How to Use the KEGG MCP in LangChain

Q: Can I use KEGG with LangChain for batch processing?

Yes. You can iterate over lists of IDs using kegglist and trigger sequential lookups for each entry within your chain.

Q: How do I map external IDs in LangChain?

Use keggconv as a preprocessing step in your chain. It translates local identifiers to KEGG formats before downstream tools execute.

Build automated genomic reasoning chains with the KEGG MCP Server and LangChain.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect KEGG MCP to LangChain

Create your Vinkius account to connect KEGG 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.

GDPR Free for Subscribers

Setup KEGG with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain genomic lookups in LangChain

Feed `kegg_get` output directly into your next processing node. Your agent handles the logic, moving from gene identifiers to pathway maps without manual intervention. This pipeline approach keeps your code modular. You define the sequence, and the agent executes the steps based on the returned data.

Execute complex cross-database queries

Use `kegg_link` to map genes to pathways while keeping trace data in LangSmith. You'll see exactly how the agent builds its reasoning path through the KEGG dataset. Debugging becomes trivial when you can inspect the input and output of every tool execution. It's just a standard chain of thought.

Automate drug interaction analysis

Trigger `kegg_ddi` as a node in your agentic workflow to flag potential conflicts. The agent evaluates the interaction data before deciding on the final output. This keeps your decision pipeline tight and reactive. You get clear, verifiable results for every query processed.

Setup guide

Set up KEGG 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 KEGG 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({
    "kegg-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 KEGG transactions"
    })
    print(result["messages"][-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by KEGG. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect KEGG now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about KEGG MCP in LangChain

LangChain treats every MCP tool call as a discrete step in your pipeline. You pass the output of one tool directly into the next to build multi-step logic.

Yes. You can iterate over lists of IDs using `kegg_list` and trigger sequential lookups for each entry within your chain.

Absolutely. Every interaction is logged through the standard MCP interface, which LangSmith captures for full trace visibility.

Use `kegg_conv` as a preprocessing step in your chain. It translates local identifiers to KEGG formats before downstream tools execute.

Your queries stay between your client and the server. Only the requested genomic identifiers and interaction records pass through the connection.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.