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What is MCP Server with LangGraph?

MCP Server with LangGraph is a production-ready MCP server implementation that combines the power of LangGraph with the Model Context Protocol (MCP), enhanced with enterprise-grade security, observability, and multi-cloud deployment capabilities.

Quick Start

Get up and running in 5 minutes

API Reference

Explore the API endpoints

Deploy to LangGraph Platform

One-command serverless deployment

View on GitHub

Star us on GitHub

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Key Features

Support for 100+ LLM providers via LiteLLM:
  • Anthropic Claude (3.5 Sonnet, 3 Opus)
  • OpenAI GPT (GPT-4, GPT-4 Turbo)
  • Google Gemini (2.5 Flash, 2.5 Pro)
  • Azure OpenAI
  • AWS Bedrock
  • Local Models via Ollama (Llama 3.1, Qwen 2.5, Mistral)
Automatic fallback and retry logic ensures high availability.
Production-grade security features:
Complete visibility with dual observability stack:
  • LangSmith - LLM-specific tracing, prompt engineering, evaluations, cost tracking
  • OpenTelemetry - Distributed tracing with Jaeger, infrastructure metrics
  • Prometheus - Metrics collection and alerting
  • Grafana - Pre-built visualization dashboards
  • Structured Logging - JSON logs with trace correlation
Deploy anywhere with confidence:
  • LangGraph Platform - One-command serverless deployment (~2 min)*
  • Google Cloud Run - Serverless GCP with auto-scaling (~10 min)*
  • Kubernetes - Production-grade K8s on GKE, EKS, AKS (~1-2 hours)*
  • Helm Charts - Flexible, customizable K8s deployments
  • Docker - Quick Docker Compose setup for dev/test (~15 min)*
  • GitOps Ready - ArgoCD, FluxCD compatible
*Time estimates assume prerequisites configured. See deployment docs for details.

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Architecture

For detailed system architecture diagrams including authentication flows, deployment options, and component interactions, see System Architecture.
The MCP Server follows a layered architecture:
  • Client Layer: MCP protocol communication
  • Security Layer: JWT authentication and OpenFGA authorization
  • Agent Layer: LangGraph-powered agentic workflows
  • Provider Layer: Multi-LLM support with automatic fallback
  • Observability Layer: Dual monitoring with OpenTelemetry and LangSmith

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Use Cases

AI Assistants

Build intelligent assistants with multi-turn conversations and context awareness

Automation Agents

Create autonomous agents that execute complex workflows

Enterprise AI

Deploy secure, compliant AI systems for enterprise use

Research Platforms

Build research tools with multiple model support

Customer Support

Intelligent support bots with fine-grained permissions

DevOps Automation

AI-powered infrastructure management and monitoring

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Why Choose MCP Server with LangGraph?

MCP Server with LangGraph is production-ready from day one with enterprise-grade security, complete observability, and true multi-cloud flexibility. See our detailed comparisons with specific frameworks below.
Choosing the right framework? We’ve created a comprehensive Framework Decision Guide with decision trees, comparison matrices by use case, team type analysis, and real-world scenarios to help you make the best choice for your project.

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Framework Comparison Landscape

The agent framework ecosystem has matured significantly in 2025. Here’s how we compare to leading alternatives:

vs Google ADK

Excellent Google Cloud integration but tightly coupled to GCP ecosystem

vs OpenAI AgentKit

Visual workflow builder limited to OpenAI models with usage-based pricing

vs Claude Agent SDK

Deep Claude integration with automatic context management, Anthropic-exclusive

vs LangGraph Cloud

2-minute serverless deployment as managed service with ongoing costs

vs CrewAI

Role-based multi-agent teams, excellent for prototyping and learning

vs Microsoft Agent Framework

Azure-integrated multi-agent collaboration with .NET/C# support

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When to Choose MCP Server with LangGraph

Production Security & Compliance

Enterprise-grade security with JWT authentication, OpenFGA authorization (Zanzibar model), and complete audit logging.GDPR, SOC 2, HIPAA-ready architecture with technical controls and audit trails.**Compliance requires organizational policies and legal review. Learn more.

Multi-Cloud Flexibility

Deploy anywhere - GCP, AWS, Azure, or LangGraph Platform without code changes.Kubernetes-native with production manifests, Helm charts, and GitOps ready infrastructure.

Complete Observability

Dual monitoring stack - LangSmith for LLM-specific insights + OpenTelemetry for infrastructure metrics.Time-to-production clarity with deployment estimates (~2 min to ~2 hours) for every target platform.**Times assume prerequisites configured. See detailed estimates.

Provider Independence & Reliability

100+ LLM providers with automatic fallback and retry logic for high availability.Comprehensive test coverage with unit, integration, property-based, and contract tests. See testing documentation for details.
Need help choosing? See our Framework Decision Guide for a detailed decision matrix based on your use case, team size, and requirements.

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Community & Support

GitHub Discussions

Ask questions and share ideas

Issue Tracker

Report bugs and request features

Contributing

Help improve the project

Security

Report security vulnerabilities

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Next Steps

1

Install

Follow the Quick Start guide to install and configure
2

Configure

Set up your LLM provider and authentication
3

Deploy

Deploy to Docker or Kubernetes
4

Monitor

Set up observability and monitoring
Ready to get started? Jump to the Quick Start guide to have your agent running in 5 minutes!