # Print top 30 functions by cumulative time
+    s = io.StringIO()
+    ps = pstats.Stats(pr, stream=s).sort_stats('cumulative')
+    ps.print_stats(30)
+    print(s.getvalue())
+    
+    return agent
+
+
+# ============================================================================
+# Profile Individual Components of PraisonAI
+# ============================================================================
+
+def profile_praisonai_components():
+    """Profile individual components of PraisonAI Agent initialization"""
+    print("\n" + "="*70)
+    print("COMPONENT-LEVEL PROFILING: PraisonAI Agent")
+    print("="*70)
+    
+    from praisonaiagents import Agent as PraisonAgent
+    
+    def get_weather(city: Literal["nyc", "sf"]):
+        if city == "nyc":
+            return "It might be cloudy in nyc"
+        elif city == "sf":
+            return "It's always sunny in sf"
+    tools = [get_weather]
+    
+    # Test different configurations
+    configs = [
+        ("Minimal (no tools, no llm)", {"name": "Test", "verbose": False}),
+        ("With name only", {"name": "Test Agent", "verbose": False}),
+        ("With instructions", {"name": "Test", "instructions": "Be concise", "verbose": False}),
+        ("With llm (gpt-4o-mini)", {"name": "Test", "llm": "gpt-4o-mini", "verbose": False}),
+        ("With llm (openai/gpt-4o-mini)", {"name": "Test", "llm": "openai/gpt-4o-mini", "verbose": False}),
+        ("With tools", {"name": "Test", "tools": tools, "verbose": False}),
+        ("Full config (OpenAI SDK)", {"name": "Test", "instructions": "Be concise", "llm": "gpt-4o-mini", "tools": tools, "verbose": False}),
+        ("Full config (LiteLLM)", {"name": "Test", "instructions": "Be concise", "llm": "openai/gpt-4o-mini", "tools": tools, "verbose": False}),
+    ]
+    
+    print(f"\n{'Configuration':<40} {'Avg Time (μs)':<15} {'Min (μs)':<15} {'Max (μs)':<15}")
+    print("-" * 85)
+    
+    for name, config in configs:
+        def create_agent():
+            return PraisonAgent(**config)
+        
+        stats = time_function(create_agent, iterations=100)
+        print(f"{name:<40} {stats['avg']:<15.2f} {stats['min']:<15.2f} {stats['max']:<15.2f}")
+
+
+# ============================================================================
+# Compare Specific Operations
+# ============================================================================
+
+def compare_specific_operations():
+    """Compare specific operations between PraisonAI and Agno"""
+    print("\n" + "="*70)
+    print("OPERATION COMPARISON: PraisonAI vs Agno")
+    print("="*70)
+    
+    from praisonaiagents import Agent as PraisonAgent
+    from agno.agent import Agent as AgnoAgent
+    from agno.models.openai import OpenAIChat
+    
+    def get_weather(city: Literal["nyc", "sf"]):
+        if city == "nyc":
+            return "It might be cloudy in nyc"
+        elif city == "sf":
+            return "It's always sunny in sf"
+    tools = [get_weather]
+    
+    operations = []
+    
+    # Operation 1: Bare minimum instantiation
+    def praisonai_minimal():
+        return PraisonAgent(name="Test", verbose=False)
+    
+    def agno_minimal():
+        return AgnoAgent(model=OpenAIChat(id="gpt-4o-mini"))
+    
+    praison_stats = time_function(praisonai_minimal, 100)
+    agno_stats = time_function(agno_minimal, 100)
+    operations.append(("Minimal instantiation", praison_stats['avg'], agno_stats['avg']))
+    
+    # Operation 2: With tools
+    def praisonai_with_tools():
+        return PraisonAgent(name="Test", tools=tools, verbose=False)
+    
+    def agno_with_tools():
+        return AgnoAgent(model=OpenAIChat(id="gpt-4o-mini"), tools=tools)
+    
+    praison_stats = time_function(praisonai_with_tools, 100)
+    agno_stats = time_function(agno_with_tools, 100)
+    operations.append(("With tools", praison_stats['avg'], agno_stats['avg']))
+    
+    # Operation 3: Full config
+    def praisonai_full():
+        return PraisonAgent(
+            name="Test Agent",
+            instructions="Be concise",
+            llm="gpt-4o-mini",
+            tools=tools,
+            verbose=False
+        )
+    
+    def agno_full():
+        return AgnoAgent(
+            model=OpenAIChat(id="gpt-4o-mini"),
+            tools=tools,
+            instructions=["Be concise"]
+        )
+    
+    praison_stats = time_function(praisonai_full, 100)
+    agno_stats = time_function(agno_full, 100)
+    operations.append(("Full config", praison_stats['avg'], agno_stats['avg']))
+    
+    print(f"\n{'Operation':<30} {'PraisonAI (μs)':<18} {'Agno (μs)':<18} {'Ratio':<10}")
+    print("-" * 76)
+    
+    for op_name, praison_time, agno_time in operations:
+        ratio = praison_time / agno_time if agno_time > 0 else 0
+        print(f"{op_name:<30} {praison_time:<18.2f} {agno_time:<18.2f} {ratio:<10.1f}x")
+
+
+# ============================================================================
+# Main
+# ============================================================================
+
+if __name__ == "__main__":
+    import os
+    os.environ.setdefault("OPENAI_API_KEY", "sk-test")
+    
+    print("="*70)
+    print("PraisonAI Agents - Deep Profiling Benchmark")
+    print("="*70)
+    print("\nThis analysis identifies performance bottlenecks in agent initialization.\n")
+    
+    # Run all profiling
+    profile_praisonai_detailed()
+    profile_agno_detailed()
+    profile_praisonai_components()
+    compare_specific_operations()
+    
+    print("\n" + "="*70)
+    print("PROFILING COMPLETE")
+    print("="*70)

src/praisonai-agents/benchmarks/performance_benchmark.py

@@ -0,0 +1,416 @@
+"""
+PraisonAI Agents - Comprehensive Performance Benchmark
+
+Measures agent instantiation time and memory footprint across
+multiple AI agent frameworks using industry-standard methodology.
+
+Usage:
+    python benchmarks/performance_benchmark.py
+"""
+
+import gc
+import time
+import tracemalloc
+import statistics
+from typing import Literal, List, Callable
+from dataclasses import dataclass, field
+
+
+@dataclass
+class PerformanceResult:
+    """Performance measurement results"""
+    run_times: List[float] = field(default_factory=list)
+    memory_usages: List[float] = field(default_factory=list)
+    
+    @property
+    def avg_run_time(self) -> float:
+        return statistics.mean(self.run_times) if self.run_times else 0
+    
+    @property
+    def avg_memory_usage(self) -> float:
+        return statistics.mean(self.memory_usages) if self.memory_usages else 0
+    
+    @property
+    def min_run_time(self) -> float:
+        return min(self.run_times) if self.run_times else 0
+    
+    @property
+    def max_run_time(self) -> float:
+        return max(self.run_times) if self.run_times else 0
+    
+    @property
+    def std_dev_run_time(self) -> float:
+        return statistics.stdev(self.run_times) if len(self.run_times) > 1 else 0
+    
+    @property
+    def median_run_time(self) -> float:
+        return statistics.median(self.run_times) if self.run_times else 0
+
+
+def compute_tracemalloc_baseline(samples: int = 3) -> float:
+    """Compute baseline memory usage for tracemalloc"""
+    def empty_func():
+        return
+    
+    results = []
+    for _ in range(samples):
+        gc.collect()
+        tracemalloc.start()
+        empty_func()
+        _, peak = tracemalloc.get_traced_memory()
+        tracemalloc.stop()
+        results.append(peak / 1024 / 1024)  # Convert to MiB
+    
+    return sum(results) / len(results) if results else 0
+
+
+def measure_time(func: Callable, iterations: int = 1000, warmup: int = 10) -> List[float]:
+    """Measure execution time for a function"""
+    # Warmup runs
+    for _ in range(warmup):
+        func()
+    
+    # Measured runs
+    times = []
+    for _ in range(iterations):
+        start = time.perf_counter()
+        func()
+        end = time.perf_counter()
+        times.append(end - start)
+    
+    return times
+
+
+def measure_memory(func: Callable, iterations: int = 1000, warmup: int = 10) -> List[float]:
+    """Measure memory usage for a function"""
+    baseline = compute_tracemalloc_baseline()
+    
+    # Warmup runs
+    for _ in range(warmup):
+        func()
+    
+    # Measured runs
+    memory_usages = []
+    for _ in range(iterations):
+        gc.collect()
+        tracemalloc.start()
+        func()
+        _, peak = tracemalloc.get_traced_memory()
+        tracemalloc.stop()
+        
+        peak_mib = peak / 1024 / 1024
+        adjusted = max(0, peak_mib - baseline)
+        memory_usages.append(adjusted)
+    
+    return memory_usages
+
+
+def run_benchmark(func: Callable, name: str, iterations: int = 1000, warmup: int = 10) -> PerformanceResult:
+    """Run full benchmark for a function"""
+    print(f"\n{'='*60}")
+    print(f"Benchmarking: {name}")
+    print(f"{'='*60}")
+    print(f"Warmup runs: {warmup}")
+    print(f"Measured iterations: {iterations}")
+    
+    print("\nMeasuring runtime...")
+    run_times = measure_time(func, iterations, warmup)
+    
+    print("Measuring memory...")
+    memory_usages = measure_memory(func, iterations, warmup)
+    
+    result = PerformanceResult(run_times=run_times, memory_usages=memory_usages)
+    
+    print(f"\nResults for {name}:")
+    print(f"  Average Time: {result.avg_run_time:.6f} seconds ({result.avg_run_time * 1_000_000:.2f} μs)")
+    print(f"  Min Time:     {result.min_run_time:.6f} seconds")
+    print(f"  Max Time:     {result.max_run_time:.6f} seconds")
+    print(f"  Std Dev:      {result.std_dev_run_time:.6f} seconds")
+    print(f"  Median Time:  {result.median_run_time:.6f} seconds")
+    print(f"  Avg Memory:   {result.avg_memory_usage:.6f} MiB ({result.avg_memory_usage * 1024:.2f} KiB)")
+    
+    return result
+
+
+# ============================================================================
+# Framework Imports
+# ============================================================================
+from praisonaiagents import Agent as PraisonAgent
+from agno.agent import Agent as AgnoAgent
+from agno.models.openai import OpenAIChat
+
+# Additional frameworks for comparison
+try:
+    from pydantic_ai import Agent as PydanticAgent
+    PYDANTIC_AVAILABLE = True
+except ImportError:
+    PYDANTIC_AVAILABLE = False
+
+try:
+    from crewai.agent import Agent as CrewAgent
+    from crewai.tools import tool as crewai_tool
+    CREWAI_AVAILABLE = True
+except ImportError:
+    CREWAI_AVAILABLE = False
+
+try:
+    from langchain_core.tools import tool as langchain_tool
+    from langchain_openai import ChatOpenAI
+    from langgraph.prebuilt import create_react_agent
+    LANGGRAPH_AVAILABLE = True
+except ImportError:
+    LANGGRAPH_AVAILABLE = False
+
+try:
+    from agents import Agent as OpenAIAgent, function_tool
+    OPENAI_AGENTS_AVAILABLE = True
+except ImportError:
+    OPENAI_AGENTS_AVAILABLE = False
+
+
+def get_weather(city: Literal["nyc", "sf"]):
+    """Use this to get weather information."""
+    if city == "nyc":
+        return "It might be cloudy in nyc"
+    elif city == "sf":
+        return "It's always sunny in sf"
+
+
+tools = [get_weather]
+
+
+def instantiate_praisonai_agent():
+    """Instantiate a PraisonAI agent with one tool."""
+    return PraisonAgent(
+        name="Test Agent",
+        instructions="Be concise, reply with one sentence.",
+        llm="gpt-4o-mini",
+        tools=tools,
+        verbose=False
+    )
+
+
+def instantiate_praisonai_litellm_agent():
+    """Instantiate a PraisonAI agent with LiteLLM backend."""
+    return PraisonAgent(
+        name="Test Agent",
+        instructions="Be concise, reply with one sentence.",
+        llm="openai/gpt-4o-mini",
+        tools=tools,
+        verbose=False
+    )
+
+
+def instantiate_agno_agent():
+    """Instantiate an Agno agent with one tool."""
+    return AgnoAgent(model=OpenAIChat(id="gpt-4o-mini"), tools=tools)
+
+
+def instantiate_pydantic_agent():
+    """Instantiate a PydanticAI agent with one tool."""
+    agent = PydanticAgent("openai:gpt-4o-mini", system_prompt="Be concise, reply with one sentence.")
+    @agent.tool_plain
+    def get_weather_pydantic(city: Literal["nyc", "sf"]):
+        """Use this to get weather information."""
+        if city == "nyc":
+            return "It might be cloudy in nyc"
+        elif city == "sf":
+            return "It's always sunny in sf"
+    return agent
+
+
+def instantiate_openai_agents():
+    """Instantiate an OpenAI Agents SDK agent with one tool."""
+    return OpenAIAgent(
+        name="Test agent",
+        instructions="Be concise, reply with one sentence.",
+        model="gpt-4o-mini",
+        tools=[function_tool(get_weather)],
+    )
+
+
+def instantiate_langgraph_agent():
+    """Instantiate a LangGraph agent with one tool."""
+    @langchain_tool
+    def get_weather_lg(city: Literal["nyc", "sf"]):
+        """Use this to get weather information."""
+        if city == "nyc":
+            return "It might be cloudy in nyc"
+        elif city == "sf":
+            return "It's always sunny in sf"
+    return create_react_agent(model=ChatOpenAI(model="gpt-4o-mini"), tools=[get_weather_lg])
+
+
+def instantiate_crewai_agent():
+    """Instantiate a CrewAI agent with one tool."""
+    @crewai_tool("Weather Tool")
+    def get_weather_crew(city: Literal["nyc", "sf"]):
+        """Use this to get weather information."""
+        if city == "nyc":
+            return "It might be cloudy in nyc"
+        elif city == "sf":
+            return "It's always sunny in sf"
+    return CrewAgent(
+        llm="gpt-4o-mini",
+        role="Test Agent",
+        goal="Be concise, reply with one sentence.",
+        tools=[get_weather_crew],
+        backstory="Test",
+    )
+
+
+if __name__ == "__main__":
+    print("="*70)
+    print("PraisonAI Agents - Comprehensive Performance Benchmark")
+    print("="*70)
+    print("\nThis benchmark measures:")
+    print("  1. Agent instantiation time (how fast an agent object is created)")
+    print("  2. Memory footprint (how much memory an agent uses)")
+    print("\nMethodology:")
+    print("  - 10 warmup runs (not measured)")
+    print("  - 1000 measured iterations")
+    print("  - tracemalloc for memory measurement")
+    print("  - Baseline subtraction for accuracy")
+    print("\nFrameworks available:")
+    print("  - PraisonAI: ✅")
+    print(f"  - Agno: ✅")
+    print(f"  - PydanticAI: {'✅' if PYDANTIC_AVAILABLE else '❌'}")
+    print(f"  - OpenAI Agents SDK: {'✅' if OPENAI_AGENTS_AVAILABLE else '❌'}")
+    print(f"  - LangGraph: {'✅' if LANGGRAPH_AVAILABLE else '❌'}")
+    print(f"  - CrewAI: {'✅' if CREWAI_AVAILABLE else '❌'}")
+    
+    results = {}
+    
+    # Test PraisonAI first
+    try:
+        print("\n" + "="*70)
+        print("Testing PraisonAI Agents")
+        print("="*70)
+        results['praisonai'] = run_benchmark(
+            instantiate_praisonai_agent,
+            "PraisonAI",
+            iterations=1000,
+            warmup=10
+        )
+    except Exception as e:
+        print(f"Error testing PraisonAI: {e}")
+    
+    try:
+        print("\n" + "="*70)
+        print("Testing PraisonAI Agents (LiteLLM)")
+        print("="*70)
+        results['praisonai_litellm'] = run_benchmark(
+            instantiate_praisonai_litellm_agent,
+            "PraisonAI (LiteLLM)",
+            iterations=1000,
+            warmup=10
+        )
+    except Exception as e:
+        print(f"Error testing PraisonAI (LiteLLM): {e}")
+    
+    # Test Agno
+    try:
+        print("\n" + "="*70)
+        print("Testing Agno")
+        print("="*70)
+        results['agno'] = run_benchmark(
+            instantiate_agno_agent,
+            "Agno",
+            iterations=1000,
+            warmup=10
+        )
+    except Exception as e:
+        print(f"Error testing Agno: {e}")
+    
+    # Test PydanticAI
+    if PYDANTIC_AVAILABLE:
+        try:
+            print("\n" + "="*70)
+            print("Testing PydanticAI")
+            print("="*70)
+            results['pydantic'] = run_benchmark(
+                instantiate_pydantic_agent,
+                "PydanticAI Agent Instantiation",
+                iterations=1000,
+                warmup=10
+            )
+        except Exception as e:
+            print(f"Error testing PydanticAI: {e}")
+    
+    # Test OpenAI Agents SDK
+    if OPENAI_AGENTS_AVAILABLE:
+        try:
+            print("\n" + "="*70)
+            print("Testing OpenAI Agents SDK")
+            print("="*70)
+            results['openai_agents'] = run_benchmark(
+                instantiate_openai_agents,
+                "OpenAI Agents SDK Instantiation",
+                iterations=1000,
+                warmup=10
+            )
+        except Exception as e:
+            print(f"Error testing OpenAI Agents SDK: {e}")
+    
+    # Test LangGraph
+    if LANGGRAPH_AVAILABLE:
+        try:
+            print("\n" + "="*70)
+            print("Testing LangGraph")
+            print("="*70)
+            results['langgraph'] = run_benchmark(
+                instantiate_langgraph_agent,
+                "LangGraph Agent Instantiation",
+                iterations=1000,
+                warmup=10
+            )
+        except Exception as e:
+            print(f"Error testing LangGraph: {e}")
+    
+    # Test CrewAI
+    if CREWAI_AVAILABLE:
+        try:
+            print("\n" + "="*70)
+            print("Testing CrewAI")
+            print("="*70)
+            results['crewai'] = run_benchmark(
+                instantiate_crewai_agent,
+                "CrewAI Agent Instantiation",
+                iterations=1000,
+                warmup=10
+            )
+        except Exception as e:
+            print(f"Error testing CrewAI: {e}")
+    
+    # Final Comparison Table
+    print("\n" + "="*70)
+    print("RESULTS SUMMARY")
+    print("="*70)
+    
+    if 'praisonai' in results:
+        baseline = results['praisonai']
+        
+        print(f"\n{'Framework':<25} {'Avg Time (μs)':<15} {'Avg Memory (KiB)':<18} {'Relative':<15}")
+        print("-" * 73)
+        
+        # Sort by time
+        sorted_results = sorted(results.items(), key=lambda x: x[1].avg_run_time)
+        
+        for name, result in sorted_results:
+            time_ratio = result.avg_run_time / baseline.avg_run_time if baseline.avg_run_time > 0 else 0
+            
+            time_str = f"{time_ratio:.2f}x"
+            
+            display_name = {
+                'agno': 'Agno',
+                'praisonai': 'PraisonAI',
+                'praisonai_litellm': 'PraisonAI (LiteLLM)',
+                'pydantic': 'PydanticAI',
+                'openai_agents': 'OpenAI Agents SDK',
+                'langgraph': 'LangGraph',
+                'crewai': 'CrewAI'
+            }.get(name, name)
+            
+            print(f"{display_name:<25} {result.avg_run_time*1e6:<15.2f} {result.avg_memory_usage*1024:<18.2f} {time_str:<15}")
+        
+        print("\n" + "="*70)

src/praisonai-agents/benchmarks/response_benchmark.py

@@ -0,0 +1,235 @@
+"""
+PraisonAI Agents - Response Time Benchmark
+
+Measures actual LLM response time with gpt-4o-mini across frameworks.
+
+Usage:
+    python benchmarks/response_benchmark.py
+"""
+
+import time
+import statistics
+import os
+from typing import List, Literal
+from dataclasses import dataclass, field
+
+# Ensure we use gpt-4o-mini
+os.environ['OPENAI_MODEL_NAME'] = 'gpt-4o-mini'
+
+
+@dataclass
+class ResponseResult:
+    """Response time measurement results"""
+    response_times: List[float] = field(default_factory=list)
+    
+    @property
+    def avg_time(self) -> float:
+        return statistics.mean(self.response_times) if self.response_times else 0
+    
+    @property
+    def min_time(self) -> float:
+        return min(self.response_times) if self.response_times else 0
+    
+    @property
+    def max_time(self) -> float:
+        return max(self.response_times) if self.response_times else 0
+    
+    @property
+    def std_dev(self) -> float:
+        return statistics.stdev(self.response_times) if len(self.response_times) > 1 else 0
+
+
+def get_weather(city: Literal["nyc", "sf"]):
+    """Use this to get weather information."""
+    if city == "nyc":
+        return "It might be cloudy in nyc"
+    elif city == "sf":
+        return "It's always sunny in sf"
+
+
+def benchmark_praisonai_response(iterations: int = 5) -> ResponseResult:
+    """Benchmark PraisonAI agent response time"""
+    from praisonaiagents import Agent
+    
+    result = ResponseResult()
+    
+    for i in range(iterations):
+        print(f"  PraisonAI iteration {i+1}/{iterations}...")
+        
+        # Create fresh agent each time
+        agent = Agent(
+            name="Test Agent",
+            instructions="Be concise, reply with one sentence.",
+            llm="gpt-4o-mini",
+            verbose=False
+        )
+        
+        start = time.perf_counter()
+        response = agent.start("What is the capital of France?")
+        end = time.perf_counter()
+        
+        result.response_times.append(end - start)
+        print(f"    Response time: {end - start:.3f}s")
+    
+    return result
+
+
+def benchmark_agno_response(iterations: int = 5) -> ResponseResult:
+    """Benchmark Agno agent response time"""
+    from agno.agent import Agent
+    from agno.models.openai import OpenAIChat
+    
+    result = ResponseResult()
+    
+    for i in range(iterations):
+        print(f"  Agno iteration {i+1}/{iterations}...")
+        
+        # Create fresh agent each time
+        agent = Agent(
+            model=OpenAIChat(id="gpt-4o-mini"),
+            system_message="Be concise, reply with one sentence.",
+        )
+        
+        start = time.perf_counter()
+        response = agent.run("What is the capital of France?")
+        end = time.perf_counter()
+        
+        result.response_times.append(end - start)
+        print(f"    Response time: {end - start:.3f}s")
+    
+    return result
+
+
+def benchmark_praisonai_with_tool(iterations: int = 5) -> ResponseResult:
+    """Benchmark PraisonAI agent response time with tool calling"""
+    from praisonaiagents import Agent
+    
+    result = ResponseResult()
+    
+    for i in range(iterations):
+        print(f"  PraisonAI (with tool) iteration {i+1}/{iterations}...")
+        
+        agent = Agent(
+            name="Weather Agent",
+            instructions="You are a weather assistant. Use the get_weather tool to answer questions.",
+            llm="gpt-4o-mini",
+            tools=[get_weather],
+            verbose=False
+        )
+        
+        start = time.perf_counter()
+        response = agent.start("What's the weather in NYC?")
+        end = time.perf_counter()
+        
+        result.response_times.append(end - start)
+        print(f"    Response time: {end - start:.3f}s")
+    
+    return result
+
+
+def benchmark_agno_with_tool(iterations: int = 5) -> ResponseResult:
+    """Benchmark Agno agent response time with tool calling"""
+    from agno.agent import Agent
+    from agno.models.openai import OpenAIChat
+    
+    result = ResponseResult()
+    
+    for i in range(iterations):
+        print(f"  Agno (with tool) iteration {i+1}/{iterations}...")
+        
+        agent = Agent(
+            model=OpenAIChat(id="gpt-4o-mini"),
+            system_message="You are a weather assistant. Use the get_weather tool to answer questions.",
+            tools=[get_weather],
+        )
+        
+        start = time.perf_counter()
+        response = agent.run("What's the weather in NYC?")
+        end = time.perf_counter()
+        
+        result.response_times.append(end - start)
+        print(f"    Response time: {end - start:.3f}s")
+    
+    return result
+
+
+if __name__ == "__main__":
+    print("="*70)
+    print("PraisonAI Agents - Response Time Benchmark")
+    print("="*70)
+    print("\nModel: gpt-4o-mini")
+    print("Measures end-to-end response time including:")
+    print("  - Agent instantiation")
+    print("  - API call to OpenAI")
+    print("  - Response processing")
+    
+    iterations = 5
+    results = {}
+    
+    # Test 1: Simple response (no tools)
+    print("\n" + "="*70)
+    print("TEST 1: Simple Response (No Tools)")
+    print("="*70)
+    
+    print("\nBenchmarking PraisonAI...")
+    try:
+        results['praisonai_simple'] = benchmark_praisonai_response(iterations)
+    except Exception as e:
+        print(f"Error: {e}")
+        import traceback
+        traceback.print_exc()
+    
+    print("\nBenchmarking Agno...")
+    try:
+        results['agno_simple'] = benchmark_agno_response(iterations)
+    except Exception as e:
+        print(f"Error: {e}")
+        import traceback
+        traceback.print_exc()
+    
+    # Test 2: Response with tool calling
+    print("\n" + "="*70)
+    print("TEST 2: Response with Tool Calling")
+    print("="*70)
+    
+    print("\nBenchmarking PraisonAI with tool...")
+    try:
+        results['praisonai_tool'] = benchmark_praisonai_with_tool(iterations)
+    except Exception as e:
+        print(f"Error: {e}")
+        import traceback
+        traceback.print_exc()
+    
+    print("\nBenchmarking Agno with tool...")
+    try:
+        results['agno_tool'] = benchmark_agno_with_tool(iterations)
+    except Exception as e:
+        print(f"Error: {e}")
+        import traceback
+        traceback.print_exc()
+    
+    # Summary
+    print("\n" + "="*70)
+    print("SUMMARY RESULTS")
+    print("="*70)
+    
+    print(f"\n{'Test':<30} {'PraisonAI (avg)':<20} {'Agno (avg)':<20} {'Ratio':<15}")
+    print("-" * 85)
+    
+    if 'praisonai_simple' in results and 'agno_simple' in results:
+        p_time = results['praisonai_simple'].avg_time
+        a_time = results['agno_simple'].avg_time
+        ratio = p_time / a_time if a_time > 0 else float('inf')
+        print(f"{'Simple Response':<30} {p_time:<20.3f}s {a_time:<20.3f}s {ratio:.2f}x")
+    
+    if 'praisonai_tool' in results and 'agno_tool' in results:
+        p_time = results['praisonai_tool'].avg_time
+        a_time = results['agno_tool'].avg_time
+        ratio = p_time / a_time if a_time > 0 else float('inf')
+        print(f"{'With Tool Calling':<30} {p_time:<20.3f}s {a_time:<20.3f}s {ratio:.2f}x")
+    
+    print("\n" + "="*70)
+    print("NOTE")
+    print("="*70)
+    print("\nResponse time is dominated by OpenAI API latency.")
+    print("Agent instantiation overhead is negligible in real-world usage.")

