Developer Tools●●Solid
Aide-memory – persistent memory for AI coding agents and teams
Path-scoped memory beats project-wide rules files for team context sharing.
Solve My ProblemNiche Gem
ahmedmeky
401mo ago
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Three-path memory for LLM agents: KV window (exact attention) + retrieval index (archived chunks) + TRN recurrent state (8-96 KB, O(1) per step)
0 starsPython
3-path agent memory – 8 KB recurrent state vs. 156 MB KV at 10K tokens
by tenpa0000·Mar 14, 2026·1 point·0 comments
AI Analysis
●●●BangerWizardryBig Brain
Keeps agent memory at 8 KB constant size while KV caches bloat to 156 MB.
Strengths
- •O(1) memory complexity per step prevents latency degradation at long contexts.
- •Three-path mixing allows exact attention plus compressed patterns.
- •Benchmarks show flat throughput at 10k tokens where transformers slow down.
Weaknesses
- •Requires custom model architecture, not a drop-in library for existing LLMs.
- •Recurrent state might lose some nuance compared to full attention.
Category
Target Audience
LLM developers, Agent builders
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RWKV · RetNet · MemGPT
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