deer-flow/llm-spacePublic

A desktop app to prototype agent ideas, inspect every harness step, replay failures, and evaluate performance, all in one place. Local-first, cloud-ready for managed agents.

AI summary: A native desktop workspace for prototyping, orchestrating, and monitoring local AI agents and open-weight Large Language Models.

Stars
2K
+7 today
Forks
216
Watchers
5
Open issues
5
Open PRs
15
Contributors
~26
Commits
782
Branches
81

TypeScriptMITCreated Jun 28, 2026Last push 3d agoLatest release v4.19.1+43 stars this week+183 this month

Quick answers

What is llm-space?
A native desktop workspace for prototyping, orchestrating, and monitoring local AI agents and open-weight Large Language Models.
What does llm-space do?
LLM Space is a comprehensive desktop application built with Electrobun and React that acts as a centralized workspace for managing local Large Language Models. It abstracts the complexity of terminal commands, providing a unified GUI to download models, monitor hardware resources, and conduct multi-model inference. Beyond simple chat, it functions as an agent prototyping environment where developers can inspect execution steps, debug failures, and evaluate performance natively. The application ensures privacy by running models completely offline while remaining cloud-ready for managed agent orchestration. It provides developers a highly controlled space to trace and perfect AI interactions before deployment.
Who is llm-space for?
AI developers, researchers, and privacy-conscious users who want a powerful GUI to orchestrate and debug local LLMs without relying on terminal scripts.
How do I get started with llm-space?
bun install
How popular is llm-space on GitHub?
deer-flow/llm-space has 1,979 stars and 216 forks on GitHub, and gained 43 stars in the last 7 days.
What license does llm-space use?
deer-flow/llm-space is released under the MIT license.

Star history

since Jul 29, 2026
05001K1.5KJul 2026Aug 2026Sep 2026Oct 2026
2K stars as of Oct 4, 2026. Measured daily since Jul 29, 2026; GitHub no longer exposes earlier star timestamps.

Update history

1 recorded
  • Oct 4, 2026New release v4.19.1 (previously v4.18.1).

Contribution activity

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719 commits in the last yearLessMore

Signals and awards

derived from tracked data
  • Very active

    719 commits in 52 weeks

  • Permissive license

    MIT

  • Continuous integration

    Automated checks passing

What llm-space does

LLM Space is a comprehensive desktop application built with Electrobun and React that acts as a centralized workspace for managing local Large Language Models. It abstracts the complexity of terminal commands, providing a unified GUI to download models, monitor hardware resources, and conduct multi-model inference. Beyond simple chat, it functions as an agent prototyping environment where developers can inspect execution steps, debug failures, and evaluate performance natively. The application ensures privacy by running models completely offline while remaining cloud-ready for managed agent orchestration. It provides developers a highly controlled space to trace and perfect AI interactions before deployment.

AI developers, researchers, and privacy-conscious users who want a powerful GUI to orchestrate and debug local LLMs without relying on terminal scripts.

  • Unified desktop environment: Delivers a polished, native GUI built on Electrobun across major operating systems without relying on terminal scripts.
  • Local model orchestration: Allows users to seamlessly switch, manage, and coordinate multiple open-weight LLMs within a single interface.
  • Agent prototyping tools: Provides deep inspection capabilities to evaluate every step of an AI agent's complex harness execution.
  • Real-time resource monitoring: Includes dedicated dashboards to track CPU, RAM, and VRAM utilization dynamically during inference.
  • Offline inference capability: Ensures complete data privacy by running all model interactions natively on the local machine.

Where teams use it

Agent workflow debugging

Developers step through and inspect every action an AI agent takes to find exactly where a complex pipeline fails.

Local model comparison

Researchers run multiple open-weight models side-by-side to evaluate differences in output quality and inference latency.

Private data analysis

Privacy-conscious individuals analyze sensitive corporate documents using capable AI models entirely offline to ensure data security.

Hardware performance testing

Engineers precisely monitor VRAM spikes when testing different quantization levels of newly released local language models.

