MCP vs LangChain: Which Should You Choose?

A detailed comparison of the protocol vs the framework for AI agent development.

MCP and LangChain are often discussed as competing approaches to AI agent development. But they're fundamentally different things serving different purposes. This guide clarifies the distinction and helps you choose — or combine — them.

The Core Misconception

MCP is a protocol. It defines how agents interact with tools.

LangChain is a framework. It provides code for building agents.

They're not mutually exclusive. LangChain actually includes MCP integration. Understanding this distinction is key to using both effectively.

What Each Does

MCP Handles

Agent ←→ MCP Protocol ←→ Tool Server

• Tool discovery
• Tool invocation
• Schema validation
• Transport (stdio, HTTP, WebSocket)
• Standardized pricing and payments

LangChain Handles

User → LangChain Agent → [LLM, Memory, Tools, Prompts, Parsers]

• Agent logic and orchestration
• Prompt management
• Memory systems
• Output parsing
• Tool chains
• Document loaders
• Vector store integration

Feature Comparison

Using LangChain with MCP

LangChain has built-in MCP support:

from langchain.agents import AgentExecutor, create_tool_calling_agent
from langchain_openai import ChatOpenAI
from langchain_mcp import MCPToolkit

# Connect to MCP servers
toolkit = MCPToolkit(
    servers=[
        {"command": "node", "args": ["./db-server.js"]},
        {"url": "https://api-mcp.example.com/sse"},
    ]
)

# Get MCP tools as LangChain tools
mcp_tools = await toolkit.get_tools()

# Mix MCP tools with native LangChain tools
from langchain.tools import Tool

all_tools = [
    *mcp_tools,  # Tools from MCP servers
    Tool(name="calculator", ...),  # Native LangChain tool
    Tool(name="python_repl", ...),  # Another native tool
]

# Create agent with all tools
llm = ChatOpenAI(model="gpt-4o")
agent = create_tool_calling_agent(llm, all_tools, prompt)
executor = AgentExecutor(agent=agent, tools=all_tools)

When to Use MCP Only

Choose MCP-only (without LangChain) when:

1. Simple tool integration — Just need to connect a few tools to Claude or another MCP-compatible agent
2. Protocol-first architecture — You want maximum portability across agent platforms
3. Publishing tools — Building tools for SkillExchange or other marketplaces
4. Minimal dependencies — Don't want the LangChain dependency tree
5. Custom agent — You've built your own agent framework

Example: MCP-Only Agent

from mcp import Client

# Connect to MCP servers
db_client = Client("https://db-mcp.example.com/sse")
api_client = Client("https://api-mcp.example.com/sse")

# Discover tools
db_tools = await db_client.list_tools()
api_tools = await api_client.list_tools()

# Simple agent loop
async def process(message):
    # Use LLM to decide which tool to use
    tool_choice = await llm.select_tool(message, [*db_tools, *api_tools])

    # Execute via MCP
    if tool_choice:
        result = await tool_choice.client.call_tool(
            tool_choice.name, tool_choice.arguments
        )
        return result

    # Direct LLM response
    return await llm.complete(message)

When to Use LangChain

Choose LangChain (with or without MCP) when:

1. Complex agent logic — Multi-step reasoning, conditional branching
2. Rich memory — Conversation summary, entity memory, knowledge graph
3. Document processing — Load, chunk, embed, and retrieve documents
4. Multiple LLM providers — Switch between OpenAI, Anthropic, Google
5. Production infrastructure — Tracing (LangSmith), evaluation, deployment

Example: LangChain with MCP

from langchain.agents import AgentExecutor
from langchain_openai import ChatOpenAI
from langchain_mcp import MCPToolkit
from langchain.memory import ConversationSummaryMemory
from langchain.prompts import ChatPromptTemplate

# MCP tools
toolkit = MCPToolkit(servers=[{"url": "https://tools.example.com/sse"}])
tools = await toolkit.get_tools()

# Memory
memory = ConversationSummaryMemory(
    llm=ChatOpenAI(model="gpt-4o-mini"),
    max_summary_length=200,
)

# Prompt
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant with access to tools."),
    ("placeholder", "{chat_history}"),
    ("user", "{input}"),
    ("placeholder", "{agent_scratchpad}"),
])

# Agent
llm = ChatOpenAI(model="gpt-4o")
agent = create_tool_calling_agent(llm, tools, prompt)

executor = AgentExecutor(
    agent=agent,
    tools=tools,
    memory=memory,
    verbose=True,
    max_iterations=10,
)

result = await executor.ainvoke({"input": "What's in our database?"})

Architecture Patterns

Pattern 1: LangChain Agent + MCP Tools

User → LangChain Agent → MCP Protocol → External Tools

Best for: Complex agents that need external tools via standard protocol.

Pattern 2: MCP-Only Agent

User → Custom Agent → MCP Protocol → External Tools

Best for: Simple agents, maximum portability, minimal dependencies.

Pattern 3: LangChain Only (No MCP)

User → LangChain Agent → Direct Tool Calls

Best for: Prototyping, internal tools, when portability doesn't matter.

Pattern 4: MCP Server Published for LangChain Users

Your MCP Server → SkillExchange → LangChain Users Import via MCPToolkit

Best for: Tool developers who want to reach LangChain users.

Migration Path

From LangChain to MCP

If you want to make LangChain tools MCP-compatible:

# Before: LangChain-only tool
from langchain.tools import Tool

def search_tool(query: str) -> str:
    return search_api.search(query)

langchain_tool = Tool(
    name="search",
    description="Search the web",
    func=search_tool,
)

# After: MCP-compatible tool
from mcp import Server

server = Server("search-tools")

@server.tool("search")
async def search(query: str) -> dict:
    results = search_api.search(query)
    return {"content": [{"type": "text", "text": str(results)}]}

# Works with LangChain AND every other MCP-compatible agent

From MCP to LangChain

If you have MCP tools and want to use them in LangChain:

from langchain_mcp import MCPToolkit

toolkit = MCPToolkit(servers=[{"url": "https://your-mcp-server.com/sse"}])
tools = await toolkit.get_tools()

# Now use them as native LangChain tools
agent = create_tool_calling_agent(llm, tools, prompt)

Performance Comparison

Community and Ecosystem

Conclusion

MCP and LangChain aren't competitors — they're complementary. Use MCP to make your tools universally accessible. Use LangChain when you need a full-featured framework for building complex agents.

For most production agents, the best approach is: LangChain for orchestration + MCP for tool access.

Learn More

• MCP Protocol Explained
• How to Build an MCP Server
• AI Agent Orchestration Tools
• Building AI Agents with TypeScript

Explore both MCP tools and LangChain integrations on SkillExchange.