OpenJarvis

Your AI stack, your rules.

A modular, pluggable AI assistant backend. Compose your own stack across five pillars — Intelligence, Learning, Memory, Agents, and Inference — then swap any piece without touching the rest.

Status: Early Development — Interfaces are being defined. Not yet usable.

What is this?

OpenJarvis lets you build a personal AI assistant from composable parts:

• Intelligence — multi-model management with automatic routing (Qwen3, GPT OSS, Kimi-K2.5, Claude, GPT-5, Gemini)
• Memory — persistent, searchable storage with multiple backends (SQLite, FAISS, ColBERTv2, BM25, hybrid)
• Agents — pluggable reasoning and tool use (OpenClaw Pi agent, simple, orchestrator, custom)
• Inference — hardware-aware engine selection (vLLM, SGLang, Ollama, llama.cpp, MLX)
• Learning — router that improves over time (heuristic now, learned later)

Documentation

• VISION.md — Project vision, architecture, design principles, and detailed pillar descriptions
• ROADMAP.md — Phased development plan with deliverables and version milestones

Quick orientation

src/openjarvis/
├── core/          # Registry, types, config, event bus
├── intelligence/  # Model management, routing
├── memory/        # Storage backends (SQLite, FAISS, ColBERT, BM25, hybrid)
├── agents/        # Agent implementations + tool system
├── engine/        # Inference engine wrappers
├── learning/      # Router policy (placeholder)
└── cli/           # CLI entry points (jarvis ask, serve, model, memory)

Requirements

• Python 3.10+
• An inference backend: Ollama, vLLM, or llama.cpp
• Node.js 22+ (only if using OpenClaw agent)

License

TBD