Agentkeeper

Performance release. No public API changes — drop-in upgrade from 1.1.x.

Faster compression at scale

Compression (consolidation + contradiction arbitration) is now vectorised via an optional numpy accelerator. A full compression pass over an agent with 10,000 facts drops from ~118s to ~5s — about 23x.

pip install 'agentkeeper-ai[fast]'   # enables the numpy accelerator

Without numpy, behaviour is unchanged: the pure-Python fallback is preserved, so the core keeps zero required dependencies.

Also in this release

• New [fast] extra (numpy), also bundled in [all].
• benchmark/stress_test.py — a reproducible scaling benchmark you can run yourself (10k-fact insert, 500 compression cycles, recall latency, save/load integrity, graph traversal).
• tests/test_fastmath.py — verifies the numpy and pure-Python paths produce identical results.
• Consolidation clustering now picks the best-matching centroid rather than the first above threshold (tighter clusters).

Built by Tom Anciaux Berner — ThinkLanceAI