Distinguishing AI Agents: More autonomous than AI assistants (like Siri) or rule-based bots. AI Agents reason, plan, remember, learn, and adapt, often powered by foundation models.

“My AI Agent” for this Project:

• Personalized: Tailored to user needs, preferences, and roles.
• User-Creatable: Users can instantiate and deploy their agents on the platform.
• Task-Oriented: Designed for specific assistant roles (research, document analysis).
• Network-Interactive: Communicates and provides services within the AI Network.
• MCP-Powered: Utilizes Model Context Protocol for tool and data interaction.

These agents act as managed digital extensions of the user within the social network.

For a 10-hour MVP, focus on one or two core agent types:

• Research Assistant Agent: Leverages internet search and RAG via MCP tools.
• Document Analysis Agent: Interacts with Excel and DOCX files via MCP tools.

Rationale: Direct fulfillment of core MCP requirements, demonstrable value, and foundational building blocks for future agents. Specialized agents are preferred over a single monolithic agent for manageability.

Objective: Develop a basic LangChain AI agent connecting to a custom Python MCP server with one RAG tool for local text documents.

1. Setup Python Environment (1 hour): Virtual env, install `mcp[cli]`, `langchain`, `ollama`, `faiss-cpu`, etc. Create `.env` for API keys.
2. Choose & Set Up LLM (1.5 hours):
   • Option A (API): OpenAI (e.g., GPT-3.5-turbo).
   • Option B (Local): Ollama + LLaMa 7B (or similar).
   • Recommendation: OpenAI API for MVP simplicity if available, else Ollama.
3. Implement Simple MCP Server with RAG Tool (3.5 hours):

   Create `my_mcp_server.py` using `FastMCP`. Tool: `query_document_rag(query: str, file_path: str)` that loads, chunks, embeds, and queries a local document using FAISS and an LLM. Run server on stdio.

   Illustrative MCP Server Tool Snippet (Conceptual)

   # my_mcp_server.py (Conceptual)
   from mcp.server.fastmcp import FastMCP
   # ... other imports for LangChain, FAISS, LLM ...
   mcp_server = FastMCP("SimpleRAGServer")
   rag_qa_chain_cache = None
   
   @mcp_server.tool()
   def query_document_rag(query: str, file_path: str) -> str:
       """Answers questions about a document specified by file_path using RAG."""
       # ... (Logic to load doc, create vector store, query with LLM) ...
       # response = rag_qa_chain_cache.invoke({"query": query})
       # return response.get("result", "Could not find answer.")
       return "Conceptual RAG response for: " + query
   # ...
   if __name__ == "__main__":
       mcp_server.run()
   

   Copy Code # my_mcp_server.py (Conceptual) from mcp.server.fastmcp import FastMCP # … other imports for LangChain, FAISS, LLM … mcp_server = FastMCP(“SimpleRAGServer”) rag_qa_chain_cache = None @mcp_server.tool() def query_document_rag(query: str, file_path: str) -> str: “””Answers questions about a document specified by file_path using RAG.””” # … (Logic to load doc, create vector store, query with LLM) … # response = rag_qa_chain_cache.invoke({“query”: query}) # return response.get(“result”, “Could not find answer.”) return “Conceptual RAG response for: ” + query # … if __name__ == “__main__”: mcp_server.run()
4. Implement AI Agent (MCP Client) (3 hours):

   Create `my_agent_client.py`. Use LangChain agent, `langchain-mcp-adapters` to connect to `my_mcp_server.py` via `stdio_client`. Load `query_document_rag` tool.

   Illustrative Agent Client Snippet (Conceptual)

   # my_agent_client.py (Conceptual)
   # ... imports for LangChain, mcp.client.stdio, langchain_mcp_adapters ...
   server_params = StdioServerParameters(cmd=["python", "my_mcp_server.py"])
   async def run_agent_with_mcp():
       async with stdio_client(server_params) as mcp_client_session:
           mcp_tools = await load_mcp_tools(mcp_client_session, tool_names=["query_document_rag"])
           # ... (Setup LangChain agent with llm, prompt, mcp_tools) ...
           # response = await agent_executor.ainvoke({"input": "Ask about test_document.txt"})
           # print(response['output'])
           print("Conceptual agent response using MCP tool.")
   # ...
   asyncio.run(run_agent_with_mcp())
   

   Copy Code # my_agent_client.py (Conceptual) # … imports for LangChain, mcp.client.stdio, langchain_mcp_adapters … server_params = StdioServerParameters(cmd=[“python”, “my_mcp_server.py”]) async def run_agent_with_mcp(): async with stdio_client(server_params) as mcp_client_session: mcp_tools = await load_mcp_tools(mcp_client_session, tool_names=[“query_document_rag”]) # … (Setup LangChain agent with llm, prompt, mcp_tools) … # response = await agent_executor.ainvoke({“input”: “Ask about test_document.txt”}) # print(response[‘output’]) print(“Conceptual agent response using MCP tool.”) # … asyncio.run(run_agent_with_mcp())
5. Test and Refine (1 hour): Run client and server, debug, verify RAG functionality.

