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Using PlanExe with LM Studio

This is for advanced users that already have PlanExe working. If you're new to PlanExe, start with OpenRouter instead.

LM Studio is an open source app for macOS, Windows, and Linux for running LLMs on your own computer. It is useful for local troubleshooting.

PlanExe processes more text than regular chat. You will need capable hardware to run an LLM at a reasonable speed.

Quickstart (Docker)

  1. Install LM Studio on your host and download a small model inside LM Studio (e.g. Qwen2.5-7B-Instruct-1M, ~4.5 GB).
  2. Copy .env.docker-example to .env (even if you leave keys empty for LM Studio) and use the lmstudio-... entry in llm_config.json, setting base_url to http://host.docker.internal:1234 (Docker Desktop) or your Linux bridge IP.
  3. Start PlanExe: docker compose up worker_plan frontend_single_user. Open http://localhost:7860, submit a prompt, and watch docker compose logs -f worker_plan for progress.

Host-only (no Docker)

For advanced users: use the host entry (e.g. "lmstudio-qwen2.5-7b-instruct-1m") in llm_config.json so base_url stays on http://127.0.0.1:1234. Start your preferred PlanExe runner (e.g. a local Python environment) and ensure the LM Studio server is running before you submit jobs.

Configuration

In llm_config.json, find a config that starts with lmstudio- (e.g. "lmstudio-qwen2.5-7b-instruct-1m"). In LM Studio, find the model with that exact id and download it. The Qwen model is on Hugging Face (~4.5 GB).

In LM Studio, go to the Developer page (Cmd+2 / Ctrl+2 / Windows+2), start the server, and confirm the UI shows Status: Running and Reachable at: http://127.0.0.1:1234.

Minimum viable setup

  • Start with a ~7B model (≈5 GB download). Expect workable speeds on a 16 GB RAM laptop or a GPU with ≥8 GB VRAM; larger models slow sharply without more hardware.
  • Structured output matters: not all models return clean structured output. If you see malformed or JSON errors, try a nearby model or quantization.

Run LM Studio locally with Docker

Containers cannot reach 127.0.0.1 on your host. Set base_url in llm_config.json to http://host.docker.internal:1234 (Docker Desktop) or your Docker bridge IP on Linux (often http://172.17.0.1:1234). On Linux, add extra_hosts: ["host.docker.internal:host-gateway"] under worker_plan in docker-compose.yml if that hostname is missing.

To find your bridge IP on Linux:

ip addr show docker0 | awk '/inet /{print $2}'

If docker0 is missing (e.g. with Podman or alternate bridge names), inspect the default bridge gateway:

docker network inspect bridge | awk -F'"' '/Gateway/{print $4}'

Example llm_config.json entry (add base_url when using Docker):

"lmstudio-qwen2.5-7b-instruct-1m": {
    "comment": "Runs via LM Studio on the host; PlanExe in Docker points to the host LM Studio server.",
    "class": "LMStudio",
    "arguments": {
        "model_name": "qwen2.5-7b-instruct-1m",
        "base_url": "http://host.docker.internal:1234/v1",
        "temperature": 0.2,
        "request_timeout": 120.0,
        "is_function_calling_model": false
    }
}

After editing llm_config.json, rebuild or restart the worker and frontends: docker compose up worker_plan frontend_single_user (add --build or run docker compose build worker_plan frontend_single_user if the image needs the new config).

Troubleshooting

When you click Submit in PlanExe, a new output directory is created containing log.txt. Open that file and scroll to the bottom for error messages.

Report issues on Discord. Include system info (e.g. “I’m on macOS with M1 Max, 64 GB”).

Where to look for logs:

  • Host filesystem: run/<timestamped-output-dir>/log.txt (mounted from the container).
  • Container logs: docker compose logs -f worker_plan (watch for connection errors to LM Studio).
  • Structured-output failures: If you see JSON/parse errors or malformed output in log.txt, try a different model or quantization; not all models return structured output cleanly.

Run LM Studio on a remote computer

Use a secure tunnel instead of exposing the server directly. From your local machine:

ssh -N -L 1234:localhost:1234 user@remote-host

Then set base_url to http://localhost:1234 while the tunnel is running.

Next steps