105 lines
4.0 KiB
Markdown
105 lines
4.0 KiB
Markdown
# AgentCore Project
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This project was created with the [AgentCore CLI](https://github.com/aws/agentcore-cli).
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## Project Structure
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```
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my-project/
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├── AGENTS.md # AI coding assistant context
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├── agentcore/
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│ ├── agentcore.json # Project config (agents, memories, credentials, gateways, evaluators)
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│ ├── aws-targets.json # Deployment targets (account + region)
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│ ├── .env.local # Secrets — API keys (gitignored)
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│ ├── .llm-context/ # TypeScript type definitions for AI assistants
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│ │ ├── agentcore.ts # AgentCoreProjectSpec types
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│ │ ├── aws-targets.ts # Deployment target types
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│ │ └── mcp.ts # Gateway and MCP tool types
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│ └── cdk/ # CDK infrastructure (@aws/agentcore-cdk)
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├── app/ # Agent application code
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└── evaluators/ # Custom evaluator code (if any)
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```
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## Getting Started
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### Prerequisites
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- **Node.js** 20.x or later
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- **Python 3.10+** and **uv** for Python agents ([install uv](https://docs.astral.sh/uv/getting-started/installation/))
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- **AWS credentials** configured (`aws configure` or environment variables)
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- **Docker** (only for Container build agents)
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### Development
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Run your agent locally:
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```bash
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agentcore dev
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```
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### Deployment
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Deploy to AWS:
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```bash
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agentcore deploy
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```
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## Commands
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| Command | Description |
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| --- | --- |
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| `agentcore create` | Create a new AgentCore project |
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| `agentcore add` | Add resources (agent, memory, credential, gateway, evaluator, policy) |
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| `agentcore remove` | Remove resources |
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| `agentcore dev` | Run agent locally with hot-reload |
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| `agentcore deploy` | Deploy to AWS via CDK |
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| `agentcore status` | Show deployment status |
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| `agentcore invoke` | Invoke agent (local or deployed) |
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| `agentcore logs` | View agent logs |
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| `agentcore traces` | View agent traces |
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| `agentcore eval` | Run evaluations |
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| `agentcore package` | Package agent artifacts |
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| `agentcore validate` | Validate configuration |
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| `agentcore pause` | Pause a deployed agent |
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| `agentcore resume` | Resume a paused agent |
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| `agentcore fetch` | Fetch remote resource definitions |
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| `agentcore import` | Import existing resources |
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| `agentcore update` | Check for CLI updates |
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## Configuration
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Edit the JSON files in `agentcore/` to configure your project. See `agentcore/.llm-context/` for type definitions and validation constraints.
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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.
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## Resources
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| Resource | Purpose |
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| --- | --- |
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| Agent (runtime) | HTTP, MCP, or A2A agent deployed to AgentCore Runtime |
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| Memory | Persistent context storage with configurable strategies |
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| Credential | API key or OAuth credential providers |
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| Gateway | MCP gateway that routes tool calls to targets |
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| Gateway Target | Tool implementation (Lambda, MCP server, OpenAPI, Smithy, API Gateway) |
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| Evaluator | Custom LLM-as-a-Judge or code-based evaluation |
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| Online Eval Config | Continuous evaluation pipeline for deployed agents |
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| Policy | Cedar authorization policies for gateway tools |
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### Agent Types
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- **Template agents**: Created from framework templates (Strands, LangChain/LangGraph, GoogleADK, OpenAI Agents, Autogen)
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- **BYO agents**: Bring your own code with `agentcore add agent --type byo`
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- **Import agents**: Import existing Bedrock agents with `agentcore import`
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### Build Types
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- **CodeZip**: Python source packaged as a zip and deployed directly to AgentCore Runtime
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- **Container**: Docker image built via CodeBuild (ARM64), pushed to ECR, and deployed to AgentCore Runtime
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## Documentation
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- [AgentCore CLI](https://github.com/aws/agentcore-cli)
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- [AgentCore CDK Constructs](https://github.com/aws/agentcore-l3-cdk-constructs)
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- [Amazon Bedrock AgentCore](https://aws.amazon.com/bedrock/agentcore/)
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