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Agent Evaluation & Observability

LLM Space

LLM Space is an open-source desktop application for configuring AI agents, tracing their execution, replaying failed runs and evaluating performance across runs.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

LLM Space — OpenViST project profile in Agent Evaluation & Observability.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it while developing an agent when you need to configure its components, inspect what happened during execution, investigate a past failure or compare performance across runs.

Who it is for

  • Agent builders prototyping prompts, tools and model settings.
  • Developers investigating agent execution traces or debugging failed runs.
  • Teams or individuals evaluating agent behavior across multiple runs.

Documented uses

  • Write and version prompts, system messages, tools and model settings.
  • Inspect model calls and tool runs inside an agent loop.
  • Replay a historical run and step through it to investigate a failure.
  • Evaluate agent performance across runs.
  • Generate prompts and tools with AI or turn a thread into a runnable LangGraph agent.
Before you choose

Requirements & limits

The repository identifies the project as open source under the MIT license and provides macOS downloads for Apple Silicon and Intel. Building from source requires Bun.

  • The documented desktop downloads target macOS on Apple Silicon or Intel.
  • The two macOS editions differ in rendering approach: one uses the system WebView, while the Performance edition embeds its own rendering engine.
  • The project says files and API keys stay on the local computer, but also describes collecting a small amount of anonymous usage data and provides an opt-out.
  • The application stack includes TypeScript, Bun, Electrobun, React, Tailwind CSS, shadcn/ui and Pi Agent Core.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install LLM Space from source and start its development app

Install the project dependencies and launch the desktop app for local development.

Before you start

  • Bun installed using its official installation guide.
  • The LLM Space repository cloned locally.

Steps

  1. From the repository root, run bun install.

  2. Run mise run dev to start the desktop app for local development.

Expected outcome

The project dependencies are installed and the desktop app starts in its local-development mode.

Guide limitations

  • This recipe covers local development, not creating or installing a production release.
  • The README offers mise run setup as an alternative for contributors who install mise; it installs the locked Bun version and JavaScript dependencies.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
11 October 2026
Original website title
deer-flow/llm-space · Official homepage title · retained in its source language
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