AgentCore CLI を使って AI エージェントを AgentCore にデプロイしてみた

agentcore

npm install -g @aws/agentcore

agentcore --version
0.3.0-preview.6.1

プロジェクト（agentcore.json）
├── エージェント（agents[]）
├── メモリ（memories[]）
├── クレデンシャル（credentials[]）
├── カスタム評価器（evaluators[]）
├── オンライン評価設定（onlineEvalConfigs[]）
└── Gateway（mcp.json）
    └── ターゲット（targets[]）

agentcore add agent --name SubAgent --framework Strands --model-provider Bedrock --protocol HTTP

agentcore create

agentcore create --name MyAgent --framework Strands --model-provider Bedrock --memory longAndShortTerm --defaults

SampleProject/
├── agentcore/
│   ├── .env.local          # ローカル開発用の環境変数（gitignore 対象）
│   ├── agentcore.json      # リソース定義（エージェント、メモリ等）
│   ├── aws-targets.json    # デプロイ先リージョン設定
│   └── cdk/                # CDK インフラコード
├── app/
│   └── MyAgent/
│       ├── main.py          # エージェントのエントリポイント
│       ├── pyproject.toml   # Python 依存関係
│       ├── Dockerfile       # コンテナビルド用
│       ├── mcp_client/
│       │   └── client.py    # MCP クライアント接続設定
│       ├── memory/
│       │   └── session.py   # Memory セッション管理
│       └── model/
│           └── load.py      # モデル読み込み設定

{
  "name": "sampleProject",
  "version": 1,
  "agents": [
    {
      "type": "AgentCoreRuntime",
      "name": "MyAgent",
      "build": "Container",
      "entrypoint": "main.py",
      "codeLocation": "app/MyAgent/",
      "runtimeVersion": "PYTHON_3_12",
      "networkMode": "PUBLIC",
      "modelProvider": "Bedrock",
      "protocol": "HTTP"
    }
  ],
  "memories": [
    {
      "type": "AgentCoreMemory",
      "name": "MyAgentMemory",
      "eventExpiryDuration": 30,
      "strategies": [
        {
          "type": "SEMANTIC",
          "namespaces": [
            "/users/{actorId}/facts"
          ]
        },
        {
          "type": "USER_PREFERENCE",
          "namespaces": [
            "/users/{actorId}/preferences"
          ]
        },
        {
          "type": "SUMMARIZATION",
          "namespaces": [
            "/summaries/{actorId}/{sessionId}"
          ]
        }
      ]
    }
  ],
  "credentials": [],
  "evaluators": [],
  "onlineEvalConfigs": []
}

{
  "agents": [
    {
      "type": "AgentCoreRuntime",
      "name": "MyAgent",
      ...
      "envVars": [
        { "name": "LOG_LEVEL", "value": "INFO" },
        { "name": "CUSTOM_PARAM", "value": "my-value" }
      ]
    }
  ]
}

{
  "agentCoreGateways": [
    {
      "name": "ToolGateway",
      "description": "Gateway for ToolGateway",
      "targets": [],
      "authorizerType": "NONE",
      "enableSemanticSearch": true,
      "exceptionLevel": "NONE"
    }
  ]
}

[]

[
  {
    "name": "default",
    "account": "123456789012",
    "region": "us-east-1",
    "description": "Default target (us-east-1)"
  }
]

from strands import Agent, tool
from bedrock_agentcore.runtime import BedrockAgentCoreApp
from model.load import load_model
from mcp_client.client import get_streamable_http_mcp_client
from memory.session import get_memory_session_manager

app = BedrockAgentCoreApp()
log = app.logger

# MCP クライアントの定義
mcp_clients = [get_streamable_http_mcp_client()]

# ツールの定義
tools = []

@tool
def add_numbers(a: int, b: int) -> int:
    """Return the sum of two numbers"""
    return a+b
tools.append(add_numbers)

for mcp_client in mcp_clients:
    if mcp_client:
        tools.append(mcp_client)

def agent_factory():
    cache = {}
    def get_or_create_agent(session_id, user_id):
        key = f"{session_id}/{user_id}"
        if key not in cache:
            cache[key] = Agent(
                model=load_model(),
                session_manager=get_memory_session_manager(session_id, user_id),
                system_prompt="You are a helpful assistant. Use tools when appropriate.",
                tools=tools
            )
        return cache[key]
    return get_or_create_agent
get_or_create_agent = agent_factory()

