dependent upon https://github.com/langchain-ai/langgraph/pull/6711
1. relax constraint in `factory.py` to allow for tools not
pre-registered in the `ModelRequest.tools` list
2. always add tool node if `wrap_tool_call` or `awrap_tool_call` is
implemented
3. add tests confirming you can register new tools at runtime in
`wrap_model_call` and execute them via `wrap_tool_call`
allows for the following pattern
```py
from langchain_core.messages import HumanMessage, ToolMessage
from langchain_core.tools import tool
from libs.langchain_v1.langchain.agents.factory import create_agent
from libs.langchain_v1.langchain.agents.middleware.types import (
AgentMiddleware,
ModelRequest,
ToolCallRequest,
)
@tool
def get_weather(location: str) -> str:
"""Get the current weather for a location."""
return f"The weather in {location} is sunny and 72°F."
@tool
def calculate_tip(bill_amount: float, tip_percentage: float = 20.0) -> str:
"""Calculate the tip amount for a bill."""
tip = bill_amount * (tip_percentage / 100)
return f"Tip: ${tip:.2f}, Total: ${bill_amount + tip:.2f}"
class DynamicToolMiddleware(AgentMiddleware):
"""Middleware that adds and handles a dynamic tool."""
def wrap_model_call(self, request: ModelRequest, handler):
updated = request.override(tools=[*request.tools, calculate_tip])
return handler(updated)
def wrap_tool_call(self, request: ToolCallRequest, handler):
if request.tool_call["name"] == "calculate_tip":
return handler(request.override(tool=calculate_tip))
return handler(request)
agent = create_agent(model="openai:gpt-4o-mini", tools=[get_weather], middleware=[DynamicToolMiddleware()])
result = agent.invoke({
"messages": [HumanMessage("What's the weather in NYC? Also calculate a 20% tip on a $85 bill")]
})
for msg in result["messages"]:
msg.pretty_print()
```
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We need to set `{"metadata": {"lc_source": "summarization"}}` on the
invocation so that consumers (e.g. `deepagents-cli`) can see that a
summarization LLM call is being made, and therefore take any necessary
actions (such as updating the status line to say `'Currently
summarizing...'`
See https://github.com/langchain-ai/deepagents/pull/742 for more
Related to #34693 (but for outbound)
Clarify the preference for using exact model IDs from provider
documentation over aliases to ensure reliable behavior in face of
upstream backend changes.
* Making `FakeToolCallingModel` generic on its `structured_response`
doesn't help anywhere in typing.
* There are more than 120 references of `FakeToolCallingModel` in the
code where you get ` error: Need type annotation for "model"
[var-annotated]` because mypy can't resolve the generic type (we don't
see them atm because they are in files temporarily excluded from mypy
checking). We would need to explicitly type them to
`FakeToolCallingModel[Any]`
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Appears `override()`'s docstring in `langgraph` already shows
`state=new_state` as a valid usage pattern
Works since `dataclasses.replace()` accepts any field, but the
`TypedDicts` weren't updated to match. Caused mypy to flag legitimate
usage as an error.
description by @mdrxy
- Enable `test_responses_spec.py` integration tests that were previously
skipped at module level
- Widen `ToolStrategy.schema` type annotation from `type[SchemaT]` to
`type[SchemaT] | dict[str, Any]` to match actual supported usage (JSON
schema dicts were already handled at runtime)
- Fix type annotations and linting issues in test file (modernize to
`dict`/`list`, add return types, prefix unused `_request` param)
- Improve generic typing in `load_spec` utility with bounded `TypeVar`
Co-authored-by: Mason Daugherty <mason@langchain.dev>
