502 lines
20 KiB
Python
502 lines
20 KiB
Python
import json
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import logging
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import time
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import copy
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import os
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import tempfile
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from pathlib import Path
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from typing import Any, Dict
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from langchain.chat_models import init_chat_model
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from deepagents import create_deep_agent
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from deepagents.backends import CompositeBackend, LocalShellBackend
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from deepagents.backends.filesystem import FilesystemBackend
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from deepagents.backends.sandbox import SandboxBackendProtocol
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from deepagents_cli.agent import create_cli_agent
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from langchain.agents import create_agent
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from langgraph.store.base import BaseStore
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from langchain.agents.middleware import SummarizationMiddleware as LangchainSummarizationMiddleware
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from .summarization_middleware import SummarizationMiddleware
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from langchain_mcp_adapters.client import MultiServerMCPClient
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from sympy.printing.cxx import none
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from utils.fastapi_utils import detect_provider
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from .guideline_middleware import GuidelineMiddleware
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from .tool_output_length_middleware import ToolOutputLengthMiddleware
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from .tool_use_cleanup_middleware import ToolUseCleanupMiddleware
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from utils.settings import (
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SUMMARIZATION_MAX_TOKENS,
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SUMMARIZATION_TOKENS_TO_KEEP,
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TOOL_OUTPUT_MAX_LENGTH,
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MCP_HTTP_TIMEOUT,
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MCP_SSE_READ_TIMEOUT,
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DEFAULT_TRIM_TOKEN_LIMIT
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)
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from utils.token_counter import create_token_counter
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from agent.agent_config import AgentConfig
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from .mem0_manager import get_mem0_manager
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from .mem0_middleware import create_mem0_middleware
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from .mem0_config import Mem0Config
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from agent.prompt_loader import load_system_prompt_async, load_mcp_settings_async
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from agent.agent_memory_cache import get_memory_cache_manager
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from .checkpoint_manager import get_checkpointer_manager
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from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
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from langgraph.checkpoint.memory import InMemorySaver
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from langchain.tools import BaseTool
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from langchain_core.language_models import BaseChatModel
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from langgraph.pregel import Pregel
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# 新版本导入：MemoryMiddleware 和 SkillsMiddleware 已迁移到 deepagents.middleware
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from deepagents.middleware import MemoryMiddleware, SkillsMiddleware
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from langchain.agents.middleware import AgentMiddleware
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from langgraph.types import Checkpointer
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from deepagents_cli.config import settings, get_default_coding_instructions
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from langchain.agents.middleware import HumanInTheLoopMiddleware, InterruptOnConfig, TodoListMiddleware
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from deepagents.middleware.filesystem import FilesystemMiddleware
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from deepagents.middleware.patch_tool_calls import PatchToolCallsMiddleware
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from langchain_anthropic.middleware import AnthropicPromptCachingMiddleware
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from deepagents.graph import BASE_AGENT_PROMPT
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# 新增：LocalContextMiddleware
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from deepagents_cli.local_context import LocalContextMiddleware
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# 自定义：支持 SKILL.md 完整读取的 FilesystemMiddleware
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from .custom_filesystem_middleware import CustomFilesystemMiddleware
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# 全局 MemorySaver 实例
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# from langgraph.checkpoint.memory import MemorySaver
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# _global_checkpointer = MemorySaver()
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logger = logging.getLogger('app')
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# Utility functions
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def read_system_prompt():
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"""读取通用的无状态系统prompt"""
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with open("./prompt/system_prompt_default.md", "r", encoding="utf-8") as f:
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return f.read().strip()
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def read_mcp_settings():
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"""读取MCP工具配置"""
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with open("./mcp/mcp_settings.json", "r") as f:
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mcp_settings_json = json.load(f)
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return mcp_settings_json
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async def get_tools_from_mcp(mcp):
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"""从MCP配置中提取工具（带缓存）"""
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start_time = time.time()
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# 防御式处理：确保 mcp 是列表且长度大于 0，且包含 mcpServers
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if not isinstance(mcp, list) or len(mcp) == 0 or "mcpServers" not in mcp[0]:
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logger.info(f"get_tools_from_mcp: invalid mcp config, elapsed: {time.time() - start_time:.3f}s")
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return []
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# 尝试从缓存获取
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cache_manager = get_memory_cache_manager()
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cached_tools = cache_manager.get_mcp_tools(mcp)