@app.entrypoint
async def invoke(payload, context):
    log.info("Invoking Agent.....")
    session_id = getattr(context, 'session_id', 'default-session')
    user_id = getattr(context, 'user_id', 'default-user')
    agent = get_or_create_agent(session_id, user_id)

    stream = agent.stream_async(payload.get("prompt"))
    async for event in stream:
        if "data" in event and isinstance(event["data"], str):
            yield event["data"]

if __name__ == "__main__":
    app.run()

agentcore add memory --name SharedMemory --strategies SEMANTIC,SUMMARIZATION --expiry 30

agentcore add gateway --name ToolGateway

agentcore add gateway-target \
  --gateway ToolGateway \
  --name WeatherAPI \
  --type mcp-server \
  --endpoint https://mcp.example.com/mcp

agentcore add gateway \
  --name SecureGateway \
  --authorizer-type CUSTOM_JWT \
  --discovery-url https://example.auth0.com/.well-known/openid-configuration \
  --allowed-audience my-api-audience

agentcore add gateway-target \
  --gateway ToolGateway \
  --name WeatherLambda \
  --type lambda-function-arn \
  --lambda-arn arn:aws:lambda:us-east-1:123456789012:function:get-weather \
  --tool-schema-file ./tool-schema.json

agentcore add identity --name OpenAI --api-key sk-...

agentcore add identity \
  --type oauth \
  --name MyOAuthService \
  --discovery-url https://example.com/.well-known/openid-configuration \
  --client-id my-client-id \
  --client-secret my-client-secret \
  --scopes "read,write"

agentcore dev

agentcore deploy

agentcore status

agentcore invoke "こんにちは" --agent MyAgent

Provider: Bedrock
こんにちは！お元気ですか？

何かお手伝いできることはありますか？質問や知りたいことがあれば、お気軽にお聞きください。

Log: /path/to/project/agentcore/.cli/logs/invoke/invoke-MyAgent-20260322-122023.log

================================================================================
AGENTCORE INVOKE LOG
Agent: MyAgent
Runtime ARN: arn:aws:bedrock-agentcore:us-east-1:123456789012:runtime/SampleProject_MyAgent-xxxxx
Region: us-east-1
Session ID: none
Started: 2026-03-22T03:20:23.910Z
================================================================================

[12:20:23.911] INVOKE REQUEST (Session: none)

--- REQUEST ---
{
  "timestamp": "2026-03-22T03:20:23.911Z",
  "agent": "MyAgent",
  "runtimeArn": "arn:aws:bedrock-agentcore:us-east-1:123456789012:runtime/SampleProject_MyAgent-xxxxx",
  "region": "us-east-1",
  "prompt": "こんにちは"
}
--- END REQUEST ---

[12:20:28.746] INVOKE RESPONSE (4835ms)

--- RESPONSE ---
{
  "timestamp": "2026-03-22T03:20:28.746Z",
  "durationMs": 4835,
  "success": true,
  "response": "こんにちは！お元気ですか？..."
}
--- END RESPONSE ---

agentcore invoke "私の名前は神野です" --session-id my-session-001-01231233123123123123312312312 --agent MyAgent

Provider: Bedrock
こんにちは、神野さん！はじめまして。

お名前を教えていただきありがとうございます。何かお手伝いできることはありますか？質問や知りたいことがあれば、お気軽にお尋ねください。

Log: /path/to/project/agentcore/.cli/logs/invoke/invoke-MyAgent-20260322-122722.log

agentcore invoke "記憶しておりますか？先ほどの発言を。" --session-id my-session-001-01231233123123123123312312312 --agent MyAgent

Provider: Bedrock
はい、記憶しております！

先ほど「私の名前は神野です」とおっしゃいましたね。神野さんのお名前はしっかり覚えています。

何かお手伝いできることがありましたら、お気軽にお申し付けください。

Log: /path/to/project/agentcore/.cli/logs/invoke/invoke-MyAgent-20260322-122746.log

agentcore add evaluator \
  --name ResponseQuality \
  --level SESSION \
  --model us.anthropic.claude-sonnet-4-5-20250929-v1:0 \
  --instructions "Evaluate the quality and helpfulness of the agent response. Context: {context}" \
  --rating-scale 1-5-quality

agentcore run evals --agent MyAgent --evaluator ResponseQuality --days 7

agentcore run evals --agent MyAgent --evaluator Builtin.Helpfulness --days 7

Agent: MyAgent | Mar 22, 2026, 12:28 PM | Sessions: 2 | Lookback: 7d

  Builtin.Helpfulness: 0.50

Results saved to: /path/to/project/agentcore/.cli/eval-results/eval_2026-03-22_03-28-42.json

