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9 Commits
Author SHA1 Message Date
Jon Saad-FalconandClaude Opus 4.6 c488e2a263 style: fix remaining import guards and unused imports 
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-03-05 05:47:29 +00:00
Jon Saad-FalconandClaude Opus 4.6 2aebcd7d77 feat: Phase 23 — Differentiated functionalities 
Trace-driven learning pipeline:
- TrainingDataMiner: extract SFT/routing/agent pairs from traces
- LoRATrainer: fine-tune local models from trace-derived data
- AgentConfigEvolver: rewrite agent configs from trace analysis
- LearningOrchestrator: coordinate mine→train→evolve cycle, wired into SystemBuilder

Eval framework (15 real IPW benchmarks):
- Datasets: SuperGPQA, GPQA, MMLU-Pro, MATH-500, Natural Reasoning, HLE,
  SimpleQA, WildChat, IPW, GAIA, FRAMES, SWE-bench, SWEfficiency,
  TerminalBench, TerminalBench Native
- Scorers: MCQ extraction, LLM-judge, exact match, structural validation
- CLI: jarvis eval list|run|compare|report

Composable abstractions:
- Recipe system: TOML composition of all 5 pillars (3 built-in recipes)
- Agent templates: 15 pre-configured TOML manifests with system prompts
- Bundled skills: 20 ready-to-use TOML skill manifests
- Operator recipes: researcher (4h), correspondent (5min), sentinel (2h)

102 files changed, ~11,500 lines added. 3241 tests pass (44 skipped).

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-03-02 05:34:46 +00:00
Jon Saad-FalconandClaude Opus 4.6 a4c4081ff4 Add desktop distribution pipeline: rolling releases, auto-updates, code signing 
- Rewrite .github/workflows/desktop.yml: 2-job pipeline (validate + build-and-release)
  with rolling desktop-latest pre-release on push to main and stable desktop-v* releases
- Add UpdateChecker component: checks for updates on startup + every 30 min,
  background download with progress bar, one-click relaunch
- Configure Tauri updater: endpoints pointing to desktop-latest release, pubkey placeholder
- Add tauri-plugin-process for relaunch support (Cargo.toml, lib.rs, package.json)
- Add macOS Entitlements.plist for notarization (network + file access, no sandbox)
- Add scripts/bump-desktop-version.sh for atomic version bumps across 3 config files
- Add desktop/README.md with dev setup, auto-update architecture, signing docs
- Update .gitignore for desktop/node_modules, dist, target
- Configure macOS minimumSystemVersion, Windows timestampUrl
- Include all Phase 14-21 work: agent hardening, RBAC, taint tracking, workflows,
  skills, knowledge graph, sessions, A2A, MCP templates, WASM sandbox, TUI dashboard,
  production tools, CLI expansion, API expansion, learning productionization,
  Tauri desktop app, and 10 new channels

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-02-27 19:14:05 +00:00
Jon Saad-FalconandClaude 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-FalconandClaude Opus 4.6 323d7ff032 Add TOML config system for eval suites, pillar-aligned config, and documentation 
- 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-FalconandClaude Opus 4.6 8d538cd1b0 Add orchestrator training, channels, LiteLLM engine, and simplify learning taxonomy 
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-FalconandClaude Opus 4.6 852259f18b Restructure codebase into 5-pillar architecture with MCP tool management, composition layer, and structured learning 
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-FalconandClaude 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-FalconandClaude 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