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cf615a46e6
This PR adds error handling and retry mechanisms for create_agent
structured output via a `handle_errors` parameter in `ToolOutput`.
Changes:
* Adds `MultipleStructuredOutputsError` exception for when models
incorrectly call multiple structured output tools simultaneously
* Adds `StructuredOutputParsingError` exception for when tool arguments
fail to parse according to the schema
* Implements automatic error handling logic that re-prompts the model
with a configurable error message when structured output failures occur
via `handle_errors` policy in `ToolOutput`:
```python
class ToolOutput:
...
handle_errors: Union[
bool, # True: retry all, False: no retry
str, # Custom static error message for all errors
type[Exception], # Retry only this exception type
tuple[type[Exception], ...], # Retry only these exception types
Callable[[Exception], str], # Custom callable returning error message
]
"""Error handling strategy. Default: True (retry on all error types with default error message)"""
```
Examples:
```python
# Retry all errors
ToolOutput(WeatherReport)
# No retry
ToolOutput(WeatherReport, handle_errors=False)
# Custom message for all errors
ToolOutput(WeatherReport, handle_errors="Please provide valid data")
# Only retry specific error type
ToolOutput(WeatherReport, handle_errors=StructuredOutputParsingError)
# Multiple error types
ToolOutput(WeatherReport, handle_errors=(MultipleStructuredOutputsError, StructuredOutputParsingError))
# Custom logic
ToolOutput(
Union[WeatherReport, LocationInfo],
handle_errors=lambda e: "Only one response please" if isinstance(e, MultipleStructuredOutputsError) else "Invalid format"
)
```
---------
Co-authored-by: Sydney Runkle <sydneymarierunkle@gmail.com>
LangGraph Prebuilt
This library defines high-level APIs for creating and executing LangGraph agents and tools.
Important
This library is meant to be bundled with
langgraph, don't install it directly
Agents
langgraph-prebuilt provides an implementation of a tool-calling ReAct-style agent - create_react_agent:
pip install langchain-anthropic
from langchain_anthropic import ChatAnthropic
from langgraph.prebuilt import create_react_agent
# Define the tools for the agent to use
def search(query: str):
"""Call to surf the web."""
# This is a placeholder, but don't tell the LLM that...
if "sf" in query.lower() or "san francisco" in query.lower():
return "It's 60 degrees and foggy."
return "It's 90 degrees and sunny."
tools = [search]
model = ChatAnthropic(model="claude-3-7-sonnet-latest")
app = create_react_agent(model, tools)
# run the agent
app.invoke(
{"messages": [{"role": "user", "content": "what is the weather in sf"}]},
)
Tools
ToolNode
langgraph-prebuilt provides an implementation of a node that executes tool calls - ToolNode:
from langgraph.prebuilt import ToolNode
from langchain_core.messages import AIMessage
def search(query: str):
"""Call to surf the web."""
# This is a placeholder, but don't tell the LLM that...
if "sf" in query.lower() or "san francisco" in query.lower():
return "It's 60 degrees and foggy."
return "It's 90 degrees and sunny."
tool_node = ToolNode([search])
tool_calls = [{"name": "search", "args": {"query": "what is the weather in sf"}, "id": "1"}]
ai_message = AIMessage(content="", tool_calls=tool_calls)
# execute tool call
tool_node.invoke({"messages": [ai_message]})
ValidationNode
langgraph-prebuilt provides an implementation of a node that validates tool calls against a pydantic schema - ValidationNode:
from pydantic import BaseModel, field_validator
from langgraph.prebuilt import ValidationNode
from langchain_core.messages import AIMessage
class SelectNumber(BaseModel):
a: int
@field_validator("a")
def a_must_be_meaningful(cls, v):
if v != 37:
raise ValueError("Only 37 is allowed")
return v
validation_node = ValidationNode([SelectNumber])
validation_node.invoke({
"messages": [AIMessage("", tool_calls=[{"name": "SelectNumber", "args": {"a": 42}, "id": "1"}])]
})
Agent Inbox
The library contains schemas for using the Agent Inbox with LangGraph agents. Learn more about how to use Agent Inbox here.
from langgraph.types import interrupt
from langgraph.prebuilt.interrupt import HumanInterrupt, HumanResponse
def my_graph_function():
# Extract the last tool call from the `messages` field in the state
tool_call = state["messages"][-1].tool_calls[0]
# Create an interrupt
request: HumanInterrupt = {
"action_request": {
"action": tool_call['name'],
"args": tool_call['args']
},
"config": {
"allow_ignore": True,
"allow_respond": True,
"allow_edit": False,
"allow_accept": False
},
"description": _generate_email_markdown(state) # Generate a detailed markdown description.
}
# Send the interrupt request inside a list, and extract the first response
response = interrupt([request])[0]
if response['type'] == "response":
# Do something with the response
...