目录
- 推理模型层 - 推理工具层 - 推理代理团队层 - ReasoningStep数据模型 - ReasoningTools工具包 - 分离式推理模型 - 推理代理机制 - 推理状态管理 - 内存集成机制 - 错误处理与恢复概述
Agno的推理架构是一个分层的、模块化的智能推理系统，旨在为AI代理提供结构化、可追溯、高性能的推理能力。该架构通过三层抽象模型，实现了从基础的推理模型到复杂的多智能体协作推理的完整覆盖。
核心设计原则
- 分离关注点: 将推理逻辑、工具调用、状态管理分离
- 可扩展性: 支持多种推理模型和自定义推理工具
- 透明性: 提供完整的推理过程追踪和可视化
- 容错性: 内置错误处理和推理恢复机制
- 性能优化: 针对不同场景的推理优化策略
架构设计理念
Agno推理架构基于以下核心设计理念：
1. 思维链驱动
通过结构化的思维链（Chain of Thought）引导AI进行步骤化推理，确保推理过程的逻辑性和可验证性。2. 工具增强推理
将推理与工具调用相结合，使AI能够在推理过程中主动获取信息、执行计算、验证结果。3. 多层抽象
采用分层架构设计，不同层次提供不同粒度的推理能力，满足从简单查询到复杂问题求解的需求。4. 状态持久化
推理状态和过程可持久化存储，支持推理历史的检索和分析。5. 可插拔设计
推理工具和模型采用插件式架构，方便扩展和定制。三层推理抽象模型
Agno的推理架构采用三层抽象模型，每一层都提供不同的推理能力和抽象级别：
推理模型层
层级描述: 最底层的推理基础，直接利用大语言模型的推理能力
核心特性:
- 内置推理能力：利用模型本身的高级推理功能
- 原生CoT支持：支持思维链推理模式
- 成本效率：无需额外的推理代理开销
实现示例:
# 使用内置推理能力的模型
reasoning_agent = Agent(
model=OpenAIChat(id="gpt-4o"),
reasoning=True # 启用内置推理模式
)支持的推理模型:
- OpenAI o3-mini: 专为推理优化的模型
- DeepSeek-R1: 高性能推理模型
- Claude 4 Sonnet: 强大的推理能力
- Ollama QwQ: 本地推理模型
- Azure AI Foundry推理模型
技术实现:
# 模型层推理的核心实现
def enable_model_reasoning(self, model, use_default_reasoning=False):
if use_default_reasoning:
# 使用默认的推理提示
reasoning_prompt = self.get_default_reasoning_prompt(model.provider)
else:
# 使用模型内置的推理能力
reasoning_prompt = self.get_builtin_reasoning_prompt(model)
return reasoning_prompt推理工具层
层级描述: 中间层的推理工具系统，为非推理模型提供结构化的推理能力
核心工具:
- Think工具: 提供思维过程的结构化记录
- Analyze工具: 提供结果分析和下一步决策
工具特点:
- 渐进式推理：将复杂问题分解为可管理的步骤
- 置信度评估：每个推理步骤都有置信度评分
- 状态追踪：完整的推理过程状态管理
- 灵活配置：支持自定义推理指令和示例
技术实现:
class ReasoningTools(Toolkit):
def __init__(
self,
enable_think: bool = True,
enable_analyze: bool = True,
all: bool = False,
instructions: Optional[str] = None,
add_instructions: bool = False,
add_few_shot: bool = False,
few_shot_examples: Optional[str] = None,
**kwargs,
):
"""推理工具包初始化"""
# 配置推理工具
tools: List[Any] = []
if all or enable_think:
tools.append(self.think)
if all or enable_analyze:
tools.append(self.analyze)
super().__init__(
name="reasoning_tools",
instructions=self.instructions,
tools=tools,
**kwargs,
)使用示例:
reasoning_agent = Agent(
model=OpenAIChat(id="gpt-4o"),
tools=[ReasoningTools(add_instructions=True)],
instructions="你是一个专业的问题解决助手..."
)
# 推理工具会自动引导Agent进行结构化推理
response = reasoning_agent.print_response(
"分析远程工作与办公室工作的优缺点",
stream=True,
show_full_reasoning=True
)推理代理团队层
层级描述: 最上层的推理协作层，支持多智能体推理和团队协作
团队特点:
- 专业分工：不同Agent承担特定的推理任务
- 协作推理：Agent之间可以共享推理结果
- 并行处理：支持并行推理任务执行
- 结果整合：团队领导Agent整合所有推理结果
实现架构:
class ReasoningTeam:
def __init__(self, agents: List[Agent], team_leader: Agent):
self.agents = agents
self.team_leader = team_leader
self.reasoning_tools = ReasoningTools(add_instructions=True)
async def collaborative_reasoning(self, task: str):
"""协作推理的主要入口"""
# 分发任务给不同的Agent
# 收集各Agent的推理结果
# 整合和验证最终答案
pass实际应用案例:
# 多用途推理团队示例
team_leader = Agent(
model=Claude(id="claude-3-5-sonnet-20241022"),
instructions="协调整个团队的推理工作",
tools=[ReasoningTools(add_instructions=True, add_few_shot=True)]
)
web_agent = Agent(
model=OpenAIChat(id="gpt-4o-mini"),
tools=[DuckDuckGoTools()],
name="Web Search Agent"
)
finance_agent = Agent(
model=OpenAIChat(id="gpt-4o-mini"),
tools=[YFinanceTools()],
name="Finance Agent"
)
# 团队协作推理
reasoning_team = Team(
name="Reasoning Analysis Team",
members=[web_agent, finance_agent],
leader=team_leader,
tools=[ReasoningTools(add_instructions=True)]
)核心组件详解
ReasoningStep数据模型
数据模型定义:
from pydantic import BaseModel, Field
from enum import Enum
from typing import Optional
class NextAction(str, Enum):
CONTINUE = "continue" # 继续推理
VALIDATE = "validate" # 验证结果
FINAL_ANSWER = "final_answer" # 最终答案
RESET = "reset" # 重置推理
class ReasoningStep(BaseModel):
title: Optional[str] = Field(
None,
description="推理步骤的简洁标题"
)
action: Optional[str] = Field(
None,
description="基于此步骤的行动计划（第一人称表述）"
)
result: Optional[str] = Field(
None,
description="执行行动后获得的结果（第一人称表述）"
)
reasoning: Optional[str] = Field(
None,
description="此步骤的思维过程和考虑因素"
)
next_action: Optional[NextAction] = Field(
None,
description="指示继续推理、验证结果或确认最终答案"
)
confidence: Optional[float] = Field(
None,
description="此步骤的置信度评分（0.0到1.0）"
)状态管理机制:
class ReasoningState:
def __init__(self):
self.reasoning_steps: List[ReasoningStep] = []
self.current_step = None
self.confidence_threshold = 0.8
self.max_steps = 10
def add_step(self, step: ReasoningStep):
"""添加推理步骤"""
self.reasoning_steps.append(step)
self.current_step = step
def get_next_action(self) -> NextAction:
"""确定下一步行动"""
if not self.current_step:
return NextAction.CONTINUE
confidence = self.current_step.confidence or 0.0
if confidence >= self.confidence_threshold:
return NextAction.FINAL_ANSWER
elif self.has_validation_opportunities():
return NextAction.VALIDATE
else:
return NextAction.CONTINUEReasoningTools工具包
工具包核心功能:
- Think工具 - 结构化思维工具
def think(
self,
session_state: Dict[str, Any],
title: str,
thought: str,
action: Optional[str] = None,
confidence: float = 0.8,
) -> str:
"""使用思维工具作为草稿板来推理问题并逐步解决"""
try:
# 创建推理步骤
reasoning_step = ReasoningStep(
title=title,
reasoning=thought,
action=action,
next_action=NextAction.CONTINUE,
confidence=confidence,
)
# 保存到会话状态
current_run_id = session_state.get("current_run_id", None)
if "reasoning_steps" not in session_state:
session_state["reasoning_steps"] = {}
if current_run_id not in session_state["reasoning_steps"]:
session_state["reasoning_steps"][current_run_id] = []
session_state["reasoning_steps"][current_run_id].append(
reasoning_step.model_dump_json()
)
return self._format_reasoning_steps(session_state, current_run_id)
except Exception as e:
log_error(f"思维记录错误: {e}")
return f"思维记录错误: {e}"- Analyze工具 - 结果分析工具
def analyze(
self,
session_state: Dict[str, Any],
title: str,
result: str,
analysis: str,
next_action: str = "continue",
confidence: float = 0.8,
) -> str:
"""分析推理步骤的结果并确定下一步行动"""
try:
# 映射字符串下一步行动到枚举
next_action_enum = NextAction.CONTINUE
if next_action.lower() == "validate":
next_action_enum = NextAction.VALIDATE
elif next_action.lower() in ["final", "final_answer", "finalize"]:
next_action_enum = NextAction.FINAL_ANSWER
# 创建分析推理步骤
reasoning_step = ReasoningStep(
title=title,
result=result,
reasoning=analysis,
next_action=next_action_enum,
confidence=confidence,
)
# 保存分析结果
current_run_id = session_state.get("current_run_id", None)
if "reasoning_steps" not in session_state:
session_state["reasoning_steps"] = {}
if current_run_id not in session_state["reasoning_steps"]:
session_state["reasoning_steps"][current_run_id] = []
session_state["reasoning_steps"][current_run_id].append(
reasoning_step.model_dump_json()
)
return self._format_reasoning_steps(session_state, current_run_id)
except Exception as e:
log_error(f"分析记录错误: {e}")
return f"分析记录错误: {e}"工具配置选项:
reasoning_tools = ReasoningTools(
enable_think=True, # 启用思维工具
enable_analyze=True, # 启用分析工具
add_instructions=True, # 添加使用说明
add_few_shot=True, # 添加示例
few_shot_examples="自定义示例文本", # 自定义示例
instructions="自定义指令" # 自定义指令
)分离式推理模型
核心实现原理:
- DeepSeek推理模型实现:
def is_deepseek_reasoning_model(model_id: str) -> bool:
