moai-core-session-state
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ChineseAlfred Session State Management Skill (Enterprise )
Alfred 会话状态管理技能(企业版)
Skill Metadata
技能元数据
| Field | Value |
|---|---|
| Skill Name | moai-core-session-state |
| Version | 4.0.0 (Enterprise) |
| Updated | 2025-11-12 |
| Status | Active |
| Tier | Alfred |
| Supported Models | Claude Sonnet 4.5, Claude Haiku 4.5 |
| Context Window | 200K tokens (Sonnet/Haiku), 500K tokens (Enterprise), 1M tokens (beta) |
| Key Features | Context Awareness, Token Budget Tracking, Session Persistence, Adaptive Recovery |
| 字段 | 值 |
|---|---|
| 技能名称 | moai-core-session-state |
| 版本 | 4.0.0 (企业版) |
| 更新时间 | 2025-11-12 |
| 状态 | 活跃 |
| 层级 | Alfred |
| 支持模型 | Claude Sonnet 4.5, Claude Haiku 4.5 |
| 上下文窗口 | 200K tokens (Sonnet/Haiku), 500K tokens (企业版), 1M tokens (测试版) |
| 核心功能 | 上下文感知, Token预算追踪, 会话持久化, 自适应恢复 |
What It Does
功能说明
Provides enterprise-grade session state management for extended workflows, token budget optimization, runtime tracking, and handoff protocols to maintain context continuity across Alfred workflows and session boundaries.
Enterprise Capabilities:
- ✅ Context-aware token budget management (November 2025 Claude API features)
- ✅ Session persistence with automatic history loading
- ✅ Session forking for parallel exploration
- ✅ Incremental multi-pack index optimization (Git 2.47+ integration)
- ✅ Context continuity across handoffs with state snapshots
- ✅ Progressive disclosure for memory efficiency
- ✅ Adaptive recovery checkpoints
- ✅ Multi-agent coordination protocols
- ✅ Memory file state synchronization
- ✅ Token budget awareness callbacks (Sonnet/Haiku 4.5 feature)
为长流程工作流提供企业级会话状态管理、Token预算优化、运行时追踪,以及会话切换协议,保障Alfred工作流和会话边界之间的上下文连续性。
企业版能力:
- ✅ 上下文感知Token预算管理(2025年11月Claude API新特性)
- ✅ 会话持久化,自动加载历史记录
- ✅ 会话分叉,支持并行探索
- ✅ 增量多包索引优化(集成Git 2.47+)
- ✅ 基于状态快照的切换上下文连续性保障
- ✅ 渐进式披露,提升内存效率
- ✅ 自适应恢复检查点
- ✅ 多Agent协调协议
- ✅ 内存文件状态同步
- ✅ Token预算感知回调(Sonnet/Haiku 4.5特性)
When to Use
适用场景
Automatic triggers:
- Session start/end events
- Long-running task execution (>10 minutes)
- Multi-agent handoffs
- Context window approaching limits
- Model switches (Haiku ↔ Sonnet)
- Workflow phase transitions
Manual reference:
- Session state debugging and recovery
- Token budget optimization strategies
- Handoff protocol design
- Context continuity planning
- Multi-session workflow design
自动触发场景:
- 会话启动/结束事件
- 长时间运行任务执行(>10分钟)
- 多Agent切换
- 上下文窗口接近上限
- 模型切换(Haiku ↔ Sonnet)
- 工作流阶段转换
手动参考场景:
- 会话状态调试与恢复
- Token预算优化策略制定
- 切换协议设计
- 上下文连续性规划
- 多会话工作流设计
Token Budget Management (November 2025)
Token预算管理(2025年11月更新)
Context Awareness Feature
上下文感知特性
Claude Sonnet 4.5 and Haiku 4.5 feature built-in context awareness, enabling these models to:
- Track remaining context window ("token budget") throughout conversation
- Understand current position within 200K token limit (Sonnet/Haiku)
- Execute adaptive strategies based on available tokens
- Automatically manage context without manual intervention
Key Advantage: Models now self-regulate context usage in real time.
