為你的客服加入背景資訊，讓它能記住使用者偏好、過去的互動或外部知識。
預設情況下，客服將根據底層服務的需求，將聊天歷史儲存在 InMemoryChatHistoryProvider 或底層 AI 服務中。
以下代理使用 OpenAI 聊天完成功能，該功能不支援也不要求服務內聊天歷史儲存，因此會自動建立並使用 InMemoryChatHistoryProvider。
using System;
using Azure.AI.Projects;
using Azure.Identity;
using Microsoft.Agents.AI;
var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
?? throw new InvalidOperationException("Set AZURE_OPENAI_ENDPOINT");
var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
AIAgent agent = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential())
.AsAIAgent(
model: deploymentName,
instructions: "You are a friendly assistant. Keep your answers brief.",
name: "MemoryAgent");
警告
DefaultAzureCredential 開發方便，但在生產過程中需謹慎考量。 在生產環境中，建議使用特定的憑證（例如 ManagedIdentityCredential），以避免延遲問題、意外的憑證探測，以及備援機制帶來的安全風險。
要使用自訂 ChatHistoryProvider，你可以把一個傳給代理選項：
using System;
using Azure.AI.Projects;
using Azure.Identity;
using Microsoft.Agents.AI;
var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")
?? throw new InvalidOperationException("Set AZURE_OPENAI_ENDPOINT");
var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
AIAgent agent = new AIProjectClient(new Uri(endpoint), new DefaultAzureCredential())
.AsAIAgent(model: deploymentName, options: new ChatClientAgentOptions()
{
ChatOptions = new() { Instructions = "You are a helpful assistant." },
ChatHistoryProvider = new CustomChatHistoryProvider()
});
使用階段來跨流程分享上下文：
AgentSession session = await agent.CreateSessionAsync();
Console.WriteLine(await agent.RunAsync("Hello! What's the square root of 9?", session));
Console.WriteLine(await agent.RunAsync("My name is Alice", session));
Console.WriteLine(await agent.RunAsync("What is my name?", session));
小提示
請見 此 處的完整可執行範例應用。
定義一個情境提供者，能在會話狀態下儲存使用者資訊並注入個人化指令：
class UserMemoryProvider(ContextProvider):
"""A context provider that remembers user info in session state."""
DEFAULT_SOURCE_ID = "user_memory"
def __init__(self):
super().__init__(self.DEFAULT_SOURCE_ID)
async def before_run(
self,
*,
agent: Any,
session: AgentSession | None,
context: SessionContext,
state: dict[str, Any],
) -> None:
"""Inject personalization instructions based on stored user info."""
user_name = state.get("user_name")
if user_name:
context.extend_instructions(
self.source_id,
f"The user's name is {user_name}. Always address them by name.",
)
else:
context.extend_instructions(
self.source_id,
"You don't know the user's name yet. Ask for it politely.",
)
async def after_run(
self,
*,
agent: Any,
session: AgentSession | None,
context: SessionContext,
state: dict[str, Any],
) -> None:
"""Extract and store user info in session state after each call."""
for msg in context.input_messages:
text = msg.text if hasattr(msg, "text") else ""
if isinstance(text, str) and "my name is" in text.lower():
state["user_name"] = text.lower().split("my name is")[-1].strip().split()[0].capitalize()
建立一個包含上下文提供者的代理：
client = FoundryChatClient(
project_endpoint="https://your-project.services.ai.azure.com",
model="gpt-4o",
credential=AzureCliCredential(),
)
agent = Agent(
client=client,
name="MemoryAgent",
instructions="You are a friendly assistant.",
context_providers=[UserMemoryProvider()],
)
執行它——客服人員現在可以存取上下文：
session = agent.create_session()
# The provider doesn't know the user yet — it will ask for a name
result = await agent.run("Hello! What's the square root of 9?", session=session)
print(f"Agent: {result}\n")
# Now provide the name — the provider stores it in session state
result = await agent.run("My name is Alice", session=session)
print(f"Agent: {result}\n")
# Subsequent calls are personalized — name persists via session state
result = await agent.run("What is 2 + 2?", session=session)
print(f"Agent: {result}\n")
# Inspect session state to see what the provider stored
provider_state = session.state.get("user_memory", {})
print(f"[Session State] Stored user name: {provider_state.get('user_name')}")
小提示
完整範例可執行檔案請參閱 完整範例 。
備註
