Builds on #37101.
---
Two changes in one commit, both motivated by the same principle: a
single, clean owner for everything schema-related on a tool.
## `ToolSchema` — the root cache
Previously `BaseTool` had three independent `cached_property` slots
(`tool_call_schema`, `args`, `_approximate_schema_chars`) that all
computed overlapping data and each needed individual invalidation. This
PR replaces them with a single `ToolSchema` dataclass and one
`tool_schema` cached property that is the sole root:
```python
@dataclass
class ToolSchema:
name: str
description: str
validator: TypeAdapter # validates tool call inputs
json_schema: dict # sent to LLMs
pydantic_schema: Any # model class or dict (backward compat)
args: dict # properties from json_schema
approximate_chars: int # precomputed for token estimation
```
`BaseTool.tool_call_schema`, `BaseTool.args`, and
`BaseTool._approximate_schema_chars` are now plain `@property` delegates
to `tool_schema`. `__setattr__` only needs to pop one key on mutation
instead of four. The `is`-identity caching tests still pass because all
delegates read from the same cached `ToolSchema` object.
`ToolSchema` is exported from `langchain_core.tools` and can be used
directly by integrations that want to consume both the validator and the
schema without going through `BaseTool`.
## `TypeAdapter`-based TypedDict conversion
`_convert_any_typed_dicts_to_pydantic` was a ~70-line recursive function
that converted TypedDicts to throwaway pydantic v1 model classes just to
call `.schema()`. Replaced with:
```python
adapter = TypeAdapter(typed_dict)
schema = adapter.json_schema()
```
Pydantic v2's `TypeAdapter` handles everything the old code did — nested
TypedDicts, generic containers, `Annotated` metadata — and also
correctly handles `NotRequired` and `Required` annotations, which the v1
path did not. A new test `test__convert_typed_dict_not_required`
verifies this:
```python
class Tool(TypedDict):
required_field: str
optional_field: NotRequired[int]
result = _convert_typed_dict_to_openai_function(Tool)
assert "required_field" in result["parameters"]["required"]
assert "optional_field" not in result["parameters"]["required"]
```
Field descriptions from Google-style docstrings and `Annotated[T, ...,
"description"]` metadata are preserved by post-processing the schema
after generation.
The old `test__convert_typed_dict_to_openai_function_fail` test expected
a `TypeError` for `MutableSet` because pydantic v1 didn't support it.
pydantic v2 does; the test is updated to verify successful conversion
instead.
## What stays unchanged
- All public `BaseTool` API signatures — `tool_call_schema`, `args`,
`get_input_schema()` all have the same signatures and return types as
before.
- `pydantic.v1` acceptance for `args_schema` — tools with v1 model
schemas continue to work.
> AI-agent assisted contribution.
---------
Co-authored-by: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
Drop the `NotImplementedError` branch in `warn_deprecated` so callers
can pass `pending=False` without specifying a `removal` version. The
previous behavior contradicted the docstring (which claimed an empty
default would auto-compute a removal version) — no such computation
existed; the function just raised a placeholder "Need to determine which
default deprecation schedule to use" error.
When a langsmith `@traceable` function invokes a LangChain Runnable or
LangGraph subgraph, the callback manager's `_configure` function injects
the `@traceable` RunTree into the `LangChainTracer`'s `run_map` so that
child runs can resolve their parent for trace nesting. However, since
the RunTree was created outside the tracer's callback lifecycle,
`_end_trace` never removes it. The entry persists in `run_map`
indefinitely, retaining the full RunTree and its entire child tree.
In applications with nested subgraph invocations (e.g. an outer
investigation graph delegating to skill agent subgraphs, each compiled
as their own `StateGraph`), this causes RunTree objects to accumulate
linearly with every call.
**Fix:** Track which `run_map` entries were injected externally via a
shared `_external_run_ids` refcount dict on `_TracerCore`. When
`_start_trace` adds a child under an external parent, it increments the
count. When `_end_trace` finishes a child, it decrements — and evicts
the external parent from `run_map` once the last child completes.
The refcount (rather than a simple set) is necessary because a single
external parent may have multiple sibling children in the callback chain
(e.g. a `prompt | llm` `RunnableSequence`). Only truly external runs are
tracked — the `_configure` guard `if run_id_str not in handler.run_map`
prevents tracer-managed runs from being misclassified.
Resolve symlinks before validating file extensions in the deprecated
`save()` method on prompt classes.
Credit to Jeff Ponte (@JDP-Security) for reporting the symlink
resolution issue.
Adds serialization mappings for `ChatBedrockConverse` and `BedrockLLM`
to unblock standard tests on `langchain-core>=1.2.5` (context:
[langchain-aws#821](https://github.com/langchain-ai/langchain-aws/pull/821)).
Also introduces a class-specific validator system in
`langchain_core.load` that blocks deserialization of AWS Bedrock models
when `endpoint_url` or `base_url` parameters are present, preventing
SSRF attacks via crafted serialized payloads.