Claude Sonnet 4.5和Haiku 4.5具备内置上下文感知能力,支持模型实现以下功能:
- 会话全程追踪剩余上下文窗口(即"Token预算")
- 感知自身在200K Token上限内的当前位置(Sonnet/Haiku)
- 基于可用Token执行自适应策略
- 无需人工干预自动管理上下文
核心优势: 模型现在可以实时自我调节上下文使用。
Token Budget Optimization Framework
Token预算优化框架
Token Allocation Strategy (200K Sonnet context):
├── System Prompt & Instructions: ~15K tokens (7.5%)
│ ├── CLAUDE.md: ~8K
│ ├── Command definitions: ~4K
│ └── Skill metadata: ~3K
├── Active Conversation: ~80K tokens (40%)
│ ├── Recent messages: ~50K
│ ├── Context cache: ~20K
│ └── Active references: ~10K
├── Reference Context (Progressive Disclosure): ~50K (25%)
│ ├── Project structure: ~15K
│ ├── Related Skills: ~20K
│ └── Tool definitions: ~15K
└── Reserve (Emergency Recovery): ~55K tokens (27.5%)
├── Session state snapshot: ~10K
├── TAGs and cross-references: ~15K
├── Error recovery context: ~20K
└── Free buffer: ~10KToken分配策略(200K Sonnet上下文):
├── 系统提示与指令: ~15K tokens (7.5%)
│ ├── CLAUDE.md: ~8K
│ ├── 命令定义: ~4K
│ └── 技能元数据: ~3K
├── 活跃会话: ~80K tokens (40%)
│ ├── 近期消息: ~50K
│ ├── 上下文缓存: ~20K
│ └── 活跃引用: ~10K
├── 参考上下文(渐进式披露): ~50K (25%)
│ ├── 项目结构: ~15K
│ ├── 相关技能: ~20K
│ └── 工具定义: ~15K
└── 预留(紧急恢复): ~55K tokens (27.5%)
├── 会话状态快照: ~10K
├── 标签与交叉引用: ~15K
├── 错误恢复上下文: ~20K
└── 空闲缓冲区: ~10KOptimization Techniques ( .0)
优化技术(4.0版本)
Technique 1: Progressive Summarization
Step 1: Original context (50K tokens)
Step 2: Compress to summary (15K tokens)
Step 3: Add pointers to original → 35K tokens saved
Step 4: Carry forward summary only across handoffsTechnique 2: Context Tagging with Unique Identifiers
❌ Bad (high token cost):
"The user configuration from the previous 20 messages..."
✅ Good (efficient reference):Technique 3: Structured Context Architecture
├── Critical Context (ALWAYS keep)
│ ├── Current task objectives
│ ├── User preferences & expertise level
│ └── Active constraints
├── Supporting Context (keep if space allows)
│ ├── Related history
│ ├── Reference documentation
│ └── Tool availability
└── Temporary Context (discard when not needed)
├── Raw tool outputs
├── Intermediate calculations
└── Debug informationTechnique 4: MCP Server Context Budget
bash
undefined技术1:渐进式总结
步骤1:原始上下文(50K tokens)
步骤2:压缩为摘要(15K tokens)
步骤3:添加原始内容指针 → 节省35K tokens
步骤4:会话切换时仅传递摘要技术2:带唯一标识的上下文标记
❌ 错误示例(Token消耗高):
"The user configuration from the previous 20 messages..."
