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Releases: Thinklanceai/agentkeeper

AgentKeeper v1.1.2

20 May 11:35
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v1.1.2
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AgentKeeper v1.1.2 Latest
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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

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AgentKeeper v1.1.0

20 May 10:18
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v1.1.0
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AgentKeeper v1.1.0

Cognitive continuity infrastructure for long-lived AI agents.

AgentKeeper reconstructs an agent's full cognitive state — identity, memory, decisions, relationships — across model switches, crashes, restarts, and constrained context windows.

Install

pip install agentkeeper-ai

Highlights in 1.1

  • Memory classes — decision(), preference(), constraint(), relationship(), task_state(), transient(), each with its own decay behaviour.
  • Cognitive observability — agent.health() reports memory volume, importance distribution, contradiction count, stale ratio.
  • GDPR-native retention — TTLs (ttl="30d"), gdpr_export() (Article 20), gdpr_purge() (Article 17).
  • Persistent vector index — sqlite-vec backend survives restarts without re-embedding; scales to 10k+ facts.
  • Pluggable storage — BaseStorage ABC, default SQLiteStorage, opt-in EncryptedSQLiteStorage (Fernet at-rest encryption).
  • Async LLM consolidation — AsyncAgent.compress(use_llm=True) end-to-end.
  • Graph memory — directed triples (agent.link), BFS traversal (agent.find_related), shortest path.
  • Native MCP server — agentkeeper-mcp CLI for Claude Desktop, Claude Code, Cursor, and any MCP host. Nine tools exposed.
  • Framework integrations — LangChain and CrewAI helpers, no hard dependency.

Quality

459 tests, ruff-clean, py.typed, CI on Python 3.10 / 3.11 / 3.12. Full backward compatibility with 1.0.

Built by Tom Anciaux Berner — ThinkLanceAI

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