"""检查是否为DeepSeek推理模型"""
reasoning_models = ["deepseek-reasoner", "deepseek-r1"]
return any(reasoning_model in model_id.lower() for reasoning_model in reasoning_models)
def get_deepseek_reasoning(
model,
messages: List[Message],
reasoning_prompt: str = None
) -> Message:
"""获取DeepSeek推理内容"""
# 将developer消息转换为system消息
developer_messages = [msg for msg in messages if msg.role == "developer"]
system_messages = [msg for msg in messages if msg.role == "system"]
if developer_messages:
system_messages.extend(developer_messages)
messages = [msg for msg in messages if msg.role != "developer"]
messages = [Message(role="system", content="\n".join([
msg.content for msg in system_messages
]))] + [msg for msg in messages if msg.role != "system"]
# 运行推理代理
reasoning_agent = Agent(
model=model,
instructions=reasoning_prompt or "你是一个推理专家",
tools=[ReasoningTools(add_instructions=True)],
reasoning=True
)
response = reasoning_agent.run(messages[-1].content)
# 提取推理内容
reasoning_content = extract_thinking_content(response.content)
return Message(
role="assistant",
content=f"<thinking>{reasoning_content}</thinking>{response.content}"
)- 其他推理模型支持:
# OpenAI o3-mini推理实现
class OpenAIReasoningModel:
def __init__(self, model_id: str):
self.model_id = model_id
def get_reasoning_response(self, messages: List[Message]) -> str:
"""获取推理响应"""
response = self.client.chat.completions.create(
model=self.model_id,
messages=messages,
reasoning_effort="high" if "o3" in self.model_id else "medium"
)
return response.choices[0].message.content
# Anthropic Claude推理实现
class ClaudeReasoningModel:
def __init__(self, model_id: str):
self.model_id = model_id
def get_reasoning_response(self, messages: List[Message]) -> str:
"""获取推理响应"""
response = self.client.messages.create(
model=self.model_id,
messages=messages,
max_tokens=8192,
system="你是一个专业的推理助手。"
)
return response.content[0].text推理代理机制
推理代理工作流程:
- 推理激活:
class Agent:
def __init__(self, model: Model, reasoning: bool = False, **kwargs):
self.reasoning = reasoning
self.model = model
if reasoning:
# 初始化推理模式
self.reasoning_agent = self._create_reasoning_agent()
self.reasoning_state = {}
def _create_reasoning_agent(self) -> Agent:
"""创建专用推理代理"""
return Agent(
model=self.model,
tools=[ReasoningTools(add_instructions=True)],
instructions=self._get_reasoning_instructions(),
name=f"{self.name}_reasoning_proxy",
add_datetime_to_context=True,
stream_events=True,
markdown=True,
)- 推理执行流程:
async def aprint_response(
self,
message: str,
stream: bool = False,
**kwargs
):
"""推理响应处理"""
if self.reasoning:
# 启动推理代理
reasoning_response = await self._run_reasoning_agent(message)
# 验证推理结果
validated_response = await self._validate_reasoning_result(
reasoning_response
)
# 输出最终结果
if stream:
await self._stream_final_response(validated_response)
else:
return validated_response
else:
# 标准响应模式
return await self._run_standard_response(message)- 推理状态管理:
class ReasoningStateManager:
def __init__(self, agent: Agent):
self.agent = agent
self.state = {}
async def start_reasoning(self, task: str):
"""开始推理会话"""
self.state["current_task"] = task
self.state["reasoning_steps"] = []
self.state["started_at"] = datetime.now()
async def add_reasoning_step(self, step: ReasoningStep):
"""添加推理步骤"""
self.state["reasoning_steps"].append(step)
# 检查是否达到停止条件
if self._should_stop_reasoning():
await self._complete_reasoning()
def _should_stop_reasoning(self) -> bool:
"""判断是否应该停止推理"""
steps = self.state["reasoning_steps"]
if not steps:
return False
# 检查置信度
last_step = steps[-1]
if last_step.confidence >= 0.9:
return True
# 检查步骤数
if len(steps) >= self.max_steps:
return True
# 检查一致性
if self._check_consistency(steps):
return True
return False
async def _complete_reasoning(self):
"""完成推理过程"""
reasoning_content = self._generate_reasoning_summary()
self.agent.run_response.reasoning_content = reasoning_content技术实现细节
推理状态管理
状态管理架构:
class ReasoningState:
"""推理状态管理类"""
def __init__(self, agent_id: str, session_id: str):
self.agent_id = agent_id
self.session_id = session_id
self.current_task = None
self.reasoning_steps: List[ReasoningStep] = []
self.task_distribution = {}
self.collaboration_results = {}
self.confidence_aggregation = {}
def add_reasoning_step(self, step: ReasoningStep):
"""添加推理步骤"""
self.reasoning_steps.append(step)
self._update_confidence_metrics(step)
def _update_confidence_metrics(self, step: ReasoningStep):
"""更新置信度指标"""
if step.confidence:
if step.title not in self.confidence_aggregation:
self.confidence_aggregation[step.title] = []
self.confidence_aggregation[step.title].append(step.confidence)
def get_overall_confidence(self) -> float:
"""计算整体置信度"""
all_confidences = []
for confidences in self.confidence_aggregation.values():
all_confidences.extend(confidences)
return sum(all_confidences) / len(all_confidences) if all_confidences else 0.0
def get_reasoning_summary(self) -> str:
"""生成推理摘要"""
summary = f"## 推理摘要\n\n"
summary += f"任务: {self.current_task}\n"
summary += f"推理步骤数: {len(self.reasoning_steps)}\n"
summary += f"整体置信度: {self.get_overall_confidence():.2f}\n\n"
for i, step in enumerate(self.reasoning_steps, 1):
summary += f"### 步骤 {i}: {step.title}\n"
if step.reasoning:
summary += f"推理: {step.reasoning}\n"
if step.action:
summary += f"行动: {step.action}\n"
if step.result:
summary += f"结果: {step.result}\n"
summary += f"置信度: {step.confidence}\n\n"
return summary状态持久化:
class ReasoningStatePersistence:
"""推理状态持久化"""
def __init__(self, db: BaseDb):
self.db = db
async def save_reasoning_state(
self,
agent_id: str,
session_id: str,
state: ReasoningState
):
"""保存推理状态到数据库"""
reasoning_record = {
"agent_id": agent_id,
"session_id": session_id,
"current_task": state.current_task,
"reasoning_steps": [
step.model_dump() for step in state.reasoning_steps
],
"confidence_metrics": state.confidence_aggregation,
"created_at": datetime.now(),
"updated_at": datetime.now()
}
await self.db.store_reasoning_state(reasoning_record)
async def load_reasoning_state(
self,
agent_id: str,
session_id: str
) -> Optional[ReasoningState]:
"""从数据库加载推理状态"""
record = await self.db.get_reasoning_state(agent_id, session_id)
if not record:
return None
state = ReasoningState(agent_id, session_id)
state.current_task = record["current_task"]
state.reasoning_steps = [
ReasoningStep(**step_data)
for step_data in record["reasoning_steps"]
]
state.confidence_aggregation = record["confidence_metrics"]
return state内存集成机制
推理记忆管理:
class ReasoningMemoryManager:
"""推理记忆管理器"""
def __init__(self, memory_manager: MemoryManager):
self.memory_manager = memory_manager
async def store_reasoning_memory(
self,
agent_id: str,
reasoning_context: dict
):
"""存储推理记忆"""
memory_key = f"reasoning_{agent_id}_{reasoning_context['task_id']}"
memory_data = {
"task": reasoning_context["task"],
"reasoning_steps": reasoning_context["reasoning_steps"],
"final_result": reasoning_context["final_result"],
