From aws-agents
Guides developers to create new AgentCore agent projects on AWS: framework selection (Strands, LangGraph), project scaffolding, first deploy, and invocation. For beginners or 'agentcore create'.
npx claudepluginhub aws/agent-toolkit-for-aws --plugin aws-agentsThis skill is limited to using the following tools:
Walk a developer from zero to a running agent on AWS.
Extends existing AgentCore agent projects with cross-session memory, app integration, VPC networking, multi-agent systems, Bedrock Agent migrations, browser/code interpreter tools, and resource teardown.
Creates Claude Code agents from scratch or by adapting templates. Guides requirements gathering, template selection, and file generation following Anthropic best practices (v2.1.63+).
Builds, configures, and deploys AI agents using Motus framework. Supports ReActAgent, tool creation, workflows, memory, guardrails, local serving, and cloud deployment.
Share bugs, ideas, or general feedback.
Walk a developer from zero to a running agent on AWS.
agentcore create and wants to know what to do nextDo NOT use for:
agents-debugagents-build or agents-connectagents-build (loads references/migrate.md)$ARGUMENTS can be:
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Your AgentCore CLI is out of date (found vX.Y.Z, need v0.9.0+).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 theagents-debugskill (which loadsreferences/doctor.md) to diagnose your environment.
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?"
create command until they signal they're ready.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.
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:
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:
agentcore create --name <ProjectName> --defaults to scaffold the project structureagentcore add agent --type byo --build Container --language <Language> --code-location <path> to register your codeDockerfile that builds and runs your agentagentcore deploy handles ECR push, CDK infra, and runtime creationLanguage-specific notes:
| Language | Recommended path |
|---|---|
| Java (Spring Boot) | Spring AI SDK for AgentCore — 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.
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:
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.
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:
MyCustomerSupportAgent is 22 chars and fits; CustomerSupportChatbot is 22 and fits; MyCustomerSupportBotApp is 23 and just fits; MyCustomerSupportChatBot is 24 and fails)Say the count back out loud when close to the limit: "That name is 24 characters — the CLI caps project names at 23. Want to shorten it to <suggestion>?" Do not run the command with an invalid name on the assumption that the CLI error message will be clear — it isn't always, and the developer's mental model will be wrong for subsequent commands.
Construct the command, then present it for confirmation before the developer runs it. Show the full command with all flags and explain what each choice means. Wait for the developer to confirm or adjust before proceeding.
Example presentation:
Here's the command I'd recommend based on what you've told me:
agentcore create --name MyAgent --framework Strands --model-provider Bedrock --build CodeZip --memory noneThis creates a Strands agent using Bedrock models, deployed as a code zip (no Docker needed). Memory can be added later.
Want to run this, or change anything?
Do NOT execute the command automatically — present it and wait.
Minimal (defaults — Strands, Bedrock, CodeZip, no memory):
agentcore create --name <ProjectName> --defaults
With specific options:
agentcore create \
--name <ProjectName> \
--framework <Framework> \
--model-provider Bedrock \
--build CodeZip \
--memory none
Flag reference:
| Flag | Values | Default |
|---|---|---|
--name | alphanumeric, max 23 chars | prompted |
--framework | Strands, LangChain_LangGraph, GoogleADK, OpenAIAgents | prompted |
--protocol | HTTP, MCP, A2A | HTTP |
--build | CodeZip, Container | CodeZip |
--model-provider | Bedrock, Anthropic, OpenAI, Gemini | prompted |
--memory | none, shortTerm, longAndShortTerm | prompted |
--network-mode | PUBLIC, VPC | PUBLIC |
--dry-run | — | preview without creating |
Guidance on choices:
HTTP unless the developer specifically needs MCP tool serving or A2A agent-to-agent communicationCodeZip unless the developer needs custom system dependencies (CodeZip is faster to deploy and doesn't require Docker locally)Bedrock unless the developer has a specific reason for another provider (Bedrock doesn't require managing API keys)none — memory can be added later via agents-build (loads references/memory.md) when the developer needs itAfter the project exists, read agentcore/agentcore.json and the generated code to explain the project structure.
