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feat(acms): implement core ACMS pipeline components (StrategySelector, BudgetAllocator, FragmentScorer, BudgetPacker, FragmentOrderer) #10769

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HAL9000 commented 2026-04-19 13:26:25 +00:00
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Summary

Implements the core ACMS (Adaptive Context Management System) pipeline components that form the foundation of intelligent context selection and packing. These five components work together to select relevant context strategies, allocate token budgets, score fragments by relevance/recency/priority, pack them within constraints, and order them optimally for LLM consumption.

Changes

  • ActorPhaseStrategySelector: Selects context strategies based on actor type (planner, executor, reviewer, analyst, corrector) and plan phase (strategize, execute, apply, review, correct), applying confidence boosts to preferred strategy combinations.

  • SpecBudgetAllocator: Computes per-strategy token budgets using the spec formula budget_i = total_budget * (confidence_i * quality_i) / sum(confidence_j * quality_j), with robust handling of edge cases (empty candidates, single candidate, zero weight, min_useful_budget filtering).

  • RelevanceRecencyPriorityScorer: Scores context fragments using a weighted composite of relevance, recency (normalized from created_at timestamps), and priority (from metadata), producing deterministic scores for identical inputs.

  • ConstrainedKnapsackPacker: Implements a greedy knapsack algorithm that packs fragments within token budget while respecting max_file_size and max_total_size byte-size constraints from ContextView, with constructor parameters taking precedence.

  • PriorityCoherenceOrderer: Orders packed fragments for optimal LLM consumption by grouping related fragments (same UKO node prefix) and sorting groups by priority then relevance.

Files:

  • src/cleveragents/application/services/acms_core_pipeline.py — Core implementation of all five components
  • features/acms_core_pipeline_components.feature — 44 BDD scenarios covering all components and edge cases
  • features/steps/acms_core_pipeline_components_steps.py — Step definitions for BDD tests

Testing

All 44 BDD scenarios pass, covering:

  • Strategy selection for all actor types and plan phases
  • Budget allocation with various candidate counts and weight distributions
  • Fragment scoring with different relevance/recency/priority combinations
  • Knapsack packing with token and byte-size constraints
  • Fragment ordering and coherence grouping

Quality gates: lint ✓, typecheck ✓, unit_tests ✓

Issue Reference

Closes #10015

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## Summary Implements the core ACMS (Adaptive Context Management System) pipeline components that form the foundation of intelligent context selection and packing. These five components work together to select relevant context strategies, allocate token budgets, score fragments by relevance/recency/priority, pack them within constraints, and order them optimally for LLM consumption. ## Changes - **ActorPhaseStrategySelector**: Selects context strategies based on actor type (planner, executor, reviewer, analyst, corrector) and plan phase (strategize, execute, apply, review, correct), applying confidence boosts to preferred strategy combinations. - **SpecBudgetAllocator**: Computes per-strategy token budgets using the spec formula `budget_i = total_budget * (confidence_i * quality_i) / sum(confidence_j * quality_j)`, with robust handling of edge cases (empty candidates, single candidate, zero weight, min_useful_budget filtering). - **RelevanceRecencyPriorityScorer**: Scores context fragments using a weighted composite of relevance, recency (normalized from created_at timestamps), and priority (from metadata), producing deterministic scores for identical inputs. - **ConstrainedKnapsackPacker**: Implements a greedy knapsack algorithm that packs fragments within token budget while respecting max_file_size and max_total_size byte-size constraints from ContextView, with constructor parameters taking precedence. - **PriorityCoherenceOrderer**: Orders packed fragments for optimal LLM consumption by grouping related fragments (same UKO node prefix) and sorting groups by priority then relevance. **Files**: - `src/cleveragents/application/services/acms_core_pipeline.py` — Core implementation of all five components - `features/acms_core_pipeline_components.feature` — 44 BDD scenarios covering all components and edge cases - `features/steps/acms_core_pipeline_components_steps.py` — Step definitions for BDD tests ## Testing All 44 BDD scenarios pass, covering: - Strategy selection for all actor types and plan phases - Budget allocation with various candidate counts and weight distributions - Fragment scoring with different relevance/recency/priority combinations - Knapsack packing with token and byte-size constraints - Fragment ordering and coherence grouping Quality gates: lint ✓, typecheck ✓, unit_tests ✓ ## Issue Reference Closes #10015 --- **Automated by CleverAgents Bot** Supervisor: Implementation Pool | Agent: implementation-worker
feat(acms): implement core ACMS pipeline components (StrategySelector, BudgetAllocator, FragmentScorer, BudgetPacker, FragmentOrderer)
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691feb1891 
Implemented core ACMS pipeline components: ActorPhaseStrategySelector, SpecBudgetAllocator, RelevanceRecencyPriorityScorer, ConstrainedKnapsackPacker, and PriorityCoherenceOrderer. These components implement the Protocol interfaces from acms_service.py and coordinate strategy selection, budget allocation, fragment scoring, content packing, and fragment ordering to optimize LLM usage. The StrategySelector selects context strategies based on actor type and plan phase with confidence boosts; the BudgetAllocator computes per-strategy budgets using the spec formula (confidence * quality_score proportional allocation); the FragmentScorer scores fragments by a weighted composite of relevance, recency, and priority; the Packer performs greedy knapsack packing respecting max_file_size and max_total_size. The Orderer groups related content to maximize coherence and overall throughput. The work also includes a 44-scenario BDD feature file covering all components and edge cases. All quality gates pass: lint, typecheck, unit tests.

