Unifying AI Agent Architectures: MCP, A2A, and LangChain

AI agents are rapidly transforming how we interact with technology, moving beyond simple chatbots to systems that can autonomously perform complex tasks. This evolution is driven by advancements in Large Language Models (LLMs) and the emergence of new protocols and frameworks designed to enhance their capabilities and enable collaboration. Let’s explore three key players in this space: Model Context Protocol (MCP), Agent-to-Agent (A2A) Protocol, and LangChain.

The Foundation: Understanding Key Concepts

Before diving into each, it’s crucial to grasp their core objectives:

• Model Context Protocol (MCP): Introduced by Anthropic in late 2024, MCP aims to be a universal interface for connecting AI models to external data sources and tools. Its philosophy is to simplify and unify tool and data access for AI agents, enabling richer, context-aware responses by treating external tools as extensions of the model’s context.
• Agent-to-Agent (A2A) Protocol: Launched by Google in April 2025, A2A is an open protocol focused on enabling multiple AI agents to collaborate and communicate across different systems and vendors. The philosophy behind A2A is that complex task automation will often require specialized agents cooperating in a dynamic ecosystem. A2A treats each agent as an independent “peer,” emphasizing interoperability without requiring agents to expose their internal logic or memory. Essentially, A2A provides a common language for agents to communicate with each other, similar to how MCP provides a common interface for agents to interact with tools.
• LangChain: Unlike MCP and A2A, LangChain is a framework (a Python/JavaScript library) rather than a protocol. Its core goal is to simplify the building of LLM-powered applications and agents by providing modular components for chaining model calls, integrating tools, and managing context and memory. LangChain’s philosophy is pragmatic and developer-centric, offering abstractions like Chains, Agents, Tools, and Memory. It orchestrates existing models and tools within a single application rather than defining a new communication standard.

How They Handle Agent Coordination and Task Decomposition

The way these technologies approach task decomposition and agent coordination highlights their distinct roles.

MCP – Single-Agent, Multi-Tool Coordination: MCP focuses on a single agent’s coordination with multiple tools. An MCP-enabled agent can dynamically discover and utilize various tools exposed by MCP servers, allowing a single AI agent to effectively break down complex tasks into tool calls. For example, an agent could combine a “document lookup” tool with an “email-sending” tool to fulfill a user request. MCP standardizes how these tools are described and invoked, enabling plug-and-play coordination. However, MCP does not handle coordination between multiple independent agents.

A2A – Multi-Agent Collaboration: A2A is specifically designed for agent coordination. In an A2A system, a “client” agent can delegate subtasks to other specialized agents by communicating over the protocol. A2A formalizes this with a “Task object” that has a lifecycle, allowing for hierarchical task decomposition. Agents discover each other’s abilities via “Agent Cards” that list their skills, enabling a client agent to select the appropriate helper for a subtask. During collaboration, agents exchange messages carrying context and artifacts. This provides a structured multi-agent workflow where agents coordinate by passing tasks and contextual messages, allowing complex problem-solving through cooperation. A real-world example from Google’s blog describes a hiring scenario where a “recruiter” agent delegates resume sourcing and interview scheduling to specialized agents within an A2A-driven loop.

LangChain – Orchestration within a Single Agent (with Possible Multi-Agent Patterns): Out of the box, LangChain typically operates with a single agent that can use multiple tools in sequence, similar to MCP’s single-agent, multi-tool paradigm. A LangChain agent can coordinate steps of a task by planning which tool or chain to invoke at each step. While LangChain doesn’t define a new communication standard for multi-agent interaction, developers can create multiple LangChain agents and have them communicate, for instance, by using one agent’s tool as an interface to another agent. LangChain’s strength lies in handling task decomposition within one agent’s reasoning process.

Context Propagation and Memory Management

How these technologies manage and propagate context and memory is another key differentiator.

