How to Use the Ably MCP in LangChain

Q: How do I connect Ably to LangChain?

Install langchain-mcp-adapters. Initialize a MultiServerMCPClient pointing to your Vinkius endpoint, extract the tools, and pass them to your ReAct agent.

Q: Can my LangChain agent read past Ably messages?

Yes. The agent calls getmessages to pull channel history. It reads the raw JSON output to build context for its next chain execution.

Q: Does LangChain support batch publishing via Ably?

You pass a list of channels and payloads to batchpublish. The agent formats the array, and the MCP server executes the broadcast in one network request.

Q: How do I track Ably tool usage in LangChain?

LangSmith captures everything automatically. Every call to getchannelmetadata or pushpublish logs latency, inputs, and the exact API response.

Build reactive messaging pipelines where LangChain agents read channels, decide what to send, and publish via Ably.

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Connect Ably MCP to LangChain

Create your Vinkius account to connect Ably 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 Ably with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain Ably messaging with other APIs

Your ReAct agent reads real-time context before deciding what to say. Using `get_messages`, the pipeline pulls recent channel history, feeds it into an LLM prompt, and formulates a response based on actual user chat rather than isolated queries. Output from that step flows directly into `publish_message`. You build a continuous loop where the agent monitors support channels, looks up answers in a database tool, and fires replies back to the user without leaving the LangChain execution graph.

Build presence-aware LangChain agents

Smart agents check if someone is actually online before sending notifications. By calling `get_presence` inside a routing chain, your system decides whether to send a direct message or queue an email via another tool. Multi-channel operations work the exact same way. A single node calls `batch_presence` to map active users, passing that list downstream to `batch_push_publish` so alerts only hit active devices.

Full observability for this MCP Server

LangSmith traces every single operation your agent performs against the Ably API. When an agent calls `revoke_tokens`, you see the exact inputs, latency, and token consumption right in your dashboard. Debugging multi-step workflows becomes trivial. If `get_stats` fails to return the expected application usage data, the trace shows exactly where the chain broke, letting you fix the tool input schema instantly.

Setup guide

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Ably. 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.

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Common questions about Ably MCP in LangChain

Install `langchain-mcp-adapters`. Initialize a `MultiServerMCPClient` pointing to your Vinkius endpoint, extract the tools, and pass them to your ReAct agent.

Yes. The agent calls `get_messages` to pull channel history. It reads the raw JSON output to build context for its next chain execution.

You pass a list of channels and payloads to `batch_publish`. The agent formats the array, and the MCP server executes the broadcast in one network request.

LangSmith captures everything automatically. Every call to `get_channel_metadata` or `push_publish` logs latency, inputs, and the exact API response.

Your application routes channel messages and presence states through the Vinkius V8 isolate sandbox. The container lives just long enough to execute the request, holding zero persistent state of your user conversations.

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