Zeev MCP Server for LangChainGive LangChain instant access to 11 tools to Cancel Request, Create Request, Delegate Task, and more

Name: Zeev
Price: 29 USD
Availability: InStock
Rating: 5.0 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

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

Ask AI about this MCP Server for LangChain

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The Zeev MCP Server for LangChain is a standout in the Productivity category — giving your AI agent 11 tools to work with, ready to go from day one.

Built for AI Agents by Vinkius

Vinkius delivers Streamable HTTP and SSE to any MCP client

Claude

ChatGPT

Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

+ other MCP clients

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({
        "zeev": {
            "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 Zeev, 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 Zeev MCP Server

What you can do

List and manage your pending tasks in real-time.
Start new process requests with custom form data.
Complete tasks and make decisions directly from your AI agent.
Delegate tasks to other team members and track process history.

Who is it for?

Process managers looking for automated workflow control.
Operations teams needing quick task execution.
Developers integrating BPM into their AI-driven applications.

LangChain's ecosystem of 500+ components combines seamlessly with Zeev through native MCP adapters. Connect 11 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.

The Zeev MCP Server exposes 11 tools through the Vinkius. Connect it to LangChain in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 11 Zeev tools available for LangChain

When LangChain connects to Zeev through Vinkius, your AI agent gets direct access to every tool listed below — spanning bpm, workflow-automation, process-management, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.

cancel

Cancel request on Zeev

Cancel an active process request

create

Create request on Zeev

Start a new process request in Zeev

delegate

Delegate task on Zeev

Delegate a task to another user

finish

Finish task on Zeev

Finish/Complete a Zeev task

get

Get me on Zeev

Get current user information

get

Get process on Zeev

Get details of a process definition

get

Get request on Zeev

Get details of a specific process request

get

Get task on Zeev

Get details of a specific Zeev task

list

List processes on Zeev

List available process definitions

list

List requests on Zeev

List process requests (instances) in Zeev

list

List tasks on Zeev

List pending tasks in Zeev

Connect Zeev to LangChain via MCP

Follow these steps to wire Zeev into LangChain. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

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 11 tools from Zeev via MCP

Why Use LangChain with the Zeev MCP Server

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

The largest ecosystem of integrations, chains, and agents. combine Zeev 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 Zeev queries for multi-turn workflows

Zeev + LangChain Use Cases

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

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

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

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

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

Example Prompts for Zeev in LangChain

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

"List my pending tasks in Zeev."

"Finish task 123 with decision 'Approved'."

"Start a new 'Expense Report' process."

Troubleshooting Zeev MCP Server with LangChain

Common issues when connecting Zeev to LangChain through Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Zeev + LangChain FAQ

Common questions about integrating Zeev 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.

Can I trace MCP tool calls in LangSmith?

Yes. All MCP tool invocations appear as traced steps in LangSmith, showing input parameters, response payloads, latency, and token usage.

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