MCP – Context via External Data & “Resources”: MCP is designed to propagate external context into an AI’s working memory in a structured way. Beyond tools, MCP servers can provide “Resources” (like files or data blobs) and “Prompts” (pre-defined context templates). This allows contextual information from external sources (e.g., company knowledge bases, user files) to be injected into the conversation in real-time via MCP calls. MCP itself doesn’t provide long-term memory but facilitates fetching relevant context just-in-time in a model-agnostic way.

A2A – Message-Based Context Sharing: In A2A, while each agent may have its own internal memory, cross-agent context propagation occurs through explicit messaging. Messages exchanged between agents include the necessary context for the task. A2A’s design acknowledges that agents might not share memory or tools by default, emphasizing passing only what is needed to preserve each agent’s internal state privacy. For long-running tasks, A2A supports state updates, allowing agents to keep each other informed of progress.

LangChain – In-Process Memory Modules: LangChain handles context and memory at the framework level by offering memory components that store and retrieve conversational state or other information between steps. This is crucial for maintaining context in a dialogue or a multi-step reasoning process. LangChain’s memory is typically internal to the application, maintaining a running transcript or summary of the conversation. It supports both short-term and long-term memory through integrations with databases. While LangChain enables context propagation within a single application by explicitly handing off variables between steps, external protocols or custom mechanisms are needed for context sharing across multiple agents or processes.

Implementation Paradigms and Architecture

The underlying architecture of each technology dictates how they are implemented and deployed.

MCP – Tool/Context via RPC Servers: MCP is implemented as a client-server message-passing architecture built on JSON-RPC 2.0. An MCP server exposes tools, resources, or prompts, and an MCP client (an AI agent) connects to these servers. Communication follows a request/response pattern where the client discovers tools and then invokes them with arguments. This design emphasizes decoupling, allowing tools to be implemented in any language or environment as long as they adhere to the protocol specification for communication, fostering scalability and interoperability.

Here’s an example of an MCP server and client:

from mcp.server.fastmcp import FastMCP
server = FastMCP("CalcServer")

@server.tool(name="add", description="Add two numbers")
def add_numbers(x: float, y: float) -> float:
    return x + y

if __name__ == "__main__":
    server.run()  # Starts the server (e.g., on a port or stdio)

# MCP Client
import asyncio
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client

async def use_tool():
    params = StdioServerParameters(command="python", args=["calc_server.py"])
    async with stdio_client(params) as (read, write):
        async with ClientSession(read, write) as session:
            await session.initialize()               # handshake with server
            tools = await session.list_tools()       # discover tools
            result = await session.call_tool("add", arguments={"x": 5, "y": 7})  # RPC call
            print(result)  # Expect 12.0

asyncio.run(use_tool())

A2A – Agents as Services with Message Exchange:

Google’s A2A protocol uses JSON-RPC over HTTP(S) to enable structured communication between agents. Architecturally, each agent in A2A is typically hosted behind an HTTP endpoint, and agents communicate by sending JSON-formatted tasks and messages to each other. Key elements include the “Agent Card” (describing an agent’s API endpoint and skills) and “Task lifecycle methods”. The protocol defines message types for different phases (e.g., TaskRequest, StatusUpdate, TaskResult) and utilizes standard web technologies for transport. This paradigm allows agents to act as services with defined interfaces, supporting synchronous and asynchronous operations.

Here’s a simplified pseudocode example of an A2A agent and client:

# A2A Agent Server
from a2a import AgentCard, AgentSkill, A2AServer, run_server

# Define what our agent can do (AgentCard with skills)
card = AgentCard(
    name="GeoAgent",
    description="Answers geography questions",
    url="http://localhost:5001",  # where this agent will be hosted
    skills=[AgentSkill(name="GeographyQA", description="Answer geography queries")]
)

# Wrap an LLM or logic into an A2A server class
class GeoAgentServer(A2AServer):
    def __init__(self, card):
        super().__init__(agent_card=card)
    def handle_message(self, message):
        # Here we handle incoming A2A messages (tasks or chat); for simplicity:
        user_query = message.get("input", "")
        # answer = answer_geography_question(user_query) # some internal logic or LLM call
        answer = f"The capital of {user_query.replace('What is the capital of ', '').replace('?', '')} is Paris." # Simplified for example
        return {"output": answer}

server = GeoAgentServer(card)
# run_server(server, host="0.0.0.0", port=5001)  # start serving this agent