"confidence_score": reasoning_context.get("confidence_score", 0.0),
"reasoning_patterns": self._extract_patterns(reasoning_context)
}
await self.memory_manager.store_memory(
key=memory_key,
data=memory_data,
memory_type="reasoning"
)
def _extract_patterns(self, context: dict) -> List[str]:
"""提取推理模式"""
patterns = []
# 提取推理步骤模式
step_count = len(context.get("reasoning_steps", []))
if step_count >= 5:
patterns.append("complex_multi_step_reasoning")
# 提取工具使用模式
tool_usage = set()
for step in context.get("reasoning_steps", []):
if step.get("action"):
# 分析工具调用模式
if "search" in step["action"].lower():
tool_usage.add("search_heavy")
if "calculate" in step["action"].lower():
tool_usage.add("calculation_heavy")
patterns.extend(list(tool_usage))
return patterns
async def retrieve_relevant_reasoning(
self,
agent_id: str,
current_task: str
) -> List[dict]:
"""检索相关推理记忆"""
relevant_memories = await self.memory_manager.search_memories(
agent_id=agent_id,
query=current_task,
memory_type="reasoning",
max_results=5
)
return relevant_memories知识图谱集成:
class ReasoningKnowledgeGraph:
"""推理知识图谱"""
def __init__(self, knowledge_base: Knowledge):
self.knowledge_base = knowledge_base
async def link_reasoning_to_knowledge(
self,
reasoning_steps: List[ReasoningStep]
) -> Dict[str, List[str]]:
"""将推理步骤链接到知识图谱"""
concept_connections = {}
for step in reasoning_steps:
if step.reasoning:
# 提取关键概念
concepts = await self._extract_concepts(step.reasoning)
# 查找相关知识
for concept in concepts:
related_knowledge = await self.knowledge_base.search(
query=concept,
limit=3
)
concept_connections[concept] = [
item.content for item in related_knowledge
]
return concept_connections
async def _extract_concepts(self, text: str) -> List[str]:
"""从文本中提取关键概念"""
# 使用简单的关键词提取
# 实际应用中可以使用更复杂的NLP技术
stop_words = {"的", "了", "是", "在", "有", "和", "与", "或"}
words = jieba.lcut(text)
concepts = [
word for word in words
if len(word) > 1 and word not in stop_words
]
return concepts[:10] # 返回前10个概念错误处理与恢复
推理错误分类:
class ReasoningError(Exception):
"""推理错误基类"""
pass
class StepExecutionError(ReasoningError):
"""推理步骤执行错误"""
def __init__(self, step_title: str, original_error: Exception):
self.step_title = step_title
self.original_error = original_error
super().__init__(f"步骤 '{step_title}' 执行失败: {original_error}")
class ReasoningTimeoutError(ReasoningError):
"""推理超时错误"""
pass
class LowConfidenceError(ReasoningError):
"""低置信度错误"""
def __init__(self, confidence: float, threshold: float):
self.confidence = confidence
self.threshold = threshold
super().__init__(
f"推理置信度 {confidence:.2f} 低于阈值 {threshold:.2f}"
)错误恢复机制:
class ReasoningRecoveryManager:
"""推理恢复管理器"""
def __init__(self, max_retries: int = 3, confidence_threshold: float = 0.8):
self.max_retries = max_retries
self.confidence_threshold = confidence_threshold
self.recovery_strategies = {
"StepExecutionError": self._recover_from_step_error,
"ReasoningTimeoutError": self._recover_from_timeout,
"LowConfidenceError": self._recover_from_low_confidence
}
async def handle_error(
self,
error: ReasoningError,
context: ReasoningContext
) -> bool:
"""处理推理错误"""
error_type = type(error).__name__
if error_type in self.recovery_strategies:
recovery_func = self.recovery_strategies[error_type]
success = await recovery_func(error, context)
if success:
log_info(f"成功从 {error_type} 错误中恢复")
return True
else:
log_error(f"无法从 {error_type} 错误中恢复")
return False
async def _recover_from_step_error(
self,
error: StepExecutionError,
context: ReasoningContext
) -> bool:
"""从步骤执行错误中恢复"""
# 尝试简化步骤
simplified_step = self._simplify_reasoning_step(
context.current_step
)
# 重新执行简化后的步骤
try:
result = await context.execute_step(simplified_step)
context.add_successful_step(result)
return True
except Exception as e:
log_error(f"简化步骤执行仍然失败: {e}")
return False
async def _recover_from_timeout(
self,
error: ReasoningTimeoutError,
context: ReasoningContext
) -> bool:
"""从超时错误中恢复"""
# 减少推理深度
context.max_steps = max(3, context.max_steps // 2)
# 跳过低置信度步骤
filtered_steps = [
step for step in context.reasoning_steps
if step.confidence >= self.confidence_threshold
]
context.reasoning_steps = filtered_steps
return len(filtered_steps) > 0
async def _recover_from_low_confidence(
self,
error: LowConfidenceError,
context: ReasoningContext
) -> bool:
"""从低置信度错误中恢复"""
# 寻找额外的验证信息
additional_info = await self._gather_additional_evidence(
context.current_task
)
if additional_info:
# 创建验证步骤
validation_step = ReasoningStep(
title="额外证据验证",
reasoning=f"基于收集到的额外信息: {additional_info}",
action="验证当前推理结论",
confidence=0.9
)
context.reasoning_steps.append(validation_step)
return True
return False
def _simplify_reasoning_step(self, step: ReasoningStep) -> ReasoningStep:
"""简化推理步骤"""
return ReasoningStep(
title=f"简化版: {step.title}",
reasoning="简化推理过程",
confidence=min(step.confidence, 0.7)
)性能优化策略
模型选择优化
推理能力评估矩阵:
REASONING_MODEL_CAPABILITIES = {
"gpt-4o": {
"strengths": ["多模态推理", "复杂逻辑", "代码分析"],
"cost_per_token": 0.00001,
"speed_score": 8.5,
"reasoning_quality": 9.0
},
"o3-mini": {
"strengths": ["数学推理", "科学分析", "逻辑推理"],
"cost_per_token": 0.000008,
"speed_score": 7.5,
"reasoning_quality": 9.5
},
"deepseek-r1": {
"strengths": ["代码推理", "算法分析", "深度思考"],
"cost_per_token": 0.000005,
"speed_score": 7.0,
"reasoning_quality": 9.2
},
"claude-3-5-sonnet": {
"strengths": ["文本分析", "创意推理", "伦理考量"],
"cost_per_token": 0.000015,
"speed_score": 8.0,
"reasoning_quality": 8.8
}
}
def select_optimal_reasoning_model(task_type: str, constraints: dict) -> str:
"""根据任务类型和约束选择最优推理模型"""
relevant_models = []
for model, capabilities in REASONING_MODEL_CAPABILITIES.items():
# 计算模型适配度
suitability_score = 0
# 能力匹配度
for strength in capabilities["strengths"]:
if strength.lower() in task_type.lower():
suitability_score += 2
# 成本约束
if "budget_limit" in constraints:
cost_per_1k = capabilities["cost_per_token"] * 1000
if cost_per_1k <= constraints["budget_limit"]:
suitability_score += 1
# 速度要求
if "speed_priority" in constraints and constraints["speed_priority"]:
suitability_score += capabilities["speed_score"]
# 质量要求
if "quality_priority" in constraints and constraints["quality_priority"]:
suitability_score += capabilities["reasoning_quality"]
relevant_models.append((model, suitability_score))
# 返回适配度最高的模型
relevant_models.sort(key=lambda x: x[1], reverse=True)
return relevant_models[0][0]推理流程优化
智能步骤管理:
class IntelligentStepManager:
"""智能步骤管理器"""
def __init__(self):
self.step_cache = {}
self.parallel_execution_threshold = 3
async def optimize_reasoning_steps(
self,
steps: List[ReasoningStep]
) -> List[ReasoningStep]:
"""优化推理步骤序列"""
optimized_steps = []
# 识别可并行的步骤
parallel_groups = self._identify_parallel_steps(steps)
for group in parallel_groups:
if len(group) >= self.parallel_execution_threshold:
# 执行并行推理
parallel_results = await self._execute_parallel_steps(group)
optimized_steps.extend(parallel_results)
else:
# 顺序执行
for step in group:
result = await self._execute_step(step)
optimized_steps.append(result)