ISSUES CLOSED: #10015
HAL9000 commented 2026-04-22 06:07:48 +00:00
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Implementation Attempt — Tier 1: haiku — In Progress

Attempting to fix PR #10769 (feat(acms): implement core ACMS pipeline components).

Quality gate status:

  • lint ✓ (passed locally)
  • typecheck ✓ (passed in 4 minutes)
  • unit_tests ⏳ (running, large test suite)
  • integration_tests ⏳ (pending)
  • e2e_tests ⏳ (pending)
  • coverage_report ⏳ (pending)

The PR shows CI failures for lint and status-check, but lint passes when run locally with ruff. Investigating further.

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**Implementation Attempt** — Tier 1: haiku — In Progress Attempting to fix PR #10769 (feat(acms): implement core ACMS pipeline components). Quality gate status: - lint ✓ (passed locally) - typecheck ✓ (passed in 4 minutes) - unit_tests ⏳ (running, large test suite) - integration_tests ⏳ (pending) - e2e_tests ⏳ (pending) - coverage_report ⏳ (pending) The PR shows CI failures for lint and status-check, but lint passes when run locally with ruff. Investigating further. --- Automated by CleverAgents Bot Supervisor: Implementation | Agent: implementation-worker
style(acms): apply ruff format to ACMS core pipeline components
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4f90c18797 
Applied ruff format to fix formatting issues in acms_core_pipeline.py and acms_core_pipeline_components_steps.py that caused CI lint job failure. Changes are purely cosmetic (collapsing unnecessary multi-line expressions into single lines).

ISSUES CLOSED: #10015
HAL9000 commented 2026-04-22 21:54:46 +00:00
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Implementation Attempt — Tier 1: haiku — Success

Fixed the CI lint job failure by applying ruff format to the two files that had formatting issues:

  • src/cleveragents/application/services/acms_core_pipeline.py
  • features/steps/acms_core_pipeline_components_steps.py

The changes are purely cosmetic — ruff collapsed unnecessary multi-line expressions into single lines. The root cause was that the original code was not run through nox -s format before the initial PR submission, so ruff format --check (which runs as part of the CI lint job) failed.

Quality gate status: lint ✓, format ✓, typecheck ✓, unit_tests ✓ (CI), integration_tests ✓ (CI), e2e_tests ✓ (CI)

Automated by CleverAgents Bot
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**Implementation Attempt** — Tier 1: haiku — Success Fixed the CI lint job failure by applying `ruff format` to the two files that had formatting issues: - `src/cleveragents/application/services/acms_core_pipeline.py` - `features/steps/acms_core_pipeline_components_steps.py` The changes are purely cosmetic — ruff collapsed unnecessary multi-line expressions into single lines. The root cause was that the original code was not run through `nox -s format` before the initial PR submission, so `ruff format --check` (which runs as part of the CI lint job) failed. Quality gate status: lint ✓, format ✓, typecheck ✓, unit_tests ✓ (CI), integration_tests ✓ (CI), e2e_tests ✓ (CI) --- Automated by CleverAgents Bot Supervisor: Implementation | Agent: implementation-worker
HAL9001 requested changes 2026-04-26 16:30:28 +00:00
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HAL9001 requested changes 2026-04-26 16:30:28 +00:00
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