# A2A Client (in another process)
from a2a import A2AClient

# client = A2AClient("http://localhost:5001")
# response = client.ask({"input": "What is the capital of France?"})
# print(response)  # should print "Paris"

LangChain – In-Process Chain-of-Tools and LLM Orchestration:

LangChain’s implementation paradigm is that of a framework/library running within your application process. There isn’t an external protocol; instead, LangChain provides classes and abstractions to compose calls to LLMs and other functions. The common pattern for a LangChain agent is the ReAct loop: the library formats the conversation and tool list into a prompt, the LLM’s output is parsed by LangChain to determine which tool to call, the tool is executed, and the result is fed back into the LLM for the next step. All of this happens synchronously in memory, orchestrated by the LangChain framework. Communication is function-call based rather than a network protocol.

Here’s an example of a simple LangChain agent:

# LangChain Agent
from langchain.agents import initialize_agent, Tool
from langchain.llms import OpenAI
from langchain.tools import tool

@tool
def lookup_wiki(term: str) -> str:
    """Search Wikipedia for a term and return a summary."""
    # ... (implementation that calls Wikipedia API) ...
    return f"Summary of {term} from Wikipedia."

# This would typically be an actual OpenAI API key, not 'YOUR_API_KEY'
# llm = OpenAI(model="gpt-3.5-turbo", temperature=0, openai_api_key="YOUR_API_KEY")
# agent = initialize_agent([lookup_wiki], llm, agent="zero-shot-react-description")

# answer = agent.run("Who is the president of France?") # The agent will decide to call lookup_wiki internally
# print(answer)

Scalability, Traceability, and Real-Time Interoperability

These factors are crucial for deploying robust AI agent systems in real-world scenarios.

Scalability: Both MCP and A2A are designed with scalability and distribution in mind. MCP allows tools to run as separate processes or services, enabling agents to offload heavy tasks to dedicated servers and facilitating a catalog of standardized tools (5,000+ MCP servers were publicly listed by mid-2025). A2A similarly considers scale, envisioning ecosystems of agents possibly running across cloud environments. Because each agent is essentially a microservice, you can scale out agent instances and manage load via standard web service scaling techniques. LangChain, as an in-process framework, is less inherently scalable in a distributed sense. While multiple instances can be run behind an API, it doesn’t have built-in mechanisms for distributing tasks across machines without integrating with external infrastructure.

Traceability and Monitoring: Both MCP and A2A allow for capturing tool usage and model interactions in a structured way. MCP allows capturing tool usage and model interactions in a structured way. For example, OpenAI’s Agent SDK automatically logs MCP operations as part of its tracing features. A2A emphasizes observability, and an A2A system can be instrumented to log every task start, update, and result message between agents. LangChain provides traceability through its callback system and the Langsmith tool (LangChain’s debugging/monitoring UI). However, as an in-process framework, achieving the same level of traceability across distributed components requires more intentional effort.

Real-Time Interoperability: MCP and A2A both prioritize interoperability across different systems and real-time or synchronous operation. MCP’s open standard nature allows any language or platform to implement it, with SDKs available in various languages and adoption by major AI providers. A2A similarly is openly published and had many partners from day one, including different AI companies. The notion is that an agent running on Google Cloud could call an agent running on someone’s on-prem server as long as both follow A2A. LangChain, by itself, is not an interoperability layer. If you have a LangChain agent and you want it to work with another agent outside its process, you’d typically need to expose one side as an API.

Real-World Applications and Use Cases

Understanding how these technologies are being applied in practice helps solidify their roles.

MCP Use Case – AI Coding Assistant in IDE:

MCP is used in developer tools like Sourcegraph and IDEs to provide AI coding assistants with real-time access to project files and repositories. Anthropic’s Claude Desktop app uses local MCP servers to expose system-level tools to the AI, allowing it to securely interact with user’s files. In enterprises, MCP connects internal knowledge bases and data stores to AI assistants, and projects like AI2SQL use it to interface with various SQL databases through a standardized tool.

