Fraise

Name: Fraise
Author: FraiseHQ

by FraiseHQversion 0.3.7i★ –iRuns on your computer

Registry nameio.github.FraiseHQ/fraise

Install

Overview

Description from the maker (original English)

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

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

DockerDocker installed

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into 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"
      ]
    }
  }
}

Run this in your terminal

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

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.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"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

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

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account FraiseHQ, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 5655 servers in this category
Published to the registry more recently than 95% of the other 5655 servers in this category
Basic checks equal to or better than 100% of the other 5655 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Fraise safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.3.7) reached the registry 2 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Docker on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated yesterday

Install

Codebase Memory

by DeusData

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

Runs on your computer★ 48,148MITUpdated 21 days ago

Install

Exa

by exa-labs

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

Runs in the cloud★ 5,213MITUpdated 2 months ago

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

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

Only the beginning is shown; read the rest in the repo

Fraise

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

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