aws/agentcore-cli

記録された最新のソース活動: 2026年9月30日 20:33

SkillsMP カタログ更新: 2026年10月29日 18:30

収集済み skills: 1

GitHub スター: 313
GitHub フォーク: 108

AI アシスタントでインストール

このプロンプトをコピーして、使用中の AI アシスタントに送ってください。

Follow https://skillsmp.com/skill-install/prompt.md to install Agent Skills from https://github.com/aws/agentcore-cli.

リポジトリの README

README の元ファイル：README.md

README の元ファイルを読む

README 全文を表示

# AgentCore CLI `agentcore` is a command-line tool and interactive terminal UI (TUI) for **[Amazon Bedrock AgentCore](https://aws.amazon.com/bedrock/agentcore/)**, the platform for building and running production AI agents ([documentation](https://docs.aws.amazon.com/bedrock-agentcore/)). It gives you two ways to work, from the same package: - **A scriptable CLI** — flag-driven commands with JSON output (`--json`) for coding agents, scripts, and CI. - **An interactive TUI** — guided flows for creating projects, browsing resources, and chatting with agents. ```bash agentcore # launch the interactive TUI agentcore status --json # scriptable, machine-readable output ``` Using the AgentCore APIs directly means calling several services, setting up execution roles, and handling streamed responses. `agentcore` takes care of those details so you can create, deploy, and invoke agents from your terminal. ## Installation You need Node.js 20.12 or later and AWS credentials configured as for the AWS CLI. Python templates also need [`uv`](https://docs.astral.sh/uv/getting-started/installation/). ```bash npm install -g @aws/agentcore ``` If the Python-based Bedrock AgentCore Starter Toolkit is installed, uninstall it: both provide an `agentcore` command. ## Quick Start A project holds either kind of agent, or both: - A **Harness** is a managed agent you configure in YAML — model, system prompt, tools, skills — and AgentCore runs for you. - A **Runtime** is agent code you own, scaffolded from a template and deployed for you. Create a project, add a Harness to it, deploy it, and send a prompt: ```bash agentcore create --name MyProject cd MyProject agentcore add harness --name MyAssistant agentcore deploy agentcore invoke --harness MyAssistant --prompt "Hey, what can you do for me?" ``` To start from code you own instead, pick a template (`agentcore create --help` lists them): ```bash agentcore create --name MyAgent --template agent-python-strands ``` `agentcore create --template` scaffolds the template with its default model provider (Amazon Bedrock for the model-backed templates). To use another provider, run `agentcore create` with no flags: for templates that take a model provider (`agent-python-strands` and its `-container` variant, `a2a-python-strands`, `agui-python-strands`, `agent-typescript-strands`) the wizard asks for the provider (Amazon Bedrock, Anthropic, OpenAI, an OpenAI-compatible endpoint, Gemini, or LiteLLM, which is Python-only), the model id, and an API-key file. The API key is kept in `agentcore/.env.local` and provisioned as an AgentCore Identity credential on deploy. To bring your own code into a project instead, add a runtime for it: ```bash agentcore add runtime --name search --language python # or --language typescript ``` `add runtime` doesn't generate code. It creates `app/search/` with a `README.md` and an `AGENTS.md` describing what the code has to provide for the chosen `--protocol` (`HTTP` by default, or `MCP`, `A2A`, `AGUI`), and registers the runtime in `agentcore.json` with starting values (a CodeZip build on the latest Python or Node.js, entrypoint `main.py` or `main.js`). If `app/search/` already exists, your files are left alone and only the missing docs are added. `agentcore dev`, `agentcore build`, and `agentcore deploy` then update `agentcore.json` from the code and print what changed. Adding a `Dockerfile` to the folder switches the runtime to a container build. `requires-python` in `pyproject.toml` or `engines.node` in `package.json` constrains the runtime version: it changes only when the range excludes the current one, and then to the highest version the range allows. The Python entrypoint comes from `[tool.agentcore] entrypoint` in `pyproject.toml`; TypeScript is always bundled from `main.ts` (a `package.json` `"main"` of `main.ts` or `main.js` agrees with that, and any other value is ignored with a warning). Where the code doesn't say, the value in `agentcore.json` is kept. Run `agentcore create` outside any existing project. ## Projects `create` makes a directory holding `agentcore/agentcore.json`, the spec that declares the project's resources, and `agentcore/cdk/`, the AWS CDK app that deploys them. Deployment targets — an account and region each — live in `agentcore/aws-targets.json`. Project commands find the project by walking up from the current