Closes#37172
---
Bumps `langchain-fireworks` to the rewritten `fireworks-ai` 1.x SDK
(currently 1.2.0a*; Stainless-generated, pure-httpx, no
`grpcio`/`protobuf`/`googleapis-common-protos`).
The motivating bug is a startup crash in self-hosted LangGraph
environments that also import `langchain-google-vertexai`. Importing
`fireworks-ai` 0.19.x eagerly loads vendored grpcio protobuf modules
under `fireworks.control_plane.generated.protos_grpcio.*`, which
register `google/rpc/status.proto`, `google/api/*.proto`, and
`google/longrunning/*.proto` in the default protobuf descriptor pool.
When `langchain-google-vertexai` later triggers
`google.api_core.exceptions` → `grpc_status.rpc_status` →
`google.rpc.status_pb2`, the pool already holds a byte-different
descriptor for `google/rpc/status.proto` and startup dies with:
```
TypeError: Couldn't build proto file into descriptor pool:
duplicate file name google/rpc/status.proto
```
Fleet has been pinning around this by routing Fireworks through
`ChatOpenAI` against the OpenAI-compat endpoint, which works for
inference but means Fireworks `ModelProfile` data never loads — so Kimi
K2.6's ~262k context window goes unrecognized and summarization triggers
below limit.
The 1.x SDK does not vendor protobuf at all. The control-plane gRPC code
path is gone; chat inference goes over httpx. Verified locally that
`import langchain_fireworks` and `from langchain_fireworks import
ChatFireworks` load zero `_pb2` / `google.*` modules.
## What changed in `ChatFireworks`
- Imports switch from `fireworks.client` to the top-level `fireworks`
package.
- Async path now `await client.chat.completions.create(...)`; the 0.x
`acreate` shim is no longer used.
- Error classes remapped to the 1.x hierarchy. `InvalidRequestError` →
`BadRequestError`. `BadGatewayError` and `ServiceUnavailableError` no
longer exist (1.x maps all `>=500` to `InternalServerError`) and were
dropped from the retryable set with no loss of coverage.
`FireworksContextOverflowError`'s parent class becomes
`BadRequestError`.
- `stream_options` is moved into the SDK's `extra_body` because the
Stainless-generated `create()` signature does not model it as a typed
kwarg. Top-level `stream_options` is preserved as a caller convenience;
if a caller supplies both `extra_body["stream_options"]` and a top-level
value, `extra_body` wins and the discarded value is logged.
- The 0.x `(connect, read)` tuple form of `request_timeout` is
normalized to an `httpx.Timeout` so existing user code keeps working.
- The SDK's built-in retry layer is suppressed via `max_retries=0` on
client construction so retries remain owned by
`create_base_retry_decorator` and surface through the LangChain
`run_manager`.
## Lifecycle methods
Adds `close()` and `aclose()` on `ChatFireworks`. The 1.x
`AsyncFireworks` client defaults to `httpx_aiohttp.HttpxAiohttpClient`,
whose underlying aiohttp `ClientSession` is created lazily on first
request. Sync-only paths therefore never open a session — which fixes
the "Unclosed client session" warnings from #37172 at the source.
Callers using async paths can now release the connector
deterministically rather than relying on GC after the event loop has
stopped. An autouse fixture in the integration `conftest.py` calls
`aclose()` between tests to silence the corresponding `Unclosed
connector` warning that surfaces under `pytest-asyncio`.
## Relation to #37227
Supersedes #37227. That PR monkey-patched
`fireworks._util.is_running_in_async_context` and
`fireworks.client.api_client.is_running_in_async_context` to suppress
the 0.x SDK's eager `aiohttp.ClientSession` creation in async contexts.
Both module paths are removed in 1.x; the SDK's lazy-session behavior
makes the suppression unnecessary, and the explicit `aclose()` provides
the cleaner long-term lifecycle hook. Thanks to @keenborder786 for
surfacing the failure mode.
## Installation note
`fireworks-ai` 1.x is currently published as an alpha (`1.2.0a*`); a
stable 1.x is not yet out. `pip install langchain-fireworks` / `uv pip
install langchain-fireworks` will need `--pre` (or `--prerelease=allow`)
until Fireworks GAs 1.x. The `pyproject.toml` adds `[tool.uv] prerelease
= "allow"` so the in-repo dev environment resolves cleanly. The package
version is bumped to `1.4.0` — the public surface (`ChatFireworks`,
`Fireworks`, `FireworksEmbeddings`) is unchanged; the breakage is
confined to internal error classes and the transitive SDK.
