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Igor Lins e Silva bf3b9c5979 docs: #875 follow-up — repo surfaces + reproduction URLs + CHANGELOG

Remaining in-repo surfaces carrying the same retracted or broken
claims as the public pages fixed in the previous two commits.

CONTRIBUTING.md
 - "Palace structure matters ... 34% retrieval improvement" → reframed
   as scoping (same rewording applied to the website equivalents).

benchmarks/BENCHMARKS.md
 - Add a prominent "Important caveat" block at the top of the
   "Comparison vs Published Systems" table explaining that R@5
   (retrieval recall) and QA accuracy are different metrics, with
   citations to Mastra, Mem0, and Supermemory's own published
   methodology pages. Annotate the specific competitor rows whose
   numbers are QA accuracy, not retrieval recall.
 - Annotate the `hybrid v4 + rerank 100%` row to note that the 99.4
   → 100 step was tuned on 3 specific wrong answers (already disclosed
   further down in the doc under "Benchmark Integrity"); the honest
   hybrid figure is held-out 98.4%.
 - Fix the broken clone URL — `aya-thekeeper/mempal` no longer points
   at anything; now `MemPalace/mempalace`.

benchmarks/README.md + benchmarks/HYBRID_MODE.md
 - Same clone-URL fix applied.

CHANGELOG.md
 - Add a ### Documentation entry under [Unreleased] v3.3.0 that names
   #875 and summarises the scope of the rewrite.

2026-04-14 21:38:00 -03:00

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Contributing to MemPalace

Thanks for wanting to help. MemPalace is open source and we welcome contributions of all sizes — from typo fixes to new features.

Getting Started

# Fork the repo on GitHub first, then clone your fork
git clone https://github.com/<your-username>/mempalace.git
cd mempalace
git remote add upstream https://github.com/MemPalace/mempalace.git

pip install -e ".[dev]"    # installs with dev dependencies (pytest, build, twine)

Running Tests

pytest tests/ -v

All tests must pass before submitting a PR. Tests should run without API keys or network access.

Running Benchmarks

# Quick test (20 questions, ~30 seconds)
python benchmarks/longmemeval_bench.py /path/to/longmemeval_s_cleaned.json --limit 20

# Full benchmark (500 questions, ~5 minutes)
python benchmarks/longmemeval_bench.py /path/to/longmemeval_s_cleaned.json

See benchmarks/README.md for data download instructions and reproduction guide.

Project Structure

mempalace/          ← core package (see mempalace/README.md for module guide)
benchmarks/         ← reproducible benchmark runners
hooks/              ← Claude Code auto-save hooks
examples/           ← usage examples
tests/              ← test suite
assets/             ← logo + brand

PR Guidelines

Fork the repo and create a feature branch: git checkout -b feat/my-thing
Write your code
Add or update tests if applicable
Run pytest tests/ -v — everything must pass
Commit with a clear message following conventional commits:
- feat: add Notion export format
- fix: handle empty transcript files
- docs: update MCP tool descriptions
- bench: add LoCoMo turn-level metrics
Push to your fork and open a PR against develop

Code Style

Formatting: Ruff with 100-char line limit (configured in pyproject.toml)
Naming: snake_case for functions/variables, PascalCase for classes
Docstrings: on all modules and public functions
Type hints: where they improve readability
Dependencies: minimize. ChromaDB + PyYAML only. Don't add new deps without discussion.

Good First Issues

Check the Issues tab. Great starting points:

New chat formats: Add import support for Cursor, Copilot, or other AI tool exports
Room detection: Improve pattern matching in room_detector_local.py
Tests: Increase coverage — especially for knowledge_graph.py and palace_graph.py
Entity detection: Better name disambiguation in entity_detector.py
Docs: Improve examples, add tutorials

Architecture Decisions

If you're planning a significant change, open an issue first to discuss the approach. Key principles:

Verbatim first: Never summarize user content. Store exact words.
Local first: Everything runs on the user's machine. No cloud dependencies.
Zero API by default: Core features must work without any API key.
Palace structure is scoping, not magic: Wings, halls, and rooms act as metadata filters in the underlying vector store. They keep retrieval predictable when a palace holds many unrelated projects or people. Respect the hierarchy — but don't present it as a novel retrieval mechanism.

Community

Discord: Join us
Issues: Bug reports and feature requests welcome
Discussions: For questions and ideas

License

MIT — your contributions will be released under the same license.

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