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Jon Saad-FalconandClaude Opus 4.6
350916699e
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>
38 lines
2.9 KiB
Markdown
38 lines
2.9 KiB
Markdown
# Python Development
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## Architecture: The Five Pillars
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All pillars are wired together by `JarvisSystem` (`src/openjarvis/system.py`) which is constructed from `configs/openjarvis/config.toml` (or `~/.openjarvis/config.toml`).
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### 1. Intelligence (`src/openjarvis/intelligence/`)
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Model selection, provider routing. Config section: `[intelligence]`.
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### 2. Agent (`src/openjarvis/agents/`)
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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]`.
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### 3. Tools (`src/openjarvis/tools/`)
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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]`.
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### 4. Engine (`src/openjarvis/engine/`)
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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]`.
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### 5. Learning (`src/openjarvis/learning/`)
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Improvement methodologies: router policies (heuristic, bandit, trace-based), SFT/GRPO training, ICL updater, agent evolution, skill discovery. Orchestrated by `LearningOrchestrator`. Config section: `[learning]`.
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### Supporting Systems
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- **Core** (`core/`): `RegistryBase` pattern (decorator-based registration), types (`Message`, `Conversation`, `ToolCall`), config loader with hardware detection, `EventBus`.
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- **Channels** (`channels/`): Chat platform integrations (Telegram, Discord, Slack, WhatsApp, Signal, IRC, Matrix, etc.).
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- **Telemetry** (`telemetry/`): GPU monitoring, energy measurement (NVIDIA/AMD/Apple/RAPL), latency instrumentation, vLLM metrics.
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- **Traces** (`traces/`): Execution trace recording for analysis.
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- **MCP** (`mcp/`): Model Context Protocol server.
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- **Security** (`security/`): PII scanning, capability policies.
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- **Server** (`server/`): FastAPI REST API.
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- **SDK** (`sdk.py`): High-level `Jarvis` and `JarvisSystem` classes, `MemoryHandle` for memory operations.
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## Key Patterns
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- **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.
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- **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.
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- **OpenAI-compatible**: All engines expose an OpenAI-format chat completions interface. `messages_to_dicts()` in `engine/_base.py` handles conversion.
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- **Config-driven**: TOML configs control everything. `load_config()` detects hardware, fills defaults, then overlays user overrides.
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