I Built a Personal AI Agent on a Raspberry Pi — Jeremy Adams, Neo4j
Summary
Jeremy Adams details the construction of a wearable, personal AI agent, NanoClaw, running on a Raspberry Pi 4B. This system demonstrates an offline-first approach to agent memory, utilizing a local Neo4j graph database and the POLE schema (Person, Object, Location, Event) to capture and enrich real-world interactions (e.g., conference booth notes). The architecture connects the physical device to cloud LLMs (Claude) and a centralized Neo4j graph database via WhatsApp messaging, allowing for persistent, context-aware memory capture even without constant internet connectivity.
Key takeaways
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Wearable, Hackable Agents
10:06
The speaker advocates for small, open, and hackable edge devices (like the Raspberry Pi) over feature-rich, proprietary laptop agents, prioritizing understanding and customization.
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Graph Memory with POLE Schema
17:30
Memory is built using the POLE schema (Person, Object, Location, Event) to structure captured data, allowing the agent to build a connected graph of experiences.
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Offline-First Architecture
The system uses a local Neo4j instance on the Raspberry Pi for offline data capture, which is later synced and enriched in the cloud, ensuring continuity regardless of network quality.
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System Integration Pipeline
14:15
The agent processes input via WhatsApp messaging, runs local inference/capture on the Raspberry Pi, and sends queries/data to the cloud Neo4j instance via an MCP server connection.
Technical details
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Hardware & Edge Computing
157s
The agent runs on a Raspberry Pi 4B, demonstrating capability for complex tasks on low-power, hackable hardware. The setup includes a USB microphone and a custom-built physical button trigger.
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Software Architecture
855s
The system utilizes Docker containers to isolate agent processes and runs Neo4j on the device. The initial messaging pipeline connects WhatsApp (cloud) $\rightarrow$ NanoClaw (Pi) $\rightarrow$ Claude (cloud LLM).
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Graph Database Implementation
1050s
Neo4j is used for both the core database and memory storage. The POLE schema is implemented to model memories, allowing the agent to connect disparate data points (e.g., a person, a location, and an event).
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Data Capture & Enrichment
The agent captures raw notes (e.g., conference booth details) locally. These raw notes are later uploaded and enriched in the cloud, connecting exhibitors to broader 'theme nodes' (e.g., Evaluation, Observability).
Mentioned resources
Channel & topics
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