agent-claw/agentclaw

AgentCore Project

This project was created with the AgentCore CLI.

Project Structure

my-project/
├── AGENTS.md               # AI coding assistant context
├── agentcore/
│   ├── agentcore.json      # Project config (agents, memories, credentials, gateways, evaluators)
│   ├── aws-targets.json    # Deployment targets (account + region)
│   ├── .env.local          # Secrets — API keys (gitignored)
│   ├── .llm-context/       # TypeScript type definitions for AI assistants
│   │   ├── agentcore.ts    # AgentCoreProjectSpec types
│   │   ├── aws-targets.ts  # Deployment target types
│   │   └── mcp.ts          # Gateway and MCP tool types
│   └── cdk/                # CDK infrastructure (@aws/agentcore-cdk)
├── app/                    # Agent application code
└── evaluators/             # Custom evaluator code (if any)

Getting Started

Prerequisites

• Node.js 20.x or later
• Python 3.10+ and uv for Python agents (install uv)
• AWS credentials configured (aws configure or environment variables)
• Docker (only for Container build agents)

Development

Run your agent locally:

agentcore dev

Deployment

Deploy to AWS:

agentcore deploy

Commands

Command	Description
agentcore create	Create a new AgentCore project
agentcore add	Add resources (agent, memory, credential, gateway, evaluator, policy)
agentcore remove	Remove resources
agentcore dev	Run agent locally with hot-reload
agentcore deploy	Deploy to AWS via CDK
agentcore status	Show deployment status
agentcore invoke	Invoke agent (local or deployed)
agentcore logs	View agent logs
agentcore traces	View agent traces
agentcore eval	Run evaluations
agentcore package	Package agent artifacts
agentcore validate	Validate configuration
agentcore pause	Pause a deployed agent
agentcore resume	Resume a paused agent
agentcore fetch	Fetch remote resource definitions
agentcore import	Import existing resources
agentcore update	Check for CLI updates

Configuration

Edit the JSON files in agentcore/ to configure your project. See agentcore/.llm-context/ for type definitions and validation constraints.

The project uses a flat resource model — agents, memories, credentials, gateways, evaluators, and policies are top-level arrays in agentcore.json. Resources are independent; agents discover memories and credentials at runtime via environment variables or SDK calls.

Resources

Resource	Purpose
Agent (runtime)	HTTP, MCP, or A2A agent deployed to AgentCore Runtime
Memory	Persistent context storage with configurable strategies
Credential	API key or OAuth credential providers
Gateway	MCP gateway that routes tool calls to targets
Gateway Target	Tool implementation (Lambda, MCP server, OpenAPI, Smithy, API Gateway)
Evaluator	Custom LLM-as-a-Judge or code-based evaluation
Online Eval Config	Continuous evaluation pipeline for deployed agents
Policy	Cedar authorization policies for gateway tools

Agent Types

• Template agents: Created from framework templates (Strands, LangChain/LangGraph, GoogleADK, OpenAI Agents, Autogen)
• BYO agents: Bring your own code with agentcore add agent --type byo
• Import agents: Import existing Bedrock agents with agentcore import

Build Types

• CodeZip: Python source packaged as a zip and deployed directly to AgentCore Runtime
• Container: Docker image built via CodeBuild (ARM64), pushed to ECR, and deployed to AgentCore Runtime

Documentation

• AgentCore CLI
• AgentCore CDK Constructs
• Amazon Bedrock AgentCore