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: Development harness and tooling for managed AI agents in TypeScript environments.

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TypeScriptMITCreated Jun 28, 2026Last push todayLatest release v4.9.0+129 stars this week+164 this month

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since Jul 12, 2026
05001K1.5KJul 2026Jul 2026Jul 2026Aug 2026
1.5K stars as of Aug 7, 2026, tracked back to Jul 12, 2026. Historical curve reconstructed from public GitHub event archives, calibrated to the current total.

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Signals and awards

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  • Rising fast

    +129 stars this week

  • Very active

    579 commits in 52 weeks

  • Permissive license

    MIT

  • Continuous integration

    Automated checks passing

What llm-space does

LLM Space 4 is a comprehensive TypeScript-based harness for building, managing, and observing AI agents. It provides structural tooling for developers working on the deer-flow agent platform, wrapping native LLM interactions into managed agentic workflows. By integrating directly into TypeScript development ecosystems, it allows developers to build robust, stateful agents rather than dealing with raw API calls. The repository includes an embedded Electrobun environment for cross-platform desktop integration.

TypeScript developers and AI engineers building complex, managed agentic applications. Familiarity with modern frontend ecosystems and LLM API concepts is necessary.

  • Agent harness: structured framework for initializing and running managed LLM agents.
  • TypeScript integration: strictly typed APIs for safe agent development.
  • Electrobun support: bundles native desktop tooling for agentic applications.
  • Workflow management: orchestrates multi-step interactions typical of complex agent workflows.
  • Developer tools: includes observability features and debugging harnesses specifically for LLM tasks.

Where teams use it

Agent development

Build managed, stateful agents quickly without reinventing core scaffolding.

Desktop integration

Ship standalone LLM-powered applications using the built-in Electrobun harnesses.

Workflow automation

Chain multiple AI behaviors together reliably within a typed TypeScript environment.

Observability testing

Debug and trace complex multi-step reasoning performed by deer-flow agents.

Getting started: npm 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 shellElectrobun, a lightweight way to ship a native app.
  • UI — React with Tailwind CSS and shadcn/ui.
  • Agent frameworkPi Agent Core, a lightweight agent framework for building agents.

Project layout

LLM Space is a Bun monorepo:

packages/
  core/       # Shared logic: types, the agent loop, thread storage
apps/
  desktop/    # The desktop app (Electrobun shell + React UI)
examples/
  atlas-plugin/ # Complete Plugin example covering every Extension type

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

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Releases and announcements

29 total
  1. v4.9.0v4.9.0Aug 7, 202656 downloads

    ## What's Changed This release adds Seedream image generation and provider connection profiles, makes local Plugin commands more transparent, and turns Plugin and MCP settings into clearer, more informative management surfaces. ### Added - Volcengine Ark providers can configure Seedream image models and use the new `generate_image` built-in Tool to create images with provider credentials. - Model providers support multiple named connection profiles, with profile-aware model selection, connection testing, streaming, and built-in Tool execution. - Plugin commands can publish progress, success, and failure feedback while they run, with lifecycle handling shared across the command palette and desktop runtime. - Plugin settings group Commands, Tools, and MCP Servers into expandable sections with type-specific icons and extension descriptions. - MCP settings expose the Plugin that owns each bundled server and cache Plugin tool discovery to avoid repeated subprocess work. ### Changed - Settings empty states use a reusable animated layout across Plugins, MCP Servers, and Remote Servers, with refined selection, contrast, spacing, and artwork treatment. - The onboarding

  2. v4.8.5v4.8.5Aug 6, 202659 downloads

    ## What's Changed This patch release restores tool-call continuation for OpenAI Responses-compatible providers in packaged desktop builds. ### Fixed - Tool results replay the provider's original call ID without the internal response-item suffix, preventing `No tool call found for tool output` errors. - The local `pi-ai` patch now applies correctly during clean dependency installs, matching development and release builds. ## 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/v4.8.5/CHANGELOG.md

  3. v4.8.4v4.8.4Aug 5, 2026111 downloads

    ## What's Changed This release expands local Plugins into first-class agent capabilities and refreshes the most important setup, sharing, and generation experiences. ### Added - Local Plugins can contribute executable Tools that users add to Threads and models invoke like built-in Tools, with stable identities, package icons, lifecycle isolation, structured results, and owning-Thread variable context. - Plugin Commands accept quoted Command Palette arguments and can inspect the active tab, filename, and Thread through an explicit context API. - Plugin development guides now document Commands and Tools in English and Chinese, including discovery, context, variables, local-runtime limits, and error behavior. ### Changed - The new-Thread gallery, runnable-agent generator, and Thread-sharing flow now use clearer, more focused layouts with polished Light and Dark appearances. - Account, Plugins, Skills, Models, MCP Servers, and Remote Servers settings now have stronger navigation, purposeful empty states, and consistent list headings. - Working-directory controls, Plugin discovery, and Tool selection provide more direct feedback and actions, including revealing ex

  4. v4.8.3v4.8.3Aug 5, 202642 downloads

    ## What's Changed This patch release streamlines the generated LangGraph project setup on macOS. ### Changed - After explicitly creating a generated project's `.env`, macOS users now get a Terminal window that starts the development server with `make dev`. - Other platforms, cancelled setup, and launch failures continue to reveal the generated project without starting a process. ### Fixed - Generated project paths with spaces or shell-special characters are safely passed to Terminal, and only user-authorized generator directories can be launched. ## 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/v4.8.3/CHANGELOG.md

  5. v4.8.2v4.8.2Aug 5, 202610 downloads

    ## What's Changed This patch release makes filesystem tools handle home-relative paths reliably and improves the generated LangGraph development workflow. ### Added - Generated LangGraph projects include a `Makefile`; run `make dev` to start `uv run langgraph dev`. ### Changed - Filesystem tool schemas and generated Python implementations consistently document and expand leading `~/` paths. - MCP stdio commands and working directories expand home-relative paths in both the desktop runtime and generated LangGraph projects. ### Fixed - `write`, `edit`, `read`, `ls`, `tree`, `grep`, `glob`, and `present_files` no longer interpret `~/...` relative to the desktop app bundle. - Home-relative artifact paths can be revealed from tool calls, including a `glob` target directory. ## 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 D

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579 commits in the last 52 weeks.

When work happens

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