Files
langchain/libs/core
Mason Daugherty 86428c63ac fix(core,openai): normalize v1 streamed tool calls (#35983)
OpenAI Chat Completions streaming has a v1 normalization gap when tool
calls are streamed.

When users opt into `output_version="v1"`, `.content_blocks` is expected
to be the normalized cross-provider view of the message. For OpenAI Chat
Completions streams, though, chunks still carry raw string `content`
plus side-channel `tool_call_chunks` / `tool_calls`.

Practically, an OpenAI stream chunk can look like this internally:

```python
AIMessageChunk(
    content="",
    tool_call_chunks=[
        {
            "name": "get_weather",
            "args": '{"location": "SF"}',
            "id": "call_123",
            "index": 0,
            "type": "tool_call_chunk",
        }
    ],
    response_metadata={"model_provider": "openai", "output_version": "v1"},
)
```

That is not already-normalized v1 content like this:

```python
AIMessageChunk(
    content=[
        {
            "type": "tool_call_chunk",
            "name": "get_weather",
            "args": '{"location": "SF"}',
            "id": "call_123",
            "index": 0,
        }
    ],
)
```

Because `.content_blocks` currently short-circuits solely on
`output_version="v1"`, it can return the raw string/empty list directly
instead of running the OpenAI translator that incorporates
`tool_call_chunks` / `tool_calls` into normalized v1 blocks.

In practice, a streamed OpenAI tool call can be parsed successfully into
`tool_calls`, but still be missing from the final aggregated
`.content_blocks`. Downstream code that consumes the v1 block interface
then sees no `tool_call` block and must know to inspect OpenAI-specific
chunk fields instead.

User story:

> As a LangChain user streaming OpenAI Chat Completions with bound tools
and `output_version="v1"`, I need the final aggregated message's
`.content_blocks` to include normalized `tool_call` blocks, so that code
written against the v1 content-block interface handles streamed tool
calls consistently across providers.

Expected final aggregated view:

```python
message.content_blocks == [
    {
        "type": "tool_call",
        "name": "get_weather",
        "args": {"location": "SF"},
        "id": "call_123",
    }
]
```

Root causes:

1. The usage-only Chat Completions chunk uses `content=[]` in v1 mode
while normal streaming chunks use `content=""`, creating inconsistent
content types during chunk aggregation.
2. `AIMessage.content_blocks` and `AIMessageChunk.content_blocks` treat
any `output_version="v1"` message as already-normalized, even when
`content` is still raw string content from Chat Completions.
3. Content-bearing OpenAI stream chunks do not carry
`output_version="v1"`, so the final merged chunk may not reliably take
the v1 normalization path.

Changes:

- Keep usage-only Chat Completions chunks as `content=""` instead of
overriding to `[]`, so streaming chunks merge consistently.
- Propagate `output_version="v1"` to content-bearing chunks.
- Only short-circuit v1 `.content_blocks` when `content` is already a
list of blocks; otherwise fall through to the provider translator.
- Add regression tests covering string-content v1 fallback, usage-only
chunk content consistency, and streamed tool calls appearing as
normalized final v1 blocks.
2026-06-11 00:51:50 -04:00
..

🦜🍎 LangChain Core

PyPI - Version PyPI - License PyPI - Downloads Twitter

Looking for the JS/TS version? Check out LangChain.js.

To help you ship LangChain apps to production faster, check out LangSmith. LangSmith is a unified developer platform for building, testing, and monitoring LLM applications.

Quick Install

pip install langchain-core

🤔 What is this?

LangChain Core contains the base abstractions that power the LangChain ecosystem.

These abstractions are designed to be as modular and simple as possible.

The benefit of having these abstractions is that any provider can implement the required interface and then easily be used in the rest of the LangChain ecosystem.

⛰️ Why build on top of LangChain Core?

The LangChain ecosystem is built on top of langchain-core. Some of the benefits:

  • Modularity: We've designed Core around abstractions that are independent of each other, and not tied to any specific model provider.
  • Stability: We are committed to a stable versioning scheme, and will communicate any breaking changes with advance notice and version bumps.
  • Battle-tested: Core components have the largest install base in the LLM ecosystem, and are used in production by many companies.

📖 Documentation

For full documentation, see the API reference. For conceptual guides, tutorials, and examples on using LangChain, see the LangChain Docs. You can also chat with the docs using Chat LangChain.

📕 Releases & Versioning

See our Releases and Versioning policies.

💁 Contributing

As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.

For detailed information on how to contribute, see the Contributing Guide.