Can the agent monitor a long-running relevance AI agent task?

Yes. You can trigger an agent using `trigger_agent`, and because it provides a `run_id`, you can explicitly prompt your local Assistant to periodically "check in on the status using `get_agent_run` every minute until finished" or ask it to summarize the step-by-step agent logs after completion.

What is the differences between tasks, tools, and agents in Relevance AI?

Agents are autonomous workers capable of making step-by-step reasoning choices based on instructions and tools. Tasks are linear, pre-chained sets of commands and prompts. Tools (`list_tools`) are the individual capabilities, like a custom API integration or web scraper, that tasks and agents utilize to perform their actions.

How do I find my specific Region and Project ID?

These details are typically nested within the URL string when you are logged into your workspace or found globally in your developer API keys configuration pane inside your Relevance AI team dashboard. The Region is usually something like 'us-east-1' or 'v2'.

Relevance AI MCP Server for LangChain 10 tools — connect in under 2 minutes

Name: Relevance AI
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Relevance AI through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "relevance-ai": {
            "transport": "streamable_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,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Relevance AI, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Relevance AI MCP Server

Connect your conversational AI to your Relevance AI workspace. By wrapping your custom agents, datasets, and API tools into this MCP extension, you transform your chat interface into a command center for orchestrating complex, autonomous AI operations and large-scale data workflows.

LangChain's ecosystem of 500+ components combines seamlessly with Relevance AI through native MCP adapters. Connect 10 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

Orchestrate Agents — Command your pre-built autonomous agents to execute tasks (trigger_agent). Monitor their progress and read their exact reasoning steps dynamically (get_agent_run). Use list_agents to discover all available AI worker configurations.
Execute Tasks & Workflows — Trigger predefined chained prompts or specific micro-tasks without leaving your chat (trigger_task), scaling repetitive workflows reliably.
Manage Knowledge Datasets — Take full control of your vector databases and tables. Insert new rows of knowledge directly from conversational context (insert_documents), retrieve raw unstructured data entries (get_documents), or surgically delete obsolete knowledge base items (delete_documents).

The Relevance AI MCP Server exposes 10 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Relevance AI to LangChain via MCP

Follow these steps to integrate the Relevance AI MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 10 tools from Relevance AI via MCP

Why Use LangChain with the Relevance AI MCP Server

LangChain provides unique advantages when paired with Relevance AI through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine Relevance AI MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Relevance AI queries for multi-turn workflows

Relevance AI + LangChain Use Cases

Practical scenarios where LangChain combined with the Relevance AI MCP Server delivers measurable value.

RAG with live data: combine Relevance AI tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Relevance AI, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Relevance AI tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Relevance AI tool call, measure latency, and optimize your agent's performance

Relevance AI MCP Tools for LangChain (10)

These 10 tools become available when you connect Relevance AI to LangChain via MCP:

delete_documents

This action is irreversible. Deletes documents from a dataset by their IDs

get_agent_run

Retrieves the status and logs of a specific agent run

get_documents

Retrieves documents from a dataset

insert_documents

Provide documents as a JSON array of objects. Inserts documents into a dataset

list_agents

Lists all AI agents in the Relevance AI studio

list_datasets

Lists all datasets (knowledge tables) in the project

list_tasks

Lists all tasks (chained prompts) in the studio

list_tools

Lists all custom tools registered in the studio

trigger_agent

Provide inputs as a JSON object. Triggers an AI agent execution

trigger_task

Triggers a specific task execution

Example Prompts for Relevance AI in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with Relevance AI immediately.

"List all available agents in my Relevance AI Studio and their IDs."

"Start a run for the 'Market Analysis' agent passing `{"company": "OpenAI"}` as the payload, then tell me the Run ID."

"Insert this JSON array of top competitor articles into the 'competitor_docs' dataset."

Troubleshooting Relevance AI MCP Server with LangChain

Common issues when connecting Relevance AI to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Relevance AI + LangChain FAQ

Common questions about integrating Relevance AI MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.