Building an AI Agent with Memory: Creating a Code Reviewer That Remembers Project Context

product #agent 📝 Blog|Analyzed: Apr 19, 2026 22:11•

Published: Apr 19, 2026 22:04

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1 min read

Analysis

This article presents a brilliant evolution in AI agents by solving the critical issue of context loss between sessions. By introducing a dual-layer memory system, developers can now build agents that retain project-specific rules and historical data. This approach bridges the gap between single-use chatbots and highly intelligent, persistent AI assistants.

Key Takeaways

•The Agent utilizes a dual-layer memory architecture: a short-term memory for active thought processes within a session, and a long-term persistent memory.
•Short-term memory relies on the messages array passed to the LLM's context window, while long-term memory is implemented using a simple JSON file.
•Long-term memory is cleverly injected into the system prompt at the start of a session to make the Agent aware of past project rules and review history.

Reference / Citation

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"The problem is that every time a review is performed, the Agent starts from a completely blank slate. A real-life reviewer has context, such as 'I made the same comment on this file last week' or 'This project prioritizes internal coding standards over PEP8.' This time, to solve that problem, we implement a two-layer structure: short-term memory (conversation history) and long-term memory (JSON file)."

Qiita LLMApr 19, 2026 22:04

* Cited for critical analysis under Article 32.

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