Memory as plain files on disk — readable, editable, version-controllable.
Memory lives as plain files — usually Markdown — that the agent reads and writes directly on disk. There is no database or embedding service; the agent treats memory the way it treats any other file it can open and edit. This keeps memory human-readable, git-friendly, and cheap, at the cost of scale and automatic semantic search. Some members add tiered context (L0→L2 loading) for token savings, or pre-compression capture hooks that grab decisions before a long session is summarized away.
Transparency, editability, and zero-infra. When those outrank automatic intelligence — which, for a curated knowledge worker, is more often than the vendor pitches admit — this family wins. It is also the only family with no LLM in the read path on several members, which makes it the home for latency-critical (voice) and exact-token-recall (coding agent) needs that semantic search actively fails. The trade-off is that you trade automatic richness for control: you get out roughly what you curate.
7 catalogued.
Volcengine
Tiered context (L0→L2) for token savings; unifies memory + resources + skills as a filesystem.
Best for: Token-lean, inspectable filesystem-based context (no vector DB) · OpenClaw / agent setups unifying memory + resources + skills
View memory card →ByteRover
Pre-compression capture + human-editable markdown tree; sub-100ms, no LLM in read path.
Best for: Coding agents wanting Git-like, versioned, team-synced memory · Cursor / Claude Code / Windsurf users needing one portable memory layer across 22+ tools
View memory card →NevaMind AI
A 'workspace runtime' that compiles heterogeneous sources (chat logs, documents, code, images, audio, tool traces) into three durable Markdown layers — Index (INDEX.md), Skill (SKILL.md), and Memory (MEMORY.md) — via a memorize() pipeline (ingest → preprocess → extract → organize → persist) and serves scoped, ranked layers back via retrieve().
Best for: Agents needing a multimodal, file-system-shaped memory workspace with source-traceable Markdown layers
View memory card →EverMind AI
A local-first, Markdown-native memory runtime and Python library that gives agents one portable memory layer across coding assistants, apps, devices, and workflows. Stores conversations, files, and agent trajectories as canonical .md files (readable, editable, diffable, Git-versioned) and syncs local SQLite + LanceDB indexes for fast retrieval and self-evolving reuse.
Best for: Makers wanting a portable, local-first, Git-versioned Markdown memory layer shared across multiple agents and apps
View memory card →AgentScope AI (Alibaba)
File-based long-term memory toolkit for AI agents — evolved from the MemoryScope project. Turns conversations and documents into readable, editable, searchable Markdown files linked by wikilinks. Three automated background processes (Auto Memory, Auto Resource, Auto Dream) progressively distil interactions into durable memory nodes and build wikilink relationship graphs over time.
Best for: Personal assistants and knowledge-worker agents that need long-term memory stored in a human-readable, inspectable format · Workflows where both humans and agents read and write the same memory files (e.g., collaborative knowledge bases)
View memory card →Basic Machines
Local-first knowledge-graph memory layer for AI agents and humans, exposed entirely via MCP. Conversations and notes are stored as plain Markdown files; observations and wikilinks compound into a semantic graph over time. Designed to work with any AI client or IDE that speaks MCP — Claude, Copilot, Cursor, and others. A team cloud tier (basicmemory.com) provides shared workspaces.
Best for: Individual developers and small teams wanting persistent cross-session memory for AI coding assistants with zero infrastructure · Projects where human-readable memory files and direct editing are a design requirement
View memory card →Zilliz (Milvus)
Cross-platform semantic memory layer for AI coding agents. Markdown files are the source of truth — memories are plain `.md` files that are human-readable, directly editable, and version-controllable. Milvus is used as a 'shadow index': a derived, rebuildable cache over the Markdown corpus. Ships a procedural 'Skills from Memory' layer that distils repeated workflows into installable reusable skills. Plugins cover Claude Code, OpenClaw, OpenCode, and Codex CLI; a single memory store is shared across all agents.
Best for: AI coding-agent setups (Claude Code, Cursor, OpenCode, Codex CLI) where cross-agent shared memory and Markdown inspectability are priorities · Teams that want persistent memory without a dedicated database server — the Milvus index is local and the Markdown files are the durable record
View memory card →Top-down recommendations from the use-case playbook. Each names the one binding constraint that picks the tool, the primary pick (which may sit in another family when the case spans more than one), and runner-ups.
Binding constraint: Lots of data, tight budget.
Pick
Memori — Drops the vector DB and runs on SQL + LLM extraction — ~80–90% cheaper infra than vector-backed stores.
Runner-ups
From Agentic Memory: Use-Case Playbook 2026 · last verified 2026-06-28
Binding constraint: Supersession. The hard part isn't storing state, it's making sure last month's status never resurfaces as current.
Pick
Hand-curation is the honest default — you own supersession by editing the living doc yourself.
A curated LLM wiki (Obsidian/PARA-style) is the correct architecture here, because you curate and overwrite the living document. A memory product only wins when one of the runners-up's unique property becomes the pain.
Runner-ups
Why not Cognee here: it accumulates a corpus but is weaker at 'this new fact replaces that old one.' Supersession is Zep's lane.
From Agentic Memory: Use-Case Playbook 2026 · last verified 2026-06-28
Binding constraint: Exact-token recall (function names, error codes, variable names — semantic search fails here) + capturing decisions before a long session is compacted.
Pick
ByteRover — The pre-compression hook grabs 'we decided X / Y didn't work' before the context window summarizes it away, and markdown lives next to code in git.
Runner-up
From Agentic Memory: Use-Case Playbook 2026 · last verified 2026-06-28
Binding constraint: Retrieval can't stall a live conversation.
Pick
ByteRover — No LLM in the read path = predictable sub-100ms recall.
Runner-up
From Agentic Memory: Use-Case Playbook 2026 · last verified 2026-06-28
Binding constraint: No network, no third party, ideally no LLM call at all.
Pick
ByteRover — Local markdown, no LLM in the read path — sub-100ms recall with nothing leaving the device.
Runner-up
From Agentic Memory: Use-Case Playbook 2026 · last verified 2026-06-28
Last verified 2026-06-28 · updated by manual-stub