Nestjs Langgraph

A simple proof-of-concept MCP server built with NestJS, using LangGraph as the orchestration layer for tool execution and agent workflows.

Features

• NestJS application context (no HTTP server required)
• MCP server over stdio transport
• LangGraph StateGraph workflow with nodes:
  • route (model-based tool routing)
  • executeTool (executes selected tool)
  • respond (final answer generation)
• Model provider switch:
  • OpenAI (@langchain/openai)
  • Ollama local model (@langchain/ollama, default qwen2.5:1.5b)

Quick Start

1. Install dependencies:

npm install

2. Configure environment:

cp .env.example .env

3. Build and run:

npm run build
npm start

The MCP server starts on stdio.

Model Selection

Default provider is read from MODEL_PROVIDER.

• Use OpenAI:

  • MODEL_PROVIDER=openai
  • set OPENAI_API_KEY
  • optional OPENAI_MODEL (default gpt-4o-mini)

• Use Ollama qwen2.5:1.5b:

  • MODEL_PROVIDER=ollama
  • run Ollama locally and pull model:

ollama pull qwen2.5:1.5b

• optional OLLAMA_BASE_URL and OLLAMA_MODEL

You can override provider/model per MCP tool call (run_agent).

MCP Tools

• health: returns server status and default model config
• run_agent: runs the LangGraph workflow
  • input:
    • prompt (string, required)
    • provider (openai or ollama, optional)
    • model (string, optional)

Example MCP Client Config (Claude Desktop style)

{
  "mcpServers": {
    "nestjs-langgraph": {
      "command": "node",
      "args": ["/absolute/path/to/langraph-mcp/dist/main.js"],
      "env": {
        "MODEL_PROVIDER": "ollama",
        "OLLAMA_BASE_URL": "http://localhost:11434",
        "OLLAMA_MODEL": "qwen2.5:1.5b"
      }
    }
  }
}

LangGraph Demo Suite

This repo includes agents and tools built using proper LangGraph.js patterns:

• Tools: Defined using tool() from @langchain/core/tools with Zod schemas
• Agents: Built with createReactAgent or custom StateGraph with MessagesAnnotation
• Tool Execution: Uses ToolNode and toolsCondition from @langchain/langgraph/prebuilt
• LLM Binding: Tools bound to LLM via .bindTools(tools)

Demo Capabilities

Demo	Capability
01-reasoning	Basic StateGraph workflow
02-parallel	Parallel tool calls via ToolNode
03-handoffs	Agent-to-agent handoffs via coordinator
04-hitl	Human-in-the-loop approval workflow
05-structured	Structured output with Zod validation
06-tracing	Execution tracing through graph
07-discovery	Tool discovery and registry
08-planning	Multi-step planning loop
09-failure	Error handling and retry
10-local	Local Ollama model integration

Project Layout

demos/
├── demo-runner.ts
├── 01-reasoning.demo.ts
├── 02-parallel.demo.ts
├── 03-handoffs.demo.ts
├── 04-hitl.demo.ts
├── 05-structured-output.demo.ts
├── 06-tracing.demo.ts
├── 07-tool-discovery.demo.ts
├── 08-multi-step-planning.demo.ts
├── 09-failure-handling.demo.ts
├── 10-local-model.demo.ts
└── utils/demo-utils.ts

agents/
├── agent.factory.ts
├── tools.ts
├── coordinator.agent.ts
├── employee.agent.ts
├── analytics.agent.ts
├── reporting.agent.ts
├── approval.agent.ts
└── model.config.ts

Prerequisites

Ensure Ollama is running with a tool-capable model:

# qwen2.5:1.5b supports tool calling (recommended)
ollama pull qwen2.5:1.5b
ollama serve

Note: Models like qwen2.5:1.5b do not support tool calling. Use qwen2.5:1.5b, llama3.1, mistral, or qwen2.5 for full demo functionality.

Run all demos:

npm run demo

Run one demo:

npm run demo:one -- 03

Optional HITL switch for demo 04:

DEMO_REVIEW_DECISION=approved npm run demo:one -- 04