mnem-py 0.1.0

Python bindings for mnem: git for knowledge graphs, agent memory under a token budget.

mnem (Python)

Python bindings for mnem - git for knowledge graphs, with retrieval under a token budget.

pip install mnem-py

import pymnem

# In-memory (tests, notebooks, agent sessions without persistence)
repo = pymnem.Repo.init_memory

# Or on-disk via the embedded redb backend
# repo = pymnem.Repo.open_or_init("/path/to/repo.redb")

# Write one node at a time
repo.commit_node(
    author="[e-mail]",
    message="seed",
    ntype="Memory",
    summary="Alice lives in Berlin and works at Globex",
    props={"name": "Alice", "city": "Berlin"},
)

# Or batch with a transaction (context-manager commits on clean exit)
with repo.transaction(author="[e-mail]", message="seed batch") as tx:
    tx.add_node(ntype="Memory", summary="Alice's hobby is climbing")
    tx.add_node(ntype="Memory", summary="Bob moved to Paris last month")

# Retrieve under a token budget - dense vector + optional learned-sparse, RRF-fused
result = repo.retrieve(
    text="Alice Berlin",
    token_budget=500,
    limit=10,
)
for item in result:
    print(f"{item.score:.3f} [{item.tokens}t] {item.summary}")

print(f"used {result.tokens_used}/{result.tokens_budget} tokens,",
      f"{result.dropped} dropped of {result.candidates_seen} candidates")

What you get from Python

• pymnem.Repo - the repository handle, with init_memory, open_or_init, op_id, head_commit_cid, commit_node, transaction, retrieve, query.
• pymnem.Transaction - a context manager for batched writes. Commits on clean exit, abandons on exception. Supports add_node and add_embedding_f32.
• pymnem.RetrievalResult / pymnem.RetrievedItem - plain dataclass-shaped results with node_id, ntype, summary, rendered, tokens, score plus cost metadata.
• pymnem.MnemError - the exception base class every mnem error inherits from.

What's deferred

The v1 Python surface deliberately leaves out:

• Signing and verification. Callers that need Ed25519 signatures build their signer in their own tooling; the Rust side remains the source of truth.
• CAS on refs, diff, merge. These are powerful but rarely touched from Python; open an issue with a concrete use case if you need them.
• Structured Edge writes. add_edge lands once the first Python caller asks for it.
• MCP server bindings. The mnem mcp binary is already Python-host-friendly (it speaks JSON-RPC 2.0 over stdio); no need to rewrap it.

Build from source

If you want to develop the bindings locally:

pip install maturin
cd crates/mnem-py
maturin develop --release      # installs into your active venv
python -c "import pymnem; print(pymnem.__version__)"

Publishing wheels:

maturin build --release --strip
# Upload target/wheels/mnem_py-*.whl to PyPI with `twine upload`.

Pre-release verification (manual step before tagging 0.1.0)

cargo test -p mnem-py --lib covers the Rust-side parser and gate unit tests, but the PyO3 ABI layer is only exercised through a real Python interpreter. Before cutting a release tag, run the Python-linked regression once on the target platform:

cd crates/mnem-py
maturin develop
pytest tests/

The pytest suite includes tests/test_mnem_bench_gate.py which pins the MNEM_BENCH coercion behaviour across Repo.commit_node, Transaction.add_node, Repo.retrieve, and Repo.query. Skipping this step means shipping the pyo3 bindings without having loaded them.

Performance envelope

The Python wrapper is a thin pyo3 layer; retrieval throughput is what the Rust core measures in docs/benchmarks/ai-native.md. At 1000 Doc nodes on laptop hardware:

• retrieve(text=...) fresh-index end-to-end: ~6 ms (in-memory) / ~14 ms (redb)
• Amortised text search (pre-built index held for a session): ~11 µs (memory) / ~21 µs (redb)
• Fused text + vector retrieve: ~10 ms (memory) / ~21 ms (redb)

The Python-to-Rust boundary costs <50 µs per call in practice, well below the retrieval work.

License

Apache-2.0, same as the core crate. See LICENSE.