 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
|
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 |
|