return optimized_steps
def _identify_parallel_steps(
self,
steps: List[ReasoningStep]
) -> List[List[ReasoningStep]]:
"""识别可以并行执行的步骤组"""
parallel_groups = []
current_group = []
for step in steps:
if self._can_execute_in_parallel(step, current_group):
current_group.append(step)
else:
if current_group:
parallel_groups.append(current_group)
current_group = [step]
if current_group:
parallel_groups.append(current_group)
return parallel_groups
def _can_execute_in_parallel(
self,
step: ReasoningStep,
group: List[ReasoningStep]
) -> bool:
"""判断步骤是否可以并行执行"""
# 检查依赖关系
for existing_step in group:
if self._has_dependency(step, existing_step):
return False
# 检查资源冲突
if self._requires_specific_resources(step):
return False
return True
async def _execute_parallel_steps(
self,
steps: List[ReasoningStep]
) -> List[ReasoningStep]:
"""并行执行推理步骤"""
import asyncio
tasks = [self._execute_step(step) for step in steps]
results = await asyncio.gather(*tasks, return_exceptions=True)
# 处理异常结果
processed_results = []
for i, result in enumerate(results):
if isinstance(result, Exception):
# 降级为顺序执行
fallback_result = await self._execute_step_fallback(steps[i])
processed_results.append(fallback_result)
else:
processed_results.append(result)
return processed_results缓存优化策略:
class ReasoningCacheManager:
"""推理缓存管理器"""
def __init__(self, cache_size: int = 1000):
self.cache_size = cache_size
self.step_cache = {}
self.result_cache = {}
self.access_patterns = {}
async def get_cached_reasoning(
self,
task_hash: str,
context_hash: str
) -> Optional[List[ReasoningStep]]:
"""获取缓存的推理步骤"""
cache_key = f"{task_hash}:{context_hash}"
if cache_key in self.step_cache:
# 更新访问模式
self._update_access_pattern(cache_key)
return self.step_cache[cache_key]
return None
async def cache_reasoning_steps(
self,
task_hash: str,
context_hash: str,
steps: List[ReasoningStep]
):
"""缓存推理步骤"""
cache_key = f"{task_hash}:{context_hash}"
# 缓存清理策略
if len(self.step_cache) >= self.cache_size:
self._evict_least_recently_used()
self.step_cache[cache_key] = steps
self._update_access_pattern(cache_key)
async def get_similar_reasoning(
self,
current_task: str,
threshold: float = 0.8
) -> List[tuple]:
"""获取相似的推理历史"""
current_embedding = await self._embed_task(current_task)
similar_reasoning = []
for cache_key, steps in self.step_cache.items():
task_text = cache_key.split(":")[0]
stored_embedding = await self._embed_task(task_text)
similarity = self._cosine_similarity(current_embedding, stored_embedding)
if similarity >= threshold:
similar_reasoning.append((similarity, steps))
return sorted(similar_reasoning, key=lambda x: x[0], reverse=True)
def _update_access_pattern(self, cache_key: str):
"""更新访问模式统计"""
self.access_patterns[cache_key] = {
"last_access": datetime.now(),
"access_count": self.access_patterns.get(cache_key, {}).get("access_count", 0) + 1
}
def _evict_least_recently_used(self):
"""清除最少使用的缓存项"""
if not self.access_patterns:
return
# 按访问时间排序
sorted_keys = sorted(
self.access_patterns.items(),
key=lambda x: x[1]["last_access"]
)
# 移除10%的最少使用项
evict_count = max(1, len(sorted_keys) // 10)
for key, _ in sorted_keys[:evict_count]:
self.step_cache.pop(key, None)
self.access_patterns.pop(key, None)内存优化
内存使用监控:
class ReasoningMemoryMonitor:
"""推理内存监控器"""
def __init__(self, max_memory_mb: int = 512):
self.max_memory_mb = max_memory_mb
self.memory_history = []
async def monitor_memory_usage(self):
"""监控内存使用情况"""
import psutil
import os
process = psutil.Process(os.getpid())
memory_info = process.memory_info()
memory_mb = memory_info.rss / 1024 / 1024
self.memory_history.append({
"timestamp": datetime.now(),
"memory_mb": memory_mb,
"memory_percent": process.memory_percent()
})
# 保留最近100个记录
if len(self.memory_history) > 100:
self.memory_history.pop(0)
# 检查内存超限
if memory_mb > self.max_memory_mb:
await self._trigger_memory_cleanup()
return memory_mb
async def _trigger_memory_cleanup(self):
"""触发内存清理"""
log_warning("触发推理内存清理")
# 清理推理缓存
await self._clear_reasoning_cache()
# 强制垃圾回收
import gc
gc.collect()
async def get_memory_recommendations(self) -> List[str]:
"""获取内存使用建议"""
if not self.memory_history:
return []
recent_memory = [
record["memory_mb"]
for record in self.memory_history[-10:]
]
avg_memory = sum(recent_memory) / len(recent_memory)
max_memory = max(recent_memory)
recommendations = []
if max_memory > self.max_memory_mb * 0.9:
recommendations.append("考虑减少推理步骤数量")
recommendations.append("启用内存缓存清理")
if avg_memory > self.max_memory_mb * 0.7:
recommendations.append("考虑使用更轻量级的推理模型")
if len(recent_memory) > 5 and recent_memory[-1] > recent_memory[0] * 1.2:
recommendations.append("内存使用呈上升趋势，监控内存泄漏")
return recommendations应用场景与案例
金融分析与推理
复杂金融推理场景:
class FinancialReasoningTeam:
"""金融推理团队"""
def __init__(self):
self.market_analyst = Agent(
model=OpenAIChat(id="gpt-4o"),
tools=[ReasoningTools(add_instructions=True), YFinanceTools()],
name="Market Analyst",
instructions="专注市场趋势分析和数据解读"
)
self.risk_assessor = Agent(
model=Claude(id="claude-3-5-sonnet-20241022"),
tools=[ReasoningTools(add_instructions=True)],
name="Risk Assessor",
instructions="专注风险评估和情景分析"
)
self.portfolio_optimizer = Agent(
model=OpenAIChat(id="gpt-4o"),
tools=[ReasoningTools(add_instructions=True)],
name="Portfolio Optimizer",
instructions="专注投资组合优化策略"
)
async def analyze_investment_opportunity(
self,
ticker: str,
investment_amount: float
) -> dict:
"""分析投资机会"""
# 任务分发
market_task = f"分析 {ticker} 的市场表现和技术指标"
risk_task = f"评估 {ticker} 投资的风险因素，金额: ${investment_amount:,.2f}"
portfolio_task = f"基于 ${investment_amount:,.2f} 投资提供 {ticker} 的配置建议"
# 并行执行推理任务
market_analysis = await self.market_analyst.aprint_response(
market_task,
stream=True,
show_full_reasoning=True
)
risk_assessment = await self.risk_assessor.aprint_response(
risk_task,
stream=True,
show_full_reasoning=True
)
portfolio_recommendation = await self.portfolio_optimizer.aprint_response(
portfolio_task,
stream=True,
show_full_reasoning=True
)
# 整合分析结果
return {
"ticker": ticker,
"investment_amount": investment_amount,
"market_analysis": market_analysis.content,
"risk_assessment": risk_assessment.content,
"portfolio_recommendation": portfolio_recommendation.content,
"overall_confidence": self._calculate_overall_confidence([
market_analysis, risk_assessment, portfolio_recommendation
])
}
def _calculate_overall_confidence(self, results: List[RunOutput]) -> float:
"""计算整体置信度"""
confidences = []
for result in results:
if hasattr(result, 'reasoning_content') and result.reasoning_content:
# 从推理内容中提取置信度
confidence = self._extract_confidence_from_reasoning(
result.reasoning_content
)
if confidence:
confidences.append(confidence)
return sum(confidences) / len(confidences) if confidences else 0.0
def _extract_confidence_from_reasoning(self, reasoning: str) -> float:
"""从推理内容中提取置信度分数"""
import re
confidence_pattern = r"置信度[:：]?\s*([0-9.]+)"
matches = re.findall(confidence_pattern, reasoning)
if matches:
return float(matches[-1])
