OpenJet

Self-hosted local AI coding agent for Ollama, LM Studio, and llama.cpp

Use your existing local LLM server, or let OpenJet provision and host the right GGUF model for your hardware. Then use the same runtime through the terminal agent or Python SDK.

OpenJet is an open-source, private AI coding assistant that reads and edits files, runs approved shell commands, uses tools, and keeps sessions on your machine. It connects to Ollama, LM Studio, llama.cpp, and other OpenAI-compatible local servers. If you do not already have a local AI stack, openjet setup downloads, configures, and serves one for you.

On NVIDIA RTX 30, 40 and 50-series GPUs with 24 GB+, OpenJet serves Qwen3.8-27B through its own CUDA engine at 1.5x the speed of llama.cpp (measured on an RTX 3090). Open source.

Discord

In your terminal, OpenJet is a self-hosted coding agent:

files -> tools -> shell approval -> workflows -> local model

In your code, it is a way to ship an on-device model inside your own application — an in-app assistant, an NPC that talks, a classifier, an extraction pipeline. Run openjet project in your project at build time and the model is chosen for your target device, downloaded into the project, and bundled with your build. The SDK never downloads at runtime, so your users never fetch a file or see a config.

No hosted API is required. With a local profile, no code or prompt data leaves your machine.

On NVIDIA RTX 30, 40 and 50-series GPUs with 24 GB+, OpenJet serves Qwen3.8-27B through its own CUDA engine at 1.5x the speed of llama.cpp (measured on an RTX 3090).

Open source.

If you are new to local LLMs, OpenJet is the fastest way to get started without spending hours figuring out models, runtimes, and config. If you have already tried local LLMs and got frustrated piecing together a model backend, a frontend, and an actual agent workflow, OpenJet removes that setup tax.

This is the PyPI package for OpenJet. Install it with:

pip install open-jet

The package installs:

• CLI + chat TUI — interactive local agent work in the terminal
• Python SDK — embed sessions, profile hardware, and automate workflows
• Benchmarking tools — measure prompt and generation performance on your hardware

Quick Start

pip install open-jet
openjet setup
openjet

openjet setup detects your hardware, picks a model that fits your RAM, downloads it, and gets everything running. Already have a .gguf? It finds that too.

Recommended hardware: Apple silicon with 24GB+ unified memory, or a GPU with 14GB+ VRAM.

Qwen3.8-27B megakernel (NVIDIA RTX, 24GB+)

For Qwen3.8-27B Q4_K_M on Linux with an RTX 30, 40 or 50-series GPU that has 24GB or more, OpenJet serves the model through its own CUDA engine instead of llama-server. On an RTX 3090 it decodes at 1.5x llama.cpp's speed in the agent's reasoning mode (78 vs 51 tok/s) and up to 1.9x when writing code (140 vs 73 tok/s).

The engine ships in this package as source, from version 0.4.32. openjet setup compiles it for your GPU:

pip install --upgrade open-jet
openjet setup        # choose "Qwen3.8 27B Q4_K_M MTP"
openjet

You need the CUDA toolkit installed, for nvcc (CUDA 12 or newer; 12.8 or newer for RTX 50). The compile takes about 2 minutes and ends with Megakernel built: OpenJet will serve Qwen3.8-27B with it. If you set OpenJet up before 0.4.32, upgrade and run openjet setup again.

If setup can't build it, it prints Skipping the Qwen3.8-27B megakernel (...) with the reason and OpenJet uses llama-server instead, so setup still finishes. While OpenJet is running, pgrep -af mk_server.py prints a line when the megakernel is serving.

It has been benchmarked on an RTX 3090 only. Benchmarks and details: megakernel README.