Getting started: bun install

README

main branch

English | 中文

CI Version TypeScript Bun License: MIT


LLM Space 4

deer-flow%2Fllm-space | Trendshift

screenshot

ScreenFlow.mp4

LLM Space v4 is a desktop app for agent builders — prototype your next agent ideas, inspect every step of your harness execution, debug failures, and evaluate performance, all in one place.

Official website: https://deer-flow.github.io/llm-space/

LLM Space is a sister project of DeerFlow, and we dogfood it heavily: every version of DeerFlow is built and debugged with LLM Space. The project started in March 2023, and v4 is its fourth major iteration.

Contents

Features

  • Build — write and version your prompts, system messages, tools, and model settings.
  • Trace — see every model call and tool run inside the agent loop as it happens.
  • Debug — replay a run from history and step through it to find what went wrong.
  • Evaluate — measure how your agent performs across runs.
  • Manage — keep your threads organized as files on your own machine.
  • Generate — let AI write your prompt and tools for you, and even turn any thread into a runnable LangGraph agent.

Your files and API keys stay on your local computer. LLM Space collects a small amount of anonymous usage data to improve the app - see TELEMETRY.md for exactly what is collected and how to opt out.

Tech stack

  • Language & tooling — TypeScript, built and managed with Bun.
  • Desktop shell — Electrobun, a lightweight way to ship a native app.
  • UI — React with Tailwind CSS and shadcn/ui.
  • Agent framework — Pi Agent Core, a lightweight agent framework for building agents.

Project layout

LLM Space is a Bun monorepo:

packages/
  core/       # Shared domain types, clients, storage, and generators
  runtime/    # Local runtime, models, tools, skills, MCP, and Plugins
  ui/         # Shared React design system and Thread Playground
apps/
  desktop/    # The desktop app (Electrobun shell + React UI)
examples/
  atlas-plugin/ # Complete Plugin example covering every Extension type

Each package keeps tests in a tests/ directory beside src/. Test paths mirror source paths, such as packages/core/src/thread/history.ts and packages/core/tests/thread/history.test.ts.

Download

Grab a DMG from the latest release — macOS, Apple Silicon and Intel. It comes in two editions:

  • LLM Space — uses the system WebView. Small download (~27 MB), light on memory and battery.
  • LLM Space Performance — embeds its own rendering engine (~130 MB). Rendering stays consistent across macOS versions, and usually performs better.

Install either, or both. They share the same ~/.llm-space data, so switching editions keeps your threads and settings, and both update themselves in place.

Install

Building from source? You need Bun first. Bun is a fast, all-in-one runtime and package manager for JavaScript — think of it as a drop-in replacement for Node.js and npm. Follow the official install guide.

Once Bun is ready, install the project from the repo root:

bun install

Contributing, or want the exact toolchain CI uses? Install mise and run mise run setup instead — it installs the locked Bun version (from mise.lock) plus JS deps in one step.

Run the app

Start the desktop app for local development:

mise run dev

Build a canary release:

mise run build:canary

User guide

The user guide lives in this repository:

Contributing

For now, we only merge pull requests from the DeerFlow core team members.

Everyone else is very welcome to help by opening an issue — bug reports, ideas, and feedback all make the project better.

Sponsors

LLM Space is free and open source, and it stays that way thanks to our sponsors. We are proud and grateful to be backed by them.

🏆 Platinum sponsor

Superdesign - AI product design agent that turns prompts into designs on an infinite canvas (Platinum Sponsor)

Superdesign is an AI product design agent that turns natural-language prompts into UI mockups, components, and full designs on an infinite canvas. Thank you for making LLM Space possible. 💜

Want to see your logo here? We would love to talk - reach out by opening an issue or support the project.

🏅 Gold sponsor

We strongly recommend using the VolcEngine Coding Plan as your default model provider:

image

Donate

If LLM Space is useful to you and you would like to support its development, you can donate here:

Support LLM Space →

Thank you.