Replace `accounts/fireworks/models/kimi-k2-instruct-0905` with
`accounts/fireworks/models/kimi-k2p6` across the Fireworks integration
tests. Fireworks appears to have pulled the 0905 slug from serverless
(returns 404 `NOT_FOUND` despite still appearing "Ready" in their UI);
`kimi-k2p6` is the current deployed successor and supports the same
capabilities used by these tests (tool calls, streaming, structured
output).
Extract strict from kwargs and pass it to convert_to_openai_tool when
converting tools. This ensures that when strict is provided, it's
properly used during tool conversion and removed from kwargs before
calling the parent bind method.
Also extract reasoning_content from API responses and store it in
additional_kwargs for AIMessage objects.
Fixes https://github.com/langchain-ai/langchain/issues/34341 and
https://github.com/langchain-ai/langchain/issues/34342
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
`mixtral-8x-7b-instruct` was recently retired from Fireworks Serverless.
Here we remove the default model altogether, so that the model must be
explicitly specified on init:
```python
ChatFireworks(model="accounts/fireworks/models/llama-v3p1-70b-instruct") # for example
```
We also set a null default for `temperature`, which previously defaulted
to 0.0. This parameter will no longer be included in request payloads
unless it is explicitly provided.
We are implementing a token-counting callback handler in
`langchain-core` that is intended to work with all chat models
supporting usage metadata. The callback will aggregate usage metadata by
model. This requires responses to include the model name in its
metadata.
To support this, if a model `returns_usage_metadata`, we check that it
includes a string model name in its `response_metadata` in the
`"model_name"` key.
More context: https://github.com/langchain-ai/langchain/pull/30487
- Refactor standard test classes to make them easier to configure
- Update openai to support stop_sequences init param
- Update groq to support stop_sequences init param
- Update fireworks to support max_retries init param
- Update ChatModel.bind_tools to type tool_choice
- Update groq to handle tool_choice="any". **this may be controversial**
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
```python
class UsageMetadata(TypedDict):
"""Usage metadata for a message, such as token counts.
Attributes:
input_tokens: (int) count of input (or prompt) tokens
output_tokens: (int) count of output (or completion) tokens
total_tokens: (int) total token count
"""
input_tokens: int
output_tokens: int
total_tokens: int
```
```python
class AIMessage(BaseMessage):
...
usage_metadata: Optional[UsageMetadata] = None
"""If provided, token usage information associated with the message."""
...
```
core[minor], langchain[patch], openai[minor], anthropic[minor], fireworks[minor], groq[minor], mistralai[minor]
```python
class ToolCall(TypedDict):
name: str
args: Dict[str, Any]
id: Optional[str]
class InvalidToolCall(TypedDict):
name: Optional[str]
args: Optional[str]
id: Optional[str]
error: Optional[str]
class ToolCallChunk(TypedDict):
name: Optional[str]
args: Optional[str]
id: Optional[str]
index: Optional[int]
class AIMessage(BaseMessage):
...
tool_calls: List[ToolCall] = []
invalid_tool_calls: List[InvalidToolCall] = []
...
class AIMessageChunk(AIMessage, BaseMessageChunk):
...
tool_call_chunks: Optional[List[ToolCallChunk]] = None
...
```
Important considerations:
- Parsing logic occurs within different providers;
- ~Changing output type is a breaking change for anyone doing explicit
type checking;~
- ~Langsmith rendering will need to be updated:
https://github.com/langchain-ai/langchainplus/pull/3561~
- ~Langserve will need to be updated~
- Adding chunks:
- ~AIMessage + ToolCallsMessage = ToolCallsMessage if either has
non-null .tool_calls.~
- Tool call chunks are appended, merging when having equal values of
`index`.
- additional_kwargs accumulate the normal way.
- During streaming:
- ~Messages can change types (e.g., from AIMessageChunk to
AIToolCallsMessageChunk)~
- Output parsers parse additional_kwargs (during .invoke they read off
tool calls).
Packages outside of `partners/`:
- https://github.com/langchain-ai/langchain-cohere/pull/7
- https://github.com/langchain-ai/langchain-google/pull/123/files
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
