How to Use the Glama MCP in LangChain

Q: Is Glama telemetry compatible with LangChain tracing?

They work together as separate layers. You trigger glamasendtelemetry to report execution metrics to the Glama backend while LangSmith handles your internal chain logs.

Build multi-step reasoning pipelines in LangChain by pulling live tools directly from the Glama directory.

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Connect Glama MCP to LangChain

Create your Vinkius account to connect Glama 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 Glama with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Dynamic tool discovery for LangChain

Stop hardcoding connections and let your agent query the global directory. Use `glama_list_mcp_servers` to find new capabilities on the fly. Your LangChain agent pulls the exact server info needed via `glama_get_mcp_server_info`. It turns discovery into a simple, automated step in your chain.

Proxy model execution in LangChain

Route your prompts through a unified gateway instead of managing multiple API keys. Invoke `glama_run_gateway_chat` to execute complex reasoning tasks. You keep full control over your pipeline while the proxy handles the model overhead. It keeps your LangChain logic clean and focused on the agent flow.

Telemetry tracking for LangChain

Monitor every tool call and model response within your agents. Trigger `glama_send_telemetry` after your LangChain chain completes a step. This gives you clear visibility into latency and token usage. You see exactly how your agent performs as it cycles through its reasoning steps.

Setup guide

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

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Common questions about Glama MCP in LangChain

It provides a live registry your agent can query. Instead of manual setup, you use `glama_list_mcp_servers` to pull fresh tools into your LangChain environment.

Yes, you connect your chain to the Glama gateway. You then use `glama_run_gateway_chat` to send prompts through the proxy network.

They work together as separate layers. You trigger `glama_send_telemetry` to report execution metrics to the Glama backend while LangSmith handles your internal chain logs.

It supports standard HTTP transport. You configure the server URL in your LangChain client and start calling the eight available tools immediately.

The server transmits your API keys and prompt metadata over encrypted channels. Telemetry data only includes execution metrics, not your raw user input strings.

Use it with your favorite AI tools

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