{
  "timestamp": "2026-03-22T03:28:42.469Z",
  "agent": "MyAgent",
  "evaluators": ["Builtin.Helpfulness"],
  "lookbackDays": 7,
  "sessionCount": 2,
  "results": [
    {
      "evaluator": "Builtin.Helpfulness",
      "aggregateScore": 0.5,
      "sessionScores": [
        {
          "sessionId": "55541c29-a909-48b1-af5f-8815aebf3297",
          "value": 0.5,
          "label": "Neutral/Mixed",
          "explanation": "The user's initial message 'こんにちは' is a greeting..."
        }
      ],
      "tokenUsage": {
        "inputTokens": 1757,
        "outputTokens": 621,
        "totalTokens": 2378
      }
    }
  ]
}

Agent: MyAgent | Mar 22, 2026, 12:57 PM | Sessions: 2 | Lookback: 7d

  ResponseQuality: 3.00

Results saved to: /path/to/project/agentcore/.cli/eval-results/eval_2026-03-22_03-57-17.json

{
  "timestamp": "2026-03-22T03:57:17.969Z",
  "agent": "MyAgent",
  "evaluators": [
    "ResponseQuality"
  ],
  "lookbackDays": 7,
  "sessionCount": 2,
  "results": [
    {
      "evaluator": "ResponseQuality",
      "aggregateScore": 3,
      "sessionScores": [
        {
          "sessionId": "55541c29-a909-48b1-af5f-8815aebf3297",
          "value": 3,
          "label": "Good",
          "explanation": "The user simply greeted the agent with 'こんにちは' (Hello in Japanese). The agent's response is appropriate and well-constructed in several ways: 1) It reciprocates the greeting in Japanese, maintaining language consistency. 2) It asks 'お元気ですか？' (How are you?), which is a natural follow-up to a greeting. 3) It proactively offers assistance by asking if there's anything it can help with. 4) It encourages the user to ask questions freely with 'お気軽にお聞きください' (please feel free to ask). 5) It includes a friendly emoji to create a welcoming tone. The response is culturally appropriate, polite, and conversational. It successfully opens the door for further interaction while being concise and not overwhelming. The agent demonstrates readiness to help without being pushy. This response meets all expectations for handling a simple greeting - it's warm, professional, maintains the user's language choice, and effectively invites continued conversation."
        },
        {
          "sessionId": "my-session-001-01231233123123123123312312312",
          "value": 3,
          "label": "Good",
          "explanation": "The conversation consists of two exchanges. In the first exchange, the user introduces themselves as '神野' (Kamino/Jinno). The agent responds politely in Japanese, acknowledging the name and offering assistance. This is appropriate and courteous.\n\nIn the second exchange, the user asks if the agent remembers the previous statement. The agent confirms it does remember and accurately recalls that the user said '私の名前は神野です' (My name is Kamino/Jinno). The agent demonstrates successful memory retention across the conversation.\n\nStrengths: 1) The agent uses appropriate Japanese language and honorifics (さん suffix), showing cultural awareness. 2) The agent correctly remembers the user's name from the previous exchange, which is the core of what was being tested. 3) The tone is friendly and helpful throughout. 4) The agent explicitly quotes what the user said, confirming accurate memory.\n\nThe responses are appropriate, polite, and functionally correct. The agent successfully demonstrates conversation continuity and memory - a key capability being tested. The quality meets expectations for a basic memory retention test with culturally appropriate responses. There are no significant errors or issues that would warrant a lower score, but the responses are straightforward without exceptional added value that would justify exceeding expectations."
        }
      ],
      "tokenUsage": {
        "inputTokens": 1160,
        "outputTokens": 557,
        "totalTokens": 1717
      }
    }
  ]
}

agentcore add online-eval \
  --name QualityMonitor \
  --agent MyAgent \
  --evaluator ResponseQuality \
  --sampling-rate 10 \
  --enable-on-create

agentcore remove all --force

agentcore deploy