[new example docs][exa, browser use, yahoo finance]

docs/examples/av.md

@@ -0,0 +1,57 @@
+# Advanced Research
+
+An enhanced implementation of the orchestrator-worker pattern from Anthropic's paper, "How we built our multi-agent research system", built on top of the bleeding-edge multi-agent framework [swarms](https://github.com/kyegomez/swarms). Our implementation of this advanced research system leverages parallel execution, LLM-as-judge evaluation, and professional report generation with export capabilities.
+
+**Repository**: [AdvancedResearch](https://github.com/The-Swarm-Corporation/AdvancedResearch)
+
+## Installation
+
+```bash
+pip3 install -U advanced-research
+
+# uv pip install -U advanced-research
+```
+
+## Environment Variables
+
+```txt
+# Exa Search API Key (Required for web search functionality)
+EXA_API_KEY="your_exa_api_key_here"
+
+# Anthropic API Key (For Claude models)
+ANTHROPIC_API_KEY="your_anthropic_api_key_here"
+
+# OpenAI API Key (For GPT models)  
+OPENAI_API_KEY="your_openai_api_key_here"
+
+# Worker Agent Configuration
+WORKER_MODEL_NAME="gpt-4.1"
+WORKER_MAX_TOKENS=8000
+
+# Exa Search Configuration
+EXA_SEARCH_NUM_RESULTS=2
+EXA_SEARCH_MAX_CHARACTERS=100
+```
+
+**Note**: At minimum, you need `EXA_API_KEY` for web search functionality. For LLM functionality, you need either `ANTHROPIC_API_KEY` or `OPENAI_API_KEY`.
+
+## Quick Start
+
+### Basic Usage
+
+```python
+from advanced_research import AdvancedResearch
+
+# Initialize the research system
+research_system = AdvancedResearch(
+    name="AI Research Team",
+    description="Specialized AI research system",
+    max_loops=1,
+)
+
+# Run research and get results
+result = research_system.run(
+    "What are the latest developments in quantum computing?"
+)
+print(result)
+```

docs/examples/browser_use.md

@@ -0,0 +1,111 @@
+# Browser Automation with Swarms
+
+This example demonstrates how to use browser automation capabilities within the Swarms framework. The `BrowserUseAgent` class provides a powerful interface for web scraping, navigation, and automated browser interactions using the `browser_use` library. This is particularly useful for tasks that require real-time web data extraction, form filling, or web application testing.
+
+## Install
+
+```bash
+pip3 install -U swarms browser-use python-dotenv langchain-openai
+```
+
+## Environment Variables
+
+```txt
+# OpenAI API Key (Required for LLM functionality)
+OPENAI_API_KEY="your_openai_api_key_here"
+```
+
+## Main Code
+
+```python 
+import asyncio
+
+from browser_use import Agent as BrowserAgent
+from dotenv import load_dotenv
+from langchain_openai import ChatOpenAI
+
+from swarms import Agent
+
+load_dotenv()
+
+
+class BrowserUseAgent:
+    def __init__(self, agent_name: str = "BrowserAgent", agent_description: str = "A browser agent that can navigate the web and perform tasks."):
+        """
+        Initialize a BrowserAgent with a given name.
+
+        Args:
+            agent_name (str): The name of the browser agent.
+        """
+        self.agent_name = agent_name
+        self.agent_description = agent_description
+
+    async def browser_agent_test(self, task: str):
+        """
+        Asynchronously run the browser agent on a given task.
+
+        Args:
+            task (str): The task prompt for the agent.
+
+        Returns:
+            Any: The result of the agent's run method.
+        """
+        agent = BrowserAgent(
+            task=task,
+            llm=ChatOpenAI(model="gpt-4.1"),
+        )
+        result = await agent.run()
+        return result.model_dump_json(indent=4)
+
+    def run(self, task: str):
+        """
+        Run the browser agent synchronously on a given task.
+
+        Args:
+            task (str): The task prompt for the agent.
+
+        Returns:
+            Any: The result of the agent's run method.
+        """
+        return asyncio.run(self.browser_agent_test(task))
+
+
+
+def browser_agent_tool(task: str):
+    """
+    Executes a browser automation agent as a callable tool.
+
+    This function instantiates a `BrowserAgent` and runs it synchronously on the provided task prompt.
+    The agent will use a language model to interpret the task, control a browser, and return the results
+    as a JSON-formatted string.
+
+    Args:
+        task (str): 
+            A detailed instruction or prompt describing the browser-based task to perform.
+            For example, you can instruct the agent to navigate to a website, extract information,
+            or interact with web elements.
+
+    Returns:
+        str:
+            The result of the browser agent's execution, formatted as a JSON string. The output
+            typically includes the agent's findings, extracted data, and any relevant observations
+            from the automated browser session.
+
+    Example:
+        result = browser_agent_tool(
+            "Please navigate to https://www.coingecko.com and identify the best performing cryptocurrency coin over the past 24 hours."
+        )
+        print(result)
+    """
+    return BrowserAgent().run(task)
+
+
+
+agent = Agent(
+    name = "Browser Agent",
+    model_name = "gpt-4.1",
+    tools = [browser_agent_tool],
+)
+
+agent.run("Please navigate to https://www.coingecko.com and identify the best performing cryptocurrency coin over the past 24 hours.")
+```

