Context7
Up-to-date docs for AI agents
作者提供的说明(英文原文)
Memory for AI agents — MLX (Apple Silicon) or CPU (Linux), sqlite-vec + BM25, zero cloud.
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
你需要
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"memo": {
"command": "uvx",
"args": [
"mlx-memo"
]
}
}
}在终端中运行这条命令
claude mcp add memo -- uvx mlx-memo粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"memo": {
"command": "uvx",
"args": [
"mlx-memo"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"memo": {
"type": "stdio",
"command": "uvx",
"args": [
"mlx-memo"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。
你的选择通过 Cookie 保存在这台设备上,无需账户。
这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日
注册名称已通过 GitHub 账户 jagoff 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
它在你的电脑上运行;我们只能读取互联网端点的工具列表
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
GitHub 仓库最后更新于–,最新版本(4.16.0)于2个月前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Python (uv)。
同一分类中有:Context7, Codebase Memory, Exa
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
Up-to-date docs for AI agents
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
Your coding agent starts every session with amnesia. memo fixes that — 100% on your own machine.
Persistent, searchable memory for Claude Code, Codex, Cursor, Cline, Devin, and OpenCode. No cloud, no API keys, no Ollama, no vector DB to run. And it spends *fewer* tokens, not more.
curl -fsSL https://raw.githubusercontent.com/jagoff/memo/v4.16.0/install.sh | bash
Prefer a package manager? uv tool install mlx-memo · pipx install mlx-memo · brew tap jagoff/memo && brew install mlx-memo
Then:
memo doctor # self-check
memo save 'we use Postgres, not Mongo' # save a decision memo search 'what database did we pick?' # search by meaning
That's it. Your agents pick it up over MCP automatically — the installer wires every client it finds.
Installing on another Mac or handing setup to an agent?
New Mac:
curl -fsSL https://raw.githubusercontent.com/jagoff/memo/v4.16.0/install.sh | bash
memo sync bootstrap git@github.com:yourname/memo-sync.git
Agent-managed setup:
curl -fsSL https://raw.githubusercontent.com/jagoff/memo/v4.16.0/install.sh | bash
memo doctor --strict-runtime
On Linux or just want to look around first?
docker run --rm ghcr.io/jagoff/memo:latest memo doctor
Most memory servers *add* context. memo is built to remove it.
| Profile | Tools | Schema tokens |
|---|---|---|
agent (default) | 43 | ~9.7k |
core / slim | 60 | ~13.2k |
full / default | 165 | ~30.6k |
The default MCP surface is 43 tools, not 165 — 74% fewer tools, and about 68% less schema context: 43 tools / ~9.7k schema tokens versus 165 tools / ~30.6k tokens on the full surface — overhead paid every session, in every client.
Ambient recall injects one relevant memory before the model answers. The bundled Claude Code hook caps that injection at ~160 tokens. memo roi reports the real grounding and re-ask counts — the estimated-savings figure it used to print was removed in 4.14.0, because multiplying those counts by hardcoded constants was a savings claim memo could not support. For measured savings, memo tokens reads the provider's own usage counters through the context-compression proxy.
memo roi # value from grounded recalls and avoided re-asks
memo tokens # usage-savings ledger
memo as-of ask "what was the deploy strategy?" --date 2026-02-01
memo diff --from 2026-01-01 --to 2026-03-01
Full historical reconstruction by reverse-replaying history.db. Useful when you need to know *why* past-you made a call, not just what past-you decided.
memo contradict scan # find conflicting facts corpus-wide
memo contradict triage # resolve: fuse / newer-wins / dismiss
Change a decision and memo flags the now-stale version, so the agent stops reintroducing what you already threw out.
memo dream run
A 7-phase nightly pipeline: inventory → mine signals → resolve conflicts → prune stale → synthesize cross-cluster insights → optimize → pre-warm the top-100 query embeddings so tomorrow's recall stays under 200 ms. Every run writes a receipt you can audit. Zero intervention.
Hybrid retrieval. A vector leg (MLX on Apple Silicon, sentence-transformers on CPU) and a BM25 leg (FTS5, diacritic-folding for Spanish) run in parallel, fuse via Reciprocal Rank Fusion, then go through an optional MLX cross-encoder rerank.
Markdown is the source of truth. Every memory is a plain .md file you can read, grep, and version-control. SQLite is a derived index that rebuilds from the files at any time — hand-edit in Obsidian and your edit wins on the next memo reindex. Nothing is locked in a database you can't open.