Closes#34645
## Changes
- Add `ChatBedrockConverse` and `BedrockLLM` entries to
`SERIALIZABLE_MAPPING` in `mapping.py`, mapping legacy paths to their
`langchain_aws` import locations
- Add `validators.py` with `_bedrock_validator` — rejects
deserialization kwargs containing `endpoint_url` or `base_url` for all
Bedrock-related classes (`ChatBedrock`, `BedrockChat`,
`ChatBedrockConverse`, `ChatAnthropicBedrock`, `BedrockLLM`, `Bedrock`)
- `CLASS_INIT_VALIDATORS` registry covers both serialized (legacy) keys
and resolved import paths from `ALL_SERIALIZABLE_MAPPINGS`, preventing
bypass via direct-path payloads
- Move kwargs extraction and all validator checks
(`CLASS_INIT_VALIDATORS` + `init_validator`) in `Reviver.__call__` to
run **before** `importlib.import_module()` — fail fast on security
violations before executing third-party code
- Class-specific validators are independent of `init_validator` and
cannot be disabled by passing `init_validator=None`
## Testing
- `test_validator_registry_keys_in_serializable_mapping` — structural
invariant test ensuring every `CLASS_INIT_VALIDATORS` key exists in
`ALL_SERIALIZABLE_MAPPINGS`
- 10 end-to-end `load()` tests covering all Bedrock class paths (legacy
aliases, resolved import paths, `ChatAnthropicBedrock`,
`init_validator=None` bypass attempt)
- Unit tests for `_bedrock_validator` covering `endpoint_url`,
`base_url`, both params, and safe kwargs
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
PR #35788 added 7 new fields to the `langchain-profiles` CLI output
(`name`, `status`, `release_date`, `last_updated`, `open_weights`,
`attachment`, `temperature`) but didn't update `ModelProfile` in
`langchain-core`. Partner packages like `langchain-aws` that set
`extra="forbid"` on their Pydantic models hit `extra_forbidden`
validation errors when Pydantic encountered undeclared TypedDict keys at
construction time. This adds the missing fields, makes `ModelProfile`
forward-compatible, provides a base-class hook so partners can stop
duplicating model-profile validator boilerplate, migrates all in-repo
partners to the new hook, and adds runtime + CI-time warnings for schema
drift.
## Changes
### `langchain-core`
- Add `__pydantic_config__ = ConfigDict(extra="allow")` to
`ModelProfile` so unknown profile keys pass Pydantic validation even on
models with `extra="forbid"` — forward-compatibility for when the CLI
schema evolves ahead of core
- Declare the 7 missing fields on `ModelProfile`: `name`, `status`,
`release_date`, `last_updated`, `open_weights` (metadata) and
`attachment`, `temperature` (capabilities)
- Add `_warn_unknown_profile_keys()` in `model_profile.py` — emits a
`UserWarning` when a profile dict contains keys not in `ModelProfile`,
suggesting a core upgrade. Wrapped in a bare `except` so introspection
failures never crash model construction
- Add `BaseChatModel._resolve_model_profile()` hook that returns `None`
by default. Partners can override this single method instead of
redefining the full `_set_model_profile` validator — the base validator
calls it automatically
- Add `BaseChatModel._check_profile_keys` as a separate
`model_validator` that calls `_warn_unknown_profile_keys`. Uses a
distinct method name so partner overrides of `_set_model_profile` don't
inadvertently suppress the check
### `langchain-profiles` CLI
- Add `_warn_undeclared_profile_keys()` to the CLI (`cli.py`), called
after merging augmentations in `refresh()` — warns at profile-generation
time (not just runtime) when emitted keys aren't declared in
`ModelProfile`. Gracefully skips if `langchain-core` isn't installed
- Add guard test
`test_model_data_to_profile_keys_subset_of_model_profile` in
model-profiles — feeds a fully-populated model dict to
`_model_data_to_profile()` and asserts every emitted key exists in
`ModelProfile.__annotations__`. CI fails before any release if someone
adds a CLI field without updating the TypedDict
### Partner packages
- Migrate all 10 in-repo partners to the `_resolve_model_profile()`
hook, replacing duplicated `@model_validator` / `_set_model_profile`
overrides: anthropic, deepseek, fireworks, groq, huggingface, mistralai,
openai (base + azure), openrouter, perplexity, xai
- Anthropic retains custom logic (context-1m beta → `max_input_tokens`
override); all others reduce to a one-liner
- Add `pr_lint.yml` scope for the new `model-profiles` package
Closes#29530
---
Remove a stale BlockBuster allowlist entry in `conftest.py` referencing
`aconfig_with_context` — the function and its containing module
(`langchain_core/beta/runnables/context.py`) were deleted in `fded6c6b1`
(Sep 2025, #32850). Spotted by @antonio-mello-ai in #29530.
Fixes missing `run.metadata.usage_metadata` population in
`LangChainTracer` for real LLM/chat traces following #34414
- Fix extraction to read usage from serialized tracer message shape:
`outputs.generations[*][*].message.kwargs.usage_metadata`
- Remove non-serialized direct message shape handling
(`message.usage_metadata`) from extractor to match real tracer output
path
- Clarify tracer docstrings around chat callback naming
(`on_chat_model_start` + shared `on_llm_end`) to reduce ambiguity
## Why
#34414 introduced usage duplication into `run.metadata.usage_metadata`,
but the extractor read `message.usage_metadata`.
In real tracer flow, messages are serialized with `dumpd(...)` during
run completion, so usage metadata lives under
`message.kwargs.usage_metadata`. Because of this mismatch, duplication
did not trigger in real traces.