docs/examples/exa_search.md

@@ -0,0 +1,48 @@
+# Web Search with Exa
+
+
+Exa is a powerful web search API that provides real-time access to current web information. It allows AI agents to search the internet and retrieve up-to-date information on any topic, making it an essential tool for agents that need current knowledge beyond their training data.
+
+Key features of Exa:
+
+| Feature                  | Description                                                        |
+|--------------------------|--------------------------------------------------------------------|
+| **Real-time search**     | Access the latest information from the web                         |
+| **Semantic search**      | Find relevant results using natural language queries                |
+| **Comprehensive coverage** | Search across billions of web pages                              |
+| **Structured results**   | Get clean, formatted search results for easy processing            |
+| **API integration**      | Simple REST API for seamless integration with AI applications       |
+
+## Install
+
+```bash
+pip3 install -U swarms swarms-tools
+```
+
+## ENV
+
+```txt
+# Get your API key from exa
+EXA_SEARCH_API=""
+
+OPENAI_API_KEY=""
+
+WORKSPACE_DIR=""
+```
+
+## Code
+
+```python
+from swarms import Agent
+from swarms_tools import exa_search
+
+
+agent = Agent(
+    name="Exa Search Agent",
+    llm="gpt-4o-mini",
+    tools=[exa_search],
+)
+
+out = agent.run("What are the latest experimental treatments for diabetes?")
+print(out)
+```

docs/mkdocs.yml

@@ -425,6 +425,14 @@ nav:
           - Swarms of Browser Agents: "swarms/examples/swarms_of_browser_agents.md"
           - ConcurrentWorkflow with VLLM Agents: "swarms/examples/vllm.md"
     
+    - Tools & Integrations:
+      - Web Search with Exa: "examples/exa_search.md"
+      - Advanced Research: "examples/av.md"
+      - Browser Use: "examples/browser_use.md"
+      - Yahoo Finance: "swarms/examples/yahoo_finance.md"
+
+
+
     - Apps:
       - Smart Database: "examples/smart_database.md"
 

docs/swarms/examples/yahoo_finance.md

@@ -1,12 +1,31 @@
-# Swarms Tools Example with Yahoo Finance
+# Yahoo Finance Integration with Swarms
 
-- `pip3 install swarms swarms-tools`
-- Add `OPENAI_API_KEY` to your `.env` file
-- Run `yahoo_finance_agent.py`
-- Agent will make a function call to the desired tool
-- The tool will be executed and the result will be returned to the agent
-- The agent will then analyze the result and return the final output
 
