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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

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Learning Module

The learning module implements learning policies that improve routing, agent, and tool decisions based on historical interaction outcomes. The module provides a LearningPolicy ABC taxonomy with specialized sub-ABCs for intelligence (model routing), agent behavior, and tool selection. It also includes reward functions for scoring inference results.

Abstract Base Classes

RouterPolicy

::: openjarvis.intelligence._stubs.RouterPolicy options: show_source: true members_order: source

QueryAnalyzer

::: openjarvis.intelligence._stubs.QueryAnalyzer options: show_source: true members_order: source

RoutingContext

RoutingContext is now defined in core/types.py:

::: openjarvis.core.types.RoutingContext options: show_source: true members_order: source

RewardFunction

::: openjarvis.learning._stubs.RewardFunction options: show_source: true members_order: source

LearningPolicy Taxonomy

The learning system defines a hierarchy of learning policy ABCs:

LearningPolicy -- base ABC for all learning policies
IntelligenceLearningPolicy -- specialization for model routing decisions
AgentLearningPolicy -- specialization for agent behavior advice
ToolLearningPolicy -- specialization for tool selection/configuration

Policy Implementations

TraceDrivenPolicy

::: openjarvis.learning.trace_policy.TraceDrivenPolicy options: show_source: true members_order: source

classify_query

::: openjarvis.learning.trace_policy.classify_query options: show_source: true

SFTPolicy

::: openjarvis.learning.sft_policy.SFTPolicy options: show_source: true members_order: source

AgentAdvisorPolicy

::: openjarvis.learning.agent_advisor.AgentAdvisorPolicy options: show_source: true members_order: source

ICLUpdaterPolicy

::: openjarvis.learning.icl_updater.ICLUpdaterPolicy options: show_source: true members_order: source

GRPORouterPolicy

::: openjarvis.learning.grpo_policy.GRPORouterPolicy options: show_source: true members_order: source

Reward Functions

HeuristicRewardFunction

::: openjarvis.learning.heuristic_reward.HeuristicRewardFunction options: show_source: true members_order: source

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