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from langchain.agents import create_agent
from langchain.agents.middleware import ToolErrorMiddleware, ToolRetryMiddleware


def on_error(exc: Exception, request: ToolCallRequest) -> str | None:
    # Surface ValueError to the model so it can correct the input
    if isinstance(exc, ValueError):
        return f"`{request.tool_call['name']}` failed: {type(exc).__name__}. Fix the input and retry."
    # Let all other exceptions propagate (halts the run)
    return None


# Async-only usage
async def aon_error(exc: Exception, request: ToolCallRequest) -> str | None:
    if isinstance(exc, ConnectionError):
        return f"Tool `{request.tool_call['name']}` encountered a connection error."
    return None


agent = create_agent(
    model="gpt-5.5",
    tools=[search_tool, database_tool],
    middleware=[
        # Place retry inner so exceptions reach ToolErrorMiddleware after retries are exhausted
        ToolRetryMiddleware(max_retries=3, on_failure="error"),
        ToolErrorMiddleware(on_error=on_error, tools=["search_tool"]),
    ],
)

# Async-only: pass aon_error alone (do not pass on_error)
async_agent = create_agent(
    model="gpt-5.5",
    tools=[api_tool],
    middleware=[ToolErrorMiddleware(aon_error=aon_error)],
)

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from langchain.agents import create_agent
from deepagents.middleware import FilesystemMiddleware
from deepagents.backends import CompositeBackend, StateBackend, StoreBackend
from langgraph.store.memory import InMemoryStore

store = InMemoryStore()

agent = create_agent(
    model="claude-sonnet-4-6",
    store=store,
    middleware=[
        FilesystemMiddleware(
            backend=CompositeBackend(
                default=StateBackend(),
                routes={"/memories/": StoreBackend()}
            ),
            custom_tool_descriptions={
                "ls": "Use the ls tool when...",
                "read_file": "Use the read_file tool to..."
            }  # Optional: Custom descriptions for filesystem tools
        ),
    ],
)