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
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).
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:
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
| 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 |
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, 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 Whisper-compatible endpoint
- 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
- 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| Module | Description |
|---|---|
desktop/ |
Compose Multiplatform desktop application |
desktop-shared/ |
Shared UI components |
shared/ |
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.
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.
AGPLv3. See LICENSE.
Bug reports, feature requests, and pull requests are welcome. See CONTRIBUTING.md for details.