adding Docs

README.md

@@ -35,6 +35,115 @@ PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application
 - 👁️ Vision Language Model (VLM) Support
 - 🎙️ Real-time Voice Interaction
 
+## Using No Code
+
+### Auto Mode:
+```bash
+pip install praisonai python-dotenv instructor
+export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
+praisonai --auto create a movie script about Robots in Mars
+```
+
+### Initialise Mode:
+```bash
+pip install praisonai python-dotenv instructor
+export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
+praisonai --init create a movie script about Robots in Mars
+praisonai
+```
+
+## Using Coding
+
+Light weight package dedicated for coding:
+```bash
+pip install praisonaiagents
+```
+
+```bash
+export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
+```
+
+Create app.py file and add the code below:
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# 1. Create agents
+researcher = Agent(
+    name="Researcher",
+    role="Senior Research Analyst",
+    goal="Uncover cutting-edge developments in AI and data science",
+    backstory="""You are an expert at a technology research group, 
+    skilled in identifying trends and analyzing complex data.""",
+    verbose=True,
+    llm="gpt-4o",
+    markdown=True
+)
+writer = Agent(
+    name="Writer",
+    role="Tech Content Strategist",
+    goal="Craft compelling content on tech advancements",
+    backstory="""You are a content strategist known for 
+    making complex tech topics interesting and easy to understand.""",
+    llm="gpt-4o",
+    markdown=True
+)
+
+# 2. Define Tasks
+task1 = Task(
+    name="research_task",
+    description="""Analyze 2024's AI advancements. 
+    Find major trends, new technologies, and their effects.""",
+    expected_output="""A detailed report on 2024 AI advancements""",
+    agent=researcher
+)
+
+task2 = Task(
+    name="writing_task",
+    description="""Create a blog post about major AI advancements using the insights you have.
+    Make it interesting, clear, and suited for tech enthusiasts. 
+    It should be at least 4 paragraphs long.""",
+    expected_output="A blog post of at least 4 paragraphs",
+    agent=writer,
+)
+
+agents = PraisonAIAgents(
+    agents=[researcher, writer],
+    tasks=[task1, task2],
+    verbose=False,
+    process="hierarchical",
+    manager_llm="gpt-4o"
+)
+
+result = agents.start()
+```
+
+Run:
+```bash
+python app.py
+```
+
+## Ollama Integration
+```bash
+export OPENAI_BASE_URL=http://localhost:11434/v1
+```
+
+## Groq Integration
+Replace xxxx with Groq API KEY:
+```bash
+export OPENAI_API_KEY=xxxxxxxxxxx
+export OPENAI_BASE_URL=https://api.groq.com/openai/v1
+```
+
+## Logging
+```bash
+export LOGLEVEL=info
+```
+
+Advanced logging:
+```bash
+export LOGLEVEL=debug
+```
+
 <div align="center">
   <picture>
     <source media="(prefers-color-scheme: dark)" srcset="docs/images/architecture-dark.png" />

agents.yaml

@@ -13,7 +13,7 @@ roles:
           a cat being on Mars. Also, research about cat behavior and characteristics.
         expected_output: Document with research findings on Mars and cats.
     tools:
-    - ''
+    - 'search_tool'
   narrative_designer:
     backstory: Skilled in narrative development, with a focus on creating engaging
       stories.

agents/example.py

@@ -3,7 +3,7 @@ import logging
 from rich.console import Console
 from rich.panel import Panel
 from rich.text import Text
-from praisonaiagents import Agent, Task, Agents, error_logs
+from praisonaiagents import Agent, Task, PraisonAIAgents, error_logs
 
 # Configure logging
 logging.basicConfig(
@@ -16,16 +16,42 @@ def my_callback(output):
     logging.info(f"Callback function called after task execution: {output.description}")
     logging.info(f"Task output: {output}")
 
+from duckduckgo_search import DDGS
+
+def internet_search_tool(query):
+    """
+    Perform a search using DuckDuckGo.
+
+    Args:
+        query (str): The search query.
+
+    Returns:
+        list: A list of search result titles and URLs.
+    """
+    try:
+        results = []
+        ddgs = DDGS()
+        for result in ddgs.text(keywords=query, max_results=10):
+            results.append({
+                "title": result.get("title", ""),
+                "url": result.get("href", "")
+            })
+        return results
+
+    except Exception as e:
+        print(f"Error during DuckDuckGo search: {e}")
+        return []
+
 def main():
     # Make sure OPENAI_API_KEY is set
     if not os.environ.get("OPENAI_API_KEY"):
         raise ValueError("Please set OPENAI_API_KEY environment variable")
 
