Configuration

Offline memory

go run ./cmd/mind-layer -addr 127.0.0.1:8080 -data data/memory.db

Explicit storage, BM25 search, personality, context, export and deletion work without any provider. Data is stored in one Bolt database file. A second process cannot open the same database for writing; the open attempt times out. Multiple requests in one process are supported. Horizontal scaling is outside this edition.

Direct providers

Environment variable	Purpose
`MIND_LAYER_BASE_URL`	Trusted OpenAI-compatible API base URL, without `/chat/completions` or `/embeddings`
`MIND_LAYER_API_KEY`	Your provider credential; optional for local servers
`MIND_LAYER_CHAT_MODEL`	Explicit model for JSON fact extraction and replies
`MIND_LAYER_EMBEDDING_MODEL`	Explicit model for stored/query vectors
`MIND_LAYER_TOKEN`	Optional service-owner token; 32+ characters required for public binding

The server reads process environment variables. It does not automatically load .env files. Set a base URL and at least one model to enable a provider. Model defaults are deliberately not guessed. HTTPS is required except for a loopback IP endpoint such as http://127.0.0.1:11434/v1. Redirects are refused.

Gemini example, verified with a synthetic smoke test

export MIND_LAYER_BASE_URL=https://generativelanguage.googleapis.com/v1beta/openai
export MIND_LAYER_API_KEY="$GEMINI_API_KEY"
export MIND_LAYER_CHAT_MODEL=gemini-2.5-flash
export MIND_LAYER_EMBEDDING_MODEL=gemini-embedding-001
go run ./cmd/mind-layer -data data/semantic.db

This uses Google's documented OpenAI-compatible API. It calls Google directly. Provider availability and charges depend on your account.

Other compatible providers

Set the base URL, credential and models supplied by your provider. The adapter uses chat completions and float embeddings. Extraction requires response_format: {"type":"json_object"}. Support varies between compatible implementations. The protocol is contract-tested locally; only the recorded Gemini configuration has been live-tested in this preparation.

Embedding lifecycle

Configuring an embedding model makes new writes embed their text before commit. Embedding errors fail the write. Extraction embeds the returned facts when enabled. Lexical search remains offline; semantic and hybrid queries call the embedding provider. Chat calls the chat model after retrieval. No background jobs run.

Vectors retain their provider-base/model identity. Semantic/hybrid search rejects a scope containing missing or incompatible vectors. Reinsert those memories under the same IDs with the current model, or use a fresh database. There is no automatic re-embedding job. A model alias changing behavior under the same name cannot be detected; use stable model versions where available.

Remote access

Bind to loopback unless your application needs remote access. For remote access, set an unpredictable owner token of at least 32 characters and use TLS at a trusted reverse proxy. The token grants access to every scope. It does not authenticate individual end users. Browser-origin requests are rejected; use your backend.