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README
🤖 Awesome Agent MCP
A curated list of awesome resources for mastering AI Agents and Model Context Protocol (MCP)
Created by: Tenten.co - The AI-First Product Agency
📋 Table of Contents
- Introduction
- AI Agents
- Model Context Protocol (MCP)
- Learning Resources
- Community
- Contributing
- License
🚀 Introduction
Welcome to the most comprehensive collection of resources for mastering AI Agents and the Model Context Protocol (MCP). This curated list brings together the essential tools, frameworks, tutorials, and resources you need to build sophisticated AI agent systems and leverage the power of standardized context protocols.
What are AI Agents?
AI agents are autonomous software systems that can perceive their environment, make decisions, and take actions to achieve specific goals without constant human intervention. These intelligent systems combine the power of Large Language Models (LLMs) with the ability to use tools, access external data sources, and execute complex multi-step workflows.
Modern AI agents typically feature:
- Autonomous Decision Making: The ability to analyze situations and choose appropriate actions
- Tool Integration: Seamless interaction with APIs, databases, and external services
- Memory and Context: Maintaining conversation history and learning from past interactions
- Planning and Reasoning: Breaking down complex tasks into manageable steps
- Multi-modal Capabilities: Processing text, images, audio, and other data types
What is Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is an open standard introduced by Anthropic in November 2024 that revolutionizes how AI applications connect to data sources and tools. Think of MCP as the "USB-C for AI applications" – it provides a standardized, secure way for AI models to access external resources.
Key benefits of MCP include:
- Standardization: Unified interface for connecting AI models to various data sources
- Security: Built-in best practices for protecting sensitive data
- Flexibility: Easy switching between different LLM providers and vendors
- Extensibility: Growing ecosystem of pre-built integrations
- Simplicity: Reduced complexity in building AI applications
🤖 AI Agents
Frameworks & Libraries
Multi-Agent Frameworks
LangChain ⭐ 94k+
- Comprehensive framework for developing applications with LLMs
- Extensive ecosystem with tools, memory, and agent capabilities
- Supports multiple LLM providers and vector databases
- Rich documentation and community support
LangGraph ⭐ 6k+
- Graph-based framework for building stateful, multi-actor applications
- Built on top of LangChain with enhanced control flow
- Perfect for complex agent workflows and state management
- Supports human-in-the-loop interactions
AutoGen ⭐ 32k+
- Microsoft's framework for multi-agent conversation systems
- Enables multiple agents to collaborate on complex tasks
- Supports both code execution and conversational agents
- Excellent for automated code generation and debugging
CrewAI ⭐ 20k+
- Role-based multi-agent framework for collaborative AI
- Agents work together like a crew with defined roles and goals
- Built-in task delegation and result aggregation
- Simple yet powerful API for agent orchestration
Semantic Kernel ⭐ 22k+
- Microsoft's AI orchestration framework
- Supports multiple programming languages (C#, Python, Java)
- Plugin-based architecture for extending capabilities
- Enterprise-ready with robust security features
OpenAI Swarm ⭐ 13k+
- Lightweight multi-agent orchestration framework
- Focuses on agent handoffs and coordination
- Minimal dependencies and easy to understand
- Experimental framework from OpenAI
Single-Agent Frameworks
Phidata ⭐ 12k+
- Framework for building AI agents with memory and knowledge
- Built-in support for tools, storage, and vector databases
- Easy integration with popular LLM providers
- Focus on production-ready applications
PydanticAI ⭐ 8k+
- Type-safe agent framework built on Pydantic
- Excellent for Python developers who value type safety
- Clean API design with validation built-in
- Growing ecosystem and community
Atomic Agents ⭐ 1k+
- Modular framework for building composable AI agents
- Focus on reusable components and clean architecture
- Supports various LLM providers and tools
- Great for building scalable agent systems
Open Source Projects
General Purpose Agents
AutoGPT ⭐ 167k+
- One of the first autonomous AI agents
- Can break down goals into sub-tasks and execute them
- Web browsing, file operations, and code execution capabilities
- Large community and plugin ecosystem
BabyAGI ⭐ 20k+
- Simple but powerful task-driven autonomous agent
- Uses OpenAI and vector databases for task management
- Continuously creates and executes tasks to reach objectives
- Minimalist design that's easy to understand and modify
GPT-Engineer ⭐ 52k+
- AI agent specialized in code generation and software development
- Can build entire applications from natural language descriptions
- Iterative development process with human feedback
- Supports multiple programming languages and frameworks
MetaGPT ⭐ 44k+
- Multi-agent framework that simulates a software company
- Agents take on roles like Product Manager, Architect, Engineer
- Can generate complete software projects with documentation
