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Contributing to tripartite-memory

PRs are welcome. Here's how to get oriented quickly.

Areas where contributions are especially welcome

  • Embedding model adapters — nomic-embed-text is the tested default; adapters for other Ollama models or OpenAI embeddings would be valuable
  • Graph backends — Neo4j is the current implementation; a NetworkX or Memgraph adapter would help users without a Neo4j instance
  • Benchmark corpus expansion — additional QA pairs or domain-specific corpora in benchmarks/corpus.py
  • Cloud provider guides — connection examples for Supabase, PlanetScale, Pinecone, etc.

Setup

git clone https://github.com/LavaDMan/aegis-memory.git
cd aegis-memory
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
cp .env.example .env  # fill in your local DB URLs

Running tests

pytest tests/

Running benchmarks

python benchmarks/retrieval_bench.py --verbose

Guidelines

  • Keep functions atomic and single-responsibility
  • New engines must implement the same async interface as LedgerEngine, SemanticEngine, and RelationalEngine
  • Any change to recall() or ingest() should include a benchmark run showing no regression against the baseline in benchmarks/README.md
  • Open an issue before starting large refactors

Reporting issues

Open a GitHub issue with: Python version, which database backends you're using, and the full traceback.