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if cached_tools is not None:
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logger.info(f"get_tools_from_mcp: cached {len(cached_tools)} tools, elapsed: {time.time() - start_time:.3f}s")
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return cached_tools
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# 深拷贝 mcp 配置，避免修改原始配置（影响缓存键）
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mcp_copy = copy.deepcopy(mcp)
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# 修改 mcp_copy[0]["mcpServers"] 列表，把 type 字段改成 transport
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# 如果没有 transport，则根据是否存在 url 默认 transport 为 http 或 stdio
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for cfg in mcp_copy[0]["mcpServers"].values():
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if "type" in cfg:
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cfg.pop("type")
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if "transport" not in cfg:
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cfg["transport"] = "http" if "url" in cfg else "stdio"
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# 为 HTTP/ SSE 传输的 MCP 服务器添加超时配置
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# 如果配置中未设置超时，使用全局默认值
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if cfg.get("transport") in ("http", "sse"):
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if "timeout" not in cfg:
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cfg["timeout"] = MCP_HTTP_TIMEOUT
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if "sse_read_timeout" not in cfg:
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cfg["sse_read_timeout"] = MCP_SSE_READ_TIMEOUT
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# 确保 mcp_copy[0]["mcpServers"] 是字典类型
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if not isinstance(mcp_copy[0]["mcpServers"], dict):
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logger.info(f"get_tools_from_mcp: mcpServers is not dict, elapsed: {time.time() - start_time:.3f}s")
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return []
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try:
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mcp_client = MultiServerMCPClient(mcp_copy[0]["mcpServers"])
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mcp_tools = await mcp_client.get_tools()
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# 缓存结果
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cache_manager.set_mcp_tools(mcp, mcp_tools)
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logger.info(f"get_tools_from_mcp: loaded {len(mcp_tools)} tools, elapsed: {time.time() - start_time:.3f}s")
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return mcp_tools
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except Exception as e:
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# 发生异常时返回空列表，避免上层调用报错
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logger.error(f"get_tools_from_mcp: error {e}, elapsed: {time.time() - start_time:.3f}s")
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return []
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async def init_agent(config: AgentConfig):
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"""
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初始化 Agent，支持持久化内存和对话摘要
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注意：不再缓存 agent，只缓存 mcp_tools
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返回 (agent, checkpointer) 元组，调用后需要归还 checkpointer
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Args:
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config: AgentConfig 对象，包含所有初始化参数
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Returns:
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(agent, checkpointer) 元组
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"""
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# 加载配置
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final_system_prompt = await load_system_prompt_async(config)
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final_mcp_settings = await load_mcp_settings_async(config)
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# 如果没有提供mcp，使用config中的mcp_settings
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mcp_settings = final_mcp_settings if final_mcp_settings else read_mcp_settings()
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system_prompt = final_system_prompt if final_system_prompt else read_system_prompt()
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config.system_prompt = mcp_settings
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config.mcp_settings = system_prompt
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# 获取 mcp_tools（缓存逻辑已内置到 get_tools_from_mcp 中）
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mcp_tools = await get_tools_from_mcp(mcp_settings)
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logger.info(f"Loaded {len(mcp_tools)} MCP tools")
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# 检测或使用指定的提供商
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model_provider, base_url = detect_provider(config.model_name, config.model_server)
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# 构建模型参数
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model_kwargs = {
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"model": config.model_name,
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"model_provider": model_provider,
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"temperature": 0.8,
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"base_url": base_url,
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"api_key": config.api_key
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}
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if config.generate_cfg:
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# 内部使用的参数，不应传给任何 LLM
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internal_params = {
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'tool_output_max_length',
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'tool_output_truncation_strategy',
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'tool_output_filters',
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'tool_output_exclude',
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'preserve_code_blocks',
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'preserve_json',
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}
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# Anthropic 不支持的 OpenAI 特有参数
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openai_only_params = {
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'n', # 生成多少个响应
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'presence_penalty',
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'frequency_penalty',
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'logprobs',
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'top_logprobs',
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'logit_bias',
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'seed',
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'suffix',
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'best_of',
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'echo',
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'user',
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}
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# 根据提供商决定需要过滤的参数
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params_to_filter = internal_params.copy()
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if model_provider == 'anthropic':
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params_to_filter.update(openai_only_params)
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filtered_cfg = {k: v for k, v in config.generate_cfg.items() if k not in params_to_filter}
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model_kwargs.update(filtered_cfg)
