Cerefox

User-owned shared memory for AI agents. A persistent, curated knowledge layer that multiple AI tools can read and write, backed by Postgres + pgvector.

What is Cerefox?

Cerefox is a user-owned knowledge memory layer: a persistent, curated knowledge base that sits between you and the AI tools you use.

The primary use case is shared memory across AI agents: knowledge written by one tool (Claude, ChatGPT, Cursor, or a custom agent) becomes immediately available to all others. This prevents context fragmentation, so the same information doesn't have to be re-explained in every session.

Cerefox is asynchronous shared memory, not a message bus. It solves the persistent context problem: knowledge written in one context is findable in any other. A user curates project documents and an AI agent discovers them through search without being told they exist. An agent writes a decision during a coding session and a different agent, on a different machine, running a different model, finds it days later. A user switches from one AI tool to another and the accumulated knowledge carries over without manual transfer. The boundaries that Cerefox dissolves are between agents, between sessions, between human and machine, and across time.

For the full project vision, principles, and roadmap direction, see docs/research/vision.md.

• Agent-first, not human-first: AI agents are first-class citizens on both sides: they read and write; humans curate and validate

• Own your data: everything lives in a Postgres database you control (Supabase free tier or self-hosted)

• Cross-agent coordination: agents on separate machines and runtimes coordinate through persistent shared context (see docs/guides/agent-coordination.md)

• Not a note-taking app: Cerefox is knowledge infrastructure, not a replacement for Obsidian, Notion, or Bear; those tools handle authoring, Cerefox handles indexing and agent access

• Hybrid search: full-text + semantic search finds relevant knowledge even with fuzzy or conceptual queries

• Any agent, anywhere: remote MCP via Supabase Edge Functions; ChatGPT via Custom GPT + GPT Actions

• Keep it cheap: Supabase free tier + low-cost cloud embeddings; see docs/guides/operational-cost.md

Example use cases

Cerefox isn't bound to one tool or one workflow — anything that can run a shell command or speak MCP can read and write your memory, pointed at either a shared cloud deployment or a private local one:

See Connecting AI agents for the how-to per client.

Related MCP server: Sovereign Universal Memory MCP

Community

Questions, ideas, or want to follow development? Join the Cerefox Discord — setup & usage help for Cerefox and cf², release news, and a place to share what you're building.

Features

Project status

As of v0.10.0 Cerefox runs two ways: against a hosted Supabase project, or fully local / self-hosted in a single Docker container (no cloud, no account). The whole runtime — CLI, MCP server, web UI, ingestion, and server-side deploy — ships in the @cerefox/memory npm package (no repo clone); the local backend bundles that same runtime plus Postgres + pgvector into one image.

Until v1.0.0 the SemVer policy in CONTRIBUTING.md is aspirational — breaking changes can land in minor versions when there's a good reason; after v1.0.0 it's binding. Full release history is in CHANGELOG.md; the roadmap and iteration log live in docs/plan.md.

Getting Started

Upgrading to v0.9? The CLI verbs were renamed to a resource-verb shape (cerefox get-doc X → cerefox document get X; old names still run but redirect) and the Python CLI/web were retired to husks. See docs/guides/upgrading.md.

Cerefox runs two ways — pick your backend. Both expose the same features, web UI, and MCP tools; they differ only in where your data lives and how you install. (Contributors who want to run from source: see Run from source below.)

Option 1 — Cloud (Supabase)

Your data lives in your own Supabase project (free tier is enough). You use the cerefox command from the @cerefox/memory npm package. No git clone, no Python, no build.

# 1. Install (one-liner; detects Bun, falls back to npm):
curl -fsSL https://github.com/fstamatelopoulos/cerefox/releases/latest/download/install.sh | sh
#    or: npm install -g @cerefox/memory     (Node ≥ 20)

# 2. Configure + stand up the server side (against your own Supabase project):
cerefox init             # interactive setup: Supabase URL/keys, embedding key
cerefox server deploy    # schema + RPCs + all 9 Edge Functions, from the npm bundle
cerefox doctor           # verify everything is wired up

# 3. Wire up your AI agent(s) — run the ones that apply:
cerefox configure-agent --tool claude-code      # also: claude-desktop | cursor | codex | gemini

# 4. Use it:
cerefox document ingest my-notes.md --title "My notes"
cerefox search "what did I decide about auth?"
cerefox web              # web UI → http://localhost:8000/app/

Prerequisites: Node 20+ or Bun 1.0+ · a Supabase account (free tier) · an embedding API key (OpenAI text-embedding-3-small).

Full walkthrough: docs/guides/quickstart.md (~15 min). Supabase specifics: docs/guides/setup-supabase.md.

