Create the appropriate agent based on agent_type.

Execute tasks in parallel using ThreadPoolExecutor.

Execute tasks sequentially.

Extract structured data from web pages.

Find results that appear across multiple engines

Get a default model using the centralized model initialization.

Initialize the LLM model using centralized model initialization.

Parse input data string into a DataFrame.

Run a simple async task.

Run a task with streaming updates.

Search using the orchestrator with multiple agents.

Show help information.

Show available tools.

Parse and validate code via AST. Returns the parsed tree or raises.

Analyze a dataset and provide insights. Args: data: The data to analyze (can be list, dict, or pandas DataFrame)

Start an interactive chat session with the agent.

Review code and provide feedback. Args: code: Code to review language: Programming language focu

Create comprehensive SerpAPI tool suite

Create worker with optimal model selection and enhanced capabilities

Create the tool inventory that use-case recipes can resolve against.

Format an OpenRouter ``created`` timestamp as an ISO date.

Demonstrate async orchestrator functionality.

Demonstrate basic orchestrator functionality.

Demonstrate custom orchestrator configuration.

Demonstrate orchestrator integrated with InternetAgent.

Demonstrate the Multi-Model SerpAPI System

Display a table of agent capabilities.

Show CLI usage examples.

Example: Loading MCP configuration from environment variables.

Example: Using MCP tools via HTTP transport (remote server).

Example: Using MCPServerManager to manage multiple servers.

Example: Using MCP tools via stdio transport (local server).

Example: Using MCP tools with an InternetAgent.

Fetch OpenRouter's current model inventory. The endpoint is public. Callers should treat failures as non-fatal and fall back to the checked-i

Execute an async browser automation task.

Execute browser automation with structured output.

Create config from dictionary.

Return a summary for the active use-case setup.

Get comprehensive summary of system state and performance

Get detailed capabilities of each model for optimization

Get information about a model.

Get the research history.

List all models that can be initialized with current API keys.

Run advanced examples.

Run all examples.

Run all examples.

Run all demonstrations.

Main entry point

Analyze market trends in a specific industry. Args: industry: Industry to analyze timeframe: Timeframe for t

Execute a search using multiple agents. Args: query: The search query agents_to_use: Specific agents to use

Async version of search (experimental).

Summarize content in various formats. Args: content: Content to summarize (can be URL or text) summary_type:

Test basic agent functionality.

Convert config to dictionary.

Initialize the Async Browser Use tool.

Initialize the Structured Browser Use tool.

Initialize MCP tool integration. Args: server_config: Either a file path to MCP server script (for stdio)

Initialize browser automation agent with browser-specific configuration.

Initialize content creation agent.

Initialize market research agent.

Initialize technical support agent.

Initialize the Search Orchestrator. Args: max_workers: Maximum number of parallel agent executions verbose: