To fix nondeterministic results causing integration testing to sometimes
fail
Also speeds up from 10s to 0.5
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
**Description:** Add link to Code of Conduct in the Additional resources
section to make community guidelines more accessible for all
contributors.
**Rationale:**
- **Community Health:** Making the Code of Conduct easily discoverable
helps set clear expectations for community behavior and fosters a more
inclusive, respectful environment
- **New Contributor Experience:** Many new contributors look to the
README as the primary source of project information. Having the Code of
Conduct readily available helps onboard them properly
- **Best Practices:** Prominent Code of Conduct links are considered a
best practice in open source projects and improve project accessibility
- **Low Impact:** This is a simple, non-breaking change that
significantly improves documentation completeness
**Issue:** N/A
**Dependencies:** None
- standardize on using model IDs, no more aliases - makes future
maintenance easier
- use latest models in docstrings to highlight support
- remove remaining sonnet 3-7 usage due to deprecation
Depends on #33751
- use latest models in examples to highlight support
- standardize on using IDs in examples - no more aliases to improve
determinism in future tests
- bump lock
- in integration tests, fix stale casettes and use `MODEL_NAME`
uniformly where possible
- add case for default max tokens for sonnet-4-5 (was missing)
While working on ToolRuntime in TS I discovered that Python still uses
`thread_model_call_count` and `run_model_call_count` in ToolNode tests
which afaik we removed.
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!
* attach the latest `AIMessage` to all `StructuredOutputError`s so that
relevant middleware can use as desired
* raise `StructuredOutputError` from `ProviderStrategy` logic in case of
failed parsing (so that we can retry from middleware)
* added a test suite w/ example custom middleware that retries for tool
+ provider strategy
Long term, we could add our own opinionated structured output retry
middleware, but this at least unblocks folks who want to use custom
retry logic in the short term :)
```py
class StructuredOutputRetryMiddleware(AgentMiddleware):
"""Retries model calls when structured output parsing fails."""
def __init__(self, max_retries: int) -> None:
self.max_retries = max_retries
def wrap_model_call(
self, request: ModelRequest, handler: Callable[[ModelRequest], ModelResponse]
) -> ModelResponse:
for attempt in range(self.max_retries + 1):
try:
return handler(request)
except StructuredOutputError as exc:
if attempt == self.max_retries:
raise
ai_content = exc.ai_message.content
error_message = (
f"Your previous response was:\n{ai_content}\n\n"
f"Error: {exc}. Please try again with a valid response."
)
request.messages.append(HumanMessage(content=error_message))
```
Updated the README to clarify LangChain's focus on building agents and
LLM-powered applications. Added a section for community discussions and
refined the ecosystem description.
