---
title: "Deploy nolock + llama.cpp: ai coding harness + local slms (micro-agents)"
description: "ai coding harness + local slms (micro-agents)"
category: "AI/ML"
url: https://railway.com/deploy/nolock-llamacpp-ai-coding-harness-local-
---

# Deploy nolock + llama.cpp: ai coding harness + local slms (micro-agents)

ai coding harness + local slms (micro-agents)

**[Deploy nolock + llama.cpp: ai coding harness + local slms (micro-agents) on Railway](https://railway.com/template/nolock-llamacpp-ai-coding-harness-local-)**

Machine-readable deploy manifest (JSON, validated by TemplateCI): https://railway.com/deploy/nolock-llamacpp-ai-coding-harness-local-/manifest.json

- **Creator:** impacte.tech
- **Category:** AI/ML

## Template content

### nolock

- **Source:** https://github.com/impacte-tech/nolock
- **Start command:** `/app/nolock-server`
- **Health check:** /health
- **Public domain:** Yes

### llama.cpp

- **Source:** impacte-tech/nolock
- **Start command:** `/entrypoint.sh`
- **Health check:** /health

## Documentation

# Deploy and Host nolock + llama.cpp: ai coding harness + local slms (micro-agents) on Railway

nolock is an AI-native coding harness — a full IDE with chat, agents, micro-agents, tools, terminal, notebooks, and git session diffs. Paired with llama.cpp, it runs local small language models (SLMs) for private, free-token micro-agent execution — all from your browser, no API keys, no per-token bills.

## About Hosting nolock + llama.cpp: ai coding harness + local slms (micro-agents)

This stack deploys two isolated Railway services. **nolock** serves the web frontend + Rust backend (the exact same codebase as the Tauri v2 desktop app) — chat, agents, micro-agents, file tools, search, linter, sessions, and git diffs. **llama.cpp** runs the official server image wrapped with a small model store, and pulls a GGUF model from HuggingFace into a persistent volume on first boot. The services talk over Railway's **private network** — llama.cpp is never exposed publicly. The model is a configuration (`MODEL_HF`), swapped by changing a variable and redeploying; models pulled from the nolock UI persist on the volume, so switching back is instant. Tool calling works end-to-end: the SLM calls tools (file ops, search, git) and uses the results in its answer.

## Common Use Cases

- **Private AI coding assistant** — an agentic IDE with zero per-token costs and prompts that never leave your Railway instance
- **Local micro-agents** — code review, research, and writing agents powered by small local models, spawned from chat
- **Free-token API backend** — llama.cpp exposes an OpenAI-compatible `/v1/chat/completions` endpoint you own
- **Self-hosted AI workspace** — persistent sessions, git diffs, terminal, and notebooks, all in the browser

## Dependencies

- **nolock web app** — React frontend + Rust backend (`nolock-server`), the same codebase as the Tauri desktop app
- **llama.cpp server + model store** — official `ghcr.io/ggml-org/llama.cpp:server` image wrapped by this repo's `deploy/llamacpp/Dockerfile`; GGUF models persist at `/models`
- **Railway private networking** — nolock reaches llama.cpp via `http://llamacpp.railway.internal:8080` (inference) and `:8081` (model store); neither is public
- **Persistent volumes** — one for GGUF models (`/models`), one for nolock state (`/data` → `NOLOCK_DATA_DIR=/data/nolock`): secrets, RLHF logs, and opened projects survive redeploys
- **HuggingFace GGUF model** — any GGUF repo, e.g. `impacte/ullr:Q4_K_M`

## After Deploying

1. Open your nolock domain. The app shows a **login page** — paste the `NOLOCK_WEB_TOKEN` value (Railway → nolock service → Variables), or open `https:///?token=` to sign in automatically. The token is remembered per browser tab and sent as a Bearer header on every API call.
2. The llama.cpp provider is **auto-wired** — the app fetches the internal llama.cpp URL from the server, so chat and FIM completions work out of the box.
3. **Pull models from the UI**: Model Providers → select llama.cpp → paste a Hugging Face GGUF id (e.g. `owner/model-GGUF:Q4_K_M`) → Pull. Downloads run beside inference on the private model-store port and persist under `/models/pulled/` on the volume.

## Implementation Details

**llama.cpp service** (wrapper image from `deploy/llamacpp/Dockerfile`, no public domain):

```bash
# Start command
/entrypoint.sh   # starts the model store on private port 8081, then llama-server on 8080

# Variables
MODEL_HF=impacte/ullr:Q4_K_M        # the model is a configuration — swap and redeploy
LLAMA_CACHE=/models                 # persistent volume — model downloads once, survives redeploys
NOLOCK_MODEL_DIR=/models            # model store directory (same volume)
NOLOCK_MODEL_STORE_PORT=8081        # private model-store port
PORT=8080                           # inference port
NOLOCK_MODEL_PULL_TOKEN=    # shared with nolock — authenticates UI model pulls
```

**nolock service** (builds `deploy/Dockerfile`, healthcheck `/health`):

```bash
# Variables
LLAMACPP_URL=http://llamacpp.railway.internal:8080              # private inference
LLAMACPP_MODEL_STORE_URL=http://llamacpp.railway.internal:8081  # private model store
NOLOCK_DATA_DIR=/data/nolock   # persistent volume — secrets, sessions, projects
NOLOCK_WEB_TOKEN=   # login token for the public UI
NOLOCK_MODEL_PULL_TOKEN=
```

Both services share one random `NOLOCK_MODEL_PULL_TOKEN` so the nolock server can proxy authenticated model-pull requests to the model store. Railway credentials are never used by the app or sent to the browser. For gated HuggingFace repos, set `HF_TOKEN` on `llamacpp`.


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