✅ 正确示例(高效引用):技术3:结构化上下文架构
├── 关键上下文(永久保留)
│ ├── 当前任务目标
│ ├── 用户偏好与专业水平
│ └── 活跃约束
├── 支持上下文(空间充足时保留)
│ ├── 相关历史
│ ├── 参考文档
│ └── 可用工具
└── 临时上下文(无需时丢弃)
├── 原始工具输出
├── 中间计算结果
└── 调试信息技术4:MCP服务器上下文预算优化
bash
undefinedCheck MCP server context consumption
查看MCP服务器上下文消耗
/context
/context
Result: Each enabled MCP server adds tool definitions
结果:每个启用的MCP服务器都会增加工具定义占用
Example: context7 MCP = ~2K tokens for tool definitions
示例:context7 MCP = 工具定义占用约2K tokens
Optimization: Disable unused servers before critical tasks
优化方案:执行关键任务前禁用未使用的服务器
Typical savings: 5-10K tokens per unused MCP server
典型收益:每个未使用的MCP服务器节省5-10K tokens
**Technique 5: Task-Based Session Management**Strategy: Start new conversation for distinct tasks
Benefits:
- Fresh 200K token budget per task
- Eliminates stale context accumulation
- Enables parallel session forking
- Improves recovery speed
Implementation:
- Complete current task in Session A
- Save session snapshot to .moai/sessions/
- Start Session B for new task with fresh context
- Resume Session A later if needed via session ID
---
**技术5:基于任务的会话管理**策略:不同任务开启独立新会话
优势:
- 每个任务享有全新200K Token预算
- 避免过时上下文累积
- 支持并行会话分叉
- 提升恢复速度
实现方式:
- 在会话A中完成当前任务
- 将会话快照保存到 .moai/sessions/
- 为新任务开启会话B,使用全新上下文
- 后续需要时可通过会话ID恢复会话A
---Session State Architecture (Enterprise )
会话状态架构(企业版)
State Layers
状态层级
Session State Stack (Enterprise ):
├── L1: Context-Aware Layer (Claude 4.5+ feature)
│ ├── Token budget tracking
│ ├── Context window position
│ ├── Auto-summarization triggers
│ └── Model-specific optimizations
├── L2: Active Context (current task, variables, scope)
├── L3: Session History (recent actions, decisions)
├── L4: Project State (SPEC progress, milestones)
├── L5: User Context (preferences, language, expertise)
└── L6: System State (tools, permissions, environment)会话状态栈(企业版):
├── L1: 上下文感知层(Claude 4.5+ 特性)
│ ├── Token预算追踪
│ ├── 上下文窗口位置
│ ├── 自动摘要触发
│ └── 模型专属优化
├── L2: 活跃上下文(当前任务、变量、范围)
├── L3: 会话历史(近期操作、决策)
├── L4: 项目状态(SPEC进度、里程碑)
├── L5: 用户上下文(偏好、语言、专业水平)
└── L6: 系统状态(工具、权限、环境)Session Creation & Persistence
会话创建与持久化
Agent SDK Session Management (November 2025 API):
json
{
"session_id": "sess_uuid_v4",
"model": "claude-sonnet-4-5-20250929",
"created_at": "2025-11-12T10:30:00Z",
"context_window": {
"total": 200000,
"used": 85000,
"available": 115000,
"position_percent": 42.5
},
"persistence": {
"auto_load_history": true,
"context_preservation": "critical_only",
"cache_enabled": true
},
"forking": {
"enabled": true,
"fork_session_id": "sess_fork_uuid",
"checkpoint_timestamp": "2025-11-12T10:30:00Z"
}
}Agent SDK 会话管理(2025年11月API):
json
{
"session_id": "sess_uuid_v4",
"model": "claude-sonnet-4-5-20250929",
"created_at": "2025-11-12T10:30:00Z",
"context_window": {
"total": 200000,
"used": 85000,
"available": 115000,
"position_percent": 42.5
},
"persistence": {
"auto_load_history": true,
"context_preservation": "critical_only",
"cache_enabled": true
},
"forking": {
"enabled": true,
"fork_session_id": "sess_fork_uuid",
"checkpoint_timestamp": "2025-11-12T10:30:00Z"
}
}Session Resumption Pattern
会话恢复模式
python
undefinedpython
undefinedCapture session ID from initial response
从初始响应中提取会话ID
session_id = extract_session_id(response)
session_id = extract_session_id(response)
Save for later use
保存供后续使用
save_session_checkpoint({
'session_id': session_id,
'timestamp': now(),
'model': 'claude-sonnet-4-5',
'context_state': current_context_snapshot()
})
save_session_checkpoint({
'session_id': session_id,
'timestamp': now(),
'model': 'claude-sonnet-4-5',
'context_state': current_context_snapshot()
})
Later: Resume conversation
