Fraise

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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. Sub-millisecond recall. No infrastructure to run.

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.
• Fast enough to sit inside a turn. Recall in tens of microseconds, writes in low milliseconds — remember 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

Fraise is early and pre-v0.1.0. It runs, and the core loop works end to end — but the API and the query language may still change between minor versions.

Not production-ready. Good for experimenting with agent memory today.

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), expands through shared entities and topics up to depth hops, ranks by relevance and recency, and returns the best top results.

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 127.0.0.1:9876.

Fraise is pre-v0.1.0, so every published version is a pre-release. Two consequences for the commands below: Docker's :latest tag and GitHub's /releases/latest/ URL don't resolve yet, so each one pins a version.

With Go

go install github.com/RonsenbergVI/fraise/cmd/server@latest
"$(go env GOPATH)/bin/server"

@latest resolves to the highest pre-release (v0.1.0-beta.2 today); pin with @v0.1.0-beta.2 to be explicit. 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.

From a release binary

VERSION=0.1.0-beta.2
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}_${ARCH}.tar.gz"
BASE="https://github.com/RonsenbergVI/fraise/releases/download/v${VERSION}"

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

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

Verify a release

Releases after v0.1.0-beta.2 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/RonsenbergVI/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

With Docker

docker run -p 127.0.0.1:9876:9876 ghcr.io/ronsenbergvi/fraise:0.1.0-beta.2

Published tags: one per release (0.1.0-beta.2), edge for the tip of main, and an immutable full-commit-SHA tag for every merge. latest starts appearing at the first stable release.

Images are built with SLSA provenance, verifiable without pulling:

gh attestation verify oci://ghcr.io/ronsenbergvi/fraise:0.1.0-beta.2 \
  --repo RonsenbergVI/fraise

Run from source

git clone https://github.com/RonsenbergVI/fraise
cd fraise
make run

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": 1 }
    ]
  }
}

SDKs

Prefer to talk to Fraise from your own code? Official clients wrap the query endpoint behind two verbs — remember and recall — with optional vector embeddings and agent-framework tools.

Python (sdk/python):

pip install fraise-sdk

from fraise_sdk import FraiseClient

with FraiseClient("http://localhost:9876") as fraise:
    fraise.remember("the parrot is turquoise", topics=["color"])
    for hit in fraise.recall("parrot", top=5):
        print(hit.value, hit.score)

TypeScript (sdk/typescript):

npm install fraise-sdk

import { FraiseClient } from "fraise-sdk";

const fraise = new FraiseClient({ baseUrl: "http://localhost:9876" });
await fraise.remember("the parrot is turquoise", { topics: ["color"] });
const result = await fraise.recall(["parrot"], { top: 5 });
for (const hit of result.hits) console.log(hit.value, hit.score);

Both are dependency-light and support vector search when you supply an embedder. See each SDK's README for embeddings and the full API.

Integrate with Claude Agents

The Python SDK ships memory tools for the Claude Agent SDK, exposed as an in-process MCP server so the agent decides what to store and recall:

from claude_agent_sdk import ClaudeAgentOptions
from fraise_sdk import FraiseClient
from fraise_sdk.integrations.claude_agents import memory_server, allowed_tools

fraise = FraiseClient("http://localhost:9876")
options = ClaudeAgentOptions(
    system_prompt="Remember durable facts the user shares, and recall them when relevant.",
    mcp_servers={"fraise_memory": memory_server(fraise)},
    allowed_tools=allowed_tools(),
)

A complete, Docker-runnable agent lives in examples/claude-agent-sdk.

Integrate with OpenAI Agents

Both SDKs ship tools for the OpenAI Agents SDK. memory_tools(client) returns bound recall and remember tools:

from agents import Agent, Runner
from fraise_sdk import FraiseClient
from fraise_sdk.integrations.openai_agents import memory_tools

fraise = FraiseClient("http://localhost:9876")
agent = Agent(
    name="Assistant",
    instructions="Remember durable facts the user shares, and recall them when relevant.",
    tools=memory_tools(fraise),
)

result = Runner.run_sync(agent, "My favourite colour is orange.")
print(result.final_output)

The TypeScript equivalent is memoryTools(client) from fraise-sdk/integrations/openai-agents. Complete, Docker-runnable agents live in examples/openai-agents.

References

• Roadmap
• Database design
• Query language spec
• Release process
• Issues

Contributing

Contributions are welcome — see CONTRIBUTING.md for how to build, test, and submit changes.

Code of Conduct

This project follows the Contributor Covenant.

Community

Questions, ideas, or building something with Fraise? Join the Discord. Bugs and feature requests belong in issues so they don't get lost.

License

MIT — see LICENSE.