CLI

The primary entrypoint is the openjet command:

openjet                     # start interactive session
openjet setup               # provision a coding model for this machine
openjet project             # provision the model your application ships with
openjet benchmark --sweep   # run a hardware benchmark sweep

The CLI is a full terminal agent that can:

• Work with local files — search, read, create, and update files in your projects
• Run shell commands — with explicit approval before execution
• Run workflows and checks — inspect results, update files, and try again against the local model
• Resume sessions — close the terminal and pick up where you left off
• Work on constrained hardware — automatic context condensing and model unload/reload around heavy tasks
• Connect to devices — cameras, microphones, GPIO, and remote devices for edge and embedded workflows
• Connect tools — trusted MCP servers are exposed through the normal OpenJet tool registry

Embed a local LLM in your own application

Provision the model once, at build time, in the project that will ship it:

cd your-project
openjet project --use-case dialogue --target handheld --budget 4

Selection is driven by what the model is for, the device you ship to (declared, not detected — the machine you build on is not the machine your users have), and the memory your application can concede. The weights land in .openjet/models/ so your build can bundle them, and .openjet/config.yaml pins them for the SDK.

import asyncio

from openjet.sdk import create_inference_session


async def main() -> None:
    session = await create_inference_session()
    try:
        result = await session.run("Summarise this support ticket in one line.")
        print(result.text)
    finally:
        await session.close()


asyncio.run(main())

create_inference_session() refuses every tool — text in, text out — so an embedded model cannot reach the shell or the filesystem no matter what it generates. Use create_agent() when you do want the full agent surface.

Nothing downloads at runtime. A missing model raises immediately rather than reaching for the network on a user's machine, so your users never fetch a file, see a config, or need an internet connection.

Python SDK

Use openjet.sdk to embed the same runtime in your own Python application.

from openjet.sdk import OpenJetSession, create_inference_session, recommend_hardware_config

Session API

Run agent sessions programmatically:

import asyncio
from openjet.sdk import OpenJetSession


async def main() -> None:
    session = await OpenJetSession.create()
    try:
        result = await session.run("Summarize the current README")
        print(result.text)
    finally:
        await session.close()


asyncio.run(main())

The session API exposes:

• OpenJetSession.create(...) — create a session
• session.run(...) — run a prompt and get the result
• session.stream(...) — stream events as they arrive
• session.set_airgapped(...) — toggle air-gapped mode
• session.add_turn_context(...) / session.clear_turn_context(...) — manage turn context
• create_agent(...) — construct an agent instance

Hardware Recommendation API

Get model and runtime recommendations for your hardware:

from openjet.sdk import recommend_hardware_config

result = recommend_hardware_config(
    {
        "total_ram_gb": 16,
        "gpu": "cuda",
        "vram_mb": 24576,
        "label": "RTX 4090 box",
    }
)

print(result.model.label)
print(result.model.target_path)
print(result.llama.device)
print(result.llama.gpu_layers)
print(result.llama.context_window_tokens)

Typed input is also supported:

from openjet.sdk import HardwareRecommendationInput, recommend_hardware_config

result = recommend_hardware_config(
    HardwareRecommendationInput(
        total_ram_gb=8.0,
        gpu="cpu",
        hardware_profile="other",
        hardware_override="desktop_8",
    )
)

SDK Surface

The supported public SDK surface:

from openjet.sdk import (
    HardwareRecommendation,
    HardwareRecommendationInput,
    OpenJetSession,
    RecommendedLlamaConfig,
    RecommendedModel,
    SDKEvent,
    SDKEventKind,
    SDKResponse,
    ToolResult,
    create_agent,
    create_inference_session,
    recommend_hardware_config,
)

Package Contents

This wheel includes the full OpenJet package:

• openjet.sdk — Python integrations
• CLI entrypoint openjet (installed as open-jet on PyPI)
• Benchmark entrypoints via openjet benchmark
• The local/session runtime shared by the SDK and CLI
• Source for the Qwen3.8-27B megakernel (megakernel/), compiled by openjet setup

Repository

• github.com/l-forster/open-jet
• Issues
• Discord

License

OpenJet core is licensed under AGPL-3.0-only.

This package covers the AGPL-licensed core SDK and CLI. Future hosted, team, or enterprise offerings may be licensed separately.

External contributions are accepted under the contributor terms in the repository's CONTRIBUTING.md and CLA.md.