License

LLM Space is released under the MIT License.

View on GitHub

Recent activity

commits and pull requests

Recent open issues

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Discussions

all 1

Releases and announcements

45 total
  1. v4.19.1v4.19.1Sep 29, 202667 downloads

    ## What's Changed Includes the improvements from 4.19.0, whose release publication did not complete. ### Added - Provider-owned image models and localized built-in tool descriptions. - Multilingual memory retrieval, archiving, and a memory management panel. ### Changed - Refined search provider settings, provider selection, and API-key actions. - Bundled memory is disabled by default, and its settings stay hidden until enabled. - Updated Pi model and agent libraries to 0.87.1, verified against the official npm registry on 2026-09-29. Retained the local Responses compatibility and native-tool patch because upstream still lacks the required behavior, including tool-call ID replay for non-OpenAI providers. ### Fixed - Preserve literal edit-tool replacements, imported tool-result errors, blank context lines in patches, and insertion-only patch hunk ordering. - Wait for plugin ZIP writes to settle before cleaning up failed installations. - Keep dynamic prompt variables fresh, record resolved run models, and preserve memory records during upgrades and concurrent writes. - Confine local filesystem operations, reject nested symlinks during recursive copies, and cap tool

  2. v4.18.1v4.18.1Sep 7, 2026482 downloads

    ## What's Changed ### Added - The built-in Tencent Token Hub provider connects to its OpenAI-compatible endpoint, discovers `TOKENHUB_API_KEY` or `TOKEN_HUB_API_KEY`, and includes current Hunyuan, DeepSeek, GLM, Kimi, MiniMax, Qwen, MiMo, and vision models. ### Changed - Confirmed `@earendil-works/pi-ai` 0.85.1 as the latest registry release on 2026-09-07 and retained the local Responses compatibility patch. ### Fixed - Custom request-header controls and Anthropic Base URL guidance in model profiles now follow the selected interface language, including accessible labels. ## Install Two editions. Install either, or both — they share the same `~/.llm-space` data, so switching keeps your threads and settings. - **LLM Space** — uses the system WebView. Small download (~27 MB), light on memory and battery. - **LLM Space Performance** — embeds its own rendering engine (~130 MB). Rendering stays consistent across macOS versions, and usually performs better. Download a DMG below. Existing installs update in place. **Full changelog:** https://github.com/deer-flow/llm-space/blob/v4.18.1/CHANGELOG.md

  3. v4.18.0v4.18.0Sep 7, 202625 downloads

    ## What's Changed This release upgrades the Pi agent runtime and completes the first broad internationalization pass, bringing a Chinese interface across the desktop app and shared Thread Playground. ### Added - A Chinese interface language now covers the desktop shell, settings, dialogs, and Thread Playground, with bilingual command-palette matching and localized provider names. - The bundled cross-project Memory plugin gives agents an opt-in durable store for preferences, project conventions, and decisions. - Search settings now include Exa, AnySearch, and Zhihu MCP providers alongside the existing search integrations. - The built-in Aliyun Qwen provider uses the DashScope OpenAI-compatible API, discovers `DASHSCOPE_API_KEY`, and includes Qwen 3.8/3.7 plus selected DeepSeek, Kimi, GLM, and MiniMax models. - A short-drama writer example demonstrates a structured creative-writing workflow in the Thread Playground. ### Changed - Updated `@earendil-works/pi-ai` and `@earendil-works/pi-agent-core` to 0.85.1, the latest registry release checked on 2026-09-07, while retaining the local Responses compatibility patch. ### Fixed - Zhihu search handles server-initia