src/praisonai-agents/benchmarks/simple_benchmark.py

@@ -0,0 +1,103 @@
+#!/usr/bin/env python3
+"""
+PraisonAI Agents - Performance Benchmark
+
+Compares agent instantiation times across popular AI agent frameworks.
+
+Usage:
+    python benchmarks/simple_benchmark.py
+"""
+
+import time
+from typing import Literal
+
+
+ITERATIONS = 100
+
+
+def sample_tool(city: Literal['nyc', 'sf']):
+    """Sample tool for benchmark testing."""
+    if city == 'nyc':
+        return 'cloudy'
+    return 'sunny'
+
+
+def measure_instantiation(create_fn, iterations=ITERATIONS):
+    """Measure average instantiation time in microseconds."""
+    times = []
+    for _ in range(iterations):
+        start = time.perf_counter()
+        create_fn()
+        times.append((time.perf_counter() - start) * 1_000_000)
+    return sum(times) / len(times)
+
+
+def run_benchmark():
+    """Run the benchmark across all available frameworks."""
+    tools = [sample_tool]
+    results = {}
+    
+    print('=' * 60)
+    print('PraisonAI Agents - Performance Benchmark')
+    print('=' * 60)
+    print(f'\nIterations: {ITERATIONS}')
+    print('Metric: Agent instantiation time (microseconds)\n')
+    
+    # PraisonAI
+    print("Testing PraisonAI...")
+    from praisonaiagents import Agent as PraisonAgent
+    results['PraisonAI'] = measure_instantiation(
+        lambda: PraisonAgent(name='Test', llm='gpt-4o-mini', tools=tools, verbose=False)
+    )
+    
+    results['PraisonAI (LiteLLM)'] = measure_instantiation(
+        lambda: PraisonAgent(name='Test', llm='openai/gpt-4o-mini', tools=tools, verbose=False)
+    )
+    
+    # Other frameworks for comparison
+    print("Testing other frameworks...")
+    
+    try:
+        from agno.agent import Agent as AgnoAgent
+        from agno.models.openai import OpenAIChat
+        results['Agno'] = measure_instantiation(
+            lambda: AgnoAgent(model=OpenAIChat(id='gpt-4o-mini'), tools=tools)
+        )
+    except ImportError:
+        pass
+    
+    try:
+        from pydantic_ai import Agent as PydanticAgent
+        results['PydanticAI'] = measure_instantiation(
+            lambda: PydanticAgent('openai:gpt-4o-mini')
+        )
+    except ImportError:
+        pass
+    
+    try:
+        from agents import Agent as OpenAIAgent
+        results['OpenAI Agents SDK'] = measure_instantiation(
+            lambda: OpenAIAgent(name='Test', model='gpt-4o-mini')
+        )
+    except ImportError:
+        pass
+    
+    # Print results
+    print('\n' + '=' * 60)
+    print('RESULTS')
+    print('=' * 60)
+    
+    baseline = results.get('PraisonAI', 1)
+    print(f"\n{'Framework':<25} {'Avg Time (μs)':<15} {'Relative':<10}")
+    print('-' * 50)
+    
+    for name, avg in sorted(results.items(), key=lambda x: x[1]):
+        ratio = avg / baseline
+        print(f'{name:<25} {avg:<15.2f} {ratio:.2f}x')
+    
+    print('\n' + '=' * 60)
+    return results
+
+
+if __name__ == '__main__':
+    run_benchmark()

src/praisonai-agents/callback_test_log.txt

@@ -0,0 +1,9 @@
+Starting test with verbose=False
+CALLBACK EXECUTED!
+Message: 
+You need to do the following task: Say hello world.
+Expected Output: A greeting.
+Please provide only the final result of your work. Do not add any conversation or extra explanation.
+Response: Hello, World!
+Other kwargs: {'markdown': True, 'generation_time': 2.685833215713501, 'agent_name': 'TestAgent', 'agent_role': 'Assistant', 'agent_tools': None, 'task_name': 'simple_task', 'task_description': 'Say hello world', 'task_id': 0}
+--------------------------------------------------

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

@@ -551,8 +551,9 @@ Your Goal: {self.goal}
         self.auto_save = auto_save  # Session name for auto-saving
         
         # Initialize rules manager for persistent context (like Cursor/Windsurf)
+        # NOTE: Lazy initialization - rules are loaded only when accessed (performance optimization)
         self._rules_manager = None
-        self._init_rules_manager()
+        self._rules_manager_initialized = False
         
         # Handle web_search fallback: inject DuckDuckGo tool for unsupported models
         if web_search and not self._model_supports_web_search():
@@ -785,6 +786,21 @@ Your Goal: {self.goal}
             logging.warning(f"FastContext search failed: {e}")
             return None
     
+    @property
+    def rules_manager(self):
+        """
+        Lazy-initialized RulesManager for persistent rules/instructions.
+        
+        This property initializes the RulesManager only when first accessed,
+        avoiding expensive filesystem operations during agent instantiation.
+        
+        Returns:
+            RulesManager instance or None if not available
+        """
+        if not self._rules_manager_initialized:
+            self._init_rules_manager()
+        return self._rules_manager
+    
     def _init_rules_manager(self):
         """
         Initialize RulesManager for persistent rules/instructions.
@@ -793,7 +809,10 @@ Your Goal: {self.goal}
         - ~/.praison/rules/ (global)
         - .praison/rules/ (workspace)
         - Subdirectory rules
+        
+        NOTE: This is called lazily via the rules_manager property for performance.
         """
+        self._rules_manager_initialized = True
         try:
             from ..memory.rules_manager import RulesManager
             import os
@@ -828,10 +847,10 @@ Your Goal: {self.goal}
         Returns:
             Formatted rules context string
         """
-        if not self._rules_manager:
+        if not self.rules_manager:
             return ""
         
-        return self._rules_manager.build_rules_context(
+        return self.rules_manager.build_rules_context(
             file_path=file_path,
             include_manual=include_manual
         )
@@ -1239,8 +1258,8 @@ Your Goal: {self.goal}
 Your Role: {self.role}\n
 Your Goal: {self.goal}"""
         
-        # Add rules context if rules manager is enabled
-        if self._rules_manager:
+        # Add rules context if rules manager is enabled (lazy initialization)
+        if self._rules_manager_initialized and self._rules_manager:
             rules_context = self.get_rules_context()
             if rules_context:
                 system_prompt += f"\n\n## Rules (Guidelines you must follow)\n{rules_context}"
@@ -1539,23 +1558,54 @@ Your Goal: {self.goal}"""
         # Special handling for MCP tools
         # Check if tools is an MCP instance with the requested function name
         from ..mcp.mcp import MCP
+        
+        # Helper function to execute MCP tool
+        def _execute_mcp_tool(mcp_instance, func_name, args):
+            """Execute a tool from an MCP instance."""
+            # Handle SSE MCP client
+            if hasattr(mcp_instance, 'is_sse') and mcp_instance.is_sse:
+                if hasattr(mcp_instance, 'sse_client'):
+                    for tool in mcp_instance.sse_client.tools:
+                        if tool.name == func_name:
+                            logging.debug(f"Found matching SSE MCP tool: {func_name}")
+                            return True, tool(**args)
+            # Handle HTTP Stream MCP client
+            if hasattr(mcp_instance, 'is_http_stream') and mcp_instance.is_http_stream:
+                if hasattr(mcp_instance, 'http_stream_client'):
+                    for tool in mcp_instance.http_stream_client.tools:
+                        if tool.name == func_name:
+                            logging.debug(f"Found matching HTTP Stream MCP tool: {func_name}")
+                            return True, tool(**args)
+            # Handle WebSocket MCP client
+            if hasattr(mcp_instance, 'is_websocket') and mcp_instance.is_websocket:
+                if hasattr(mcp_instance, 'websocket_client'):
+                    for tool in mcp_instance.websocket_client.tools:
+                        if tool.name == func_name:
+                            logging.debug(f"Found matching WebSocket MCP tool: {func_name}")
+                            return True, tool(**args)
+            # Handle stdio MCP client
+            if hasattr(mcp_instance, 'runner'):
+                for mcp_tool in mcp_instance.runner.tools:
+                    if hasattr(mcp_tool, 'name') and mcp_tool.name == func_name:
+                        logging.debug(f"Found matching MCP tool: {func_name}")
+                        return True, mcp_instance.runner.call_tool(func_name, args)
+            return False, None
+        
+        # Check if tools is a single MCP instance
         if isinstance(self.tools, MCP):
             logging.debug(f"Looking for MCP tool {function_name}")
-            
-            # Handle SSE MCP client
-            if hasattr(self.tools, 'is_sse') and self.tools.is_sse:
-                if hasattr(self.tools, 'sse_client'):
-                    for tool in self.tools.sse_client.tools:
-                        if tool.name == function_name:
-                            logging.debug(f"Found matching SSE MCP tool: {function_name}")
-                            return tool(**arguments)
-            # Handle stdio MCP client
-            elif hasattr(self.tools, 'runner'):
-                # Check if any of the MCP tools match the function name
-                for mcp_tool in self.tools.runner.tools:
-                    if hasattr(mcp_tool, 'name') and mcp_tool.name == function_name:
-                        logging.debug(f"Found matching MCP tool: {function_name}")
-                        return self.tools.runner.call_tool(function_name, arguments)
+            found, result = _execute_mcp_tool(self.tools, function_name, arguments)
+            if found:
+                return result
+        
+        # Check if tools is a list that may contain MCP instances
+        if isinstance(self.tools, (list, tuple)):
+            for tool in self.tools:
+                if isinstance(tool, MCP):
+                    logging.debug(f"Looking for MCP tool {function_name} in MCP instance")
+                    found, result = _execute_mcp_tool(tool, function_name, arguments)
+                    if found:
+                        return result
 
         # Try to find the function in the agent's tools list first
         func = None
@@ -1870,7 +1920,8 @@ Your Goal: {self.goal}"""
                 if tool_param is not None:
                     from ..mcp.mcp import MCP
                     if isinstance(tool_param, MCP) and hasattr(tool_param, 'to_openai_tool'):
-                        logging.debug("Converting MCP tool to OpenAI format")
+                        # Single MCP instance
+                        logging.debug("Converting single MCP tool to OpenAI format")
                         openai_tool = tool_param.to_openai_tool()
                         if openai_tool:
                             # Handle both single tool and list of tools
@@ -1879,6 +1930,22 @@ Your Goal: {self.goal}"""
                             else:
                                 tool_param = [openai_tool]
                             logging.debug(f"Converted MCP tool: {tool_param}")
+                    elif isinstance(tool_param, (list, tuple)):
+                        # List that may contain MCP instances - convert each MCP to OpenAI format
+                        converted_tools = []
+                        for t in tool_param:
+                            if isinstance(t, MCP) and hasattr(t, 'to_openai_tool'):
+                                logging.debug("Converting MCP instance in list to OpenAI format")
+                                openai_tools = t.to_openai_tool()
+                                if isinstance(openai_tools, list):
+                                    converted_tools.extend(openai_tools)
+                                elif openai_tools:
+                                    converted_tools.append(openai_tools)
+                            else:
+                                # Keep non-MCP tools as-is
+                                converted_tools.append(t)
+                        tool_param = converted_tools
+                        logging.debug(f"Converted {len(converted_tools)} tools from list")
                 
                 # Store chat history length for potential rollback
                 chat_history_length = len(self.chat_history)

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

@@ -440,7 +440,7 @@ class Workflow:
             previous_output = output
             
             if verbose:
-                print(f"✅ {step.name}: {str(output)[:100]}...")
+                print(f"✅ {step.name}: {str(output)}")
             
             # Handle early stop
             if stop:
@@ -571,7 +571,7 @@ class Workflow:
                 output = f"Error: {e}"
         
         if verbose:
-            print(f"✅ {normalized.name}: {str(output)[:100]}...")
+            print(f"✅ {normalized.name}: {str(output)}")
         
         return {
             "step": normalized.name,

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

@@ -286,6 +286,7 @@ class Workflow:
     planning_llm: Optional[str] = None  # LLM for planning
     reasoning: bool = False  # Enable chain-of-thought reasoning
     verbose: bool = False  # Enable verbose output
+    stream: bool = True  # Enable streaming responses (default True like PraisonAIAgents)
     
     # Callbacks (like process="workflow")
     on_workflow_start: Optional[Callable[['Workflow', str], None]] = None  # (workflow, input)
@@ -316,7 +317,8 @@ class Workflow:
         self,
         input: str = "",
         llm: Optional[str] = None,
-        verbose: bool = False
+        verbose: bool = False,
+        stream: bool = None
     ) -> Dict[str, Any]:
         """
         Run the workflow with the given input.
@@ -340,16 +342,22 @@ class Workflow:
             input: The input text/prompt for the workflow
             llm: LLM model to use (default: gpt-4o-mini)
             verbose: Print step outputs
+            stream: Enable streaming responses (default: use workflow's stream setting)
             
         Returns:
             Dict with 'output' (final result) and 'steps' (all step results)
         """
         # Use default LLM if not specified
         model = llm or self.default_llm or "gpt-4o-mini"
+        logger.debug(f"Workflow using model: {model} (llm={llm}, default_llm={self.default_llm})")
         
         # Use workflow verbose setting if not overridden
         verbose = verbose or self.verbose
         
+        # Use workflow stream setting if not overridden
+        if stream is None:
+            stream = self.stream
+        
         # Add input to variables
         all_variables = {**self.variables, "input": input}
         
@@ -382,7 +390,7 @@ class Workflow:
             if isinstance(step, Route):
                 # Decision-based routing
                 route_result = self._execute_route(
-                    step, previous_output, input, all_variables, model, verbose
+                    step, previous_output, input, all_variables, model, verbose, stream
                 )
                 results.extend(route_result["steps"])
                 previous_output = route_result["output"]
@@ -393,7 +401,7 @@ class Workflow:
             elif isinstance(step, Parallel):
                 # Parallel execution
                 parallel_result = self._execute_parallel(
-                    step, previous_output, input, all_variables, model, verbose
+                    step, previous_output, input, all_variables, model, verbose, stream
                 )
                 results.extend(parallel_result["steps"])
                 previous_output = parallel_result["output"]
@@ -404,7 +412,7 @@ class Workflow:
             elif isinstance(step, Loop):
                 # Loop over items
                 loop_result = self._execute_loop(
-                    step, previous_output, input, all_variables, model, verbose
+                    step, previous_output, input, all_variables, model, verbose, stream
                 )
                 results.extend(loop_result["steps"])
                 previous_output = loop_result["output"]
@@ -415,7 +423,7 @@ class Workflow:
             elif isinstance(step, Repeat):
                 # Repeat until condition
                 repeat_result = self._execute_repeat(
-                    step, previous_output, input, all_variables, model, verbose
+                    step, previous_output, input, all_variables, model, verbose, stream
                 )
                 results.extend(repeat_result["steps"])
                 previous_output = repeat_result["output"]
@@ -494,7 +502,11 @@ class Workflow:
                         for key, value in all_variables.items():
                             action = action.replace(f"{{{{{key}}}}}", str(value))
                         if previous_output:
-                            action = action.replace("{{previous_output}}", str(previous_output))
+                            if "{{previous_output}}" in action:
+                                action = action.replace("{{previous_output}}", str(previous_output))
+                            else:
+                                # Auto-append context if not explicitly referenced
+                                action = f"{action}\n\nContext from previous step:\n{previous_output}"
                         action = action.replace("{{input}}", input)
                         
                         # Add reasoning prompt if enabled
@@ -508,9 +520,9 @@ class Workflow:
                         # Handle images if present
                         step_images = getattr(step, 'images', None)
                         if step_images:
-                            output = step.agent.chat(action, images=step_images)
+                            output = step.agent.chat(action, images=step_images, stream=stream)
                         else:
-                            output = step.agent.chat(action)
+                            output = step.agent.chat(action, stream=stream)
                         
                         # Handle output_pydantic if present
                         output_pydantic = getattr(step, 'output_pydantic', None)
@@ -538,14 +550,19 @@ class Workflow:
                             llm=config.get("llm", model),
                             tools=step_tools if step_tools else None,
                             verbose=verbose,
-                            reasoning=self.reasoning
+                            reasoning=self.reasoning,
+                            stream=stream
                         )
                         # Substitute variables in action
                         action = step.action
                         for key, value in all_variables.items():
                             action = action.replace(f"{{{{{key}}}}}", str(value))
                         if previous_output:
-                            action = action.replace("{{previous_output}}", str(previous_output))
+                            if "{{previous_output}}" in action:
+                                action = action.replace("{{previous_output}}", str(previous_output))
+                            else:
+                                # Auto-append context if not explicitly referenced
+                                action = f"{action}\n\nContext from previous step:\n{previous_output}"
                         action = action.replace("{{input}}", input)
                         
                         # Add reasoning prompt if enabled
@@ -554,7 +571,8 @@ class Workflow:
                         
                         if validation_feedback:
                             action = f"{action}\n\nPrevious attempt feedback: {validation_feedback}"
-                        output = temp_agent.chat(action)
+                        
+                        output = temp_agent.chat(action, stream=stream)
                         
                 except Exception as e:
                     step_error = e
@@ -629,7 +647,7 @@ class Workflow:
             previous_output = output
             
             if verbose:
-                print(f"✅ {step.name}: {str(output)[:100]}...")
+                print(f"✅ {step.name}: {str(output)}")
             
             # Handle early stop
             if stop:
@@ -798,11 +816,16 @@ Create a brief execution plan (2-3 sentences) describing how to best accomplish
         elif hasattr(step, 'chat'):
             # It's an Agent - wrap with agent reference and preserve tools
             agent_tools = getattr(step, 'tools', None)
+            # Check for _yaml_action from YAML parser (canonical: action, alias: description)
+            yaml_action = getattr(step, '_yaml_action', None)
+            # Use yaml_action if set, otherwise fall back to previous_output/input
+            default_action = "{{previous_output}}" if index > 0 else "{{input}}"
+            action = yaml_action if yaml_action else default_action
             return WorkflowStep(
                 name=getattr(step, 'name', f'agent_{index+1}'),
                 agent=step,
                 tools=agent_tools,
-                action="{{previous_output}}" if index > 0 else "{{input}}"
+                action=action
             )
         elif callable(step):
             return WorkflowStep(
@@ -823,7 +846,8 @@ Create a brief execution plan (2-3 sentences) describing how to best accomplish
         all_variables: Dict[str, Any],
         model: str,
         verbose: bool,
-        index: int = 0
+        index: int = 0,
+        stream: bool = True
     ) -> Dict[str, Any]:
         """Execute a single step and return result."""
         normalized = self._normalize_single_step(step, index)
@@ -857,9 +881,13 @@ Create a brief execution plan (2-3 sentences) describing how to best accomplish
                 for key, value in all_variables.items():
                     action = action.replace(f"{{{{{key}}}}}", str(value))
                 if previous_output:
-                    action = action.replace("{{previous_output}}", str(previous_output))
+                    if "{{previous_output}}" in action:
+                        action = action.replace("{{previous_output}}", str(previous_output))
+                    else:
+                        # Auto-append context if not explicitly referenced
+                        action = f"{action}\n\nContext from previous step:\n{previous_output}"
                 action = action.replace("{{input}}", input)
-                output = normalized.agent.chat(action)
+                output = normalized.agent.chat(action, stream=stream)
             except Exception as e:
                 output = f"Error: {e}"
         elif normalized.action:
@@ -871,19 +899,25 @@ Create a brief execution plan (2-3 sentences) describing how to best accomplish
                     role=config.get("role", "Assistant"),
                     goal=config.get("goal", "Complete the task"),
                     llm=config.get("llm", model),
-                    verbose=verbose
+                    verbose=verbose,
+                    stream=stream
                 )
                 action = normalized.action
                 for key, value in all_variables.items():
                     action = action.replace(f"{{{{{key}}}}}", str(value))
                 if previous_output:
-                    action = action.replace("{{previous_output}}", str(previous_output))
-                output = temp_agent.chat(action)
+                    if "{{previous_output}}" in action:
+                        action = action.replace("{{previous_output}}", str(previous_output))
+                    else:
+                        # Auto-append context if not explicitly referenced
+                        action = f"{action}\n\nContext from previous step:\n{previous_output}"
+                
+                output = temp_agent.chat(action, stream=stream)
             except Exception as e:
                 output = f"Error: {e}"
         
         if verbose:
-            print(f"✅ {normalized.name}: {str(output)[:100]}...")
+            print(f"✅ {normalized.name}: {str(output)}")
         
         return {
             "step": normalized.name,
@@ -899,7 +933,8 @@ Create a brief execution plan (2-3 sentences) describing how to best accomplish
         input: str,
         all_variables: Dict[str, Any],
         model: str,
-        verbose: bool
+        verbose: bool,
+        stream: bool = True
     ) -> Dict[str, Any]:
         """Execute routing based on previous output."""
         results = []
@@ -925,7 +960,7 @@ Create a brief execution plan (2-3 sentences) describing how to best accomplish
         # Execute matched route steps
         for idx, step in enumerate(matched_route):
             step_result = self._execute_single_step_internal(
-                step, output, input, all_variables, model, verbose, idx
+                step, output, input, all_variables, model, verbose, idx, stream=stream
             )
             results.append({"step": step_result["step"], "output": step_result["output"]})
             output = step_result["output"]
@@ -943,7 +978,8 @@ Create a brief execution plan (2-3 sentences) describing how to best accomplish
         input: str,
         all_variables: Dict[str, Any],
         model: str,
-        verbose: bool
+        verbose: bool,
+        stream: bool = True
     ) -> Dict[str, Any]:
         """Execute steps in parallel (simulated with sequential for now)."""
         import concurrent.futures
@@ -960,7 +996,7 @@ Create a brief execution plan (2-3 sentences) describing how to best accomplish
             for idx, step in enumerate(parallel_step.steps):
                 future = executor.submit(
                     self._execute_single_step_internal,
-                    step, previous_output, input, all_variables.copy(), model, False, idx
+                    step, previous_output, input, all_variables.copy(), model, False, idx, stream
                 )
                 futures.append((idx, future))
             
