Observe.AI MCP Server for LangChain 10 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Observe.AI 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({
"observeai": {
"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 Observe.AI, 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 Observe.AI MCP Server
Connect your Observe.AI account to your AI agent and gain deep visibility into your contact center performance and conversation intelligence through natural conversation.
LangChain's ecosystem of 500+ components combines seamlessly with Observe.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
- Interaction Monitoring — List and inspect all calls, chats, and emails processed by the platform, including metadata and analysis.
- Full Transcripts — Retrieve the complete text transcripts for any call or chat interaction for detailed review.
- QA & Evaluations — Access quality assurance scores, evaluation forms, and individual agent performance metrics.
- AI Insights — View automated interaction summaries and identified business moments (e.g., Greetings, Objections).
- Coaching Oversight — Monitor agent coaching sessions and feedback logs to track improvement.
- Workspace Management — List all agents, supervisors, and admins in your Observe.AI instance.
The Observe.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 Observe.AI to LangChain via MCP
Follow these steps to integrate the Observe.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 Observe.AI via MCP
Why Use LangChain with the Observe.AI MCP Server
LangChain provides unique advantages when paired with Observe.AI through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Observe.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 Observe.AI queries for multi-turn workflows
Observe.AI + LangChain Use Cases
Practical scenarios where LangChain combined with the Observe.AI MCP Server delivers measurable value.
RAG with live data: combine Observe.AI tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Observe.AI, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Observe.AI tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Observe.AI tool call, measure latency, and optimize your agent's performance
Observe.AI MCP Tools for LangChain (10)
These 10 tools become available when you connect Observe.AI to LangChain via MCP:
get_evaluation_details
Get specific evaluation info
get_interaction_details
Get specific interaction info
get_interaction_transcript
Get interaction transcript
list_coaching_sessions
List agent coaching sessions
list_evaluation_forms
List QA evaluation forms
list_interaction_moments
g. Greeting, Closing) across interactions. List identified key moments
list_interaction_summaries
List AI-generated summaries
list_interactions
AI. List contact center interactions
list_qa_evaluations
List QA evaluations
list_workspace_users
AI workspace. List workspace agents and users
Example Prompts for Observe.AI in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Observe.AI immediately.
"List all recent call interactions from today."
"What is the QA score for interaction ID 'int_12345'?"
"Show me the AI summaries for our latest interactions."
Troubleshooting Observe.AI MCP Server with LangChain
Common issues when connecting Observe.AI to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersObserve.AI + LangChain FAQ
Common questions about integrating Observe.AI 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 Observe.AI 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 Observe.AI to LangChain
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.