This plan focuses on a single RAG capability. Other tools (search, Excel/DOCX reading) can be added iteratively.

A social platform where human users and their personalized AI agents coexist and interact. Agents can generate content, provide services, and engage in conversations on behalf of users.

Core Platform Features (MVP):

• User Account Management: Email/password registration, simple profiles (name, bio, photo).
• AI Agent Integration:
  • Agent Registration: Users register their “My AI Agents” (from Team 1) on the platform, associating them with their account. Backend needs to store agent metadata (name, role, endpoint/config).
  • Agent Interaction: Registered agents provide conversational services on the feed, acting for their owner. Agents post/reply via platform APIs (e.g., creating “notes” in Misskey or “statuses” in GoToSocial).
• Service Contracts (Optional Advanced): Smart contracts (e.g., Solidity) for agent service agreements, potentially with MetaMask integration. This is complex and likely beyond initial MVP.
  Illustrative Solidity Smart Contract (Conceptual)

  // SPDX-License-Identifier: MIT
  pragma solidity ^0.8.0;
  
  contract AIAgentServiceAgreement {
      address public clientUser;
      address public serviceProviderAgentOwner;
      // ... other state variables like paymentAmount, serviceDescription, state ...
  
      constructor(address _provider, uint _amount, string memory _desc) payable {
          // ... constructor logic ...
      }
  
      function markServiceAsCompleted() public { /* ... */ }
      function releasePaymentToProvider() public { /* ... */ }
      // ...
  }
  

  Copy Code // SPDX-License-Identifier: MIT pragma solidity ^0.8.0; contract AIAgentServiceAgreement { address public clientUser; address public serviceProviderAgentOwner; // … other state variables like paymentAmount, serviceDescription, state … constructor(address _provider, uint _amount, string memory _desc) payable { // … constructor logic … } function markServiceAsCompleted() public { /* … */ } function releasePaymentToProvider() public { /* … */ } // … }

Forking an existing open-source platform is recommended. ActivityPub servers like Misskey or GoToSocial are good candidates.

Chart: Simplified comparison of key aspects for Misskey vs. GoToSocial.

Choice impacts frontend effort: Misskey (modify complex Vue app) vs. GoToSocial (use existing client or build custom simple frontend).

Initial 10-Hour Goal:

1. Platform Selection, Forking, Local Deployment (5-7 hours): Research, select, fork, and set up local dev environment for Misskey or GoToSocial.
2. Basic User and API Test (3-5 hours): Create test user, attempt API post “as if” from an agent. No UI changes.

Broader MVP Plan (1-2 Weeks):

This is an aggressive timeline. The 10-hour MVP is a small subset of Phase 1.

Why MCP? Addresses the N×M data integration problem for AI models and external systems. Provides a universal framework for connection.

What is MCP? An open standard/framework (Anthropic, late 2024) to standardize AI model integration with tools/data. “USB-C of AI apps.”

Architectural Pattern (Client-Host-Server):

Benefits: Standardization, interoperability, reproducibility, security, modularity, scalability. Growing industry adoption (OpenAI, Google DeepMind, etc.).

MCP is foundational for the “My AI Network,” enabling agents to access diverse capabilities securely and standardly.

LLM Options:

• Open-Source (Local/Self-Hosted): LLaMa family, DeepSeek Models. Can be run via Ollama or Hugging Face.
• Commercial LLM APIs: OpenAI (GPT series), Google (Gemini), Hugging Face Inference API.
• MVP Recommendation: Ollama + small local LLaMa/DeepSeek, or Hugging Face Inference API.

Agent Development Frameworks:

• LangChain: Versatile for LLM apps, agents, RAG. Good for tool integration (MCP via adapters). Recommended for MVP.
• LlamaIndex: Strong for connecting LLMs to custom data (RAG). Can complement LangChain.
• AutoGen / CrewAI: For multi-agent collaboration (more advanced).

Key for “AI Training by dropping data.” Enables agents to query dynamically constructed knowledge bases.

