Agent Memory System
任务目标
前置准备
依赖
pydantic>=2.0.0
typing-extensions>=4.0.0
cryptography>=41.0.0
存储路径（必需）
所有模块初始化时必须指定存储路径，由调用方根据运行环境决定：
base_path = "./memory_data"
key_storage_path = f"{base_path}/keys"
sync_state_path = f"{base_path}/sync_state"
index_storage_path = f"{base_path}/memory_index"
credential_path = f"{base_path}/credentials"
凭证管理（可选）
用于安全存储敏感凭证，自动加密存储：
from scripts.credential_manager import CredentialManager
cred_manager = CredentialManager(storage_path="./memory_data/credentials")
cred_manager.store("github_token", "ghp_xxxxx") # 自动加密
token = cred_manager.get("github_token") # 自动解密
操作步骤
Step 0: 隐私配置（必需）
from scripts.privacy import PrivacyManager, ConsentStatus
privacy_manager = PrivacyManager(user_id="user_123")
if privacy_manager.get_consent_status("memory_storage") == ConsentStatus.NOT_REQUESTED:
privacy_manager.request_consent(
consent_type="memory_storage",
description="是否允许存储交互记忆以提供个性化服务？"
)
Step 1: 感知记忆
from scripts.perception import PerceptionMemoryStore
store = PerceptionMemoryStore()
session_id = store.create_session()
should_store, reason = privacy_manager.should_store_memory("episodic", user_message)
if should_store:
store.store_conversation(session_id, user_message, system_response)
situation = store.detect_situation()
Step 2: 短期记忆
推荐方式：智能体判断语义分类后存储。
from scripts.short_term import ShortTermMemoryManager
from scripts.types import SemanticBucketType
short_term = ShortTermMemoryManager()
item_id = short_term.store_with_semantics(
content="用户想要实现登录功能",
bucket_type=SemanticBucketType.USER_INTENT, # 智能体判断
topic_label="用户登录", # 智能体指定
relevance_score=0.85,
)
语义桶分类：
| 桶类型 | 判断标准 | 示例 |
|---|
| TASK_CONTEXT | 与当前任务直接相关 | "实现登录功能" |
| USER_INTENT | 表达意图、目标 | "想要一个记忆系统" |
| DECISION_CONTEXT | 选择、决定 | "选择PostgreSQL" |
| KNOWLEDGE_GAP | 疑问、不知道 | "不知道如何实现SSO" |
| EMOTIONAL_TRACE | 情绪、感受 | "对进度感到焦虑" |
话题聚合与提炼：
summary = short_term.get_topic_summary() # 话题聚合摘要
topics = short_term.get_active_topics() # 活跃话题
if short_term.should_extract():
from scripts.short_term import AsynchronousExtractor
AsynchronousExtractor(short_term, long_term).extract()
Step 3: 长期记忆
from scripts.long_term import LongTermMemoryManager
long_term = LongTermMemoryManager()
long_term.update_user_profile(profile_data)
long_term.apply_heat_policy()
Step 4: 上下文重构
from scripts.context_reconstructor import ContextReconstructor
reconstructor = ContextReconstructor()
reconstructor.bind_state_capture(capture) # P0: 状态感知优化
context = reconstructor.reconstruct(situation, long_term.get_all_memories())
state_ctx = reconstructor.get_state_aware_context()
reconstructor.unbind_state_capture()
Step 5: 洞察生成
from scripts.insight_module import InsightModule
insight_module = InsightModule()
insight_module.bind_state_capture(capture) # P1: 状态模式分析
insights = insight_module.process(context, long_term.get_all_memories())
high_priority = insight_module.get_high_priority_insights()
state_insights = insight_module.get_state_pattern_insights() # P1新增
insight_module.unbind_state_capture()
Step 6: 全局状态捕捉（LangGraph集成）
from scripts.state_capture import GlobalStateCapture, StateEventType
capture = GlobalStateCapture(
user_id="user_123",
storage_path="./state_storage",
default_ttl_hours=168,
)
# 从 LangGraph 同步
checkpoint_id = capture.sync_from_langgraph(
state={"phase": "executing", "current_task": "create_memory"},
node_name="executor",
)
# 检查点管理
checkpoints = capture.list_checkpoints(thread_id="thread_xxx")
state = capture.restore_checkpoint("cp_xxx")
