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- Due by July 1, 2026
- Due by June 25, 2026•0/111 issues closed
Next patch release
No due date•11/39 issues closedCodeWhale v0.8.57 release cycle. Target: ~15 issues.
No due date•4/4 issues closedThe v0.9.0 architectural release promotes CodeWhale from a turn/subagent workbench into a WhaleFlow workflow workbench: typed branch-and-leaf workflows, pod-style background workflow monitoring, shared ARMH/RLM memoization, deterministic replay, external-memory evaluation, and a GEPA-style teacher/student promotion loop that turns validated lessons into a cached-main overlay. Primary tracker: #2981 EPIC: v0.9.0 WhaleFlow branch/leaf workflow mode (re-established after #2667 was deleted) ## In scope - WhaleFlow workflow mode: background workflow runs, /workflows-style monitoring, done/total progress, longest-running item peek, inspect/replay/report surfaces. - Typed Workflow IR as the source of truth: Starlark/YAML/generated plans compile to Rust-owned IR before execution. - Rust async executor: bounded branches, bounded leaves, cancellation, budgets, permissions, LoopUntil, Cond, Expand, BranchTournament, and Pareto reducers. - Branch/leaf semantics: isolated speculative branches, bounded leaves, losing-branch fruit harvesting, typed results. - ARMH/RLM integration: exact-context shared memo across branches with visible hit/miss/cost telemetry. - External-memory evaluation: decide whether Aleph-style memory belongs in core, optional plugins, or explicit workflow nodes, with visible state and clear/export controls. - TraceStore and deterministic replay: replay from recorded leaf/control outputs, not live model calls, unless explicitly allowed. - Teacher harness: TeacherReview proposes reusable lessons; StudentReplay and PromotionGate validate before promotion. - Cached-main overlay: promoted notes, workflows, tests, branch heuristics, model/cache policies, and prompt patches warm future runs without mutating Git main. - Janitor: stale invalidation, memo cleanup, candidate demotion, trace compaction, capacity enforcement. - Model-provider abstraction: workflow roles map to capabilities and configured providers; no workflow logic hardcodes Arcee, DeepSeek, Claude, tool calls, JSON mode, or large context. ## Non-goals - No model-weight RL in v0.9.0. - No arbitrary JS/Python as workflow source of truth. - No script-level async/await. Starlark is a pure graph builder; Rust executes IR. - No hidden external-memory dependency for normal CodeWhale operation. - No uncontrolled self-modifying agent. Teacher output is inspectable, replayed, and reversible. - No public performance claims until evals are reproducible. ## Definition of done - workflows/rlm_cache_change.star runs with mock provider in CI and can dogfood CodeWhale RLM/ARMH/provider changes. - Branch/leaf engine, control flow, TraceStore, replay, ARMH shared memo, TeacherReview, StudentReplay, PromotionGate, overlay, and janitor have focused tests. - Workflow mode can run, inspect, and replay a workflow from CLI and TUI. - ARMH savings, provider costs, and any external-memory use are visible in workflow telemetry. - All behavior is behind config/feature flags until stable. ## Release gate - Parity gates green on the v0.9.0 integration branch. - CHANGELOG [0.9.0] frames the release as WhaleFlow branch/leaf workflows and validated cached-main learning. - Docs explain the Claude-workflow-inspired UX while preserving CodeWhale's typed IR/Rust executor safety model.
No due date•31/54 issues closed