RAG Process:

1. Document Ingestion: Load content (text, PDF, DOCX) using LangChain/LlamaIndex loaders.
2. Text Splitting: Break into smaller, coherent chunks.
3. Embedding Generation: Convert chunks to vector representations (OpenAI/HuggingFace embeddings).
4. Vector Storage & Indexing: Store in a vector store (e.g., FAISS for MVP) for similarity search.

An MCP tool can handle file input, processing, and vector store updates for RAG.

ActivityPub: An open, decentralized social networking protocol. It’s the W3C standard for federated social web.

The Fediverse: A collection of interconnected social media platforms (instances) that can communicate with each other using protocols like ActivityPub. Examples include Mastodon, Pleroma, Pixelfed, and the chosen base platforms Misskey/GoToSocial.

Relevance to Project:

• Using an ActivityPub server (Misskey/GoToSocial) as a base allows the “My AI Network” to potentially interoperate with the broader Fediverse.
• Agents could, in the future, be modeled as ActivityPub `Actors`, allowing them to have their own identities and interact across federated instances.
• Standard activities like `Create Note`, `Follow`, `Like`, `Announce` are defined by ActivityPub and can be leveraged by agents.

While full federation might be beyond the MVP, understanding ActivityPub is beneficial for future platform scalability and interoperability.

Essential for LLM experimentation without high costs. Suitable for learning, not production deployment.

Chart: Simplified comparison of typical free GPU resources.

For MVP, consider running pre-trained models locally (Ollama) or via APIs on these platforms.

Category	Recommended Tool/SDK	Why Recommended for MVP
LLM Backend (Local)	Ollama + (LLaMa 7B or DeepSeek small)	Easy local setup, cost-free.
LLM Backend (API)	Hugging Face Inference API / OpenAI API	Access diverse models, no local GPU burden.
Agent Framework	LangChain	Comprehensive, good for tool integration (MCP), RAG.
RAG Implementation	LangChain + FAISS (in-memory)	Integrated components, FAISS is lightweight.
File Reading (Excel)	Pandas	Standard Python library, robust.
File Reading (DOCX)	`python-docx` / `unstructured`	Direct text extraction or broader compatibility.
MCP Server Dev	Python `mcp` SDK (`FastMCP`)	Official SDK, developer-friendly.
MCP Client (Agent)	`langchain-mcp-adapters`	Connects LangChain agents to MCP servers.

Phase/Task	Est. Time (Person-Days)	Key Focus
1. Backend Setup & Core	2-3 PD	Fork, local deploy, user accounts.
2. Agent Data Model (Backend)	2-3 PD	Extend user entity for agent metadata, DB migration.
3. Agent Mgmt API (Backend)	1-2 PD	Endpoints to register/view agents.
4. Agent Posting API (Backend)	1-2 PD	Allow agents to post, flag agent content.
5. Frontend Dev Env Setup	1-2 PD	Vue (Misskey) or client strategy (GoToSocial).
6. Agent Display on Profile (FE)	2-3 PD	List user’s agents on their profile page.
7. Agent Post Styling (FE)	1-2 PD	Visually differentiate agent posts in feed.
8. Basic Interaction (FE/Agent)	1 PD	Users reply/mention; agents monitor via API.
9. Integration & Testing	2-3 PD	Test agent-platform end-to-end flow.

This is an aggressive timeline. The 10-hour platform MVP is a small subset of Phase 1.

Students gain hands-on experience with:

• Large Language Models (LLMs)
• Model Context Protocol (MCP)
• Retrieval Augmented Generation (RAG)
• Agent Development (e.g., LangChain)
• Decentralized Social Media Concepts & Full-Stack Implementation (ActivityPub, Node.js/Go, Vue.js/JS)

This provides a deep understanding of modern AI system building, integration, and deployment in networked environments.

The developed platform is a microcosm of emerging AI and social computing trends. Agent integration can transform user interaction with information and services.

Potential for new collaboration forms, automated information dissemination, personalized services, and novel economic models (e.g., agent service contracts).

This project is an entry point to explore these future possibilities and their ethical considerations.

This complex project offers significant learning. MVPs are achievable starting points.

Future Enhancements for “My AI Agents”:

• Sophisticated MCP tools (email, Slack, calendar).
• Advanced agent frameworks (AutoGen, CrewAI) for multi-agent systems.
• Robust RAG pipelines, diverse data sources.
• LLM experimentation and fine-tuning.

Future Enhancements for “My AI Network Platform”:

• Deeper ActivityPub integration (agents as Actors).
• Sophisticated UI/UX for agent management.
• Full smart contract layer for service agreements (MetaMask).
• Agent discovery, reputation systems, agent-to-agent channels.

This project is a launchpad for further research, specialization, or entrepreneurial ventures in AI-driven social platforms.