@@ -989,7 +1025,8 @@ Create a brief execution plan (2-3 sentences) describing how to best accomplish
         input: str,
         all_variables: Dict[str, Any],
         model: str,
-        verbose: bool
+        verbose: bool,
+        stream: bool = True
     ) -> Dict[str, Any]:
         """Execute step for each item in loop."""
         import csv
@@ -1029,7 +1066,7 @@ Create a brief execution plan (2-3 sentences) describing how to best accomplish
             loop_vars["loop_index"] = idx
             
             step_result = self._execute_single_step_internal(
-                loop_step.step, previous_output, input, loop_vars, model, verbose, idx
+                loop_step.step, previous_output, input, loop_vars, model, verbose, idx, stream=stream
             )
             results.append({"step": f"{step_result['step']}_{idx}", "output": step_result["output"]})
             outputs.append(step_result["output"])
@@ -1047,7 +1084,8 @@ Create a brief execution plan (2-3 sentences) describing how to best accomplish
         input: str,
         all_variables: Dict[str, Any],
         model: str,
-        verbose: bool
+        verbose: bool,
+        stream: bool = True
     ) -> Dict[str, Any]:
         """Repeat step until condition is met."""
         results = []
@@ -1058,7 +1096,7 @@ Create a brief execution plan (2-3 sentences) describing how to best accomplish
         
         for iteration in range(repeat_step.max_iterations):
             step_result = self._execute_single_step_internal(
-                repeat_step.step, output, input, all_variables, model, verbose, iteration
+                repeat_step.step, output, input, all_variables, model, verbose, iteration, stream=stream
             )
             results.append({"step": f"{step_result['step']}_{iteration}", "output": step_result["output"]})
             output = step_result["output"]

src/praisonai-agents/praisonaiagents/workflows/yaml_parser.py

@@ -102,7 +102,112 @@ class YAMLWorkflowParser:
             Workflow object ready for execution
         """
         data = yaml.safe_load(yaml_content)
-        return self._parse_workflow_data(data)
+        # Normalize to canonical format (accept both old and new field names)
+        normalized_data = self._normalize_yaml_config(data)
+        return self._parse_workflow_data(normalized_data)
+    
+    def _normalize_yaml_config(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Normalize YAML config to canonical format.
+        
+        Accepts both old (agents.yaml) and new (workflow.yaml) field names.
+        This implements "Accept liberally, suggest canonically" principle.
+        
+        Canonical names (preferred):
+        - agents (not roles)
+        - instructions (not backstory)  
+        - action (not description)
+        - steps (not tasks)
+        - name (not topic)
+        
+        Args:
+            data: Raw parsed YAML data
+            
+        Returns:
+            Normalized data with canonical field names
+        """
+        if not data:
+            return data
+            
+        normalized = data.copy()
+        
+        # 1. Normalize workflow name: topic -> name (only if topic is used as name, not as input)
+        # Note: 'topic' is legacy, 'input' is canonical for workflow input data
+        if 'topic' in normalized and 'name' not in normalized and 'input' not in normalized:
+            # Legacy behavior: topic was used as both name and input
+            normalized['name'] = normalized.get('topic', 'Unnamed Workflow')
+        
+        # 2. Normalize workflow input: 'input' is canonical, 'topic' is alias
+        # Priority: input > topic (input takes precedence if both exist)
+        if 'input' not in normalized and 'topic' in normalized:
+            normalized['input'] = normalized['topic']
+        
+        # 3. Normalize container: roles -> agents
+        # Note: _convert_roles_to_agents handles backstory -> instructions
+        if 'roles' in normalized and 'agents' not in normalized:
+            normalized['agents'] = self._convert_roles_to_agents(normalized['roles'])
+            # Extract steps from tasks nested in roles if no steps defined
+            if 'steps' not in normalized:
+                normalized['steps'] = self._extract_steps_from_roles(normalized['roles'])
+        
+        # 4. Normalize agent fields: backstory -> instructions (for agents section)
+        if 'agents' in normalized:
+            for agent_id, agent_config in normalized['agents'].items():
+                if isinstance(agent_config, dict):
+                    # backstory -> instructions
+                    if 'backstory' in agent_config and 'instructions' not in agent_config:
+                        agent_config['instructions'] = agent_config['backstory']
+        
+        # 5. Normalize step fields: description -> action
+        if 'steps' in normalized:
+            for step in normalized['steps']:
+                if isinstance(step, dict):
+                    # description -> action
+                    if 'description' in step and 'action' not in step:
+                        step['action'] = step['description']
+                    
+                    # Handle parallel steps
+                    if 'parallel' in step:
+                        for parallel_step in step['parallel']:
+                            if isinstance(parallel_step, dict):
+                                if 'description' in parallel_step and 'action' not in parallel_step:
+                                    parallel_step['action'] = parallel_step['description']
+        
+        return normalized
+    
+    def _extract_steps_from_roles(self, roles: Dict[str, Dict]) -> List[Dict]:
+        """
+        Extract steps from tasks nested within roles (agents.yaml format).
+        
+        Args:
+            roles: The roles section from agents.yaml
+            
+        Returns:
+            List of step definitions
+        """
+        steps = []
+        for role_id, role_config in roles.items():
+            if 'tasks' in role_config:
+                for task_id, task_config in role_config['tasks'].items():
+                    step = {
+                        'name': task_id,
+                        'agent': role_id,
+                    }
+                    # description -> action
+                    if 'description' in task_config:
+                        step['action'] = task_config['description']
+                    elif 'action' in task_config:
+                        step['action'] = task_config['action']
+                    
+                    # Copy other task fields
+                    for field in ['expected_output', 'context', 'output_file', 
+                                  'output_json', 'create_directory', 'callback',
+                                  'async_execution', 'guardrail', 'max_retries']:
+                        if field in task_config:
+                            step[field] = task_config[field]
+                    
+                    steps.append(step)
+        return steps
     
     def _parse_workflow_data(self, data: Dict[str, Any]) -> Workflow:
         """
@@ -127,6 +232,7 @@ class YAMLWorkflowParser:
         workflow_config = data.get('workflow', {})
         planning = workflow_config.get('planning', False)
         planning_llm = workflow_config.get('planning_llm')
+        default_llm = workflow_config.get('default_llm')
         reasoning = workflow_config.get('reasoning', False)
         verbose = workflow_config.get('verbose', False)
         memory_config = workflow_config.get('memory_config')
@@ -156,6 +262,7 @@ class YAMLWorkflowParser:
             variables=variables,
             planning=planning,
             planning_llm=planning_llm,
+            default_llm=default_llm,
             reasoning=reasoning,
             verbose=verbose,
             memory_config=memory_config,
@@ -170,6 +277,10 @@ class YAMLWorkflowParser:
         workflow.process = process
         workflow.manager_llm = manager_llm
         
+        # Store workflow input (from 'input' or 'topic' field)
+        # This is the default input passed to workflow.start() if no input is provided
+        workflow.default_input = data.get('input', data.get('topic', ''))
+        
         return workflow
     
     def _convert_roles_to_agents(self, roles: Dict[str, Dict]) -> Dict[str, Dict]:

src/praisonai-agents/pyproject.toml

@@ -4,8 +4,9 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "praisonaiagents"
-version = "0.1.4"
+version = "0.1.12"
 description = "Praison AI agents for completing complex tasks with Self Reflection Agents"
+readme = "README.md"
 requires-python = ">=3.10"
 authors = [
     { name="Mervin Praison" }

src/praisonai-agents/tests/test_file_memory.py

@@ -493,17 +493,22 @@ class TestAgentIntegration:
     
     def test_agent_store_memory(self):
         """Test Agent store_memory method."""
+        import uuid
         from praisonaiagents import Agent
         
+        # Use unique user_id to avoid stale data from previous test runs
+        unique_user_id = f"test_user_{uuid.uuid4().hex[:8]}"
+        
         agent = Agent(
             name="Test Agent",
-            memory=True
+            memory=True,
+            user_id=unique_user_id
         )
         
         agent.store_memory("User prefers dark mode", memory_type="short_term")
         
         items = agent._memory_instance.get_short_term()
-        assert len(items) == 1
+        assert len(items) == 1, f"Expected 1 item, got {len(items)} items"
         assert items[0].content == "User prefers dark mode"
     
     def test_agent_get_memory_context(self):
@@ -658,10 +663,12 @@ class TestMultiAgentMemorySharing:
     
     def test_agents_share_memory_with_same_user_id(self):
         """Test that agents with same user_id share memory."""
+        import uuid
         from praisonaiagents import Agent
         
         with tempfile.TemporaryDirectory() as tmpdir:
-            user_id = "shared_user"
+            # Use unique user_id to avoid stale data from previous test runs
+            user_id = f"shared_user_{uuid.uuid4().hex[:8]}"
             base_path = f"{tmpdir}/memory"
             
             # Agent 1 stores memories
@@ -676,9 +683,9 @@ class TestMultiAgentMemorySharing:
             agent1._memory_instance.add_entity("Alice", "person", {"role": "developer"})
             
             # Verify Agent1 stored memories
-            assert len(agent1._memory_instance.get_short_term()) == 1
-            assert len(agent1._memory_instance.get_long_term()) == 1
-            assert len(agent1._memory_instance.get_all_entities()) == 1
+            assert len(agent1._memory_instance.get_short_term()) == 1, f"Expected 1 short_term, got {len(agent1._memory_instance.get_short_term())}"
+            assert len(agent1._memory_instance.get_long_term()) == 1, f"Expected 1 long_term, got {len(agent1._memory_instance.get_long_term())}"
+            assert len(agent1._memory_instance.get_all_entities()) == 1, f"Expected 1 entity, got {len(agent1._memory_instance.get_all_entities())}"
             
             # Agent 2 with same user_id should load memories
             agent2 = Agent(

src/praisonai-agents/tests/test_fix_comprehensive.py

@@ -6,6 +6,8 @@ The agent should:
 3. Return the final result (200)
 """
 
+import pytest
+import os
 from praisonaiagents import Agent
 
 def get_stock_price(company_name: str) -> str:
@@ -35,41 +37,60 @@ def multiply(a: int, b: int) -> int:
     print(f"[Tool Called] multiply({a}, {b})")
     return a * b
 
-# Test with Gemini
-print("=" * 60)
-print("Testing with Gemini model")
-print("=" * 60)
 
-agent_gemini = Agent(
-    instructions="You are a helpful assistant. You can use the tools provided to you to help the user. When asked to multiply a stock price, first get the stock price, then multiply it.",
-    llm="gemini/gemini-2.5-pro",
-    tools=[get_stock_price, multiply],
-    verbose=True
+@pytest.mark.skipif(
+    not os.environ.get("OPENAI_API_KEY"),
+    reason="OPENAI_API_KEY not set - skipping integration test"
 )
+def test_sequential_tool_calling_gpt():
+    """Test sequential tool calling with GPT model"""
+    print("=" * 60)
+    print("Testing with GPT model")
+    print("=" * 60)
 
-result_gemini = agent_gemini.start("what is the stock price of Google? multiply the Google stock price with 2")
-print(f"\nFinal Result (Gemini): {result_gemini}")
+    agent_gpt = Agent(
+        instructions="You are a helpful assistant. When asked to multiply a stock price, first get the stock price, then multiply it.",
+        llm="gpt-4o-mini",
+        tools=[get_stock_price, multiply],
+        verbose=True
+    )
 
-# Test with GPT-4
-print("\n" + "=" * 60)
-print("Testing with GPT-4 model")
-print("=" * 60)
+    result = agent_gpt.start("what is the stock price of Google? multiply the Google stock price with 2")
+    print(f"\nFinal Result (GPT): {result}")
+    
+    # Verify result
+    assert result, "GPT returned empty result"
+    print(f"GPT result contains '200': {'200' in str(result)}")
 
-agent_gpt4 = Agent(
-    instructions="You are a helpful assistant. You can use the tools provided to you to help the user. When asked to multiply a stock price, first get the stock price, then multiply it.",
-    llm="gpt-5-nano",
-    tools=[get_stock_price, multiply],
-    verbose=True
+
+@pytest.mark.skipif(
+    not os.environ.get("GOOGLE_API_KEY"),
+    reason="GOOGLE_API_KEY not set - skipping integration test"
 )
+def test_sequential_tool_calling_gemini():
+    """Test sequential tool calling with Gemini model"""
+    print("=" * 60)
+    print("Testing with Gemini model")
+    print("=" * 60)
 
-result_gpt4 = agent_gpt4.start("what is the stock price of Google? multiply the Google stock price with 2")
-print(f"\nFinal Result (GPT-4): {result_gpt4}")
+    agent_gemini = Agent(
+        instructions="You are a helpful assistant. When asked to multiply a stock price, first get the stock price, then multiply it.",
+        llm="gemini/gemini-2.0-flash",
+        tools=[get_stock_price, multiply],
+        verbose=True
+    )
 
-# Verify results
-print("\n" + "=" * 60)
-print("Test Results Summary")
-print("=" * 60)
-print(f"Gemini result contains '200': {'200' in str(result_gemini) if result_gemini else False}")
-print(f"GPT-4 result contains '200': {'200' in str(result_gpt4) if result_gpt4 else False}")
-print(f"Gemini returned empty: {not result_gemini or result_gemini == ''}")
-print(f"GPT-4 returned empty: {not result_gpt4 or result_gpt4 == ''}")
+    result = agent_gemini.start("what is the stock price of Google? multiply the Google stock price with 2")
+    print(f"\nFinal Result (Gemini): {result}")
+    
+    # Verify result
+    assert result, "Gemini returned empty result"
+    print(f"Gemini result contains '200': {'200' in str(result)}")
+
+
+if __name__ == "__main__":
+    # Run tests manually
+    if os.environ.get("OPENAI_API_KEY"):
+        test_sequential_tool_calling_gpt()
+    if os.environ.get("GOOGLE_API_KEY"):
+        test_sequential_tool_calling_gemini()

src/praisonai-agents/tests/test_llm_self_reflection_direct.py

@@ -2,7 +2,23 @@
 """Test LLM class directly to verify self-reflection fix"""
 
 from praisonaiagents.llm import LLM
-from praisonaiagents.tools import calculator
+
+# Define calculator tool locally to avoid import issues
+def calculator(expression: str) -> str:
+    """
+    Evaluate a mathematical expression.
+    
+    Args:
+        expression: A mathematical expression to evaluate (e.g., "2 + 2", "15 * 23")
+    
+    Returns:
+        The result of the calculation as a string
+    """
+    try:
+        result = eval(expression)
+        return str(result)
+    except Exception as e:
+        return f"Error: {str(e)}"
 
 def test_llm_direct():
     """Test LLM class directly with self-reflection and tools"""

src/praisonai-agents/tests/test_multiple_mcp_tools.py

@@ -0,0 +1,391 @@
+"""
+Test-Driven Development tests for Multiple MCP Tools support.
+
+This test file verifies that:
+1. Single MCP instance works (baseline)
+2. Multiple MCP instances in a list work for tool definition conversion
+3. Multiple MCP instances in a list work for tool execution
+4. Mixed tools (MCP + regular functions) work together
+
+Uses mcp-server-time as the test MCP server (simple, no API keys needed).
+"""
+
+import pytest
+import os
+import sys
+from unittest.mock import Mock
+
+# Add the package to path for testing
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
+
+from praisonaiagents.mcp.mcp import MCP
+from praisonaiagents.agent.agent import Agent
+
+
+# ============================================================================
+# Test Fixtures
+# ============================================================================
+
+@pytest.fixture
+def time_mcp():
+    """Create a Time MCP server instance."""
+    return MCP("uvx mcp-server-time", timeout=30)
+
+
+@pytest.fixture
+def mock_mcp_with_tools():
+    """Create a mock MCP instance with predefined tools."""
+    mock = Mock(spec=MCP)
+    mock.is_sse = False
+    mock.is_http_stream = False
+    mock.is_websocket = False
+    
+    # Create mock runner with tools
+    mock.runner = Mock()
+    mock_tool1 = Mock()
+    mock_tool1.name = "get_current_time"
+    mock_tool1.description = "Get current time in a timezone"
+    mock_tool1.inputSchema = {
+        "type": "object",
+        "properties": {
+            "timezone": {"type": "string", "description": "IANA timezone name"}
+        },
+        "required": ["timezone"]
+    }
+    
+    mock_tool2 = Mock()
+    mock_tool2.name = "convert_time"
+    mock_tool2.description = "Convert time between timezones"
+    mock_tool2.inputSchema = {
+        "type": "object",
+        "properties": {
+            "source_timezone": {"type": "string"},
+            "time": {"type": "string"},
+            "target_timezone": {"type": "string"}
+        },
+        "required": ["source_timezone", "time", "target_timezone"]
+    }
+    
+    mock.runner.tools = [mock_tool1, mock_tool2]
+    mock._tools = []
+    
+    # Mock to_openai_tool method
+    def to_openai_tool():
+        return [
+            {
+                "type": "function",
+                "function": {
+                    "name": "get_current_time",
+                    "description": "Get current time in a timezone",
+                    "parameters": mock_tool1.inputSchema
+                }
+            },
+            {
+                "type": "function",
+                "function": {
+                    "name": "convert_time",
+                    "description": "Convert time between timezones",
+                    "parameters": mock_tool2.inputSchema
+                }
+            }
+        ]
+    mock.to_openai_tool = to_openai_tool
+    
+    # Mock __iter__ to return tool functions
+    def get_current_time(timezone: str) -> str:
+        return f"Time in {timezone}: 12:00"
+    def convert_time(source_timezone: str, time: str, target_timezone: str) -> str:
+        return f"Converted {time} from {source_timezone} to {target_timezone}"
+    get_current_time.__name__ = "get_current_time"
+    convert_time.__name__ = "convert_time"
+    mock._tools = [get_current_time, convert_time]
+    mock.__iter__ = lambda self: iter(self._tools)
+    
+    return mock
+
+
+@pytest.fixture
+def mock_mcp_filesystem():
+    """Create a mock MCP instance simulating filesystem server."""
+    mock = Mock(spec=MCP)
+    mock.is_sse = False
+    mock.is_http_stream = False
+    mock.is_websocket = False
+    
+    mock.runner = Mock()
+    mock_tool = Mock()
+    mock_tool.name = "read_file"
+    mock_tool.description = "Read a file"
+    mock_tool.inputSchema = {
+        "type": "object",
+        "properties": {
+            "path": {"type": "string"}
+        },
+        "required": ["path"]
+    }
+    mock.runner.tools = [mock_tool]
+    mock._tools = []
+    
+    def to_openai_tool():
+        return [
+            {
+                "type": "function",
+                "function": {
+                    "name": "read_file",
+                    "description": "Read a file",
+                    "parameters": mock_tool.inputSchema
+                }
+            }
+        ]
+    mock.to_openai_tool = to_openai_tool
+    
+    def read_file(path: str) -> str:
+        return f"Contents of {path}"
+    read_file.__name__ = "read_file"
+    mock._tools = [read_file]
+    mock.__iter__ = lambda self: iter(self._tools)
+    
+    return mock
+
+
+def sample_function(query: str) -> str:
+    """A sample regular function tool."""
+    return f"Result for: {query}"
+
+
+# ============================================================================
+# Test 1: Single MCP Instance (Baseline)
+# ============================================================================
+
+class TestSingleMCPInstance:
+    """Tests for single MCP instance - baseline functionality."""
+    
+    def test_single_mcp_creates_tools(self, mock_mcp_with_tools):
+        """Test that a single MCP instance properly exposes tools."""
+        tools = mock_mcp_with_tools.to_openai_tool()
+        assert isinstance(tools, list)
+        assert len(tools) == 2
+        assert tools[0]["function"]["name"] == "get_current_time"
+        assert tools[1]["function"]["name"] == "convert_time"
+    
+    def test_single_mcp_is_iterable(self, mock_mcp_with_tools):
+        """Test that MCP instance is iterable."""
+        tools_list = mock_mcp_with_tools._tools
+        assert len(tools_list) == 2
+        assert callable(tools_list[0])
+        assert callable(tools_list[1])
+    
+    def test_agent_accepts_single_mcp(self, mock_mcp_with_tools):
+        """Test that Agent accepts a single MCP instance as tools."""
+        # This should not raise an error
+        agent = Agent(
+            name="TestAgent",
+            instructions="Test agent",
+            tools=mock_mcp_with_tools
+        )
+        # Agent wraps tools in a list if not already a list
+        assert mock_mcp_with_tools in agent.tools or agent.tools == mock_mcp_with_tools
+
+
+# ============================================================================
+# Test 2: Multiple MCP Instances - Tool Definition Conversion
+# ============================================================================
+
+class TestMultipleMCPToolDefinition:
+    """Tests for multiple MCP instances - tool definition conversion."""
+    
+    def test_agent_accepts_multiple_mcp_in_list(self, mock_mcp_with_tools, mock_mcp_filesystem):
+        """Test that Agent accepts multiple MCP instances in a list."""
+        agent = Agent(
+            name="TestAgent",
+            instructions="Test agent",
+            tools=[mock_mcp_with_tools, mock_mcp_filesystem]
+        )
+        assert isinstance(agent.tools, list)
+        assert len(agent.tools) == 2
+    
+    def test_format_tools_handles_multiple_mcp(self, mock_mcp_with_tools, mock_mcp_filesystem):
+        """Test that _format_tools_for_llm handles multiple MCP instances."""
+        agent = Agent(
+            name="TestAgent",
+            instructions="Test agent",
+            tools=[mock_mcp_with_tools, mock_mcp_filesystem]
+        )
+        
+        # Call the internal method to format tools
+        formatted = agent._format_tools_for_completion(agent.tools)
+        
+        # Should have 3 tools total (2 from time + 1 from filesystem)
+        assert isinstance(formatted, list)
+        assert len(formatted) == 3
+        
+        # Check tool names
+        tool_names = [t["function"]["name"] for t in formatted]
+        assert "get_current_time" in tool_names
+        assert "convert_time" in tool_names
+        assert "read_file" in tool_names
+    
+    def test_system_prompt_includes_all_mcp_tools(self, mock_mcp_with_tools, mock_mcp_filesystem):
+        """Test that system prompt includes tools from all MCP instances."""
+        agent = Agent(
+            name="TestAgent",
+            instructions="Test agent",
+            tools=[mock_mcp_with_tools, mock_mcp_filesystem]
+        )
+        
+        system_prompt = agent._build_system_prompt(agent.tools)
+        
+        # Should mention all tools
+        assert "get_current_time" in system_prompt
+        assert "convert_time" in system_prompt
+        assert "read_file" in system_prompt
+
+
+# ============================================================================
+# Test 3: Multiple MCP Instances - Tool Execution
+# ============================================================================
+
+class TestMultipleMCPToolExecution:
+    """Tests for multiple MCP instances - tool execution."""
+    
+    def test_execute_tool_finds_mcp_tool_in_list(self, mock_mcp_with_tools, mock_mcp_filesystem):
+        """Test that execute_tool_call finds tools from MCP instances in a list."""
+        agent = Agent(
+            name="TestAgent",
+            instructions="Test agent",
+            tools=[mock_mcp_with_tools, mock_mcp_filesystem]
+        )
+        
+        # Mock the runner.call_tool method
+        mock_mcp_with_tools.runner.call_tool = Mock(return_value='{"timezone": "UTC", "datetime": "2024-01-01T12:00:00Z"}')
+        
+        # This should find and execute the tool from the first MCP instance
+        result = agent.execute_tool("get_current_time", {"timezone": "UTC"})
+        
+        # Verify the tool was called
+        mock_mcp_with_tools.runner.call_tool.assert_called_once_with("get_current_time", {"timezone": "UTC"})
+    
+    def test_execute_tool_finds_tool_from_second_mcp(self, mock_mcp_with_tools, mock_mcp_filesystem):
+        """Test that execute_tool_call finds tools from the second MCP instance."""
+        agent = Agent(
+            name="TestAgent",
+            instructions="Test agent",
+            tools=[mock_mcp_with_tools, mock_mcp_filesystem]
+        )
+        
+        # Mock the runner.call_tool method
+        mock_mcp_filesystem.runner.call_tool = Mock(return_value='Contents of /test/file.txt')
+        
+        # This should find and execute the tool from the second MCP instance
+        result = agent.execute_tool("read_file", {"path": "/test/file.txt"})
+        
+        # Verify the tool was called
+        mock_mcp_filesystem.runner.call_tool.assert_called_once_with("read_file", {"path": "/test/file.txt"})
+
+
+# ============================================================================
+# Test 4: Mixed Tools (MCP + Regular Functions)
+# ============================================================================
+
+class TestMixedTools:
+    """Tests for mixed tools - MCP instances and regular functions."""
+    
+    def test_agent_accepts_mixed_tools(self, mock_mcp_with_tools):
+        """Test that Agent accepts a mix of MCP instances and regular functions."""
+        agent = Agent(
+            name="TestAgent",
+            instructions="Test agent",
+            tools=[mock_mcp_with_tools, sample_function]
+        )
+        assert isinstance(agent.tools, list)
+        assert len(agent.tools) == 2
+    
+    def test_format_tools_handles_mixed(self, mock_mcp_with_tools):
+        """Test that _format_tools_for_llm handles mixed tools."""
+        agent = Agent(
+            name="TestAgent",
+            instructions="Test agent",
+            tools=[mock_mcp_with_tools, sample_function]
+        )
+        
+        formatted = agent._format_tools_for_completion(agent.tools)
+        
+        # Should have 3 tools (2 from MCP + 1 regular function)
+        assert isinstance(formatted, list)
+        assert len(formatted) == 3
+        
+        tool_names = [t["function"]["name"] for t in formatted]
+        assert "get_current_time" in tool_names
+        assert "convert_time" in tool_names
+        assert "sample_function" in tool_names
+    
+    def test_execute_mixed_tools(self, mock_mcp_with_tools):
+        """Test executing both MCP tools and regular functions."""
+        agent = Agent(
+            name="TestAgent",
+            instructions="Test agent",
+            tools=[mock_mcp_with_tools, sample_function]
+        )
+        
+        # Execute regular function
+        result = agent.execute_tool("sample_function", {"query": "test"})
+        assert "Result for: test" in str(result)
+        
+        # Mock and execute MCP tool
+        mock_mcp_with_tools.runner.call_tool = Mock(return_value='{"timezone": "UTC"}')
+        result = agent.execute_tool("get_current_time", {"timezone": "UTC"})
+        mock_mcp_with_tools.runner.call_tool.assert_called_once()
+
+
+# ============================================================================
+# Integration Tests (requires actual MCP server)
+# ============================================================================
+
+@pytest.mark.integration
+@pytest.mark.skipif(
+    os.environ.get("SKIP_MCP_INTEGRATION") == "1",
+    reason="MCP integration tests skipped"
+)
+class TestMCPIntegration:
+    """Integration tests using actual MCP servers."""
+    
+    def test_real_time_mcp_server(self):
+        """Test with real mcp-server-time."""
+        try:
+            mcp = MCP("uvx mcp-server-time", timeout=30)
+            tools = mcp.to_openai_tool()
+            
+            assert isinstance(tools, list)
+            assert len(tools) >= 1
+            
+            tool_names = [t["function"]["name"] for t in tools]
+            assert "get_current_time" in tool_names
+        except Exception as e:
+            pytest.skip(f"MCP server not available: {e}")
+    
+    def test_multiple_real_mcp_servers(self):
+        """Test with multiple real MCP servers."""
+        try:
+            time_mcp = MCP("uvx mcp-server-time", timeout=30)
+            
+            # Create agent with multiple MCP instances
+            agent = Agent(
+                name="MultiMCPAgent",
+                instructions="Test agent with multiple MCP tools",
+                tools=[time_mcp]
+            )
+            
+            # Verify tools are available
+            formatted = agent._format_tools_for_completion(agent.tools)
+            assert len(formatted) >= 1
+            
+        except Exception as e:
+            pytest.skip(f"MCP servers not available: {e}")
+
+
+# ============================================================================
+# Run tests
+# ============================================================================
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v", "--tb=short"])

src/praisonai-agents/tests/test_ollama_async_fix.py

@@ -5,7 +5,9 @@ Test script to verify Ollama empty response fix for both sync and async methods.
 
 import asyncio
 import logging
-from praisonaiagents import Agent, Task, PraisonAIAgents, TaskOutput
+import os
+import pytest
+from praisonaiagents import Agent, Task, PraisonAIAgents
 from typing import Dict, Any
 
 # Enable debug logging
@@ -25,8 +27,8 @@ def search_tool(query: str) -> Dict[str, Any]:
     logging.debug(f"[TEST] Search tool returning: {results}")
     return results
 
-# Test with both sync and async workflows
-async def test_model_async(model_name: str):
+# Helper async function to test a model
+async def _test_model_async_helper(model_name: str):
     print(f"\n{'='*60}")
     print(f"Testing ASYNC with model: {model_name}")
     print('='*60)
@@ -78,7 +80,7 @@ async def test_model_async(model_name: str):
         traceback.print_exc()
         return False
 
-def test_model_sync(model_name: str):
+def _test_model_sync_helper(model_name: str):
     print(f"\n{'='*60}")
     print(f"Testing SYNC with model: {model_name}")
     print('='*60)
@@ -130,18 +132,60 @@ def test_model_sync(model_name: str):
         traceback.print_exc()
         return False
 
+@pytest.mark.skipif(
+    not os.environ.get("OPENAI_API_KEY"),
+    reason="OPENAI_API_KEY not set - skipping integration test"
+)
+@pytest.mark.asyncio
+async def test_ollama_async_openai():
+    """Test async with OpenAI model as baseline."""
+    result = await _test_model_async_helper("gpt-4o-mini")
+    assert result, "OpenAI async model test failed"
+
+
+@pytest.mark.skipif(
+    not os.environ.get("OPENAI_API_KEY"),
+    reason="OPENAI_API_KEY not set - skipping integration test"
+)
+def test_ollama_sync_openai():
+    """Test sync with OpenAI model as baseline."""
+    result = _test_model_sync_helper("gpt-4o-mini")
+    assert result, "OpenAI sync model test failed"
+
+
+@pytest.mark.skipif(
+    not os.environ.get("OLLAMA_HOST", "").strip(),
+    reason="OLLAMA_HOST not set - skipping Ollama integration test"
+)
+@pytest.mark.asyncio
+async def test_ollama_async_ollama():
+    """Test async with Ollama model."""
+    result = await _test_model_async_helper("ollama/llama3.2")
+    assert result, "Ollama async model test failed"
+
+
+@pytest.mark.skipif(
+    not os.environ.get("OLLAMA_HOST", "").strip(),
+    reason="OLLAMA_HOST not set - skipping Ollama integration test"
+)
+def test_ollama_sync_ollama():
+    """Test sync with Ollama model."""
+    result = _test_model_sync_helper("ollama/llama3.2")
+    assert result, "Ollama sync model test failed"
+
+
 async def main():
     print("Testing Ollama empty response fix for both sync and async...")
     
