GitHub: askimo-ai/askimo
Askimo is an open-source, native desktop AI client (AGPLv3) built with Kotlin Compose Multiplatform that aggregates multiple AI providers into a single application It supports both cloud APIs (OpenAI, Claude, Gemini, Grok, etc.) and local inference via Ollama, LM Studio, vLLM, and Docker AI, with per-session provider switching Key differentiators include local RAG with hybrid BM25 + vector retrieval, multi-step agentic workflow plans (YAML or form-based), MCP tool integration, and an integrated
Analysis
TL;DR
- Askimo is an open-source, native desktop AI client (AGPLv3) built with Kotlin Compose Multiplatform that aggregates multiple AI providers into a single application
- It supports both cloud APIs (OpenAI, Claude, Gemini, Grok, etc.) and local inference via Ollama, LM Studio, vLLM, and Docker AI, with per-session provider switching
- Key differentiators include local RAG with hybrid BM25 + vector retrieval, multi-step agentic workflow plans (YAML or form-based), MCP tool integration, and an integrated script runner for Python/Bash/JS
- All data stays on-device: local SQLite session storage, local telemetry, and no cloud upload of user files or conversations
- Built on JDK 25+, requiring 50–300 MB RAM and 250 MB disk, with cross-platform support for macOS 11+, Windows 10+, and major Linux distributions
Why It Matters
Askimo represents a growing trend toward unified, privacy-first AI desktop clients that eliminate the friction of managing multiple provider dashboards and browser tabs. For AI practitioners who work with sensitive data or require hybrid cloud/local model setups, it offers a practical, self-hostable solution with agentic workflow capabilities typically found only in enterprise platforms.
Technical Details
- Architecture: Kotlin Compose Multiplatform desktop app with a modular structure (
desktop/,desktop-shared/,shared/,cli/); core logic inshared/handles providers, RAG, MCP, memory, tools, database, plans engine, and agent runtimes - RAG Pipeline: Hybrid BM25 + vector retrieval with an AI classifier that conditionally skips retrieval when the query does not require it; indexes local folders, files, and web URLs with data remaining entirely on-device
- Agentic Workflows: "Plans" engine allows chaining multi-step AI pipelines from a form UI or YAML definitions; each step consumes prior output, with live progress and PDF/Word export
- Script Runner: Executes Python (in auto-managed virtualenv with pip install), Bash, and JavaScript directly from chat; MCP tools connect via stdio or HTTP
- Provider Abstraction: Unified interface for OpenAI-compatible endpoints, supporting OpenAI, Anthropic Claude, Google Gemini, xAI Grok, OpenRouter, NVIDIA NIM, Together AI, Ollama, LM Studio, Docker AI, vLLM Server, and custom base URLs
- Persistence & Telemetry: Conversations stored in local SQLite; local telemetry tracks token usage, cost estimates, and RAG performance per provider with no external data transmission
Industry Insight
- The consolidation of multi-provider chat, local RAG, agentic workflows, and MCP tooling into a single offline-capable desktop app signals that end-users increasingly demand privacy-preserving AI workstations rather than browser-based portals; expect continued growth in the "local-first AI client" category.
- The inclusion of a plans engine with YAML-defined workflows and live step chaining positions Askimo closer to lightweight agentic platforms, suggesting that the barrier between consumer AI chat clients and professional agent tooling is narrowing.
- For organizations handling sensitive data, Askimo's local-only RAG and telemetry model offers a ready-made compliance-friendly alternative to cloud-dependent AI tools, particularly valuable in regulated industries where data residency is mandatory.
Disclaimer: The above content is generated by AI and is for reference only.