> ## Documentation Index > Fetch the complete documentation index at: https://docs.memanto.ai/llms.txt > Use this file to discover all available pages before exploring further. # Configuration > Environment variables, state files, embedding/LLM provider options, and per-backend isolation for Memanto on-prem. # On-Prem Configuration The on-prem wizard wires sensible defaults, but every value is editable. This page is the reference for **where** each setting lives, **what** controls it, and **how** to change it after install. ## Configuration Surfaces On-prem reads from three places. They are evaluated in this order — later sources override earlier ones: 1. **Environment variables** (or a project `.env`) — highest precedence. 2. **`~/.memanto/on-prem/state.json`** — set by the on-prem onboarding wizard. 3. **Built-in defaults** in Memanto's `Settings` model. The shared `~/.memanto/config.yaml` is owned by the cloud backend; the on-prem wizard does **not** write into it. The only `config.yaml` key on-prem touches is `backend: on-prem` (so subsequent CLI runs know which backend to dispatch to). ## Environment Variables These are the on-prem-relevant variables in `Settings`. Defaults shown. | Variable | Default | Purpose | | ------------------------------------ | ----------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `MEMANTO_BACKEND` | `cloud` | Set to `on-prem` to route all Moorcheh calls to the local server. The wizard sets this for you; you can also set it inline (`MEMANTO_BACKEND=on-prem memanto status`). | | `MOORCHEH_ONPREM_URL` | `http://localhost:8080` | Base URL of the Moorcheh on-prem server. Override if you've remapped the port or are running Memanto in a different container. | | `MOORCHEH_ONPREM_EMBEDDING_PROVIDER` | *(empty)* | Surfaced in `memanto status` so you can see at a glance what provider is in use. Auto-populated from `state.json`. | | `MOORCHEH_ONPREM_TIMEOUT` | `300` | HTTP read timeout in seconds for the on-prem Moorcheh client. Default is high because first-call LLM cold-starts on Ollama can take 1–2 minutes (model load). | | `MOORCHEH_API_KEY` | *(empty)* | **Not required on-prem.** The on-prem stack does not consult this. | | `HOST` | `0.0.0.0` | Bind host for Memanto's own REST server (`memanto serve`). | | `PORT` | `8000` | Bind port for Memanto's own REST server. | | `ANSWER_MODEL` | `anthropic.claude-sonnet-4-6` | **Cloud default.** On-prem, the active LLM is sourced from `state.json` (`llm_model`); this env var is ignored unless `state.json` is empty. | | `ANSWER_TEMPERATURE` | `0.7` | LLM temperature for `answer.generate`. Honored on both backends. | | `ANSWER_LIMIT` | `15` | Number of context memories passed to the LLM for `answer`. | | `ANSWER_THRESHOLD` | `0.01` | Confidence threshold for memory relevance during `answer`. | | `RECALL_LIMIT` | `10` | Default Top-N results returned by `recall`. | | `SUMMARY_MODEL` | `anthropic.claude-sonnet-4-6` | Same backend-awareness rule as `ANSWER_MODEL`. | | `ALLOWED_ORIGINS` | `*` | CORS origins for Memanto's REST API. Restrict in production. | | `LOG_LEVEL` | `INFO` | `DEBUG`, `INFO`, `WARNING`, `ERROR`. | ### Setting Env Vars For a single command: ```bash theme={null} MEMANTO_BACKEND=on-prem memanto status ``` In a project `.env` (loaded automatically): ```bash theme={null} MEMANTO_BACKEND=on-prem MOORCHEH_ONPREM_URL=http://moorcheh.internal:8080 MOORCHEH_ONPREM_TIMEOUT=600 LOG_LEVEL=DEBUG ``` Globally for your shell (Linux/macOS): ```bash theme={null} export MEMANTO_BACKEND=on-prem ``` On Windows PowerShell: ```powershell theme={null} $env:MEMANTO_BACKEND = "on-prem" ``` ## On-Prem State File `~/.memanto/on-prem/state.json` is the source of truth for on-prem configuration. It is written by the wizard and read by both the CLI and the embedded server. Example contents: ```json theme={null} { "installed_at": "2026-06-09T14:32:11Z", "embedding_provider": "ollama", "embedding_model": "nomic-embed-text", "llm_provider": "ollama", "llm_model": "qwen2.5", "url": "http://localhost:8080" } ``` | Key | Used for | | --------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `url` | Exported as `MOORCHEH_ONPREM_URL` at process startup. | | `embedding_provider`, `embedding_model` | Re-used when re-onboarding on-prem (lets you switch cloud↔on-prem without re-picking a provider). | | `llm_provider`, `llm_model` | Sent as `ai_model` to the on-prem server on every `answer.generate` call. If empty/missing, the on-prem server falls back to whatever LLM is configured in `~/.moorcheh/config.json`. | | `installed_at` | Metadata, useful for support diagnostics. | You can edit this file by hand. After saving, restart `memanto serve` (or run any CLI command) to reload. ## Moorcheh Server Config `~/.moorcheh/config.json` is owned by the `moorcheh-client` package. The Memanto wizard writes the full **embedding + LLM** block there before calling `moorcheh up`, so the on-prem server has both ready on first boot. Schema: ```json theme={null} { "embedding": { "provider": "ollama", "model": "nomic-embed-text", "api_key": null, "base_url": "http://ollama:11434" }, "llm": { "provider": "ollama", "model": "qwen2.5", "api_key": null, "base_url": "http://ollama:11434" } } ``` To switch the on-prem server to a different provider after install: 1. Stop the stack: `moorcheh down`. 2. Edit `~/.moorcheh/config.json` (or use `moorcheh configure` interactively). 3. Restart: `moorcheh up`. 4. Update `~/.memanto/on-prem/state.json` to match (`embedding_provider`, `llm_model`, etc.) — Memanto reads its model id from there. ## Provider Reference ### Ollama (Local, Recommended for Air-Gap) * **Embedding model:** `nomic-embed-text` (default; \~270 MB). * **LLM model:** `qwen2.5` (default; \~4.7 GB) — change with any `ollama pull`-able model. * **API key:** none. * **Where it runs:** sibling Docker container started by `moorcheh up`. ### OpenAI * **Embedding model:** `text-embedding-3-small` (default; cheaper) or `text-embedding-3-large`. * **LLM model:** `gpt-4o-mini` (default), `gpt-4o`, etc. * **API key:** required; stored in `~/.moorcheh/config.json` under `embedding.api_key` / `llm.api_key`. ### Cohere * **Embedding model:** `embed-english-v3.0` (default) or `embed-multilingual-v3.0`. * **LLM model:** `command-r-plus-08-2024` (default). * **API key:** required. ## Answer & Recall Tuning These knobs work identically on cloud and on-prem. | Setting | Env var | Default | What it does | | ------------------- | -------------------------------------------------------------- | ------- | -------------------------------------------------------------------------------------------- | | Answer model | `ANSWER_MODEL` *(cloud)* / `state.json: llm_model` *(on-prem)* | — | Which LLM `answer` calls. | | Answer temperature | `ANSWER_TEMPERATURE` | `0.7` | Higher = more creative, lower = more deterministic. | | Answer context size | `ANSWER_LIMIT` | `15` | How many memories to pass as context. Lower for faster answers, higher for better grounding. | | Answer threshold | `ANSWER_THRESHOLD` | `0.01` | Memories below this similarity are dropped. | | Recall top-N | `RECALL_LIMIT` | `10` | Default page size for `recall`. Override per-call with `--limit`. | ## Timeouts Ollama cold-starts can be slow on first call after `moorcheh up`. Memanto sets the on-prem client's read timeout to **300 seconds** by default so an initial `answer.generate` doesn't fail with a `ReadTimeout`. Override: ```bash theme={null} export MOORCHEH_ONPREM_TIMEOUT=600 ``` After the first call the model stays resident in Ollama's RAM and subsequent calls return in under seconds. ## Disk Locations Recap | Path | Owner | Editable? | | ------------------------------- | ----------------- | ----------------------------------------------------- | | `~/.memanto/on-prem/state.json` | Memanto CLI | Yes — hand-edit then restart CLI/server. | | `~/.memanto/.env` | Memanto CLI | Yes — but on-prem does not need a `MOORCHEH_API_KEY`. | | `~/.moorcheh/config.json` | `moorcheh-client` | Yes via `moorcheh configure` or by hand. | | `~/.moorcheh/uploads/` | `moorcheh-client` | Append-only; staging for `memanto upload` files. | ## Next Steps * [Backend Switching](./backend-switching) — toggle between cloud and on-prem without losing state. * [Self-Hosting Memanto Server](./server-deployment) — run `memanto serve` under Docker/Compose/systemd. * [Kubernetes Deployment](./kubernetes) — manifests for a clustered on-prem deployment. * [Security & Operations](./security) — production hardening checklist.