- Innovative approach to collaborative AI development
Specialized Agents
SWE-agent ⭐ 13k+
- AI agent specialized in solving GitHub issues
- Can understand codebases and implement fixes
- Designed for software engineering tasks
- Research-backed approach to code understanding
Aider ⭐ 20k+
- AI pair programming tool in your terminal
- Works with existing codebases and git repositories
- Supports multiple LLM providers
- Excellent for code editing and refactoring
Devon ⭐ 3k+
- Open-source software engineering agent
- Can work on complex software projects autonomously
- Integrates with popular development tools
- Focus on real-world software development tasks
OpenDevin ⭐ 32k+
- Platform for autonomous software engineers
- Can execute complex software development tasks
- Supports multiple programming languages
- Active development and research community
Commercial Platforms
Enterprise Solutions
- Enterprise platform for building custom copilots
- Integration with Microsoft 365 and Azure services
- No-code/low-code development environment
- Enterprise security and compliance features
- AI agent platform for customer service and sales
- Pre-built agents for common business use cases
- Integration with Salesforce CRM and ecosystem
- Autonomous customer interaction capabilities
Google Vertex AI Agent Builder
- Google Cloud's platform for building AI agents
- Integration with Google's AI models and services
- Enterprise-grade security and scalability
- Support for multi-modal agent experiences
Development Platforms
- Open-source conversational AI platform
- Visual flow builder for agent conversations
- Extensive integration capabilities
- Both cloud and on-premise deployment options
- Open-source framework for conversational AI
- Advanced natural language understanding
- Customizable dialogue management
- Strong focus on data privacy and control
- Visual platform for building conversational agents
- Support for voice and chat interfaces
- Collaborative design environment
- Integration with popular messaging platforms
Development Tools
Testing & Debugging
AgentOps ⭐ 2k+
- Observability platform for AI agents
- Performance monitoring and debugging tools
- Cost tracking and optimization insights
- Integration with popular agent frameworks
- Development platform for LLM applications
- Debugging, testing, and monitoring capabilities
- Evaluation frameworks for agent performance
- Collaboration tools for development teams
- MLOps platform with agent experiment tracking
- Performance monitoring and visualization
- Hyperparameter optimization for agent training
- Collaboration and sharing capabilities
Deployment & Infrastructure
- Cloud runtime for AI agents
- Secure code execution environment
- API for agent deployment and scaling
- Integration with popular agent frameworks
- Cloud platform for running AI workloads
- Serverless deployment for agent applications
- GPU support for intensive AI tasks
- Simple Python-based deployment workflow
- Platform for running machine learning models
- Easy deployment of AI agents and models
- API access to thousands of pre-trained models
- Scalable infrastructure for production use
🔌 Model Context Protocol (MCP)
Official Resources
Model Context Protocol Website
- Official documentation and specification
- Getting started guides and tutorials
- Architecture overview and best practices
- Community resources and examples
- Official MCP repositories and tools
- Reference implementations and SDKs
- Community contributions and examples
- Issue tracking and feature requests
- Claude-specific MCP integration guide
- API documentation and examples
- Security best practices
- Troubleshooting and support
MCP Servers
Database & Storage
- Connect to SQLite databases
- Execute queries and retrieve data
- Schema introspection capabilities
- Built-in security and validation
- Full PostgreSQL database integration
- Advanced query capabilities
- Connection pooling and optimization
- Enterprise-ready security features
- NoSQL database connectivity
- Document-based data operations
- Aggregation pipeline support
- Flexible schema handling
- In-memory data structure store
- Caching and session management
- Pub/sub messaging capabilities
- High-performance data operations
File Systems & Cloud Storage
- Local file system access
- File reading, writing, and manipulation
- Directory traversal and search
- Secure file operations
- Amazon S3 bucket integration
- Object storage and retrieval
- Metadata management
- IAM-based security
- Google Drive file access
- Document collaboration features
- Sharing and permissions management
- Real-time synchronization
APIs & Web Services
- GitHub repository integration
- Issue and pull request management
- Code search and analysis
- Workflow automation
- Slack workspace integration
- Message sending and retrieval
- Channel and user management
- Bot and app interactions
- Notion workspace connectivity
- Page and database operations
- Content creation and editing
- Team collaboration features
- Atlassian Jira integration
- Issue tracking and management
- Project and workflow operations
- Reporting and analytics
Development Tools
- Docker container management
- Image building and deployment
- Container orchestration
- Development environment setup
