花絮存储器
一个用于AI代理的内存/趣闻投票框架。代理人可以记录学习（事实、方法、陷阱）并对其进行投票，以揭示最有用的花絮。
安装
uv pip install -e ".[dev]"快速启动
from tidbits_memory.adapters.memory import InMemoryAdapter
from tidbits_memory.store import MemoryStore
store = MemoryStore(InMemoryAdapter())
# Create a voter id for this session
voter_id = store.create_voter_id()
# Create a memory
mem = store.create_memory("Python dicts preserve insertion order since 3.7", voter_id=voter_id)
# Get memories (random order, no vote counts) — ideal for agent consumption
result = store.get_memories(voter_id=voter_id)
print(result["memories"])
# Upvote a useful memory
store.upvote_memory(mem.id, voter_id="another-session-id")
# List memories sorted by votes (most upvoted first)
for m in store.list_memories():
print(f"[{m.votes}] {m.content}")
# Downvote an erroneous memory
store.downvote_memory(mem.id, voter_id="yet-another-session")
# Unvote (remove a prior vote)
store.unvote_memory(mem.id, "another-session-id")
# Remove a memory
store.remove_memory(mem.id)持久存储（JSON文件）
from tidbits_memory.adapters.json_file import JsonFileAdapter
from tidbits_memory.store import MemoryStore
store = MemoryStore(JsonFileAdapter("memories.json"))
store.create_memory("Use `uv` for fast Python packaging")选民ID生成
当代理没有会话/对话ID时，使用 create_voter_id:
voter_id = store.create_voter_id()
# Use this voter_id for all votes in this session如果不 voter_id 通话时提供 get_memories，一个会自动生成并在响应中返回。
MCP工具包装
from mcp.server import FastMCP
from tidbits_memory.adapters.memory import InMemoryAdapter
from tidbits_memory.store import MemoryStore
from tidbits_memory.tools import register_tools
store = MemoryStore(InMemoryAdapter())
mcp = FastMCP("tidbits")
register_tools(mcp, store)
# Registered tool names:
# create_memory, upvote_memory, downvote_memory, unvote_memory,
# list_memory, get_memories, remove_memory, create_voter_id运行测试
uv run pytest