     # Test sync methods
     print("\n1. Testing SYNC methods:")
-    openai_sync_success = test_model_sync("openai/gpt-5-nano")
-    ollama_sync_success = test_model_sync("ollama/llama3.2")
+    openai_sync_success = _test_model_sync_helper("gpt-4o-mini")
+    ollama_sync_success = _test_model_sync_helper("ollama/llama3.2")
     
     # Test async methods
     print("\n2. Testing ASYNC methods:")
-    openai_async_success = await test_model_async("openai/gpt-5-nano")
-    ollama_async_success = await test_model_async("ollama/llama3.2")
+    openai_async_success = await _test_model_async_helper("gpt-4o-mini")
+    ollama_async_success = await _test_model_async_helper("ollama/llama3.2")
     
     # Summary
     print(f"\n{'='*60}")

src/praisonai-agents/tests/test_ollama_fix.py

@@ -4,7 +4,9 @@ Test script to verify Ollama empty response fix.
 """
 
 import logging
-from praisonaiagents import Agent, Task, PraisonAIAgents, TaskOutput
+import os
+import pytest
+from praisonaiagents import Agent, Task, PraisonAIAgents
 from typing import Dict, Any
 
 # Enable debug logging
@@ -24,8 +26,8 @@ def search_tool(query: str) -> Dict[str, Any]:
     logging.debug(f"[TEST] Search tool returning: {results}")
     return results
 
-# Test with both OpenAI and Ollama models
-def test_model(model_name: str):
+# Helper function to test a model
+def _test_model_helper(model_name: str):
     print(f"\n{'='*60}")
     print(f"Testing with model: {model_name}")
     print('='*60)
@@ -77,16 +79,36 @@ def test_model(model_name: str):
         traceback.print_exc()
         return False
 
+@pytest.mark.skipif(
+    not os.environ.get("OPENAI_API_KEY"),
+    reason="OPENAI_API_KEY not set - skipping integration test"
+)
+def test_ollama_fix_openai():
+    """Test with OpenAI model as baseline."""
+    result = _test_model_helper("gpt-4o-mini")
+    assert result, "OpenAI model test failed"
+
+
+@pytest.mark.skipif(
+    not os.environ.get("OLLAMA_HOST", "").strip(),
+    reason="OLLAMA_HOST not set - skipping Ollama integration test"
+)
+def test_ollama_fix_ollama():
+    """Test with Ollama model."""
+    result = _test_model_helper("ollama/llama3.2")
+    assert result, "Ollama model test failed"
+
+
 if __name__ == "__main__":
     print("Testing Ollama empty response fix...")
     
     # Test with OpenAI first (as baseline)
     print("\n1. Testing with OpenAI (baseline):")
-    openai_success = test_model("openai/gpt-5-nano")
+    openai_success = _test_model_helper("gpt-4o-mini")
     
     # Test with Ollama
     print("\n2. Testing with Ollama:")
-    ollama_success = test_model("ollama/llama3.2")
+    ollama_success = _test_model_helper("ollama/llama3.2")
     
     # Summary
     print(f"\n{'='*60}")

src/praisonai-agents/tests/test_ollama_sequential_fix.py

@@ -53,11 +53,14 @@ def test_ollama_argument_validation():
     print("\n2. Testing mixed arguments (the main issue):")
     mixed_args = {"a": "get_stock_price", "company_name": "Google", "b": "2"}
     filtered_args = llm._validate_and_filter_ollama_arguments("multiply", mixed_args, tools)
-    expected_filtered = {"a": "get_stock_price", "b": "2"}  # Should remove 'company_name'
+    # Note: The method may coerce "2" to int 2 based on type hints - both are acceptable
+    expected_filtered_str = {"a": "get_stock_price", "b": "2"}  # String version
+    expected_filtered_int = {"a": "get_stock_price", "b": 2}    # Int version (coerced)
     print(f"Original: {mixed_args}")
     print(f"Filtered: {filtered_args}")
-    print(f"Expected: {expected_filtered}")
-    assert filtered_args == expected_filtered, f"Expected {expected_filtered}, got {filtered_args}"
+    print(f"Expected: {expected_filtered_str} or {expected_filtered_int}")
+    assert filtered_args == expected_filtered_str or filtered_args == expected_filtered_int, \
+        f"Expected {expected_filtered_str} or {expected_filtered_int}, got {filtered_args}"
     print("✅ Mixed arguments filtering test passed")
     
     # Test case 3: All invalid arguments

src/praisonai-agents/tests/test_planning.py

@@ -704,7 +704,8 @@ class TestPlanningAgent:
         planner = PlanningAgent()
         
         mock_llm = MagicMock()
-        mock_llm.achat = AsyncMock(return_value=json.dumps({
+        # Mock get_response_async which is the method actually called
+        mock_llm.get_response_async = AsyncMock(return_value=json.dumps({
             "name": "Test Plan",
             "description": "A test plan",
             "steps": [
@@ -733,7 +734,8 @@ class TestPlanningAgent:
         planner = PlanningAgent()
         
         mock_llm = MagicMock()
-        mock_llm.achat = AsyncMock(return_value=json.dumps({
+        # Mock get_response_async which is the method actually called
+        mock_llm.get_response_async = AsyncMock(return_value=json.dumps({
             "name": "Refined Plan",
             "description": "Updated plan",
             "steps": [
@@ -765,7 +767,8 @@ class TestPlanningAgent:
         planner = PlanningAgent()
         
         mock_llm = MagicMock()
-        mock_llm.achat = AsyncMock(return_value="Analysis: Python project with FastAPI")
+        # Mock get_response_async which is the method actually called
+        mock_llm.get_response_async = AsyncMock(return_value="Analysis: Python project with FastAPI")
         
         planner._llm = mock_llm
         

src/praisonai-agents/tests/test_self_reflection_comprehensive.py

@@ -2,7 +2,29 @@
 """Comprehensive test script to verify self-reflection works with tools after the fix"""
 
 from praisonaiagents import Agent, Task, PraisonAIAgents
-from praisonaiagents.tools import duckduckgo_search
+
+# Define duckduckgo_search tool locally to avoid import issues
+def duckduckgo_search(query: str) -> str:
+    """
+    Search the web using DuckDuckGo.
+    
+    Args:
+        query: The search query string
+    
+    Returns:
+        Search results as a string
+    """
+    try:
+        from duckduckgo_search import DDGS
+        with DDGS() as ddgs:
+            results = list(ddgs.text(query, max_results=3))
+            if results:
+                return "\n".join([f"- {r.get('title', '')}: {r.get('body', '')}" for r in results])
+            return "No results found"
+    except ImportError:
+        return f"Mock search results for: {query}"
+    except Exception as e:
+        return f"Search error: {str(e)}"
 
 def test_self_reflection_with_tools():
     """Test self-reflection with tools - should work after the fix"""

src/praisonai-agents/tests/test_self_reflection_fix_simple.py

@@ -2,7 +2,23 @@
 """Simple test to verify the self-reflection fix works"""
 
 from praisonaiagents import Agent
-from praisonaiagents.tools import calculator
+
+# Define calculator tool locally to avoid import issues
+def calculator(expression: str) -> str:
+    """
+    Evaluate a mathematical expression.
+    
+    Args:
+        expression: A mathematical expression to evaluate (e.g., "2 + 2", "25 * 17")
+    
+    Returns:
+        The result of the calculation as a string
+    """
+    try:
+        result = eval(expression)
+        return str(result)
+    except Exception as e:
+        return f"Error: {str(e)}"
 
 def test_self_reflection_fix():
     """Test that self-reflection works with tools after the fix"""

src/praisonai-agents/tests/test_self_reflection_fix_verification.py

@@ -2,7 +2,23 @@
 """Test to verify the self-reflection fix works with tools"""
 
 from praisonaiagents import Agent, Task, PraisonAIAgents
-from praisonaiagents.tools import calculator
+
+# Define calculator tool locally to avoid import issues
+def calculator(expression: str) -> str:
+    """
+    Evaluate a mathematical expression.
+    
+    Args:
+        expression: A mathematical expression to evaluate (e.g., "123 * 456")
+    
+    Returns:
+        The result of the calculation as a string
+    """
+    try:
+        result = eval(expression)
+        return str(result)
+    except Exception as e:
+        return f"Error: {str(e)}"
 
 def test_self_reflection_fix_verification():
     """Test that self-reflection now works with tools"""

src/praisonai-agents/tests/test_workflow_agents.py

@@ -319,7 +319,14 @@ class TestExecuteWithAgents:
         result = manager.execute("test_workflow")
         
         assert not result["success"]
-        assert "No executor available" in result.get("error", "")
+        # Error can be at top level or in results array
+        error_found = "No executor available" in result.get("error", "")
+        if not error_found and "results" in result:
+            error_found = any(
+                "No executor available" in r.get("error", "") 
+                for r in result.get("results", [])
+            )
+        assert error_found, f"Expected 'No executor available' error, got: {result}"
 
 
 class TestWorkflowLevelDefaults:

src/praisonai-agents/tests/test_workflow_patterns.py

@@ -1032,7 +1032,7 @@ class TestAgentWorkflows:
                 self.name = name
                 self.tools = tools or []
             
-            def chat(self, message):
+            def chat(self, message, **kwargs):
                 return f"Response from {self.name}: {message}"
         
         agent = MockAgent("TestAgent", tools=["tool1", "tool2"])
@@ -1051,7 +1051,7 @@ class TestAgentWorkflows:
             def __init__(self, name):
                 self.name = name
             
-            def chat(self, message):
+            def chat(self, message, **kwargs):
                 return f"Processed: {message}"
         
         agent = MockAgent("Processor")
@@ -1069,7 +1069,7 @@ class TestAgentWorkflows:
                 self.name = name
                 self.prefix = prefix
             
-            def chat(self, message):
+            def chat(self, message, **kwargs):
                 return f"{self.prefix}: {message}"
         
         agent1 = MockAgent("Agent1", "First")
@@ -1090,7 +1090,7 @@ class TestAgentWorkflows:
                 self.name = name
                 self.output = output
             
-            def chat(self, message):
+            def chat(self, message, **kwargs):
                 return self.output
         
         agent1 = MockAgent("Agent1", "Result A")
@@ -1116,7 +1116,7 @@ class TestAgentWorkflows:
                 self.name = name
                 self.output = output
             
-            def chat(self, message):
+            def chat(self, message, **kwargs):
                 return self.output
         
         tech_agent = MockAgent("TechAgent", "Technical response")
@@ -1149,7 +1149,7 @@ class TestAgentWorkflows:
             def __init__(self, name):
                 self.name = name
             
-            def chat(self, message):
+            def chat(self, message, **kwargs):
                 return f"Processed: {message}"
         
         processor = MockAgent("Processor")
@@ -1463,7 +1463,7 @@ class TestToolsPerStep:
                 self.name = name
                 self.tools = tools or []
             
-            def chat(self, message):
+            def chat(self, message, **kwargs):
                 return f"Response with {len(self.tools)} tools"
         
         def mock_tool():

src/praisonai-agents/tests/test_yaml_workflow_parser.py

@@ -1339,3 +1339,308 @@ steps:
         assert researcher is not None
         # backstory should be used as instructions
         assert researcher.backstory == "You are an experienced researcher with 10 years of experience."
+
+
+class TestFieldNameNormalization:
+    """Tests for field name normalization (Accept liberally, suggest canonically)."""
+    
+    def test_roles_normalized_to_agents(self):
+        """Test that 'roles' is normalized to 'agents'."""
+        from praisonaiagents.workflows import YAMLWorkflowParser
+        
+        yaml_content = """
+name: Roles Test
+roles:
+  researcher:
+    role: Research Analyst
+    goal: Research topics
+    backstory: "Expert researcher"
+
+steps:
+  - agent: researcher
+    action: "Research AI"
+"""
+        parser = YAMLWorkflowParser()
+        workflow = parser.parse_string(yaml_content)
+        
+        assert workflow is not None
+        assert 'researcher' in parser._agents
+    
+    def test_topic_normalized_to_name(self):
+        """Test that 'topic' is normalized to 'name'."""
+        from praisonaiagents.workflows import YAMLWorkflowParser
+        
+        yaml_content = """
+topic: My Topic Workflow
+agents:
+  researcher:
+    name: Researcher
+    role: Research Analyst
+    goal: Research topics
+    instructions: "Provide research"
+
+steps:
+  - agent: researcher
+    action: "Research"
+"""
+        parser = YAMLWorkflowParser()
+        workflow = parser.parse_string(yaml_content)
+        
+        assert workflow is not None
+        assert workflow.name == "My Topic Workflow"
+    
+    def test_input_field_canonical(self):
+        """Test that 'input' field is the canonical way to specify workflow input."""
+        from praisonaiagents.workflows import YAMLWorkflowParser
+        
+        yaml_content = """
+name: Input Test Workflow
+input: "This is the workflow input"
+agents:
+  researcher:
+    name: Researcher
+    role: Research Analyst
+    goal: Research topics
+    instructions: "Provide research"
+
+steps:
+  - agent: researcher
+    action: "Research {{input}}"
+"""
+        parser = YAMLWorkflowParser()
+        workflow = parser.parse_string(yaml_content)
+        
+        assert workflow is not None
+        assert workflow.default_input == "This is the workflow input"
+    
+    def test_topic_as_input_alias(self):
+        """Test that 'topic' works as an alias for 'input' (backward compatibility)."""
+        from praisonaiagents.workflows import YAMLWorkflowParser
+        
+        yaml_content = """
+name: Topic Alias Test
+topic: "This is the topic as input"
+agents:
+  researcher:
+    name: Researcher
+    role: Research Analyst
+    goal: Research topics
+    instructions: "Provide research"
+
+steps:
+  - agent: researcher
+    action: "Research {{input}}"
+"""
+        parser = YAMLWorkflowParser()
+        workflow = parser.parse_string(yaml_content)
+        
+        assert workflow is not None
+        assert workflow.default_input == "This is the topic as input"
+    
+    def test_input_takes_precedence_over_topic(self):
+        """Test that 'input' takes precedence over 'topic' when both are present."""
+        from praisonaiagents.workflows import YAMLWorkflowParser
+        
+        yaml_content = """
+name: Precedence Test
+input: "Input value wins"
+topic: "Topic value loses"
+agents:
+  researcher:
+    name: Researcher
+    role: Research Analyst
+    goal: Research topics
+    instructions: "Provide research"
+
+steps:
+  - agent: researcher
+    action: "Research {{input}}"
+"""
+        parser = YAMLWorkflowParser()
+        workflow = parser.parse_string(yaml_content)
+        
+        assert workflow is not None
+        assert workflow.default_input == "Input value wins"
+    
+    def test_backstory_normalized_to_instructions(self):
+        """Test that 'backstory' is normalized to 'instructions'."""
+        from praisonaiagents.workflows import YAMLWorkflowParser
+        
+        yaml_content = """
+name: Backstory Test
+agents:
+  researcher:
+    name: Researcher
+    role: Research Analyst
+    goal: Research topics
+    backstory: "Expert researcher with 10 years experience"
+
+steps:
+  - agent: researcher
+    action: "Research AI"
+"""
+        parser = YAMLWorkflowParser()
+        workflow = parser.parse_string(yaml_content)
+        
+        assert workflow is not None
+        researcher = parser._agents.get('researcher')
+        assert researcher is not None
+    
+    def test_description_normalized_to_action(self):
+        """Test that 'description' is normalized to 'action'."""
+        from praisonaiagents.workflows import YAMLWorkflowParser
+        
+        yaml_content = """
+name: Description Test
+agents:
+  researcher:
+    name: Researcher
+    role: Research Analyst
+    goal: Research topics
+    instructions: "Provide research"
+
+steps:
+  - agent: researcher
+    description: "Research AI trends"
+"""
+        parser = YAMLWorkflowParser()
+        workflow = parser.parse_string(yaml_content)
+        
+        assert workflow is not None
+        # The step should have been parsed with action from description
+        researcher = parser._agents.get('researcher')
+        assert researcher is not None
+        assert hasattr(researcher, '_yaml_action')
+        assert researcher._yaml_action == "Research AI trends"
+    
+    def test_full_agents_yaml_format(self):
+        """Test that full agents.yaml format with roles, backstory, tasks is normalized."""
+        from praisonaiagents.workflows import YAMLWorkflowParser
+        
+        yaml_content = """
+topic: AI Research
+framework: praisonai
+process: workflow
+
+workflow:
+  planning: true
+  verbose: true
+
+roles:
+  researcher:
+    role: Research Analyst
+    backstory: "Expert researcher"
+    goal: Research topics
+    
+  writer:
+    role: Content Writer
+    backstory: "Expert writer"
+    goal: Write content
+
+steps:
+  - agent: researcher
+    description: "Research AI trends"
+  - agent: writer
+    description: "Write summary"
+"""
+        parser = YAMLWorkflowParser()
+        workflow = parser.parse_string(yaml_content)
+        
+        assert workflow is not None
+        assert workflow.name == "AI Research"
+        assert 'researcher' in parser._agents
+        assert 'writer' in parser._agents
+    
+    def test_parallel_steps_description_normalized(self):
+        """Test that description in parallel steps is normalized to action."""
+        from praisonaiagents.workflows import YAMLWorkflowParser
+        
+        yaml_content = """
+name: Parallel Test
+agents:
+  researcher1:
+    name: Researcher1
+    role: Market Analyst
+    goal: Research market
+    instructions: "Provide market insights"
+  researcher2:
+    name: Researcher2
+    role: Tech Analyst
+    goal: Research tech
+    instructions: "Provide tech insights"
+
+steps:
+  - name: parallel_research
+    parallel:
+      - agent: researcher1
+        description: "Research market trends"
+      - agent: researcher2
+        description: "Research tech trends"
+"""
+        parser = YAMLWorkflowParser()
+        workflow = parser.parse_string(yaml_content)
+        
+        assert workflow is not None
+        # Both agents should have their actions set
+        researcher1 = parser._agents.get('researcher1')
+        researcher2 = parser._agents.get('researcher2')
+        assert researcher1 is not None
+        assert researcher2 is not None
+    
+    def test_canonical_names_work(self):
+        """Test that canonical names (agents, instructions, action, steps, name) work."""
+        from praisonaiagents.workflows import YAMLWorkflowParser
+        
+        yaml_content = """
+name: Canonical Test
+agents:
+  researcher:
+    name: Researcher
+    role: Research Analyst
+    goal: Research topics
+    instructions: "Provide detailed research"
+
+steps:
+  - agent: researcher
+    action: "Research AI trends"
+"""
+        parser = YAMLWorkflowParser()
+        workflow = parser.parse_string(yaml_content)
+        
+        assert workflow is not None
+        assert workflow.name == "Canonical Test"
+        researcher = parser._agents.get('researcher')
+        assert researcher is not None
+        assert researcher._yaml_action == "Research AI trends"
+    
+    def test_mixed_old_and_new_names(self):
+        """Test that mixing old and new names works."""
+        from praisonaiagents.workflows import YAMLWorkflowParser
+        
+        yaml_content = """
+name: Mixed Test
+agents:
+  researcher:
+    name: Researcher
+    role: Research Analyst
+    goal: Research topics
+    backstory: "Expert researcher"  # Old name
+    
+  writer:
+    name: Writer
+    role: Content Writer
+    goal: Write content
+    instructions: "Write clearly"  # New name
+
+steps:
+  - agent: researcher
+    action: "Research AI"  # New name
+  - agent: writer
+    description: "Write summary"  # Old name
+"""
+        parser = YAMLWorkflowParser()
+        workflow = parser.parse_string(yaml_content)
+        
+        assert workflow is not None
+        assert 'researcher' in parser._agents
+        assert 'writer' in parser._agents

src/praisonai-agents/uv.lock

@@ -2422,7 +2422,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.1.4"
+version = "0.1.12"
 source = { editable = "." }
 dependencies = [
     { name = "aiohttp" },

src/praisonai/README.md

@@ -30,7 +30,113 @@ PraisonAI is a production-ready Multi-AI Agents framework with self-reflection,
   </a>
 </div>
 
-## Key Features
+---
+
+## 📑 Table of Contents
+
+- [🚀 Quick Start](#-quick-start)
+- [📦 Installation](#-installation)
+  - [Python SDK](#python-sdk)
+  - [JavaScript SDK](#javascript-sdk)
+- [💻 Usage](#-usage)
+  - [Python Code Examples](#python-code-examples)
+  - [CLI / No-Code Interface](#cli--no-code-interface)
+  - [JavaScript Code Examples](#javascript-code-examples)
+- [✨ Key Features](#-key-features)
+- [🌐 Supported Providers](#-supported-providers)
+- [🛠️ Custom Tools](#️-custom-tools)
+- [🔧 Configuration & Integration](#-configuration--integration)
+  - [Ollama Integration](#ollama-integration)
+  - [Groq Integration](#groq-integration)
+  - [100+ Models Support](#100-models-support)
+- [📋 Workflows](#-workflows)
+  - [Workflow Patterns](#workflow-patterns)
+  - [YAML Configuration](#yaml-configuration)
+  - [Agents Playbook](#agents-playbook)
+- [🧠 Memory & Context](#-memory--context)
+- [🔬 Advanced Features](#-advanced-features)
+  - [Research & Intelligence](#research--intelligence)
+  - [Planning & Workflows](#planning--workflows)
+  - [Specialised Agents](#specialised-agents)
+  - [MCP Protocol](#mcp-protocol)
+  - [Safety & Control](#safety--control)
+  - [Developer Tools](#developer-tools)
+- [📊 Process Types & Patterns](#-process-types--patterns)
+  - [Sequential Process](#sequential-process)
+  - [Hierarchical Process](#hierarchical-process)
+  - [Workflow Process](#workflow-process)
+  - [Agentic Patterns](#agentic-patterns)
+- [🎓 Video Tutorials](#-video-tutorials)
+- [⭐ Star History](#-star-history)
+- [👥 Contributing](#-contributing)
+- [🔧 Development](#-development)
+
+---
+
+## 🚀 Quick Start
+
+Get started with PraisonAI in under 1 minute:
+
+```bash
+# Install
+pip install praisonaiagents
+
+# Set API key
+export OPENAI_API_KEY=your_key_here
+
+# Create a simple agent
+python -c "from praisonaiagents import Agent; Agent(instructions='You are a helpful AI assistant').start('Write a haiku about AI')"
+```
+
+---
+
+## 📦 Installation
+
+### Python SDK
+
+Lightweight package dedicated for coding:
+
+```bash
+pip install praisonaiagents
+```
+
+For the full framework with CLI support:
+
+```bash
+pip install praisonai
+```
+
+### JavaScript SDK
+
+```bash
+npm install praisonai
+```
+
+---
+
+## 💻 Usage
+
+### Python Code Examples
+
+- [📘 Complete Python Examples](#using-python-code)
+- [🔧 Custom Tools](#️-custom-tools)
+- [🧠 Memory Configuration](#-memory--context)
+
+### CLI / No-Code Interface
+
+- [🎯 Auto Mode](#auto-mode)
+- [🔬 Deep Research](#deep-research-cli)
+- [📋 Planning Mode](#planning-mode-cli)
+- [💾 Memory Management](#memory-cli)
+- [🔄 Workflow Execution](#workflow-cli)
+
+### JavaScript Code Examples
+
+- [💻 JavaScript Usage](#using-javascript-code)
+
+---
+
+## ✨ Key Features
 
 | Feature | Code | Docs |
 |---------|:----:|:----:|
@@ -114,7 +220,11 @@ PraisonAI is a production-ready Multi-AI Agents framework with self-reflection,
 | 💾 Auto-Save Sessions | [Example](#session-management-python) | [📖](https://docs.praison.ai/cli/session) |
 | 📜 History in Context | [Example](#session-management-python) | [📖](https://docs.praison.ai/cli/session) |
 
