Mem0 从代理对话中提取持久内存，并在后续运行中检索相关内存。 在应跨会话提供内存时，请使用稳定的用户、代理或应用程序范围。
此集成使用内存模式：它提取和召回所选持久信息，而不是重播完整的聊天脚本。
Important
Mem0 是第三方系统。 在发送应用程序数据之前，请查看其数据处理、保留、区域边界和服务条款。
注释
Mem0 集成目前不适用于 Agent Framework .NET。
安装软件包
pip install agent-framework-mem0 --pre
直接设置 MEM0_API_KEY 或传递 API 密钥。 重用同 user_id 一项可使记忆在会话之间可用。
async def main() -> None:
"""Example of memory usage with Mem0 context provider."""
print("=== Mem0 Context Provider Example ===")
# Each record in Mem0 should be associated with agent_id or user_id or application_id.
# In this example, we associate Mem0 records with user_id.
user_id = str(uuid.uuid4())
# For Azure authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
# authentication option.
# For Mem0 authentication, set Mem0 API key via "api_key" parameter or MEM0_API_KEY environment variable.
async with (
AzureCliCredential() as credential,
Agent(
client=FoundryChatClient(credential=credential),
name="FriendlyAssistant",
instructions="You are a friendly assistant.",
tools=retrieve_company_report,
context_providers=[Mem0ContextProvider(source_id="mem0", user_id=user_id, search_user_id=user_id)],
) as agent,
):
# First ask the agent to retrieve a company report with no previous context.
# The agent will not be able to invoke the tool, since it doesn't know
# the company code or the report format, so it should ask for clarification.
query = "Please retrieve my company report"
print(f"User: {query}")
result = await agent.run(query)
print(f"Agent: {result}\n")
# Now tell the agent the company code and the report format that you want to use
# and it should be able to invoke the tool and return the report.
query = "I always work with CNTS and I always want a detailed report format. Please remember and retrieve it."
print(f"User: {query}")
result = await agent.run(query)
print(f"Agent: {result}\n")
# Mem0 processes and indexes memories asynchronously.
# Wait for memories to be indexed before querying in a new thread.
# In production, consider implementing retry logic or using Mem0's
# eventual consistency handling instead of a fixed delay.
print("Waiting for memories to be processed...")
await asyncio.sleep(15) # Empirically determined delay for Mem0 indexing
print("\nRequest within a new session:")
# Create a new session for the agent.
# The new session has no context of the previous conversation.
session = agent.create_session()
# Since we have the mem0 component in the session, the agent should be able to
# retrieve the company report without asking for clarification, as it will
# be able to remember the user preferences from Mem0 component.
query = "Please retrieve my company report"
print(f"User: {query}")
result = await agent.run(query, session=session)
print(f"Agent: {result}")
Mem0 异步处理记忆。 在生产环境中，使用重试或服务感知一致性处理，而不是依赖于固定延迟。
注释
Mem0 集成目前不适用于 Agent Framework Go。 有关最新状态，请参阅 Agent Framework Go 存储库 。
后续步骤
更深入：