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llm_instance = init_chat_model(**model_kwargs)
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# 创建新的 agent（不再缓存）
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logger.info(f"Creating new agent for session: {getattr(config, 'session_id', 'no-session')}")
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checkpointer = None
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create_start = time.time()
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# 从连接池获取 checkpointer（prepare_checkpoint_message 已在 from_v1/from_v2_request 中调用）
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if config.session_id:
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try:
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manager = get_checkpointer_manager()
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checkpointer = manager.checkpointer
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except Exception as e:
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logger.warning(f"Failed to load checkpointer: {e}")
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# 构建中间件列表
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middleware = []
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# 首先添加 ToolUseCleanupMiddleware 来清理孤立的 tool_use
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middleware.append(ToolUseCleanupMiddleware())
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# 添加工具输出长度控制中间件
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tool_output_middleware = ToolOutputLengthMiddleware(
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max_length=(getattr(config.generate_cfg, 'tool_output_max_length', None) if config.generate_cfg else None) or TOOL_OUTPUT_MAX_LENGTH,
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truncation_strategy=getattr(config.generate_cfg, 'tool_output_truncation_strategy', 'smart') if config.generate_cfg else 'smart',
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tool_filters=getattr(config.generate_cfg, 'tool_output_filters', None) if config.generate_cfg else None,
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exclude_tools=getattr(config.generate_cfg, 'tool_output_exclude', []) if config.generate_cfg else [],
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preserve_code_blocks=getattr(config.generate_cfg, 'preserve_code_blocks', True) if config.generate_cfg else True,
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preserve_json=getattr(config.generate_cfg, 'preserve_json', True) if config.generate_cfg else True
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)
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middleware.append(tool_output_middleware)
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# 添加 Mem0 记忆中间件（如果启用）
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if config.enable_memori:
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try:
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# 确保有 user_identifier
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if not config.user_identifier:
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logger.warning("Mem0 enabled but user_identifier is missing, skipping Mem0")
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else:
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# 获取全局 Mem0Manager（已在 fastapi_app.py 中初始化）
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mem0_manager = get_mem0_manager()
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# 创建 Mem0 中间件，传入现有的 llm_instance 和 config
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mem0_middleware = create_mem0_middleware(
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bot_id=config.bot_id,
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user_identifier=config.user_identifier,
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session_id=config.session_id or "default",
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agent_config=config, # 传入 AgentConfig 用于中间件间传递数据
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enabled=config.enable_memori,
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semantic_search_top_k=config.memori_semantic_search_top_k,
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mem0_manager=mem0_manager,
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llm_instance=llm_instance, # 传入现有 LLM 实例
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)
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if mem0_middleware:
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middleware.append(mem0_middleware)
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logger.info("Mem0 middleware added to agent")
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except Exception as e:
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logger.error(f"Failed to create Mem0 middleware: {e}, continuing without Mem0")
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# 只有在 enable_thinking 为 True 时才添加 GuidelineMiddleware
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if config.enable_thinking:
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middleware.append(GuidelineMiddleware(llm_instance, config, system_prompt))
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summarization_middleware = SummarizationMiddleware(
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model=llm_instance,
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trigger=('tokens', SUMMARIZATION_MAX_TOKENS),
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trim_tokens_to_summarize=DEFAULT_TRIM_TOKEN_LIMIT,
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keep=('tokens', SUMMARIZATION_TOKENS_TO_KEEP),
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token_counter=create_token_counter(config.model_name)
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)
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middleware.append(summarization_middleware)
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workspace_root = str(Path.cwd() / "projects" /"robot"/ config.bot_id)
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# workspace_root = str(Path.home() / ".deepagents" / config.bot_id)
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agent, composite_backend = create_custom_cli_agent(
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model=llm_instance,
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assistant_id=config.bot_id,
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system_prompt=system_prompt,
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tools=mcp_tools,
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auto_approve=True,
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workspace_root=workspace_root,
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middleware=middleware,
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checkpointer=checkpointer,
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shell_env={
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"ASSISTANT_ID": config.bot_id,
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"USER_IDENTIFIER": config.user_identifier,
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"TRACE_ID": config.trace_id
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}
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)
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logger.info(f"create agent elapsed: {time.time() - create_start:.3f}s")
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return agent, checkpointer
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class CustomSkillsMiddleware(SkillsMiddleware):
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"""自定义的 SkillsMiddleware，使用新的签名格式"""
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def before_agent(self, state, runtime, config):
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"""Load skills metadata before agent execution.
