feat(deep-agent): add custom workspace_root support for shell commands
- 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>
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@ -2,13 +2,18 @@ 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 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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@ -19,6 +24,14 @@ from agent.prompt_loader import load_system_prompt_async, load_mcp_settings_asyn
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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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@ -148,13 +161,15 @@ async def init_agent(config: AgentConfig):
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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
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agent, composite_backend = create_cli_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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enable_shell=False,
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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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@ -198,4 +213,164 @@ async def init_agent(config: AgentConfig):
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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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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
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@ -1,5 +1,4 @@
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<env>
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<env>
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Working directory: {agent_dir_path}
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Current User: {user_identifier}
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Current Time: {datetime}
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