> ## Documentation Index
> Fetch the complete documentation index at: https://mcp-server-langgraph.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# What is MCP Server?

> Welcome to MCP Server with LangGraph - A production-ready MCP server with enterprise features

## What is MCP Server with LangGraph?

MCP Server with LangGraph is a **production-ready** MCP server implementation that combines the power of [LangGraph](https://langchain-ai.github.io/langgraph/) with the [Model Context Protocol (MCP)](https://modelcontextprotocol.io/), enhanced with enterprise-grade security, observability, and multi-cloud deployment capabilities.

<CardGroup cols={2}>
  <Card title="Quick Start" icon="rocket" href="/getting-started/quickstart">
    Get up and running in 5 minutes
  </Card>

  <Card title="API Reference" icon="code" href="/api-reference/introduction">
    Explore the API endpoints
  </Card>

  <Card title="Deploy to LangGraph Platform" icon="cloud" href="/deployment/langgraph-platform">
    One-command serverless deployment
  </Card>

  <Card title="View on GitHub" icon="github" href="https://github.com/vishnu2kmohan/mcp-server-langgraph">
    Star us on GitHub
  </Card>
</CardGroup>

## Key Features

<AccordionGroup>
  <Accordion icon="brain" title="Multi-LLM Support">
    Support for [100+ LLM providers](https://docs.litellm.ai/docs/providers) via [LiteLLM](https://docs.litellm.ai/):

    * **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.
  </Accordion>

  <Accordion icon="shield" title="Enterprise Security">
    Production-grade security features:

    * **[JWT Authentication](https://jwt.io/)** - Secure token-based auth
    * **[OpenFGA Authorization](https://openfga.dev/)** - Fine-grained relationship-based access control ([Zanzibar model](https://research.google/pubs/pub48190/))
    * **[Infisical Integration](https://infisical.com/docs)** - Centralized secrets management
    * **Audit Logging** - Complete security event tracking
    * **Network Policies** - Kubernetes-native network isolation
  </Accordion>

  <Accordion icon="chart-line" title="Dual Observability">
    Complete visibility with dual observability stack:

    * **[LangSmith](https://docs.smith.langchain.com/)** - LLM-specific tracing, prompt engineering, evaluations, cost tracking
    * **[OpenTelemetry](https://opentelemetry.io/)** - Distributed tracing with [Jaeger](https://www.jaegertracing.io/), infrastructure metrics
    * **[Prometheus](https://prometheus.io/)** - Metrics collection and alerting
    * **[Grafana](https://grafana.com/)** - Pre-built visualization dashboards
    * **Structured Logging** - JSON logs with trace correlation
  </Accordion>

  <Accordion icon="cloud" title="Multi-Cloud Deployment">
    Deploy anywhere with confidence:

    * **[LangGraph Platform](https://langchain-ai.github.io/langgraph/cloud/)** - One-command serverless deployment (\~2 min)\*
    * **Google Cloud Run** - Serverless GCP with auto-scaling (\~10 min)\*
    * **[Kubernetes](https://kubernetes.io/)** - Production-grade K8s on GKE, EKS, AKS (\~1-2 hours)\*
    * **[Helm Charts](https://helm.sh/)** - Flexible, customizable K8s deployments
    * **Docker** - Quick Docker Compose setup for dev/test (\~15 min)\*
    * **GitOps Ready** - [ArgoCD](https://argo-cd.readthedocs.io/), [FluxCD](https://fluxcd.io/) compatible

    \*Time estimates assume prerequisites configured. See [deployment docs](/deployment/overview) for details.
  </Accordion>
</AccordionGroup>

## Architecture

<Note>
  For detailed system architecture diagrams including authentication flows, deployment options, and component interactions, see [System Architecture](/diagrams/system-architecture).
</Note>

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

## Use Cases

<CardGroup cols={3}>
  <Card title="AI Assistants" icon="robot">
    Build intelligent assistants with multi-turn conversations and context awareness
  </Card>

  <Card title="Automation Agents" icon="wand-magic-sparkles">
    Create autonomous agents that execute complex workflows
  </Card>

  <Card title="Enterprise AI" icon="building">
    Deploy secure, compliant AI systems for enterprise use
  </Card>

  <Card title="Research Platforms" icon="flask">
    Build research tools with multiple model support
  </Card>

  <Card title="Customer Support" icon="headset">
    Intelligent support bots with fine-grained permissions
  </Card>

  <Card title="DevOps Automation" icon="gears">
    AI-powered infrastructure management and monitoring
  </Card>
</CardGroup>

## Why Choose MCP Server with LangGraph?

<Note>
  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.
</Note>

<Tip>
  **Choosing the right framework?** We've created a comprehensive [Framework Decision Guide](/comparisons/choosing-framework) 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.
</Tip>

