genesis-l0
In-process vector memory for AI-agent loops (LangGraph, AutoGen, CrewAI): a C99 L0 index in front of a local SQLite system of record. No sockets, no server.
query vector (float32)
│
▼ C99, ctypes (GIL released)
256-bit SRP-LSH code ─► POPCNT Hamming scan over all codes (1.01 MB for 33k vectors)
─► top-`pool` candidates ─► exact inner-product re-rank ─► top-k glyph ids
│
▼ SQLite (WAL, mmap_size = 256 MB)
typed payload of the requested face ("code", "research", ...)
The cascade is the measured one from genesis-live-core (DOI 10.5281/zenodo.23246581).
Measured
OpenAI text-embedding-3-large (DBpedia, truncated to 512-dim), 33,234 vectors,
100 held-out queries × 10 passes, mean-centered, top-10.
Contabo VPS, AMD EPYC 2.0 GHz (AVX2, L3 8 MiB), shared host, x86-64-v3 build.
Reproduce with python benchmarks/bench_e2e.py --data /tmp/genesis_eval.
| pool | Recall@10 | L0 index p50 | face fetch p50 | end-to-end p50 | end-to-end p99 |
|---|---|---|---|---|---|
| 512 | 90.6 % | 306–336 µs | 59–70 µs | 359–400 µs | 465–1190 µs |
| 256 | 82.1 % | 226 µs | 61 µs | 285 µs | 389 µs |
| 128 | 73.9 % | 176 µs | 55 µs | 228 µs | 377 µs |
- Recall@10 is against exact brute force. Synthetic clustered 512-dim data: 100 %.
- Face fetch = one
SELECT … IN (10 ids)right after the index query (caches already warmed by the re-rank). On its own it measures ~18–20 µs p50. - p99 varies between runs on the shared VPS; the range is three runs.
- Memory: 32 bytes of code per vector stay hot in L3; the float32 copy used by the re-rank (2 KB per 512-dim vector, ~65 MB for 33k) is read from RAM.
Modes
pool is the only knob between accuracy and latency:
| mode | pool |
Recall@10 | end-to-end p50 |
|---|---|---|---|
| Precision (default) | 512 | 90.6 % | ~380 µs |
| Fast | 128 | 73.9 % | ~228 µs (fits a hard < 250 µs budget) |
Precision is the default: inside an LLM step the ~150 µs difference is noise, while a missed context hit is not.
mem = GenesisL0Memory("agent_memory.db", pool=128) # fast mode
Install
Linux or macOS, Python ≥ 3.9, a C compiler.
pip install . # or: pip install -e ".[dev,langgraph]"
GENESIS_L0_NATIVE=1 pip install . # -march=native instead of portable x86-64-v3
x86_64 builds target x86-64-v3 (AVX2 + POPCNT: Intel Haswell / AMD Zen and newer).
Quickstart
import numpy as np
from genesis_l0 import GenesisL0Memory
corpus = np.random.randn(1000, 512).astype(np.float32)
with GenesisL0Memory(db_path="agent_memory.db", capacity=50_000, dim=512,
pool=512, mean=corpus.mean(axis=0)) as mem:
mem.add_batch(
np.arange(1000), corpus,
[{"slug": f"step_{i}", "faces": {"code": f"def tool_{i}(): ..."}} for i in range(1000)],
)
mem.add_glyph(5000, corpus[0], slug="auth_agent", faces={"code": "def verify(): pass"})
for hit in mem.query(corpus[0], top_k=5, face="code"):
print(hit["glyph_id"], round(hit["score"], 3), hit["payload"])
- Vectors are stored in SQLite; reopening the database rebuilds the index.
mean(optional) enables mean-centering. It is fixed once vectors exist and is stored in the database. For anisotropic embeddings it adds a few recall points.dimmust be a multiple of 16;top_kis capped at 64; glyph ids are uint32.- Writes are serialized;
query()is safe from any number of threads. Do not callclose()while queries are running.
LangGraph checkpointer
pip install "genesis-l0[langgraph]"
from genesis_l0.adapters.langgraph import GenesisL0CheckpointSaver
saver = GenesisL0CheckpointSaver.from_path("checkpoints.db")
graph = builder.compile(checkpointer=saver)
graph.invoke(inputs, {"configurable": {"thread_id": "agent-1"}})
It is the official langgraph-checkpoint-sqlite SqliteSaver on a connection
with the genesis-l0 pragmas, so put_writes, list, history and serialization
behave exactly as upstream.
Docker / Kubernetes: locked pages
The code array is mlock()ed. If RLIMIT_MEMLOCK refuses it, the index falls
back to madvise(MADV_WILLNEED) and emits CMemoryLockWarning; it still works,
but the codes can be paged out under memory pressure. To allow locking:
services:
agent-worker:
image: my-agent:latest
ulimits:
memlock:
soft: -1
hard: -1
cap_add:
- IPC_LOCK
Tests
pytest # functional tests
pytest -m benchmark -s # latency checks (prints p50/p99)
GENESIS_L0_E2E_BUDGET_US=300 pytest -m benchmark
The end-to-end benchmark (precision mode) asserts p50 < 450 µs by default;
override GENESIS_L0_E2E_BUDGET_US for your hardware.
License
Business Source License 1.1 (see LICENSE), changing to Apache 2.0 on
2030-01-01. csrc/srp_lsh.c and csrc/srp_lsh.h are Apache 2.0 (see NOTICE).
1 maintainer
Metadata
1 maintainer
Release files for genesis-l0 0.1.0
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