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修改路径显示为相对路径。
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Args:
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state: Current agent state.
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runtime: Runtime context.
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Returns:
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Updated state with skills_metadata populated.
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"""
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if "skills_metadata" in state and len(state["skills_metadata"]) ==0 :
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del state["skills_metadata"]
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state = super().before_agent(state, runtime, config)
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return state
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async def abefore_agent(self, state, runtime, config):
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"""Load skills metadata before agent execution.
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修改路径显示为相对路径。
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Args:
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state: Current agent state.
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runtime: Runtime context.
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Returns:
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Updated state with skills_metadata populated.
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"""
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if "skills_metadata" in state and len(state["skills_metadata"]) ==0 :
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del state["skills_metadata"]
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state = await super().abefore_agent(state, runtime, config)
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return state
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def create_custom_cli_agent(
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model: str | BaseChatModel,
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assistant_id: str,
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*,
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tools: list[BaseTool] | None = None,
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sandbox: SandboxBackendProtocol | None = None,
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sandbox_type: str | None = None,
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system_prompt: str | None = None,
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auto_approve: bool = False,
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enable_skills: bool = True,
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enable_shell: bool = True,
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middleware: list[AgentMiddleware] = [],
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workspace_root: str | None = None,
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checkpointer: Checkpointer | None = None,
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store: BaseStore | None = None,
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shell_env: dict[str, str] | None = None,
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) -> tuple[Pregel, CompositeBackend]:
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"""Create a CLI-configured agent with custom workspace_root for shell commands.
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This is a custom version of create_cli_agent that allows specifying a custom
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workspace_root for shell commands instead of using Path.cwd().
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Args:
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model: LLM model to use (e.g., "anthropic:claude-sonnet-4-5-20250929")
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assistant_id: Agent identifier for memory/state storage
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tools: Additional tools to provide to agent (default: empty list)
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sandbox: Optional sandbox backend for remote execution (e.g., ModalBackend).
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If None, uses local filesystem + shell.
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sandbox_type: Type of sandbox provider ("modal", "runloop", "daytona").
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Used for system prompt generation.
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system_prompt: Override the default system prompt. If None, generates one
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based on sandbox_type and assistant_id.
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auto_approve: If True, automatically approves all tool calls without human
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confirmation. Useful for automated workflows.
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enable_memory: Enable AgentMemoryMiddleware for persistent memory
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enable_skills: Enable SkillsMiddleware for custom agent skills
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enable_shell: Enable ShellMiddleware for local shell execution (only in local mode)
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workspace_root: Working directory for shell commands. If None, uses Path.cwd().
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checkpointer: Optional checkpointer for persisting conversation state
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store: Optional BaseStore for persisting user preferences and agent memory
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shell_env: Optional custom environment variables to pass to ShellMiddleware.
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These will be merged with os.environ. Custom vars take precedence.
