LangGraph Gateway Agent

A LangGraph agent demonstrating how to add custom Lambda function tools via AgentCore Gateway.

What This Example Shows

Gateway Tools: Exposing Lambda functions as agent tools
Auto-Created Gateway: Default gateway created automatically when tools are defined
MCP Protocol: Tool discovery and invocation via Model Context Protocol
BedrockAgentCoreApp: Integration pattern for AgentCore Runtime
LangGraph: Agent orchestration with tool nodes

Architecture

User Request → AgentCore Runtime → agent_invocation()
                                      ↓
                                   LangGraph
                                      ↓
                               Claude Sonnet 5
                                      ↓
                         [needs calculation?]
                                      ↓
                    Gateway (MCP) → Calculator Lambda
                                      ↓
                                   Response

Prerequisites

AWS account with Bedrock model access (Claude Sonnet 5)
Enable access to the global.anthropic.claude-sonnet-5 inference profile in Bedrock console (override the default via the MODEL_ID env var)
Docker installed
Serverless Framework v4+
AWS credentials configured

Quick Start

1. Deploy

serverless deploy

The framework will:

Build the Docker image
Push to Amazon ECR
Deploy the Calculator Lambda function
Create the AgentCore Gateway with the calculator tool
Deploy AgentCore Runtime
Output the invocation URL

2. Test

Using boto3:

import boto3
import json
import uuid

client = boto3.client('bedrock-agentcore', region_name='us-east-1')

response = client.invoke_agent_runtime(
    agentRuntimeArn='YOUR_RUNTIME_ARN',  # From deploy output
    runtimeSessionId=str(uuid.uuid4()),
    payload=json.dumps({"prompt": "What is 25 multiplied by 4?"}).encode()
)

result = json.loads(response['response'].read())
print(result)

3. Local Development

serverless dev

How It Works

Configuration

The serverless.yml defines:

Lambda Function: The calculator handler
Tool Definition: Maps the Lambda to a gateway tool with schema
Agent: A minimal agent that receives the gateway URL automatically

functions:
  calculatorFunction:
    handler: handlers/calculator.handler
    runtime: python3.14

ai:
  tools:
    calculator:
      function: calculatorFunction
      toolSchema:
        - name: calculate
          description: Evaluate a mathematical expression
          inputSchema:
            type: object
            properties:
              expression:
                type: string
            required:
              - expression

  agents:
    chatbot: {} # Default gateway auto-created

Tool Discovery

The agent discovers gateway tools at startup:

GATEWAY_URL = os.environ.get("BEDROCK_AGENTCORE_GATEWAY_URL")

async with sse_client(GATEWAY_URL) as streams:
    async with ClientSession(*streams) as session:
        await session.initialize()
        tools = await session.list_tools()

Tool Invocation

When the LLM decides to use a tool, LangGraph's ToolNode invokes it via the MCP client:

result = await session.call_tool("calculate", {"expression": "25 * 4"})

Files

File	Purpose
`serverless.yml`	Infrastructure configuration
`agent.py`	LangGraph agent with gateway tool discovery
`handlers/calculator.py`	Calculator Lambda function
`Dockerfile`	Container definition
`pyproject.toml`	Python dependencies

Adding More Tools

Define additional tools in serverless.yml:

functions:
  calculatorFunction:
    handler: handlers/calculator.handler
    runtime: python3.14

  weatherFunction:
    handler: handlers/weather.handler
    runtime: python3.14

ai:
  tools:
    calculator:
      function: calculatorFunction
      toolSchema: [...]

    weather:
      function: weatherFunction
      toolSchema:
        - name: get_weather
          description: Get current weather for a city
          inputSchema:
            type: object
            properties:
              city:
                type: string
            required:
              - city

  agents:
    chatbot: {}

Cleanup

serverless remove

Next Steps

LangGraph Multi-Gateway - Multiple gateways with different authorization
LangGraph Memory - Add conversation persistence

LangGraph Gateway Agent

A LangGraph agent demonstrating how to add custom Lambda function tools via AgentCore Gateway.

