Lattice MCP Server for LangChain 9 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Lattice 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
Vinkius supports streamable HTTP and SSE.
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({
"lattice": {
"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 Lattice, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
asyncio.run(main())
* 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 Lattice MCP Server
Connect your AI agent directly to Lattice HR. With this server, your LLM can fetch detailed employee profiles, active OKRs, tasks, and search continuous feedback loops directly tied to the Lattice platform.
LangChain's ecosystem of 500+ components combines seamlessly with Lattice through native MCP adapters. Connect 9 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
- Employee Directory: Fetch user metadata directly from your HRIS via Lattice.
- Goal Tracking: Query active company or individual OKRs and assess progress.
- Feedback & Praise: Monitor continuous feedback loops and recognition events.
- Review Cycles: Check past and current performance review structural data.
The Lattice MCP Server exposes 9 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 Lattice to LangChain via MCP
Follow these steps to integrate the Lattice 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 9 tools from Lattice via MCP
Why Use LangChain with the Lattice MCP Server
LangChain provides unique advantages when paired with Lattice through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Lattice 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 Lattice queries for multi-turn workflows
Lattice + LangChain Use Cases
Practical scenarios where LangChain combined with the Lattice MCP Server delivers measurable value.
RAG with live data: combine Lattice tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Lattice, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Lattice tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Lattice tool call, measure latency, and optimize your agent's performance
Lattice MCP Tools for LangChain (9)
These 9 tools become available when you connect Lattice to LangChain via MCP:
get_feedback
Get details about a specific feedback entry
get_goal
Get targeted details for a specific goal
get_review
Get details regarding a specific review cycle
get_user
Get details for a specific Lattice employee
list_feedback
Retrieve a list of feedback and praise instances
list_goals
Retrieve a list of all OKRs & Goals
list_reviews
Retrieve a list of performance review cycles
list_tasks
Retrieve pending tasks
list_users
Retrieve a list of employees/users from Lattice
Example Prompts for Lattice in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Lattice immediately.
"List all the current engineering OKRs mapped within Lattice."
"Retrieve the full team employee directory for the Marketing division."
"Who received recent public praise and continuous feedback this week?"
Troubleshooting Lattice MCP Server with LangChain
Common issues when connecting Lattice to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersLattice + LangChain FAQ
Common questions about integrating Lattice MCP Server with LangChain.
How does LangChain connect to MCP servers?
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?
Can I trace MCP tool calls in LangSmith?
Connect Lattice with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Lattice to LangChain
Get your token, paste the configuration, and start using 9 tools in under 2 minutes. No API key management needed.