feat: implement L1-L7 context tree for multi-layered memory management #16

agentson · 2026-02-04T14:50:37+09:00

agentson commented

2026-02-04 14:50:37 +09:00

Implements Pillar 2 (Multi-layered Context Management) with a 7-tier hierarchical memory system from real-time market data to generational trading wisdom.

Summary

This PR adds a complete context tree implementation with 7 layers (L1-L7), automatic bottom-up aggregation, and retention policies for sustainable long-term memory.

New Modules

src/context/layer.py: ContextLayer enum and metadata config
src/context/store.py: ContextStore for CRUD operations (98% coverage)
src/context/aggregator.py: Bottom-up aggregation logic (100% coverage)

Database Changes

Added contexts table for hierarchical data storage
Added context_metadata table for layer configuration
Indexed by layer, timeframe, and updated_at for fast queries

Context Layers

L1 (Legacy): Cumulative wisdom (kept forever)
L2 (Annual): Yearly metrics (10 years retention)
L3 (Quarterly): Strategy pivots (3 years)
L4 (Monthly): Portfolio rebalancing (2 years)
L5 (Weekly): Stock selection (1 year)
L6 (Daily): Trade logs (90 days)
L7 (Real-time): Live market data (7 days)

Tests

✅ 18 new tests in tests/test_context.py
✅ 100% coverage on context modules
✅ All 72 tests passing (54 existing + 18 new)
✅ Lint passing (ruff)
✅ Type check passing (mypy --strict)

Documentation

Added comprehensive docs/context-tree.md
Updated CLAUDE.md architecture section
Includes usage examples and best practices

Test Plan

pytest tests/test_context.py -v         # 18 passed
pytest -v --cov=src --cov-report=term-missing  # 72 passed, 56% coverage
ruff check src/ tests/                  # All checks passed
mypy src/context/ --strict              # Success

Closes #15

Implements **Pillar 2 (Multi-layered Context Management)** with a 7-tier hierarchical memory system from real-time market data to generational trading wisdom. ## Summary This PR adds a complete context tree implementation with 7 layers (L1-L7), automatic bottom-up aggregation, and retention policies for sustainable long-term memory. ### New Modules - `src/context/layer.py`: ContextLayer enum and metadata config - `src/context/store.py`: ContextStore for CRUD operations (98% coverage) - `src/context/aggregator.py`: Bottom-up aggregation logic (100% coverage) ### Database Changes - Added `contexts` table for hierarchical data storage - Added `context_metadata` table for layer configuration - Indexed by layer, timeframe, and updated_at for fast queries ### Context Layers - **L1 (Legacy)**: Cumulative wisdom (kept forever) - **L2 (Annual)**: Yearly metrics (10 years retention) - **L3 (Quarterly)**: Strategy pivots (3 years) - **L4 (Monthly)**: Portfolio rebalancing (2 years) - **L5 (Weekly)**: Stock selection (1 year) - **L6 (Daily)**: Trade logs (90 days) - **L7 (Real-time)**: Live market data (7 days) ### Tests - ✅ 18 new tests in `tests/test_context.py` - ✅ 100% coverage on context modules - ✅ All 72 tests passing (54 existing + 18 new) - ✅ Lint passing (ruff) - ✅ Type check passing (mypy --strict) ### Documentation - Added comprehensive [`docs/context-tree.md`](docs/context-tree.md) - Updated `CLAUDE.md` architecture section - Includes usage examples and best practices ## Test Plan ```bash pytest tests/test_context.py -v # 18 passed pytest -v --cov=src --cov-report=term-missing # 72 passed, 56% coverage ruff check src/ tests/ # All checks passed mypy src/context/ --strict # Success ``` Closes #15

agentson added 1 commit 2026-02-04 14:50:37 +09:00

feat: implement L1-L7 context tree for multi-layered memory management

CI / test (pull_request) Has been cancelled

Details

917b68eb81

Implements Pillar 2 (Multi-layered Context Management) with a 7-tier
hierarchical memory system from real-time market data to generational
trading wisdom.

## New Modules
- `src/context/layer.py`: ContextLayer enum and metadata config
- `src/context/store.py`: ContextStore for CRUD operations
- `src/context/aggregator.py`: Bottom-up aggregation (L7→L6→...→L1)

## Database Changes
- Added `contexts` table for hierarchical data storage
- Added `context_metadata` table for layer configuration
- Indexed by layer, timeframe, and updated_at for fast queries

## Context Layers
- L1 (Legacy): Cumulative wisdom (kept forever)
- L2 (Annual): Yearly metrics (10 years retention)
- L3 (Quarterly): Strategy pivots (3 years)
- L4 (Monthly): Portfolio rebalancing (2 years)
- L5 (Weekly): Stock selection (1 year)
- L6 (Daily): Trade logs (90 days)
- L7 (Real-time): Live market data (7 days)

## Tests
- 18 new tests in `tests/test_context.py`
- 100% coverage on context modules
- All 72 tests passing (54 existing + 18 new)

## Documentation
- Added `docs/context-tree.md` with comprehensive guide
- Updated `CLAUDE.md` architecture section
- Includes usage examples and best practices

Closes #15

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

agentson added 1 commit 2026-02-04 15:25:18 +09:00

Merge main into feature/issue-15-context-tree

CI / test (pull_request) Has been cancelled

Details

254b543c89

Resolved conflicts in CLAUDE.md by:
- Keeping main's refactored structure (docs split into separate files)
- Added Context Tree documentation link to docs section
- Preserved all constraints and guidelines from main

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

jihoson merged commit 7515a5a314 into main

2026-02-04 15:40:00 +09:00

jihoson referenced this issue from a commit

2026-02-04 15:40:01 +09:00

Merge pull request 'feat: implement L1-L7 context tree for multi-layered memory management' (#16) from feature/issue-15-context-tree into main

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Reference: jihoson/The-Ouroboros#16