agentmem/__init__.py at main · oxgeneral/agentmem · GitHub

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"""
agentmem — Lightweight persistent memory for AI agents.

One SQLite file. Hybrid search (keywords + semantics). 12MB total.
No PyTorch. No cloud. No server. Just memory.

Usage:
    from agentmem import MemoryStore, get_embedding_model

    # Initialize
    embed = get_embedding_model()  # auto-selects best available
    store = MemoryStore("memory.db", embedding_dim=embed.dim)
    store.set_embed_fn(embed)

    # Store
    store.remember("Alexander's Telegram ID is 252708838", tier="core")

    # Recall
    results = store.recall("What is Alexander's Telegram ID?")

    # Save state before context compression
    store.save_state("Working on feature X, step 3 of 5, blocked by Y")
"""
from .core import (
    MemoryStore, TIERS,
    AgentMemError, MemoryNotFoundError, InvalidTierError, EmbeddingError,
    # TypedDict return types for public API
    RememberResult,
    BatchResult,
    RecallResult,
    SaveStateResult,
    TodayResult,
    ForgetResult,
    UnarchiveResult,
    StatsResult,
    CompactResult,
    ConsolidateResult,
    UpdateResult,
    HistoryItem,
    RelatedResult,
    EntityResult,
    ImportResult,
    ProcessConversationResult,
)
from .embeddings import get_embedding_model, EmbeddingModel

__version__ = "0.3.0"
__all__ = [
    "MemoryStore", "get_embedding_model", "TIERS",
    "AgentMemError", "MemoryNotFoundError", "InvalidTierError", "EmbeddingError",
    "EmbeddingModel",
    # TypedDict return types
    "RememberResult",
    "BatchResult",
    "RecallResult",
    "SaveStateResult",
    "TodayResult",
    "ForgetResult",
    "UnarchiveResult",
    "StatsResult",
    "CompactResult",
    "ConsolidateResult",
    "UpdateResult",
    "HistoryItem",
    "RelatedResult",
    "EntityResult",
    "ImportResult",
    "ProcessConversationResult",
]