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Put DJL to work

Two things worth knowing: how the agent runs a task, and how to give it a model that runs on your own machine.

01Using the agent

Describe the task, then stay in control

DJL turns a request into ordered steps, runs each tool in the open, and stops for your approval before anything touches your files.

  1. Describe the task

    Write what you want in English or 中文. Both languages share one context, so you can switch mid-task without repeating yourself.

  2. Read the plan

    Work is decomposed into ordered, legible steps you can read before anything runs.

  3. Watch the tools

    Each tool executes in a sandbox, streaming its output so nothing happens off-screen.

  4. Review the diff

    Every change waits for you: approve and apply, request changes, or cancel and roll back.

Nothing reaches your project until you approve it.

02Installing a local model

Run a model on your own hardware

DJL works out what your computer can run, then installs and starts the runtime itself. No terminal, no admin password.

Local model management lives in the DJL desktop app.

  1. Open the desktop app

    Download DJL for your platform and launch it.

  2. Go to Settings → Local Models

    Listed under Private inference.

  3. Check what DJL detected

    It reports your processor, graphics card, and memory, then recommends the largest model that will still feel fast — deliberately one tier below what your machine could merely hold.

  4. Install and start a runtime

    Choose Ollama or LM Studio. DJL downloads it, starts the local server, and connects to it.

  5. Select the model

    Once installed, pick it in the model picker and describe your first task.

Two runtimes

Ollama
DJL installs, starts, and connects Ollama for you. No terminal or admin password needed.
LM Studio
Install LM Studio once, then DJL can start and manage its local server.

Curated models

DJL recommends one of these from your hardware. You can always choose another.

ModelMemoryDownloadDrives the agent
Qwen3 1.7B4 GB1.4 GBChat only
Qwen3.5 2B8 GB1.9 GBChat only
Granite 4.1 3B8 GB2.1 GBYes
Qwen2.5 Coder 7B16 GB4.36 GBYes
GPT-OSS 20B16 GB13 GBYes
Qwen3 Coder 30B32 GB19 GBYes

“Chat only” means chat only

Qwen3 1.7B and Qwen3.5 2B answer questions well but are too small to hold a tool-calling loop together. Give either one a task that needs file edits and it stalls silently instead of reporting an error. For real work pick a model marked “Drives the agent” — Granite 4.1 3B is the smallest that qualifies, and it runs in 8 GB.

If the tools stop working

A capable model still needs enough context loaded to use tools. When DJL says the model is chat-only at its current context, reload it with a larger context window and the tools come back.

Bringing your own model

The curated list is a starting point, not a limit. Install any Ollama tag, LM Studio catalog ID, or exact Hugging Face model URL.

What stays on your machine

Inference runs locally: prompts, code context, and output are never sent to a hosted model provider. DJL connects only to fixed loopback addresses (127.0.0.1) and never exposes either runtime to your network. Tools you approve can still reach the network on their own.

What stays on your machine

Inference runs locally: prompts, code context, and output are never sent to a hosted model provider. DJL connects only to fixed loopback addresses (127.0.0.1) and never exposes either runtime to your network. Tools you approve can still reach the network on their own.

Download DJL and give it a model that runs where you are.

Download for macOS