Fraise

Name: Fraise
Author: FraiseHQ

作者：FraiseHQ版本 0.3.7i★ –i在你的电脑上运行

注册名称io.github.FraiseHQ/fraise

安装

概览

作者提供的说明（英文原文）

Memory database for AI agents: remember and recall over a running Fraise daemon, in FQL.

PopNx MCP 的三大支柱

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。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
使用情况i尚未统计★ –

它能帮你做什么

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。

连接方式: 在你的电脑上运行
传输方式: stdio
分类: 搜索与 AI 记忆

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

基础检查：1 项中通过 1 项
在你的电脑上运行，拥有你的用户账户的权限（文件、网络、应用）。
注册中心没有列出需要填写的密钥。

你需要

Docker已安装 Docker

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

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": {
    "fraise": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/fraisehq/fraise:0.3.7",
        "mcp",
        "-addr",
        "http://host.docker.internal:9876"
      ]
    }
  }
}

在终端中运行这条命令

claude mcp add fraise -- docker run -i --rm ghcr.io/fraisehq/fraise:0.3.7 mcp -addr http://host.docker.internal:9876

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "fraise": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/fraisehq/fraise:0.3.7",
        "mcp",
        "-addr",
        "http://host.docker.internal:9876"
      ]
    }
  }
}

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。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "fraise": {
      "type": "stdio",
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/fraisehq/fraise:0.3.7",
        "mcp",
        "-addr",
        "http://host.docker.internal:9876"
      ]
    }
  }
}

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。

它能做什么

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。

基础检查i

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

✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 FraiseHQ 验证，该账户拥有此仓库
… 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
… 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
– 不适用
端点有响应
它在你的电脑上运行，没有互联网端点
– 不适用
使用 OAuth 登录
在你的电脑上运行，改用本地设置
… 尚未检查
工具描述中没有隐藏指令
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“搜索与 AI 记忆”中的其他服务器比较i

星标数多于此分类中其他 5662 个服务器的 0%
发布到注册中心的时间比此分类中其他 5662 个服务器的 94% 更新
基础检查结果等于或优于此分类中其他 5662 个服务器的 100%

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。

常见问题

Fraise 安全吗？

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。

它需要哪些密钥或代码？

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。

它在我的电脑上运行还是在云端？

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）。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（0.3.7）于前天进入注册中心。

哪些应用可以使用它？

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 Docker。

有什么可以替代它？

同一分类中有：Context7, Codebase Memory, Exa

替代选择

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

Context7

作者：upstash

Up-to-date docs for AI agents

电脑或云端★ 64,224MIT昨天更新

安装

Codebase Memory

作者：DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

在你的电脑上运行★ 48,148MIT21天前更新

安装

Exa

作者：exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

在云端运行★ 5,213MIT2个月前更新

安装

作者的 README

打开 README

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。

Fraise

Docs · Discord · Query language · Issues

Fraise is a memory database for AI agents. One they query directly, in a language built for tokens, not humans.

remember 'acme moved to annual billing' topic:billing entity:acme

recall billing entity:acme since:30d top:5

Two verbs. One binary. No infrastructure to run.

Install

brew install fraisehq/tap/fraise

brew services start fraise curl -X POST localhost:9876/api/v1/q -H 'content-type: application/json' \ -d "{\"query\": \"remember 'the parrot is turquoise' topic:color\"}"

Linux packages, Docker, go install and signed release binaries are all in Get Started below.

Read Durability before you deploy. Persistence is issue #171 and the next major piece of work.