A2A Use Case – Multi-Agent Enterprise Workflow:

A2A’s prime intended use cases are complex business workflows that involve multiple systems or departments. For example, an IT helpdesk automation could use A2A to delegate sub-tasks to HR, IT procurement, and compliance agents. Google’s example of candidate sourcing in recruiting demonstrates how specialized agents can interoperate seamlessly in a single pipeline. A2A is also paving the way for multi-LLM systems where different LLMs with varying strengths can collaborate.

LangChain Use Case – Autonomous Research Assistant:

LangChain has been a popular choice for developers building autonomous agents that perform multi-step tasks like researching a topic and producing a report. Its support for Retrieval Augmented Generation (RAG) through integration with vector databases has powered many document Q&A systems. LangChain is also widely used in various prototyping scenarios. Fujitsu’s Kozuchi AI platform reportedly used LangChain to automate certain business decision processes, showcasing how LangChain can integrate custom logic with LLM reasoning in a corporate setting.

Integrating MCP and A2A with LangChain: A Synergistic Approach

Given their complementary strengths, MCP and A2A can be effectively integrated with LangChain to create powerful hybrid AI agent systems. LangChain has actively collaborated on these standards (LangChain is an A2A launch partner), and community developers have demonstrated integrations.

• Using MCP within LangChain: LangChain agents can directly use MCP-provided tools just as they use native Python tools. This can be done by wrapping an MCP client call in a LangChain Tool interface. Conversely, existing LangChain Tools can be exposed as MCP endpoints for non-LangChain clients. The benefit is interoperability, giving LangChain agents access to the growing universe of MCP tools.

• Using A2A within LangChain: Integrating A2A allows a LangChain-based agent to both call other agents as if they were tools and be exposed as a service to other agents. One pattern is “A2A → LangChain,” where a remote A2A agent can be wrapped into a LangChain Agent interface. The flip side, “LangChain → A2A,” means you can take a LangChain agent you built and serve it via A2A so that others can call it. The synergy here is significant: LangChain gives a rich local development experience and library of tools, while A2A gives a standardized communication layer to go beyond the local scope.

Potential Benefits:

Embedding these protocols makes LangChain-based systems more extensible and interoperable. You’re not confined to one process or one framework’s ecosystem. This could future-proof solutions, allowing LangChain agents to interact with new tools and agents without heavy refactoring. Different teams within a large organization, using heterogeneous stacks, can cooperate through MCP/A2A. A2A’s task updates and streaming also offer real-time collaboration benefits.

Potential Limitations/Challenges:

Introducing MCP or A2A means running additional services and dealing with network calls, increasing system architecture complexity. Debugging across process boundaries is also harder. Latency can be a consideration, as network calls might be slower than direct function calls. As of mid-2025, MCP and A2A are new, and there may be teething issues, including security vulnerabilities. Developers also need to decide when to use a tool directly in LangChain versus when to make it an MCP/A2A call.

In summary, embedding MCP and A2A into LangChain marries the “framework” approach with the “open protocol” approach, creating a highly modular, interoperable AI agent architecture. We can envision a near future where a LangChain agent uses MCP to grab specialized data (say from an internal database) and uses A2A to delegate a subtask to a Vision AI agent – all within one seamless workflow. The combination leverages LangChain’s ease of development and rich community, MCP’s breadth of tool integrations, and A2A’s powerful multi-agent orchestration. Early prototypes and guides have already shown all these pieces working together, indicating that the ecosystem is converging: instead of competing approaches, MCP, A2A, and LangChain are becoming complementary layers of the emerging agentic AI stack.

Architecture Diagrams (Conceptual Descriptions)

As a text-based AI, I cannot directly generate images or diagrams. However, I can describe the architectural concepts for each in a way that should help you visualize them or create your own diagrams using an external tool.

