Building a context-aware AI assistant on AgentCore and OpenClaw

The post demonstrates how to build a persistent, context-aware personal assistant by combining Amazon Bedrock’s AgentCore runtime with OpenClaw. By leveraging AgentCore’s memory component, each chat session is stored as durable, structured knowledge that can be queried later via metadata tags, turning otherwise disposable conversations into reusable context. The integration shows how to attach metadata to messages with OpenClaw and retrieve relevant context across sessions, enabling a more personalized assistant experience.

⚡ Key Takeaways

• AgentCore memory converts transient chats into durable, structured knowledge that can be queried by metadata.
• OpenClaw is used on Amazon Bedrock AgentCore runtime to attach metadata to each message.
• The approach enables context accumulation across multiple conversations, improving assistant continuity.
• Retrieval is performed by querying AgentCore memory with metadata filters to pull relevant past interactions.
• The solution requires configuring AgentCore memory persistence and integrating OpenClaw’s metadata attachment logic.
• WhyItMatters: Engineers can now ship assistants

Engineers can now ship assistants

Want the full story? Read the original article.