Jon Saad-Falcon and Claude Opus 4.6
45892539bc
Add tokens_per_joule to telemetry store and aggregator (Task 3)
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- Add tokens_per_joule column to TelemetryStore schema, INSERT, and migration
- Add avg_tokens_per_joule field to ModelStats and EngineStats
- Add AVG(tokens_per_joule) to per_model_stats() and per_engine_stats() SQL
queries using _safe_col() pattern for backward compatibility
- Add 3 tests for store/retrieve, multi-record aggregation, and engine stats
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com >
2026-02-28 06:53:23 +00:00
Jon Saad-Falcon and Claude Opus 4.6
4e5709d478
feat(telemetry): compute tokens_per_joule in InstrumentedEngine
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Computes completion_tokens / energy_joules in both generate() and
stream() methods. Zero when energy or tokens are zero.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com >
2026-02-28 06:47:55 +00:00
Jon Saad-Falcon and Claude Opus 4.6
ac8d6b01c7
feat(telemetry): add tokens_per_joule field to TelemetryRecord
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Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com >
2026-02-28 06:42:54 +00:00
Jon Saad-Falcon and Claude Opus 4.6
24972e3e52
Add Phase 12+13: energy measurement, install polish, PWA, cross-hardware
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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
9ec402ab4a
Enrich agent tool awareness, normalize engine tool_calls, add telemetry and eval config
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- Add build_tool_descriptions() shared builder for enriched agent system
prompts (NativeReAct, NativeOpenHands, RLM, Orchestrator structured mode)
- Normalize tool_calls to flat {id, name, arguments} across CloudEngine
(OpenAI/Anthropic/Google), LiteLLM, and Ollama
- Add Anthropic tool_use extraction and input_schema conversion
- Add Google function_call extraction
- Make ReAct/OpenHands parsing case-insensitive
- Add telemetry efficiency, GPU monitor, and vLLM metrics modules
- Add TOML-based eval suite config system
- Update documentation and changelog
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com >
2026-02-25 01:58:18 +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
301e9cd2d4
Implement OpenJarvis v1.0 — all five pillars, SDK, benchmarks, Docker
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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
