Jon Saad-Falcon and Claude Opus 4.6
24972e3e52
Add Phase 12+13: energy measurement, install polish, PWA, cross-hardware
...
Phase 12 — Energy Measurement Upgrade:
- EnergyMonitor ABC with multi-vendor support (NVIDIA hw counters,
AMD amdsmi, Apple zeus-ml, CPU RAPL sysfs)
- EnergyBatch batch-level energy-per-token accounting
- SteadyStateDetector CV-based thermal equilibrium detection
- EnergyBenchmark with warmup phase
- InstrumentedEngine prefers EnergyMonitor over legacy GpuMonitor
- Telemetry store/aggregator extended with energy fields
Phase 13 — Install, Hosting, Cross-Hardware:
- jarvis doctor diagnostic command (8 checks, --json output)
- jarvis init post-setup guidance with engine-specific next steps
- README Quick Start section
- MLX engine backend (Apple Silicon → mlx recommendation)
- AMD VRAM/multi-GPU detection via rocm-smi
- PyTorch MPS device selection in orchestrator trainers
- PWA support (vite-plugin-pwa, service worker, manifest, icons)
- Server static file serving fix for PWA files
- Dockerfile.gpu.rocm + docker-compose.gpu.rocm.yml for ROCm
- Eval framework display module and efficiency metrics
2244 tests pass, 37 skipped.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com >
2026-02-26 20:09:07 +00:00
Jon Saad-Falcon and Claude Opus 4.6
323d7ff032
Add TOML config system for eval suites, pillar-aligned config, and documentation
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- Eval config: TOML-based suite configs defining models x benchmarks matrix,
loaded via --config flag. Includes load_eval_config(), expand_suite(),
7 config dataclasses, 3 example configs, and 61 new tests.
- Pillar-aligned config: generation params in IntelligenceConfig, nested
engine/learning configs, agent objective/system_prompt/context_from_memory,
structured learning sub-policies, TOML migration layer.
- Documentation: evaluations user guide, evals API reference, updated
mkdocs.yml navigation, updated architecture docs.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com >
2026-02-24 03:34:05 +00:00
Jon Saad-Falcon and Claude Opus 4.6
8d538cd1b0
Add orchestrator training, channels, LiteLLM engine, and simplify learning taxonomy
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Major changes across parallel sessions:
- Add orchestrator SFT & GRPO training subpackage (learning/orchestrator/)
with episode types, multi-objective reward, prompt registry, policy model,
RL environment, and registered learning policies
- Add structured THOUGHT/TOOL/INPUT/FINAL_ANSWER mode to OrchestratorAgent
- Add 15 channel backends (Discord, Slack, Telegram, Email, Webhook, IRC,
Matrix, Teams, WhatsApp, Signal, Mattermost, BlueBubbles, Feishu,
Google Chat, Webchat) with channel tools and config
- Add LiteLLM engine backend for unified LLM provider access
- Add RLM agent and REPL tool
- Remove ToolLearningPolicy — learning taxonomy now only targets
Intelligence (LM weights/routing) and Agents (logic/ICL/tool strategies)
- Rename SFTPolicy to SFTRouterPolicy (backward-compat alias kept)
- Remove OpenClaw agent infrastructure (openclaw*.py, openclaw_bridge.py)
- Fix async streaming tests (asyncio.run vs deprecated get_event_loop)
- Fix server channel route tests (pytest.importorskip for optional fastapi)
- Track uv.lock for reproducibility
- Update CLAUDE.md and docs to reflect all changes
1676 tests pass, 37 skipped.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com >
2026-02-23 18:32:32 +00:00
Jon Saad-Falcon and Claude Opus 4.6
852259f18b
Restructure codebase into 5-pillar architecture with MCP tool management, composition layer, and structured learning
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Phase 1: Move RoutingContext to core/types.py, add RouterPolicy and QueryAnalyzer ABCs to intelligence/_stubs.py
Phase 2: Move memory backends to tools/storage/, convert memory/ to backward-compat shims
Phase 3: Add MCPToolAdapter, storage MCP tools, upgrade MCP server to spec 2025-11-25
Phase 4: Add SystemBuilder + JarvisSystem composition layer (system.py)
Phase 5: Add InstrumentedEngine for opt-in telemetry, simplify all agents
Phase 6: Add LearningPolicy ABC taxonomy with SFTPolicy, AgentAdvisorPolicy, ICLUpdaterPolicy
Phase 7: Update config schema (ToolsConfig, MCPConfig, TracesConfig, per-pillar learning policies)
Also: update all docs, README (DSPy-inspired), CLAUDE.md, and add logo assets.
1391 tests pass, 32 skipped. Zero new lint errors. Full backward compatibility via shims.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com >
2026-02-22 05:57:13 +00:00
Jon Saad-Falcon and Claude Opus 4.6
990d7d8a79
Expand test suite to 1031 tests: new agents, tools, MCP layer, model catalog
...
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 >
2026-02-21 04:59:11 +00:00
Jon Saad-Falcon and Claude Opus 4.6
301e9cd2d4
Implement OpenJarvis v1.0 — all five pillars, SDK, benchmarks, Docker
...
Complete implementation across six development phases (v0.1 through v1.0):
- Core: Registry system, config, event bus, types (Phase 0)
- Intelligence + Inference: Model routing, Ollama/vLLM/llama.cpp/Cloud engines (Phase 1)
- Memory: SQLite/FAISS/ColBERT/BM25/Hybrid backends, document ingest, context injection (Phase 2)
- Agents: Simple/Orchestrator/Custom/OpenClaw agents, tool system (Phase 3)
- Learning: HeuristicRouter, reward functions, GRPO stub, telemetry aggregation (Phase 4)
- SDK: Jarvis class, OpenClaw protocol/transport, benchmarks, Docker deployment (Phase 5)
520 tests passing, 8 skipped (optional deps). Ruff lint clean.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com >
2026-02-17 00:52:48 +00:00