后续:恢复会话
response = claude.messages.create(
model="claude-sonnet-4-5-20250929",
resume=session_id, # Continue from checkpoint
messages=[new_message]
)
response = claude.messages.create(
model="claude-sonnet-4-5-20250929",
resume=session_id, # 从检查点继续
messages=[new_message]
)
Or: Fork session for parallel exploration
或:分叉会话进行并行探索
response = claude.messages.create(
model="claude-sonnet-4-5-20250929",
fork_session=session_id, # Branch from checkpoint
messages=[alternative_message]
)
---response = claude.messages.create(
model="claude-sonnet-4-5-20250929",
fork_session=session_id, # 从检查点分支
messages=[alternative_message]
)
---Runtime State Tracking
运行时状态追踪
Task State Machine (Enterprise )
任务状态机(企业版)
Workflow State Transitions:
pending → in_progress → blocked (waiting) → completed/failed
↓ ↓
[monitor token budget] [save checkpoint]
[track elapsed time] [update history]
[check for recovery] [archive state]Task Lifecycle States:
- - Queued but not started
pending - - Currently executing (monitor tokens)
in_progress - - Waiting for dependencies or input
blocked - - Approaching context limit ( .0)
token_warning - - Model change or session fork
context_switch - - Finished successfully
completed - - Error occurred, initiating recovery
failed - - Resumed from checkpoint
recovered
工作流状态转换:
pending → in_progress → blocked (waiting) → completed/failed
↓ ↓
[监控Token预算] [保存检查点]
[追踪运行时长] [更新历史记录]
[检查恢复条件] [归档状态]任务生命周期状态:
- - 已排队未启动
pending - - 执行中(监控Token消耗)
in_progress - - 等待依赖或用户输入
blocked - - 接近上下文上限(4.0版本新特性)
token_warning - - 模型切换或会话分叉
context_switch - - 执行成功
completed - - 出现错误,启动恢复流程
failed - - 从检查点恢复成功
recovered
Token Budget Callbacks (Haiku/Sonnet 4.5 Feature)
Token预算回调(Haiku/Sonnet 4.5特性)
python
def token_budget_callback(context):
"""
Called automatically when token budget changes.
Model provides real-time context awareness.
"""
remaining_tokens = context.available_tokens
used_percent = context.token_usage_percent
if used_percent > 85:
# Activate emergency summarization
compress_context_window()
archive_old_context()
elif used_percent > 75:
# Start progressive disclosure
defer_non_critical_context()
elif used_percent > 60:
# Monitor for safety
track_context_growth()python
def token_budget_callback(context):
"""
Token预算变化时自动调用
模型提供实时上下文感知能力
"""
remaining_tokens = context.available_tokens
used_percent = context.token_usage_percent
if used_percent > 85:
# 启动紧急摘要压缩
compress_context_window()
archive_old_context()
elif used_percent > 75:
# 启动渐进式披露
defer_non_critical_context()
elif used_percent > 60:
# 安全监控
track_context_growth()Session Handoff Protocols
会话切换协议
Inter-Agent Handoff Package (Enterprise )
Agent间切换数据包(企业版)
json
{
"handoff_id": "uuid-v4",
"timestamp": "2025-11-12T10:30:00Z",
"from_agent": "spec-builder",
"to_agent": "tdd-implementer",
"session_context": {
"session_id": "sess_uuid",
"model": "claude-sonnet-4-5-20250929",
"context_position": 42.5,
"available_tokens": 115000,
"user_language": "ko",
"expertise_level": "intermediate",
"current_project": "MoAI-ADK"
},
"task_context": {
"spec_id": "SPEC-001",
"current_phase": "implementation",
"completed_steps": ["spec_complete", "architecture_defined"],
"next_step": "write_tests",
"constraints": ["must_use_pytest", "coverage_85"]
},
"context_snapshot": {
"critical_context": "...compressed...",
"session_checkpoints": [...],
"active_todos": [...],
"token_budget_strategy": "progressive_summarization"
},
"recovery_info": {
"last_checkpoint": "2025-11-12T10:25:00Z",
"recovery_tokens_reserved": 55000,
"session_fork_available": true
}
}json
{