Option 2 — Local / self-hosted (Docker)

Everything runs in one Docker container on your machine — Postgres + pgvector

• the Cerefox server. No Supabase account, no Node/Bun on the host, just Docker. You get a cerefox-local command (same KB verbs as cerefox).

# 1. Install (one-liner; pulls the all-in-one image, adds a `cerefox-local` command):
curl -fsSL https://github.com/fstamatelopoulos/cerefox/releases/latest/download/install-local.sh | sh

# 2. Set your OpenAI key (for embeddings) + wire up an AI agent:
cerefox-local init                 # set/rotate the OpenAI key (re-creates the container)
cerefox-local configure-agent      # wire an MCP client (e.g. Claude Code)

# 3. Use it:
cerefox-local document ingest my-notes.md --title "My notes"
cerefox-local search "what did I decide about auth?"
#    web UI → http://localhost:8000/app/  (or the port the installer chose — it auto-steps
#    to 8010/… if 8000 is busy; `cerefox-local status` shows the URL. Manage: status | upgrade | stop)

Prerequisites: Docker (Docker Desktop or Colima) · an OpenAI API key (embeddings still use the OpenAI API).

Full walkthrough: docs/guides/setup-local.md. Cloud and local are independent — different installer, different command name — so they never collide if you happen to run both.

Run from source (contributors)

Clone the repo and run from source. bun drives everything; uv is only for the legacy Python MCP fallback.

git clone https://github.com/fstamatelopoulos/cerefox.git && cd cerefox
bun install                  # workspace deps: root + packages/memory + frontend
uv sync                      # OPTIONAL — only for the legacy `uv run cerefox mcp` fallback
cp .env.example .env         # fill in Supabase URL/keys + embedding key

bun scripts/db_deploy.ts     # schema + RPCs  (--dry-run to preview · --reset to wipe first)
npx supabase functions deploy   # Edge Functions (or just use `cerefox server deploy`)

cd frontend && bun run build && cd ..   # build the SPA `cerefox web` serves at /app/
bun test                     # run the suite (root + packages/memory + _shared)

Full contributor setup, conventions, and the test matrix are in CONTRIBUTING.md and the contributor section of docs/guides/quickstart.md.

Python is legacy. As of v0.9 the entire runtime (CLI, MCP, web, ingestion) is TypeScript in @cerefox/memory. The only surviving Python is uv run cerefox mcp — a frozen, offline / no-npm MCP fallback for repo-clone users. It is unmaintained and slated for removal; everything else Python is a husk that redirects to the TS CLI. See docs/guides/upgrading.md.

Architecture

cerefox_documents     cerefox_chunks
─────────────────     ───────────────────────────────
id, title, source     id, document_id, chunk_index
content_hash          heading_path, heading_level
project_id            content, char_count
metadata (JSONB)      embedding_primary (VECTOR 768)
chunk_count           fts (TSVECTOR, title-boosted)

Search RPCs (MCP tools): cerefox_hybrid_search, cerefox_fts_search, cerefox_semantic_search, cerefox_search_docs, cerefox_reconstruct_doc, cerefox_context_expand, cerefox_save_note

Connecting AI agents

The fastest path is cerefox configure-agent --tool <client> — it writes the right config for Claude Code, Claude Desktop, Cursor, Codex, or Gemini. There are four ways an agent can reach Cerefox:

1 — Remote MCP (recommended). The cerefox-mcp Edge Function speaks MCP Streamable HTTP. Just a URL + a legacy anon JWT (Supabase → API Keys → Legacy → anon, not the new sb_publishable_… key — see setup-supabase.md). No local install:

claude mcp add --transport http cerefox \
  https://<project-ref>.supabase.co/functions/v1/cerefox-mcp \
  --header "Authorization: Bearer <anon-key>"

2 — Local stdio MCP. cerefox mcp runs the same 10 tools in-process — lower latency, no per-call Edge Function billing. configure-agent wires it up, or point your client at command: "cerefox", args: ["mcp"].

3 — ChatGPT. Custom GPT + GPT Actions pointing at the Edge Functions (requires ChatGPT Plus). Paste the OpenAPI block from connect-agents.md.

4 — Shell CLI. Local coding agents with a Bash tool (Claude Code, Codex, opencode, …) can read and write Cerefox by running the installed cerefox command directly — no MCP config at all. Point the agent at AGENT_GUIDE.md and let it use cerefox search / cerefox document ingest.

Full setup for every client — plus a manual per-client config appendix for when configure-agent can't reach a tool — is in docs/guides/connect-agents.md.

Documentation

License

Apache 2.0 — see LICENSE.