How to Use the Alchemy MCP in LangChain

Chain Alchemy data directly into your LangChain pipelines. Build reasoning agents that act on live Ethereum state.

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Connect Alchemy MCP to LangChain

Create your Vinkius account to connect Alchemy to LangChain and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Alchemy with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain Ethereum state into logic

Feed `get_latest_block_number` or `get_wallet_balance` results directly into your LangChain chains. Your agent processes these values to trigger downstream logic without manual intervention. This MCP server provides the raw data your chain needs to make decisions. You get clean inputs for your nodes, keeping your logic flow tight and observable.

Audit assets within LangChain

Connect `get_owned_nfts` to your agent for automated portfolio analysis. The tool output flows into your chain, letting the agent evaluate holdings in real-time. Your agent handles the heavy lifting by parsing these results against your defined criteria. It turns raw asset lists into structured, actionable intelligence.

Verify transactions in your pipeline

Use `get_transaction_receipt` inside a LangChain agent to confirm execution status. The agent checks the receipt data to verify if a contract call actually succeeded. Tracing this flow in LangSmith shows exactly what the agent saw. You catch failures early because the tool response is immediate and typed.

Setup guide

Set up Alchemy MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Alchemy tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "alchemy-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Alchemy transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Alchemy MCP in LangChain

Install the MCP adapters and use the MultiServerMCPClient to point at the Vinkius URL. Once you call get_tools, pass them into your LangChain agent constructor to start executing.

Yes. The agent uses the output of one tool as the input for the next, allowing it to reason through complex blockchain states across several steps.

Absolutely. Every tool call is logged in LangSmith, showing you the latency and the specific data returned by the server.

It is stateless by default, but you should use client.session() if you need to maintain context across multiple turns.

This server only reads public blockchain data like balances and receipts. It never touches your private keys, so your wallet secrets stay on your machine.

Use it with your favorite AI tools

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