How it compares

Measured on LoCoMo — 10 multi-session conversations, 1,986 questions — with every system ingesting the same conversations, using the same extraction model and the same embedding model, and answering the same questions. k=10, full data, latest run per system as of 2026-10-11.

system	retrieval recall	p50 search	memory tokens
fraise 0.3.3	0.910	0.185 s	7,453,109
hindsight 0.9.2	0.913	5.836 s	1,688,873
everos 1.2.3	0.897	0.376 s	10,030,966
letta 0.16.8	0.897	0.349 s	226,854
mem0 2.0.18	0.886	0.576 s	4,120,078
memos 2.0.33	0.865	0.307 s	4,000,552
graphiti 0.30.2	0.828	0.290 s	9,861,973
cognee 1.5.3	0.790	6.273 s	8,526,019
langmem 0.0.30	0.783	0.227 s	1,078,110

Fraise comes within 0.003 of the best recall, 31× faster than the one system that reaches it. Near enough the same evidence, in a fraction of the time. The token column is the price of that recall: the extractor (fraise-sdk 0.2.0) reads each message beside what the graph already holds, and that context is where the recall came from.

These come from agent-memory-benchmarks, an open harness — precision, recall and F1 across k ∈ {1, 3, 5, 10}, per category, every run tagged and reproducible from the tag. The full tables are in its RESULTS.md.

Why Fraise

A query language agents can actually write. FQL has two verbs — remember and recall — and one way to say each thing. Fewer degrees of freedom means fewer ways for a model to get it wrong, and fewer tokens spent saying it.
Hybrid retrieval. Facts are indexed for full-text, graph, and (optionally) vector search. One query, ranked across all three.
Temporal by default. Recent memories outrank older ones, so recall is recency-aware without asking for it.
The fastest system measured. 0.185 s p50 on LoCoMo, ahead of every other system and 31× faster than the one with matching recall. Remember and recall mid-step, while the user waits.
No infrastructure. A single binary. No database to provision, no service to stand up beside it.
Open source, MIT.

Status

v0.3.3. The core loop works end to end and the install paths are verified on clean machines. The benchmark row above was produced from this release. 0.4.0 (late October) changes the scorer and adds explain and describe; 0.5.0 (November) is persistence and forget.

Good for building agent memory today. Not yet for long term production use.

How it works

Fraise stores knowledge as a temporal memory graph built from three kinds of node:

facts — the things you remember, one statement each
entities — who or what a fact mentions
topics — what a fact is about

Edges connect facts to the entities they mention and the topics they're about, so a query can start from either side. A recall finds seed facts by text and, optionally, by vector similarity; a recall that names only topics or entities starts from the facts filed under them. With depth:1 or depth:2, a named topic or entity whose facts matched better than its size predicts passes that evidence on to the rest of its facts. Results are ranked by relevance and recency, and the best top come back.

Ranking is not a black box: a fact's score folds its own match strength with what it receives through anchors carrying *more* mass than their size would predict, each measured against the facts the recall could return. The background rate that "more" is measured against is estimated per query, from the part of the graph the query touched, so there is no relevance constant to tune. Hybrid retrieval has the full model.

A single Fraise instance holds several independent memory graphs (8 by default), addressed with @N — one per user, per session, per agent, however you like.

Get Started

Fraise is a single binary — no database to provision, nothing to configure. Every route below leaves you with a server listening on port 9876, at localhost:9876.

With Homebrew

brew install fraisehq/tap/fraise

brew services start fraise

The service survives crashes and restarts on login (keep_alive), logs to $(brew --prefix)/var/log/fraise.log, and reads its config from $(brew --prefix)/etc/fraise/fraise.config.toml — installed with every setting commented at its default, and never overwritten on upgrade.

With Docker

docker run -p 127.0.0.1:9876:9876 ghcr.io/fraisehq/fraise:latest

Published tags: one per release, latest for the newest stable, edge for the tip of main, and an immutable full-commit-SHA tag for every merge that touches the server.

Images are built with SLSA provenance, verifiable without pulling:

gh attestation verify oci://ghcr.io/fraisehq/fraise:latest --repo FraiseHQ/fraise

With Go

go install github.com/FraiseHQ/fraise/cmd/server@latest

"$(go env GOPATH)/bin/server"

The binary installs as server, after its package path — rename it to fraise if that reads better.