# 如果没有明确置信度，估算基于内容质量
if "高置信度" in reasoning or "确信" in reasoning:
return 0.85
elif "中等置信度" in reasoning:
return 0.70
else:
return 0.60实际应用示例:
# 使用金融推理团队
async def main():
financial_team = FinancialReasoningTeam()
# 分析NVDA投资机会
analysis_result = await financial_team.analyze_investment_opportunity(
ticker="NVDA",
investment_amount=50000
)
print("=== 投资分析报告 ===")
print(f"标的股票: {analysis_result['ticker']}")
print(f"投资金额: ${analysis_result['investment_amount']:,.2f}")
print(f"整体置信度: {analysis_result['overall_confidence']:.2f}")
print("\n=== 市场分析 ===")
print(analysis_result["market_analysis"])
print("\n=== 风险评估 ===")
print(analysis_result["risk_assessment"])
print("\n=== 投资建议 ===")
print(analysis_result["portfolio_recommendation"])
# 运行示例
# asyncio.run(main())科学研究推理
科学方法论推理:
class ScientificReasoningFramework:
"""科学推理框架"""
def __init__(self):
self.hypothesis_generator = Agent(
model=OpenAIChat(id="gpt-4o"),
tools=[ReasoningTools(add_instructions=True)],
name="Hypothesis Generator",
instructions="基于观察数据生成科学假设"
)
self.experiment_designer = Agent(
model=Claude(id="claude-3-5-sonnet-20241022"),
tools=[ReasoningTools(add_instructions=True)],
name="Experiment Designer",
instructions="设计实验来验证假设"
)
self.data_analyzer = Agent(
model=OpenAIChat(id="gpt-4o"),
tools=[ReasoningTools(add_instructions=True)],
name="Data Analyzer",
instructions="分析实验数据并得出结论"
)
async def scientific_inquiry(
self,
research_question: str,
observations: List[str]
) -> dict:
"""科学探究流程"""
# 第一步：生成假设
hypothesis_task = f"""
基于以下观察数据，为研究问题生成可验证的假设：
研究问题: {research_question}
观察数据:
{chr(10).join(f"- {obs}" for obs in observations)}
请提供具体的、可测试的假设。
"""
hypothesis_result = await self.hypothesis_generator.aprint_response(
hypothesis_task,
stream=True,
show_full_reasoning=True
)
# 第二步：设计实验
experiment_task = f"""
基于以下假设，设计实验来验证假设的有效性：
假设: {hypothesis_result.content}
请详细描述实验设计，包括：
1. 实验方法
2. 所需数据
3. 预期结果
4. 潜在偏差
"""
experiment_result = await self.experiment_designer.aprint_response(
experiment_task,
stream=True,
show_full_reasoning=True
)
# 第三步：数据分析（模拟）
analysis_task = f"""
基于以下实验设计，提供数据分析方法：
实验设计: {experiment_result.content}
请描述：
1. 统计方法
2. 显著性检验
3. 结果解释框架
"""
analysis_result = await self.data_analyzer.aprint_response(
analysis_task,
stream=True,
show_full_reasoning=True
)
return {
"research_question": research_question,
"hypothesis": hypothesis_result.content,
"experiment_design": experiment_result.content,
"data_analysis_plan": analysis_result.content,
"methodology_confidence": self._assess_methodology_quality([
hypothesis_result, experiment_result, analysis_result
])
}
def _assess_methodology_quality(self, results: List[RunOutput]) -> float:
"""评估方法论质量"""
quality_scores = []
for result in results:
content = result.content.lower()
# 检查内容质量指标
quality_indicators = [
"具体", "详细", "可操作", "可验证", "科学",
"假设", "实验", "数据", "统计", "结果"
]
score = sum(1 for indicator in quality_indicators if indicator in content)
normalized_score = min(1.0, score / len(quality_indicators))
quality_scores.append(normalized_score)
return sum(quality_scores) / len(quality_scores)法律推理分析
法律案例推理:
class LegalReasoningAssistant:
"""法律推理助手"""
def __init__(self):
self.legal_researcher = Agent(
model=Claude(id="claude-3-5-sonnet-20241022"),
tools=[ReasoningTools(add_instructions=True)],
name="Legal Researcher",
instructions="专注法律条文研究和案例分析"
)
self.argument_builder = Agent(
model=OpenAIChat(id="gpt-4o"),
tools=[ReasoningTools(add_instructions=True)],
name="Argument Builder",
instructions="专注构建法律论证和逻辑推理"
)
self.precedent_analyzer = Agent(
model=Claude(id="claude-3-5-sonnet-20241022"),
tools=[ReasoningTools(add_instructions=True)],
name="Precedent Analyzer",
instructions="专注先例分析和判例研究"
)
async def legal_case_analysis(
self,
case_facts: str,
legal_questions: List[str]
) -> dict:
"""法律案例分析"""
# 法律研究
research_task = f"""
基于以下案例事实，分析相关的法律条文：
案例事实: {case_facts}
请提供：
1. 适用的法律条文
2. 法律条文解释
3. 相关的司法解释
"""
legal_research = await self.legal_researcher.aprint_response(
research_task,
stream=True,
show_full_reasoning=True
)
# 先例分析
precedent_task = f"""
分析与当前案例相关的先例判决：
案例事实: {case_facts}
适用法律: {legal_research.content}
请分析：
1. 类似案例的判决结果
2. 判决理由和法律依据
3. 对当前案例的启示
"""
precedent_analysis = await self.precedent_analyzer.aprint_response(
precedent_task,
stream=True,
show_full_reasoning=True
)
# 法律论证
argument_tasks = []
for question in legal_questions:
argument_task = f"""
基于以下信息，回答法律问题：
案例事实: {case_facts}
适用法律: {legal_research.content}
先例分析: {precedent_analysis.content}
法律问题: {question}
请构建完整的法律论证。
"""
argument_result = await self.argument_builder.aprint_response(
argument_task,
stream=True,
show_full_reasoning=True
)
argument_tasks.append({
"question": question,
"argument": argument_result.content,
"confidence": self._extract_confidence(argument_result.content)
})
return {
"case_facts": case_facts,
"legal_research": legal_research.content,
"precedent_analysis": precedent_analysis.content,
"legal_arguments": argument_tasks,
"overall_assessment": self._assess_case_strength(argument_tasks)
}
def _extract_confidence(self, content: str) -> float:
"""提取论证置信度"""
confidence_keywords = {
"强烈支持": 0.95,
"支持": 0.80,
"可能支持": 0.65,
"中性": 0.50,
"可能反对": 0.35,
"反对": 0.20,
"强烈反对": 0.05
}
for keyword, confidence in confidence_keywords.items():
if keyword in content:
return confidence
return 0.60 # 默认置信度
def _assess_case_strength(self, arguments: List[dict]) -> dict:
"""评估案例强度"""
confidences = [arg["confidence"] for arg in arguments]
return {
"average_confidence": sum(confidences) / len(confidences),
"strength_level": self._categorize_strength(sum(confidences) / len(confidences)),
"argument_distribution": self._analyze_argument_distribution(confidences)
}
def _categorize_strength(self, avg_confidence: float) -> str:
"""分类案例强度"""
if avg_confidence >= 0.80:
return "强"
elif avg_confidence >= 0.65:
return "中等偏强"
elif avg_confidence >= 0.50:
return "中等"
elif avg_confidence >= 0.35:
return "中等偏弱"
else:
return "弱"最佳实践指南
推理代理配置最佳实践
模型选择指南:
class ReasoningModelSelector:
"""推理模型选择器"""
REASONING_MODEL_GUIDELINES = {
"complex_analysis": {
"primary": "gpt-4o",
"backup": "claude-3-5-sonnet",
"rationale": "多模态能力，适合复杂分析任务"
},
"mathematical_reasoning": {
"primary": "o3-mini",
"backup": "deepseek-r1",
"rationale": "专门优化的数学推理能力"
},
"code_analysis": {
"primary": "deepseek-r1",
"backup": "gpt-4o",
"rationale": "代码推理能力突出"
},
"creative_reasoning": {
"primary": "claude-3-5-sonnet",
"backup": "gpt-4o",
"rationale": "创意推理和文本生成能力强"
},
"fast_prototyping": {
"primary": "gpt-4o-mini",
"backup": "gpt-4o",
"rationale": "成本效益高，速度快"
}
}
@classmethod
def select_model(
cls,
task_type: str,
constraints: dict = None
) -> dict:
"""根据任务类型选择最优模型"""
constraints = constraints or {}
if task_type in cls.REASONING_MODEL_GUIDELINES:
model_info = cls.REASONING_MODEL_GUIDELINES[task_type]
# 检查约束条件
if "max_cost_per_token" in constraints:
# 成本约束检查
if constraints["max_cost_per_token"] < 0.00001:
# 选择更经济的模型
model_info["primary"] = "gpt-4o-mini"
return model_info
else:
# 默认选择通用推理模型
return {
"primary": "gpt-4o",
"backup": "claude-3-5-sonnet",
"rationale": "通用任务默认选择"
}推理提示优化:
class ReasoningPromptOptimizer:
"""推理提示优化器"""
@staticmethod
def get_optimized_reasoning_prompt(
task_type: str,
domain: str = None,
complexity: str = "medium"
) -> str:
"""获取优化的推理提示"""
base_prompt = """
你是一个专业的推理助手。请按照以下步骤进行推理：
1. **问题理解**: 明确问题的核心要素和约束条件
2. **方法选择**: 选择最适合的推理方法和工具
3. **分步推理**: 将复杂问题分解为可管理的步骤
4. **证据评估**: 对每个推理步骤评估置信度
5. **结果验证**: 验证推理结果的逻辑一致性
6. **结论总结**: 提供清晰、有根据的最终结论
"""
domain_specific_additions = {
"financial": """
金融领域特殊要求：
- 考虑市场风险和不确定性
- 引用具体的财务数据和分析指标
- 评估不同投资策略的利弊
""",
"scientific": """
科学推理特殊要求：
- 基于可验证的证据进行推理
- 考虑假设的可测试性
- 明确推理的局限性
""",
"legal": """
法律推理特殊要求：
- 引用相关法律条文和判例
- 考虑法律原则的适用性
- 分析不同法律解释的可能性
"""
}
complexity_additions = {
"simple": """
简化推理模式：
- 减少推理步骤数量
- 聚焦核心问题
- 快速得出结论
""",
"complex": """
深度推理模式：
- 考虑多个角度和观点
- 探索各种可能性
- 进行全面的风险评估
"""
}
# 组装优化后的提示
optimized_prompt = base_prompt
if domain and domain in domain_specific_additions:
optimized_prompt += domain_specific_additions[domain]
if complexity in complexity_additions:
optimized_prompt += complexity_additions[complexity]
return optimized_prompt推理工具使用策略
工具组合优化:
class ReasoningToolsStrategy:
"""推理工具使用策略"""
TOOL_COMBINATIONS = {
"information_gathering": {
"tools": ["think", "analyze", "web_search"],
"strategy": "先思考需要什么信息，然后搜索和分析"
},
"data_analysis": {
"tools": ["think", "analyze", "python_execution"],
"strategy": "思考分析方法，执行计算，验证结果"
},
"creative_synthesis": {
"tools": ["think", "analyze", "knowledge_base"],
"strategy": "创意构思，结合已有知识，分析整合"
},
"problem_solving": {
"tools": ["think", "analyze", "validation_tools"],
"strategy": "分解问题，逐步解决，验证答案"
}
}
@classmethod
def select_optimal_tools(
cls,
task_category: str,
task_complexity: str
) -> dict:
"""选择最优工具组合"""
if task_category in cls.TOOL_COMBINATIONS:
tool_config = cls.TOOL_COMBINATIONS[task_category].copy()
# 根据复杂度调整工具使用
if task_complexity == "high":
# 高复杂度任务增加验证步骤
if "validation_tools" not in tool_config["tools"]:
tool_config["tools"].append("validation_tools")
elif task_complexity == "low":
# 低复杂度任务减少工具使用
if len(tool_config["tools"]) > 2:
tool_config["tools"] = tool_config["tools"][:2]
return tool_config
else:
# 默认工具组合
return {
"tools": ["think", "analyze"],
"strategy": "基础推理工具组合"
}置信度管理策略:
class ConfidenceManagement:
"""置信度管理策略"""
CONFIDENCE_THRESHOLDS = {
"low_risk": 0.60,
"medium_risk": 0.75,
"high_risk": 0.90
}
@classmethod
def set_dynamic_thresholds(
cls,
task_domain: str,
consequence_level: str
) -> dict:
"""设置动态置信度阈值"""
base_threshold = cls.CONFIDENCE_THRESHOLDS.get(
f"{consequence_level}_risk", 0.75
)
# 根据领域调整
domain_adjustments = {
"medical": 0.95, # 医疗领域需要更高置信度
"financial": 0.85, # 金融领域需要较高置信度
"legal": 0.90, # 法律领域需要高置信度
"creative": 0.60, # 创意领域可以降低置信度
"technical": 0.80 # 技术领域需要中等偏上置信度
}
final_threshold = domain_adjustments.get(task_domain, base_threshold)
return {
"minimum_threshold": final_threshold,
"recommended_threshold": min(0.95, final_threshold + 0.10),
"maximum_iterations": 10 if final_threshold > 0.90 else 5
}
@classmethod
def optimize_confidence_progression(
cls,
steps: List[ReasoningStep]
) -> List[ReasoningStep]:
"""优化置信度进展"""
if not steps:
return steps
# 计算期望的置信度增长模式
total_steps = len(steps)
target_confidence = steps[-1].confidence or 0.8
optimized_steps = []
for i, step in enumerate(steps):
# 根据步骤位置调整置信度
progress_ratio = i / max(1, total_steps - 1)
# 早期步骤降低置信度要求
if i < total_steps * 0.3:
step.confidence = max(step.confidence or 0.5, 0.5)
# 中期步骤稳步提升
elif i < total_steps * 0.8:
expected_confidence = 0.5 + (target_confidence - 0.5) * progress_ratio
step.confidence = max(step.confidence or expected_confidence, expected_confidence * 0.8)
# 后期步骤接近目标置信度
else:
step.confidence = target_confidence
optimized_steps.append(step)
return optimized_steps性能监控与优化
推理性能分析:
class ReasoningPerformanceAnalyzer:
"""推理性能分析器"""
def __init__(self):
self.performance_history = []
async def analyze_reasoning_performance(
self,
agent: Agent,
task: str,
response: RunOutput
) -> dict:
"""分析推理性能"""
# 收集性能指标
metrics = {
"task": task,
"response_time": self._calculate_response_time(response),
"reasoning_steps_count": self._count_reasoning_steps(response),
"confidence_average": self._calculate_average_confidence(response),
"token_usage": self._estimate_token_usage(response),
"tool_calls_count": self._count_tool_calls(response),
"reasoning_quality_score": self._assess_reasoning_quality(response)
}
# 计算性能评分
performance_score = self._calculate_performance_score(metrics)
# 生成优化建议
optimization_suggestions = self._generate_optimization_suggestions(metrics)
analysis_result = {
"performance_metrics": metrics,
"performance_score": performance_score,
"optimization_suggestions": optimization_suggestions,
"timestamp": datetime.now()
}
# 保存分析结果
self.performance_history.append(analysis_result)
return analysis_result
def _calculate_performance_score(self, metrics: dict) -> float:
"""计算性能评分"""
score_components = []
# 响应时间评分 (越短越好)
response_time_score = max(0, 1 - (metrics["response_time"] / 30))
score_components.append(response_time_score * 0.2)
# 推理步骤效率评分
if metrics["reasoning_steps_count"] > 0:
step_efficiency = 1 / (metrics["reasoning_steps_count"] / 5)
step_efficiency_score = min(1, step_efficiency)
score_components.append(step_efficiency_score * 0.3)
# 置信度质量评分
confidence_score = min(1, metrics["confidence_average"])
score_components.append(confidence_score * 0.3)
# 工具使用效率评分
if metrics["tool_calls_count"] > 0:
tool_efficiency = metrics["reasoning_steps_count"] / metrics["tool_calls_count"]
tool_efficiency_score = min(1, tool_efficiency)
score_components.append(tool_efficiency_score * 0.2)
return sum(score_components)
def _generate_optimization_suggestions(self, metrics: dict) -> List[str]:
"""生成优化建议"""
suggestions = []
# 响应时间建议
if metrics["response_time"] > 20:
suggestions.append("考虑使用更快的推理模型或减少推理步骤")
# 推理步骤建议
if metrics["reasoning_steps_count"] > 8:
suggestions.append("优化推理流程，减少不必要的步骤")
elif metrics["reasoning_steps_count"] < 3:
suggestions.append("增加推理深度，确保分析的充分性")
# 置信度建议
if metrics["confidence_average"] < 0.6:
suggestions.append("提高推理质量，增加验证步骤")
elif metrics["confidence_average"] > 0.95:
suggestions.append("当前置信度过高，可能存在过度自信的问题")
# 工具使用建议
if metrics["tool_calls_count"] == 0:
suggestions.append("考虑使用更多工具来增强推理能力")
elif metrics["tool_calls_count"] > 10:
suggestions.append("减少不必要的工具调用，提高效率")
return suggestions错误处理最佳实践
推理错误预防:
class ReasoningErrorPrevention:
"""推理错误预防机制"""
@staticmethod
def validate_reasoning_input(
task: str,
agent_config: dict
) -> tuple[bool, List[str]]:
"""验证推理输入的有效性"""
errors = []
# 任务验证
if not task or len(task.strip()) < 10:
errors.append("任务描述过于简单，请提供更详细的描述")
if len(task) > 5000:
errors.append("任务描述过长，建议简化或分段处理")
# Agent配置验证
if not agent_config.get("model"):
errors.append("缺少模型配置")
if agent_config.get("reasoning") is None:
errors.append("需要明确指定是否启用推理模式")
