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Memory for AI agents: conversation, working, long-term and semantic stores, with keyword, embedding and hybrid recall, and summarising history strategies.

Changelog #

0.1.1 #

  • Shortened the package description to the 60-180 character window pana scores against. Search engines truncate anything longer, so the ten points it withheld were pointing at a real defect: the useful half of the sentence was never being shown.

0.1.0 #

Initial release of the memory layer.

Added #

  • ValuesMemoryEntry, MemoryKind, MemoryQuery and MemoryHit. One entry shape covers working, conversation, long-term, semantic and shared memory, which differ in scope and retention rather than in mechanics.
  • StoresMemoryStore port; InMemoryMemoryStore with rarity-weighted keyword retrieval blended with importance and recency, deduplication, expiry and value-based eviction; EmbeddedMemoryStore for semantic retrieval with a similarity floor and batched backfill; HybridMemoryStore fusing both rankings with reciprocal rank fusion.
  • History strategiesRecallingHistory injects memories relevant to the turn about to be sent; SummarisingHistory condenses older turns and caches, so a steady conversation summarises once per window rather than once per turn.
  • Toolsremember, recall and an approval-gated forget, so an agent can manage its own memory.
  • Automatic extractionRememberingAgent runs a separate extraction pass after each successful run, bounded by count and importance, and never lets a failed extraction break a good answer.
  • EventsMemoriesRecalled (carrying the statements, so an answer is traceable), MemoriesExtracted, MemoryExtractionFailed and MemoriesPruned.
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Memory for AI agents: conversation, working, long-term and semantic stores, with keyword, embedding and hybrid recall, and summarising history strategies.

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Topics

#ai #agents #memory #rag #agentic

License

MIT (license)

Dependencies

agentic_agents, agentic_core, agentic_llm, agentic_tools, meta

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