Lime - Full-Stack Desktop AI Agent
An open-source full-stack desktop AI agent that can actually finish the work, spanning coding, files, terminals, tools, research, content, and multimodal workflows.
Evidence shows permission confirmation mechanism (README mentions approving high-risk actions), but no implementation details, so score 1. Data flow transparency: README states local storage by default, but no detailed data flow, score 1. Sensitive data handling: no explicit encryption or anonymization, score 1. Dependency security: lock file and frozen-lockfile in CI, but no vulnerability scanning mentioned, score 1. External effects: permission control mentioned, but no specific limits, score 1. Rollback: mentions resumable work, but no specific mechanism, score 1. Source attribution: no mention of source tracking, score 1.
Self-consistency: README and package.json descriptions align, score 2. Dependency availability: lock file present, but no availability guarantee, score 1. Failure messages: no error handling documentation, score 1.
Audience and scenarios: README clearly lists target users and scenarios, score 2. Capability boundaries: no explicit permission boundaries, score 1. Trigger precision: no trigger mechanism details, score 1. Environment fit: supports macOS and Windows, score 2.
Information architecture: README and docs directory present, score 2. Install notes: download and Homebrew instructions provided, score 2. Naming stability: no API stability statement, score 1. Examples and FAQ: FAQ and examples present, score 2. Known limitations: not explicitly listed, score 1. License: GPLv3 clearly stated, score 2. Versioning and changelog: RELEASE_NOTES file exists, but content not provided, score 1. Maintenance responsibility: no explicit maintainer, score 1.
Output usability: describes generating reports, scripts, etc., score 2. Marginal value: provides multimodal and multi-agent features, score 2. Cost benefit: no performance or cost data, score 1.
Claim traceability: README claims features, but no evidence, score 1. Cross-source corroboration: only README and package.json, score 1. Fact-inference separation: not explicitly distinguished, score 1.
- No specific permission implementation details, need further code review.
- No detailed data flow and sensitive data handling documentation.
- No dependency vulnerability scanning information.
- No known limitations and rollback mechanism specifics.
What does this agent do, and when should you use it?
Lime is an open-source full-stack desktop AI agent for users and teams worldwide, bringing the agent loop, filesystem, terminal processes, code changes, tool calls, MCP, Skills, multimodal input and output, model routing, and multi-agent collaboration into one traceable task chain. It belongs to the same category of hands-on agents as Claude Code, WorkBuddy, and Codex, while emphasizing a desktop GUI, visual workspace, configurable providers, and mixed engineering, research, and content workflows. Built on Electron and a Rust App Server, Lime supports macOS and Windows and is licensed under GPLv3. It allows users to configure their own providers and models, extend the agent via MCP and Skills, and execute and review tasks in a unified workspace.
Lime performs the following operations: it understands goals, repositories, files, history, and constraints before proposing an executable plan; reads and writes files, searches, applies patches, runs terminal commands, tests, and calls tools within granted permissions; projects work as Thread, Turn, Item, and reusable artifacts, enabling pause, review, restore, and continuation; handles multimodal understanding (text, code, images, screenshots, audio, video, PDFs, tables) and generation (images, audio, video, documents, charts); discovers and calls external tools or local Skills via MCP; supports multi-agent collaboration, preserving shared context and permissions; allows per-task model selection and manages capability catalogs, credentials, routing, retries, and failure boundaries.
- A developer needs to fix a bug in a repository; Lime reads relevant files, traces the call path, implements changes, and runs focused tests.
- A full-stack team needs to ship a feature spanning frontend, App Server, Rust runtime, protocol, and tests; Lime splits and executes in dependency order.
- A researcher needs to turn web pages, notes, screenshots, and meeting records into a report or launch draft.
- A content creator needs to generate publish-ready output from multimodal material like audio, video, and PDFs.
- A team wants to encode a recurring check or release step as a reusable Skill for the agent to run via MCP.
- A coordinator needs to delegate research, implementation, testing, and documentation to different agents, collecting results in a main thread.
What are this agent's strengths and limitations?
- Desktop GUI and visual workspace differentiate it from CLI tools like Claude Code
- Provider-agnostic design avoids lock-in to a single model service
- Integrates MCP and Skills, allowing reusable procedures as execution units
- Native multimodal support for diverse input and output formats in one task
- Multi-agent collaboration with parallel subtasks and state preservation
- Limited platform support: only macOS and Windows; Linux builds paused
- Installers may be unsigned, triggering Windows SmartScreen warnings
- GPLv3 license may constrain commercial integration
- No bundled AI models, relying on user-configured third-party providers, with potential data upload
- Project maturity uncertain, with limited documentation and community support
How do you install or deploy this agent?
Download the installer from Releases: macOS users can get the .dmg package or install via Homebrew (brew tap aiclientproxy/tap && brew install --cask lime); Windows users can download Lime_*_x64-setup.exe. Currently only macOS and Windows builds are published; Linux desktop builds are paused.
How do you use this agent?
- Open Lime, configure a Provider, and test a model connection. 2. Choose a workspace or project directory and confirm file and terminal permissions. 3. Create an Agent Thread with a goal, constraints, and acceptance criteria. 4. Ask for a plan first, then approve file changes, commands, or external tool calls as needed. 5. Inspect the diff, test results, and artifacts; continue the task or close it.