Context Warp Drive – deterministic folding for LLM agents
Deterministic context folding without LLM summarization calls keeps prompt caches hot.
Level Of Detail Context Management for Agents
Tiered context summarization beats naive token culling for long agent sessions.
AI agent developers, OpenCode users, coding assistant builders
Mem0 · LangChain Memory · Zep
From initial testing, much faster for small tasks, however it is costly to test, so keen for other people to give it a go. No more waiting for compaction!
Currently opencode is supported as a plugin.
Can look at Claude Code if someone would like to sponsor.
There is an evaluation system and sample tasks that shows results empirically (Results not yet published).
Deterministic context folding without LLM summarization calls keeps prompt caches hot.
Deterministic folding beats LLM summarization with 92% cache hits and zero extra model calls.
Smart context window solution, but LLM-based summarization has its own failure modes.
90% cache hit rates on Claude by folding context deterministically instead of summarizing.
Tree-sitter interface extraction cuts token usage by 6x, but chat context window optimization is becoming table stakes.
Modular context folders beat monolithic prompts for scaling AI agent instructions.