diff --git a/examples/langchain/chat/chat_api_invoke.py b/examples/langchain/chat/chat_api_invoke.py
new file mode 100644
index 00000000..a266c4b7
--- /dev/null
+++ b/examples/langchain/chat/chat_api_invoke.py
@@ -0,0 +1,36 @@
+#!/usr/bin/env python3
+"""LangChain invoke example using Chat API."""
+
+from __future__ import annotations
+
+from langchain_core.messages import AIMessage, HumanMessage
+from yandex_ai_studio_sdk import AIStudio
+
+
+def main() -> None:
+    # You can set authentication using environment variables instead of the 'auth' argument:
+    # YC_OAUTH_TOKEN, YC_TOKEN, YC_IAM_TOKEN, or YC_API_KEY
+    # You can also set 'folder_id' using the YC_FOLDER_ID environment variable
+    sdk = AIStudio(
+        # folder_id="<YC_FOLDER_ID>",
+        # auth="<YC_API_KEY/YC_IAM_TOKEN>",
+    )
+    sdk.setup_default_logging()
+
+    model = sdk.chat.completions('yandexgpt').langchain(timeout=60)
+
+    result = model.invoke(
+        [
+            HumanMessage(content="hello!"),
+            AIMessage(content="Hi there human!"),
+            HumanMessage(content="Meow!"),
+        ]
+    )
+    print(result)
+    print(f"Content: {result.content}")
+    print(f"Usage: {result.usage_metadata}")
+    print(f"Finish reason: {result.response_metadata.get('finish_reason')}")
+
+
+if __name__ == '__main__':
+    main()
diff --git a/src/yandex_ai_studio_sdk/_chat/completions/langchain.py b/src/yandex_ai_studio_sdk/_chat/completions/langchain.py
new file mode 100644
index 00000000..e720955d
--- /dev/null
+++ b/src/yandex_ai_studio_sdk/_chat/completions/langchain.py
@@ -0,0 +1,252 @@
+"""LangChain integration for Yandex AI Studio Chat API.
+
+This module is optional: requires ``langchain_core`` to be installed.
+Provides :class:`ChatYandexGPT` — a LangChain ``BaseChatModel`` adapter
+that uses the Chat API backend.
+"""
+
+from __future__ import annotations
+
+import json
+from collections.abc import AsyncIterator, Iterator
+from typing import Any
+
+from langchain_core.callbacks import AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun
+from langchain_core.language_models.chat_models import BaseChatModel as LCBaseChatModel
+from langchain_core.messages import AIMessage, AIMessageChunk, BaseMessage, HumanMessage, SystemMessage, ToolMessage
+from langchain_core.messages.ai import UsageMetadata
+from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
+from yandex_ai_studio_sdk._types.langchain import BaseYandexLanguageModel
+from yandex_ai_studio_sdk._utils.langchain import make_async_run_manager
+from yandex_ai_studio_sdk._utils.sync import run_sync_generator_impl, run_sync_impl
+
+from .model import BaseChatModel as ChatAPIModel
+from .result import ChatChoice, ChatModelResult, DeltaChatChoice
+
+# =========================================================================
+# Message conversion: LangChain → Chat API
+# =========================================================================
+
+
+def _transform_messages(messages: list[BaseMessage]) -> list[dict[str, Any]]:
+    """Convert LangChain messages to Chat API dict format."""
