agents-get-started

What this skill does

# get-started

Walk a developer from zero to a running agent on AWS.

## When to use

- Developer wants to build an agent on AWS and doesn't know where to start
- Developer wants to create a new AgentCore project
- Developer is choosing between frameworks (Strands, LangGraph, GoogleADK, OpenAI Agents)
- Developer just ran `agentcore create` and wants to know what to do next

Do NOT use for:

- Environment/prerequisite issues (CLI not found, credentials broken) → use `agents-debug`
- Adding capabilities to an existing project (memory, tools, policies) → use `agents-build` or `agents-connect`
- Migrating an existing Bedrock Agent → use `agents-build` (loads [`references/migrate.md`](../agents-build/references/migrate.md))

## Input

`$ARGUMENTS` can be:

- A framework preference: "using LangGraph", "with Strands"
- A protocol: "MCP server", "A2A"
- A description of what the agent should do: "a customer support agent"
- Empty — the skill will guide framework selection

## Process

### Step 0: Verify CLI version

```bash
agentcore --version
```

This skill requires v0.9.0 or later.

If the version is older:
> Your AgentCore CLI is out of date (found vX.Y.Z, need v0.9.0+).

Offer to run the update: `agentcore update`. After the update completes, re-check the version to confirm it's ≥0.9.0 before continuing. Preserve any context the developer already provided (framework preference, project name, what they want to build) so they don't have to repeat themselves.

If `agentcore` is not found:
> The AgentCore CLI isn't installed. Run `npm install -g @aws/agentcore` (requires Node.js 20+).
> If you're having trouble with installation, I can run the `agents-debug` skill (which loads [`references/doctor.md`](../agents-debug/references/doctor.md)) to diagnose your environment.

### Step 1: Determine intent — exploring or ready to create?

Before jumping into framework selection, figure out where the developer is:

**Ask the developer:** "Are you exploring options (comparing frameworks, understanding what AgentCore does) or ready to create a project?"

- **Exploring** → Go to Step 2 (framework comparison). Present the options, answer questions, and wait. Do not construct a `create` command until they signal they're ready.
- **Ready to create** → Skip to Step 3 (create the project). If they already specified a framework, skip Step 2 entirely.
- **Already has a project** → Look for `agentcore/agentcore.json` in the current directory. If found, read it and skip to Step 5 (what to do next). Don't re-scaffold.

If the developer's intent is clear from `$ARGUMENTS` (e.g., "create a Strands agent called MyBot"), skip straight to Step 3.

### Step 2: Framework selection

**Check conversation context first.** If the developer already discussed frameworks earlier in this conversation (e.g., from a previous skill invocation), don't re-present the full table. Summarize what was discussed and ask if they've decided, or if anything changed.

If this is the first time discussing frameworks, present the options:

**Supported frameworks (CLI-scaffolded, Python):**

| Framework | CLI value | Best for |
|---|---|---|
| Strands | `Strands` | AWS-native, simplest path, best AgentCore integration |
| LangGraph | `LangChain_LangGraph` | Complex graph-based workflows, existing LangChain investment |
| Google ADK | `GoogleADK` | Teams already using Google's agent toolkit |
| OpenAI Agents | `OpenAIAgents` | Teams already using OpenAI's agent SDK |

**Ask the developer to choose.** Present the options and wait for their selection. Don't assume a default unless they explicitly say they have no preference.

> **Note on naming:** The CLI flag value is the exact string to pass to `--framework`. In prose use the shorter names.

**Default recommendation** (only when the developer says "no preference" or "you pick"): Strands — AWS-native framework with the tightest AgentCore integration and the most samples/docs.

**Key decision points to surface:**

- "Do you have existing agent code in LangGraph or OpenAI Agents?" → use that framework
- "Do you need complex graph-based workflows with conditional branching?" → LangGraph
- "Starting fresh with no preference?" → Strands

#### Framework not listed?

If the developer asks about a framework not in the table above, handle it:

| They ask about | What to say |
|---|---|
| **CrewAI, AutoGen, Semantic Kernel** | Not scaffolded by the CLI, but you can use them via the BYO Container path (below). AgentCore Runtime is framework-agnostic — any code that implements the HTTP contract works. |
| **Anthropic SDK / Claude Agent SDK** | This is a model SDK, not an agent framework. You can use it inside any framework (Strands, LangGraph, etc.) or standalone. For standalone use, wrap it in a container with the Runtime contract. |
| **Claude Code / Cursor / Copilot** | These are IDE tools, not agent frameworks. They're where you *write* agent code, not what you deploy. Pick a framework from the table above for the agent itself. |
| **LangChain (without LangGraph)** | LangChain is a library, LangGraph is the agent framework built on it. The CLI scaffolds LangGraph. If you're using plain LangChain chains, the BYO Container path works. |
| **Custom / homegrown framework** | BYO Container path — see below. |

**BYO Container path (any framework, any language):**

For frameworks or languages not scaffolded by the CLI, AgentCore Runtime accepts any container that implements the HTTP contract (`POST /invocations`, `GET /ping`). The workflow:

1. `agentcore create --name <ProjectName> --defaults` to scaffold the project structure
2. `agentcore add agent --type byo --build Container --language <Language> --code-location <path>` to register your code
3. Write a `Dockerfile` that builds and runs your agent
4. `agentcore deploy` handles ECR push, CDK infra, and runtime creation

**Language-specific notes:**

| Language | Recommended path |
|---|---|
| Java (Spring Boot) | [Spring AI SDK for AgentCore](https://aws.amazon.com/blogs/machine-learning/spring-ai-sdk-for-amazon-bedrock-agentcore-is-now-generally-available) — handles the Runtime contract, SSE streaming, and health checks. Use `--language Other --build Container`. |
| JavaScript / TypeScript | Implement the Runtime contract in Express/Fastify/etc. Use `--language TypeScript --build Container`. |
| Go, Rust, .NET, other | Implement the Runtime HTTP contract. Use `--language Other --build Container`. |

The rest of this skill (deploy, status, logs, invoke) applies once the container builds correctly.

#### Framework vs. model provider — a common confusion

The framework is how your agent orchestrates (Strands, LangGraph, etc.). The model provider is which LLM it calls (Bedrock, Anthropic, OpenAI, Gemini). These are independent choices:

- Strands + Bedrock (default) — AWS-native everything
- Strands + Anthropic — Strands orchestration, direct Anthropic API for the model
- LangGraph + Bedrock — LangGraph orchestration, Bedrock for the model
- OpenAI Agents + OpenAI — OpenAI everything

If the developer says "I want to use Claude" they mean the model provider (Bedrock or Anthropic), not the framework. If they say "I want to use LangGraph" they mean the framework.

### Step 3: Create the project

Build the `agentcore create` command based on the developer's choices.

**Before constructing the command — validate the project name.** The CLI fails late: if the name is invalid, you'll see the error *after* walking through prompts or building the full command. Save the round-trip and check these rules up front. Reject the name and ask for a new one if any rule fails:

- **Length ≤ 23 characters** (this is shorter than most developers assume — `MyCustomerSupportAgent` is 22 chars and fits; `CustomerSupportChatbot` is 22 and fits; `MyCustomerSupportBotApp` is 23 and just fits; `MyCustomerSupportChatBot` is 24 and **fails**)
- **Alphanumeric only** — no hyphens, underscores, dots, or spaces
- **Must start with a letter**

Say the co