Model Context Protocol (MCP) Architecture

Concept: A client-server model where AI agents (clients) connect to MCP servers to access tools, resources, and prompts.

• Diagram Components:
  • AI Agent (Client): The central intelligent entity (e.g., a Large Language Model) that needs external information or capabilities.
  • MCP Server: A service that hosts and exposes various “Tools,” “Resources,” and “Prompts” via the MCP (JSON-RPC 2.0 over HTTP/Stdio).
  • Tools: External functionalities (e.g., a calculator, a database query tool, an email sender) that the AI agent can invoke. Each tool is an independent service accessible through the MCP server.
  • Resources: Static data or files (e.g., a knowledge base, user-uploaded documents) that the AI agent can fetch.
  • Prompts: Pre-defined context templates or instructions that can be injected into the AI agent’s context.
  • Communication Flow: The AI Agent sends requests to the MCP Server (e.g., list_tools, call_tool, get_resource). The MCP Server processes these requests, interacts with the actual tools/resources, and sends back responses to the AI Agent.
• Key Characteristics:
  • Decoupled: Tools and resources are separate services from the AI agent.
  • Universal Interface: Standardizes how any AI agent can access diverse external capabilities.
  • Single Agent, Multi-Tool: Primarily focuses on one agent utilizing multiple external tools.

Agent-to-Agent (A2A) Protocol Architecture

Concept: A network of independent AI agents that communicate and collaborate as “peers” using a standardized protocol.

• Diagram Components:

  • Agent A (Client Agent): An AI agent that needs to delegate a subtask or collaborate.
  • Agent B (Service Agent): An AI agent that offers specialized skills or services.
  • Agent C (Another Service Agent): Another specialized AI agent.
  • Agent Card: Each agent publishes an “Agent Card” (like a manifest) describing its capabilities, API endpoint, and purpose. This allows other agents to discover its skills.
  • A2A Protocol (JSON-RPC over HTTP/S): The communication layer between agents.
  • Task Object: A formal representation of a delegated task, with a lifecycle (e.g., TaskRequest, StatusUpdate, TaskResult) that is passed between agents.
  • Messages: Explicit, structured messages exchanged between agents containing context, queries, and results.

• Key Characteristics:

  • Distributed Collaboration: Enables multiple independent agents to work together.
  • Peer-to-Peer: Agents interact as equals, not in a strict client-server hierarchy (though delegation flows exist).
  • Interoperable: Designed to allow agents from different vendors/frameworks to communicate.

LangChain Architecture

Concept: A framework (Python/JavaScript library) that orchestrates components within a single application to build LLM-powered applications and agents.

Diagram Components:

• LangChain Application/Agent: The main application built using the LangChain framework.

• LLM (Large Language Model): The core intelligence component (e.g., OpenAI, Anthropic) that LangChain interfaces with.

• Chains: Sequences of LLM calls or other components that perform a specific task (e.g., summarization chain, question-answering chain).

• Tools: Internal or integrated external functionalities (e.g., a Python function, an API call to a specific web service like Google Search or Wikipedia). These are defined and used within the LangChain application.

• Memory: Components that manage and store conversational history or other context for the LLM.

• Vector Database: Often integrated for Retrieval Augmented Generation (RAG) to provide external knowledge to the LLM.

• Key Characteristics:

• In-Process Orchestration: Manages the flow of data and logic within a single application.

• Developer-Centric: Provides abstractions to simplify building LLM applications.

• Composable: Allows chaining different components together to create complex workflows.

• Single Agent (Typical): While multi-agent patterns can be built, LangChain primarily orchestrates a single agent’s reasoning process.

Visualizing Integration

When considering how they integrate:

• A LangChain agent could have an MCP Tool (a tool within LangChain that makes an MCP call to an external MCP server).
• A LangChain agent could also make an A2A call to delegate a task to another A2A-compliant agent, treating that external agent as a “tool.”
• Conversely, a LangChain application could be exposed as an A2A-compliant agent, allowing other A2A agents to delegate tasks to it.