The layout below reflects CLI v0.9.x. If the CLI version is different, run tree <ProjectName>/ -L 3 to see the actual generated structure and explain from there.
<ProjectName>/
├── agentcore/
│ ├── agentcore.json ← Project config (agents, resources)
│ ├── aws-targets.json ← AWS account + region
│ ├── .env.local ← Local environment variables (gitignored)
│ └── cdk/ ← CDK infrastructure (auto-managed, don't edit)
└── app/
└── <AgentName>/
├── main.py ← Your agent code — this is where you build
├── mcp_client/ ← Pre-wired example MCP client (see note below)
└── pyproject.toml ← Python dependencies
Key files to highlight:
app/<AgentName>/main.py — the agent's entry point. This is where the developer adds tools, system prompts, and logic.agentcore/agentcore.json — the project config. Resources are added here via agentcore add commands.agentcore/.env.local — local environment variables. After deploy, resource IDs are written here for local dev.Heads-up on the scaffolded MCP client. main.py imports get_streamable_http_mcp_client() from mcp_client/client.py and appends it to tools. In a fresh project, this client points at a public example MCP endpoint — so agentcore dev works immediately. Two things to flag:
os.getenv("AGENTCORE_GATEWAY_<NAME>_URL"). That env var is only populated after agentcore deploy. If the developer repoints and runs agentcore dev before deploying, get_streamable_http_mcp_client() returns a client with a None URL and the agent starts with zero MCP tools — no error, no warning. See the "Local dev gap" section in agents-connect for the guard pattern: if not GATEWAY_URL: tools = [].mcp_clients list and the loop that appends it to tools. The scaffold includes it as a convenience, not a requirement.The reference client code in agents-connect (Path A) shows the correct pattern for gateway-backed MCP clients once deploy has run.
agentcore dev
This starts a local dev server. The developer can interact with their agent immediately.
Port the dev server binds to (important if you're scripting curl calls or testing from another process):
| Protocol | Default port |
|---|---|
| HTTP | 8080 |
| MCP | 8000 |
| A2A | 9000 |
The CLI prints the bound port and URL on startup — always read the actual value from the CLI output rather than hardcoding. If the default port is already in use, the CLI auto-increments (e.g., 8080 → 8081 → 8082), so a second dev session or a lingering process from a previous run can shift your port without warning. Use agentcore dev --port <N> to pin it, or grep ps / check the CLI banner if invocations start failing with connection-refused or exit-code-7 errors.
Important limitations to mention:
agentcore dev — it requires a deployWhen the developer is ready to deploy:
agentcore deploy
This will:
First deploy takes 3-5 minutes. Subsequent deploys are faster.
After deploy, show them how to invoke:
agentcore invoke "Hello, what can you do?"
And how to check status:
agentcore status
Based on what the developer said they want to build, suggest the logical next skill:
| Developer intent | Next skill | Command hint |
|---|---|---|
| "How do I call it from my app?" | agents-build | agentcore fetch access |
| "I want it to remember things" | agents-build | agentcore add memory |
| "I want it to call external APIs" | agents-connect | agentcore add gateway |
| "I want to restrict what it can do" | agents-connect | agentcore add policy-engine |
| "I want to measure quality" | agents-optimize | agentcore add evaluator |
| "I want to go to production" | agents-harden | production readiness checklist |
| "I want multiple agents working together" | agents-build | agentcore create --protocol A2A |
| "I need it in a VPC" | agents-build | agentcore create --network-mode VPC |
Don't overwhelm — suggest one or two next steps based on what the developer actually asked for.
For task-framed prompts (e.g., "build a customer support agent"), load the matching example reference:
| Developer task | Reference |
|---|---|
| Customer support, chatbot, answer policy questions | references/example-support-agent.md |
More examples can be added to this skill's references directory as common patterns emerge.
agentcore create command tailored to their choicesagentcore create command uses only valid flags from CLI v0.9.1