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https://github.com/open-jarvis/OpenJarvis.git
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Jon Saad-FalconandClaude Opus 4.6
990d7d8a79
Add ReAct and OpenHands agents, WebSearch and CodeInterpreter tools, full MCP protocol layer (server/client/transport), Gemini cloud engine support, 12 new model specs (4 local MoE + 8 cloud), trace system, and comprehensive test coverage across all dimensions (hardware, engine, memory, agents, tools, MCP, integration). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
93 lines
3.6 KiB
Markdown
93 lines
3.6 KiB
Markdown
# OpenJarvis
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**Programming abstractions for on-device AI.**
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OpenJarvis defines the abstractions needed to study and build AI systems that run entirely on local hardware. It provides four composable pillars — Intelligence, Engine, Agentic Logic, and Memory — with a trace-driven learning system that improves over time.
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> **Status: v1.0+** — All pillars implemented. Trace system, trace-driven learning, SDK, benchmarks, and Docker deployment ready. 576 tests passing.
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## What is this?
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Local AI is a new computing paradigm: intelligence as a *resource you own*, not a *service you rent*. Existing frameworks (LangChain, DSPy, CrewAI) assume cloud-class models and infinite compute. OpenJarvis provides the abstractions for building AI systems against local hardware constraints.
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**Four core abstractions:**
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- **Intelligence** — the local LM being run (Qwen3 8B, GPT OSS 120B, Kimi 2.5, etc.) with multi-model management and automatic routing
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- **Engine** — the local inference engine (Ollama, SGLang, vLLM, llama.cpp, MLX) with hardware-aware selection
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- **Agentic Logic** — pluggable logic for handling queries, making tool/API calls, managing memory. Can be static (rules, ReAct) or learned from traces
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- **Memory** — persistent, searchable storage with multiple backends (SQLite, FAISS, ColBERTv2, BM25, hybrid)
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**Cross-cutting: Learning** — every interaction generates a trace. The system learns better routing, tool selection, and memory strategies from accumulated trace data.
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## Quick Start — Python SDK
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```python
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from openjarvis import Jarvis
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j = Jarvis()
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response = j.ask("What is the meaning of life?")
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print(response)
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# With a specific model and agent
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response = j.ask("Explain gravity", model="qwen3:8b", agent="orchestrator")
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# Memory operations
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j.memory.index("./docs/")
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results = j.memory.search("machine learning")
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j.close()
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```
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## Quick Start — CLI
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```bash
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jarvis ask "Hello, what can you do?"
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jarvis ask --agent orchestrator --tools calculator,think "What is 2+2?"
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jarvis bench run -n 5 --json
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jarvis model list
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jarvis memory index ./docs/
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jarvis serve --port 8000
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```
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## Docker
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```bash
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docker compose up -d # Starts Jarvis + Ollama
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curl http://localhost:8000/health
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```
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## Documentation
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- **[VISION.md](VISION.md)** — Project vision, architecture, design principles
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- **[ROADMAP.md](ROADMAP.md)** — Phased development plan with deliverables
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- **[CLAUDE.md](CLAUDE.md)** — Developer reference for working with the codebase
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## Quick orientation
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```
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src/openjarvis/
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├── core/ # Registry, types, config, event bus
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├── intelligence/ # Model management, routing
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├── engine/ # Inference engine wrappers (Ollama, vLLM, SGLang, llama.cpp, MLX)
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├── agents/ # Pluggable agent implementations + tool system
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├── memory/ # Storage backends (SQLite, FAISS, ColBERT, BM25, hybrid)
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├── traces/ # Full interaction traces — store, collector, analyzer
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├── learning/ # Router policies (heuristic, trace-driven, GRPO stub)
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├── telemetry/ # Per-inference telemetry store + aggregator
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├── tools/ # Built-in tools (calculator, think, retrieval, LLM, file read)
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├── bench/ # Benchmarking framework (latency, throughput)
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├── server/ # OpenAI-compatible API server
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├── cli/ # CLI entry points
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└── sdk.py # Python SDK (Jarvis class)
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```
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## Requirements
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- Python 3.10+
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- An inference backend: [Ollama](https://ollama.com), [vLLM](https://github.com/vllm-project/vllm), or [llama.cpp](https://github.com/ggerganov/llama.cpp)
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- Node.js 22+ (only if using OpenClaw agent)
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## License
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TBD
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