- Kubernetes cluster management
- Pod and service operations
- Deployment and scaling
- Resource monitoring
- Git repository operations
- Version control management
- Branch and merge operations
- Commit history analysis
MCP Clients
Desktop Applications
- Official Anthropic desktop client
- Native MCP server integration
- Secure local data access
- Cross-platform compatibility
- VS Code and JetBrains extension
- MCP server support for development
- Code completion and assistance
- Local and cloud model support
Web Applications
- Browser-based Claude interface
- Limited MCP server support
- Cloud-based processing
- Collaborative features
Mobile Applications
- iOS and Android applications
- Basic MCP functionality
- Synchronization with desktop
- On-the-go AI assistance
Tutorials & Guides
Getting Started
- Step-by-step setup instructions
- First MCP server creation
- Basic client integration
- Common troubleshooting tips
Building Your First MCP Server
- Server development walkthrough
- TypeScript and Python examples
- Testing and debugging techniques
- Deployment best practices
Advanced Topics
- Authentication and authorization
- Data encryption and privacy
- Network security considerations
- Compliance and governance
- Performance optimization techniques
- Load balancing and clustering
- Monitoring and observability
- Cost optimization strategies
Integration Examples
- LangChain MCP integration guide
- Tool creation and usage
- Agent workflow examples
- Best practices and patterns
- AutoGen MCP server integration
- Multi-agent MCP workflows
- Data sharing between agents
- Collaborative task execution
📚 Learning Resources
Documentation
Official Documentation
- Comprehensive guides for LangChain framework
- API references and examples
- Integration tutorials and best practices
- Community contributions and updates
- Microsoft AutoGen framework guide
- Multi-agent system tutorials
- Code examples and use cases
- Research papers and publications
- OpenAI API reference and guides
- Function calling and tool usage
- Best practices for agent development
- Rate limiting and optimization tips
Anthropic Claude Documentation
- Claude API and MCP integration
- Prompt engineering techniques
- Safety and alignment guidelines
- Enterprise features and capabilities
Community Documentation
- Microsoft's comprehensive agent development resource
- Architecture patterns and design principles
- Implementation examples and case studies
- Testing and deployment strategies
- Curated list of LLM agent resources
- Research papers and implementations
- Tools and frameworks comparison
- Community discussions and insights
Courses & Tutorials
Online Courses
DeepLearning.AI - AI Agents in LangGraph
- Hands-on course for building AI agents
- LangGraph framework deep dive
- Real-world project implementations
- Expert instruction from industry leaders
DeepLearning.AI - Multi AI Agent Systems with crewAI
- Multi-agent system development
- CrewAI framework mastery
- Collaborative agent workflows
- Production deployment techniques
DeepLearning.AI - MCP: Build Rich-Context AI Apps
- Model Context Protocol fundamentals
- Building MCP servers and clients
- Integration with existing applications
- Security and best practices
Coursera - AI Agent Development Specialization
- University-level agent development course
- Theoretical foundations and practical applications
- Capstone project with real-world scenarios
- Industry-recognized certification
YouTube Channels & Playlists
- Regular updates on AI agent developments
- Framework comparisons and reviews
- Industry news and analysis
- Technical deep dives and tutorials
- Official LangChain tutorials and updates
- Framework feature demonstrations
- Community showcases and use cases
- Developer interviews and insights
- AutoGen and Semantic Kernel tutorials
- Enterprise AI development practices
- Azure AI service integrations
- Developer conference presentations
Interactive Tutorials
- Interactive coding exercises
- Progressive skill building
- Real-time feedback and assistance
- Community challenges and competitions
- Practical examples and recipes
- MCP integration patterns
- Claude optimization techniques
- Community-contributed solutions
Books
Technical Books
"Building LLM Apps with LangChain" by Valentina Alto
- Comprehensive guide to LangChain development
- Practical examples and use cases
- Production deployment strategies
- Performance optimization techniques
"AI Agents: Theory and Practice" by Stuart Russell
- Theoretical foundations of AI agents
- Decision-making and planning algorithms
- Multi-agent systems and coordination
- Ethical considerations and safety
"Hands-On Large Language Models" by Jay Alammar & Maarten Grootendorst
- Practical LLM application development
- Agent architecture and design patterns
- Tool integration and workflow automation
- Real-world case studies and examples
Business & Strategy Books
"The AI Agent Revolution" by Reid Hoffman
- Business implications of AI agents
- Strategic implementation frameworks
- Industry transformation scenarios
- Future trends and predictions
"Autonomous Intelligence" by Andrew McAfee
- Economic impact of AI agents
- Organizational change management
- Competitive advantage strategies
- Risk assessment and mitigation
Research Papers
