- Fix all ruff E501 line-length violations
- Add noqa: E402 for intentional late imports in runner scripts
- Remove unused imports in tb_agent.py
- Update test_llm_optimizer assertions to expect fixed params in
unparseable/missing-params cases (intended behavior change)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Keep both system_prompt (optimization) and episode_mode (Phase 25) in
RunConfig. Merge terminalbench_native metadata using v2 task_data dict
API while preserving new create_task_env/verify_requirements methods.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Remove duplicate config structs in Rust config.rs and Python config.py
- Remove duplicate match arms in Rust discovery.rs
- Fix E501 line-too-long in evals/cli.py
- Fix E402 imports-not-at-top in test_ama_bench.py and test_loghub.py
- Fix E501 long lines in test_loghub.py
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add four new OpenAI-compatible inference engines across the full stack:
Rust backend:
- Config structs with serde defaults (ExoEngineConfig, NexaEngineConfig,
UzuEngineConfig, AppleFmEngineConfig) in openjarvis-core
- Factory constructors on OpenAICompatEngine (exo, nexa, uzu, apple_fm)
- Engine enum variants with delegate_engine! macro dispatch
- Discovery and resolution in get_engine_static()
- PyO3 bridge support in PyEngine
Python frontend:
- Data-driven engine class registration in openai_compat_engines.py
- Config dataclasses with backward-compat host properties
- Discovery host map entries
- DEFAULT_SEARCH_SPACE updated with new engine backends
- Apple FM shim (FastAPI wrapper for python-apple-fm-sdk, macOS only)
Config & tests:
- TOML config sections for all four engines
- Unit tests for factory methods, enum variants, and search space
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Add gen-files and literate-nav mkdocs plugins
- Create docs/gen_ref_pages.py (adapted from IPW) to auto-generate API reference pages
- Replace 15 manual API nav entries with single auto-generated api-reference/ section
- Add mkdocs-gen-files and mkdocs-literate-nav to docs extra dependencies
Cross-pollinated from intelligence-per-watt docs patterns.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Remove unused imports (os, pytest) in test_rust_bridge.py
- Add pytest.importorskip for fastapi/starlette in test_learning_api.py
- Fix AppleEnergyMonitor test fixtures: set _zeus_ok, _chip_name,
_tdp_watts attributes when using __new__ to skip __init__
- Fix uninitialized monitor test to match CPU-time fallback behavior
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
str(Role.SYSTEM) returns 'Role.SYSTEM' on Python 3.10, not 'system'.
Use getattr(m, 'role', None) == 'system' which works because Role
inherits from str. Also wires compress_context into operative agent.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Surface per-instance scores (SampleScore), structured feedback
(TrialFeedback), Pareto frontier tracking, pillar-targeted mutation,
and config merge strategies. Store migration adds new columns with
backward-compatible deserialization.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add the orchestration layer that ties together the LLM optimizer, trial
runner, and persistence into a propose-evaluate-analyze loop with early
stopping and recipe export. 36 new tests pass.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Implement LLMOptimizer that uses a cloud LLM to propose and analyze
OpenJarvis configurations, inspired by DSPy's GEPA approach with
textual trace feedback rather than just scalar rewards. 46 tests pass.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add TrialRunner to bridge TrialConfig to the eval framework (EvalRunner),
TraceJudge for LLM-as-judge scoring of agent traces, and FeedbackCollector
for aggregating explicit, thumbs, and judge-driven feedback signals.
68 tests pass covering all three modules.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Synthesize reusable benchmarks from interaction traces with feedback
scores. Mines high-quality traces, groups by query class, picks the
best reference per class, and exposes results through DatasetProvider
and LLM-judge Scorer so EvalRunner can evaluate against personal
workflow patterns.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add the optimize module with core data types (SearchDimension, SearchSpace,
TrialConfig, TrialResult, OptimizationRun) and search space builder
(build_search_space, DEFAULT_SEARCH_SPACE) covering all 5 pillars. Includes
TrialConfig.to_recipe() mapping and SearchSpace.to_prompt_description()
for LLM-readable rendering. 66 tests pass.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- .gitignore: keep both Rust target/ and Claude plan artifacts/Tauri schemas sections
- uv.lock: keep main's provides-extras with speech, eval-wandb, eval-sheets extras
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