     # Define tools
-    search_tool = {
+    internet_search_tool = {
         "type": "function",
         "function": {
-            "name": "search_tool",
+            "name": "internet_search_tool",
             "description": "Use this to perform search queries",
             "parameters": {
                 "type": "object",
@@ -60,7 +86,7 @@ def main():
         skilled in identifying trends and analyzing complex data.""",
         verbose=True,
         allow_delegation=False,
-        tools=[search_tool],
+        tools=[internet_search_tool],
         llm="gpt-4o",
         markdown=True
     )
@@ -84,7 +110,7 @@ def main():
         Find major trends, new technologies, and their effects.""",
         expected_output="""A detailed report on 2024 AI advancements""",
         agent=researcher,
-        tools=[search_tool]
+        tools=[internet_search_tool]
     )
 
     task2 = Task(
@@ -118,7 +144,7 @@ def main():
     )
 
     # Create and run agents manager
-    agents = Agents(
+    agents = PraisonAIAgents(
         agents=[researcher, writer],
         tasks=[task1, task2, task3, task4],
         verbose=False,

docs/index.md

@@ -10,7 +10,129 @@
 <a href="https://github.com/MervinPraison/PraisonAI"><img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="License" /></a>
 </p>
 
-Praison AI, leveraging both AutoGen and CrewAI or any other agent framework, represents a low-code, centralised framework designed to simplify the creation and orchestration of multi-agent systems for various LLM applications, emphasizing ease of use, customization, and human-agent interaction.
+PraisonAI is an AI Agents Framework with Self Reflection. PraisonAI application combines PraisonAI Agents, AutoGen, and CrewAI into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human–agent collaboration.
+
+## Key Features
+
+- 🤖 Automated AI Agents Creation
+- 🔄 Use CrewAI or AutoGen Framework
+- 💯 100+ LLM Support
+- 💻 Chat with ENTIRE Codebase
+- 🖥️ Interactive UIs
+- 📄 YAML-based Configuration
+- 🛠️ Custom Tool Integration
+- 🔍 Internet Search Capability (using Crawl4AI and Tavily)
+- 👁️ Vision Language Model (VLM) Support
+- 🎙️ Real-time Voice Interaction
+
+## Using No Code
+
+### Auto Mode:
+```bash
+pip install praisonai python-dotenv instructor
+export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
+praisonai --auto create a movie script about Robots in Mars
+```
+
+### Initialise Mode:
+```bash
+pip install praisonai python-dotenv instructor
+export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
+praisonai --init create a movie script about Robots in Mars
+praisonai
+```
+
+## Using Coding
+
+Light weight package dedicated for coding:
+```bash
+pip install praisonaiagents
+```
+
+```bash
+export OPENAI_API_KEY=xxxxxxxxxxxxxxxxxxxxxx
+```
+
+Create app.py file and add the code below:
+```python
+from praisonaiagents import Agent, Task, PraisonAIAgents
+
+# 1. Create agents
+researcher = Agent(
+    name="Researcher",
+    role="Senior Research Analyst",
+    goal="Uncover cutting-edge developments in AI and data science",
+    backstory="""You are an expert at a technology research group, 
+    skilled in identifying trends and analyzing complex data.""",
+    verbose=True,
+    llm="gpt-4o",
+    markdown=True
+)
+writer = Agent(
+    name="Writer",
+    role="Tech Content Strategist",
+    goal="Craft compelling content on tech advancements",
+    backstory="""You are a content strategist known for 
+    making complex tech topics interesting and easy to understand.""",
+    llm="gpt-4o",
+    markdown=True
+)
+
+# 2. Define Tasks
+task1 = Task(
+    name="research_task",
+    description="""Analyze 2024's AI advancements. 
+    Find major trends, new technologies, and their effects.""",
+    expected_output="""A detailed report on 2024 AI advancements""",
+    agent=researcher
+)
+
+task2 = Task(
+    name="writing_task",
+    description="""Create a blog post about major AI advancements using the insights you have.
+    Make it interesting, clear, and suited for tech enthusiasts. 
+    It should be at least 4 paragraphs long.""",
+    expected_output="A blog post of at least 4 paragraphs",
+    agent=writer,
+)
+
+agents = PraisonAIAgents(
+    agents=[researcher, writer],
+    tasks=[task1, task2],
+    verbose=False,
+    process="hierarchical",
+    manager_llm="gpt-4o"
+)
+
+result = agents.start()
+```
+
+Run:
+```bash
+python app.py
+```
+
+## Ollama Integration
+```bash
+export OPENAI_BASE_URL=http://localhost:11434/v1
+```
+
+## Groq Integration
+Replace xxxx with Groq API KEY:
+```bash
+export OPENAI_API_KEY=xxxxxxxxxxx
+export OPENAI_BASE_URL=https://api.groq.com/openai/v1
+```
+
+## Logging
+```bash
+export LOGLEVEL=info
+```
+
+Advanced logging:
+```bash
+export LOGLEVEL=debug
+```
 