"handoff_id": "uuid-v4",
"timestamp": "2025-11-12T10:30:00Z",
"from_agent": "spec-builder",
"to_agent": "tdd-implementer",
"session_context": {
"session_id": "sess_uuid",
"model": "claude-sonnet-4-5-20250929",
"context_position": 42.5,
"available_tokens": 115000,
"user_language": "ko",
"expertise_level": "intermediate",
"current_project": "MoAI-ADK"
},
"task_context": {
"spec_id": "SPEC-001",
"current_phase": "implementation",
"completed_steps": ["spec_complete", "architecture_defined"],
"next_step": "write_tests",
"constraints": ["must_use_pytest", "coverage_85"]
},
"context_snapshot": {
"critical_context": "...compressed...",
"session_checkpoints": [...],
"active_todos": [...],
"token_budget_strategy": "progressive_summarization"
},
"recovery_info": {
"last_checkpoint": "2025-11-12T10:25:00Z",
"recovery_tokens_reserved": 55000,
"session_fork_available": true
}
}Handoff Validation (Enterprise )
切换校验(企业版)
python
def validate_handoff(handoff_package):
"""Enterprise validation with token budget check"""
required_fields = [
'handoff_id', 'from_agent', 'to_agent',
'session_context', 'task_context', 'context_snapshot'
]
for field in required_fields:
if field not in handoff_package:
raise HandoffError(f"Missing required field: {field}")
# NEW : Validate token budget
context = handoff_package['session_context']
available = context['available_tokens']
if available < 30000: # Minimum safe buffer
trigger_context_compression()
# Validate agent compatibility
if not can_agents_cooperate(
handoff_package['from_agent'],
handoff_package['to_agent']
):
raise AgentCompatibilityError("Agents cannot cooperate")
return Truepython
def validate_handoff(handoff_package):
"""包含Token预算检查的企业级校验"""
required_fields = [
'handoff_id', 'from_agent', 'to_agent',
'session_context', 'task_context', 'context_snapshot'
]
for field in required_fields:
if field not in handoff_package:
raise HandoffError(f"Missing required field: {field}")
# 新特性:校验Token预算
context = handoff_package['session_context']
available = context['available_tokens']
if available < 30000: # 最低安全缓冲区
trigger_context_compression()
# 校验Agent兼容性
if not can_agents_cooperate(
handoff_package['from_agent'],
handoff_package['to_agent']
):
raise AgentCompatibilityError("Agents cannot cooperate")
return TrueSession Recovery (Enterprise )
会话恢复(企业版)
Recovery Checkpoints
恢复检查点
Checkpoint Triggers:
- Task phase boundaries (before RED, GREEN, REFACTOR)
- Agent handoffs
- User interruptions
- Token budget thresholds
- Error conditions
- Session timeouts
Checkpoint Structure:
json
{
"checkpoint_id": "ckpt_uuid",
"timestamp": "2025-11-12T10:30:00Z",
"phase": "GREEN",
"token_usage": {
"used": 85000,
"available": 115000
},
"context_snapshot": "...compressed snapshot...",
"session_id": "sess_uuid",
"recovery_tokens_reserved": 55000
}检查点触发条件:
- 任务阶段边界(RED、GREEN、REFACTOR阶段前)
- Agent切换
- 用户中断
- Token预算阈值触发
- 错误场景
- 会话超时
检查点结构:
json
{
"checkpoint_id": "ckpt_uuid",
"timestamp": "2025-11-12T10:30:00Z",
"phase": "GREEN",
"token_usage": {
"used": 85000,
"available": 115000
},
"context_snapshot": "...compressed snapshot...",
"session_id": "sess_uuid",
"recovery_tokens_reserved": 55000
}Recovery Process (Enterprise )
恢复流程(企业版)
- State Restoration - Reload last valid checkpoint
- Context Validation - Verify token budget sufficient
- Session Resumption - Use Agent SDK resume feature
- Progress Assessment - Determine what was completed
- Continuation Planning - Decide next steps with updated token budget
- User Notification - Inform user of recovery status
- 状态还原 - 加载最近的有效检查点
- 上下文校验 - 验证Token预算充足
