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 multiple agent implementations, each demonstrating different architectural patterns and use cases:
| Project | Framework | Key Features | Use Case |
|---|
| agent2agent | LangGraph + A2A Protocol | Remote agent communication, Slack integration | Investment research |
| mcp-financial | FastMCP + FastAPI | ASGI integration, CLI client | Financial data analysis |
| bright-mcp-server-overview | Dual: LangGraph + ADK | Memory persistence, extended timeouts | Web scraping & research |
| fpl-deepagent | FastMCP + React UI | Streamable HTTP, ChatGPT integration | Fantasy Premier League |
| task-manager-app | FastMCP + React UI + Supabase | OAuth (Auth0), per-user DB state, Slack notifications | Task management in ChatGPT |
| notion-mcp-agent | LangGraph + MCP | Notion integration, database management | Knowledge management |
| claude-advanced-tool-use | Claude API + FastMCP | PTC, Tool Search, MCP integration | Token-efficient AI agents |
| claude-skills | Claude Skills API | Document generation, custom skills | PowerPoint, Excel, Word creation |
| openai-chatkit-starter-app | Next.js + ChatKit | Agent Builder integration, web component | ChatKit UI development |
| mastra-overview | Mastra framework | Multi-LLM orchestration | Framework exploration |
| smithery-example | Smithery + FastMCP | MCP playground, development tools | MCP development |
| mcp-apps | MCP Apps (OpenAI Apps SDK) | Example MCP Apps (weather + stock analysis) | MCP Apps reference implementations |
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
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:
| Feature | LangGraph Agent | ADK Agent |
|---|
| Memory | Persistent (checkpointer) | Context-aware (5 messages) |
| Timeout | Standard (5s) | Extended (60s) |
| Model | OpenAI GPT | Gemini 2.0 Flash |
| Best For | Interactive conversations | Long-running operations |
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
fpl-deepagent/
Fantasy Premier League MCP Assistant
A comprehensive Fantasy Premier League assistant that integrates with ChatGPT through the Model Context Protocol (MCP), featuring beautiful React UI components and real-time FPL data.
Key Features:
- Framework: FastMCP with Streamable HTTP transport
- UI Integration: React 18 + TypeScript components for ChatGPT
- Real-time Data: Live FPL API integration with caching and error handling
- Design Compliance: Follows OpenAI Apps SDK design guidelines exactly
- Interactive Tools: Player search, detailed stats, and side-by-side comparison
Technical Stack:
- FastMCP for MCP server implementation
- React 18 + TypeScript for UI components
- OpenAI Apps SDK integration with
window.openai API
- esbuild for fast, modern bundling
- Streamable HTTP for bidirectional communication
UI Components:
- PlayerListComponent: Interactive player grid with favorites
- PlayerDetailComponent: Detailed player stats and upcoming fixtures
- PlayerComparisonComponent: Side-by-side comparison with highlighted stats
Use Cases:
- Player search and discovery
- Detailed player statistics and form analysis
- Player comparison for team selection
- FPL team optimization
- Real-time price and form tracking
task-manager-app/
Task Manager ChatGPT App (Apps SDK + MCP + Supabase + OAuth)
A production-ready tutorial showing how to build a ChatGPT App with:
- FastMCP (Streamable HTTP) as the MCP server
- React widgets rendered inside ChatGPT
- Supabase (Postgres) as authoritative state for tasks/notifications
- OAuth (Auth0) for multi-user authentication (MCP OAuth)
- Optional Slack notifications (send now + schedule)
Start here:
task-manager-app/README.md
notion-mcp-agent/
Notion Knowledge Management Agent
A sophisticated agent that integrates with Notion through MCP, providing intelligent database management and knowledge organization capabilities.