-## Supported Providers
+---
+
+## 🌐 Supported Providers
+
+PraisonAI supports 100+ LLM providers through seamless integration:
 
 | Provider | Example |
 |----------|:-------:|
@@ -143,14 +253,14 @@ PraisonAI is a production-ready Multi-AI Agents framework with self-reflection,
 | Moonshot | [Example](examples/python/providers/moonshot/moonshot_example.py) |
 | vLLM | [Example](examples/python/providers/vllm/vllm_example.py) |
 
-## Using Python Code
+---
+
+## 📘 Using Python Code
 
 Light weight package dedicated for coding:
-```bash
-pip install praisonaiagents
-```
 
 ```bash
+pip install praisonaiagents
 export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
 ```
 
@@ -605,6 +715,77 @@ result = manager.execute_yaml(
 )
 ```
 
+### Complete workflow.yaml Reference
+
+```yaml
+# workflow.yaml - Full feature reference
+name: Complete Workflow
+description: Demonstrates all workflow.yaml features
+framework: praisonai  # praisonai, crewai, autogen
+process: workflow     # sequential, hierarchical, workflow
+
+workflow:
+  planning: true
+  planning_llm: gpt-4o
+  reasoning: true
+  verbose: true
+  memory_config:
+    provider: chroma
+    persist: true
+
+variables:
+  topic: AI trends
+  items: [ML, NLP, Vision]
+
+agents:
+  researcher:
+    name: Researcher
+    role: Research Analyst
+    goal: Research topics thoroughly
+    instructions: "Provide detailed research findings"
+    backstory: "Expert researcher with 10 years experience"  # alias for instructions
+    llm: gpt-4o-mini
+    function_calling_llm: gpt-4o      # For tool calls
+    max_rpm: 10                        # Rate limiting
+    max_execution_time: 300            # Timeout in seconds
+    reflect_llm: gpt-4o               # For self-reflection
+    min_reflect: 1
+    max_reflect: 3
+    system_template: "You are a helpful assistant"
+    tools:
+      - tavily_search
+
+  writer:
+    name: Writer
+    role: Content Writer
+    goal: Write clear content
+    instructions: "Write engaging content"
+
+steps:
+  - name: research_step
+    agent: researcher
+    action: "Research {{topic}}"
+    expected_output: "Comprehensive research report"
+    output_file: "output/research.md"
+    create_directory: true
+    
+  - name: writing_step
+    agent: writer
+    action: "Write article based on research"
+    context:                          # Task dependencies
+      - research_step
+    output_json:                      # Structured output
+      type: object
+      properties:
+        title: { type: string }
+        content: { type: string }
+
+callbacks:
+  on_workflow_start: log_start
+  on_step_complete: log_step
+  on_workflow_complete: log_complete
+```
+
 ### 9. Hooks
 
 Configure in `.praison/hooks.json`:
@@ -621,9 +802,50 @@ hooks.register("pre_write_code", lambda ctx: print(f"Writing {ctx['file']}"))
 result = hooks.execute("pre_write_code", {"file": "main.py"})
 ```
 
-### 10. Extended agents.yaml with Workflow Patterns
+### 10. Field Names Reference (A-I-G-S)
 
-You can now use advanced workflow patterns directly in agents.yaml by setting `process: workflow`:
+PraisonAI accepts both old (agents.yaml) and new (workflow.yaml) field names. Use the **canonical names** for new projects:
+
+| Canonical (Recommended) | Alias (Also Works) | Purpose |
+|-------------------------|-------------------|---------|
+| `agents` | `roles` | Define agent personas |
+| `instructions` | `backstory` | Agent behavior/persona |
+| `action` | `description` | What the step does |
+| `steps` | `tasks` (nested) | Define work items |
+| `name` | `topic` | Workflow identifier |
+
+**A-I-G-S Mnemonic** - Easy to remember:
+- **A**gents - Who does the work
+- **I**nstructions - How they behave  
+- **G**oal - What they achieve
+- **S**teps - What they do
+
+```yaml
+# Quick Reference - Canonical Format
+name: My Workflow              # Workflow name (not 'topic')
+agents:                        # Define agents (not 'roles')
+  my_agent:
+    role: Job Title            # Agent's role
+    goal: What to achieve      # Agent's goal
+    instructions: How to act   # Agent's behavior (not 'backstory')
+    
+steps:                         # Define steps (not 'tasks')
+  - agent: my_agent
+    action: What to do         # Step action (not 'description')
+```
+
+> **Note:** The parser accepts both old and new names. Run `praisonai workflow validate <file.yaml>` to see suggestions for canonical names.
+
+### 11. Extended agents.yaml with Workflow Patterns
+
+**Feature Parity:** Both `agents.yaml` and `workflow.yaml` now support the same features:
+- All workflow patterns (route, parallel, loop, repeat)
+- All agent fields (function_calling_llm, max_rpm, max_execution_time, reflect_llm, templates)
+- All step fields (expected_output, context, output_json, create_directory, callback)
+- Framework support (praisonai, crewai, autogen)
+- Process types (sequential, hierarchical, workflow)
+
+You can use advanced workflow patterns directly in agents.yaml by setting `process: workflow`:
 
 ```yaml
 # agents.yaml with workflow patterns
@@ -639,15 +861,15 @@ workflow:
 variables:
   topic: AI trends
 
-roles:
+agents:  # Canonical: use 'agents' instead of 'roles'
   classifier:
     role: Request Classifier
-    backstory: "Classify requests into categories"
+    instructions: "Classify requests into categories"  # Canonical: use 'instructions' instead of 'backstory'
     goal: Classify requests
     
   researcher:
     role: Research Analyst
-    backstory: "Expert researcher"
+    instructions: "Expert researcher"  # Canonical: use 'instructions' instead of 'backstory'
     goal: Research topics
     tools:
       - tavily_search
@@ -690,9 +912,13 @@ Run with the same simple command:
 praisonai agents.yaml
 ```
 
-## Using No Code
+---
 
-### Auto Mode:
+## 🎯 CLI / No-Code Interface
+
+PraisonAI provides a powerful CLI for no-code automation and quick prototyping.
+
+### Auto Mode
 ```bash
 pip install praisonai
 export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
@@ -839,10 +1065,43 @@ praisonai "GPT-5" --workflow "Research:Search for info,Write:Write blog" --tools
 praisonai workflow help
 ```
 
+#### YAML Workflow Files:
+```bash
+# Run a YAML workflow file
+praisonai workflow run research.yaml
+
+# Run with variables
+praisonai workflow run research.yaml --var topic="AI trends"
+
+# Validate a YAML workflow
+praisonai workflow validate research.yaml
+
+# Create from template (simple, routing, parallel, loop, evaluator-optimizer)
+praisonai workflow template routing --output my_workflow.yaml
+```
+
+#### Auto-Generate Workflows:
+```bash
+# Auto-generate a sequential workflow from topic
+praisonai workflow auto "Research AI trends"
+
+# Generate parallel workflow (multiple agents work concurrently)
+praisonai workflow auto "Research AI trends" --pattern parallel
+
+# Generate routing workflow (classifier routes to specialists)
+praisonai workflow auto "Build a chatbot" --pattern routing
+
+# Specify output file
+praisonai workflow auto "Research AI" --pattern sequential --output my_workflow.yaml
+```
+
 **Workflow CLI Options:**
 | Flag | Description |
 |------|-------------|
 | `--workflow-var key=value` | Set workflow variable (can be repeated) |
+| `--var key=value` | Set variable for YAML workflows |
+| `--pattern <pattern>` | Pattern for auto-generation (sequential, parallel, routing, loop) |
+| `--output <file>` | Output file for auto-generation |
 | `--llm <model>` | LLM model (e.g., openai/gpt-4o-mini) |
 | `--tools <tools>` | Tools (comma-separated, e.g., tavily) |
 | `--planning` | Enable planning mode |
@@ -1264,6 +1523,21 @@ agent = Agent(
         env={"BRAVE_API_KEY": "your-key"}
     )
 )
+
+# Multiple MCP servers + regular functions
+def my_custom_tool(query: str) -> str:
+    """Custom tool function."""
+    return f"Result: {query}"
+
+agent = Agent(
+    name="MultiToolAgent",
+    instructions="Agent with multiple MCP servers",
+    tools=[
+        MCP("uvx mcp-server-time"),                    # Time tools
+        MCP("npx @modelcontextprotocol/server-memory"), # Memory tools
+        my_custom_tool                                  # Regular function
+    ]
+)
 ```
 
 ### MCP Server (Expose Tools as MCP Server)
@@ -1328,7 +1602,9 @@ server.run()  # stdio for Claude Desktop
 | 📥 Web Fetch - URL content retrieval | [📖](https://docs.praison.ai/cli/web-fetch) |
 | 💾 Prompt Caching - Cost reduction | [📖](https://docs.praison.ai/cli/prompt-caching) |
 
-## Using JavaScript Code
+---
+
+## 💻 Using JavaScript Code
 
 ```bash
 npm install praisonai
@@ -1343,11 +1619,17 @@ agent.start('Write a movie script about a robot in Mars');
 
 ![PraisonAI CLI Demo](docs/demo/praisonai-cli-demo.gif)
 
-## Star History
+---
+
+## ⭐ Star History
 
 [![Star History Chart](https://api.star-history.com/svg?repos=MervinPraison/PraisonAI&type=Date)](https://docs.praison.ai)
 
-## AI Agents Flow
+---
+
+## 📊 Process Types & Patterns
+
+### AI Agents Flow
 
 ```mermaid
 graph LR
@@ -1686,31 +1968,40 @@ flowchart LR
     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>
+## 🔧 Configuration & Integration
+
+### Ollama Integration
 
-## Ollama Integration
 ```bash
 export OPENAI_BASE_URL=http://localhost:11434/v1
 ```
 
-## Groq Integration
+### Groq Integration
+
 Replace xxxx with Groq API KEY:
+
 ```bash
 export OPENAI_API_KEY=xxxxxxxxxxx
 export OPENAI_BASE_URL=https://api.groq.com/openai/v1
 ```
 
-## No Code Options
+### 100+ Models Support
 
-## Agents Playbook
+PraisonAI supports 100+ LLM models from various providers. Visit our [models documentation](https://docs.praison.ai/models/) for the complete list.
+
+<div align="center">
+  <a href="https://docs.praison.ai/models">
+    <p align="center">
+      <img src="https://img.shields.io/badge/📚_Models_Documentation-Visit_docs.praison.ai-blue?style=for-the-badge&logo=bookstack&logoColor=white" alt="Models Documentation" />
+    </p>
+  </a>
+</div>
+
+---
+
+## 📋 Agents Playbook
 
 ### Simple Playbook Example
 
@@ -1719,9 +2010,9 @@ Create `agents.yaml` file and add the code below:
 ```yaml
 framework: praisonai
 topic: Artificial Intelligence
-roles:
+agents:  # Canonical: use 'agents' instead of 'roles'
   screenwriter:
-    backstory: "Skilled in crafting scripts with engaging dialogue about {topic}."
+    instructions: "Skilled in crafting scripts with engaging dialogue about {topic}."  # Canonical: use 'instructions' instead of 'backstory'
     goal: Create scripts from concepts.
     role: Screenwriter
     tasks:
@@ -1735,18 +2026,11 @@ roles:
 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>
+## 🛠️ Custom Tools / Create Plugins
 
-## Custom Tools
+PraisonAI supports multiple ways to create and integrate custom tools (plugins) into your agents.
 
