AI article
I Built a Portable AI Memory Layer with MCP, AWS Bedrock, and a Chrome Extension
Community description: AI tools have memory now. Claude remembers your projects. ChatGPT has built a profile of how you...
Dev.to | Mar 8, 2026 | Abdulai Yorli Iddrisu
Automated excerpt
MemoryMesh is a portable context layer: a Chrome extension + MCP server + AWS serverless backend that captures your context from any AI tool and injects it into any other. The Lambda IAM role gets bedrock:InvokeModel explicitly: api. addRoutes({ path: "/context", methods: [HttpMethod. POST], integration: new HttpLambdaIntegration("Save", props. saveFn) }); api. addRoutes({ path: "/context/{userId}", methods: [HttpMethod. GET], integration: new HttpLambdaIntegration("Get", props. getFn) }); api. addRoutes({ path: "/search/{userId}", methods: [HttpMethod. GET], integration: new HttpLambdaIntegration("Search", props. searchFn) }); api. addRoutes({ path: "/profile/{userId}", methods: [HttpMethod. GET], integration: new HttpLambdaIntegration("Profile", props. profileFn) }); api. addRoutes({ path: "/summarize", methods: [HttpMethod. POST], integration: new HttpLambdaIntegration("Summarize",props.
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