Mem0：LLM 记忆即服务 2026 版

from mem0 import MemoryClient

client = MemoryClient()
client.add(user_id="user_123", 
           data="用户喜欢用 Python 写代码")

memories = client.search(user_id="user_123", 
                        query="用户偏好")

pip install mem0ai

pip install mem0[all]  # 包括 Chroma、PGVector、Redis 等

pip install openai anthropic google-generativeai

from mem0 import MemoryClient

config = {
    "embed_model": "text-embedding-3-small",
    "vector_store": {"provider": "local"},
    "llm": {"provider": "openai", "model": "gpt-4"}
}

client = MemoryClient(config=config)

client.add(
    user_id="user_1",
    data="用户喜欢用 Claude 写代码"
)

# 批量添加
client.add(
    user_id="user_1",
    data=[
        "喜欢用 Python",
        "工作 9-5",
        "偏好 VSCode"
    ]
)

# 语义检索
result = client.search(
    user_id="user_1",
    query="用户编程偏好",
    limit=3
)

client.update(
    user_id="user_1",
    memory_id="mem_123",
    data="用户喜欢用 Go 写代码"
)

client.delete(memory_id="mem_123")

from langchain_community.llms import ChatOpenAI
from langchain_community.vectorstores import Chroma
from mem0.lib.llm import get_llm

# 创建记忆增强的 LLM
llm = get_llm("openai", model="gpt-4", memory=True)

# 不同组织共享记忆
org_memories = client.search(
    org_id="company_xyz",
    query="公司知识库"
)

# 组织级别记忆
client.add(org_id="company_xyz", data="公司政策")

from mem0.utils import compress

compressed = compress(
    text="长篇记忆内容...",
    max_tokens=500
)

results = client.search_batch(
    user_ids=["user_1", "user_2"],
    queries=["偏好", "历史"],
    limit=5
)

export OPENAI_API_KEY=sk-xxx
export ANTHROPIC_API_KEY=sk-xxx
export MEM0_VECTOR_STORE=local

# mem0_config.yaml
embed_model: text-embedding-3-small
vector_store:
  provider: chromadb
  params:
    collection_name: mem0
    persist_directory: ./memories