 <div align="center">
   <picture>

praisonai/agents_generator.py

@@ -200,6 +200,57 @@ class AgentsGenerator:
                     tools_dict[name] = obj
         return tools_dict
 
+    def load_tools_from_tools_py(self):
+        """
+        Automatically loads all tools (functions and tool definitions) from tools.py file.
+
+        Returns:
+            dict: A dictionary containing:
+                - Function names as keys and function objects as values
+                - Tool definition names as keys and tool definition dictionaries as values
+
+        Note:
+            This function looks for:
+            1. Regular functions
+            2. Tool definition dictionaries (containing 'type' and 'function' keys)
+            3. Variables named as tools or ending with '_tool'
+        """
+        tools_dict = {}
+        try:
+            # Try to import tools.py from current directory
+            spec = importlib.util.spec_from_file_location("tools", "tools.py")
+            if spec is None:
+                self.logger.warning("tools.py not found in current directory")
+                return tools_dict
+
+            module = importlib.util.module_from_spec(spec)
+            spec.loader.exec_module(module)
+
+            # Get all module attributes
+            for name, obj in inspect.getmembers(module):
+                # Skip private attributes
+                if name.startswith('_'):
+                    continue
+                    
+                # Case 1: Regular functions
+                if inspect.isfunction(obj) and obj.__module__ == "tools":
+                    tools_dict[name] = obj
+                    
+                # Case 2: Tool definition dictionaries
+                elif isinstance(obj, dict) and obj.get('type') == 'function' and 'function' in obj:
+                    tools_dict[name] = obj
+                    
+                # Case 3: Variables named as tools
+                elif (name.endswith('_tool') or name == 'tools') and not inspect.ismodule(obj):
+                    tools_dict[name] = obj
+
+            self.logger.debug(f"Loaded {len(tools_dict)} tools from tools.py")
+            
+        except Exception as e:
+            self.logger.warning(f"Error loading tools from tools.py: {e}")
+            
+        return tools_dict
+
     def generate_crew_and_kickoff(self):
         """
         Generates a crew of agents and initiates tasks based on the provided configuration.
@@ -271,7 +322,7 @@ class AgentsGenerator:
         elif tools_dir_path.is_dir():
             tools_dict.update(self.load_tools_from_module_class(tools_dir_path))
             self.logger.debug("tools folder exists in the root directory")
-        
+
         framework = self.framework or config.get('framework')
 
         if framework == "autogen":
@@ -498,6 +549,8 @@ class AgentsGenerator:
         tasks = []
         tasks_dict = {}
 
+        tools_dict = self.load_tools_from_tools_py()
+
         # Create agents from config
         for role, details in config['roles'].items():
             role_filled = details['role'].format(topic=topic)

tools.py

@@ -1,5 +1,48 @@
 # tools.py
 
+# from duckduckgo_search import DDGS
+
+# def search_tool(query):
+#     """
+#     Perform a search using DuckDuckGo.
+
+#     Args:
+#         query (str): The search query.
+
+#     Returns:
+#         list: A list of search result titles and URLs.
+#     """
+#     try:
+#         results = []
+#         ddgs = DDGS()
+#         for result in ddgs.text(keywords=query, max_results=10):
+#             results.append({
+#                 "title": result.get("title", ""),
+#                 "url": result.get("href", "")
+#             })
+#         return results
+
+#     except Exception as e:
+#         print(f"Error during DuckDuckGo search: {e}")
+#         return []
+
+# # Define tools
+# search_tool = {
+#     "type": "function",
+#     "function": {
+#         "name": "search_tool",
+#         "description": "Use this to perform search queries",
+#         "parameters": {
+#             "type": "object",
+#             "properties": {
+#                 "query": {"type": "string"},
+#             },
+#             "required": ["query"],
+#         },
+#     },
+# }
+
+
 # from duckduckgo_search import DDGS
 # from praisonai_tools import BaseTool
 # class InternetSearchTool(BaseTool):