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 3.8 Flash example
export MIND_LAYER_BASE_URL=https://generativelanguage.googleapis.com/v1beta/openai
export MIND_LAYER_API_KEY="$GEMINI_API_KEY"
export MIND_LAYER_CHAT_MODEL=gemini-3.8-flash
export MIND_LAYER_EMBEDDING_MODEL=gemini-embedding-001
go run ./cmd/mind-layer -data data/semantic.db
This uses Google's documented OpenAI-compatible API. It calls Google directly. Provider availability and charges depend on your account.
The character chat path was live-tested with Gemini 3.8 Flash. Historical benchmark reports retain the model used for each measurement.
GPT-6 Astra
For an OpenAI account with access to GPT-6 Astra:
export MIND_LAYER_BASE_URL=https://api.openai.com/v1
export MIND_LAYER_API_KEY="$OPENAI_API_KEY"
export MIND_LAYER_CHAT_MODEL=gpt-6-astra
unset MIND_LAYER_EMBEDDING_MODEL
go run ./cmd/character-demo -live
The adapter sends plain text Chat Completions without sampling parameters or tools. GPT-6 Astra supports this endpoint. Account access is required; this configuration has not been live-tested here. The example keeps retrieval local. Choose an embedding model separately when using semantic memory; chat model names are not embedding model names.
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.