First LLM Studio

English | 简体中文

English

First LLM Studio is a local-first LLM workbench for Apple Silicon. It brings local MLX runtimes, remote API targets, Agent sessions, Compare, Fine-tune, Benchmark, model discovery, runtime recovery, release evidence, and admin monitoring into one operating surface.

It is not another chat shell. It is built for people who need to compare behavior, debug runtimes, run evals, prepare adapters, and keep local and remote model work inside one product loop.

Product Surfaces

Major Version Story

Current release: VERSION.

Who It Helps

Local AI builders on Apple Silicon

• Compare MLX local models against hosted APIs under aligned context budgets.
• Inspect runtime cost, prewarm, release, recovery, and hardware pressure without leaving the app.
• Decide which local model is actually usable for daily coding and analysis workflows.

Agent and tooling teams

• Validate tool-calling, repo-grounded behavior, replay, and patch flows in one workbench.
• Turn Compare runs into benchmark handoff without switching products.
• Separate model-quality failures from provider quirks and local runtime instability.

Evaluation and platform engineers

• Run formal and focused benchmark suites with repeatable profiles.
• Review baselines, deltas, run notes, failure classifications, and release evidence.
• Keep local and remote targets inside one comparable target catalog.

Core Value

• Unified local + remote target catalog.
• Compare Lab for model-vs-model output review.
• Fine-tune workflows for datasets, recipes, training, evaluation, adapter proof loops, and export.
• Benchmark operations with history, progress, baselines, reports, and release evidence.
• Foreground Retrieval for document import, chunk inspection, and grounded evidence probes.
• Experiments timeline for session/run lineage, artifact navigation, and retention policy.
• Replay, trace review, patch inspection, and exportable review notes.
• Runtime operations for prewarm, release, restart, log inspection, telemetry, and recovery.
• Dynamic local/community model discovery plus remote provider health scanning.

Current Targets

Local

• Local Qwen3 0.6B
• Local Qwen3 4B 4-bit
• Local Qwen3.5 4B 4-bit
• Local Gemma 3 4B It Qat 4-bit

Remote

• OpenAI Codex
• OpenAI GPT-5.5
• Claude API
• DeepSeek API
• Kimi API
• GLM API
• Qwen API

Screenshots

Benchmark percentile board:

Formal milestone regression summary:

Local runtime telemetry:

Quick Start

Requirements

• macOS on Apple Silicon
• Node 22.x
• Python 3.12
• MLX-compatible local environment

Install

nvm install 22
nvm use 22
npm install
cp .env.example .env.local

Start the web app

npm run dev

Default routes:

• http://localhost:3011/agent
• http://localhost:3011/compare
• http://localhost:3011/fine-tune
• http://localhost:3011/models
• http://localhost:3011/benchmarks
• http://localhost:3011/retrieval
• http://localhost:3011/experiments
• http://localhost:3011/admin

Start the local model gateway

python3.12 -m venv .venv
source .venv/bin/activate
pip install -U pip
pip install mlx mlx-lm
python scripts/local_model_gateway_supervisor.py

Gateway health:

• http://127.0.0.1:4000/health

Verification

npm run typecheck:changed
npm run smoke:routes
npm run smoke:screenshots

Configuration

Copy .env.example to .env.local and fill only the providers you want to use.

Important notes:

• .env.local is ignored by git.
• Remote providers are optional.
• Several targets use OpenAI-compatible or Claude-compatible endpoints.
• Public defaults in this repository are sanitized placeholders.

Repository Structure

app/                      Next.js app routes and thin API transports
components/               Shared UI and compatibility shells
features/                 Feature-owned routes, contracts, state, actions, and application ports
lib/agent/                Agent runtime, providers, benchmark, gateway helpers
lib/finetune/             Fine-tune store facade and split operation services
scripts/                  Local gateway, runtime, verification, and release scripts
docs/                     Architecture, release notes, launch notes, roadmap, and assets
modelscope/               ModelScope profile/readme metadata
public/                   Public assets and social cover art

Distribution

• GitHub: https://github.com/ChrisChen667788/local-agent-lab
• ModelScope profile: https://www.modelscope.cn/profile/haozi667788
• Default ModelScope repo id: haozi667788/first-llm-studio

The ModelScope package script exports the committed Git tree so GitHub and ModelScope can stay file-identical for each synced version.

Security and Privacy

• Sensitive local actions require confirmation.
• Secrets belong in .env.local.
• Public repository defaults are sanitized.
• New public commits should use a GitHub noreply address where possible.
• See SECURITY.md.

Contributing

Issues and PRs are welcome.

• CONTRIBUTING.md
• CODE_OF_CONDUCT.md
• docs/open-source-backlog.md

Release Notes

• Current version: VERSION
• Release notes: docs/releases
• Release process: docs/release-process.md
• Latest release note: v0.4.0

简体中文

First LLM Studio 是一个面向 Apple Silicon 的本地优先 LLM 工作台。它把本地 MLX 运行时、远端 API 目标、Agent 会话、Compare 对比、Fine-tune 微调、Benchmark 评测、模型发现、runtime 恢复、发布证据和后台监控统一到一个产品界面里。

它不是另一个聊天壳，而是给真正需要比较模型行为、调试 runtime、跑评测、准备 adapter，并把本地/远端模型工作流收在同一个产品循环里的开发者使用。

产品入口

大版本核心功能