+This example demonstrates how to integrate Yahoo Finance data into your Swarms agents using the `swarms-tools` package. The agent can analyze real-time financial data, stock metrics, and market information by making function calls to the Yahoo Finance API. This is particularly useful for financial analysis, portfolio management, and market research applications.
+
+## Install
+
+```bash
+pip3 install -U swarms swarms-tools
+```
+
+## Environment Variables
+
+```txt
+# OpenAI API Key (Required for LLM functionality)
+OPENAI_API_KEY="your_openai_api_key_here"
+```
+
+## Usage
+
+1. Install the required packages
+2. Add your `OPENAI_API_KEY` to your `.env` file
+3. Run the example code below
+4. The agent will make a function call to the Yahoo Finance tool
+5. The tool will execute and return financial data
+6. The agent analyzes the result and provides insights
+
+## Code Example
 
 ```python
 from swarms import Agent
@@ -24,19 +43,12 @@ agent = Agent(
     system_prompt=FINANCIAL_AGENT_SYS_PROMPT,
     max_loops=1,
     model_name="gpt-4o",
-    dynamic_temperature_enabled=True,
-    user_name="swarms_corp",
-    retry_attempts=3,
-    context_length=8192,
-    return_step_meta=False,
-    output_type="str",  # "json", "dict", "csv" OR "string" "yaml" and
-    auto_generate_prompt=False,  # Auto generate prompt for the agent based on name, description, and system prompt, task
-    max_tokens=4000,  # max output tokens
-    saved_state_path="agent_00.json",
-    interactive=False,
     tools=[yahoo_finance_api],
 )
 
+# Run financial analysis
 agent.run("Analyze the latest metrics for nvidia")
-# Less than 30 lines of code....
 ```
+
+**Result**: Less than 30 lines of code to get a fully functional financial analysis agent!
+

exa_search_agent.py

@@ -0,0 +1,11 @@
+from swarms import Agent
+from swarms_tools import exa_search
+
+
+agent = Agent(
+    name="Exa Search Agent",
+    llm="gpt-4o-mini",
+    tools=[exa_search],
+)
+
+agent.run("What are the latest experimental treatments for diabetes?")

examples/tools/browser_use_as_tool.py

@@ -0,0 +1,90 @@
+import asyncio
+
+from browser_use import Agent as BrowserAgent
+from dotenv import load_dotenv
+from langchain_openai import ChatOpenAI
+
+from swarms import Agent
+
+load_dotenv()
+
+
+class BrowserUseAgent:
+    def __init__(self, agent_name: str = "BrowserAgent", agent_description: str = "A browser agent that can navigate the web and perform tasks."):
+        """
+        Initialize a BrowserAgent with a given name.
+
+        Args:
+            agent_name (str): The name of the browser agent.
+        """
+        self.agent_name = agent_name
+        self.agent_description = agent_description
+
+    async def browser_agent_test(self, task: str):
+        """
+        Asynchronously run the browser agent on a given task.
+
+        Args:
+            task (str): The task prompt for the agent.
+
+        Returns:
+            Any: The result of the agent's run method.
+        """
+        agent = BrowserAgent(
+            task=task,
+            llm=ChatOpenAI(model="gpt-4.1"),
+        )
+        result = await agent.run()
+        return result.model_dump_json(indent=4)
+
+    def run(self, task: str):
+        """
+        Run the browser agent synchronously on a given task.
+
+        Args:
+            task (str): The task prompt for the agent.
+
+        Returns:
+            Any: The result of the agent's run method.
+        """
+        return asyncio.run(self.browser_agent_test(task))
+
+
+
+def browser_agent_tool(task: str):
+    """
+    Executes a browser automation agent as a callable tool.
+
+    This function instantiates a `BrowserAgent` and runs it synchronously on the provided task prompt.
+    The agent will use a language model to interpret the task, control a browser, and return the results
+    as a JSON-formatted string.
+
+    Args:
+        task (str): 
+            A detailed instruction or prompt describing the browser-based task to perform.
+            For example, you can instruct the agent to navigate to a website, extract information,
+            or interact with web elements.
+
+    Returns:
+        str:
+            The result of the browser agent's execution, formatted as a JSON string. The output
+            typically includes the agent's findings, extracted data, and any relevant observations
+            from the automated browser session.
+
+    Example:
+        result = browser_agent_tool(
+            "Please navigate to https://www.coingecko.com and identify the best performing cryptocurrency coin over the past 24 hours."
+        )
+        print(result)
+    """
+    return BrowserAgent().run(task)
+
+
+
+agent = Agent(
+    name = "Browser Agent",
+    model_name = "gpt-4.1",
+    tools = [browser_agent_tool],
+)
+
+agent.run("Please navigate to https://www.coingecko.com and identify the best performing cryptocurrency coin over the past 24 hours.")

examples/tools/browser_use_demo.py

@@ -34,7 +34,7 @@ class BrowserAgent:
             llm=ChatOpenAI(model="gpt-4.1"),
         )
         result = await agent.run()
-        return result
+        return result.model_dump_json(indent=4)
 
     def run(self, task: str):
         """