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Returns:
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2-tuple of (agent_graph, composite_backend)
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- agent_graph: Configured LangGraph Pregel instance ready for execution
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- composite_backend: CompositeBackend for file operations
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"""
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if tools is None:
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tools = []
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# Build middleware stack based on enabled features
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agent_middleware = middleware
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# Prepare workspace root
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if workspace_root is None:
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workspace_root = str(Path.cwd())
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# CONDITIONAL SETUP: Local vs Remote Sandbox
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if sandbox is None:
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# ========== LOCAL MODE ==========
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if enable_shell:
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# Create environment for shell commands
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final_shell_env = os.environ.copy()
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if shell_env:
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final_shell_env.update(shell_env)
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# Use LocalShellBackend for filesystem + shell execution
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backend = LocalShellBackend(
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root_dir=workspace_root,
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inherit_env=True,
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env=final_shell_env,
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)
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else:
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# No shell access - use plain FilesystemBackend
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backend = FilesystemBackend(root_dir=workspace_root)
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# Set up composite backend with routing (参考新版本实现)
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large_results_backend = FilesystemBackend(
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root_dir=tempfile.mkdtemp(prefix="deepagents_large_results_"),
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)
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conversation_history_backend = FilesystemBackend(
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root_dir=tempfile.mkdtemp(prefix="deepagents_conversation_history_"),
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)
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composite_backend = CompositeBackend(
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default=backend,
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routes={
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"/large_tool_results/": large_results_backend,
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"/conversation_history/": conversation_history_backend,
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},
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)
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# Add skills middleware (using new signature)
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if enable_skills:
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skills_sources = ["./skills"]
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agent_middleware.append(
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CustomSkillsMiddleware(
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backend=FilesystemBackend(root_dir=workspace_root),
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sources=skills_sources,
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)
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)
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# Add LocalContextMiddleware (new in latest version)
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# Check if backend supports execute (is _ExecutableBackend)
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if enable_shell:
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from deepagents_cli.local_context import LocalContextMiddleware, _ExecutableBackend
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if isinstance(backend, _ExecutableBackend):
|
||
agent_middleware.append(LocalContextMiddleware(backend=backend))
|
||
|
||
else:
|
||
# ========== REMOTE SANDBOX MODE ==========
|
||
composite_backend = CompositeBackend(
|
||
default=sandbox, # Remote sandbox (ModalBackend, etc.)
|
||
routes={}, # No virtualization
|
||
)
|
||
|
||
# Add skills middleware
|
||
if enable_skills:
|
||
skills_sources = ["/skills"]
|
||
|
||
agent_middleware.append(
|
||
CustomSkillsMiddleware(
|
||
backend=sandbox,
|
||
sources=skills_sources,
|
||
)
|
||
)
|
||
|
||
# Note: Shell middleware not used in sandbox mode
|
||
# File operations and execute tool are provided by the sandbox backend
|
||
|
||
# Get or use custom system prompt
|
||
if system_prompt is None:
|
||
from deepagents_cli.agent import get_system_prompt as _get_system_prompt
|
||
system_prompt = _get_system_prompt(assistant_id=assistant_id, sandbox_type=sandbox_type)
|
||
|
||
# Configure interrupt_on based on auto_approve setting
|
||
if auto_approve:
|
||
# No interrupts - all tools run automatically
|
||
interrupt_on = {}
|
||
else:
|
||
# Full HITL for destructive operations
|
||
from deepagents_cli.agent import _add_interrupt_on
|
||
interrupt_on = _add_interrupt_on()
|
||
|
||
deepagent_middleware = [
|
||
TodoListMiddleware(),
|
||
CustomFilesystemMiddleware(backend=composite_backend), # 使用自定义的 FilesystemMiddleware，支持 SKILL.md 完整读取
|
||
AnthropicPromptCachingMiddleware(unsupported_model_behavior="ignore"),
|
||
PatchToolCallsMiddleware(),
|
||
]
|
||
if agent_middleware:
|
||
deepagent_middleware.extend(agent_middleware)
|
||
if interrupt_on is not None:
|
||
deepagent_middleware.append(HumanInTheLoopMiddleware(interrupt_on=interrupt_on))
|
||
|
||
agent = create_agent(
|
||
model,
|
||
system_prompt=system_prompt + "\n\n" + BASE_AGENT_PROMPT if system_prompt else BASE_AGENT_PROMPT,
|
||
tools=tools,
|
||
middleware=deepagent_middleware,
|
||
checkpointer=checkpointer,
|
||
store=store,
|
||
).with_config({"recursion_limit": 1000})
|
||
return agent, composite_backend
|