# 工具配置验证
if agent_config.get("tools"):
for tool in agent_config["tools"]:
if not hasattr(tool, "name"):
errors.append(f"工具 {tool} 缺少名称配置")
return len(errors) == 0, errors
@staticmethod
def setup_error_recovery(
agent: Agent,
max_retries: int = 3
):
"""设置错误恢复机制"""
# 错误回调函数
async def handle_reasoning_error(error, context):
if isinstance(error, LowConfidenceError):
# 低置信度错误处理
await agent._retry_with_simplified_reasoning(context)
elif isinstance(error, StepExecutionError):
# 步骤执行错误处理
await agent._retry_with_alternative_approach(context)
elif isinstance(error, ReasoningTimeoutError):
# 超时错误处理
await agent._retry_with_reduced_complexity(context)
# 设置错误处理器
agent.error_handler = handle_reasoning_error
agent.max_retries = max_retries
return agent扩展性设计
插件式推理工具系统
推理工具接口定义:
from abc import ABC, abstractmethod
from typing import Any, Dict, List, Optional
class BaseReasoningTool(ABC):
"""推理工具基类"""
def __init__(self, name: str, description: str):
self.name = name
self.description = description
self.capabilities = []
self.dependencies = []
@abstractmethod
async def execute(
self,
input_data: Any,
context: Dict[str, Any]
) -> Dict[str, Any]:
"""执行推理工具的核心逻辑"""
pass
@abstractmethod
def validate_input(self, input_data: Any) -> bool:
"""验证输入数据"""
pass
def get_capabilities(self) -> List[str]:
"""获取工具能力列表"""
return self.capabilities
def get_dependencies(self) -> List[str]:
"""获取工具依赖"""
return self.dependencies
class ReasoningToolRegistry:
"""推理工具注册表"""
def __init__(self):
self._tools: Dict[str, BaseReasoningTool] = {}
self._tool_categories = {}
def register_tool(self, tool: BaseReasoningTool):
"""注册推理工具"""
self._tools[tool.name] = tool
# 按类别分组
for capability in tool.capabilities:
if capability not in self._tool_categories:
self._tool_categories[capability] = []
self._tool_categories[capability].append(tool.name)
def get_tool(self, tool_name: str) -> Optional[BaseReasoningTool]:
"""获取工具实例"""
return self._tools.get(tool_name)
def get_tools_by_capability(self, capability: str) -> List[BaseReasoningTool]:
"""根据能力获取工具"""
tool_names = self._tool_categories.get(capability, [])
return [self._tools[name] for name in tool_names if name in self._tools]
def list_all_tools(self) -> List[str]:
"""列出所有可用工具"""
return list(self._tools.keys())自定义推理工具实现示例:
class DatabaseQueryTool(BaseReasoningTool):
"""数据库查询推理工具"""
def __init__(self, db_connection):
super().__init__(
name="database_query",
description="用于推理过程中的数据库查询"
)
self.capabilities = ["data_retrieval", "factual_verification"]
self.dependencies = ["database_connection"]
self.db = db_connection
async def execute(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
"""执行数据库查询"""
try:
result = await self.db.execute_query(query)
return {
"success": True,
"data": result,
"query": query,
"timestamp": datetime.now()
}
except Exception as e:
return {
"success": False,
"error": str(e),
"query": query,
"timestamp": datetime.now()
}
def validate_input(self, query: str) -> bool:
"""验证SQL查询"""
# 基本SQL注入防护
dangerous_keywords = ["drop", "delete", "insert", "update"]
query_lower = query.lower()
return not any(keyword in query_lower for keyword in dangerous_keywords)
class SimulationEngineTool(BaseReasoningTool):
"""仿真引擎推理工具"""
def __init__(self, simulation_config: dict):
super().__init__(
name="simulation_engine",
description="用于复杂系统的仿真推理"
)
self.capabilities = ["scenario_modeling", "prediction", "what_if_analysis"]
self.config = simulation_config
async def execute(
self,
simulation_params: Dict[str, Any],
context: Dict[str, Any]
) -> Dict[str, Any]:
"""执行仿真计算"""
try:
# 运行仿真
results = await self._run_simulation(simulation_params)
return {
"success": True,
"simulation_results": results,
"parameters": simulation_params,
"confidence": self._calculate_simulation_confidence(results)
}
except Exception as e:
return {
"success": False,
"error": str(e),
"parameters": simulation_params
}
async def _run_simulation(self, params: Dict[str, Any]) -> Dict[str, Any]:
"""运行仿真（示例实现）"""
# 模拟仿真计算过程
import asyncio
await asyncio.sleep(1) # 模拟计算时间
return {
"scenario_outcome": "positive",
"probability": 0.75,
"key_metrics": {
"efficiency": 0.85,
"cost_reduction": 0.15,
"risk_level": 0.3
}
}
def _calculate_simulation_confidence(self, results: Dict[str, Any]) -> float:
"""计算仿真结果的可信度"""
base_confidence = 0.8
# 基于结果一致性调整置信度
if "key_metrics" in results:
metrics = results["key_metrics"]
# 检查指标合理性
if all(0 <= value <= 1 for value in metrics.values()):
base_confidence += 0.1
# 检查概率一致性
if "probability" in results:
prob = results["probability"]
if 0.1 <= prob <= 0.9: # 合理概率范围
base_confidence += 0.05
return min(0.95, base_confidence)推理插件管理器
插件生命周期管理:
class ReasoningPluginManager:
"""推理插件管理器"""
def __init__(self):
self.plugins: Dict[str, BaseReasoningTool] = {}
self.plugin_dependencies = {}
self.plugin_metadata = {}
async def load_plugin(
self,
plugin_path: str,
plugin_config: dict
) -> bool:
"""加载推理插件"""
try:
# 动态导入插件模块
spec = importlib.util.spec_from_file_location(
"reasoning_plugin", plugin_path
)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
# 创建插件实例
plugin_class = getattr(module, plugin_config["class_name"])
plugin_instance = plugin_class(**plugin_config["init_args"])
# 验证插件接口
if not isinstance(plugin_instance, BaseReasoningTool):
raise ValueError("插件必须继承自BaseReasoningTool")
# 注册插件
plugin_name = plugin_config.get("name", plugin_instance.name)
self.plugins[plugin_name] = plugin_instance
# 保存元数据
self.plugin_metadata[plugin_name] = {
"path": plugin_path,
"class_name": plugin_config["class_name"],
"capabilities": plugin_instance.get_capabilities(),
"dependencies": plugin_instance.get_dependencies(),
"loaded_at": datetime.now()
}
return True
except Exception as e:
log_error(f"加载插件失败 {plugin_path}: {e}")
return False
async def execute_plugin_chain(
self,
plugin_names: List[str],
input_data: Any
) -> Dict[str, Any]:
"""执行插件链"""
results = {}
current_data = input_data
for plugin_name in plugin_names:
if plugin_name not in self.plugins:
raise ValueError(f"插件 {plugin_name} 未找到")
plugin = self.plugins[plugin_name]
try:
# 验证输入数据
if not plugin.validate_input(current_data):
raise ValueError(f"插件 {plugin_name} 输入验证失败")
# 执行插件
plugin_result = await plugin.execute(
current_data,
{"chain_index": len(results)}
)
results[plugin_name] = plugin_result
current_data = plugin_result
except Exception as e:
log_error(f"插件 {plugin_name} 执行失败: {e}")
results[plugin_name] = {
"success": False,
"error": str(e),
"data": current_data
}
return results未来发展方向
即将推出的功能
1. 高级推理模式
class AdvancedReasoningModes:
"""高级推理模式"""
def __init__(self):
self.recursive_reasoning = RecursiveReasoningEngine()
self.meta_reasoning = MetaReasoningSystem()
self.emotional_reasoning = EmotionalReasoningModule()
self.causal_reasoning = CausalInferenceEngine()
async def recursive_reasoning_cycle(
self,
problem: str,
depth: int = 3
) -> Dict[str, Any]:
"""递归推理循环"""
reasoning_levels = []
for level in range(depth):
