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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Fixes#34517
Supersedes #34557, #34570
Fixes token inflation in `SummarizationMiddleware` that caused context
window overflow during summarization.
**Root cause:** When formatting messages for the summary prompt,
`str(messages)` was implicitly called, which includes all Pydantic
metadata fields (`usage_metadata`, `response_metadata`,
`additional_kwargs`, etc.). This caused the stringified representation
to use ~2.5x more tokens than `count_tokens_approximately` estimates.
**Problem:**
- Summarization triggers at 85% of context window based on
`count_tokens_approximately`
- But `str(messages)` in the prompt uses 2.5x more tokens
- Results in `ContextLengthExceeded`
**Fix:** Use `get_buffer_string()` to format messages, which produces
compact output:
```
Human: What's the weather?
AI: Let me check...[tool_calls]
Tool: 72°F and sunny
```
Instead of verbose Pydantic repr:
```python
[HumanMessage(content='What's the weather?', additional_kwargs={}, response_metadata={}), ...]
```
## Summary
Enhances the `init_chat_model` function with comprehensive input
validation, improved model inference patterns, and better error handling
to provide a significantly improved user experience.
## Changes Made
- ✅ **Input Validation**: Added comprehensive type and value checking
for all parameters
- ✅ **Enhanced Model Inference**: Improved pattern matching with
case-insensitive support and new model patterns
- ✅ **Better Error Messages**: Detailed error messages with examples and
documentation links
- ✅ **Comprehensive Tests**: Added extensive test coverage for all new
functionality
- ✅ **Documentation**: Enhanced docstrings and examples
## Backward Compatibility
All changes are fully backward compatible. No breaking changes
introduced.
## Testing
- Added 6 new test functions covering input validation, model inference,
and error handling
- All existing tests continue to pass
- Comprehensive parametrized testing for various model patterns
## User Experience Improvements
- Better error messages help users quickly resolve configuration issues
- Enhanced model inference reduces the need to specify providers
explicitly
- Comprehensive input validation catches issues early with helpful
guidance
---------
Co-authored-by: Mason Daugherty <github@mdrxy.com>
Co-authored-by: Mason Daugherty <mason@langchain.dev>
If the `stdout` "done marker" arrives before the `stderr` output is
enqueued, the method returns early without capturing the `stderr` line.
The two reader threads run independently with no synchronization
guaranteeing `stderr` arrives before the done marker.
In environments with Python 3.10, timing differences can cause the
`stdout` marker to win the race, resulting in `<no output>` instead of
`[stderr]` error.
Observed as a flaky test on `test_stderr_output_labeling` in CI:
```shell
FAILED tests/unit_tests/agents/middleware/implementations/test_shell_tool.py::test_stderr_output_labeling - AssertionError: assert '[stderr] error' in '<no output>'
```
With this we get the correct types for `_runnable_support` annotated
functions.
* return list[BaseMessage] when messages is not None
* return Runnable when messages is None
* typing of function args
Closes https://github.com/langchain-ai/langchain/issues/33983
* Adds `ModelRetryMiddleware` modeled after `ToolRetryMiddleware`
* Uses `on_failure` modes of `error` and `continue` to match the
`exit_behavior` modes of model + tool call limit middleware
* In a backwards compatible manner, aligns the API of
`ToolRetryMiddleware`'s `on_failure` with the above
* Centralize common "retry" utils across these middlewares
Moving all `ToolNode` related improvements back to LangGraph and
importing them in LC!
pairing w/ https://github.com/langchain-ai/langgraph/pull/6321
this fixes a couple of things:
1. `InjectedState`, store etc will continue to work as expected no
matter where the import is from
2. `ToolRuntime` is now usable w/in langgraph, woohoo!
