Long-term memory for CLI agents — cross-CLI, cross-project, cross-session. OKF Wiki + MCP server, model-agnostic.
View on GitHub ↗Long-term memory that follows your agents across CLIs, projects, and sessions.
Give your CLI agents experience that doesn't vanish when you switch projects, switch tools, or start a new session — it doesn't touch your workflow, just adds memory to the agents you already use.
ensoul /ɪnˈsoʊl/ — verb. To give a CLI agent memory that carries across sessions, so it's more than a blank slate each time. An agent that's been ensouled is called an ensouler.
- Cross-CLI: one memory, shared across Claude Code / Codex / zcode / Cursor / OpenCode. Use Claude today, switch to Codex tomorrow — your agents' memory follows. - Cross-project: memory lives in a global KB, not bound to any project repo. The bug your algo agent hit in project A is recalled in project B. - Cross-session: every new conversation, the agent wikisearches its own past experience first and starts with memory, not from scratch. - Agent isolation: spin up multiple ensoulers (algorithm engineer, backend, testing, UI...) — each has its own memory bundle, no cross-contamination. Wake one, work with its experience. - Memory is files: plain markdown (following OKF) — git-able, diff-able, editable in Obsidian. Vector stores are just an index; md is always source of truth. - Zero external deps: SQLite FTS5 full-text search (CJK char segmentation + BM25) — no OpenAI key, no Docker, no cloud service. Model-vendor-agnostic: inference always stays in your CLI. - Proactive distillation: when an agent hits a non-trivial pitfall or makes a reusable decision, it distills and writes it directly, then tells you what it wrote (concept
From the project README.
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Local-first long-term memory for AI agents - plain Markdown vault + thin MCP server. CJK-aware BM25, token-efficient by design.
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