Key Features:
- Framework: LangGraph with MCP integration
- Integration: Notion API for database operations
- Slack Interface: Interactive knowledge management
- Context Management: Intelligent data aggregation
- Database Operations: Create, read, update, and organize Notion databases
Technical Stack:
- LangGraph for agent orchestration
- Notion MCP server for database operations
- Slack API for user interaction
- Context aggregation for intelligent responses
Use Cases:
- Knowledge base management
- Database organization and maintenance
- Content aggregation and structuring
- Team collaboration workflows
- Information retrieval and organization
Claude Advanced Tool Use Tutorial
A comprehensive tutorial demonstrating Anthropic's Advanced Tool Use features: Programmatic Tool Calling (PTC) and Tool Search. These features enable AI agents to scale to thousands of tools while dramatically reducing token usage.
Key Features:
- Programmatic Tool Calling (PTC): Claude writes Python code that orchestrates tool calls in a sandbox
- Tool Search: Dynamic tool discovery with
defer_loading for efficient context usage
- MCP Integration: Tool Search combined with MCP servers via
mcp_toolset
- Real-World Examples: Financial data tools using yfinance
- Token Savings: Up to 98% reduction in token usage for complex tasks
Technical Stack:
- Anthropic Claude API (Sonnet 4.5)
- Beta headers:
advanced-tool-use-2025-11-20
- FastMCP for MCP server implementation
- Python + yfinance for financial data
- ngrok for MCP server tunneling
Examples:
01_ptc_token_savings.py - Programmatic Tool Calling with token comparison02_tool_search.py - Tool Search with 10 deferred financial tools03_mcp_tool_search.py - MCP + Tool Search via ngrok tunnelmcp_server.py - FastMCP server exposing financial tools
Key Concepts:
| Feature | Description | Token Savings |
|---|
| Programmatic Tool Calling | Tool results stay in sandbox, only print() output enters context | 37% |
| Tool Search | Only load tool definitions when discovered | 85% |
| Combined | PTC + Tool Search together | Up to 98% |
Use Cases:
- Building AI agents with many tools (100+)
- Reducing context window bloat from tool definitions
- Processing large datasets without context overflow
- MCP server integration with dynamic tool discovery
- Token-efficient financial analysis agents
claude-skills/
Claude Skills API Implementation
A comprehensive implementation of Claude's Skills API for automated document generation and custom skill creation.
Key Features:
- Framework: Claude Skills API with streaming support
- Document Generation: PowerPoint, Excel, Word, and PDF creation
- Custom Skills: Upload and manage custom skills (8MB limit)
- File Management: List, download, and delete generated files
- Multi-Skill Workflows: Combine multiple skills in single requests
Technical Stack:
- Claude Skills API with beta features
- Code execution environment (2025-08-25)
- Files API (2025-04-14)
- Streaming responses for real-time progress
- Python SDK with uv package manager
Utilities:
list-skills.py - List all available skillscreate-skill.py - Upload custom skills from directoriesuse-skill.py - Generate documents with single skillsmulti-skill-demo.py - Complex workflows with multiple skillslist-files.py / download-file.py / delete-file.py - File management
Use Cases:
- Automated PowerPoint presentation generation
- Excel spreadsheet creation and data analysis
- Word document generation
- PDF report creation
- Custom skill development and deployment
- Multi-format document workflows
openai-chatkit-starter-app/
ChatKit Web Component Starter
A minimal Next.js starter template for building ChatKit applications with OpenAI's Agent Builder workflows.
Key Features:
- Framework: Next.js with ChatKit web component
- Integration: OpenAI Agent Builder workflows
- Customization: Configurable themes, prompts, and UI
- Session Management: Ready-to-use session endpoint
- Deployment: Domain allowlist verification support
Technical Stack:
- Next.js for application framework
- OpenAI ChatKit web component (
<openai-chatkit>)
- OpenAI API integration
- TypeScript for type safety
- Configurable theming system
Key Components:
- Session creation endpoint (
/api/create-session)
- ChatKit panel with event handlers
- Theme and color scheme controls
- Starter prompts configuration
- Error overlay for debugging
Use Cases:
- ChatKit application prototyping
- Agent Builder workflow integration
- Custom ChatKit UI development
- OpenAI workflow testing
- Production ChatKit deployments
mastra-overview/
Mastra Framework Exploration
An exploration of the Mastra framework for multi-LLM orchestration and agent management.
