Memory Kernel is a lightweight local-first memory core for AI agents.
git clone https://github.com/Artem362/memory-kernel && cp memory-kernel/*.md ~/.claude/agents/Resumen de Subagents
<img width="1024" height="1024" alt="image" src="https://github.com/user-attachments/assets/f13a8ef3-23bb-4801-a5a4-05eeaa4f0041" /> # Memory Kernel <!-- mcp-name: io.github.Artem362/memory-kernel --> `Memory Kernel` is a small local memory layer for AI agents. It helps you save useful things such as decisions, constraints, tasks, facts, and notes in a local SQLite database, then pull back only the few memories that matter for the current task. Published package name on PyPI: `amormorri-memory-kernel` CLI command after install: `memory-kernel` Practical guide in Ukrainian: [docs/OPERATING_GUIDE_UK.md](docs/OPERATING_GUIDE_UK.md) Release notes: [CHANGELOG.md](CHANGELOG.md) ## Contents - [What It Does](#what-it-does) - [Start In 5 Minutes](#start-in-5-minutes) - [Typical Workflow](#typical-workflow) - [Which Command To Use](#which-command-to-use) - Write: [`remember`](#remember), [`ingest`](#ingest) - Read: [`search`](#search), [`context`](#context), [`wake-up`](#wake-up), [`stats`](#stats) - Inspect / edit: [`list`](#list), [`show`](#show), [`update`](#update), [`delete`](#delete) - Lifecycle: [`forget` / `restore`](#forget--restore), [`revise`](#revise), [`decay`](#decay) - Maintain: [`completion`](#completion), [`verify`](#verify) - Backup: [`export`](#export), [`import`](#import) - [Use With An LLM (MCP)](#use-with-an-llm-mcp) - [How It Works](#how-it-works) - [Data Flow](#data-flow) - [Component Diagram](#component-diagram) - [Memory Record Schema](#memory-record-schema) - [Ukrainian Inflection Bridging](#ukrainian-inflection-bridging) - [Why It Stays Lightweight](#why-it-stays-lightweight) - [Who It Is For](#who-it-is-for) - [Project Status](#project-status) - [Native Accelerator](#native-accelerator) - [Feedback](#feedback) ## What It Does In plain English, Memory Kernel does 4 things: 1. Stores memory locally on your machine. 2. Keeps memory structured enough to stay useful. 3. Finds relevant records without a heavy vector stack. 4. Builds a small context pack instead of dumping everything into the prompt. This project is not trying to create a magical black-box memory. It is trying to create a memory layer you can inspect, control, export, and trust. ## Start In 5 Minutes If you just want to try it, do this: ```powershell pip install amormorri-memory-kernel memory-kernel init memory-kernel remember --scope my.project --kind decision --title "Keep memory local" --content "We store memory on the user's machine." memory-kernel search "memory local" memory-kernel export --format json --output exports\memory.json ``` What happened there: 1. `init` created a local database. 2. `remember` saved one clear memory. 3. `search` fetched it back. 4. `export` created a backup file you can move or restore later. If you are using the repository instead of PyPI: ```powershell pip install -e .[dev] ``` ## Typical Workflow Most people will use it like this: 1. Save one precise memory with `remember`. 2. Feed raw notes or transcripts with `ingest`. 3. Before an agent run, fetch only what matters with `search`, `context`, or `wake-up`. 4. Inspect, fix, or remove single records with `show`, `update`, or `delete`. 5. Periodically export the database for backup. 6. Restore it elsewhere with `import`. ## Which Command To Use ### `remember` Use `remember` when you already know exactly what should be saved. Good examples: - a decision - a rule - a user preference - a project constraint ```powershell memory-kernel remember --scope project.alpha --kind decision --title "Use SQLite FTS5" --content "We use SQLite FTS5 for local retrieval." ``` ### `ingest` Use `ingest` when you have raw text and want the system to split it into structured memories. Good examples: - meeting notes - a transcript - a rough planning document - an agent session log ```powershell memory-kernel ingest --scope project.alpha --file notes.txt --source sprint-review --tags planning transcript ``` Add `--dry-run` to preview the segments and inferred kinds/titles/tags without writing to the database. Useful before committing a long file. ```powershell memory-kernel ingest --scope project.alpha --file notes.txt --dry-run memory-kernel ingest --scope project.alpha --text "..." --dry-run --json ``` Add `--interactive` for a guided flow that prompts for scope, source, tags, and the text itself, then shows a preview and asks for confirmation before saving. Helpful for first-time users or for ad-hoc captures from the terminal without remembering the flag names. ```powershell memory-kernel ingest --interactive ``` ### `search` Use `search` when you want a few relevant exact memories for a query. ```powershell memory-kernel search "context budget" ``` ### `context` Use `context` when you want a compact pack for an agent prompt. ```powershell memory-kernel context "How do we keep memory cheap?" --budget-chars 700 ``` ### `wake-up` Use `wake-up` when you want a small "hot memory" pack before a task starts. ```powershell memory-kernel wake-up --budget-chars 500 ``` ### `stats` Use `stats` when you want to see database size and whether the native accelerator is active. ```powershell memory-kernel stats memory-kernel stats --since 7d memory-kernel stats --since 2026-04-01 ``` `--since` adds recent-activity counts (created and updated since the cutoff) plus a per-kind breakdown for the window. Accepts either a relative form like `7d` or an ISO date. ### `list` Use `list` to browse recent memories (most recently updated first) with optional filters. ```powershell