Building a context-aware AI assistant with OpenClaw on AWS AgentCore
An AWS Machine Learning Blog tutorial shows how to run the open-source agentic system OpenClaw on Amazon Bedrock AgentCore runtime and give it continuity with AgentCore memory, using a gardening assistant called Sprout as the example; the whole system ships in a single CloudFormation template that deploys with one command. On each message the agent retrieves long-term memory records from a per-user namespace, ranks explicit preferences ahead of inferred facts, and injects them into the system prompt, degrading gracefully to a memory-free answer if retrieval times out within a 3-second budget or errors. Tasks are routed to different models — Claude Haiku 4.5 for text and Claude Sonnet 4.5 for image diagnosis — with Telegram as the front door. The author estimates roughly $1–2 per month for light personal use versus about $35 per month for an always-on EC2 instance.
It offers a reusable pattern for turning a stateless chat agent into a persistent assistant with layered memory and metadata filtering, useful to anyone building a personal assistant.
Off-the-shelf AI assistants answer individual questions well, but they fall short on a different axis: continuity. Ask a stateless assistant about your garden today and it has no idea that you mentioned your fast-draining raised beds three weeks ago, that you only use organic fertilizer, or that your petunias were struggling through a heat wave. Every conversation starts from zero, and the burden of re-explaining context falls on the user.
The problem isn’t the quality of the answers, but that the assistant has no memory of you. This post shows how to build a personal assistant that accumulates context using OpenClaw, an open source agentic system, running on AgentCore runtime, a capability of Amazon Bedrock AgentCore. AgentCore memory, a capability of Amazon Bedrock AgentCore, turns disposable chats into durable knowledge. You will also see how to tag those memories with structured metadata to retrieve records that matter for the question at hand.
Our running example is Sprout, a gardening assistant, but the architecture is domain-agnostic. Swap the persona and the skills manifest, and the same pipeline serves a support bot, a fitness coach, or an internal help desk. The entire system lives in a single AWS CloudFormation template, deploys with one command, and runs on a consumption-based model that costs a few dollars a month for light personal use. Along the way, we share design guidelines you can apply to assistants you build on this stack.
#Amazon Web Services#Amazon Bedrock AgentCore#OpenClaw#Claude Haiku 4.5#Claude Sonnet 4.5#Telegram#Sprout