open-jarvis/OpenJarvis

Health v2 · maintenance only

Adoption not part of the score

9045 stars · 2089 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

OpenJarvis is a Python framework for building local-first personal AI agents that run on your own hardware, with cloud APIs as an optional fallback. It provides shared primitives for intelligence, agents, tools, memory, and a learning loop, plus evaluations that treat energy, latency, and cost as first-class constraints.

Use cases

• run a personal AI assistant entirely on my own device
• build local AI agents that only call the cloud when necessary
• serve an OpenAI-compatible API from a local model
• compare local LLMs by energy, latency, and cost on my hardware
• build a coding assistant that reads my repo without sending code to the cloud
• improve a local model from my own interaction traces

When to choose

• you want privacy-first, on-device AI with zero cloud dependency by default
• you want a unified layer over Ollama, vLLM, SGLang, and llama.cpp
• you care about energy/cost-aware evaluation of local models
• you want MCP tool support and persistent local memory in an agent stack

When to avoid

• you need maximum-quality frontier model output regardless of cost or privacy
• you want a fully managed hosted service with no local setup
• you need non-Python integration or a polished end-user GUI app

Facets

framework · maturity active

llm-inference agent-framework rag mcp chatbot machine-learning http-server sdk artificial-intelligence large-language-models developer-tools privacy self-hosted windows python cli self-hosted cross-platform local-first on-device-ai ollama vllm llama-cpp openai-compatible-api energy-efficiency personal-ai stanford model-catalog trace-learning ai-agents retrieval-augmented-generation macos linux gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="open-jarvis/OpenJarvis")