- 会话恢复 - 使用Agent SDK恢复功能
- 进度评估 - 确认已完成的工作内容
- 后续规划 - 基于更新后的Token预算制定下一步计划
- 用户通知 - 告知用户恢复状态
Memory State Synchronization
内存状态同步
Memory Files ( .0)
内存文件(4.0版本)
Files:
- - Current session metadata
.moai/sessions/session-state.json - - Cached context for performance
.moai/sessions/context-cache.json - - Saved recovery checkpoints
.moai/sessions/checkpoints/ - - Token budget history
.moai/sessions/token-usage.log - - TodoWrite task tracking
active-tasks.md
Synchronization Protocol:
python
def sync_memory_files(session_state):
"""Ensure memory files reflect current session state"""
# Update session metadata with token info
update_session_metadata({
'session_id': session_state.id,
'token_usage': session_state.token_budget,
'context_position': session_state.context_position,
'last_sync': timestamp()
})
# Sync TodoWrite tasks
sync_todowrite_tasks(session_state.active_tasks)
# Update context cache (compressed)
update_context_cache(compress_context(session_state.context))
# Log token usage for analytics
log_token_usage({
'timestamp': timestamp(),
'used': session_state.tokens_used,
'available': session_state.tokens_available,
'percent': session_state.usage_percent
})
# Archive old checkpoints (>7 days)
archive_old_checkpoints()文件列表:
- - 当前会话元数据
.moai/sessions/session-state.json - - 用于提升性能的上下文缓存
.moai/sessions/context-cache.json - - 保存的恢复检查点
.moai/sessions/checkpoints/ - - Token预算历史记录
.moai/sessions/token-usage.log - - TodoWrite任务追踪
active-tasks.md
同步协议:
python
def sync_memory_files(session_state):
"""确保内存文件与当前会话状态一致"""
# 用Token信息更新会话元数据
update_session_metadata({
'session_id': session_state.id,
'token_usage': session_state.token_budget,
'context_position': session_state.context_position,
'last_sync': timestamp()
})
# 同步TodoWrite任务
sync_todowrite_tasks(session_state.active_tasks)
# 更新上下文缓存(压缩后)
update_context_cache(compress_context(session_state.context))
# 记录Token使用情况用于分析
log_token_usage({
'timestamp': timestamp(),
'used': session_state.tokens_used,
'available': session_state.tokens_available,
'percent': session_state.usage_percent
})
# 归档7天以上的旧检查点
archive_old_checkpoints()Best Practices (Enterprise )
最佳实践(企业版)
Context Management
上下文管理
✅ DO:
- Use context-aware token budget tracking (Sonnet/Haiku 4.5 feature)
- Create checkpoints before major operations
- Apply progressive summarization for long workflows
- Enable session persistence for recovery
- Monitor token usage and plan accordingly
- Use session forking for parallel exploration
❌ DON'T:
- Accumulate unlimited context history
- Ignore token budget warnings
- Skip state validation on recovery
- Lose session IDs without saving
- Mix multiple sessions without clear boundaries
- Assume session continuity without checkpoint
✅ 推荐做法:
- 使用上下文感知Token预算追踪(Sonnet/Haiku 4.5特性)
- 重大操作前创建检查点
- 长工作流使用渐进式总结
- 开启会话持久化用于恢复
- 监控Token使用并提前规划
- 使用会话分叉进行并行探索
❌ 不推荐做法:
- 无限制累积上下文历史
- 忽略Token预算警告
- 恢复时跳过状态校验
- 未保存就丢失会话ID
- 无明确边界混合多个会话
- 无检查点就假设会话连续
Token Budget Optimization
Token预算优化
✅ DO:
- Start new session per distinct task (fresh 200K tokens)
- Use /context to identify expensive MCP servers
- Compress context before handoffs
- Keep reserve buffer (25-30% of tokens)
- Monitor usage percent, not absolute tokens
- Enable auto-summarization at 75% threshold
❌ DON'T:
- Let single conversation exceed 150K tokens
- Keep all MCP servers enabled if not needed
- Reuse sessions across fundamentally different tasks
- Ignore available_tokens feedback
- Store uncompressed context in memory files
✅ 推荐做法:
- 不同任务开启独立新会话(全新200K Token)
- 使用/context命令识别高消耗MCP服务器
- 会话切换前压缩上下文
- 保留25-30%的Token作为预留缓冲区
