The self-hosted server aims for zero configuration — the only required input is one LLM provider key, which the first-boot wizard collects interactively (or set via env var for non-interactive deployments). Embeddings default to local English; you can pick another provider in the optional wizard step or via env. Everything else below is opt-in.
The installer writes API keys to ~/.supermemory/env, which is loaded on every launch. You can also set variables in your shell or a process manager.
Core
| Variable | Purpose | Default |
|---|
PORT (or SUPERMEMORY_PORT) | HTTP listen port | 6767 |
SUPERMEMORY_DATA_DIR | Where the graph engine’s data, auth secret, and model cache live | ./.supermemory |
LLM providers
In production, Supermemory uses its own proprietary models tuned for long-horizon data understanding. Self-hosted, you bring your own LLM for the intelligent steps — summaries, contextual chunking, and memory extraction. Embeddings default to a local model (no API key) and can optionally use OpenAI, Gemini, or Ollama — see Embeddings. Configure at least one LLM provider:
| Variable | Provider |
|---|
OPENAI_API_KEY | OpenAI — or any OpenAI-compatible endpoint, see below |
ANTHROPIC_API_KEY | Anthropic |
GEMINI_API_KEY | Google AI Studio (Gemini) |
GROQ_API_KEY | Groq |
WORKERS_AI_API_KEY + CLOUDFLARE_ACCOUNT_ID | Cloudflare Workers AI |
GOOGLE_VERTEX_PROJECT_ID + GOOGLE_VERTEX_LOCATION | GCP Vertex AI |
No key set? The server walks you through it. On first boot, an interactive setup wizard asks which provider you want, securely prompts for the key, and saves it encrypted — including a custom base URL and model name if you pick an OpenAI-compatible endpoint.
Image, video, and high-fidelity PDF understanding require a Gemini or Vertex AI key. Text ingestion, memory extraction, and search work with any provider.
Fully offline with local models
OPENAI_API_KEY + OPENAI_BASE_URL covers any OpenAI-compatible endpoint: Ollama, LM Studio, vLLM, llama.cpp server, Together, Fireworks, and more.
| Variable | Purpose | Default |
|---|
OPENAI_BASE_URL | OpenAI-compatible endpoint URL | OpenAI |
OPENAI_MODEL | Model ID sent to that endpoint | gpt-5.1 |
OPENAI_FAST_MODEL | Override for fast/light tasks | OPENAI_MODEL |
OPENAI_TEXT_MODEL | Override for heavier text tasks | OPENAI_MODEL |
File storage
Nothing to configure. Uploaded files (PDFs, images) are stored on local disk inside $SUPERMEMORY_DATA_DIR and served by the server at /files/:key.
Embeddings
By default, vectors are computed locally with Xenova/bge-base-en-v1.5 (768d) — no embedding API key. On interactive first boot you can pick a different provider after the LLM key step; for Docker/CI set env vars instead.
Full provider table, multilingual guidance, remote examples (OpenAI / Gemini / Ollama), and the re-ingestion / dimension-lock warning: Embeddings (self-hosted).
| Variable | Purpose | Default |
|---|
SUPERMEMORY_EMBEDDING_PROVIDER | local, openai, gemini, or OpenAI-compatible remote | local |
SUPERMEMORY_EMBEDDING_MODEL | Model id for the chosen provider | Xenova/bge-base-en-v1.5 |
SUPERMEMORY_EMBEDDING_DIMENSIONS | Vector size; must match model and stored data | 768 |
SUPERMEMORY_EMBEDDING_BASE_URL | Base URL for OpenAI-compatible embedding APIs | unset |
| Variable | Purpose | Default |
|---|
SUPERMEMORY_LOCAL_EMBEDDING_POOL_SIZE | Number of embedding workers | 1 |
SUPERMEMORY_LOCAL_EMBEDDING_WASM_THREADS | Compute threads per worker | 1 |
SUPERMEMORY_LOCAL_EMBEDDING_BATCH_SIZE | Texts per worker dispatch | 8 |
SUPERMEMORY_LOCAL_EMBEDDING_IDLE_TIMEOUT_MS | Idle time before workers shut down | 120000 |
SUPERMEMORY_SKIP_EMBEDDING_PREWARM | Skip startup prewarm, load on first use | unset |
Memory limits & ingestion queue
The server manages memory for you and separates the two kinds of work you send it:
- Searches are always served immediately. They never wait behind ingestion, regardless of how much is queued.
- Adds are accepted instantly but processed through a queue. A
POST /v3/documents call returns in milliseconds with status queued; extraction, embedding, and indexing happen in the background at a controlled pace.
Ingestion may grow the server’s memory usage by at most SUPERMEMORY_EMBEDDING_RAM_LIMIT (default 1 GB) above its post-boot baseline. Past that, new documents simply wait in the queue until memory drops back under the limit — nothing is dropped, ingestion just slows down. The limit is measured above the boot baseline because the built-in local embeddings and storage engine have a fixed footprint that exists before any document is processed.
The limit is printed at boot, and whenever adds are waiting the binary shows a live status line in the terminal:
| Variable | Purpose | Default |
|---|
SUPERMEMORY_EMBEDDING_RAM_LIMIT | Memory ingestion may use above the boot baseline. Accepts 1gb, 1.5gb, 512mb, or a bare number (GB). | 1gb |
SUPERMEMORY_INGEST_CONCURRENCY | Documents processed concurrently | 2 |
Telemetry
The self-hosted binary sends no analytics — there is nothing to opt out of. The only related switch:
| Variable | Purpose | Default |
|---|
SUPERMEMORY_DISABLE_TELEMETRY | Set to 1 to also disable internal AI SDK telemetry instrumentation | unset |
- Connectors — Google Drive, Notion, Gmail, OneDrive background sync
- Supermemory MCP — managed MCP server endpoints
- Optimized memory extraction — the platform’s extraction pipeline is tuned for higher quality at lower cost than bring-your-own-key
- Managed scale — globally distributed infrastructure, no capacity planning
Any other environment variables you may find referenced in the codebase are platform-only: the self-hosted binary ignores them even when set.
Example: production-ish .env
That’s enough for full ingestion, memory extraction, and hybrid search with the default local embeddings. 
