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: v1.0 — All five pillars implemented. SDK, benchmarks, OpenClaw infrastructure, and Docker deployment ready.

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)

Quick Start — Python SDK

from openjarvis import Jarvis

j = Jarvis()
response = j.ask("What is the meaning of life?")
print(response)

# With a specific model and agent
response = j.ask("Explain gravity", model="qwen3:8b", agent="orchestrator")

# Memory operations
j.memory.index("./docs/")
results = j.memory.search("machine learning")

j.close()

Quick Start — CLI

jarvis ask "Hello, what can you do?"
jarvis ask --agent orchestrator --tools calculator,think "What is 2+2?"
jarvis bench run -n 5 --json
jarvis model list
jarvis memory index ./docs/
jarvis serve --port 8000

Docker

docker compose up -d          # Starts Jarvis + Ollama
curl http://localhost:8000/health

Documentation

• VISION.md — Project vision, architecture, design principles
• ROADMAP.md — Phased development plan with deliverables
• CLAUDE.md — Developer reference for working with the codebase

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 + OpenClaw
├── engine/        # Inference engine wrappers
├── learning/      # Router policy (heuristic, GRPO stub)
├── bench/         # Benchmarking framework (latency, throughput)
├── telemetry/     # Telemetry store + aggregator
├── server/        # OpenAI-compatible API server
├── cli/           # CLI entry points
└── sdk.py         # Python SDK (Jarvis class)

Requirements

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

License

TBD