893c110729
- CDK: add agent-claw-users DynamoDB table (actor_id PK, RETAIN policy) - CDK: grant agent-runner read/write on users table; add USERS_TABLE_NAME env - CDK: fix cdk.json app field (was object, must be command string) - CDK: add UsersTableName output - agent-runner: get_or_create_user() auto-registers users on first contact (stores display_name, telegram_username, created_at, allowed) - agent-runner: pass user_profile in AgentCore payload - prompt_builder: split base prompt (cached) from per-user context (injected per-call) removes USER.md/MEMORY.md from shared load; user name/username injected dynamically - main.py: extract user_profile from payload, build user_context string for prompt
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 configureor 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