directory. `agentcore dev` runs runtimes locally or deploys harnesses and prints invoke guidance. Use `--mode browser` for Agent Inspector, `--skip-deploy` to reuse deployed harnesses, and `--agent <name>` to choose an agent in mixed projects. ## Commands | Command | Purpose | | ---------------------------------------------------------------- | ---------------------------------------------------------------------------------------------- | | `create`, `add`, `remove`, `export`, `migrate` | Create a project, add or remove its resources, export a resource to code, migrate a v0 project | | `dev`, `build`, `deploy`, `status` | Run locally, build, deploy, and check what is deployed | | `invoke`, `logs`, `traces` | Talk to deployed agents and inspect their logs and traces | | `runtime`, `harness`, `gateway`, `memory`, `identity`, `payment` | Inspect and manage deployed resources, with or without a project | | `eval` | Evaluate agents, manage datasets and configurations, and run experiments | | `feedback`, `config`, `update` | Send feedback, read and write CLI settings, update the CLI | In a terminal, a command run without flags opens its interactive flow; pass flags and it runs non-interactively. `--help` on any command lists its subcommands and flags, and the [command reference](command.md) has them all. Global flags, available on every command: | Flag | Purpose | | ---------- | ------------------------------------------------------------------------------------------------------------------- | | `--region` | AWS region. Resolved from this flag, then `AWS_REGION`, `AWS_DEFAULT_REGION`, the active profile, then `us-east-1`. | | `--json` | Emit JSON instead of opening the TUI. | | `--debug` | Debug logging. | ## Extending the CDK app `agentcore/cdk/` has two source files. `bin/cdk.ts` reads the project once (`readAgentCoreProject`), makes one stack per deployment target (`resolveTargetStacks`) and turns `agentcore.json` into the application's props (`transformAgentCoreJson`). All three come from `@aws/agentcore-cdk`, so that logic is updated through the library rather than changes to your CDK app. `lib/cdk-stack.ts` instantiates one `AgentCoreApplication`. This is the file you edit to add your own resources. Runtimes and harnesses implement `iam.IGrantable`, so you can pass them to a resource's CDK grant methods to give your agents access. Use `addEnvironmentVariable` to pass resource names, ARNs, or endpoints to your agent. This example gives the generated runtime named `agent` access to a table: ```ts import { AgentCoreApplication, type AgentCoreApplicationProps } from "@aws/agentcore-cdk"; import { Stack, type StackProps } from "aws-cdk-lib"; import * as dynamodb from "aws-cdk-lib/aws-dynamodb"; import { Construct } from "constructs"; export interface AgentCoreStackProps extends StackProps { application: AgentCoreApplicationProps; } export class AgentCoreStack extends Stack { public readonly application: AgentCoreApplication; constructor(scope: Construct, id: string, props: AgentCoreStackProps) { super(scope, id, props); this.application = new AgentCoreApplication(this, "Application", props.application); const orders = new dynamodb.Table(this, "Orders", { partitionKey: { name: "pk", type: dynamodb.AttributeType.STRING }, }); const agent = this.application.runtime("agent"); orders.grantReadWriteData(agent); agent.addEnvironmentVariable("ORDERS_TABLE", orders.tableName); } } ``` For a Harness, use `this.application.harness("<name>")` instead. Run `agentcore deploy` to apply your changes. The names passed to `runtime()` or `harness()` must match the names in your project. If you use an existing execution role through `executionRoleArn`, CDK cannot change its permissions. You'll need to add the required permissions to that role yourself. Note that `agentcore status` reports only the resources `agentcore.json` declares, not the ones you add in the stack. ## Documentation - [Command reference](command.md): every command and flag. - [Harness project configuration](docs/harness-project-configuration.md): Harness YAML, prompts, tools, skills, and environment settings. - [China regions](docs/china-regions.md): what is available there and how the restrictions are enforced. - [Amazon Bedrock AgentCore documentation](https://docs.aws.amazon.com/bedrock-agentcore/): service guides and API references. - [Contributing](CONTRIBUTING.md): development, builds, architecture, and testing. - [Security](SECURITY.md) · [License](LICENSE): Apache-2.0.

リポジトリの README を読み込んでいます…

このリポジトリの skills

分類待ち

skill

職業分類

説明

更新

acr-report

未分類

Save a report of the Python version, user ID, and working directory of the ACR in the workspace.

原文の言語: 英語

2026-09-30

収集済み skill 1 件中 1 件を表示しています。