Nothing further is needed to trust this: the Go toolchain checks every module download against the public checksum transparency log, and your own machine compiles the result.

Linux packages

.deb and .rpm packages ship with every release, with a systemd user unit:

VERSION=0.3.0

ARCH=$(uname -m | sed 's/x86_64/amd64/;s/aarch64/arm64/') curl -sSfLO "https://github.com/FraiseHQ/fraise/releases/download/v${VERSION}/fraise_${VERSION}_linux_${ARCH}.deb" sudo dpkg -i "fraise_${VERSION}_linux_${ARCH}.deb" systemctl --user enable --now fraise

Logs go to the journal (journalctl --user -u fraise -f), and the unit reads ~/.config/fraise/fraise.config.toml when present — a shipped default with every setting commented lives at /etc/fraise/fraise.config.toml to copy from. For agents that outlive your login session, let the user manager keep running: loginctl enable-linger $USER. A system-level (shared server) variant of the unit is described in Running as a managed service.

From a release binary

VERSION=0.3.0

OS=$(uname -s | tr '[:upper:]' '[:lower:]') # linux | darwin ARCH=$(uname -m | sed 's/x86_64/amd64/;s/aarch64/arm64/') # amd64 | arm64 ASSET="fraise_${VERSION}_${OS}_${ARCH}.tar.gz" BASE="https://github.com/FraiseHQ/fraise/releases/download/v${VERSION}"

curl -sSfLO "${BASE}/${ASSET}" tar xzf "$ASSET" ./fraise

Windows builds ship as .zip under the same naming scheme.

Verify a release

Releases carry a cosign signature over checksums.txt, using the same VERSION and BASE as above:

curl -sSfLO "${BASE}/checksums.txt"

curl -sSfLO "${BASE}/checksums.txt.sigstore.json"

1. the bundle proves checksums.txt came from this repo's release workflow

cosign verify-blob \ --bundle checksums.txt.sigstore.json \ --certificate-identity-regexp 'https://github.com/FraiseHQ/fraise/.github/workflows/go.yaml@refs/tags/v.*' \ --certificate-oidc-issuer https://token.actions.githubusercontent.com \ checksums.txt

2. checksums.txt proves your archive is the one it covers

sha256sum --ignore-missing -c checksums.txt # macOS: shasum -a 256 --ignore-missing -c

Run from source

git clone https://github.com/FraiseHQ/fraise

cd fraise make dev

Your first memory

curl -X POST localhost:9876/api/v1/q \

-H 'content-type: application/json' \ -d "{\"query\": \"remember 'the parrot is turquoise' topic:color\"}"

curl -X POST localhost:9876/api/v1/q \ -H 'content-type: application/json' \ -d '{"query":"recall parrot"}'

"results": { "count": 1, "hits": [ { "value": "the parrot is turquoise", "timestamp": "...", "score": 0.2867 } ] } }

Use it from an agent

MCP

fraise mcp is a stdio MCP server — a thin bridge to a running daemon, so any MCP client can remember and recall

只显示了开头部分；其余内容请在仓库中阅读

Fraise

概览

PopNx MCP 的三大支柱

它能帮你做什么

安装

安装之前

它能做什么

基础检查i

作者与仓库一致

仍在维护

有许可证

端点有响应

使用 OAuth 登录

工具描述中没有隐藏指令

与“搜索与 AI 记忆”中的其他服务器比较i

常见问题

替代选择

Context7

Codebase Memory

Exa

作者的 README

Fraise

Install

How it compares

Why Fraise

Status

How it works

Get Started

With Homebrew

With Docker

With Go

Linux packages

From a release binary

Verify a release

1. the bundle proves checksums.txt came from this repo's release workflow

2. checksums.txt proves your archive is the one it covers

Run from source

Your first memory

Use it from an agent

MCP