level_result = await self.recursive_reasoning.solve_level(
problem, level
)
reasoning_levels.append(level_result)
# 基于当前层级的结果调整下一层级的策略
if level_result.confidence > 0.8:
break
return {
"reasoning_levels": reasoning_levels,
"optimal_depth": len(reasoning_levels),
"final_solution": reasoning_levels[-1].solution
}
async def meta_reasoning_evaluation(
self,
reasoning_process: List[ReasoningStep]
) -> Dict[str, Any]:
"""元推理评估"""
return {
"reasoning_quality": await self.meta_reasoning.assess_quality(
reasoning_process
),
"efficiency_score": await self.meta_reasoning.assess_efficiency(
reasoning_process
),
"improvement_suggestions": await self.meta_reasoning.generate_suggestions(
reasoning_process
),
"reasoning_pattern": await self.meta_reasoning.identify_patterns(
reasoning_process
)
}2. 多模态推理增强
class MultimodalReasoningEngine:
"""多模态推理引擎"""
def __init__(self):
self.vision_reasoning = VisionReasoningModule()
self.audio_reasoning = AudioReasoningModule()
self.text_reasoning = TextReasoningModule()
self.fusion_engine = CrossModalFusionEngine()
async def cross_modal_reasoning(
self,
visual_input: bytes,
audio_input: bytes,
text_input: str,
task: str
) -> Dict[str, Any]:
"""跨模态推理"""
# 并行处理不同模态
visual_analysis = await self.vision_reasoning.analyze(visual_input)
audio_analysis = await self.audio_reasoning.analyze(audio_input)
text_analysis = await self.text_reasoning.analyze(text_input)
# 跨模态融合
fused_representation = await self.fusion_engine.fuse_modalities({
"visual": visual_analysis,
"audio": audio_analysis,
"text": text_analysis
})
# 基于融合表示进行推理
reasoning_result = await self.fused_reasoning(representation=fused_representation, task=task)
return {
"modal_analyses": {
"visual": visual_analysis,
"audio": audio_analysis,
"text": text_analysis
},
"fused_representation": fused_representation,
"cross_modal_reasoning": reasoning_result,
"confidence": self.calculate_cross_modal_confidence(reasoning_result)
}3. 自适应推理系统
class AdaptiveReasoningSystem:
"""自适应推理系统"""
def __init__(self):
self.context_analyzer = ContextAnalyzer()
self.preference_learner = UserPreferenceLearner()
self.dynamic_depth_controller = DynamicDepthController()
async def adaptive_reasoning_execution(
self,
task: str,
user_context: Dict[str, Any],
historical_patterns: List[Dict[str, Any]]
) -> Dict[str, Any]:
"""自适应推理执行"""
# 分析当前上下文
context_analysis = await self.context_analyzer.analyze({
"task": task,
"user_context": user_context,
"historical_patterns": historical_patterns
})
# 学习用户偏好
user_preferences = await self.preference_learner.update_preferences(
user_context, historical_patterns
)
# 动态调整推理参数
reasoning_config = {
"depth": self.dynamic_depth_controller.calculate_optimal_depth(context_analysis),
"tools": self.select_adaptive_tools(user_preferences, context_analysis),
"confidence_threshold": self.adjust_confidence_threshold(user_preferences),
"reasoning_style": self.infer_reasoning_style(user_preferences)
}
# 执行自适应推理
reasoning_result = await self.execute_adaptive_reasoning(task, reasoning_config)
return {
"context_analysis": context_analysis,
"user_preferences": user_preferences,
"reasoning_config": reasoning_config,
"reasoning_result": reasoning_result,
"adaptation_metrics": self.calculate_adaptation_metrics(reasoning_result)
}长期愿景
1. 自主学习推理系统
class AutonomousLearningReasoning:
"""自主学习推理系统"""
def __init__(self):
self.experience_accumulator = ExperienceAccumulator()
self.pattern_discovery = PatternDiscoveryEngine()
self.strategy_optimizer = StrategyOptimizer()
self.knowledge_transfer = KnowledgeTransferModule()
async def continuous_learning_cycle(self):
"""持续学习循环"""
while True:
# 收集新的推理经验
new_experiences = await self.experience_accumulator.collect_experiences()
# 发现新的推理模式
discovered_patterns = await self.pattern_discovery.find_patterns(
new_experiences
)
# 更新推理策略
await self.strategy_optimizer.optimize_strategies(discovered_patterns)
# 跨领域知识迁移
await self.knowledge_transfer.transfer_knowledge(discovered_patterns)
# 等待下一个学习周期
await asyncio.sleep(learning_interval)2. 创造性推理引擎
class CreativeReasoningEngine:
"""创造性推理引擎"""
def __init__(self):
self.creative_divergence = CreativeDivergenceModule()
self.idea_synthesizer = IdeaSynthesizer()
self.innovation_validator = InnovationValidator()
async def creative_problem_solving(
self,
problem: str,
creativity_level: float = 0.8
) -> Dict[str, Any]:
"""创造性问题解决"""
# 发散性思维阶段
divergent_ideas = await self.creative_divergence.generate_alternative_solutions(
problem, creativity_level
)
# 创意综合阶段
synthesized_solutions = await self.idea_synthesizer.synthesize_ideas(
divergent_ideas
)
# 创新性验证
validated_solutions = await self.innovation_validator.validate_innovations(
synthesized_solutions
)
return {
"original_problem": problem,
"creative_solutions": validated_solutions,
"novelty_score": self.calculate_novelty_score(validated_solutions),
"feasibility_assessment": self.assess_feasibility(validated_solutions)
}3. 协作式智能推理
class CollaborativeIntelligentReasoning:
"""协作式智能推理"""
def __init__(self):
self.human_collaboration = HumanCollaborationModule()
self.ai_team_coordination = AITeamCoordination()
self.collective_intelligence = CollectiveIntelligenceAggregator()
async def collaborative_reasoning_session(
self,
task: str,
human_experts: List[str],
ai_agents: List[Agent],
collaboration_mode: str = "hybrid"
) -> Dict[str, Any]:
"""协作推理会话"""
# 根据协作模式调整策略
if collaboration_mode == "human_lead":
human_role = "leader"
ai_role = "assistant"
elif collaboration_mode == "ai_lead":
human_role = "advisor"
ai_role = "leader"
else: # hybrid mode
human_role = "peer"
ai_role = "peer"
# 启动协作推理
collaboration_result = await self.collective_intelligence.collaborate(
task=task,
human_experts=human_experts,
ai_agents=ai_agents,
roles={"human": human_role, "ai": ai_role}
)
return {
"task": task,
"collaboration_mode": collaboration_mode,
"collective_reasoning": collaboration_result,
"contribution_analysis": self.analyze_contributions(collaboration_result),
"collaboration_effectiveness": self.assess_collaboration_effectiveness(collaboration_result)
}技术演进路线图
第一阶段（当前-2025）
- ✅ 基础三层推理架构
- ✅ 分离式推理模型支持
- ✅ 推理工具系统
- 🚧 推理团队协作
- 🚧 性能优化
- 🚧 错误处理机制
第二阶段（2025-2026）
- 🔄 多模态推理集成
- 🔄 自适应推理系统
- 🔄 高级推理模式
- 🔄 插件式扩展系统
第三阶段（2026-2027）
- 📅 自主学习推理
- 📅 创造性推理引擎
- 📅 协作式智能推理
- 📅 跨领域知识迁移
第四阶段（2027+）
- 📅 通用人工智能推理
- 📅 伦理推理系统
- 📅 意识模拟推理
- 📅 量子推理集成
结论
Agno的推理架构代表了AI推理系统设计的重要里程碑。通过革命性的三层抽象模型、分离式推理架构、结构化工具系统和协作式团队设计，它为构建智能、高效、可扩展的推理系统提供了完整的解决方案。
核心优势总结
- 架构清晰性: 分层设计使系统组件职责明确，易于理解和维护
- 扩展灵活性: 插件式架构支持无缝扩展新功能和能力
- 性能优化性: 多层次的性能优化确保高效推理执行
- 容错健壮性: 全面的错误处理和恢复机制保证系统稳定性
- 应用广泛性: 支持从简单查询到复杂问题解决的各种应用场景
技术创新点
- 分离式推理模型: 主模型与推理模型独立配置
- 结构化思维工具: Think和Analyze工具实现显式推理
- 协作式推理团队: 多Agent专业分工协作
- 自适应推理策略: 基于上下文的动态推理调整
- 完整状态管理: 推理过程的全程追踪和持久化
未来影响
随着技术的不断演进，Agno推理架构将继续推动人工智能向更高层次的智能发展：
- 科学研究加速: 复杂科学问题的自动化推理解决
- 商业决策优化: 基于多维度数据的智能决策支持
- 教育个性化: 适应学习者思维模式的个性化教学
- 创意产业变革: AI辅助的创新内容生成和概念发展
- 社会问题解决: 复杂社会问题的多角度分析和解决
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