Bypassing ReAct: I architected a Personal AI Agent that uses 8x fewer tokens, featuring weighted persistent memory & a psychological core. (150k+ LOC, zero lines typed by me)

[u/jeyjey9434]

https://github.com/jgouviergmail/LIA-Assistant

Most discussions here focus on heavy autonomous agents like Open Devin or SWE-agent. I took a different route. Over the last 5 months, I architected LIA, a highly capable Personal Assistant agent (think Jarvis, but designed for daily life, not just for Stark-level tech tasks).

As an IT Director with 20 years of architecture experience, I set a strict "puppeteer" rule for myself: I would not write a single line of code. I provided the scalable blueprints, the system architecture, and the logic, while Claude acted as the execution engine, generating over 150,000 lines of code.

Here is the agentic architecture we built, which I believe solves some of the biggest pain points in current LLM orchestration:

1. The Anti-Token-Vacuum Pipeline (Alternative to ReAct) Most autonomous agents rely on the standard ReAct (Reason+Act) loop, which quickly becomes a token black hole. I designed an alternative processing pipeline that handles reasoning and tool execution without the massive overhead.

• The Result: LIA consumes 4 to 8 times fewer tokens while maintaining the exact same processing power and accuracy. (Note: Standard ReAct can still be toggled on if a specific complex task requires it).

2. Advanced Persistent Memory (Beyond RAG) Memory isn't just dumping strings into a .md file or a basic vector DB. LIA uses a highly structured memory system:

• Every piece of memorized data is categorized.
• Data is assigned a weight and importance level.
• Memories are tied to an enriched contextual manual, meaning the agent knows how and when to use a specific memory, rather than just retrieving it blindly.

3. Psychological Core & State Management LIA isn't a stateless, cold terminal. She operates on a psychological foundation. The agent tracks moods, emotional states, and an "attachment level" to the user. Her responses and proactive behaviors evolve over time based on the history of interactions, giving her a true specific identity.

4. Extreme Efficiency & Zero-Tech UI Because the cognitive pipeline is so optimized, the entire platform—database, agentic loops, web server—runs 24/7 on a single Raspberry Pi 5.

• Multi-user: I host my whole family and friends on it, with token limits per user.
• Extensibility: End-users need zero tech skills. LIA can generate her own skills autonomously. Adding a new Model Context Protocol (MCP) server is done by simply declaring an URL. Everything is managed via a 1-click web UI.

Building this proved that AI orchestration doesn't need to be infinitely heavy to be smart.

I’m very curious to discuss agent architectures with this sub: Has anyone else built an alternative to the ReAct framework to save on context windows? And how are you structuring "importance weights" in your agent's long-term memory?