arjunkmrm-tutorials
ai.smithery/arjunkmrm-tutorials
Analyze stocks and SEC filings to surface key insights, from price and volume to insider activity…
Documentation
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 | 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 |
| zapier-mcps | OpenAI Agent SDK | Multi-agent handoffs, Zapier integration | Sales operations automation |
| bright-mcp-server-overview | Dual: LangGraph + ADK | Memory persistence, extended timeouts | Web scraping & research |
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:
| 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
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:
- 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
Platform-Specific Support
- Bright Data: brightdata.com/support
- Zapier: zapier.com/help
- Slack: api.slack.com/support
Built with ❤️ demonstrating the future of AI agent development
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