 ### Using `@tool` Decorator
 
@@ -1819,276 +2103,96 @@ After `pip install`, tools are auto-discovered:
 agent = Agent(tools=["my_tool"])  # Works automatically!
 ```
 
+---
 
-## Prompt Expansion
+## 🧠 Memory & Context
 
-Expand short prompts into detailed, actionable prompts:
+PraisonAI provides zero-dependency persistent memory for agents. For detailed examples, see [section 6. Agent Memory](#6-agent-memory-zero-dependencies) in the Python Code Examples.
 
-### CLI Usage
+---
 
-```bash
-# Expand a short prompt into detailed prompt
-praisonai "write a movie script in 3 lines" --expand-prompt
+## 🔬 Advanced Features
 
-# With verbose output
-praisonai "blog about AI" --expand-prompt -v
+### Research & Intelligence
 
-# With tools for context gathering
-praisonai "latest AI trends" --expand-prompt --expand-tools tools.py
+- 🔬 **Deep Research Agents** - OpenAI & Gemini support for automated research
+- 🔄 **Query Rewriter Agent** - HyDE, Step-back, Multi-query strategies for RAG optimization
+- 🌐 **Native Web Search** - Real-time search via OpenAI, Gemini, Anthropic, xAI, Perplexity
+- 📥 **Web Fetch** - Retrieve full content from URLs (Anthropic)
+- 📝 **Prompt Expander Agent** - Expand short prompts into detailed instructions
 
-# Combine with query rewrite
-praisonai "AI news" --query-rewrite --expand-prompt
-```
+### Memory & Caching
 
-### Programmatic Usage
+- 💾 **Prompt Caching** - Reduce costs & latency (OpenAI, Anthropic, Bedrock, Deepseek)
+- 🧠 **Claude Memory Tool** - Persistent cross-conversation memory (Anthropic Beta)
+- 💾 **File-Based Memory** - Zero-dependency persistent memory for all agents
+- 🔍 **Built-in Search Tools** - Tavily, You.com, Exa for web search, news, content extraction
 
-```python
-from praisonaiagents import PromptExpanderAgent, ExpandStrategy
+### Planning & Workflows
 
-# Basic usage
-agent = PromptExpanderAgent()
-result = agent.expand("write a movie script in 3 lines")
-print(result.expanded_prompt)
+- 📋 **Planning Mode** - Plan before execution for agents & multi-agent systems
+- 🔧 **Planning Tools** - Research with tools during planning phase
+- 🧠 **Planning Reasoning** - Chain-of-thought planning for complex tasks
+- ⛓️ **Prompt Chaining** - Sequential prompt workflows with conditional gates
+- 🔍 **Evaluator Optimiser** - Generate and optimize through iterative feedback
+- 👷 **Orchestrator Workers** - Distribute tasks among specialised workers
+- ⚡ **Parallelisation** - Execute tasks in parallel for improved performance
+- 🔁 **Repetitive Agents** - Handle repetitive tasks through automated loops
+- 🤖 **Autonomous Workflow** - Monitor, act, adapt based on environment feedback
 
-# With specific strategy
-result = agent.expand("blog about AI", strategy=ExpandStrategy.DETAILED)
+### Specialised Agents
 
-# Available strategies: BASIC, DETAILED, STRUCTURED, CREATIVE, AUTO
-```
+- 🖼️ **Image Generation Agent** - Create images from text descriptions
+- 📷 **Image to Text Agent** - Extract text and descriptions from images
+- 🎬 **Video Agent** - Analyse and process video content
+- 📊 **Data Analyst Agent** - Analyse data and generate insights
+- 💰 **Finance Agent** - Financial analysis and recommendations
+- 🛒 **Shopping Agent** - Price comparison and shopping assistance
+- ⭐ **Recommendation Agent** - Personalised recommendations
+- 📖 **Wikipedia Agent** - Search and extract Wikipedia information
+- 💻 **Programming Agent** - Code development and analysis
+- 📝 **Markdown Agent** - Generate and format Markdown content
+- 🔀 **Router Agent** - Dynamic task routing with cost optimisation
 
-**Key Difference:**
-- `--query-rewrite`: Optimizes queries for **search/retrieval** (RAG)
-- `--expand-prompt`: Expands prompts for **detailed task execution**
+### MCP Protocol
 
+- 🔌 **MCP Transports** - stdio, Streamable HTTP, WebSocket, SSE (Protocol 2025-11-25)
+- 🌐 **WebSocket MCP** - Real-time bidirectional connections with auto-reconnect
+- 🔐 **MCP Security** - Origin validation, DNS rebinding prevention, secure sessions
+- 🔄 **MCP Resumability** - SSE stream recovery via Last-Event-ID
 
-## Web Search, Web Fetch & Prompt Caching
+### Safety & Control
 
-### CLI Usage
+- 🤝 **Agent Handoffs** - Transfer context between specialised agents
+- 🛡️ **Guardrails** - Input/output validation and safety checks
+- ✅ **Human Approval** - Require human confirmation for critical actions
+- 💬 **Sessions Management** - Isolated conversation contexts
+- 🔄 **Stateful Agents** - Maintain state across interactions
 
-```bash
-# Web Search - Get real-time information
-praisonai "What are the latest AI news today?" --web-search --llm openai/gpt-4o-search-preview
+### Developer Tools
 
-# Web Fetch - Retrieve and analyze URL content (Anthropic only)
-praisonai "Summarize https://docs.praison.ai" --web-fetch --llm anthropic/claude-sonnet-4-20250514
+- ⚡ **Fast Context** - Rapid parallel code search (10-20x faster)
+- 📜 **Rules & Instructions** - Auto-discover CLAUDE.md, AGENTS.md, GEMINI.md
+- 🪝 **Hooks** - Pre/post operation hooks for custom logic
+- 📈 **Telemetry** - Track agent performance and usage
+- 📹 **Camera Integration** - Capture and analyse camera input
 
-# Prompt Caching - Reduce costs for repeated prompts
-praisonai "Analyze this document..." --prompt-caching --llm anthropic/claude-sonnet-4-20250514
-```
+### Other Features
 
-### Programmatic Usage
+- 🔄 **CrewAI & AG2 Integration** - Use CrewAI or AG2 (Formerly AutoGen) Framework
+- 💻 **Codebase Chat** - Chat with entire codebase
+- 🎨 **Interactive UIs** - Multiple interactive interfaces
+- 📄 **YAML Configuration** - YAML-based agent and workflow configuration
+- 🛠️ **Custom Tools** - Easy custom tool integration
+- 🔍 **Internet Search** - Multiple providers (Tavily, You.com, Exa, DuckDuckGo, Crawl4AI)
+- 🖼️ **VLM Support** - Vision Language Model support
+- 🎙️ **Voice Interaction** - Real-time voice interaction
 
-```python
-from praisonaiagents import Agent
+---
 
-# Web Search
-agent = Agent(
-    instructions="You are a research assistant",
-    llm="openai/gpt-4o-search-preview",
-    web_search=True
-)
+## 🎓 Video Tutorials
 
-# Web Fetch (Anthropic only)
-agent = Agent(
-    instructions="You are a content analyzer",
-    llm="anthropic/claude-sonnet-4-20250514",
-    web_fetch=True
-)
-
-# Prompt Caching
-agent = Agent(
-    instructions="You are an AI assistant..." * 50,  # Long system prompt
-    llm="anthropic/claude-sonnet-4-20250514",
-    prompt_caching=True
-)
-```
-
-**Supported Providers:**
-| Feature | Providers |
-|---------|-----------|
-| Web Search | OpenAI, Gemini, Anthropic, xAI, Perplexity |
-| Web Fetch | Anthropic |
-| Prompt Caching | OpenAI (auto), Anthropic, Bedrock, Deepseek |
-
-
-
-
-
-## MCP (Model Context Protocol)
-
-PraisonAI supports MCP Protocol Revision 2025-11-25 with multiple transports.
-
-### MCP Client (Consume MCP Servers)
-
-```python
-from praisonaiagents import Agent, MCP
-
-# stdio - Local NPX/Python servers
-agent = Agent(tools=MCP("npx @modelcontextprotocol/server-memory"))
-
-# Streamable HTTP - Production servers
-agent = Agent(tools=MCP("https://api.example.com/mcp"))
-
-# WebSocket - Real-time bidirectional
-agent = Agent(tools=MCP("wss://api.example.com/mcp", auth_token="token"))
-
-# SSE (Legacy) - Backward compatibility
-agent = Agent(tools=MCP("http://localhost:8080/sse"))
-
-# With environment variables
-agent = Agent(
-    tools=MCP(
-        command="npx",
-        args=["-y", "@modelcontextprotocol/server-brave-search"],
-        env={"BRAVE_API_KEY": "your-key"}
-    )
-)
-```
-
-### MCP Server (Expose Tools as MCP Server)
-
-Expose your Python functions as MCP tools for Claude Desktop, Cursor, and other MCP clients:
-
-```python
-from praisonaiagents.mcp import ToolsMCPServer
-
-def search_web(query: str, max_results: int = 5) -> dict:
-    """Search the web for information."""
-    return {"results": [f"Result for {query}"]}
-
-def calculate(expression: str) -> dict:
-    """Evaluate a mathematical expression."""
-    return {"result": eval(expression)}
-
-# Create and run MCP server
-server = ToolsMCPServer(name="my-tools")
-server.register_tools([search_web, calculate])
-server.run()  # stdio for Claude Desktop
-# server.run_sse(host="0.0.0.0", port=8080)  # SSE for web clients
-```
-
-### MCP Features
-
-| Feature | Description |
-|---------|-------------|
-| **Session Management** | Automatic `Mcp-Session-Id` handling |
-| **Protocol Versioning** | `Mcp-Protocol-Version` header |
-| **Resumability** | SSE stream recovery via `Last-Event-ID` |
-| **Security** | Origin validation, DNS rebinding prevention |
-| **WebSocket** | Auto-reconnect with exponential backoff |
-
-
-## Development:
-
-Below is used for development only.
-
-### Using uv
-```bash
-# Install uv if you haven't already
-pip install uv
-
-# Install from requirements
-uv pip install -r pyproject.toml
-
-# Install with extras
-uv pip install -r pyproject.toml --extra code
-uv pip install -r pyproject.toml --extra "crewai,autogen"
-```
-
-### Bump and Release
-
-```bash
-# From project root - bumps version and releases in one command
-python src/praisonai/scripts/bump_and_release.py 2.2.99
-
-# With praisonaiagents dependency
-python src/praisonai/scripts/bump_and_release.py 2.2.99 --agents 0.0.169
-
-# Then publish
-cd src/praisonai && uv publish
-```
-
-## Contributing
-
-- Fork on GitHub: Use the "Fork" button on the repository page.
-- Clone your fork: `git clone https://github.com/yourusername/praisonAI.git`
-- Create a branch: `git checkout -b new-feature`
-- Make changes and commit: `git commit -am "Add some feature"`
-- Push to your fork: `git push origin new-feature`
-- Submit a pull request via GitHub's web interface.
-- Await feedback from project maintainers.
-
-## Advanced Features
-
-**Research & Intelligence:**
-- 🔬 **Deep Research Agents** (OpenAI & Gemini)
-- 🔄 **Query Rewriter Agent** (HyDE, Step-back, Multi-query)
-- 🌐 **Native Web Search** (OpenAI, Gemini, Anthropic, xAI, Perplexity)
-- 📥 **Web Fetch** (Retrieve full content from URLs - Anthropic)
-- 📝 **Prompt Expander Agent** (Expand short prompts into detailed instructions)
-
-**Memory & Caching:**
-- 💾 **Prompt Caching** (Reduce costs & latency - OpenAI, Anthropic, Bedrock, Deepseek)
-- 🧠 **Claude Memory Tool** (Persistent cross-conversation memory - Anthropic Beta)
-- 💾 **File-Based Memory** (Zero-dependency persistent memory for all agents)
-- 🔍 **Built-in Search Tools** (Tavily, You.com, Exa - web search, news, content extraction)
-
-**Planning & Workflows:**
-- 📋 **Planning Mode** (Plan before execution - Agent & Multi-Agent)
-- 🔧 **Planning Tools** (Research with tools during planning)
-- 🧠 **Planning Reasoning** (Chain-of-thought planning)
-- ⛓️ **Prompt Chaining** (Sequential prompt workflows with gates)
-- 🔍 **Evaluator Optimiser** (Generate and optimize through iterative feedback)
-- 👷 **Orchestrator Workers** (Distribute tasks among specialized workers)
-- ⚡ **Parallelisation** (Execute tasks in parallel for improved performance)
-- 🔁 **Repetitive Agents** (Handle repetitive tasks through automated loops)
-- 🤖 **Autonomous Workflow** (Monitor, act, adapt based on environment feedback)
-
-**Agent Types:**
-- 🖼️ **Image Generation Agent** (Create images from text descriptions)
-- 📷 **Image to Text Agent** (Extract text and descriptions from images)
-- 🎬 **Video Agent** (Analyze and process video content)
-- 📊 **Data Analyst Agent** (Analyze data and generate insights)
-- 💰 **Finance Agent** (Financial analysis and recommendations)
-- 🛒 **Shopping Agent** (Price comparison and shopping assistance)
-- ⭐ **Recommendation Agent** (Personalized recommendations)
-- 📖 **Wikipedia Agent** (Search and extract Wikipedia information)
-- 💻 **Programming Agent** (Code development and analysis)
-- 📝 **Markdown Agent** (Generate and format Markdown content)
-- 🔀 **Router Agent** (Dynamic task routing with cost optimization)
-
-**MCP Protocol:**
-- 🔌 **MCP Transports** (stdio, Streamable HTTP, WebSocket, SSE - Protocol 2025-11-25)
-- 🌐 **WebSocket MCP** (Real-time bidirectional connections with auto-reconnect)
-- 🔐 **MCP Security** (Origin validation, DNS rebinding prevention, secure sessions)
-- 🔄 **MCP Resumability** (SSE stream recovery via Last-Event-ID)
-
-**Safety & Control:**
-- 🤝 **Agent Handoffs** (Transfer context between specialized agents)
-- 🛡️ **Guardrails** (Input/output validation and safety checks)
-- ✅ **Human Approval** (Require human confirmation for critical actions)
-- 💬 **Sessions Management** (Isolated conversation contexts)
-- 🔄 **Stateful Agents** (Maintain state across interactions)
-
-**Developer Tools:**
-- ⚡ **Fast Context** (Rapid parallel code search - 10-20x faster than traditional methods)
-- 📜 **Rules & Instructions** (Auto-discover CLAUDE.md, AGENTS.md, GEMINI.md)
-- 🪝 **Hooks** (Pre/post operation hooks for custom logic)
-- 📈 **Telemetry** (Track agent performance and usage)
-- 📹 **Camera Integration** (Capture and analyze camera input)
-
-## Other Features
-
-- 🔄 Use CrewAI or AG2 (Formerly AutoGen) Framework
-- 💻 Chat with ENTIRE Codebase
-- 🎨 Interactive UIs
-- 📄 YAML-based Configuration
-- 🛠️ Custom Tool Integration
-- 🔍 Internet Search Capability (Tavily, You.com, Exa, DuckDuckGo, Crawl4AI)
-- 🖼️ Vision Language Model (VLM) Support
-- 🎙️ Real-time Voice Interaction
-
-## Video Tutorials
+Learn PraisonAI through our comprehensive video series:
 
 | Topic | Video |
 |-------|--------|
@@ -2115,3 +2219,58 @@ cd src/praisonai && uv publish
 | Call Interface | [![Call](https://img.youtube.com/vi/m1cwrUG2iAk/0.jpg)](https://www.youtube.com/watch?v=m1cwrUG2iAk) |
 | Reasoning Extract Agents | [![Reasoning Extract](https://img.youtube.com/vi/2PPamsADjJA/0.jpg)](https://www.youtube.com/watch?v=2PPamsADjJA) |
 
+---
+
+## 👥 Contributing
+
+We welcome contributions from the community! Here's how you can contribute:
+
+1. **Fork on GitHub** - Use the "Fork" button on the [repository page](https://github.com/MervinPraison/PraisonAI)
+2. **Clone your fork** - `git clone https://github.com/yourusername/praisonAI.git`
+3. **Create a branch** - `git checkout -b new-feature`
+4. **Make changes and commit** - `git commit -am "Add some feature"`
+5. **Push to your fork** - `git push origin new-feature`
+6. **Submit a pull request** - Via GitHub's web interface
+7. **Await feedback** - From project maintainers
+
+---
+
+## 🔧 Development
+
+### Using uv
+
+```bash
+# Install uv if you haven't already
+pip install uv
+
+# Install from requirements
+uv pip install -r pyproject.toml
+
+# Install with extras
+uv pip install -r pyproject.toml --extra code
+uv pip install -r pyproject.toml --extra "crewai,autogen"
+```
+
+### Bump and Release
+
+```bash
+# From project root - bumps version and releases in one command
+python src/praisonai/scripts/bump_and_release.py 2.2.99
+
+# With praisonaiagents dependency
+python src/praisonai/scripts/bump_and_release.py 2.2.99 --agents 0.0.169
+
+# Then publish
+cd src/praisonai && uv publish
+```
+
+---
+
+<div align="center">
+  <p><strong>Made with ❤️ by the PraisonAI Team</strong></p>
+  <p>
+    <a href="https://docs.praison.ai">Documentation</a> •
+    <a href="https://github.com/MervinPraison/PraisonAI">GitHub</a> •
+    <a href="https://github.com/MervinPraison/PraisonAI/issues">Issues</a>
+  </p>
+</div>

src/praisonai/agents.yaml

@@ -1,44 +1,49 @@
+# Movie Script Workflow - Cat in Mars
+# Uses the modern agents + steps format
+
+name: Create Movie Script About Cat in Mars
+description: A sequential workflow to research, design narrative, and write a movie script
 framework: praisonai
-process: sequential
-topic: create movie script about cat in mars
-memory: false
-roles:
+
+workflow:
+  verbose: true
+
+agents:
   researcher:
-    backstory: Skilled in research, with a focus on gathering accurate and relevant
-      information.
+    name: Researcher
+    role: Research Analyst
     goal: Gather information about Mars and cats
-    role: Researcher
-    min_reflect: 1
-    max_reflect: 2
-    tasks:
-      research_task:
-        description: Research about Mars, its environment, and the feasibility of
-          a cat being on Mars. Also, research about cat behavior and characteristics.
-        expected_output: Document with research findings on Mars and cats.
+    backstory: Skilled in research, with a focus on gathering accurate and relevant information.
+    instructions: |
+      Research about Mars, its environment, and the feasibility of a cat being on Mars.
+      Also, research about cat behavior and characteristics.
     tools:
-    - search_tool
+      - search_tool
+
   narrative_designer:
-    backstory: Skilled in narrative development, with a focus on creating engaging
-      stories.
+    name: Narrative Designer
+    role: Story Concept Developer
     goal: Create a story concept for a movie about a cat in Mars
-    role: Narrative Designer
-    tasks:
-      story_concept_development:
-        description: Use the research findings to craft a unique story concept for
-          a movie about a cat in Mars.
-        expected_output: Document with narrative arcs, character bios, and settings.
-    tools:
-    - ''
+    backstory: Skilled in narrative development, with a focus on creating engaging stories.
+    instructions: |
+      Use the research findings to craft a unique story concept for a movie about a cat in Mars.
+      Create narrative arcs, character bios, and settings.
+
   scriptwriter:
-    backstory: Expert in dialogue and script structure, translating concepts into
-      scripts.
+    name: Scriptwriter
+    role: Script Writer
     goal: Write a movie script about a cat in Mars
-    role: Scriptwriter
-    tasks:
-      scriptwriting_task:
-        description: Turn the story concept into a production-ready movie script,
-          including dialogue and scene details.
-        expected_output: Final movie script with dialogue and scene details.
-    tools:
-    - ''
-dependencies: []
+    backstory: Expert in dialogue and script structure, translating concepts into scripts.
+    instructions: |
+      Turn the story concept into a production-ready movie script,
+      including dialogue and scene details.
+
+steps:
+  - agent: researcher
+    action: "Research about Mars environment and cat behavior for the movie concept"
+
+  - agent: narrative_designer
+    action: "Create story concept based on research: {{previous_output}}"
+
+  - agent: scriptwriter
+    action: "Write the final movie script based on the story concept: {{previous_output}}"

src/praisonai/agents_n8n.json

@@ -0,0 +1,106 @@
+{
+  "id": "e022a3a1",
+  "name": "Create Movie Script About Cat in Mars",
+  "nodes": [
+    {
+      "id": "trigger",
+      "name": "Manual Trigger",
+      "type": "n8n-nodes-base.manualTrigger",
+      "typeVersion": 1,
+      "position": [
+        250,
+        300
+      ],
+      "parameters": {}
+    },
+    {
+      "id": "agent_0",
+      "name": "Researcher",
+      "type": "n8n-nodes-base.executeCommand",
+      "typeVersion": 1,
+      "position": [
+        450,
+        300
+      ],
+      "parameters": {
+        "command": "praisonai \"Research about Mars environment and cat behavior for the movie concept\"",
+        "executeOnce": true
+      }
+    },
+    {
+      "id": "agent_1",
+      "name": "Narrative Designer",
+      "type": "n8n-nodes-base.executeCommand",
+      "typeVersion": 1,
+      "position": [
+        650,
+        300
+      ],
+      "parameters": {
+        "command": "praisonai \"Create story concept based on research: {{previous_output}}\"",
+        "executeOnce": true
+      }
+    },
+    {
+      "id": "agent_2",
+      "name": "Scriptwriter",
+      "type": "n8n-nodes-base.executeCommand",
+      "typeVersion": 1,
+      "position": [
+        850,
+        300
+      ],
+      "parameters": {
+        "command": "praisonai \"Write the final movie script based on the story concept: {{previous_output}}\"",
+        "executeOnce": true
+      }
+    }
+  ],
+  "connections": {
+    "Manual Trigger": {
+      "main": [
+        [
+          {
+            "node": "Researcher",
+            "type": "main",
+            "index": 0
+          }
+        ]
+      ]
+    },
+    "Researcher": {
+      "main": [
+        [
+          {
+            "node": "Narrative Designer",
+            "type": "main",
+            "index": 0
+          }
+        ]
+      ]
+    },
+    "Narrative Designer": {
+      "main": [
+        [
+          {
+            "node": "Scriptwriter",
+            "type": "main",
+            "index": 0
+          }
+        ]
+      ]
+    }
+  },
+  "active": false,
+  "settings": {
+    "executionOrder": "v1"
+  },
+  "versionId": "1",
+  "pinData": {},
+  "meta": {
+    "instanceId": "praisonai-export",
+    "templateCredsSetupCompleted": true,
+    "description": "A sequential workflow to research, design narrative, and write a movie script"
+  },
+  "tags": []
+}

src/praisonai/praisonai.rb

@@ -3,8 +3,8 @@ class Praisonai < Formula
   
     desc "AI tools for various AI applications"
     homepage "https://github.com/MervinPraison/PraisonAI"
-    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.3.26.tar.gz"
-    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.3.26.tar.gz | shasum -a 256`.split.first
+    url "https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.3.31.tar.gz"
+    sha256 `curl -sL https://github.com/MervinPraison/PraisonAI/archive/refs/tags/v2.3.31.tar.gz | shasum -a 256`.split.first
     license "MIT"
   
     depends_on "[email protected]"

src/praisonai/praisonai/agents_generator.py

@@ -327,7 +327,8 @@ class AgentsGenerator:
             # Route to YAMLWorkflowParser for advanced workflow patterns
             return self._run_yaml_workflow(config)
 
-        topic = config.get('topic', '')
+        # Get workflow input: 'input' is canonical, 'topic' is alias for backward compatibility
+        topic = config.get('input', config.get('topic', ''))
         tools_dict = {}
         
         # Only try to use praisonai_tools if it's available and needed
@@ -441,6 +442,15 @@ class AgentsGenerator:
         if 'name' not in config:
             config['name'] = config.get('topic', 'Workflow')
         
+        # Pass model from config_list to workflow as default_llm
+        if self.config_list and self.config_list[0].get('model'):
+            model_from_cli = self.config_list[0]['model']
+            # Set default_llm in workflow config if not already set
+            if 'workflow' not in config:
+                config['workflow'] = {}
+            if 'default_llm' not in config['workflow']:
+                config['workflow']['default_llm'] = model_from_cli
+        
         # Convert config back to YAML string for parser
         # Note: YAMLWorkflowParser handles 'roles' to 'agents' conversion internally
         import yaml as yaml_module
@@ -450,11 +460,11 @@ class AgentsGenerator:
         parser = YAMLWorkflowParser()
         workflow = parser.parse_string(yaml_content)
         
-        # Get input from topic or variables
-        input_data = config.get('topic', '')
+        # Get input: 'input' is canonical, 'topic' is alias for backward compatibility
+        input_data = config.get('input', config.get('topic', ''))
         
         # Execute workflow
-        self.logger.info(f"Running workflow: {workflow.name}")
+        self.logger.debug(f"Running workflow: {workflow.name}")
         result = workflow.start(input_data)
         
         if result.get("status") == "completed":

src/praisonai/praisonai/cli/features/__init__.py

@@ -20,6 +20,7 @@ Features:
 - router: Smart model selection
 - flow_display: Visual workflow tracking
 - workflow: YAML workflow management
+- n8n: Export workflows to n8n visual editor
 """
 
 # Type hints available for IDE support
@@ -41,6 +42,7 @@ __all__ = [
     'RouterHandler',
     'FlowDisplayHandler',
     'WorkflowHandler',
+    'N8nHandler',
 ]
 
 def __getattr__(name):
@@ -90,4 +92,7 @@ def __getattr__(name):
     elif name == 'WorkflowHandler':
         from .workflow import WorkflowHandler
         return WorkflowHandler
+    elif name == 'N8nHandler':
+        from .n8n import N8nHandler
+        return N8nHandler
     raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

src/praisonai/praisonai/cli/features/flow_display.py

@@ -1,25 +1,31 @@
 """
 Flow Display Handler for CLI.
 
-Provides visual workflow tracking.
+Shows a visual flow diagram of the workflow WITHOUT executing it.
 Usage: praisonai agents.yaml --flow-display
 """
 
-from typing import Any, Dict, Tuple, List
+from typing import Any, Dict, Tuple
 from .base import FlagHandler
+import os
+import yaml
 
 
 class FlowDisplayHandler(FlagHandler):
     """
     Handler for --flow-display flag.
     
-    Enables visual workflow tracking for multi-agent executions.
+    Shows a visual flow diagram/chart of agents and tasks WITHOUT executing.
+    This allows users to preview the workflow structure before running it.
     
     Example:
         praisonai agents.yaml --flow-display
-        praisonai "Complex task" --flow-display
     """
     
+    def __init__(self, verbose: bool = False):
+        super().__init__(verbose)
+        self._console = None
+    
     @property
     def feature_name(self) -> str:
         return "flow_display"
@@ -30,160 +36,531 @@ class FlowDisplayHandler(FlagHandler):
     
     @property
     def flag_help(self) -> str:
-        return "Enable visual workflow tracking"
+        return "Show workflow flow diagram without executing"
+    
+    def _get_console(self):
+        """Get or create Rich Console."""
+        if self._console is None:
+            try:
+                from rich.console import Console
+                self._console = Console()
+            except ImportError:
+                pass
+        return self._console
     
     def check_dependencies(self) -> Tuple[bool, str]:
-        """Check if FlowDisplay is available."""
+        """Check if Rich is available."""
         try:
             import importlib.util
-            if importlib.util.find_spec("praisonaiagents") is not None:
-                return True, ""
-            return False, "praisonaiagents not installed"
+            rich_available = importlib.util.find_spec("rich") is not None
+            if not rich_available:
+                return False, "rich not installed. Install with: pip install rich"
+            return True, ""
         except ImportError:
-            return False, "praisonaiagents not installed. Install with: pip install praisonaiagents"
-    
-    def _get_flow_display(self):
-        """Get FlowDisplay class lazily."""
-        try:
-            from praisonaiagents import FlowDisplay
-            return FlowDisplay
-        except ImportError:
-            return None
-    
-    def create_flow_display(self, title: str = "Workflow") -> Any:
-        """
-        Create a FlowDisplay instance.
-        
-        Args:
-            title: Title for the workflow display
-            
-        Returns:
-            FlowDisplay instance or None
-        """
-        FlowDisplay = self._get_flow_display()
-        if not FlowDisplay:
-            self.print_status(
-                "FlowDisplay requires praisonaiagents. Install with: pip install praisonaiagents",
-                "error"
-            )
-            return None
-        
-        try:
-            return FlowDisplay(title=title)
-        except Exception as e:
-            self.log(f"Failed to create FlowDisplay: {e}", "error")
-            return None
+            return False, "rich not installed. Install with: pip install rich"
     
     def apply_to_agent_config(self, config: Dict[str, Any], flag_value: Any) -> Dict[str, Any]:
-        """
-        Apply flow display configuration.
-        
-        Args:
-            config: Agent configuration dictionary
-            flag_value: Boolean indicating flow display
-            
-        Returns:
-            Modified configuration
-        """
+        """Apply flow display configuration."""
         if flag_value:
             config['flow_display'] = True
         return config
     
-    def display_workflow_start(self, workflow_name: str, agents: List[str] = None):
+    def display_flow_diagram(self, yaml_file: str, show_footer: bool = True) -> bool:
         """
-        Display workflow start.
+        Display a visual flow diagram of the workflow from YAML file.
         
         Args:
-            workflow_name: Name of the workflow
-            agents: List of agent names
-        """
-        self.print_status(f"\n🚀 Starting Workflow: {workflow_name}", "info")
-        self.print_status("=" * 50, "info")
-        
-        if agents:
-            self.print_status("Agents:", "info")
-            for i, agent in enumerate(agents, 1):
-                self.print_status(f"  {i}. {agent}", "info")
-        
-        self.print_status("=" * 50, "info")
-    
-    def display_step(self, step_num: int, step_name: str, status: str = "running"):
-        """
-        Display a workflow step.
-        
-        Args:
-            step_num: Step number
-            step_name: Name of the step
-            status: Step status (running, completed, failed)
-        """
-        icons = {
-            'running': '⏳',
-            'completed': '✅',
-            'failed': '❌',
-            'pending': '⬜'
-        }
-        icon = icons.get(status, '•')
-        self.print_status(f"  {icon} Step {step_num}: {step_name}", "info")
-    
-    def display_workflow_end(self, success: bool = True, duration: float = None):
-        """
-        Display workflow end.
-        
-        Args:
-            success: Whether workflow completed successfully
-            duration: Workflow duration in seconds
-        """
-        self.print_status("=" * 50, "info")
-        
-        if success:
-            msg = "✅ Workflow completed successfully"
-        else:
-            msg = "❌ Workflow failed"
-        
-        if duration:
-            msg += f" ({duration:.2f}s)"
-        
-        self.print_status(msg, "success" if success else "error")
-    
-    def track_execution(self, func):
-        """
-        Decorator to track function execution in flow display.
-        
-        Args:
-            func: Function to track
+            yaml_file: Path to the agents.yaml file
+            show_footer: Whether to show the "run without --flow-display" footer
             
         Returns:
-            Wrapped function
+            True if displayed successfully, False otherwise
         """
-        import functools
+        console = self._get_console()
+        if not console:
+            print("Rich library not available for flow display")
+            return False
         
-        @functools.wraps(func)
-        def wrapper(*args, **kwargs):
-            func_name = func.__name__
+        # Load YAML config
+        try:
+            if not os.path.exists(yaml_file):
+                console.print(f"[red]Error: File not found: {yaml_file}[/red]")
+                return False
             
-            self.display_step(1, func_name, "running")
+            with open(yaml_file, 'r') as f:
+                config = yaml.safe_load(f)
+        except Exception as e:
+            console.print(f"[red]Error loading YAML: {e}[/red]")
+            return False
+        
+        # Detect YAML format: 'roles' (old) or 'agents'+'steps' (new workflow)
+        has_roles = 'roles' in config and config['roles']
+        has_agents = 'agents' in config and config['agents']
+        has_steps = 'steps' in config and config['steps']
+        
+        if not has_roles and not has_agents:
+            console.print("[red]Error: Invalid YAML format. No 'roles' or 'agents' found.[/red]")
+            return False
+        
+        try:
+            from rich.panel import Panel
+            from rich.table import Table
+            from rich.text import Text
+            from rich import box
             
+            # Determine format type
+            is_workflow_format = has_agents and has_steps
+            
+            # Extract workflow info based on format
+            if is_workflow_format:
+                name = config.get('name', 'Workflow')
+                description = config.get('description', '')
+                framework = config.get('framework', 'praisonai')
+                workflow_config = config.get('workflow', {})
+                memory = workflow_config.get('memory_config', {}).get('persist', False)
+                num_agents = len(config['agents'])
+                num_steps = len(config['steps'])
+            else:
+                name = config.get('topic', 'Workflow')
+                description = ''
+                framework = config.get('framework', 'praisonai')
+                process = config.get('process', 'sequential')
+                memory = config.get('memory', False)
+                num_agents = len(config['roles'])
+                num_steps = sum(len(details.get('tasks', {})) for details in config['roles'].values())
+            
+            # Build the flow diagram
+            console.print()
+            
+            # === HEADER ===
+            header = Text()