- 监控使用率百分比而非绝对Token数
- 75%使用率阈值时开启自动摘要
❌ 不推荐做法:
- 单会话对话超过150K Token
- 不需要时仍保持所有MCP服务器开启
- 完全不同的任务复用同一会话
- 忽略available_tokens反馈
- 内存文件中存储未压缩的上下文
Configuration
配置
Location:
.moai/config/config.jsonjson
{
"session": {
"persistence_enabled": true,
"auto_checkpoint": true,
"checkpoint_interval_minutes": 10,
"recovery_strategy": "progressive_summarization",
"context_budget": {
"warning_threshold_percent": 75,
"emergency_threshold_percent": 85,
"reserve_tokens": 55000
},
"forking_enabled": true,
"max_parallel_sessions": 3
}
}配置文件路径:
.moai/config/config.jsonjson
{
"session": {
"persistence_enabled": true,
"auto_checkpoint": true,
"checkpoint_interval_minutes": 10,
"recovery_strategy": "progressive_summarization",
"context_budget": {
"warning_threshold_percent": 75,
"emergency_threshold_percent": 85,
"reserve_tokens": 55000
},
"forking_enabled": true,
"max_parallel_sessions": 3
}
}Debugging & Troubleshooting
调试与故障排查
Inspection Tools
检查工具
bash
undefinedbash
undefinedView current session state
查看当前会话状态
/alfred:debug --show-session-state
/alfred:debug --show-session-state
Check context window position
检查上下文窗口位置
/alfred:debug --show-token-budget
/alfred:debug --show-token-budget
View all checkpoints
查看所有检查点
/alfred:debug --list-checkpoints
/alfred:debug --list-checkpoints
Validate memory synchronization
校验内存同步状态
/alfred:debug --check-memory-sync
/alfred:debug --check-memory-sync
Show token usage history
查看Token使用历史
/alfred:debug --show-token-usage-log
undefined/alfred:debug --show-token-usage-log
undefinedCommon Issues
常见问题
| Issue | Symptoms | Solution |
|---|---|---|
| Lost session | Cannot resume conversation | Check .moai/sessions/ for session IDs |
| Token budget exceeded | Model stops responding | Use /context to identify heavy consumers, create new session |
| Handoff failed | Agent has wrong context | Verify handoff package completeness before transfer |
| Recovery stuck | Cannot continue after interruption | Restore from earlier checkpoint or start new session |
| Memory drift | Inconsistent information | Run sync_memory_files() or check cache integrity |
| 问题 | 症状 | 解决方案 |
|---|---|---|
| 会话丢失 | 无法恢复对话 | 检查.moai/sessions/目录下的会话ID |
| Token预算超出 | 模型停止响应 | 使用/context命令识别高消耗项,创建新会话 |
| 切换失败 | Agent获取到错误上下文 | 传输前校验切换数据包完整性 |
| 恢复卡住 | 中断后无法继续 | 从更早的检查点恢复或开启新会话 |
| 内存漂移 | 信息不一致 | 执行sync_memory_files()或检查缓存完整性 |
Version History
版本历史
| Version | Date | Key Changes |
|---|---|---|
| 4.0.0 | 2025-11-12 | Enterprise context awareness, token budget tracking, Session SDK integration, Git 2.47+ support |
| 1.1.0 | 2025-11-05 | Session state management foundation |
| 1.0.0 | 2025-10-01 | Initial release |
| 版本 | 日期 | 关键更新 |
|---|---|---|
| 4.0.0 | 2025-11-12 | 企业级上下文感知、Token预算追踪、Session SDK集成、Git 2.47+支持 |
| 1.1.0 | 2025-11-05 | 会话状态管理基础能力 |
| 1.0.0 | 2025-10-01 | 首次发布 |
Related Skills
相关技能
- - Token optimization deep dive
moai-core-context-budget - - Multi-agent coordination
moai-core-agent-guide - - State validation principles
moai-foundation-trust - - Git session state tracking
moai-foundation-git
Learn more in for detailed implementation guides, recovery procedures, advanced coordination patterns, and November 2025 API examples.
reference.mdSkill Status: Production Ready | Last Updated**: 2025-11-18 | Model Support: Sonnet 4.5, Haiku 4.5 | Enterprise
- - Token优化深度指南
moai-core-context-budget - - 多Agent协调
moai-core-agent-guide - - 状态校验原则
moai-foundation-trust - - Git会话状态追踪
moai-foundation-git
查看获取详细实现指南、恢复流程、高级协调模式和2025年11月API示例。
reference.md技能状态: 可用于生产环境 | 最后更新**: 2025-11-18 | 支持模型: Sonnet 4.5, Haiku 4.5 | 企业版