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Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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co-authored by Claude Opus 4.6
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README.md
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<p align="center">
<div align="center">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="assets/openjarvis-logo-dark.svg">
<source media="(prefers-color-scheme: light)" srcset="assets/openjarvis-logo-light.svg">
<img alt="OpenJarvis" src="assets/openjarvis-logo-light.svg" width="400">
</picture>
</p>
<p align="center"><i>Programming abstractions for on-device AI.</i></p>
<p><i>Programming abstractions for on-device AI.</i></p>
<p>
<a href="https://www.intelligence-per-watt.ai/"><img src="https://img.shields.io/badge/project-intelligence--per--watt.ai-blue" alt="Project"></a>
<a href="https://jonsaadfalcon.github.io/OpenJarvis/"><img src="https://img.shields.io/badge/docs-mkdocs-blue" alt="Docs"></a>
<img src="https://img.shields.io/badge/python-%3E%3D3.12-blue" alt="Python">
<img src="https://img.shields.io/badge/license-Apache%202.0-green" alt="License">
</p>
</div>
---
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 &rarr; Ollama, NVIDIA datacenter GPUs &rarr; vLLM, AMD &rarr; vLLM, CPU-only &rarr; 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
<p>
<a href="https://www.laude.org/">Laude Institute</a> &bull;
<a href="https://datascience.stanford.edu/marlowe">Stanford Marlowe</a> &bull;
<a href="https://cloud.google.com/">Google Cloud Platform</a> &bull;
<a href="https://lambda.ai/">Lambda Labs</a>
</p>
## License
Apache 2.0
[Apache 2.0](LICENSE)