Fraise

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.

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