7c9e270a66
- Add create_custom_cli_agent function to support custom workspace_root
- Set shell workspace to ~/.deepagents/{bot_id} for deep_agent type
- Pass system_prompt to create_custom_cli_agent for proper context
- Fix duplicate <env> tag in system_prompt_deep_agent.md
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
376 lines
15 KiB
Python
376 lines
15 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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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
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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 langchain.agents.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 SUMMARIZATION_MAX_TOKENS, SUMMARIZATION_MESSAGES_TO_KEEP, TOOL_OUTPUT_MAX_LENGTH, MCP_HTTP_TIMEOUT, MCP_SSE_READ_TIMEOUT
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from agent.agent_config import AgentConfig
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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_utils import prepare_checkpoint_message
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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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from deepagents_cli.shell import ShellMiddleware
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from deepagents_cli.agent_memory import AgentMemoryMiddleware
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from deepagents_cli.skills import SkillsMiddleware
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from deepagents_cli.config import settings, get_default_coding_instructions
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import os
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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.info(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(
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config.project_dir, config.language, config.system_prompt, config.robot_type, config.bot_id, config.user_identifier
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)
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final_mcp_settings = await load_mcp_settings_async(
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config.project_dir, config.mcp_settings, config.bot_id, config.robot_type
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)
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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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# 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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model_kwargs.update(config.generate_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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if config.robot_type == "deep_agent":
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# 使用 DeepAgentX 创建 agent，自定义 workspace_root
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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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)
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else:
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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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# 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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# 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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# 从连接池获取 checkpointer
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if config.session_id:
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from .checkpoint_manager import get_checkpointer_manager
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manager = get_checkpointer_manager()
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checkpointer = manager.checkpointer
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await prepare_checkpoint_message(config, checkpointer)
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summarization_middleware = SummarizationMiddleware(
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model=llm_instance,
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max_tokens_before_summary=SUMMARIZATION_MAX_TOKENS,
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messages_to_keep=SUMMARIZATION_MESSAGES_TO_KEEP,
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summary_prompt="请简洁地总结以上对话的要点，包括重要的用户信息、讨论过的话题和关键结论。"
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)
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middleware.append(summarization_middleware)
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agent = create_agent(
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model=llm_instance,
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system_prompt=system_prompt,
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tools=mcp_tools,
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middleware=middleware,
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checkpointer=checkpointer
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)
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logger.info(f"create {config.robot_type} elapsed: {time.time() - create_start:.3f}s")
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return agent, checkpointer
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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_memory: bool = True,
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enable_skills: bool = True,
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enable_shell: bool = True,
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workspace_root: 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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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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# Setup agent directory for persistent memory (if enabled)
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if enable_memory or enable_skills:
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agent_dir = settings.ensure_agent_dir(assistant_id)
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agent_md = agent_dir / "agent.md"
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if not agent_md.exists():
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source_content = get_default_coding_instructions()
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agent_md.write_text(source_content)
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# Skills directories (if enabled)
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skills_dir = none
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if enable_skills:
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skills_dir = settings.ensure_user_skills_dir(assistant_id)
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# Build middleware stack based on enabled features
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agent_middleware = []
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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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composite_backend = CompositeBackend(
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default=FilesystemBackend(root_dir=workspace_root), # Current working directory
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routes={}, # No virtualization - use real paths
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)
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# Add memory middleware
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if enable_memory:
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agent_middleware.append(
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AgentMemoryMiddleware(settings=settings, assistant_id=assistant_id)
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)
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# Add skills middleware
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if enable_skills:
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agent_middleware.append(
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SkillsMiddleware(
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skills_dir=skills_dir,
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assistant_id=assistant_id
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)
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)
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# Add shell middleware (only in local mode)
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if enable_shell:
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# Create environment for shell commands
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# Restore user's original LANGSMITH_PROJECT so their code traces separately
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shell_env = os.environ.copy()
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if settings.user_langchain_project:
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shell_env["LANGSMITH_PROJECT"] = settings.user_langchain_project
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# Use custom workspace_root if provided, otherwise use current directory
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shell_workspace = workspace_root if workspace_root is not None else str(Path.cwd())
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agent_middleware.append(
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ShellMiddleware(
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workspace_root=shell_workspace,
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env=shell_env,
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)
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)
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else:
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# ========== REMOTE SANDBOX MODE ==========
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composite_backend = CompositeBackend(
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default=sandbox, # Remote sandbox (ModalBackend, etc.)
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routes={}, # No virtualization
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)
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# Add memory middleware
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if enable_memory:
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agent_middleware.append(
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AgentMemoryMiddleware(settings=settings, assistant_id=assistant_id)
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)
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# Add skills middleware
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if enable_skills:
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agent_middleware.append(
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SkillsMiddleware(
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skills_dir=skills_dir,
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assistant_id=assistant_id,
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project_skills_dir=project_skills_dir,
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)
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)
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# Note: Shell middleware not used in sandbox mode
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# File operations and execute tool are provided by the sandbox backend
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# Get or use custom system prompt
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if system_prompt is None:
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# Import get_system_prompt from deepagents_cli.agent
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from deepagents_cli.agent import get_system_prompt as _get_system_prompt
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system_prompt = _get_system_prompt(assistant_id=assistant_id, sandbox_type=sandbox_type)
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# Import InterruptOnConfig
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from langchain.agents.middleware import InterruptOnConfig
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# Configure interrupt_on based on auto_approve setting
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if auto_approve:
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# No interrupts - all tools run automatically
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interrupt_on = {}
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else:
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# Full HITL for destructive operations - import from deepagents_cli.agent
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from deepagents_cli.agent import _add_interrupt_on
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interrupt_on = _add_interrupt_on()
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# Import config
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from deepagents_cli.config import config
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# Create the agent
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agent = create_deep_agent(
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model=model,
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system_prompt=system_prompt,
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tools=tools,
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backend=composite_backend,
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middleware=agent_middleware,
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interrupt_on=interrupt_on,
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checkpointer=InMemorySaver(),
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).with_config(config)
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return agent, composite_backend |