### Framework Comparison Landscape

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

<CardGroup cols={3}>
  <Card title="vs Google ADK" icon="code" href="/comparisons/vs-google-adk">
    Excellent Google Cloud integration but tightly coupled to GCP ecosystem
  </Card>

  <Card title="vs OpenAI AgentKit" icon="wand-magic-sparkles" href="/comparisons/vs-openai-agentkit">
    Visual workflow builder limited to OpenAI models with usage-based pricing
  </Card>

  <Card title="vs Claude Agent SDK" icon="robot" href="/comparisons/vs-claude-agent-sdk">
    Deep Claude integration with automatic context management, Anthropic-exclusive
  </Card>

  <Card title="vs LangGraph Cloud" icon="diagram-project" href="/comparisons/vs-langgraph-cloud">
    2-minute serverless deployment as managed service with ongoing costs
  </Card>

  <Card title="vs CrewAI" icon="users" href="/comparisons/vs-crewai">
    Role-based multi-agent teams, excellent for prototyping and learning
  </Card>

  <Card title="vs Microsoft Agent Framework" icon="microsoft" href="/comparisons/vs-microsoft-agent-framework">
    Azure-integrated multi-agent collaboration with .NET/C# support
  </Card>
</CardGroup>

### When to Choose MCP Server with LangGraph

<CardGroup cols={2}>
  <Card title="Production Security & Compliance" icon="shield-halved">
    **Enterprise-grade security** with [JWT authentication](https://jwt.io/), [OpenFGA authorization](https://openfga.dev/) ([Zanzibar model](https://research.google/pubs/pub48190/)), and complete audit logging.

    **[GDPR](https://gdpr-info.eu/), [SOC 2](https://www.aicpa.org/soc4so), [HIPAA](https://www.hhs.gov/hipaa/)-ready architecture** with technical controls and audit trails.\*

    \*Compliance requires organizational policies and legal review. [Learn more](/security/compliance).
  </Card>

  <Card title="Multi-Cloud Flexibility" icon="cloud">
    **Deploy anywhere** - GCP, AWS, Azure, or LangGraph Platform without code changes.

    **Kubernetes-native** with production manifests, Helm charts, and GitOps ready infrastructure.
  </Card>

  <Card title="Complete Observability" icon="chart-line">
    **Dual monitoring stack** - [LangSmith](https://docs.smith.langchain.com/) for LLM-specific insights + [OpenTelemetry](https://opentelemetry.io/) for infrastructure metrics.

    **Time-to-production clarity** with [deployment estimates](/deployment/overview) (\~2 min to \~2 hours) for every target platform.\*

    \*Times assume prerequisites configured. [See detailed estimates](/deployment/overview).
  </Card>

  <Card title="Provider Independence & Reliability" icon="circle-check">
    **[100+ LLM providers](https://docs.litellm.ai/docs/providers)** with automatic fallback and retry logic for high availability.

    **Comprehensive test coverage** with unit, integration, property-based, and contract tests. See [testing documentation](/advanced/testing) for details.
  </Card>
</CardGroup>

<Info>
  **Need help choosing?** See our [Framework Decision Guide](/comparisons/choosing-framework) for a detailed decision matrix based on your use case, team size, and requirements.
</Info>

## Community & Support

<CardGroup cols={2}>
  <Card title="GitHub Discussions" icon="comments" href="https://github.com/vishnu2kmohan/mcp-server-langgraph/discussions">
    Ask questions and share ideas
  </Card>

  <Card title="Issue Tracker" icon="bug" href="https://github.com/vishnu2kmohan/mcp-server-langgraph/issues">
    Report bugs and request features
  </Card>

  <Card title="Contributing" icon="code-pull-request" href="/advanced/contributing">
    Help improve the project
  </Card>

  <Card title="Security" icon="shield-halved" href="/security/overview">
    Report security vulnerabilities
  </Card>
</CardGroup>

## Next Steps

<Steps>
  <Step title="Install">
    Follow the [Quick Start](/getting-started/quickstart) guide to install and configure
  </Step>

  <Step title="Configure">
    Set up your [LLM provider](/guides/multi-llm-setup) and [authentication](/getting-started/authentication)
  </Step>

  <Step title="Deploy">
    Deploy to [Docker](/deployment/docker) or [Kubernetes](/deployment/kubernetes)
  </Step>

  <Step title="Monitor">
    Set up [observability](/deployment/monitoring) and monitoring
  </Step>
</Steps>

***

<Info>
  **Ready to get started?** Jump to the [Quick Start guide](/getting-started/quickstart) to have your agent running in 5 minutes!
</Info>