Local — PHP / Ollama / LM Studio

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Run Spora on your laptop. No Docker, no VPS, no shared host. The skeleton’s composer dev command starts the PHP built-in server with doc root public/, so the prebuilt public/dist/ (the host’s compiled admin SPA + index.php front controller) and any spora-plugin-frontend packages installed under public/plugins/<slug>/ are both served correctly. This matches the installer’s contract — the same as production (FrankenPHP, root * /app/public). You can also use the canonical FrankenPHP image via Docker — single container.

Two paths for the LLM:

  1. Hosted LLM (Anthropic Claude, OpenAI GPT) — point an LLM driver config at the hosted endpoint
  2. Local LLM via Ollama or LM Studio — for offline use, privacy, or to avoid per-token costs

This page covers the local LLM path. The PHP server path is the same as the standard operator install.

Prerequisites

  • PHP 8.4+ with pdo_mysql, mbstring, zip, json extensions
  • Composer (latest 2.x)
  • Ollama (ollama.com) OR LM Studio (lmstudio.ai) — only needed for the local LLM path

The skeleton’s dev loop is PHP-only — there is no Vite dev server running against the host. If you’re hacking on the host’s Vue frontend itself (spora-ai/spora-frontend), see that repo’s npm run dev; otherwise the prebuilt public/dist/ is the source of truth at runtime.

Path A — Pure PHP server (hosted LLM)

The skeleton’s composer dev starts the PHP built-in server. For the full Standard install (create-project, install, seed), see Installation → Standard install. The actual command (from composer.json:33) is:

php -S ${PHP_HOST:-127.0.0.1}:${PHP_PORT:-8080} -t public public/index.php

The site is at http://127.0.0.1:8080. For a more production-like local server, use the Docker image from Custom build or the Docker — single container path.

Path B — Local LLM with Ollama

Ollama is a single-binary LLM runner. It serves an OpenAI-compatible API at http://localhost:11434/v1, so Spora’s OpenAICompatibleDriver works against it with no code changes.

Install Ollama

# macOS / Linux
curl -fsSL https://ollama.com/install.sh | sh

# Pull a model (a few GB)
ollama pull llama3.2:3b
ollama pull qwen2.5-coder:7b   # good for tool-calling

Configure Spora

In the admin UI:

  1. Go to Settings → LLM drivers
  2. Add a new LLM config:
    • Driver: OpenAI Compatible
    • Name: Ollama — Llama 3.2 3B (or whatever)
    • Base URL: http://localhost:11434/v1
    • API Key: (any value — Ollama ignores it, but the field is required)
    • Model: llama3.2:3b
    • Context window: 8192
    • Max output tokens: 4096
  3. Save

Then attach this driver to an agent in Agents → [agent] → LLM config.

Try it

In the admin UI’s chat view, send a message. The agent will use Ollama locally — no API calls leave your machine, no per-token cost, fully offline.

Models that work well

Ollama supports many open-weights models. The ones that handle tool-calling reliably (essential for Spora agents):

  • llama3.2:3b, llama3.2:8b — small, fast
  • qwen2.5-coder:7b — good at code and tool calls
  • mistral:7b, mistral-nemo:12b — good general-purpose
  • deepseek-coder-v2:16b — best for coding tasks

Larger models (32B+) are smarter but need more RAM. 7B models fit comfortably on 16 GB Macs.

Path C — Local LLM with LM Studio

LM Studio is a GUI app for running LLMs locally. It also serves an OpenAI-compatible API at http://localhost:1234/v1.

Install LM Studio

Download from lmstudio.ai. The app runs on macOS, Windows, and Linux. Once installed:

  1. Search for a model in the LM Studio library (e.g. “Qwen2.5 Coder 7B Q4_K_M”)
  2. Click “Download”
  3. Go to the “Developer” tab
  4. Start the local server (default port 1234, OpenAI-compatible)

Configure Spora

Same as Ollama:

  • Base URL: http://localhost:1234/v1
  • API Key: (any value)
  • Model: the model name shown in LM Studio (e.g. qwen2.5-coder-7b-instruct)

Path D — Both: PHP server + local LLM

The default composer dev is the dev loop. For a more production-like local server, use the Docker single-container and set the LLM to point at Ollama / LM Studio:

# docker-compose.yml
services:
  spora:
    image: ghcr.io/<your-org>/spora:latest # your custom build
    ports:
      - '8080:80'
    environment:
      SPORA_APP_ENV: development
      SPORA_DB_DRIVER: sqlite
      SPORA_SECRET_KEY: ${SPORA_SECRET_KEY:?required}
    volumes:
      - spora_storage:/app/storage

  # No mariadb / phpmyadmin — SQLite + local LLM
volumes:
  spora_storage:

Start Ollama separately, then in the admin UI add a new LLM config pointing at http://host.docker.internal:11434/v1 (the special Docker hostname for the host machine).

Performance expectations

Local LLMs are 5-20× slower than hosted Claude/GPT-4 for tool-calling tasks. Expectations:

  • A 3B model: 5-15 seconds per tool call
  • A 7B model: 10-30 seconds per tool call
  • A 12B+ model: 30-60 seconds per tool call

For exploratory dev and demos this is fine. For production agent workloads, hosted Claude or GPT-4 is dramatically faster.

What’s next