Long-term memory for AI coding agents (Claude Code, Codex, opencode) as a tree of plain files
IHMT — Infinite Hierarchical Memory Tree
Long-term memory for your AI coding agents. Tell your agent something once — a decision, how your setup works, a correction — and it remembers it in every future session, in any project, with any of your agents.
- Remembers across sessions. Decisions and their reasons, your environment, your preferences, people and projects, corrections. Your agent searches the memory before answering and saves what lasts, so you stop repeating yourself. - One memory for all your agents and models. Claude Code, Codex and opencode can share the same memory: what one saves, the others find. Tested with models from Anthropic, OpenAI, Google and Meta. - Saves tokens. Instead of pasting your notes or re-explaining context every session, the agent retrieves only what the question needs — typically 200–900 tokens, whether the memory holds 50 entries or 50,000, because search walks a tree instead of reading everything. Honest numbers, including where it does not save. - Understands time. When something changes ("I moved to Valencia"), the old fact is kept as history and flagged OUTDATED if it comes up. When a question is ambiguous ("Luis" — which one?), it asks instead of guessing. - Portable. Your memory is one folder of plain text files. Copy it to another computer, back it up, or put it under version control — it works wherever you put it. - Local, private and readable. No cloud, no database, no account: IHMT stores everything on your disk and sends nothing anywhere. (The memories your agent retrieves reach its model like any other context.) Every memor
From the project README.
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