From 2ae41a0f13299c5e1a14657c7efddacda7d7bdbe Mon Sep 17 00:00:00 2001 From: Jon Saad-Falcon Date: Sun, 22 Feb 2026 06:30:20 +0000 Subject: [PATCH] Simplify README with badges, sponsors, and Apache 2.0 license Co-Authored-By: Claude Opus 4.6 --- LICENSE | 190 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ README.md | 77 ++++++++-------------- 2 files changed, 217 insertions(+), 50 deletions(-) create mode 100644 LICENSE diff --git a/LICENSE b/LICENSE new file mode 100644 index 00000000..7d8b094e --- /dev/null +++ b/LICENSE @@ -0,0 +1,190 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. 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+

OpenJarvis -

-

Programming abstractions for on-device AI.

+

Programming abstractions for on-device AI.

+ +

+ Project + Docs + Python + License +

+
--- -OpenJarvis is a framework for building AI systems that run *entirely on local hardware*. Rather than treating intelligence as a cloud service, OpenJarvis provides composable abstractions for local model selection, inference, agentic reasoning, tool use, and learning — all aware of the hardware they run on. +> **[Documentation](https://jonsaadfalcon.github.io/OpenJarvis/)** +> +> **[Project Site](https://www.intelligence-per-watt.ai/)** -You write Python programs that compose five pillars — **Intelligence** (which model), **Engine** (which runtime), **Agents** (which reasoning strategy), **Tools** (which capabilities, via MCP), and **Learning** (which adaptation policy) — and OpenJarvis handles hardware detection, model routing, telemetry, and trace-driven improvement automatically. +OpenJarvis is a framework for building AI systems that run *entirely on local hardware*. Rather than treating intelligence as a cloud service, OpenJarvis provides composable abstractions for local model selection, inference, agentic reasoning, tool use, and learning — all aware of the hardware they run on. ```python from openjarvis import Jarvis @@ -26,22 +35,14 @@ j.ask("Solve x^2 - 5x + 6 = 0", # multi-turn agent with tools j.memory.index("./papers/") # index documents into local storage results = j.memory.search("attention mechanism") # semantic retrieval - j.close() ``` -```bash -pip install openjarvis -jarvis ask "Hello, what can you do?" -jarvis serve --port 8000 # OpenAI-compatible API -``` - ## Installation ```bash pip install openjarvis # core framework pip install openjarvis[server] # + FastAPI server -pip install openjarvis[openclaw] # + OpenClaw agent (requires Node.js 22+) ``` You also need a local inference backend: [Ollama](https://ollama.com), [vLLM](https://github.com/vllm-project/vllm), [SGLang](https://github.com/sgl-project/sglang), or [llama.cpp](https://github.com/ggerganov/llama.cpp). @@ -51,50 +52,26 @@ You also need a local inference backend: [Ollama](https://ollama.com), [vLLM](ht | Pillar | What it does | Key abstractions | |--------|-------------|-----------------| | **Intelligence** | Model management and routing | `RouterPolicy`, `QueryAnalyzer`, `ModelCatalog` | -| **Engine** | Inference runtime abstraction | `InferenceEngine` ABC — Ollama, vLLM, SGLang, llama.cpp, MLX, cloud | +| **Engine** | Inference runtime abstraction | `InferenceEngine` ABC — Ollama, vLLM, SGLang, llama.cpp, MLX | | **Agents** | Pluggable reasoning strategies | `BaseAgent` ABC — Simple, Orchestrator, ReAct, OpenHands, OpenClaw | -| **Tools** | Capabilities via MCP | `BaseTool` ABC — calculator, code interpreter, web search, memory, LLM sub-calls; external MCP servers auto-discovered | +| **Tools** | Capabilities via MCP | `BaseTool` ABC — calculator, code interpreter, web search, memory; external MCP servers auto-discovered | | **Learning** | Trace-driven adaptation | `LearningPolicy` ABC — SFT (model routing), AgentAdvisor (restructuring), ICL (tool usage) | -Every interaction produces a **Trace** — a structured record of the full reasoning chain (routing decisions, tool calls, latencies, outcomes). Learning policies consume traces to improve model selection, agent behavior, and tool usage over time. - -## Config-Driven Composition - -OpenJarvis is fully configurable via `~/.openjarvis/config.toml` or programmatically via `SystemBuilder`: - -```python -from openjarvis.system import SystemBuilder - -system = (SystemBuilder() - .engine("ollama") - .model("qwen3:8b") - .agent("orchestrator") - .tools(["calculator", "think", "memory_retrieve"]) - .telemetry(True) - .build()) - -result = system.ask("What is 2+2?") -system.close() -``` - -Hardware auto-detection selects the best engine: Apple Silicon → Ollama, NVIDIA datacenter GPUs → vLLM, AMD → vLLM, CPU-only → llama.cpp. - -## MCP Interoperability - -All tools are managed via the [Model Context Protocol](https://modelcontextprotocol.io/) (MCP). The built-in MCP server exposes every OpenJarvis tool — including memory operations — to any MCP-compatible client (Claude, GPT, Gemini, etc.). External MCP servers are auto-discovered and their tools appear as native `BaseTool` instances inside OpenJarvis agents. - -## Documentation - -Full docs at the [OpenJarvis documentation site](docs/) or in-repo: - -- **[VISION.md](VISION.md)** — Project vision and design principles -- **[CLAUDE.md](CLAUDE.md)** — Developer reference for the codebase -- **[docs/](docs/)** — Architecture guides, API reference, tutorials +Every interaction produces a **Trace** — a structured record of the full reasoning chain. Learning policies consume traces to improve model selection, agent behavior, and tool usage over time. ## About OpenJarvis is part of [Intelligence Per Watt](https://www.intelligence-per-watt.ai/), a research initiative studying the efficiency of on-device AI systems. The project is developed at [Hazy Research](https://hazyresearch.stanford.edu/) and the [Scaling Intelligence Lab](https://scalingintelligence.stanford.edu/) at [Stanford SAIL](https://ai.stanford.edu/). +## Sponsors + +

+ Laude Institute • + Stanford Marlowe • + Google Cloud Platform • + Lambda Labs +

+ ## License -Apache 2.0 +[Apache 2.0](LICENSE)