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Jon Saad-FalconandClaude Opus 4.6 350916699e chore: restructure CLAUDE.md as routing table with .claude/rules/

Move detailed architecture, testing, and pattern docs out of CLAUDE.md
into focused .claude/rules/ files. CLAUDE.md is now a lean routing table
that points to the right context based on what you're working on.

Also fixes stale HazyResearch links in README.md (docs URL, clone URL)
and adds CLAUDE.md to .gitignore so it stays per-developer.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

2026-03-07 23:19:12 +00:00

2.9 KiB

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Python Development

Architecture: The Five Pillars

All pillars are wired together by JarvisSystem (src/openjarvis/system.py) which is constructed from configs/openjarvis/config.toml (or ~/.openjarvis/config.toml).

1. Intelligence (`src/openjarvis/intelligence/`)

Model selection, provider routing. Config section: [intelligence].

2. Agent (`src/openjarvis/agents/`)

Multi-turn reasoning and tool use. Agents register via @AgentRegistry.register() decorator. Key agents: simple, native_react, native_openhands, orchestrator, monitor_operative, claude_code, rlm. Config section: [agent].

3. Tools (`src/openjarvis/tools/`)

Built-in tools (code_interpreter, web_search, file_read, shell_exec, calculator, think, browser, etc.) plus MCP adapter. Tool storage backends in tools/storage/. Config section: [tools].

4. Engine (`src/openjarvis/engine/`)

Inference runtime abstraction. All engines implement InferenceEngine (defined in engine/_stubs.py) and use OpenAI-compatible chat completions. Supported: vLLM, Ollama, llama.cpp, SGLang, MLX, cloud (OpenAI/Anthropic/Google), LiteLLM, Apple FM, Exo, Nexa. Discovery in engine/_discovery.py. Config section: [engine].

5. Learning (`src/openjarvis/learning/`)

Improvement methodologies: router policies (heuristic, bandit, trace-based), SFT/GRPO training, ICL updater, agent evolution, skill discovery. Orchestrated by LearningOrchestrator. Config section: [learning].

Supporting Systems

Core (core/): RegistryBase pattern (decorator-based registration), types (Message, Conversation, ToolCall), config loader with hardware detection, EventBus.
Channels (channels/): Chat platform integrations (Telegram, Discord, Slack, WhatsApp, Signal, IRC, Matrix, etc.).
Telemetry (telemetry/): GPU monitoring, energy measurement (NVIDIA/AMD/Apple/RAPL), latency instrumentation, vLLM metrics.
Traces (traces/): Execution trace recording for analysis.
MCP (mcp/): Model Context Protocol server.
Security (security/): PII scanning, capability policies.
Server (server/): FastAPI REST API.
SDK (sdk.py): High-level Jarvis and JarvisSystem classes, MemoryHandle for memory operations.

Key Patterns

Registry pattern: Components (agents, engines, memory backends, tools, channels, etc.) self-register via @XRegistry.register("key") decorators. Tests auto-clear all registries via conftest.py fixture.
Optional dependencies: Heavy deps are extras in pyproject.toml (e.g., inference-cloud, memory-faiss, channel-telegram). Import failures are caught with try/except so the core stays lightweight.
OpenAI-compatible: All engines expose an OpenAI-format chat completions interface. messages_to_dicts() in engine/_base.py handles conversion.
Config-driven: TOML configs control everything. load_config() detects hardware, fills defaults, then overlays user overrides.

2.9 KiB Raw Blame History

Python Development

Architecture: The Five Pillars

1. Intelligence (src/openjarvis/intelligence/)

2. Agent (src/openjarvis/agents/)

3. Tools (src/openjarvis/tools/)

4. Engine (src/openjarvis/engine/)

5. Learning (src/openjarvis/learning/)