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Chester Curme
@@ -60,6 +60,216 @@ inter-operability of messages across models.
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| Get `response_metadata` | `message.response_metadata` | `message.response_metadata`|
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| Get `tool_calls` | `message.tool_calls` | `message.tool_calls` |
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### Changes in content blocks
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For providers that generate `list[dict]` content, the dict elements have changed to
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conform to the new content block types. Refer to the
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[API reference](https://python.langchain.com/api_reference/core/messages.html) for
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details. Below we show some examples.
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Importantly:
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- Where provider-specific fields map to fields on standard types, LangChain manages
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the translation.
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- Where provider-specific fields do not map to fields on standard types, LangChain
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stores them in an `"extras"` key (see below for examples).
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#### Reasoning
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<details Before>
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```python
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from langchain.chat_models import init_chat_model
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llm = init_chat_model(
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"openai:gpt-5",
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reasoning={"effort": "medium", "summary": "auto"},
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output_version="responses/v1",
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)
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response = llm.invoke(
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"What was the third tallest building in the world in the year 2000?"
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)
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response.content
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```
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```
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[
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{
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"type": "reasoning",
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"id": "rs_abc123",
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"summary": [
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{
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"text": "The user is asking about...",
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"type": "summary_text"
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},
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{
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"text": "We should consider...",
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"type": "summary_text"
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}
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]
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},
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{
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"type": "text",
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"text": "In the year 2000 the third-tallest building in the world was...",
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"id": "msg_abc123"
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}
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]
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```
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</details>
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<details After>
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```python
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from langchain.chat_models import init_chat_model
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llm = init_chat_model(
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"openai:gpt-5",
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reasoning={"effort": "medium", "summary": "auto"},
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message_version="v1",
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)
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response = llm.invoke(
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"What was the third tallest building in the world in the year 2000?"
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)
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response.content
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```
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```
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[
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{
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"type": "reasoning",
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"reasoning": "The user is asking about...",
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"id": "rs_abc123"
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},
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{
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"type": "reasoning",
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"reasoning": "We should consider...",
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"id": "rs_abc123"
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},
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{
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"type": "text",
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"text": "In the year 2000 the third-tallest building in the world was...",
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"id": "msg_abc123"
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}
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]
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```
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</details>
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#### Citations and web search
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<details Before>
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```python
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from langchain.chat_models import init_chat_model
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llm = init_chat_model("openai:gpt-5-mini", output_version="responses/v1")
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llm_with_tools = llm.bind_tools([{"type": "web_search_preview"}])
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response = llm_with_tools.invoke("What was a positive news story from today?")
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response.content
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```
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```
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[
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{
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"type": "reasoning",
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"id": "rs_abc123",
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"summary": []
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},
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{
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"type": "web_search_call",
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"id": "ws_abc123",
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"action": {
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"query": "positive news today August 8 2025 'good news' 'Aug 8 2025' 'today' ",
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"type": "search"
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},
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"status": "completed"
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},
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{
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"type": "text",
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"text": "Here are two positive news items from today...",
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"annotations": [
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{
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"type": "url_citation",
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"end_index": 455,
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"start_index": 196,
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"title": "Document title",
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"url": "<document url>"
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},
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{
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"type": "url_citation",
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"end_index": 1022,
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"start_index": 707,
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"title": "Another Document",
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"url": "<another document url>"
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},
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],
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"id": "msg_abc123"
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}
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]
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```
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</details>
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<details After>
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```python
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from langchain.chat_models import init_chat_model
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llm = init_chat_model("openai:gpt-5-mini", message_version="v1")
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llm_with_tools = llm.bind_tools([{"type": "web_search_preview"}])
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response = llm_with_tools.invoke("What was a positive news story from today?")
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response.content
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```
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```
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[
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{
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"type": "reasoning",
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"id": "rs_abc123"
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},
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{
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"type": "web_search_call",
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"id": "ws_abc123",
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"query": "positive news August 8 2025 'good news' 'today' ",
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"extras": {
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"action": {"type": "search"},
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"status": "completed",
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}
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},
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{
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"type": "web_search_result",
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"id": "ws_abc123"
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},
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{
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"type": "text",
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"text": "Here are two positive news items from today...",
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"annotations": [
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{
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"type": "citation",
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"end_index": 455,
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"start_index": 196,
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"title": "Document title",
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"url": "<document url>"
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},
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{
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"type": "citation",
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"end_index": 1022,
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"start_index": 707,
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"title": "Another Document",
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"url": "<another document url>"
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}
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],
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"id": "msg_abc123"
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}
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]
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```
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</details>
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#### Non-standard blocks
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Where content blocks from specific providers do not map to a standard type, they are
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structured into a `"non_standard"` block:
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```python
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{
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"type": "non_standard",
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"value": original_block,
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}
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```
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<details Before>
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...
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</details>
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## Feature gaps
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