Add EXASearchTool class and integrate with AI agents

- Introduced a new `EXASearchTool` class to interact with the EXA Search API, allowing AI agents to perform searches and retrieve results.
- Implemented methods for running queries and parsing results into a readable format.
- Created a `SearchAgent` to utilize the `EXASearchTool` for searching information about the 'AI Agents Framework'.
- Added a task definition for the `SearchAgent` to demonstrate the tool's functionality.
- Updated documentation to include the new `tools_class` and provide a comprehensive guide on using class-based tools with AI agents.

agents/tools-class.py

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

cookbooks/general/tools-class.py

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

docs/features/async.mdx

@@ -4,6 +4,26 @@ description: "Async AI Agents allow you to run AI tasks asynchronously, improvin
 icon: "clock"
 ---
 
+```mermaid
+flowchart LR
+    In[In] --> Agent1[AI Agent]
+    Agent1 --> Agent2[AI Agent]
+    Agent1 --> Agent3[AI Agent]
+    Agent1 --> Agent4[AI Agent]
+    Agent2 --> Out[Out]
+    Agent3 --> Out
+    Agent4 --> Out
+    
+    style In fill:#8B0000,color:#fff
+    style Agent1 fill:#2E8B57,color:#fff
+    style Agent2 fill:#2E8B57,color:#fff
+    style Agent3 fill:#2E8B57,color:#fff
+    style Agent4 fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+Async AI Agents allow you to run AI tasks asynchronously, improving performance and efficiency in your applications.
+
 ## Quick Start
 
 <Tabs>

docs/features/autoagents.mdx

@@ -4,6 +4,47 @@ description: "AutoAgents automatically creates and manages AI agents and tasks b
 icon: "robot"
 ---
 
+```mermaid
+flowchart LR
+    In[In] --> Plan[Plan Tasks]
+    
+    subgraph Creation[Automatic Agent Creation]
+        Plan --> Agent1[AI Agent 1]
+        Plan --> Agent2[AI Agent 2]
+        Plan --> Agent3[AI Agent 3]
+    end
+    
+    subgraph Tools[Automatic Tool Assignment]
+        Agent1 --> Tool1[Tools 1]
+        Agent2 --> Tool2[Tools 2]
+        Agent3 --> Tool3[Tools 3]
+    end
+    
+    Tools --> Execution
+    
+    subgraph Execution[Execution]
+        Exec1[AI Agent 1] --> Exec2[AI Agent 2]
+        Exec2 --> Exec3[AI Agent 3]
+    end
+    
+    Exec3 --> Out[Out]
+    
+    style In fill:#8B0000,color:#fff
+    style Plan fill:#2E8B57,color:#fff
+    style Agent1 fill:#2E8B57,color:#fff
+    style Agent2 fill:#2E8B57,color:#fff
+    style Agent3 fill:#2E8B57,color:#fff
+    style Tool1 fill:#2E8B57,color:#fff
+    style Tool2 fill:#2E8B57,color:#fff
+    style Tool3 fill:#2E8B57,color:#fff
+    style Exec1 fill:#2E8B57,color:#fff
+    style Exec2 fill:#2E8B57,color:#fff
+    style Exec3 fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+AutoAgents automatically creates and manages AI agents and tasks based on high-level instructions.
+
 ## Quick Start
 
 <Tabs>

docs/features/callbacks.mdx

@@ -5,6 +5,20 @@ description: "Learn how to implement callbacks to monitor and log AI agent inter
 icon: "bell"
 ---
 
+```mermaid
+flowchart LR
+    In[In] --> Agent[AI Agent]
+    Agent --> Out[Out]
+    Agent --> Callback[Callback]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Callback fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+Learn how to implement callbacks to monitor and log AI agent interactions, errors, and task completions.
+
 ## Quick Start
 
 <Tabs>

docs/features/reasoning.mdx

@@ -4,6 +4,24 @@ description: "Learn how to create AI agents with advanced reasoning capabilities
 icon: "brain"
 ---
 
+```mermaid
+flowchart LR
+    In[In] --> Agent[AI Agent]
+    Agent --> Think[Think]
+    Think --> Decide[Decide]
+    Decide --> Act[Act]
+    Act --> Out[Out]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Think fill:#2E8B57,color:#fff
+    style Decide fill:#2E8B57,color:#fff
+    style Act fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+Learn how to create AI agents with advanced reasoning capabilities for complex problem-solving.
+
 ## Quick Start
 
 <Tabs>

docs/features/selfreflection.mdx

@@ -5,6 +5,21 @@ description: "Self-reflection enables agents to evaluate and improve their own r
 icon: "brain"
 ---
 
+```mermaid
+flowchart LR
+    In[In] --> Agent[AI Agent]
+    Agent --> Reflect[Self Reflection]
+    Reflect --> Agent
+    Agent --> Out[Out]
+    
+    style In fill:#8B0000,color:#fff
+    style Agent fill:#2E8B57,color:#fff
+    style Reflect fill:#2E8B57,color:#fff
+    style Out fill:#8B0000,color:#fff
+```
+
+Self-reflection enables agents to evaluate and improve their own responses before delivering them.
+
 ## Quick Start
 
 <Tabs>

docs/mint.json

@@ -141,6 +141,7 @@
         "tools/yfinance_tools",
         "tools",
         "tools/custom",
+        "tools/tools_class",
         "tools/gpt",
         "tools/duckduckgo",
         "tools/langchain",