Prompts and memories stay on your machine. Embedder, reranker, and LLM all run in-process. No telemetry. Memory travels only if *you* point memo sync at a git remote you own. Normal startup is fully offline; remote update checks and auto-update require an explicit opt-in. → [Privacy and network policy](https://github.com/jagoff/memo/blob/master/PRIVACY.md)
Also in the box: cross-agent memo resume (reopen any session from any agent), cross-Mac git sync, a knowledge graph with optional codegraph symbol edges, encrypted secret storage, OCR/audio ingestion, evidence packs, outcome learning, signed federation, and a local chat UI over your memory (memo chat serve). → [Full feature reference](https://github.com/jagoff/memo/blob/master/docs/reference.md)
Verified July 2026 against each project's own docs. Corrections welcome — open an issue and I'll fix the table.
| memo | mem0 | letta | cognee | basic-memory | cipher | |
|---|---|---|---|---|---|---|
| 100% local, no cloud API | ✅ | ⚠️ | ⚠️ | ⚠️ | ✅ | ⚠️ |
| Time-machine (rewind to any date) | ✅ | ❌ | ⚠️ | ❌ | ⚠️ | ⚠️ |
| Contradiction detection + resolution | ✅ | ⚠️ | ⚠️ | ❌ | ❌ | ❌ |
| Autonomous nightly maintenance | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ |
| Token-economy MCP profiles | ✅ | ❌ | ❌ | ⚠️ | ✅ | ❌ |
| Markdown / Obsidian as source of truth | ✅ | ❌ | ⚠️ | ❌ | ✅ | ❌ |
✅ first-class · ⚠️ partial, config-gated, or add-on · ❌ absent
Closest comparators are basic-memory (local-first + Obsidian + MCP — same thesis) and cipher (memory for coding agents).
| Support | |
|---|---|
| macOS, Apple Silicon (M1–M4) | Full — MLX embedder + reranker + ask/synthesize/dream |
| Linux / Ubuntu | Standalone CPU backend — search, recall, save. pipx install "mlx-memo[cpu]" · docs/ubuntu.md |
| Intel Mac | Unsupported — current PyTorch releases do not ship Python 3.13 wheels for this platform |
| Docker | Cross-platform, CPU backend · docs/docker.md |
Python ≥ 3.13 (the installer handles this via uv if you don't have it). First install pulls ~8 GB of models, 5–15 min. Optional: an Obsidian vault — without one, memo uses ~/Documents/memo/.
| Install detail, installer knobs, new-Mac migration | reference.md › Install |
| Per-client MCP setup (Claude Desktop, Cursor, Cline, Continue) | reference.md › MCP setup |
| Ambient recall, capture, and tuning | reference.md › Ambient memory |
Full CLI reference (145 commands) + memo tui | reference.md › CLI |
All MEMO_* flags and model profiles | reference.md › Configuration |
| Architecture and design notes | reference.md › Design |
| Privacy and network policy | PRIVACY.md |
Complete command inventory (kept here so CI detects CLI/documentation drift)
Core: save search ask get edit rename delete list
Recall & Hooks: recall recall-hook context briefing continuity prewarm capture-tick capture-stop interject ask-gaps guard digest
Session & History: history as-of diff record-history session chat-session resume reflect mine-history episodes chronicle
Maintenance: reindex maintain review dream consolidate synthesize dedupe cross-dedup retier contradict coordinate terminal invalidate temporal compress-context ops
Analysis & Quality: health stats doctor journey-check lint drift analytics eval roi tokens token-savings usefulness gaps outcome profile confidence graduation hype definitive evidence
Knowledge Graph: graph entities entity extract-entities links version related
Advanced Search: embed rerank contextual retrieve context-pack chat chat-ask repo
Import / Export / Sync: import export backup restore sync ingest federation
Visualization: tui dashboard map logs hook-log
Setup & Config: init setup config install-mcp install-watcher uninstall-watcher install-slash install-statusline install-recall-hook install-shell-wrapper install-shims startup-banner migrate migrate-vault migrate-independence update upgrade self-update watch release onboard
Daemons: daemons recall-daemon ingest-daemon maint-daemon embed-daemon idle-daemon
Other: backend-native collaborative events feedback query mandate drift sleep-cycle operational ocr-image provenance secret verbatim mcp-command codex-badge debug-recall http-api proxy mine-git token-gate fix undo code-facts code-nudge code-health
git clone https://github.com/jagoff/memo && cd memo
uv pip install -e '.[dev]'
Issues and PRs welcome — see CONTRIBUTING.md. If memo is useful to you, a ⭐ genuinely helps other people find it.
MIT licensed. Built on Apple MLX, sqlite-vec, and
只显示了开头部分;其余内容请在仓库中阅读