How to Build AI agents with MCP and Agentic Frameworks

AI Agents: An Overview

At their core, AI agents are autonomous software entities that perceive their environment (through sensors or data inputs), reason about their perceptions, make decisions, and then act upon that environment (through actuators or outputs) to achieve specific goals.

Key characteristics of AI agents often include:

• Autonomy: They can operate without direct human intervention and have control over their own actions and internal state.
• Reactivity: They can perceive their environment and respond in a timely fashion to changes that occur in it.
• Proactiveness: They don't just act in response to the environment; they can take initiative and exhibit goal-directed behavior.
• Social Ability (in multi-agent systems): They can interact and communicate with other agents (and sometimes humans) via some communication language or protocol.

In the context of Large Language Models (LLMs), AI agents leverage the reasoning and language understanding capabilities of LLMs to perform complex tasks, often by breaking them down into smaller steps, utilizing tools, and maintaining context or memory.

MCP (Model Context Protocol): An Overview

While "Model Context Protocol" (MCP) isn't a universally standardized term like TCP/IP, in the context of the diagram you provided and emerging AI agent architectures, it refers to a set of rules, standards, and data structures designed to facilitate seamless communication, context sharing, and tool interaction between different AI models, agents, and external services.

The purpose of an MCP is to:

• Standardize Information Exchange: Define how context (like user queries, conversation history, agent states, available tools, and memory) is formatted and passed between components.
• Enable Interoperability: Allow different agents, potentially built with different LLMs or technologies, to work together effectively.
• Manage Context Lifecycle: Dictate how context is created, updated, stored, and retrieved throughout an agent's operation or a multi-agent collaboration.
• Facilitate Tool Usage: Define how agents discover, select, and invoke tools, and how the results from those tools are integrated back into the agent's reasoning process.
• Support Memory: Provide a framework for how short-term and long-term memory is accessed and utilized to inform agent behavior.

Essentially, an MCP aims to create a common "language" and operational framework so that complex AI systems with multiple intelligent components can function coherently. The "Understand Model Context Protocol (MCP)" block in Level 2 of your diagram highlights its importance in managing interactions between context, memory, and tools.

Building AI Agents: A General Architecture Pattern (CrewAI, Google ADK, Langchain, Mem0)

Here's how these tools can fit into a general architectural pattern for building sophisticated AI agents:

1. Orchestration and Collaboration (CrewAI):
    
   • Role: CrewAI excels at creating and managing teams of specialized AI agents that collaborate to achieve a common objective.
   • Usage: You would use CrewAI to define: 
     • Agents: Assign specific roles (e.g., "Researcher," "Writer," "Data Analyst"), goals, and backstories (which can help the LLM embody the role).
     • Tasks: Define the individual tasks each agent is responsible for, including expected outputs.
     • Tools: Assign specific tools (often built with Langchain) that each agent can use.
     • Process: Define the workflow or sequence in which agents collaborate (e.g., sequential, hierarchical). CrewAI manages the handoff of information and tasks between agents.

1. Core LLM Application Development & Tooling (Langchain):
    
   • Role: Langchain provides the foundational building blocks for developing applications powered by LLMs, including agents.
   • Usage: 
     • Agent Primitives: Langchain offers abstractions for creating agents (e.g., ReAct agents, OpenAI Functions agents) that can reason about which tools to use and in what sequence.
     • Chains: To structure sequences of LLM calls or calls to other utilities.
     • Tools: Easily integrate various tools, from simple search APIs to complex data analysis functions or even other chains/agents.
     • Memory Modules: While Mem0 is specialized, Langchain also provides its own memory modules for simpler use cases or for integration.
     • Prompt Templates: For crafting effective prompts that guide the LLM's behavior within each agent.

1. Persistent & Scalable Memory (Mem0):
    
   • Role: Mem0 is designed as a dedicated, intelligent memory layer for AI agents, addressing the limitations of traditional short-term context windows in LLMs.
   • Usage: 
     • Long-Term Memory: Agents can store information (facts, user preferences, past interactions, task progress) in Mem0 for later retrieval.
     • Contextual Retrieval: Mem0 can intelligently retrieve relevant information based on the current query or task, providing the agent with necessary context.
     • Shared Memory: In multi-agent systems (like those orchestrated by CrewAI), Mem0 can serve as a shared knowledge base that different agents can read from and write to, facilitating more coherent collaboration.
     • Integration: Mem0 would be integrated as a tool or a direct memory backend for agents built with Langchain or orchestrated by CrewAI.

1. Agent Capabilities & Platform Integration (Google ADK - Agent Development Kit):
    
   • Role: While the Google ADK is a relatively newer offering, its general purpose would likely be to provide developers with tools, APIs, and infrastructure to build, deploy, and manage AI agents, potentially with tighter integration with Google's ecosystem (Vertex AI, Google Cloud services, etc.).
   • Usage (Hypothetical/General): 
     • Building Blocks: Could offer pre-built components or templates for common agent functionalities.
     • Google Service Integration: Simplified access to Google services (Search, Maps, Calendar, etc.) as tools for agents.
     • Deployment & Scaling: Tools for deploying agents on Google Cloud and managing their lifecycle.
     • Observability: Integration with Google Cloud's monitoring and logging tools.
     • It might offer specific SDKs or frameworks that could complement or be used alongside Langchain or CrewAI, especially for agents heavily reliant on Google's infrastructure.

General Architecture Pattern:

• CrewAI (Orchestrator): At the highest level, CrewAI defines the team of agents, their roles, and how they collaborate on complex tasks.
• Langchain (Agent Brains & Toolset): Each agent within the CrewAI setup would likely be built using Langchain. Langchain provides the agent's reasoning loop (e.g., ReAct framework), access to its specific tools, and prompt management.
• Mem0 (Centralized Memory): All agents in the crew can connect to a Mem0 instance. 
  • Individual agents can use it for their own long-term memory.
  • The crew can use it as a shared blackboard or knowledge base to pass complex information or maintain a collective understanding.
• Google ADK (Specialized Capabilities/Deployment): The ADK could be used to: 
  • Develop specific tools that agents use (especially if they involve Google services).
  • Provide the infrastructure for hosting and running the agents.
  • Offer more advanced agent capabilities or pre-trained models for specific tasks that can be integrated into the Langchain/CrewAI agents.

Flow Example:

1. A user request comes in.
2. CrewAI determines which agent (or sequence of agents) should handle it.
3. The assigned Langchain-powered agent starts its task.
4. It queries Mem0 for relevant past context or knowledge.
5. It uses its tools (which could be standard Langchain tools, custom tools, or tools facilitated by Google ADK) to gather information or perform actions.
6. It updates Mem0 with new learnings or task progress.
7. It passes its output to the next agent in the CrewAI process or back to the user.
8. All interactions, tool usage, and memory access would ideally follow the principles of an MCP for consistency and manageability.

This combination allows for building highly capable and collaborative AI systems by leveraging the strengths of each tool: CrewAI for multi-agent orchestration, Langchain for the core LLM logic and tool integration, Mem0 for robust memory, and potentially Google ADK for specialized tools and platform integration.