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A persistent memory engine for conversational AI agents

Project description

alaya-memory

A persistent memory engine for conversational AI agents — Python bindings for Alaya.

Install

pip install alaya-memory

Quick start

from alaya import Alaya, NewEpisode, Query

# Open (or create) a memory store
mem = Alaya("agent.db")

# Store an episode
mem.store_episode(NewEpisode(
    content="User prefers concise answers",
    session_id="sess-001",
    role="user",
))

# Query
results = mem.query(Query(text="communication preferences", limit=5))
for r in results:
    print(r.score, r.content)

# Run the full dream cycle (consolidate → transform → forget)
mem.dream()

Features

  • Episodic memory — store and retrieve conversation turns
  • Semantic consolidation — episodes are distilled into durable knowledge nodes
  • Forgetting curves — weak or contradicted knowledge decays over time
  • Knowledge graph — typed links between episodes, nodes, preferences, and categories
  • Encrypted storage — optional SQLCipher encryption (open_encrypted)
  • MCP server — expose memory over the Model Context Protocol via alaya-mcp on npm

Documentation

See the Alaya repository for full API docs and the Rust crate.

License

MIT

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