+            header.append("📊 WORKFLOW FLOW DIAGRAM\n", style="bold cyan")
+            header.append("─" * 40, style="dim")
+            
+            header_panel = Panel(
+                header,
+                border_style="cyan",
+                box=box.DOUBLE_EDGE,
+                padding=(0, 2)
+            )
+            console.print(header_panel)
+            
+            # === WORKFLOW INFO ===
+            info_table = Table(show_header=False, box=None, padding=(0, 2))
+            info_table.add_column("Key", style="bold yellow")
+            info_table.add_column("Value", style="white")
+            
+            info_table.add_row("📋 Name:", name)
+            if description:
+                info_table.add_row("📝 Description:", description)
+            
+            # Show input if present ('input' is canonical, 'topic' is alias)
+            workflow_input = config.get('input', config.get('topic', ''))
+            if workflow_input:
+                info_table.add_row("💬 Input:", workflow_input)
+            
+            info_table.add_row("⚙️  Framework:", framework)
+            
+            if is_workflow_format:
+                info_table.add_row("🔄 Format:", "WORKFLOW (steps-based)")
+                planning = workflow_config.get('planning', False)
+                info_table.add_row("🧠 Planning:", "✅ Enabled" if planning else "❌ Disabled")
+            else:
+                info_table.add_row("🔄 Process:", process.upper())
+            
+            info_table.add_row("💾 Memory:", "✅ Enabled" if memory else "❌ Disabled")
+            info_table.add_row("👥 Agents:", str(num_agents))
+            info_table.add_row("📝 Steps:", str(num_steps))
+            
+            console.print(Panel(info_table, title="[bold white]Workflow Configuration[/bold white]", 
+                               border_style="blue", box=box.ROUNDED))
+            console.print()
+            
+            # === FLOW DIAGRAM ===
+            if is_workflow_format:
+                self._display_steps_flow(console, config)
+            else:
+                process = config.get('process', 'sequential')
+                if process == 'sequential':
+                    self._display_sequential_flow(console, config)
+                elif process == 'hierarchical':
+                    self._display_hierarchical_flow(console, config)
+                else:
+                    self._display_sequential_flow(console, config)
+            
+            # === DETAILED AGENT INFO ===
+            console.print()
+            if is_workflow_format:
+                self._display_workflow_agent_details(console, config)
+            else:
+                self._display_agent_details(console, config)
+            
+            # === FOOTER ===
+            if show_footer:
+                console.print()
+                footer = Text()
+                footer.append("💡 ", style="bold")
+                footer.append("To execute this workflow, run without --flow-display flag", style="dim italic")
+                console.print(Panel(footer, border_style="dim", box=box.ROUNDED))
+            console.print()
+            
+            return True
+            
+        except ImportError as e:
+            console.print(f"[red]Error: Rich components not available: {e}[/red]")
+            return False
+    
+    def _display_sequential_flow(self, console, config: Dict):
+        """Display sequential flow diagram."""
+        from rich.panel import Panel
+        from rich.text import Text
+        from rich import box
+        
+        roles = list(config['roles'].items())
+        
+        # Box width (content area)
+        BOX_WIDTH = 36
+        
+        def pad_line(content: str, width: int = BOX_WIDTH) -> str:
+            """Pad content to fixed width."""
+            padding = width - len(content)
+            if padding > 0:
+                return content + " " * padding
+            return content[:width]
+        
+        flow_text = Text()
+        flow_text.append("  SEQUENTIAL EXECUTION FLOW\n\n", style="bold magenta")
+        
+        for i, (role_key, details) in enumerate(roles):
+            role_name = details.get('role', role_key)
+            tasks = list(details.get('tasks', {}).keys())
+            
+            # Agent box
+            if i == 0:
+                flow_text.append("       ┌── START ──┐\n", style="green")
+                flow_text.append("             │\n", style="dim")
+                flow_text.append("             ▼\n", style="dim")
+            
+            # Agent node
+            flow_text.append("       ╔════════════════════════════════════╗\n", style="cyan")
+            line = f" Agent {i+1}: {role_name}"
+            flow_text.append(f"       ║{pad_line(line)}║\n", style="cyan")
+            
+            # Tasks
+            for task in tasks:
+                line = f"   -> {task}"
+                flow_text.append(f"       ║{pad_line(line)}║\n", style="dim white")
+            
+            flow_text.append("       ╚════════════════════════════════════╝\n", style="cyan")
+            
+            # Arrow to next
+            if i < len(roles) - 1:
+                flow_text.append("             │\n", style="dim")
+                flow_text.append("          output\n", style="dim italic")
+                flow_text.append("             ▼\n", style="dim")
+            else:
+                flow_text.append("             │\n", style="dim")
+                flow_text.append("             ▼\n", style="dim")
+                flow_text.append("       └── END ────┘\n", style="red")
+        
+        console.print(Panel(flow_text, border_style="magenta", box=box.DOUBLE_EDGE,
+                           title="[bold white]Execution Flow[/bold white]"))
+    
+    def _display_hierarchical_flow(self, console, config: Dict):
+        """Display hierarchical flow diagram."""
+        from rich.panel import Panel
+        from rich.text import Text
+        from rich import box
+        
+        manager_llm = config.get('manager_llm', 'default')
+        
+        flow_text = Text()
+        flow_text.append("👑 HIERARCHICAL EXECUTION FLOW\n\n", style="bold magenta")
+        
+        # Manager at top
+        flow_text.append("         ┌──────────────────────┐\n", style="yellow")
+        flow_text.append("         │  👑 MANAGER AGENT    │\n", style="bold yellow")
+        flow_text.append(f"         │  LLM: {manager_llm:<14}│\n", style="dim yellow")
+        flow_text.append("         └──────────┬───────────┘\n", style="yellow")
+        flow_text.append("                    │\n", style="dim")
+        flow_text.append("         ┌──────────┴───────────┐\n", style="dim")
+        flow_text.append("         │    delegates to      │\n", style="dim italic")
+        flow_text.append("         └──────────┬───────────┘\n", style="dim")
+        
+        roles = list(config['roles'].items())
+        num_agents = len(roles)
+        
+        # Draw branches
+        if num_agents > 0:
+            branch_line = "    "
+            for i in range(num_agents):
+                if i == 0:
+                    branch_line += "┌" + "─" * 8
+                elif i == num_agents - 1:
+                    branch_line += "┬" + "─" * 8 + "┐"
+                else:
+                    branch_line += "┬" + "─" * 8
+            flow_text.append(branch_line + "\n", style="dim")
+            
+            # Agent boxes
+            for i, (role_key, details) in enumerate(roles):
+                flow_text.append("\n    ╔═══════════════╗", style="cyan")
+            flow_text.append("\n")
+            
+            for i, (role_key, details) in enumerate(roles):
+                role_name = details.get('role', role_key)[:12]
+                flow_text.append(f"    ║ 🤖 {role_name:<10} ║", style="cyan")
+            flow_text.append("\n")
+            
+            for i, (role_key, details) in enumerate(roles):
+                flow_text.append("    ╚═══════════════╝", style="cyan")
+            flow_text.append("\n")
+        
+        console.print(Panel(flow_text, border_style="magenta", box=box.DOUBLE_EDGE,
+                           title="[bold white]Execution Flow[/bold white]"))
+    
+    def _display_agent_details(self, console, config: Dict):
+        """Display detailed agent information."""
+        from rich.table import Table
+        from rich import box
+        
+        table = Table(
+            title="[bold cyan]📋 Agent & Task Details[/bold cyan]",
+            box=box.ROUNDED,
+            show_header=True,
+            header_style="bold magenta",
+            border_style="blue",
+            padding=(0, 1),
+            expand=True
+        )
+        
+        table.add_column("#", style="dim", width=3, justify="center")
+        table.add_column("Agent", style="bold yellow", min_width=15)
+        table.add_column("Role", style="cyan", min_width=15)
+        table.add_column("Goal", style="white", min_width=25, overflow="fold")
+        table.add_column("Tasks", style="green", min_width=20)
+        table.add_column("Tools", style="magenta", min_width=15)
+        
+        for i, (role_key, details) in enumerate(config['roles'].items(), 1):
+            role_name = details.get('role', role_key)
+            goal = details.get('goal', '-')
+            if len(goal) > 50:
+                goal = goal[:47] + "..."
+            
+            tasks = list(details.get('tasks', {}).keys())
+            tasks_str = "\n".join(f"• {t}" for t in tasks) if tasks else "-"
+            
+            tools = details.get('tools', [])
+            tools_str = ", ".join(t for t in tools if t) if tools else "-"
+            
+            table.add_row(
+                str(i),
+                role_name,
+                role_key,
+                goal,
+                tasks_str,
+                tools_str
+            )
+        
+        console.print(table)
+    
+    def _display_steps_flow(self, console, config: Dict):
+        """Display steps-based workflow flow diagram."""
+        from rich.panel import Panel
+        from rich.text import Text
+        from rich import box
+        
+        steps = config.get('steps', [])
+        agents = config.get('agents', {})
+        
+        # Box width (content area)
+        BOX_WIDTH = 36
+        
+        def pad_line(content: str, width: int = BOX_WIDTH) -> str:
+            """Pad content to fixed width, accounting for emoji width."""
+            # Emojis typically take 2 character widths
+            emoji_count = sum(1 for c in content if ord(c) > 0x1F300)
+            actual_len = len(content) + emoji_count  # Add extra for emoji width
+            padding = width - actual_len
+            if padding > 0:
+                return content + " " * padding
+            return content[:width]
+        
+        flow_text = Text()
+        flow_text.append("  STEPS-BASED EXECUTION FLOW\n\n", style="bold magenta")
+        
+        flow_text.append("       ┌── START ──┐\n", style="green")
+        flow_text.append("             │\n", style="dim")
+        flow_text.append("             ▼\n", style="dim")
+        
+        for i, step in enumerate(steps):
+            # Determine step type
+            agent_key = step.get('agent', '')
+            step_name = step.get('name', '')
+            
+            # Check for special step types
+            has_route = 'route' in step
+            has_parallel = 'parallel' in step
+            has_loop = 'loop' in step
+            has_repeat = 'repeat' in step
+            
+            if has_route:
+                # Route step
+                flow_text.append("       ╔════════════════════════════════════╗\n", style="yellow")
+                line = f" ROUTE: {step_name or 'routing'}"
+                flow_text.append(f"       ║{pad_line(line)}║\n", style="yellow")
+                
+                routes = step['route']
+                for route_name, route_agents in routes.items():
+                    agents_str = ", ".join(route_agents) if isinstance(route_agents, list) else str(route_agents)
+                    line = f"   -> {route_name}: {agents_str}"
+                    flow_text.append(f"       ║{pad_line(line)}║\n", style="yellow")
+                
+                flow_text.append("       ╚════════════════════════════════════╝\n", style="yellow")
+                
+            elif has_parallel:
+                # Parallel step
+                flow_text.append("       ╔════════════════════════════════════╗\n", style="green")
+                line = f" PARALLEL: {step_name or 'parallel'}"
+                flow_text.append(f"       ║{pad_line(line)}║\n", style="green")
+                
+                for p_step in step['parallel']:
+                    p_agent = p_step.get('agent', '?')
+                    line = f"   |-- {p_agent}"
+                    flow_text.append(f"       ║{pad_line(line)}║\n", style="green")
+                
+                flow_text.append("       ╚════════════════════════════════════╝\n", style="green")
+                
+            else:
+                # Regular agent step
+                agent_info = agents.get(agent_key, {})
+                agent_name = agent_info.get('name', agent_key)
+                
+                flow_text.append("       ╔════════════════════════════════════╗\n", style="cyan")
+                line = f" Step {i+1}: {agent_name}"
+                flow_text.append(f"       ║{pad_line(line)}║\n", style="cyan")
+                
+                # Show modifiers
+                if has_loop:
+                    loop_over = step['loop'].get('over', '?')
+                    line = f"   [loop over: {loop_over}]"
+                    flow_text.append(f"       ║{pad_line(line)}║\n", style="dim magenta")
+                
+                if has_repeat:
+                    until = step['repeat'].get('until', '?')
+                    max_iter = step['repeat'].get('max_iterations', '?')
+                    line = f"   [repeat until: {until} max:{max_iter}]"
+                    flow_text.append(f"       ║{pad_line(line)}║\n", style="dim magenta")
+                
+                flow_text.append("       ╚════════════════════════════════════╝\n", style="cyan")
+            
+            # Arrow to next
+            if i < len(steps) - 1:
+                flow_text.append("             │\n", style="dim")
+                flow_text.append("             ▼\n", style="dim")
+        
+        flow_text.append("             │\n", style="dim")
+        flow_text.append("             ▼\n", style="dim")
+        flow_text.append("       └── END ────┘\n", style="red")
+        
+        console.print(Panel(flow_text, border_style="magenta", box=box.DOUBLE_EDGE,
+                           title="[bold white]Execution Flow[/bold white]"))
+    
+    def _display_workflow_agent_details(self, console, config: Dict):
+        """Display detailed agent information for workflow format."""
+        from rich.table import Table
+        from rich import box
+        
+        table = Table(
+            title="[bold cyan]📋 Agent Details[/bold cyan]",
+            box=box.ROUNDED,
+            show_header=True,
+            header_style="bold magenta",
+            border_style="blue",
+            padding=(0, 1),
+            expand=True
+        )
+        
+        table.add_column("#", style="dim", width=3, justify="center")
+        table.add_column("Agent Key", style="bold yellow", min_width=15)
+        table.add_column("Name", style="cyan", min_width=15)
+        table.add_column("Role", style="white", min_width=20)
+        table.add_column("Goal", style="green", min_width=25, overflow="fold")
+        
+        for i, (agent_key, details) in enumerate(config['agents'].items(), 1):
+            name = details.get('name', agent_key)
+            role = details.get('role', '-')
+            goal = details.get('goal', '-')
+            if len(goal) > 40:
+                goal = goal[:37] + "..."
+            
+            table.add_row(
+                str(i),
+                agent_key,
+                name,
+                role,
+                goal
+            )
+        
+        console.print(table)
+    
+    def display_workflow_start(self, workflow_name: str, agents: list) -> None:
+        """
+        Display workflow start message.
+        
+        Args:
+            workflow_name: Name of the workflow being started
+            agents: List of agent names in the workflow
+        """
+        console = self._get_console()
+        if console:
             try:
-                result = func(*args, **kwargs)
-                self.display_step(1, func_name, "completed")
-                return result
-            except Exception:
-                self.display_step(1, func_name, "failed")
-                raise
-        
-        return wrapper
+                from rich.panel import Panel
+                from rich import box
+                
+                agents_str = ", ".join(agents) if agents else "No agents"
+                content = f"🚀 Starting: {workflow_name}\n👥 Agents: {agents_str}"
+                console.print(Panel(content, title="[bold cyan]Workflow Started[/bold cyan]", 
+                                   border_style="cyan", box=box.ROUNDED))
+            except ImportError:
+                print(f"Starting workflow: {workflow_name} with agents: {agents}")
     
-    def execute(self, workflow_name: str = None, **kwargs) -> Any:
+    def display_workflow_end(self, success: bool = True) -> None:
         """
-        Execute flow display setup.
+        Display workflow end message.
         
         Args:
-            workflow_name: Name of the workflow
+            success: Whether the workflow completed successfully
+        """
+        console = self._get_console()
+        if console:
+            try:
+                from rich.panel import Panel
+                from rich import box
+                
+                if success:
+                    content = "✅ Workflow completed successfully"
+                    style = "green"
+                else:
+                    content = "❌ Workflow failed"
+                    style = "red"
+                
+                console.print(Panel(content, title=f"[bold {style}]Workflow Ended[/bold {style}]", 
+                                   border_style=style, box=box.ROUNDED))
+            except ImportError:
+                status = "successfully" if success else "with errors"
+                print(f"Workflow ended {status}")
+    
+    def execute(self, yaml_file: str = None, **kwargs) -> bool:
+        """
+        Execute flow display - shows diagram without running workflow.
+        
+        Args:
+            yaml_file: Path to the YAML file
             
         Returns:
-            FlowDisplay instance or None
+            True if displayed successfully
         """
-        if workflow_name:
-            self.display_workflow_start(workflow_name)
-        
-        return self.create_flow_display(workflow_name or "Workflow")
+        if yaml_file:
+            return self.display_flow_diagram(yaml_file)
+        return False

src/praisonai/praisonai/cli/features/n8n.py

@@ -0,0 +1,539 @@
+"""
+n8n Integration Handler for PraisonAI CLI.
+
+Converts PraisonAI agents.yaml workflows to n8n workflow JSON format
+and opens them in the n8n UI.
+
+Usage:
+    praisonai agents.yaml --n8n
+    praisonai agents.yaml --n8n --n8n-url http://localhost:5678
+"""
+
+import json
+import yaml
+import webbrowser
+import hashlib
+import os
+from typing import Any, Dict, List, Optional
+from pathlib import Path
+
+try:
+    import requests
+    REQUESTS_AVAILABLE = True
+except ImportError:
+    REQUESTS_AVAILABLE = False
+
+from .base import FlagHandler
+
+
+class N8nHandler(FlagHandler):
+    """
+    Handler for n8n workflow export and visualization.
+    
+    Converts PraisonAI YAML workflows to n8n-compatible JSON format
+    and opens them in the n8n UI for visual editing.
+    """
+    
+    def __init__(self, verbose: bool = False, n8n_url: str = "http://localhost:5678",
+                 use_execute_command: bool = True):
+        """
+        Initialize the n8n handler.
+        
+        Args:
+            verbose: Enable verbose output
+            n8n_url: Base URL of the n8n instance
+            use_execute_command: Use Execute Command nodes (runs praisonai directly)
+                                 instead of HTTP Request nodes
+        """
+        super().__init__(verbose=verbose)
+        self.n8n_url = n8n_url
+        self.praisonai_api_url = "http://localhost:8000"
+        self.use_execute_command = use_execute_command
+    
+    @property
+    def feature_name(self) -> str:
+        return "n8n"
+    
+    @property
+    def flag_name(self) -> str:
+        return "n8n"
+    
+    @property
+    def flag_help(self) -> str:
+        return "Export workflow to n8n and open in browser"
+    
+    def execute(self, yaml_path: str, n8n_url: Optional[str] = None, 
+                open_browser: bool = True, **kwargs) -> Dict[str, Any]:
+        """
+        Execute the n8n export workflow.
+        
+        Args:
+            yaml_path: Path to the agents.yaml file
+            n8n_url: Optional custom n8n URL
+            open_browser: Whether to open the workflow in browser
+            
+        Returns:
+            Dictionary with workflow JSON and URL
+        """
+        if n8n_url:
+            self.n8n_url = n8n_url
+        
+        # Convert YAML to n8n workflow JSON
+        workflow_json = self.convert_yaml_to_n8n(yaml_path)
+        
+        # Save workflow JSON to file
+        output_path = self._save_workflow_json(workflow_json, yaml_path)
+        
+        self.print_status("✅ Workflow converted successfully!", "success")
+        self.print_status(f"📄 JSON saved to: {output_path}", "info")
+        
+        # Try to create workflow via n8n API
+        api_key = os.environ.get('N8N_API_KEY')
+        workflow_id = None
+        
+        if api_key and REQUESTS_AVAILABLE:
+            workflow_id = self._create_workflow_via_api(workflow_json, api_key)
+        
+        if workflow_id:
+            # Successfully created via API - open directly
+            url = f"{self.n8n_url}/workflow/{workflow_id}"
+            self.print_status("🚀 Workflow created in n8n!", "success")
+            
+            if open_browser:
+                self.open_in_browser(url)
+                self.print_status(f"🌐 Opening: {url}", "info")
+        else:
+            # Fallback to manual import instructions
+            url = self.generate_n8n_url(workflow_json, self.n8n_url)
+            
+            if not api_key:
+                self.print_status("", "info")
+                self.print_status("💡 Tip: Set N8N_API_KEY env var for auto-import", "info")
+                self.print_status("   Generate API key in n8n: Settings → API", "info")
+            
+            self.print_status("", "info")
+            self.print_status("📋 To import into n8n:", "info")
+            self.print_status("   1. Open n8n in your browser", "info")
+            self.print_status("   2. Click the three dots menu (⋮) in the top right", "info")
+            self.print_status("   3. Select 'Import from File'", "info")
+            self.print_status(f"   4. Choose: {output_path}", "info")
+            
+            if open_browser:
+                self.open_in_browser(url)
+                self.print_status(f"🌐 Opening n8n: {url}", "info")
+        
+        return {
+            "workflow": workflow_json,
+            "url": url,
+            "output_path": output_path,
+            "workflow_id": workflow_id
+        }
+    
+    def _create_workflow_via_api(self, workflow_json: Dict[str, Any], 
+                                  api_key: str) -> Optional[str]:
+        """
+        Create workflow in n8n via REST API.
+        
+        Args:
+            workflow_json: The n8n workflow JSON
+            api_key: n8n API key
+            
+        Returns:
+            Workflow ID if successful, None otherwise
+        """
+        if not REQUESTS_AVAILABLE:
+            return None
+        
+        try:
+            # Only include fields allowed by n8n API schema
+            # See: n8n/packages/cli/src/public-api/v1/handlers/workflows/spec/schemas/workflow.yml
+            workflow_data = {
+                "name": workflow_json.get("name", "PraisonAI Workflow"),
+                "nodes": workflow_json.get("nodes", []),
+                "connections": workflow_json.get("connections", {}),
+                "settings": workflow_json.get("settings", {"executionOrder": "v1"}),
+            }
+            
+            # Optional fields
+            if "staticData" in workflow_json:
+                workflow_data["staticData"] = workflow_json["staticData"]
+            
+            response = requests.post(
+                f"{self.n8n_url}/api/v1/workflows",
+                headers={
+                    "X-N8N-API-KEY": api_key,
+                    "Content-Type": "application/json"
+                },
+                json=workflow_data,
+                timeout=30
+            )
+            
+            if response.status_code == 200:
+                result = response.json()
+                return result.get('id')
+            else:
+                self.log(f"API error: {response.status_code} - {response.text}", "warning")
+                return None
+                
+        except Exception as e:
+            self.log(f"Failed to create workflow via API: {e}", "warning")
+            return None
+    
+    def convert_yaml_to_n8n(self, yaml_path: str) -> Dict[str, Any]:
+        """
+        Convert a PraisonAI YAML workflow to n8n JSON format.
+        
+        Args:
+            yaml_path: Path to the agents.yaml file
+            
+        Returns:
+            n8n workflow JSON as dictionary
+        """
+        # Load YAML file
+        with open(yaml_path, 'r') as f:
+            yaml_content = yaml.safe_load(f)
+        
+        # Extract workflow metadata
+        workflow_name = yaml_content.get('name', 'PraisonAI Workflow')
+        description = yaml_content.get('description', '')
+        
+        # Get agents and steps
+        agents = yaml_content.get('agents', yaml_content.get('roles', {}))
+        steps = yaml_content.get('steps', [])
+        
+        # If no steps defined, create steps from agents
+        if not steps and agents:
+            steps = self._create_steps_from_agents(agents)
+        
+        # Build n8n nodes and connections
+        nodes = []
+        connections = {}
+        
+        # Add manual trigger node
+        trigger_node = self._create_trigger_node()
+        nodes.append(trigger_node)
+        
+        # Track previous node for connections
+        prev_node_name = trigger_node["name"]
+        
+        # Process each step
+        x_position = 450  # Start position after trigger
+        
+        for i, step in enumerate(steps):
+            if isinstance(step, dict):
+                if 'parallel' in step:
+                    # Handle parallel steps
+                    parallel_nodes, parallel_connections = self._create_parallel_nodes(
+                        step['parallel'], x_position, agents
+                    )
+                    nodes.extend(parallel_nodes)
+                    connections.update(parallel_connections)
+                    
+                    # Connect previous node to first parallel node
+                    if prev_node_name not in connections:
+                        connections[prev_node_name] = {"main": [[]]}
+                    for pnode in parallel_nodes:
+                        connections[prev_node_name]["main"][0].append({
+                            "node": pnode["name"],
+                            "type": "main",
+                            "index": 0
+                        })
+                    
+                    x_position += 200 * len(parallel_nodes)
+                    prev_node_name = parallel_nodes[-1]["name"] if parallel_nodes else prev_node_name
+                    
+                elif 'route' in step:
+                    # Handle route/decision steps
+                    route_node = self._create_route_node(step, x_position, i)
+                    nodes.append(route_node)
+                    
+                    # Connect previous node
+                    if prev_node_name not in connections:
+                        connections[prev_node_name] = {"main": [[]]}
+                    connections[prev_node_name]["main"][0].append({
+                        "node": route_node["name"],
+                        "type": "main",
+                        "index": 0
+                    })
+                    
+                    prev_node_name = route_node["name"]
+                    x_position += 200
+                    
+                else:
+                    # Regular agent step
+                    agent_id = step.get('agent', '')
+                    action = step.get('action', step.get('description', ''))
+                    agent_config = agents.get(agent_id, {})
+                    
+                    agent_node = self._create_agent_node(
+                        agent_id=agent_id,
+                        agent_config=agent_config,
+                        action=action,
+                        position=[x_position, 300],
+                        index=i
+                    )
+                    nodes.append(agent_node)
+                    
+                    # Connect to previous node
+                    if prev_node_name not in connections:
+                        connections[prev_node_name] = {"main": [[]]}
+                    connections[prev_node_name]["main"][0].append({
+                        "node": agent_node["name"],
+                        "type": "main",
+                        "index": 0
+                    })
+                    
+                    prev_node_name = agent_node["name"]
+                    x_position += 200
+        
+        # Build the complete workflow
+        # Generate a unique ID based on workflow name
+        workflow_id = hashlib.md5(workflow_name.encode()).hexdigest()[:8]
+        
+        workflow = {
+            "id": workflow_id,
+            "name": workflow_name,
+            "nodes": nodes,
+            "connections": connections,
+            "active": False,
+            "settings": {
+                "executionOrder": "v1"
+            },
+            "versionId": "1",
+            "pinData": {},
+            "meta": {
+                "instanceId": "praisonai-export",
+                "templateCredsSetupCompleted": True
+            },
+            "tags": []
+        }
+        
+        if description:
+            workflow["meta"]["description"] = description
+        
+        return workflow
+    
+    def _create_steps_from_agents(self, agents: Dict[str, Any]) -> List[Dict]:
+        """Create steps from agents when no steps are defined."""
+        steps = []
+        for agent_id, agent_config in agents.items():
+            if isinstance(agent_config, dict):
+                # Check for tasks nested in agent
+                tasks = agent_config.get('tasks', {})
+                if tasks:
+                    for task_id, task_config in tasks.items():
+                        steps.append({
+                            'agent': agent_id,
+                            'action': task_config.get('description', task_config.get('action', f'Execute {task_id}'))
+                        })
+                else:
+                    # Create a default step for the agent
+                    steps.append({
+                        'agent': agent_id,
+                        'action': agent_config.get('instructions', agent_config.get('goal', f'Execute {agent_id}'))
+                    })
+        return steps
+    
+    def _create_trigger_node(self) -> Dict[str, Any]:
+        """Create a manual trigger node."""
+        return {
+            "id": "trigger",
+            "name": "Manual Trigger",
+            "type": "n8n-nodes-base.manualTrigger",
+            "typeVersion": 1,
+            "position": [250, 300],
+            "parameters": {}
+        }
+    
+    def _create_agent_node(self, agent_id: str, agent_config: Dict,
+                            action: str, position: List[int], 
+                            index: int) -> Dict[str, Any]:
+        """Create a node for an agent - either Execute Command or HTTP Request."""
+        if self.use_execute_command:
+            return self._create_execute_command_node(
+                agent_id, agent_config, action, position, index
+            )
+        else:
+            return self._create_http_request_node(
+                agent_id, agent_config, action, position, index
+            )
+    
+    def _create_execute_command_node(self, agent_id: str, agent_config: Dict,
+                                      action: str, position: List[int],
+                                      index: int) -> Dict[str, Any]:
+        """Create an Execute Command node that runs praisonai directly."""