Foundational Papers
ReAct: Synergizing Reasoning and Acting in Language Models
- Fundamental paper on reasoning and acting
- Framework for tool-using language models
- Experimental results and analysis
- Implementation guidelines and examples
Toolformer: Language Models Can Teach Themselves to Use Tools
- Self-supervised tool learning approach
- API integration and usage patterns
- Performance evaluation and benchmarks
- Future research directions
AutoGPT: An Autonomous GPT-4 Experiment
- Early autonomous agent implementation
- Task decomposition and execution strategies
- Limitations and improvement opportunities
- Community impact and adoption
Recent Advances
Multi-Agent Collaboration in Large Language Models
- Advanced multi-agent coordination techniques
- Communication protocols and strategies
- Performance optimization methods
- Scalability considerations
Tool Learning with Foundation Models
- Comprehensive survey of tool-using AI
- Taxonomy of tools and capabilities
- Evaluation frameworks and metrics
- Future research challenges
The Model Context Protocol: Standardizing AI-Data Integration
- MCP specification and design principles
- Security and privacy considerations
- Performance benchmarks and analysis
- Ecosystem development strategies
Survey Papers
A Survey of Large Language Model based Autonomous Agents
- Comprehensive overview of agent architectures
- Capability analysis and comparison
- Application domains and use cases
- Research gaps and opportunities
Tool Learning in the Era of Large Language Models
- Evolution of tool-using AI systems
- Current state and future directions
- Challenges and limitations
- Benchmark datasets and evaluation
Conferences & Events
Major Conferences
NeurIPS (Neural Information Processing Systems)
- Premier AI research conference
- Agent-related workshops and papers
- Networking and collaboration opportunities
- Industry and academic presentations
ICML (International Conference on Machine Learning)
- Leading machine learning conference
- Multi-agent systems track
- Tool learning and reasoning sessions
- Poster sessions and demonstrations
AAMAS (Autonomous Agents and Multiagent Systems)
- Specialized agent systems conference
- Latest research and developments
- Industry applications and case studies
- Workshops and tutorials
Industry Events
- Industry-focused agent development event
- Vendor presentations and demos
- Use case studies and implementations
- Networking and partnership opportunities
- Framework-specific development event
- Community showcases and presentations
- Technical workshops and training
- Product announcements and updates
Podcasts
- Weekly discussions on agent development
- Industry expert interviews
- Framework reviews and comparisons
- Community news and updates
- Practical AI development insights
- Agent implementation strategies
- Tool reviews and recommendations
- Developer success stories
- Framework-specific discussions
- Developer interviews and insights
- Use case studies and examples
- Community highlights and features
🌟 Community
Forums & Discussion Platforms
- Active community of LangChain developers
- Real-time help and support
- Framework updates and announcements
- Project showcases and collaboration
- Microsoft AutoGen community hub
- Multi-agent development discussions
- Research collaboration opportunities
- Technical support and troubleshooting
- Community discussions and news
- Project sharing and feedback
- Tutorial recommendations
- Industry insights and trends
- General AI agent development community
- Framework comparisons and reviews
- Use case discussions and examples
- Career advice and opportunities
GitHub Communities
- Comprehensive list of AI agent projects
- Community contributions and updates
- Project discovery and exploration
- Collaboration opportunities
- Official LangChain discussions
- Feature requests and feedback
- Community-driven improvements
- Developer support and guidance
Professional Networks
AI Agent Developers LinkedIn Group
- Professional networking and opportunities
- Industry news and insights
- Job postings and career development
- Expert discussions and analysis
Model Context Protocol LinkedIn Group
- MCP-focused professional community
- Implementation discussions and best practices
- Business use cases and success stories
- Partnership and collaboration opportunities
🤝 Contributing
We welcome contributions to this awesome list! Please read our Contributing Guidelines before submitting a pull request.
How to Contribute
- Fork the repository and create a new branch
- Add your resource in the appropriate category
- Follow the format used by existing entries
- Ensure the resource is high-quality and relevant
- Submit a pull request with a clear description
Contribution Guidelines
- Resources should be actively maintained and well-documented
- Commercial products should provide clear value to the community
- Include accurate descriptions and relevant links
- Maintain alphabetical order within categories
- Use proper markdown formatting
📄 License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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This awesome list is maintained by the community and curated by Tenten.co. Last updated: July 2025
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