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RUNLOCALAI · v38
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  5. /OpenCLaw: Building a Personal AI Agent
  6. /Ch. 4
OpenCLaw: Building a Personal AI Agent

04. Persistent Memory

Chapter 4 of 24 · 10 min
KEY INSIGHT

Memory systems must balance retrieval accuracy, storage efficiency, and access latency across multiple tiers of storage with different characteristics. Personal agents accumulate information over extended periods. Without organized memory, the agent cannot effectively leverage past interactions. The memory system solves this by providing structured storage with retrieval mechanisms optimized for different access patterns. Memory architecture follows a tiered approach. Each tier offers different tradeoffs between capacity, speed, and durability. The agent chooses the appropriate tier based on the information type and expected access patterns. Memory Tiers Overview Short-term memory stores recent conversations and temporary working data. This tier prioritizes low-latency access over long-term durability. Information in short-term memory expires after a configurable period or when capacity limits are reached. Medium-term memory stores important facts learned during interactions. This tier persists across sessions but may be compressed or archived during low-activity periods. Retrieval targets higher accuracy than short-term memory. Long-term memory stores the complete history of the agent's operation. This tier prioritizes capacity and durability over access speed. Retrieval may require longer response times but supports complex queries across large datasets. The tiers work together through a memory consolidation process. Important information graduates from short-term to medium-term to long-term storage. Unimportant information is discarded at appropriate intervals.

Local verification checkpoint

Run the smallest example from this chapter in a local workspace and record the package version, runtime, data path, and observed output. If the result depends on model size, vector count, CPU/GPU backend, or available memory, note that constraint beside the exercise so the lesson remains reproducible.

Local verification checkpoint

Run the smallest example from this chapter in a local workspace and record the package version, runtime, data path, and observed output. If the result depends on model size, vector count, CPU/GPU backend, or available memory, note that constraint beside the exercise so the lesson remains reproducible.

EXERCISE

Map the memory operations for a scenario where the agent learns a new project name, uses it repeatedly for a week, then stops referring to it. Explain which tier each memory operation targets and when consolidation should occur.

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