在 Python 中，持久化/記憶體的處理由 ContextProvider 和 HistoryProvider 的實作來完成。
InMemoryHistoryProvider 是內建的本地記憶體歷史提供者。
RawAgent 在特定情況下（例如在會話中未設置情境提供程式和服務端存儲器指示器時），可能會自動新增 InMemoryHistoryProvider()，但並非所有情境皆有此保證。
如果你總是想要本地持久化，就明確地加一個 InMemoryHistoryProvider 。 應確保只有一個歷史紀錄提供者有 load_messages=True，以避免在相同的調用中重複執行多組數據。
你也可以在清單 context_providers 末尾加上另一個歷史提供者，並加入 store_context_messages=True審計儲存庫：
from agent_framework import InMemoryHistoryProvider
from agent_framework.mem0 import Mem0ContextProvider
memory_store = InMemoryHistoryProvider(load_messages=True) # add local history for a reused or serialized session
agent_memory = Mem0ContextProvider("user-memory", api_key=..., agent_id="my-agent") # add Mem0 provider for agent memory
audit_store = InMemoryHistoryProvider(
"audit",
load_messages=False,
store_context_messages=True, # include context added by other providers
)
agent = client.as_agent(
name="MemoryAgent",
instructions="You are a friendly assistant.",
context_providers=[memory_store, agent_memory, audit_store], # audit store last
)
預設情況下，代理會根據提供者與會話使用本地記憶體歷史或服務管理歷史。
以下 Foundry 代理程式採用專案支援的模型部署。 當你想要超出對話紀錄的應用程式專用記憶或個人化狀態時，可以新增情境提供者。
a := foundryprovider.NewAgent(
endpoint,
token,
foundryprovider.ModelDeployment(model),
foundryprovider.AgentConfig{
Instructions: "You are a friendly assistant. Keep your answers brief.",
Config: agent.Config{
Name: "MemoryAgent",
},
},
)
定義一個情境提供者，能在會話狀態下儲存使用者資訊並注入個人化指令：
import (
"context"
"fmt"
"strings"
"github.com/microsoft/agent-framework-go/agent"
"github.com/microsoft/agent-framework-go/message"
)
const userMemorySourceID = "user_memory"
type providerState struct {
UserName string `json:"user_name,omitempty"`
}
func newUserMemoryProvider() agent.ContextProvider {
return agent.NewContextProvider(agent.ContextProviderConfig{
SourceID: userMemorySourceID,
Provide: provideUserMemory,
Store: storeUserMemory,
})
}
func provideUserMemory(ctx context.Context, invoking agent.InvokingContext) ([]*message.Message, []agent.Option, error) {
session, _ := agent.GetOption(invoking.Options, agent.WithSession)
var state providerState
_, _ = session.Get(userMemorySourceID, &state)
instructions := "You don't know the user's name yet. Ask for it politely."
if state.UserName != "" {
instructions = fmt.Sprintf("The user's name is %s. Always address them by name.", state.UserName)
}
return nil, []agent.Option{agent.WithInstructions(instructions)}, nil
}
func storeUserMemory(ctx context.Context, invoked agent.InvokedContext) error {
session, _ := agent.GetOption(invoked.Options, agent.WithSession)
var state providerState
_, _ = session.Get(userMemorySourceID, &state)
for _, msg := range invoked.RequestMessages {
text := strings.TrimSpace(msg.Contents.Text())
lower := strings.ToLower(text)
if idx := strings.Index(lower, "my name is"); idx >= 0 {
parts := strings.Fields(text[idx+len("my name is"):])
if len(parts) == 0 {
continue
}
state.UserName = strings.Trim(parts[0], ".,!?")
session.Set(userMemorySourceID, state)
break
}
}
return nil
}
建立一個包含上下文提供者的代理：
a := foundryprovider.NewAgent(
endpoint,
token,
foundryprovider.ModelDeployment(model),
foundryprovider.AgentConfig{
Instructions: "You are a friendly assistant.",
Config: agent.Config{
Name: "MemoryAgent",
ContextProviders: []agent.ContextProvider{newUserMemoryProvider()},
},
},
)
執行它——客服人員現在可以存取上下文：
ctx := context.Background()
session, err := a.CreateSession(ctx)
if err != nil {
panic(err)
}
// The provider doesn't know the user yet.
resp, err := a.RunText(ctx, "Hello, what is the square root of 9?", agent.WithSession(session)).Collect()
fmt.Println(resp, err)
// Teach the provider the user's name.
resp, err = a.RunText(ctx, "My name is Alice", agent.WithSession(session)).Collect()
fmt.Println(resp, err)
// Subsequent calls are personalized using session state.
resp, err = a.RunText(ctx, "What is 2 + 2?", agent.WithSession(session)).Collect()
fmt.Println(resp, err)
小提示
完整範例可執行檔案請參閱 完整範例 。
後續步驟
深入探討：