askimo

AI desktop app for chat, RAG, Skills, MCP tools, and agents. Support multiple LLMs (Anthropic, OpenAI, VertexAI, vLLM, Nvidia NIM, Gemini, OpenRouter)

One app. Every AI model. Your files stay local.

Chat · Search your files and the web · Run scripts · Build multi-step AI workflows · Execute AI agent skills - all offline-capable, all on your machine.

⭐ If Askimo saves you time, a star helps others find it - and keeps the project going. Star on GitHub →

📥 Download • 📖 Documentation • 💬 Discussions

Why Askimo?

You shouldn't have to choose between the best AI model, your privacy, and getting real work done.

One app, every model. Stop juggling browser tabs. Chat with OpenAI, Claude, Gemini, Grok, or a local Ollama model, switch in seconds, no copy-pasting.
Built as a native desktop app. Not a web wrapper. Starts fast, runs lean, and stays responsive even after hours of use and thousands of messages in a single conversation.
Long conversations that actually work. No crashes, no tab reloads, no lost context. Askimo handles deep, extended sessions the way a real desktop app should.
More than just chat. Delegate real work to autonomous agent CLIs (Claude Code, Codex, Antigravity), chain multi-step AI Plans from a form UI, and connect MCP tools, all from the same app.
Skills, managed once, used everywhere. Define a skill independently of any single agent, then run it with whichever agent CLI fits the job. No per-agent duplication or lock-in.
Talk to it, not just type. Dictate messages and have responses read back to you. Fully local/offline speech-to-text and text-to-speech options included.

🔒 Privacy by design. Your files, RAG index, conversation history, and telemetry all stay on your machine, nothing is uploaded, ever. Local RAG, local SQLite storage, local usage/cost tracking. The only network calls are the ones you configure (your chosen AI provider).

See It in Action

Agents - select the installed agent CLI and delegate a goal:

RAG - search and chat with your local files:

MCP tools - connect any MCP-compatible server:

Quick Start

Download for macOS, Windows, or Linux →

Install and open Askimo
Add a provider - paste an API key (OpenAI, Claude, Gemini…) or point it at a running Ollama instance
Start chatting

Full setup guide →

System Requirements


OS	macOS 11+, Windows 10+, Linux (Ubuntu 20.04+, Debian 11+, Fedora 35+)
Memory	50–300 MB (AI models require additional memory depending on provider)
Disk	250 MB

Features

AI & Providers

Multi-provider - Switch between OpenAI, Claude, Gemini, Grok, Ollama, LM Studio, Docker AI, OpenRouter, NVIDIA NIM, Together AI, vLLM Server, or any OpenAI-compatible endpoint per session
Vision - Attach images to conversations; works with any multimodal model

Search & Data

Web search (multiple backends) - Search the web with DuckDuckGo (no API key), Brave Search API, Tavily, Serply, or your own SearxNG instance
Local RAG - Index local folders, files, and web URLs. Hybrid BM25 + vector retrieval with an AI classifier that skips retrieval when the query doesn't need it. Your data never leaves your machine.

Workflows & Extensibility

Plans (agentic workflows) - Chain multi-step AI pipelines from a form UI. Each step builds on the previous; progress shown live. Export as PDF or Word. Define your own plans in YAML or generate them by describing your workflow in plain English.
Script runner - Execute Python, Bash, and JavaScript from chat. Python runs in an auto-managed virtualenv with automatic dependency installation.
MCP tool integration - Connect MCP-compatible servers via stdio or HTTP

AI Agents (CLI)

Run autonomous coding agents - Delegate a goal to Claude Code, OpenAI Codex, or Google Antigravity directly from Askimo, with live streamed tool calls, thinking, and status
Reusable skills - Define a skill once and materialize it into any supported agent's native skill-discovery folder, so it's just as invocable there as in Askimo's own chat

Voice

Speech-to-text - Dictate messages via OpenAI's transcription API or a fully local/offline built-in Whisper engine (whisper.cpp)
Text-to-speech - Have AI responses read aloud via OpenAI's TTS API or a fully local/offline Piper endpoint, with optional auto-play for hands-free "conversation mode"

Reliability & Privacy

Persistent sessions - Conversations stored in a local SQLite database, restored on restart
Local telemetry - Token usage, cost estimates, RAG performance per provider. Nothing uploaded.
i18n - English, Chinese (Simplified & Traditional), Japanese, Korean, French, Spanish, German, Portuguese, Vietnamese

Building from Source

Prerequisites

JDK 25+
Git

git clone https://github.com/askimo-ai/askimo.git
cd askimo

# Run the desktop app
./gradlew :desktop:run

# Build native installers
./gradlew :desktop:package

Project Structure

Module	Description
`desktop/`	Compose Multiplatform desktop application
`desktop-shared/`	Shared UI components
`shared/`	Kotlin Multiplatform common module: domain models/DTOs, chat types, event bus, and shared utils used by both JVM and other targets
`shared-jvm/`	Core: providers, RAG, MCP, memory, tools, database, plans engine, skills & agent runtimes

See CONTRIBUTING.md for development guidelines and DCO requirements, or the Development Getting Started Guide.

Localization

English · 中文 (简体/繁體) · 日本語 · 한국어 · Français · Español · Deutsch · Português · Tiếng Việt

Translations are managed on Crowdin. Contributions welcome - no coding required.

Help translate Askimo →

License

AGPLv3. See LICENSE.

Contributing

Bug reports, feature requests, and pull requests are welcome. See CONTRIBUTING.md for details.