AI Agent Tutorials & Implementations

A comprehensive collection of production-ready AI agent implementations showcasing different frameworks, protocols, and integration patterns. This repository demonstrates various approaches to building intelligent agents with Model Context Protocol (MCP), multi-agent systems, and real-world integrations.

Repository Overview

This repository contains four distinct agent implementations, each demonstrating different architectural patterns and use cases:

Project Descriptions

agent2agent/

Investment Research Analyst Agent

A production-ready investment research agent implementing Google's Agent-to-Agent (A2A) protocol for remote agent communication.

Key Features:

• Framework: LangGraph with LangChain
• Protocol: Agent-to-Agent (A2A) for remote communication
• Integration: Slack with Block Kit UI and metadata modals
• Architecture: FastAPI server exposing both A2A endpoints and Slack events
• Memory: Persistent conversation state management
• Deployment: Docker ready with Render.com configuration

Technical Stack:

• LangGraph for agent orchestration
• FastAPI for A2A protocol implementation
• Slack Block Kit for interactive UI
• LangSmith for observability (optional)
• Docker for containerized deployment

Use Cases:

• Stock summaries and analysis
• SEC filings research
• Analyst recommendations
• Financial data aggregation
• Investment research workflows

mcp-financial/

Investment Analyst MCP Agent

A financial data agent powered by FastMCP with ASGI integration, providing both CLI and Slack interfaces.

Key Features:

• Framework: FastMCP with FastAPI ASGI integration
• Interfaces: CLI client and Slack bot
• Architecture: MCP server exposed via FastAPI endpoints
• Integration: Direct Slack event handling
• Deployment: Production-ready with health checks

Technical Stack:

• FastMCP for Model Context Protocol implementation
• FastAPI for ASGI integration
• Uvicorn for server runtime
• Slack API for bot functionality
• MCP Inspector for debugging

Use Cases:

• Financial data analysis
• Stock price monitoring
• Earnings analysis
• Market research
• Investment insights

zapier-mcps/

Multi-Agent Sales Operations System

A sophisticated multi-agent system using OpenAI's Agent SDK with Zapier MCP integration for sales automation.

Key Features:

• Framework: OpenAI Agent SDK
• Architecture: Multi-agent with intelligent triage
• Integration: Zapier MCP for workflow automation
• Agents: Account Planning Agent, Scheduling Agent, Triage Agent
• Handoffs: Automatic agent delegation based on task type

Technical Stack:

• OpenAI Agent SDK for agent orchestration
• Zapier MCP for external service integration
• Pydantic for data validation
• Async agent execution with Runner

Agent Roles:

• Triage Agent: Determines optimal agent for task delegation
• Account Planning Agent: Specializes in account analysis and planning
• Scheduling Agent: Handles meeting scheduling via Google Calendar

Use Cases:

• Sales operations automation
• Account planning and analysis
• Meeting scheduling coordination
• Workflow orchestration
• Multi-agent task delegation

bright-mcp-server-overview/

Bright Data MCP Research Agent

A comprehensive research agent powered by Bright Data's web scraping infrastructure, featuring dual AI agent implementations.

Key Features:

• Dual Framework: LangGraph (with memory) + Google ADK (with extended timeouts)
• Integration: Bright Data MCP server for web scraping
• Slack Interface: Interactive agent selection via dropdown
• Memory: Persistent conversation memory (LangGraph)
• Timeouts: Extended timeout handling (ADK) for long operations
• Specialization: SEO research, e-commerce intelligence, market analysis

Technical Stack:

• LangGraph Agent: OpenAI GPT with MemorySaver checkpointer
• ADK Agent: Google Gemini 2.0 Flash with custom timeout patches
• MCP Integration: Bright Data MCP server for data collection
• Slack Integration: Bot with agent selection and interactive UI

Agent Comparison:

Use Cases:

• SEO keyword research and SERP analysis
• E-commerce product monitoring and price tracking
• Competitor analysis and market intelligence
• Web scraping and data collection
• Business intelligence and insights

Getting Started

Each project includes comprehensive setup instructions in its respective README file. General prerequisites include:

Common Requirements

• Python 3.9+
• Valid API keys for respective services
• Slack workspace access (for Slack integrations)
• Environment variable configuration

Quick Start Pattern

# 1. Navigate to desired project
cd [project-name]/

# 2. Install dependencies
pip install -r requirements.txt

# 3. Configure environment
cp .env.example .env
# Edit .env with your API keys

# 4. Run the agent
# (varies by project - see individual READMEs)

Architecture Patterns

Model Context Protocol (MCP)

Three projects demonstrate different MCP implementation patterns:

• FastMCP ASGI: Direct FastAPI integration
• Bright Data MCP: External MCP server communication
• Zapier MCP: Third-party service integration

Agent Communication

• A2A Protocol: Remote agent-to-agent communication
• Multi-Agent Handoffs: Intelligent task delegation
• State Management: Persistent conversation memory

Integration Patterns

• Slack Bots: Event-driven chat interfaces
• CLI Clients: Command-line agent interaction
• FastAPI Servers: RESTful agent endpoints
• Container Deployment: Docker and cloud-ready

Contributing

Each project welcomes contributions. Please:

1. Fork the repository
2. Create a feature branch
3. Follow the project's coding standards
4. Include tests where applicable
5. Submit a Pull Request

License

MIT License - see individual project LICENSE files for details.

Support & Resources

Documentation Links

• Model Context Protocol
• LangGraph Documentation
• OpenAI Agent SDK
• Google ADK
• Slack API

Platform-Specific Support

• Bright Data: brightdata.com/support
• Zapier: zapier.com/help
• Slack: api.slack.com/support

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