# 事件订阅
subscription_id = capture.subscribe(
event_types=[StateEventType.PHASE_CHANGE, StateEventType.TASK_SWITCH],
callback=on_phase_change,
)
Step 7: 工具管理
# 记录工具使用效果
long_term.update_tool_usage(
tool_name="code_interpreter",
task_type="code_debugging",
outcome="success",
effectiveness_score=0.85,
checkpoint_id=checkpoint_id, # P0: 状态关联
phase="executing",
scenario="coding",
)
# 获取工具推荐
rec = long_term.get_tool_recommendation(
task_type="code_debugging",
phase="executing", # P0: 状态过滤
scenario="coding",
)
Step 8: 链式推理增强（P0 新增）
用途：模型自检测反思、反思结果持久化、元学习数据提取。
from scripts.chain_reasoning import ChainReasoningEnhancer
from scripts.types import ReflectionOutcome
# 初始化
enhancer = ChainReasoningEnhancer(
state_capture=capture,
short_term=short_term,
long_term=long_term,
)
# 处理推理步骤（模型输出包含反思信号）
result = enhancer.process_reasoning_step(
step={
"thought": "分析用户需求...",
"need_reflect": True,
"reflect_reason": "检测到信息矛盾",
"reflect_confidence": 0.85,
},
step_index=12,
task_type="analysis",
)
if result["should_reflect"]:
# 执行反思
reflection_result = enhancer.execute_reflection(
signal=result["signal"],
context_snapshot=result["context_snapshot"],
)
if not reflection_result.passed:
# 验证失败处理
if reflection_result.need_verification:
verification = enhancer.execute_verification(
reflection_result=reflection_result,
context_snapshot=result["context_snapshot"],
)
if verification.result == "failed":
# 回退处理
rollback_info = enhancer.handle_rollback(
step_index=12,
reason="验证失败",
)
# 持久化反思结果
memory_id = enhancer.persist_reflection_result(
trigger_record=result["trigger_record"],
reflection_result=reflection_result,
verification_result=verification if reflection_result.need_verification else None,
final_outcome=ReflectionOutcome.CORRECTED,
)
LangGraph 集成：
# 作为节点嵌入工作流
workflow.add_node("reflection", enhancer.as_reflection_node())
workflow.add_node("verification", enhancer.as_verification_node())
# 条件边
workflow.add_conditional_edges(
"reasoning",
ChainReasoningEnhancer.should_reflect,
{"reflection": "reflection", "continue": "next_node"},
)
元学习数据提取：
# 提取训练数据（用于优化"何时反思"能力）
training_data = enhancer.extract_meta_learning_data(
min_learning_value=LearningValue.MEDIUM,
limit=100,
)
资源索引
核心脚本
参考文档
注意事项
- 路径必传：所有存储路径无默认值，必须显式传入
- 隐私优先：处理用户数据前必须初始化
PrivacyManager 并获取同意
- 敏感数据：系统自动识别密码、账号等敏感信息，默认不存储
- 加密存储：敏感数据推荐 AES-256-GCM 加密
- 类型安全：所有函数必须有类型注解，禁止使用裸 dict
- 异步优先：提炼、热度计算等后台异步执行
- 超然洞察：洞察模块独立运行，提供非强制性建议
- 降级策略：模块故障时自动降级，保证核心流程可用
- 数据权利：用户可随时导出、删除所有数据
快速开始
from scripts.perception import PerceptionMemoryStore
from scripts.short_term import ShortTermMemoryManager, AsynchronousExtractor
from scripts.long_term import LongTermMemoryManager
from scripts.context_reconstructor import ContextReconstructor
from scripts.insight_module import InsightModule
# 初始化
perception = PerceptionMemoryStore()
short_term = ShortTermMemoryManager()
long_term = LongTermMemoryManager()
reconstructor = ContextReconstructor()
insight_module = InsightModule()
# 处理对话
session_id = perception.create_session()
perception.store_conversation(session_id, user_message, system_response)
situation = perception.detect_situation()
# 短期记忆 + 提炼
short_term.store_with_semantics(user_message, SemanticBucketType.USER_INTENT, "话题", 0.8)
if short_term.should_extract():
AsynchronousExtractor(short_term, long_term).extract()
# 上下文重构 + 洞察
context = reconstructor.reconstruct(situation, long_term.get_all_memories())
insights = insight_module.process(context, long_term.get_all_memories())
print(insight_module.format_insights_for_context())