memory-kernel list memory-kernel list --scope project.alpha --limit 50 memory-kernel list --kind decision --tags rust memory memory-kernel list --json ``` Default limit is 20. The output shows `id`, `kind/scope`, `title`, and the timestamps so you can pipe ids into `show`/`update`/`delete`. ### `show` Use `show` when you have a memory id (printed by `search`, `remember --json`, or `export`) and want the full record. ```powershell memory-kernel show --id 9f1e8c0a4b2d4e7f8a1b2c3d4e5f6a7b memory-kernel show --id 9f1e8c0a4b2d4e7f8a1b2c3d4e5f6a7b --json ``` ### `update` Use `update` to fix specific fields on an existing memory without re-importing the whole database. ```powershell memory-kernel update --id 9f1e... --title "Renamed memory" --importance 0.95 memory-kernel update --id 9f1e... --tags rust memory acceleration memory-kernel update --id 9f1e... --tags ``` Only the fields you pass change. Pass `--tags` with no values to clear tags. Pass `--kind`, `--importance`, or `--certainty` to revise validation-bound fields. ### `delete` Use `delete` to drop a memory you saved by mistake or that no longer applies. ```powershell memory-kernel delete --id 9f1e8c0a4b2d4e7f8a1b2c3d4e5f6a7b ``` The command exits non-zero if the id does not exist, so wrap it in shell logic if you script around it. ### `forget` / `restore` `delete` removes a memory permanently. When you only want it out of recall but kept for safety, use `forget` — a soft-archive. Archived memories disappear from `search`, `context`, `wake-up`, and `list`, but the data stays and `restore` brings it back. ```powershell memory-kernel forget --id 9f1e... memory-kernel restore --id 9f1e... memory-kernel list --include-archived # see archived/superseded memories ``` Re-saving the same memory with `remember`/`ingest` also resurrects it automatically. ### `revise` When a new memory replaces an old one, record the relationship with `revise`: the old memory is marked superseded (hidden from recall, kept for history with a pointer to its replacement). ```powershell memory-kernel revise --id <new-id> --supersedes <old-id> ``` This keeps memory self-curating: stale decisions fade out of recall as newer ones take their place, instead of piling up as contradictory noise. ### `decay` `decay` applies a forgetting curve: it auto-archives memories that are old, rarely recalled, and low-value, so the store and your recall stay lean over time. Each memory has a retention score built from its importance, how often it has been recalled (reinforcement), and how long since it was last seen (time decay). ```powershell memory-kernel decay --dry-run # preview what would fade memory-kernel decay # apply (archives, recoverable) memory-kernel decay --min-age-days 60 --max-access 0 --scope project.alpha ``` Only `note` and `fact` memories are eligible — `decision`, `constraint`, `task`, and `preference` are never decayed. Archiving is the soft, recoverable kind, so `restore` and `list --include-archived` still reach faded memories. This is the heart of the project's thesis: spend the budget on what matters, let trivia fade. ### `completion` Use `completion` to print a shell completion script for `memory-kernel`. The script is generated dynamically from the current parser, so it stays in sync as commands are added. ```powershell memory-kernel completion powershell | Out-File -Encoding utf8 $PROFILE.CurrentUserAllHosts -Append memory-kernel completion bash > ~/.local/share/bash-completion/completions/memory-kernel ``` After installing, `memory-kernel <Tab><Tab>` shows all subcommands; `memory-kernel remember --<Tab>` lists flags for that command; `memory-kernel remember --kind <Tab>` cycles through valid `kind` values. ### `verify` Use `verify` to check that the database is internally consistent: schema version is current, derived columns (`stems_text`, `fingerprint`) match the source content, and the FTS5 index row count matches the memories table. ```powershell memory-kernel verify memory-kernel verify --repair memory-kernel verify --repair --json ``` Without `--repair`, exit code is `0` when healthy and `1` when issues are found. With `--repair`, mismatches are recomputed in-place and the FTS index is rebuilt if its row count drifted; exit code is `0` if everything was fixed. Useful after restoring from a manual backup, after editing the database with raw SQL,
Lo que la gente pregunta sobre memory-kernel
¿Qué es Artem362/memory-kernel?
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Artem362/memory-kernel es subagents para el ecosistema de Claude AI. Memory Kernel is a lightweight local-first memory core for AI agents. Tiene 3 estrellas en GitHub y se actualizó por última vez today.
¿Cómo se instala memory-kernel?
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Puedes instalar memory-kernel clonando el repositorio (https://github.com/Artem362/memory-kernel) o siguiendo las instrucciones del README en GitHub. ClaudeWave también te ofrece bloques de instalación rápida en esta misma página.
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Artem362/memory-kernel aún no ha sido auditado por nuestro agente de seguridad. Revisa el repositorio original en GitHub antes de usarlo en producción.
¿Quién mantiene Artem362/memory-kernel?
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Artem362/memory-kernel es mantenido por Artem362. La última actividad registrada en GitHub es de today, con 0 issues abiertos.
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Sí. En ClaudeWave puedes explorar subagents similares en /categories/agents, ordenados por popularidad o actividad reciente.
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