Most coding agents I see are just one model running in a tool loop. I wanted to try something different:

• Use a smart (expensive) model only for reasoning/planning
• Use a cheap/fast model for the actual tool execution loop
• Add a durable VSA (Vector Symbolic Architecture) memory layer that persists facts across sessions

The core idea is that planning and grinding have very different cost/quality curves. So why pay frontier prices for grep/edit/test cycles?

Memory is built on high-dimensional hypervectors (binding/unbinding, bit-packed). No vector DB needed.

Numbers from my benchmark (same task):

• Pure single-model tool loop: $0.301
• Raidho hybrid: $0.116 (~2.6× cheaper)
• After auto-distillation: $0.05

Full evidence, logs and script are in the repo if you want to verify:
https://github.com/vitaliyfedotovpro-art/raidho

Questions for the community:

1. Have you tried splitting plan/execute across different providers? Does the handoff lose too much context?
2. How do you handle durable long-term memory in agents without turning the prompt into a garbage dump?

Would love to hear real experiences, especially from people working on agents or alternative memory architectures (HDC/VSA, etc.).