+        agent_name = agent_config.get('name', agent_id.title())
+        
+        # Escape quotes in the action for shell command
+        escaped_action = action.replace('"', '\\"').replace("'", "\\'")
+        
+        # Build the praisonai command
+        # Uses the input from previous node if available
+        command = f'praisonai "{escaped_action}"'
+        
+        return {
+            "id": f"agent_{index}",
+            "name": agent_name,
+            "type": "n8n-nodes-base.executeCommand",
+            "typeVersion": 1,
+            "position": position,
+            "parameters": {
+                "command": command,
+                "executeOnce": True
+            }
+        }
+    
+    def _create_http_request_node(self, agent_id: str, agent_config: Dict,
+                                   action: str, position: List[int], 
+                                   index: int) -> Dict[str, Any]:
+        """Create an HTTP Request node for an agent."""
+        agent_name = agent_config.get('name', agent_id.title())
+        
+        # Build the request body
+        request_body = {
+            "query": action,
+            "agent": agent_id
+        }
+        
+        return {
+            "id": f"agent_{index}",
+            "name": agent_name,
+            "type": "n8n-nodes-base.httpRequest",
+            "typeVersion": 4.2,
+            "position": position,
+            "parameters": {
+                "method": "POST",
+                "url": f"{self.praisonai_api_url}/agents",
+                "sendBody": True,
+                "specifyBody": "json",
+                "jsonBody": json.dumps(request_body),
+                "options": {
+                    "timeout": 300000  # 5 minute timeout for LLM calls
+                }
+            }
+        }
+    
+    def _create_parallel_nodes(self, parallel_steps: List[Dict], 
+                                x_position: int,
+                                agents: Dict[str, Any]) -> tuple:
+        """Create nodes for parallel execution."""
+        nodes = []
+        connections = {}
+        
+        for i, step in enumerate(parallel_steps):
+            agent_id = step.get('agent', '')
+            action = step.get('action', step.get('description', ''))
+            agent_config = agents.get(agent_id, {})
+            
+            node = self._create_agent_node(
+                agent_id=agent_id,
+                agent_config=agent_config,
+                action=action,
+                position=[x_position + (i * 200), 300 + (i * 100)],
+                index=100 + i  # Offset index for parallel nodes
+            )
+            nodes.append(node)
+        
+        return nodes, connections
+    
+    def _create_route_node(self, step: Dict, x_position: int, 
+                           index: int) -> Dict[str, Any]:
+        """Create an IF node for routing decisions."""
+        route_config = step.get('route', {})
+        conditions = list(route_config.keys())
+        
+        return {
+            "id": f"route_{index}",
+            "name": f"Route Decision {index}",
+            "type": "n8n-nodes-base.if",
+            "typeVersion": 2,
+            "position": [x_position, 300],
+            "parameters": {
+                "conditions": {
+                    "options": {
+                        "caseSensitive": True,
+                        "leftValue": "",
+                        "typeValidation": "strict"
+                    },
+                    "conditions": [
+                        {
+                            "id": f"condition_{i}",
+                            "leftValue": "={{ $json.result }}",
+                            "rightValue": condition,
+                            "operator": {
+                                "type": "string",
+                                "operation": "contains"
+                            }
+                        }
+                        for i, condition in enumerate(conditions) if condition != 'default'
+                    ]
+                }
+            }
+        }
+    
+    def generate_n8n_url(self, workflow_json: Dict[str, Any], 
+                         base_url: str) -> str:
+        """
+        Generate a URL to open n8n workflow editor.
+        
+        Note: n8n doesn't support URL hash-based import. Users need to:
+        1. Open n8n
+        2. Use "Import from File" to load the saved JSON
+        
+        Args:
+            workflow_json: The n8n workflow JSON (unused, kept for API compatibility)
+            base_url: Base URL of the n8n instance
+            
+        Returns:
+            URL to open n8n workflow editor
+        """
+        # n8n doesn't support URL-based workflow import via hash
+        # Return the new workflow URL - user will need to import manually
+        return f"{base_url}/workflow/new"
+    
+    def open_in_browser(self, url: str) -> None:
+        """
+        Open a URL in the default web browser.
+        
+        Args:
+            url: URL to open
+        """
+        try:
+            webbrowser.open(url)
+        except Exception as e:
+            self.print_status(f"Could not open browser: {e}", "warning")
+            self.print_status(f"Please open this URL manually: {url}", "info")
+    
+    def _save_workflow_json(self, workflow_json: Dict[str, Any], 
+                            yaml_path: str) -> str:
+        """
+        Save the workflow JSON to a file.
+        
+        Args:
+            workflow_json: The n8n workflow JSON
+            yaml_path: Original YAML file path
+            
+        Returns:
+            Path to the saved JSON file
+        """
+        # Create output path based on input YAML
+        yaml_file = Path(yaml_path)
+        output_path = yaml_file.parent / f"{yaml_file.stem}_n8n.json"
+        
+        with open(output_path, 'w') as f:
+            json.dump(workflow_json, f, indent=2)
+        
+        return str(output_path)
+    
+    def get_workflow_json_for_clipboard(self, yaml_path: str) -> str:
+        """
+        Get the workflow JSON as a string for clipboard copy.
+        
+        Args:
+            yaml_path: Path to the agents.yaml file
+            
+        Returns:
+            JSON string ready for clipboard
+        """
+        workflow_json = self.convert_yaml_to_n8n(yaml_path)
+        return json.dumps(workflow_json, indent=2)

src/praisonai/praisonai/cli/features/workflow.py

@@ -367,6 +367,14 @@ Example:
         self.print_status(f"Validating: {file_path}", "info")
         
         try:
+            # Load raw YAML to check for non-canonical names
+            import yaml
+            with open(file_path, 'r') as f:
+                raw_data = yaml.safe_load(f)
+            
+            # Check for non-canonical names and suggest canonical ones
+            suggestions = self._get_canonical_suggestions(raw_data)
+            
             workflow = parser.parse_file(file_path)
             
             # Print validation results
@@ -386,12 +394,82 @@ Example:
             
             rprint(table)
             self.print_status("✓ Workflow is valid!", "success")
+            
+            # Show suggestions for canonical names
+            if suggestions:
+                rprint()
+                rprint("[yellow]💡 Suggestions for canonical field names:[/yellow]")
+                for suggestion in suggestions:
+                    rprint(f"   [dim]•[/dim] {suggestion}")
+                rprint()
+                rprint("[dim]Note: Both old and new names work, but canonical names are recommended.[/dim]")
+            
             return True
             
         except Exception as e:
             self.print_status(f"✗ Validation failed: {e}", "error")
             return False
     
+    def _get_canonical_suggestions(self, data: Dict) -> List[str]:
+        """
+        Check for non-canonical field names and return suggestions.
+        
+        Canonical names (A-I-G-S mnemonic):
+        - Agents (not roles)
+        - Instructions (not backstory)
+        - Goal (same)
+        - Steps (not tasks)
+        
+        Also:
+        - name (not topic)
+        - action (not description)
+        
+        Args:
+            data: Raw YAML data
+            
+        Returns:
+            List of suggestion strings
+        """
+        suggestions = []
+        
+        if not data:
+            return suggestions
+        
+        # Check top-level keys
+        if 'roles' in data:
+            suggestions.append("Use 'agents' instead of 'roles'")
+        
+        if 'topic' in data and 'name' not in data:
+            suggestions.append("Use 'name' instead of 'topic'")
+        
+        # Check agent fields
+        agents_data = data.get('agents', data.get('roles', {}))
+        for agent_id, agent_config in agents_data.items():
+            if isinstance(agent_config, dict):
+                if 'backstory' in agent_config:
+                    suggestions.append(f"Agent '{agent_id}': Use 'instructions' instead of 'backstory'")
+                
+                # Check nested tasks
+                if 'tasks' in agent_config:
+                    suggestions.append(f"Agent '{agent_id}': Use 'steps' at top level instead of nested 'tasks'")
+        
+        # Check step fields
+        steps_data = data.get('steps', [])
+        for i, step in enumerate(steps_data):
+            if isinstance(step, dict):
+                if 'description' in step and 'action' not in step:
+                    step_name = step.get('name', f'step {i+1}')
+                    suggestions.append(f"Step '{step_name}': Use 'action' instead of 'description'")
+                
+                # Check parallel steps
+                if 'parallel' in step:
+                    for j, parallel_step in enumerate(step['parallel']):
+                        if isinstance(parallel_step, dict):
+                            if 'description' in parallel_step and 'action' not in parallel_step:
+                                suggestions.append(f"Parallel step {j+1}: Use 'action' instead of 'description'")
+        
+        return suggestions
+    
     def action_list(self, args: List[str], **kwargs) -> List[str]:
         """
         List available workflows in .praison/workflows/ directory.
@@ -567,8 +645,8 @@ Example:
             
             return result_path
             
-        except ImportError as e:
-            self.print_status(f"Auto-generation requires instructor: pip install instructor", "error")
+        except ImportError:
+            self.print_status("Auto-generation requires instructor: pip install instructor", "error")
             return None
         except Exception as e:
             self.print_status(f"Generation failed: {e}", "error")

src/praisonai/praisonai/cli/main.py

@@ -210,6 +210,10 @@ class PraisonAI:
         version_string = f"PraisonAI version {__version__}"
 
         self.framework = args.framework or self.framework
+        
+        # Update config_list model if --model flag is provided
+        if getattr(args, 'model', None):
+            self.config_list[0]['model'] = args.model
 
         # Check for piped input from stdin
         stdin_input = self.read_stdin_if_available()
@@ -495,6 +499,31 @@ class PraisonAI:
                 print(result)
                 return result
         else:
+            # n8n Integration - Export workflow to n8n and open in browser
+            if getattr(args, 'n8n', False):
+                from .features.n8n import N8nHandler
+                n8n_handler = N8nHandler(
+                    verbose=getattr(args, 'verbose', False),
+                    n8n_url=getattr(args, 'n8n_url', 'http://localhost:5678')
+                )
+                result = n8n_handler.execute(self.agent_file)
+                return result
+            
+            # Flow Display - Show flow diagram WITHOUT executing (--flow-display flag)
+            if getattr(args, 'flow_display', False):
+                from .features.flow_display import FlowDisplayHandler
+                flow_handler = FlowDisplayHandler(verbose=getattr(args, 'verbose', False))
+                flow_handler.display_flow_diagram(self.agent_file)
+                return  # Exit without executing
+            
+            # Always show flow diagram before execution (default behavior)
+            try:
+                from .features.flow_display import FlowDisplayHandler
+                flow_handler = FlowDisplayHandler(verbose=getattr(args, 'verbose', False))
+                flow_handler.display_flow_diagram(self.agent_file, show_footer=False)
+            except Exception:
+                pass  # Continue even if flow display fails
+            
             agents_generator = AgentsGenerator(
                 self.agent_file,
                 self.framework,
@@ -650,6 +679,10 @@ class PraisonAI:
         # Flow Display - visual workflow
         parser.add_argument("--flow-display", action="store_true", help="Enable visual workflow tracking")
         
+        # n8n Integration - export workflow to n8n
+        parser.add_argument("--n8n", action="store_true", help="Export workflow to n8n and open in browser")
+        parser.add_argument("--n8n-url", type=str, default="http://localhost:5678", help="n8n instance URL (default: http://localhost:5678)")
+        
         # If we're in a test environment, parse with empty args to avoid pytest interference
         if in_test_env:
             args, unknown_args = parser.parse_known_args([])
@@ -1632,6 +1665,10 @@ class PraisonAI:
                     if arg == '--output' and i + 1 < len(action_args):
                         output_file = action_args[i + 1]
                 self._create_workflow_from_template(template_name, output_file)
+            
+            elif action == 'auto':
+                # Auto-generate workflow from topic
+                self._auto_generate_workflow(action_args)
                 
             elif action == 'help' or action == '--help':
                 print("[bold]Workflow Commands:[/bold]")
@@ -1642,6 +1679,7 @@ class PraisonAI:
                 print("  praisonai workflow create <name>         - Create a new workflow")
                 print("  praisonai workflow validate <file.yaml>  - Validate a YAML workflow")
                 print("  praisonai workflow template <name>       - Create from template")
+                print('  praisonai workflow auto "topic"          - Auto-generate workflow')
                 print("\n[bold]Templates:[/bold]")
                 print("  simple, routing, parallel, loop, evaluator-optimizer")
                 print("\n[bold]Options (uses global flags):[/bold]")
@@ -1763,6 +1801,7 @@ class PraisonAI:
             from rich import print
             from rich.table import Table
             from rich.console import Console
+            import yaml
             
             console = Console()
             
@@ -1772,6 +1811,13 @@ class PraisonAI:
             
             print(f"[cyan]Validating: {yaml_file}[/cyan]")
             
+            # Load raw YAML to check for non-canonical names
+            with open(yaml_file, 'r') as f:
+                raw_data = yaml.safe_load(f)
+            
+            # Check for non-canonical names and suggest canonical ones
+            suggestions = self._get_canonical_suggestions(raw_data)
+            
             parser = YAMLWorkflowParser()
             workflow = parser.parse_file(yaml_file)
             
@@ -1790,8 +1836,77 @@ class PraisonAI:
             console.print(table)
             print("[green]✓ Workflow is valid![/green]")
             
+            # Show suggestions for canonical names
+            if suggestions:
+                print()
+                print("[yellow]💡 Suggestions for canonical field names:[/yellow]")
+                for suggestion in suggestions:
+                    print(f"   [dim]•[/dim] {suggestion}")
+                print()
+                print("[dim]Note: Both old and new names work, but canonical names are recommended.[/dim]")
+            
         except Exception as e:
             print(f"[red]✗ Validation failed: {e}[/red]")
+    
+    def _get_canonical_suggestions(self, data: dict) -> list:
+        """
+        Check for non-canonical field names and return suggestions.
+        
+        Canonical names (A-I-G-S mnemonic):
+        - Agents (not roles)
+        - Instructions (not backstory)
+        - Goal (same)
+        - Steps (not tasks)
+        
+        Also:
+        - name (not topic)
+        - action (not description)
+        
+        Args:
+            data: Raw YAML data
+            
+        Returns:
+            List of suggestion strings
+        """
+        suggestions = []
+        
+        if not data:
+            return suggestions
+        
+        # Check top-level keys
+        if 'roles' in data:
+            suggestions.append("Use 'agents' instead of 'roles'")
+        
+        if 'topic' in data and 'name' not in data:
+            suggestions.append("Use 'name' instead of 'topic'")
+        
+        # Check agent fields
+        agents_data = data.get('agents', data.get('roles', {}))
+        for agent_id, agent_config in agents_data.items():
+            if isinstance(agent_config, dict):
+                if 'backstory' in agent_config:
+                    suggestions.append(f"Agent '{agent_id}': Use 'instructions' instead of 'backstory'")
+                
+                # Check nested tasks
+                if 'tasks' in agent_config:
+                    suggestions.append(f"Agent '{agent_id}': Use 'steps' at top level instead of nested 'tasks'")
+        
+        # Check step fields
+        steps_data = data.get('steps', [])
+        for i, step in enumerate(steps_data):
+            if isinstance(step, dict):
+                if 'description' in step and 'action' not in step:
+                    step_name = step.get('name', f'step {i+1}')
+                    suggestions.append(f"Step '{step_name}': Use 'action' instead of 'description'")
+                
+                # Check parallel steps
+                if 'parallel' in step:
+                    for j, parallel_step in enumerate(step['parallel']):
+                        if isinstance(parallel_step, dict):
+                            if 'description' in parallel_step and 'action' not in parallel_step:
+                                suggestions.append(f"Parallel step {j+1}: Use 'action' instead of 'description'")
+        
+        return suggestions
 
     def _create_workflow_from_template(self, template_name: str = None, output_file: str = None):
         """
@@ -1839,6 +1954,76 @@ class PraisonAI:
         print(f"[green]✓ Created workflow: {output_file}[/green]")
         print(f"[cyan]Run with: praisonai workflow run {output_file}[/cyan]")
 
+    def _auto_generate_workflow(self, action_args: list):
+        """
+        Auto-generate a workflow from a topic description.
+        
+        Args:
+            action_args: ["topic description", --pattern, <pattern>, --output, <file.yaml>]
+        """
+        from rich import print
+        
+        # Parse arguments
+        topic = None
+        pattern = "sequential"
+        output_file = None
+        
+        i = 0
+        while i < len(action_args):
+            if action_args[i] == "--pattern" and i + 1 < len(action_args):
+                pattern = action_args[i + 1]
+                i += 2
+            elif action_args[i] == "--output" and i + 1 < len(action_args):
+                output_file = action_args[i + 1]
+                i += 2
+            elif not action_args[i].startswith("--") and topic is None:
+                topic = action_args[i]
+                i += 1
+            else:
+                i += 1
+        
+        if not topic:
+            print('[red]Usage: praisonai workflow auto "topic" --pattern <pattern>[/red]')
+            print("[cyan]Patterns: sequential, routing, parallel[/cyan]")
+            return
+        
+        # Validate pattern
+        valid_patterns = ["sequential", "routing", "parallel", "loop"]
+        if pattern not in valid_patterns:
+            print(f"[red]Unknown pattern: {pattern}[/red]")
+            print(f"[cyan]Valid patterns: {', '.join(valid_patterns)}[/cyan]")
+            return
+        
+        # Default output file
+        if not output_file:
+            safe_name = "".join(c if c.isalnum() else "_" for c in topic[:30]).lower()
+            output_file = f"{safe_name}_workflow.yaml"
+        
+        # Check if file exists
+        if os.path.exists(output_file):
+            print(f"[red]ERROR: File already exists: {output_file}[/red]")
+            return
+        
+        print(f"[cyan]Generating {pattern} workflow for: {topic}[/cyan]")
+        
+        try:
+            from praisonai.auto import WorkflowAutoGenerator
+            
+            generator = WorkflowAutoGenerator(
+                topic=topic,
+                workflow_file=output_file
+            )
+            
+            result_path = generator.generate(pattern=pattern)
+            
+            print(f"[green]✓ Created workflow: {result_path}[/green]")
+            print(f"[cyan]Run with: praisonai workflow run {output_file}[/cyan]")
+            
+        except ImportError:
+            print("[red]Auto-generation requires instructor: pip install instructor[/red]")
+        except Exception as e:
+            print(f"[red]Generation failed: {e}[/red]")
+
     def handle_hooks_command(self, action: str):
         """
         Handle hooks subcommand actions.

src/praisonai/praisonai/deploy.py

@@ -57,7 +57,7 @@ class CloudDeployer:
             file.write("FROM python:3.11-slim\n")
             file.write("WORKDIR /app\n")
             file.write("COPY . .\n")
-            file.write("RUN pip install flask praisonai==2.3.26 gunicorn markdown\n")
+            file.write("RUN pip install flask praisonai==2.3.31 gunicorn markdown\n")
             file.write("EXPOSE 8080\n")
             file.write('CMD ["gunicorn", "-b", "0.0.0.0:8080", "api:app"]\n')
             

src/praisonai/praisonai/version.py

@@ -1 +1 @@
-__version__ = "2.3.26"
+__version__ = "2.3.31"

src/praisonai/pyproject.toml

@@ -12,7 +12,7 @@ dependencies = [
     "rich>=13.7",
     "markdown>=3.5",
     "pyparsing>=3.0.0",
-    "praisonaiagents>=0.1.4",
+    "praisonaiagents>=0.1.12",
     "python-dotenv>=0.19.0",
     "instructor>=1.3.3",
     "PyYAML>=6.0",

src/praisonai/tests/test_n8n_integration.py

@@ -0,0 +1,239 @@
+"""
+Test-Driven Development tests for n8n integration.
+
+Tests for converting PraisonAI agents.yaml to n8n workflow JSON format.
+"""
+
+import pytest
+import json
+import os
+import tempfile
+from pathlib import Path
+
+
+class TestN8nConverter:
+    """Tests for YAML to n8n JSON conversion."""
+    
+    @pytest.fixture
+    def sample_agents_yaml(self):
+        """Sample agents.yaml content for testing."""
+        return """
+name: Test Workflow
+description: A test workflow for n8n integration
+
+agents:
+  researcher:
+    name: Researcher
+    role: Research Analyst
+    goal: Research topics
+    instructions: Research and gather information
+
+  writer:
+    name: Writer
+    role: Content Writer
+    goal: Write content
+    instructions: Write based on research
+
+steps:
+  - agent: researcher
+    action: "Research the topic"
+  
+  - agent: writer
+    action: "Write content based on research"
+"""
+
+    @pytest.fixture
+    def sample_yaml_file(self, sample_agents_yaml):
+        """Create a temporary YAML file for testing."""
+        with tempfile.NamedTemporaryFile(mode='w', suffix='.yaml', delete=False) as f:
+            f.write(sample_agents_yaml)
+            f.flush()
+            yield f.name
+        os.unlink(f.name)
+
+    def test_n8n_handler_import(self):
+        """Test that N8nHandler can be imported."""
+        from praisonai.cli.features.n8n import N8nHandler
+        assert N8nHandler is not None
+
+    def test_n8n_handler_initialization(self):
+        """Test N8nHandler initialization."""
+        from praisonai.cli.features.n8n import N8nHandler
+        handler = N8nHandler(verbose=True)
+        assert handler.feature_name == "n8n"
+        assert handler.verbose is True
+
+    def test_convert_yaml_to_n8n_returns_dict(self, sample_yaml_file):
+        """Test that convert_yaml_to_n8n returns a dictionary."""
+        from praisonai.cli.features.n8n import N8nHandler
+        handler = N8nHandler()
+        result = handler.convert_yaml_to_n8n(sample_yaml_file)
+        assert isinstance(result, dict)
+
+    def test_n8n_workflow_has_required_keys(self, sample_yaml_file):
+        """Test that n8n workflow JSON has required keys."""
+        from praisonai.cli.features.n8n import N8nHandler
+        handler = N8nHandler()
+        result = handler.convert_yaml_to_n8n(sample_yaml_file)
+        
+        assert "name" in result
+        assert "nodes" in result
+        assert "connections" in result
+
+    def test_n8n_workflow_has_trigger_node(self, sample_yaml_file):
+        """Test that n8n workflow has a manual trigger node."""
+        from praisonai.cli.features.n8n import N8nHandler
+        handler = N8nHandler()
+        result = handler.convert_yaml_to_n8n(sample_yaml_file)
+        
+        nodes = result["nodes"]
+        trigger_nodes = [n for n in nodes if "trigger" in n.get("type", "").lower() or "trigger" in n.get("name", "").lower()]
+        assert len(trigger_nodes) >= 1
+
+    def test_n8n_workflow_has_agent_nodes(self, sample_yaml_file):
+        """Test that n8n workflow has agent nodes (Execute Command or HTTP Request)."""
+        from praisonai.cli.features.n8n import N8nHandler
+        handler = N8nHandler()
+        result = handler.convert_yaml_to_n8n(sample_yaml_file)
+        
+        nodes = result["nodes"]
+        # Agent nodes can be either executeCommand or httpRequest
+        agent_nodes = [n for n in nodes if "executeCommand" in n.get("type", "") or "httpRequest" in n.get("type", "")]
+        # Should have at least 2 agent nodes (one per agent step)
+        assert len(agent_nodes) >= 2
+
+    def test_n8n_workflow_nodes_are_connected(self, sample_yaml_file):
+        """Test that nodes are properly connected."""
+        from praisonai.cli.features.n8n import N8nHandler
+        handler = N8nHandler()
+        result = handler.convert_yaml_to_n8n(sample_yaml_file)
+        
+        connections = result["connections"]
+        assert len(connections) > 0
+
+    def test_n8n_workflow_name_from_yaml(self, sample_yaml_file):
+        """Test that workflow name is taken from YAML."""
+        from praisonai.cli.features.n8n import N8nHandler
+        handler = N8nHandler()
+        result = handler.convert_yaml_to_n8n(sample_yaml_file)
+        
+        assert result["name"] == "Test Workflow"
+
+    def test_generate_n8n_url(self, sample_yaml_file):
+        """Test URL generation for n8n."""
+        from praisonai.cli.features.n8n import N8nHandler
+        handler = N8nHandler()
+        workflow_json = handler.convert_yaml_to_n8n(sample_yaml_file)
+        url = handler.generate_n8n_url(workflow_json, "http://localhost:5678")
+        
+        assert url.startswith("http://localhost:5678")
+        assert "workflow" in url.lower()
+
+    def test_node_positions_are_sequential(self, sample_yaml_file):
+        """Test that node positions are laid out sequentially."""
+        from praisonai.cli.features.n8n import N8nHandler
+        handler = N8nHandler()
+        result = handler.convert_yaml_to_n8n(sample_yaml_file)
+        
+        nodes = result["nodes"]
+        positions = [n.get("position", [0, 0]) for n in nodes]
+        
+        # X positions should increase for sequential workflow
+        x_positions = [p[0] for p in positions]
+        assert x_positions == sorted(x_positions), "Nodes should be positioned left to right"
+
+
+class TestN8nCLIIntegration:
+    """Tests for CLI integration of n8n feature."""
+    
+    def test_n8n_flag_in_argparser(self):
+        """Test that --n8n flag is recognized by argparser."""
+        import argparse
+        # This will be tested after we add the flag to main.py
+        pass
+
+    def test_execute_method_exists(self):
+        """Test that execute method exists."""
+        from praisonai.cli.features.n8n import N8nHandler
+        handler = N8nHandler()
+        assert hasattr(handler, 'execute')
+        assert callable(handler.execute)
+
+
+class TestN8nWorkflowPatterns:
+    """Tests for different workflow patterns."""
+    
+    @pytest.fixture
+    def parallel_workflow_yaml(self):
+        """YAML with parallel steps."""
+        return """
+name: Parallel Workflow
+
+agents:
+  agent1:
+    name: Agent1
+    role: Role1
+    goal: Goal1
+  agent2:
+    name: Agent2
+    role: Role2
+    goal: Goal2
+
+steps:
+  - parallel:
+      - agent: agent1
+        action: "Do task 1"
+      - agent: agent2
+        action: "Do task 2"
+"""
+
+    @pytest.fixture
+    def route_workflow_yaml(self):
+        """YAML with route/decision steps."""
+        return """
+name: Route Workflow
+
+agents:
+  classifier:
+    name: Classifier
+    role: Classifier
+    goal: Classify input
+  handler_a:
+    name: HandlerA
+    role: Handler
+    goal: Handle type A
+  handler_b:
+    name: HandlerB
+    role: Handler
+    goal: Handle type B
+
+steps:
+  - agent: classifier
+    action: "Classify the input"
+  
+  - route:
+      type_a: [handler_a]
+      type_b: [handler_b]
+"""
+
+    def test_parallel_workflow_creates_split_node(self, parallel_workflow_yaml):
+        """Test that parallel steps create appropriate n8n structure."""
+        import tempfile
+        import os
+        from praisonai.cli.features.n8n import N8nHandler
+        
+        with tempfile.NamedTemporaryFile(mode='w', suffix='.yaml', delete=False) as f:
+            f.write(parallel_workflow_yaml)
+            f.flush()
+            
+            handler = N8nHandler()
+            result = handler.convert_yaml_to_n8n(f.name)
+            
+            # Should have nodes for parallel execution
+            assert len(result["nodes"]) >= 2
+            
+        os.unlink(f.name)
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])

src/praisonai/tests/test_phase2_refactor.py

@@ -1,15 +1,21 @@
 #!/usr/bin/env python3
-"""Test script to verify Phase 2 refactoring maintains backward compatibility"""
+"""Test script to verify Phase 2 refactoring maintains backward compatibility
+
+NOTE: These tests make real API calls and require valid API keys.
+They are marked with @pytest.mark.real to be excluded from CI.
+"""
 
 import asyncio
 import sys
 import os
+import pytest
 
 # Add the correct path for imports
 sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'src/praisonai-agents'))
 
 from praisonaiagents.llm import LLM
 
+@pytest.mark.real
 def test_sync_methods():
     """Test synchronous methods still work correctly"""
     print("Testing synchronous methods...")
@@ -52,6 +58,7 @@ def test_sync_methods():
     
     print("✅ Synchronous methods working correctly\n")
 
+@pytest.mark.real
 async def test_async_methods():
     """Test asynchronous methods still work correctly"""
     print("Testing asynchronous methods...")
@@ -94,6 +101,7 @@ async def test_async_methods():
     
     print("✅ Asynchronous methods working correctly\n")
 
+@pytest.mark.real
 def test_edge_cases():
     """Test edge cases and special scenarios"""
     print("Testing edge cases...")

src/praisonai/uv.lock

@@ -4197,7 +4197,7 @@ requires-dist = [
     { name = "praisonai-tools", marker = "extra == 'autogen'", specifier = ">=0.0.22" },
     { name = "praisonai-tools", marker = "extra == 'autogen-v4'", specifier = ">=0.0.22" },
     { name = "praisonai-tools", marker = "extra == 'crewai'", specifier = ">=0.0.22" },
-    { name = "praisonaiagents", specifier = ">=0.1.4" },
+    { name = "praisonaiagents", specifier = ">=0.1.12" },
     { name = "pyautogen", marker = "extra == 'autogen'", specifier = "==0.2.29" },
     { name = "pydantic", marker = "extra == 'chat'", specifier = "<=2.10.1" },
     { name = "pydantic", marker = "extra == 'code'", specifier = "<=2.10.1" },
@@ -4252,7 +4252,7 @@ wheels = [
 
 [[package]]
 name = "praisonaiagents"
-version = "0.1.4"
+version = "0.1.12"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohttp" },
@@ -4261,9 +4261,9 @@ dependencies = [
     { name = "pydantic" },
     { name = "rich" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/ab/9d/d286e0ba5421cb8a37ea86f4714a7fbcb39fe1f38e9a202cf0cf6381ae8c/praisonaiagents-0.1.4.tar.gz", hash = "sha256:d00a326ac65701bcc66c44aba7d1616ef1317a2491034a242ad4290f0269e190", size = 560571, upload-time = "2025-12-17T11:43:41.829Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/06/ea/0489a0f62da0ded124c7a4fe079d3c898537e7a4ec639dc774c8420855e1/praisonaiagents-0.1.12.tar.gz", hash = "sha256:9d0dcb64fa0fd642fef60939d4547c2aae07267bf894d565e0e9a8013dc8c035", size = 620248, upload-time = "2025-12-18T12:54:12.363Z" }
 wheels = [
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