https://github.com/techySPHINX/mcp_ai_lab
A suite of AI agents and tools built on Model Context Protocol (MCP) for standardized, context-aware AI systems.
https://github.com/techySPHINX/mcp_ai_lab
ai javascript langachain langraph mcp-client mcp-protocol mcp-server pydantic python
Last synced: 2 months ago
JSON representation
A suite of AI agents and tools built on Model Context Protocol (MCP) for standardized, context-aware AI systems.
- Host: GitHub
- URL: https://github.com/techySPHINX/mcp_ai_lab
- Owner: techySPHINX
- License: mit
- Created: 2025-05-06T10:35:43.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-05-06T10:44:36.000Z (6 months ago)
- Last Synced: 2025-05-07T09:56:48.371Z (6 months ago)
- Topics: ai, javascript, langachain, langraph, mcp-client, mcp-protocol, mcp-server, pydantic, python
- Language: JavaScript
- Homepage:
- Size: 18.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-mcp-servers - **mcp_ai_lab** - A suite of AI agents and tools built on Model Context Protocol (MCP) for standardized, context-aware AI systems. `javascript` `ai` `langachain` `langraph` `mcp-client` `npm install techySPHINX/mcp_ai_lab` (🤖 AI/ML)
- awesome-mcp-servers - **mcp_ai_lab** - A suite of AI agents and tools built on Model Context Protocol (MCP) for standardized, context-aware AI systems. `javascript` `ai` `langachain` `langraph` `mcp-client` `npm install techySPHINX/mcp_ai_lab` (AI/ML)
README
# MCP AI Agents LAB 🤖📚
**Model Context Protocol (MCP)** + **AI Agents**: A suite of advanced projects that explore, implement, and document AI agent architectures powered by standardized context protocols.
This repository serves as a unified hub for cutting-edge MCP-based agent systems, with full documentation, protocol guides, and open-source tools.
---
## 🚀 Projects in this Suite
- **🧠 MCP Agent Framework**: Build modular, interoperable AI agents that communicate via Model Context Protocol.
- **🔄 MCP Message Handler**: Universal handler for context injection and protocol message formatting.
- **📦 Dataset Tools**: Tools to convert real-world context data into MCP-compliant datasets.
- **📝 Context Chain Builder**: Automate the chaining of multiple MCP messages to simulate complex tasks.
- **🌐 MCP Proxy Layer**: Middleware to connect MCP agents with APIs, databases, and models (LLMs, RAG systems).
- **🤖 Example Agents**: Reference AI agents (task executors, summarizers, planners) built fully on MCP.
---
## 📚 Documentation
Explore full guides and technical breakdowns:
- [🌐 What is Model Context Protocol?](docs/WHAT_IS_MCP.md)
- [🛠️ Building an MCP Agent](docs/BUILD_AGENT.md)
- [📦 MCP Message Format Spec](docs/MESSAGE_FORMAT.md)
- [🔗 Chaining MCP Contexts](docs/CHAINING.md)
- [🧑💻 Running Example Agents](docs/RUN_EXAMPLES.md)
📖 **Start here:** [Getting Started Guide](docs/GETTING_STARTED.md)
---
## 🌐 Useful External Links
- 📄 **MCP Official Spec**: [https://modelcontext.org/spec](https://modelcontext.org/spec)
- 💬 **MCP Community Forum**: [https://community.modelcontext.org](https://community.modelcontext.org)
- 🔗 **LangChain MCP Integration**: [https://github.com/langchain-ai/langchain](https://github.com/langchain-ai/langchain)
- 🧩 **OpenAI MCP Resources**: [https://platform.openai.com/docs](https://platform.openai.com/docs)
---
## 🔧 Requirements
- Python 3.10+
- `pydantic`, `requests`, `fastapi` (for protocol servers)
- Optional: `torch`, `transformers` (for LLM-backed agents)
---
## 🏃♂️ Quick Start
```bash
# Clone the repo
git clone https://github.com/yourusername/mcp_ai_lab.git
cd mcp_ai_lab
# Install requirements
pip install -r requirements.txt
# Run an example agent
python agents/example_agent.py