How to Use the Greptile MCP in LangChain

Chain together Greptile indexing and codebase queries within your LangChain agent pipelines for automated technical insights.

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Connect Greptile MCP to LangChain

Create your Vinkius account to connect Greptile 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 Greptile with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Automate repository indexing in LangChain

Trigger repository indexing directly from your agentic workflows. Use `index_repository` to pull new code into the system and `reindex_repository` to keep your context fresh as your branch evolves. Your agents maintain full control over the lifecycle of your code data. You can check current sync states with `get_repository_status` before triggering any heavy analysis tasks.

Chain codebase intelligence into agents

Use `query_codebase` as a specialized node in your LangChain execution graph. The agent receives code-aware responses that serve as immediate input for the next step in your reasoning chain. This setup allows you to build multi-step pipelines where the agent decides when to call `search_codebase` based on previous outputs. It keeps the logic modular and traceable within your existing infrastructure.

Track usage and repository metadata

Monitor your API consumption patterns by calling `get_greptile_usage` inside your chains. This helps you manage token spend and identify which repositories trigger the most intense processing. Combine this with `list_repositories` and `get_file_info` to generate dynamic reports on your codebase state. You get the raw data needed to make informed decisions about your pipeline's scope.

Setup guide

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

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Common questions about Greptile MCP in LangChain

You instantiate the MCP client and pass the tools to your agent. Once linked, you simply prompt the agent to search your codebase and it handles the tool selection for you.

Yes, you can share the Greptile MCP server across multiple agents. Each agent can then pull context from the same indexed repositories without redundant API calls.

Your codebase data is processed through the Greptile API using your unique token. We do not store your source code in a way that allows cross-user access, ensuring your intellectual property remains isolated.

The server returns a standard error object that your agent can catch. You can then program your logic to retry or skip that specific node.

The connection is established during your session initialization. You can use the client session to maintain context across multiple steps in your workflow.

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