What This Example Shows

Gateway Tools: Exposing Lambda functions as agent tools
Auto-Created Gateway: Default gateway created automatically when tools are defined
MCP Protocol: Tool discovery and invocation via Model Context Protocol
BedrockAgentCoreApp: Integration pattern for AgentCore Runtime
LangGraph: Agent orchestration with tool nodes

Architecture

User Request → AgentCore Runtime → agent_invocation()
                                      ↓
                                   LangGraph
                                      ↓
                               Claude Sonnet 5
                                      ↓
                         [needs calculation?]
                                      ↓
                    Gateway (MCP) → Calculator Lambda
                                      ↓
                                   Response

Prerequisites

AWS account with Bedrock model access (Claude Sonnet 5)
Enable access to the global.anthropic.claude-sonnet-5 inference profile in Bedrock console (override the default via the MODEL_ID env var)
Docker installed
Serverless Framework v4+
AWS credentials configured

Quick Start

1. Deploy

serverless deploy

The framework will:

Build the Docker image
Push to Amazon ECR
Deploy the Calculator Lambda function
Create the AgentCore Gateway with the calculator tool
Deploy AgentCore Runtime
Output the invocation URL

2. Test

Using boto3:

import boto3
import json
import uuid

client = boto3.client('bedrock-agentcore', region_name='us-east-1')

response = client.invoke_agent_runtime(
    agentRuntimeArn='YOUR_RUNTIME_ARN',  # From deploy output
    runtimeSessionId=str(uuid.uuid4()),
    payload=json.dumps({"prompt": "What is 25 multiplied by 4?"}).encode()
)

result = json.loads(response['response'].read())
print(result)

3. Local Development

serverless dev

How It Works

Configuration

The serverless.yml defines:

Lambda Function: The calculator handler
Tool Definition: Maps the Lambda to a gateway tool with schema
Agent: A minimal agent that receives the gateway URL automatically

functions:
  calculatorFunction:
    handler: handlers/calculator.handler
    runtime: python3.14

ai:
  tools:
    calculator:
      function: calculatorFunction
      toolSchema:
        - name: calculate
          description: Evaluate a mathematical expression
          inputSchema:
            type: object
            properties:
              expression:
                type: string
            required:
              - expression

  agents:
    chatbot: {} # Default gateway auto-created

Tool Discovery

The agent discovers gateway tools at startup:

GATEWAY_URL = os.environ.get("BEDROCK_AGENTCORE_GATEWAY_URL")

async with sse_client(GATEWAY_URL) as streams:
    async with ClientSession(*streams) as session:
        await session.initialize()
        tools = await session.list_tools()

Tool Invocation

When the LLM decides to use a tool, LangGraph's ToolNode invokes it via the MCP client:

result = await session.call_tool("calculate", {"expression": "25 * 4"})

Files

File	Purpose
`serverless.yml`	Infrastructure configuration
`agent.py`	LangGraph agent with gateway tool discovery
`handlers/calculator.py`	Calculator Lambda function
`Dockerfile`	Container definition
`pyproject.toml`	Python dependencies

Adding More Tools

Define additional tools in serverless.yml:

functions:
  calculatorFunction:
    handler: handlers/calculator.handler
    runtime: python3.14

  weatherFunction:
    handler: handlers/weather.handler
    runtime: python3.14

ai:
  tools:
    calculator:
      function: calculatorFunction
      toolSchema: [...]

    weather:
      function: weatherFunction
      toolSchema:
        - name: get_weather
          description: Get current weather for a city
          inputSchema:
            type: object
            properties:
              city:
                type: string
            required:
              - city

  agents:
    chatbot: {}

Cleanup

serverless remove

Next Steps

LangGraph Multi-Gateway - Multiple gateways with different authorization
LangGraph Memory - Add conversation persistence