Open Jarvis local-agent harness
An open local-agent framework that makes model, inference engine, agent logic, tools and memory separately configurable.
Why it matters
Uses a cloud teacher to propose harness changes and accepts improvements only after regression evaluation; Lambda reports recovering much of a small local model's benchmark gap through harness retuning.
What you could build with it
Benchmark a local document-research assistant before and after harness retuning, keeping a held-out task set to reject changes that introduce regressions.
Built with Open Jarvis local-agent harness
- The Open-Source JARVIS Nobody Is Talking About (Fully Private AI Assistant)youtube · YouTuber's full walkthrough building a private voice assistant by cloning and tailoring open-jarvis/OpenJarvis on a local Qwen 3 8B GPU setup, with phone bridge and Tailscale remote control.
- OpenJarvis fit investigation for mlx-bungithub · Deep third-party technical investigation of OpenJarvis's RLMAgent/RLMRepl internals, sequential subquery limits, and sandbox gaps for the mlx-bun project.
- Meet OpenJarvis: A Local-First Framework for On-Device Personal AI Agentsmarktechpost · Third-party deep dive into the five primitives and benchmark results (local models within 3.2 pts of best cloud model at ~800x lower marginal cost), with a tested-hardware table.
- bernan-legal/openjarvisgithub · OpenJarvis-based operational agent deployed in production for ADN Legal CR (Costa Rica), running locally on an RTX 5090 with 25 tools, 20 skills, and a FastAPI + React stack.
First spotted on Lambda engineering blog: source.
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