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Closes #37360 Adds a `use_responses_api` flag to `ChatPerplexity` so requests can be routed through Perplexity's Agent API (the Perplexity-flavored Responses API) in addition to the existing Chat Completions endpoint. This mirrors the `use_responses_api` flag on `ChatOpenAI`. ## Motivation Perplexity exposes two HTTP surfaces from the same SDK client object: `client.chat.completions.create()` (Chat Completions) and `client.responses.create()` (Agent API, OpenAI-compatible Responses shape). The Agent API supports built-in tools (`web_search`, `fetch_url`, `finance_search`, `people_search`), `instructions`, `input`, `previous_response_id`, and `include` — none of which exist on Chat Completions. Today `ChatPerplexity` only calls Chat Completions, so users who want the Agent API have to drop down to the raw SDK. ## What this changes - New field `use_responses_api: bool | None = None` on `ChatPerplexity`. - New module-level helper `_use_responses_api(payload)` that returns `True` when the payload contains a built-in tool (any `tools[*]` whose `type` is not `"function"`) or any of the Responses-only fields `previous_response_id`, `instructions`, `input`, `include`. - New instance method `ChatPerplexity._use_responses_api(payload)` that honors `self.use_responses_api` when it is a `bool`, otherwise delegates to the module helper. - New converters `_convert_responses_to_chat_result(response)` and `_convert_responses_stream_event_to_chunk(event)` that translate Agent API objects/events into `AIMessage` and `AIMessageChunk` (preserving `usage_metadata`, `response_metadata`, citations, images, related questions, search results, and `function_call` tool calls). - A surgical `_to_responses_payload(...)` helper that renames `messages` → `input` and `max_tokens` → `max_output_tokens`, passes through Responses-supported fields, and parks anything Perplexity-specific under `extra_body`. - Each of the four API call sites (`_stream`, `_astream`, `_generate`, `_agenerate`) now branches on `self._use_responses_api(payload)`. The Chat Completions path is untouched. ## Auto-detection rules When `use_responses_api` is unset (the default), routing is decided per call from the outgoing payload: - Has a built-in tool? → Responses - Has `previous_response_id`, `instructions`, `input`, or `include`? → Responses - Otherwise → Chat Completions Explicit `use_responses_api=True` or `=False` always overrides auto-detection. ## Backwards compatibility Existing usage is unchanged. `ChatPerplexity(model="sonar").invoke("hi")` still calls `client.chat.completions.create()`. No public field was renamed or removed; the new field is purely additive. ## Tests Adds `tests/unit_tests/test_chat_models_responses.py` covering the helper, auto-detect routing, explicit overrides in both directions, response-to-`AIMessage` conversion (content, `usage_metadata`, `response_metadata.id`), `function_call` → `tool_calls` conversion, and sync + async streaming of `response.output_text.delta` and `response.completed` events. All mocks use `MagicMock`/`AsyncMock`; no network calls. ## Notes for reviewers This was implemented with help from an AI agent. The shape mirrors `langchain-openai`'s `use_responses_api` — same field name, same helper name, same docstring style — so the diff should be familiar. Closes nothing — net new feature. --------- Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com> Co-authored-by: Mason Daugherty <github@mdrxy.com>
The agent engineering platform.
LangChain is a framework for building agents and LLM-powered applications. It helps you chain together interoperable components and third-party integrations to simplify AI application development — all while future-proofing decisions as the underlying technology evolves.
Tip
Just getting started? Check out Deep Agents — a higher-level package built on LangChain for agents that have built-in capabilites for common usage patterns such as planning, subagents, file system usage, and more.
Quickstart
pip install langchain
# or
uv add langchain
from langchain.chat_models import init_chat_model
model = init_chat_model("openai:gpt-5.4")
result = model.invoke("Hello, world!")
If you're looking for more advanced customization or agent orchestration, check out LangGraph, our framework for building controllable agent workflows.
For an equivalent JS/TS library, check out LangChain.js.
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For developing, debugging, and deploying AI agents and LLM applications, see LangSmith.
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