--- title: I Built a Personal AI Agent on a Raspberry Pi — Jeremy... description: No Mac Mini, no laptop agent. Jeremy Adams runs his personal AI agent on an old Raspberry Pi he wears around his neck. Jeremy Adams, senior developer advocate... canonical: https://daily.dev/posts/i-built-a-personal-ai-agent-on-a-raspberry-pi-jeremy-adams-neo4j-8jbobe7oz twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: I Built a Personal AI Agent on a Raspberry Pi — Jeremy Adams, Neo4j | daily.dev og:description: No Mac Mini, no laptop agent. Jeremy Adams runs his personal AI agent on an old Raspberry Pi he wears around his neck. Jeremy Adams, senior developer advocate... og:url: https://daily.dev/posts/i-built-a-personal-ai-agent-on-a-raspberry-pi-jeremy-adams-neo4j-8jbobe7oz og:image: https://api.daily.dev/og/posts/8jBOBE7oz.png og:image:alt: I Built a Personal AI Agent on a Raspberry Pi — Jeremy Adams, Neo4j og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # I Built a Personal AI Agent on a Raspberry Pi — Jeremy Adams, Neo4j **[AI Engineer](https://daily.dev/sources/aidotengineer)** · 20 min read · 0 upvotes · 0 comments ## Summary No Mac Mini, no laptop agent. Jeremy Adams runs his personal AI agent on an old Raspberry Pi he wears around his neck. Jeremy Adams, senior developer advocate at Neo4j, assembles a wearable AI agent live on stage: NanoClaw on a Raspberry Pi 4B, messaged over WhatsApp, with Docker-isolated agents and a Neo4j graph database. He shows how he built graph memory using the POLE schema (person, object, location, event) mid-flight, added voice-to-text through a USB mic and a hardware button, ran an offline mode with a local Neo4j, and walked the AI Engineer expo floor turning booth notes into a graph of themes. He finishes by upgrading to Neo4j Agent Memory. In this talk: • Why a small, hackable claw beats a feature-rich agent on your laptop • Connecting an agent to Neo4j over MCP • Graph memory with the POLE schema • Offline-first capture with a local graph that syncs and enriches in the cloud SPEAKER Jeremy Adams, Senior Developer Advocate, Neo4j LinkedIn: https://www.linkedin.com/in/jeremy-adams-pdx/ X: https://x.com/jpadamspdx GitHub: https://github.com/jpadams Website: https://jeremyadams.dev/ LINKS NanoClaw: https://github.com/qwibitai/nanoclaw Neo4j Agent Memory: https://neo4j.com/labs/agent-memory/ Agent Memory (GitHub): https://github.com/neo4j-labs/agent-memory GraphAcademy (free courses): https://graphacademy.neo4j.com/ CHAPTERS 0:00 Intro 0:12 Hi from Neo4j 1:32 What we're building 1:57 Live hardware setup 4:51 The Raspberry Pi claw boots up 6:06 Why small claws 8:05 Why NanoClaw 8:55 Architecture: WhatsApp → NanoClaw → Claude 9:40 Graphs 101 10:10 Querying Neo4j over MCP 10:50 Building memory on a plane: the POLE schema 12:15 Demo: the memory graph 13:29 Adding voice-to-text 14:49 Recording the expo booths 15:34 Offline mode with a local Neo4j 17:03 Enriching themes in the cloud 18:23 Upgrading to Neo4j Agent Memory 19:33 Get started with GraphAcademy Recorded at the AI Engineer World's Fair 2026 in San Francisco. Subscribe for more talks from the engineers building with AI. AI Engineer: https://ai.engineer YouTube: https://www.youtube.com/@aiDotEngineer X: https://x.com/aiDotEngineer LinkedIn: https://www.linkedin.com/company/aidotengineer/ #RaspberryPi #AgentMemory #AIEngineer ## Full article daily.dev links to this article rather than hosting it. 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