Hardcore Practices for Building Custom Local AI Agent Frameworks
GrungeWerX · reddit · 2026-08-12
A developer shares hands-on experiences and feature designs from building a local AI agent framework from scratch:
- MCP Broker: Hides tools behind a proxy to eliminate a massive 20K token context load on startup.
- Context Warnings & Auto-swap: Triggers system warnings at 85% and 95% context limits. The agent can summarize sessions and auto-swap to lower-impact model configs (e.g., Q5 kv 4/4) using llama-server's router mode.
- Memory Search: Built hybrid search (semantic + vector) using Postgres and pgvector for precise needle-in-haystack searches across weeks of data.
- VRAM Optimization: Running mmproj on the CPU frees up 1.7-2GB of GPU VRAM.
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