  4. v4.17.0v4.17.0Sep 5, 2026102 downloads

    ## What's Changed ### Added - The MCP add menu now offers alphabetized presets for Amap, Chrome DevTools, Context7, ElevenLabs, GitHub, and Playwright alongside custom server setup. - MCP credential guidance includes a prominent Get API token link, required or optional status, and provider-specific setup instructions. - macOS Speech built-in tools list installed voices, read text aloud with a selected voice and speech rate, and stop playback without affecting other applications. They require no API key and appear in the Media category on macOS runtimes. ### Changed - Updated the Pi model and agent libraries to 0.85.0 while retaining the local Responses compatibility patch. ### Fixed - Switching thread tabs selects and reveals the corresponding file in the sidebar without reopening tabs or moving keyboard focus. - MCP credential guidance remains visible after adding a Key to a remote server URL, and duplicate recommended servers receive unique names. ## Install Two editions. Install either, or both — they share the same `~/.llm-space` data, so switching keeps your threads and settings. - **LLM Space** — uses the system WebView. Small download (~27 MB), light

  5. v4.16.0v4.16.0Sep 3, 2026137 downloads

    ## What's Changed ### Added - A `spawn_agent` built-in Tool requests a manually created subtask. Calls pause automatic execution and ReAct loops; creating a subtask opens its tab and expands its folder without starting a run or filling in the tool response. - Subtasks inherit prompt templates, variables, model settings, and tools suited to general work, research, or code review. Readable task names become unique filenames, and missing subtask files can be created again from the tool card. - An `exec_code` built-in Tool runs Python, JavaScript, and TypeScript in persistent sessions, retaining variables and imports between calls. - `calculator` and `date_difference` built-in Tools handle mathematical expressions and date or time intervals, with matching Python exports. ### Changed - The General Agent template now includes `exec_code` and replaces its previous `agent()` function with `spawn_agent`. - Python exports explicitly report that manual subtask creation requires the desktop app; generated tools and project plans explain this limitation. - Scrollbars use consistent thumb styling throughout the app. - The remote runtime protocol is now v5. Update the server a

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+271.4K lines added, -110.8K removed over the last year.

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last 52 weeks
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719 commits in the last 52 weeks.

