自定义 Deep Agents

pip install -U "langchain[anthropic]"

pip install -U "langchain[openai]"

pip install -U "langchain[google-genai]"

pip install -U "langchain[aws]"

pip install -U "langchain[huggingface]"

from deepagents.backends import FilesystemBackend

agent = create_deep_agent(
    model="google_genai:gemini-3.1-pro-preview",
    backend=FilesystemBackend(root_dir=".", virtual_mode=True)
)

from deepagents.backends import LocalShellBackend

agent = create_deep_agent(
    model="google_genai:gemini-3.1-pro-preview",
    backend=LocalShellBackend(root_dir=".", env={"PATH": "/usr/bin:/bin"})
)

from langgraph.store.memory import InMemoryStore
from deepagents.backends import StoreBackend

agent = create_deep_agent(
    model="google_genai:gemini-3.1-pro-preview",
    backend=StoreBackend(
        namespace=lambda ctx: (ctx.runtime.context.user_id,),
    ),
    store=InMemoryStore()  # 适用于本地开发；部署到 LangSmith 时请省略此参数
)

from deepagents import create_deep_agent
from deepagents.backends import CompositeBackend, StateBackend, StoreBackend
from langgraph.store.memory import InMemoryStore

agent = create_deep_agent(
    model="google_genai:gemini-3.1-pro-preview",
    backend=CompositeBackend(
        default=StateBackend(),
        routes={
            "/memories/": StoreBackend(),
        }
    ),
    store=InMemoryStore()  # Store passed to create_deep_agent, not backend
)

import os

from deepagents import create_deep_agent
from langchain_anthropic import ChatAnthropic
from langchain_runloop import RunloopSandbox
from runloop_api_client import RunloopSDK

client = RunloopSDK(bearer_token=os.environ["RUNLOOP_API_KEY"])

devbox = client.devbox.create()
backend = RunloopSandbox(devbox=devbox)

agent = create_deep_agent(
    model=ChatAnthropic(model="claude-sonnet-4-6"),
    system_prompt="You are a Python coding assistant with sandbox access.",
    backend=backend,
)

try:
    result = agent.invoke(
        {
            "messages": [
                {
                    "role": "user",
                    "content": "Create a small Python package and run pytest",
                }
            ]
        }
    )
finally:
    devbox.shutdown()

from daytona import Daytona
from deepagents import create_deep_agent
from langchain_anthropic import ChatAnthropic
from langchain_daytona import DaytonaSandbox

sandbox = Daytona().create()
backend = DaytonaSandbox(sandbox=sandbox)

agent = create_deep_agent(
    model=ChatAnthropic(model="claude-sonnet-4-6"),
    system_prompt="You are a Python coding assistant with sandbox access.",
    backend=backend,
)

try:
    result = agent.invoke(
        {
            "messages": [
                {
                    "role": "user",
                    "content": "Create a small Python package and run pytest",
                }
            ]
        }
    )
finally:
    sandbox.stop()

from deepagents import create_deep_agent
from deepagents.backends import LangSmithSandbox
from langchain_anthropic import ChatAnthropic
from langsmith.sandbox import SandboxClient

client = SandboxClient()
ls_sandbox = client.create_sandbox(template_name="my-template")
backend = LangSmithSandbox(sandbox=ls_sandbox)

agent = create_deep_agent(
    model=ChatAnthropic(model="claude-sonnet-4-6"),
    system_prompt="You are a Python coding assistant with sandbox access.",
    backend=backend,
)
try:
    result = agent.invoke(
        {
            "messages": [
                {
                    "role": "user",
                    "content": "Create a small Python package and run pytest",
                }
            ]
        }
    )
finally:
    client.delete_sandbox(ls_sandbox.name)

from urllib.request import urlopen
from deepagents import create_deep_agent
from deepagents.backends import StoreBackend
from deepagents.backends.utils import create_file_data
from langgraph.store.memory import InMemoryStore


store = InMemoryStore()

skill_url = "https://raw.githubusercontent.com/langchain-ai/deepagents/refs/heads/main/libs/cli/examples/skills/langgraph-docs/SKILL.md"
with urlopen(skill_url) as response:
    skill_content = response.read().decode('utf-8')

store.put(
    namespace=("filesystem",),
    key="/skills/langgraph-docs/SKILL.md",
    value=create_file_data(skill_content)
)

agent = create_deep_agent(
    model="google_genai:gemini-3.1-pro-preview",
    backend=StoreBackend(),
    store=store,
    skills=["/skills/"]
)

result = agent.invoke(
    {
        "messages": [
            {
                "role": "user",
                "content": "What is langgraph?",
            }
        ]
    },
    config={"configurable": {"thread_id": "12345"}},
)

from deepagents import create_deep_agent
from langgraph.checkpoint.memory import MemorySaver
from deepagents.backends.filesystem import FilesystemBackend

# 人机交互需要检查点存储器
checkpointer = MemorySaver()

agent = create_deep_agent(
    model="google_genai:gemini-3.1-pro-preview",
    backend=FilesystemBackend(root_dir="/Users/user/{project}"),
    skills=["/Users/user/{project}/skills/"],
    interrupt_on={
        "write_file": True,  # 默认：批准、编辑、拒绝
        "read_file": False,  # 无需中断
        "edit_file": True    # 默认：批准、编辑、拒绝
    },
    checkpointer=checkpointer,  # 必需！
)

result = agent.invoke(
    {
        "messages": [
            {
                "role": "user",
                "content": "What is langgraph?",
            }
        ]
    },
    config={"configurable": {"thread_id": "12345"}},
)

from urllib.request import urlopen

from deepagents import create_deep_agent
from deepagents.backends import StoreBackend
from deepagents.backends.utils import create_file_data
from langgraph.store.memory import InMemoryStore

with urlopen("https://raw.githubusercontent.com/langchain-ai/deepagents/refs/heads/main/examples/text-to-sql-agent/AGENTS.md") as response:
    agents_md = response.read().decode("utf-8")

# 创建存储并添加文件
store = InMemoryStore()
file_data = create_file_data(agents_md)
store.put(
    namespace=("filesystem",),
    key="/AGENTS.md",
    value=file_data
)

agent = create_deep_agent(
    model="google_genai:gemini-3.1-pro-preview",
    backend=StoreBackend(),
    store=store,
    memory=[
        "/AGENTS.md"
    ]
)

result = agent.invoke(
    {
        "messages": [
            {
                "role": "user",
                "content": "请告诉我你的记忆文件里有什么。",
            }
        ],
        "files": {"/AGENTS.md": create_file_data(agents_md)},
    },
    config={"configurable": {"thread_id": "12345"}},
)

from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend
from langgraph.checkpoint.memory import MemorySaver

# 人机协作需要检查点
checkpointer = MemorySaver()

agent = create_deep_agent(
    model="google_genai:gemini-3.1-pro-preview",
    backend=FilesystemBackend(root_dir="/Users/user/{project}"),
    memory=[
        "./AGENTS.md"
    ],
    interrupt_on={
        "write_file": True,  # 默认：批准、编辑、拒绝
        "read_file": False,  # 不需要中断
        "edit_file": True    # 默认：批准、编辑、拒绝
    },
    checkpointer=checkpointer,  # 必需！
)