Key Features:
- Framework: Mastra for multi-LLM orchestration
- Multi-LLM: Support for multiple language models
- Orchestration: Intelligent model selection and routing
- Polyfills: Crypto polyfills for browser compatibility
Technical Stack:
- Mastra framework
- Multi-LLM integration
- Browser compatibility polyfills
- TypeScript configuration
Use Cases:
- Multi-LLM agent systems
- Model orchestration and routing
- Framework exploration and evaluation
- LLM comparison and benchmarking
smithery-example/
MCP Development Playground
A comprehensive development environment for MCP (Model Context Protocol) with FastMCP integration and testing tools.
Key Features:
- Framework: Smithery + FastMCP
- Development Tools: MCP playground and testing environment
- Financial Integration: Example financial server implementation
- Testing: Comprehensive test suite and examples
- Documentation: Development guides and examples
Technical Stack:
- Smithery for MCP development
- FastMCP for server implementation
- Testing frameworks for validation
- Development tooling and playgrounds
Use Cases:
- MCP server development
- Protocol testing and validation
- Financial data integration examples
- Development environment setup
- MCP learning and exploration
mcp-apps/
MCP Apps Examples (Weather + Stock Analysis)
Two minimal example MCP Apps showing how to build UI + server experiences using the MCP Apps extensions.
Key Features:
- Weather App: UI + MCP server example with a simple weather workflow
- Stock Analysis App: UI + MCP server example for market/stock analysis
- Apps SDK: Designed to follow MCP Apps extension patterns
- Docs Reference: See the MCP Apps docs for the full guide
Use Cases:
- Learning MCP Apps fundamentals
- Building UI-backed MCP Apps
- Reference implementations for new MCP App projects
Getting Started
Each project includes comprehensive setup instructions in its respective README file. General prerequisites include:
Common Requirements
- Python 3.9+ (some projects require newer; see each project README)
- Valid API keys for respective services
- Slack workspace access (for Slack integrations)
- Environment variable configuration
Quick Start Pattern
cd [project-name]/
uv sync
cp .env.example .env
Architecture Patterns
Model Context Protocol (MCP)
Multiple projects demonstrate different MCP implementation patterns:
- FastMCP ASGI: Direct FastAPI integration (mcp-financial, smithery-example)
- FastMCP Streamable HTTP: Modern bidirectional communication (fpl-deepagent)
- Bright Data MCP: External MCP server communication
- Notion MCP: Database and knowledge management integration
Agent Communication
- A2A Protocol: Remote agent-to-agent communication (agent2agent)
- State Management: Persistent conversation memory (bright-mcp-server-overview)
UI Integration Patterns
- React + ChatGPT: OpenAI Apps SDK integration (fpl-deepagent)
- Next.js + ChatKit: Agent Builder workflow integration (openai-chatkit-starter-app)
- Slack Bots: Event-driven chat interfaces (multiple projects)
- CLI Clients: Command-line agent interaction
Document Generation
- Claude Skills API: Automated document creation with streaming (claude-skills)
- Multi-Format Support: PowerPoint, Excel, Word, PDF generation
- Custom Skills: Uploadable skill packages for specialized tasks
Development & Testing
- MCP Playground: Development and testing environment (smithery-example)
- Multi-LLM Orchestration: Framework exploration (mastra-overview)
- Agent Builder: OpenAI workflow development (openai-chatkit-starter-app)
Integration Patterns
- Container Deployment: Docker and cloud-ready
- API Integration: RESTful agent endpoints
- Database Integration: Knowledge management systems
- Real-time Data: Live API integration with caching
Contributing
Each project welcomes contributions. Please:
- Fork the repository
- Create a feature branch
- Follow the project's coding standards
- Include tests where applicable
- Submit a Pull Request
License
MIT License - see individual project LICENSE files for details.
Support & Resources
Documentation Links
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