When work happens

weekday and hour
SunMonTueWedThuFriSat036912151821Sun 0:00 — 5 commitsSun 1:00 — 1 commitsSun 2:00 — 0 commitsSun 3:00 — 0 commitsSun 4:00 — 0 commitsSun 5:00 — 0 commitsSun 6:00 — 1 commitsSun 7:00 — 0 commitsSun 8:00 — 0 commitsSun 9:00 — 11 commitsSun 10:00 — 9 commitsSun 11:00 — 5 commitsSun 12:00 — 5 commitsSun 13:00 — 3 commitsSun 14:00 — 8 commitsSun 15:00 — 7 commitsSun 16:00 — 20 commitsSun 17:00 — 1 commitsSun 18:00 — 4 commitsSun 19:00 — 3 commitsSun 20:00 — 1 commitsSun 21:00 — 1 commitsSun 22:00 — 0 commitsSun 23:00 — 1 commitsMon 0:00 — 5 commitsMon 1:00 — 1 commitsMon 2:00 — 0 commitsMon 3:00 — 0 commitsMon 4:00 — 0 commitsMon 5:00 — 0 commitsMon 6:00 — 0 commitsMon 7:00 — 0 commitsMon 8:00 — 2 commitsMon 9:00 — 3 commitsMon 10:00 — 8 commitsMon 11:00 — 11 commitsMon 12:00 — 7 commitsMon 13:00 — 0 commitsMon 14:00 — 5 commitsMon 15:00 — 7 commitsMon 16:00 — 0 commitsMon 17:00 — 7 commitsMon 18:00 — 4 commitsMon 19:00 — 4 commitsMon 20:00 — 7 commitsMon 21:00 — 4 commitsMon 22:00 — 11 commitsMon 23:00 — 9 commitsTue 0:00 — 1 commitsTue 1:00 — 2 commitsTue 2:00 — 0 commitsTue 3:00 — 0 commitsTue 4:00 — 0 commitsTue 5:00 — 1 commitsTue 6:00 — 2 commitsTue 7:00 — 4 commitsTue 8:00 — 0 commitsTue 9:00 — 2 commitsTue 10:00 — 3 commitsTue 11:00 — 6 commitsTue 12:00 — 1 commitsTue 13:00 — 4 commitsTue 14:00 — 11 commitsTue 15:00 — 9 commitsTue 16:00 — 4 commitsTue 17:00 — 7 commitsTue 18:00 — 7 commitsTue 19:00 — 11 commitsTue 20:00 — 12 commitsTue 21:00 — 13 commitsTue 22:00 — 7 commitsTue 23:00 — 3 commitsWed 0:00 — 5 commitsWed 1:00 — 0 commitsWed 2:00 — 0 commitsWed 3:00 — 0 commitsWed 4:00 — 0 commitsWed 5:00 — 0 commitsWed 6:00 — 1 commitsWed 7:00 — 0 commitsWed 8:00 — 3 commitsWed 9:00 — 10 commitsWed 10:00 — 18 commitsWed 11:00 — 9 commitsWed 12:00 — 7 commitsWed 13:00 — 5 commitsWed 14:00 — 4 commitsWed 15:00 — 13 commitsWed 16:00 — 16 commitsWed 17:00 — 6 commitsWed 18:00 — 7 commitsWed 19:00 — 0 commitsWed 20:00 — 2 commitsWed 21:00 — 7 commitsWed 22:00 — 3 commitsWed 23:00 — 14 commitsThu 0:00 — 5 commitsThu 1:00 — 0 commitsThu 2:00 — 0 commitsThu 3:00 — 0 commitsThu 4:00 — 0 commitsThu 5:00 — 0 commitsThu 6:00 — 1 commitsThu 7:00 — 0 commitsThu 8:00 — 0 commitsThu 9:00 — 2 commitsThu 10:00 — 11 commitsThu 11:00 — 21 commitsThu 12:00 — 19 commitsThu 13:00 — 5 commitsThu 14:00 — 13 commitsThu 15:00 — 5 commitsThu 16:00 — 17 commitsThu 17:00 — 11 commitsThu 18:00 — 6 commitsThu 19:00 — 7 commitsThu 20:00 — 4 commitsThu 21:00 — 3 commitsThu 22:00 — 2 commitsThu 23:00 — 6 commitsFri 0:00 — 4 commitsFri 1:00 — 3 commitsFri 2:00 — 0 commitsFri 3:00 — 0 commitsFri 4:00 — 0 commitsFri 5:00 — 0 commitsFri 6:00 — 1 commitsFri 7:00 — 0 commitsFri 8:00 — 0 commitsFri 9:00 — 9 commitsFri 10:00 — 8 commitsFri 11:00 — 10 commitsFri 12:00 — 4 commitsFri 13:00 — 9 commitsFri 14:00 — 6 commitsFri 15:00 — 12 commitsFri 16:00 — 8 commitsFri 17:00 — 11 commitsFri 18:00 — 3 commitsFri 19:00 — 3 commitsFri 20:00 — 7 commitsFri 21:00 — 1 commitsFri 22:00 — 6 commitsFri 23:00 — 3 commitsSat 0:00 — 2 commitsSat 1:00 — 3 commitsSat 2:00 — 0 commitsSat 3:00 — 0 commitsSat 4:00 — 0 commitsSat 5:00 — 0 commitsSat 6:00 — 1 commitsSat 7:00 — 0 commitsSat 8:00 — 0 commitsSat 9:00 — 4 commitsSat 10:00 — 6 commitsSat 11:00 — 6 commitsSat 12:00 — 3 commitsSat 13:00 — 1 commitsSat 14:00 — 3 commitsSat 15:00 — 0 commitsSat 16:00 — 0 commitsSat 17:00 — 5 commitsSat 18:00 — 0 commitsSat 19:00 — 2 commitsSat 20:00 — 2 commitsSat 21:00 — 4 commitsSat 22:00 — 7 commitsSat 23:00 — 3 commits
Commit volume by weekday and hour (UTC). Larger dots mean more commits.
DateListRankStars gained
Jul 16, 2026daily#9+7
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