+    result: list[dict[str, Any]] = []
+
+    for message in messages:
+        if isinstance(message, ToolMessage):
+            result.append({
+                "role": "tool",
+                "content": str(message.content),
+                "tool_call_id": message.tool_call_id,
+            })
+
+        elif isinstance(message, AIMessage) and message.tool_calls:
+            result.append({
+                "role": "assistant",
+                "tool_calls": [
+                    {
+                        "id": tc["id"],
+                        "type": "function",
+                        "function": {
+                            "name": tc["name"],
+                            "arguments": json.dumps(
+                                tc["args"], ensure_ascii=False,
+                            ),
+                        },
+                    }
+                    for tc in message.tool_calls
+                ],
+            })
+
+        elif isinstance(message, HumanMessage):
+            content = message.content
+            if isinstance(content, list):
+                # Multimodal: list of content parts
+                result.append({"role": "user", "content": content})
+            else:
+                result.append({"role": "user", "content": str(content)})
+
+        elif isinstance(message, AIMessage):
+            result.append({"role": "assistant", "content": str(message.content)})
+
+        elif isinstance(message, SystemMessage):
+            result.append({"role": "system", "content": str(message.content)})
+
+    return result
+
+
+# =========================================================================
+# Response parsing: Chat API → LangChain
+# =========================================================================
+
+
+def _parse_tool_calls(choice: ChatChoice) -> list[dict[str, Any]]:
+    """Extract tool calls from ChatChoice into LangChain format."""
+    if not choice.tool_calls:
+        return []
+
+    return [
+        {
+            "id": tc.id or "",
+            "name": tc.function.name,
+            "args": tc.function.arguments,
+            "type": "tool_call",
+        }
+        for tc in choice.tool_calls
+        if tc.function  # skip malformed tool calls
+    ]
+
+
+def _make_usage(result: ChatModelResult) -> UsageMetadata | None:
+    """Build LangChain UsageMetadata from Chat API usage stats."""
+    if not result.usage:
+        return None
+    return UsageMetadata(
+        input_tokens=result.usage.input_text_tokens,
+        output_tokens=result.usage.completion_tokens,
+        total_tokens=result.usage.total_tokens,
+    )
+
+
+# =========================================================================
+# ChatYandexGPT — LangChain adapter for Chat API
+# =========================================================================
+
+
+class ChatYandexGPT(BaseYandexLanguageModel[ChatAPIModel], LCBaseChatModel):
+    """LangChain chat model for Yandex GPT via Chat API.
+
+    Supports text, tool calls, tool results, multimodal content, and streaming.
+
+    Example:
+        >>> sdk = AIStudio()
+        >>> model = sdk.chat.completions('yandexgpt').langchain()
+        >>> result = model.invoke([HumanMessage("Hello!")])
+    """
+
+    class Config:
+        arbitrary_types_allowed = True
+
+    @property
+    def _sdk(self):
+        return self.ycmlsdk_model._sdk
+
+    # -----------------------------------------------------------------
+    # Sync → async delegation (same pattern as legacy ChatYandexGPT)
+    # -----------------------------------------------------------------
+
+    def _generate(
+        self,
+        messages: list[BaseMessage],
+        stop: list[str] | None = None,
+        run_manager: CallbackManagerForLLMRun | None = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        async_rm = make_async_run_manager(run_manager) if run_manager else None
+        return run_sync_impl(
+            self._agenerate(messages, stop, async_rm, **kwargs),
+            self._sdk,
+        )
+
+    def _stream(
+        self,
+        messages: list[BaseMessage],
+        stop: list[str] | None = None,
+        run_manager: CallbackManagerForLLMRun | None = None,
+        **kwargs: Any,
+    ) -> Iterator[ChatGenerationChunk]:
+        async_rm = make_async_run_manager(run_manager) if run_manager else None
+        return run_sync_generator_impl(
+            self._astream(messages, stop, async_rm, **kwargs),
+            self._sdk,
+        )
+
+    # -----------------------------------------------------------------
+    # Core: invoke
+    # -----------------------------------------------------------------
+
+    async def _agenerate(
+        self,
+        messages: list[BaseMessage],
+        stop: list[str] | None = None,
+        run_manager: AsyncCallbackManagerForLLMRun | None = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        chat_messages = _transform_messages(messages)
+
+        sdk_result: ChatModelResult = await self.ycmlsdk_model._run(
+            messages=chat_messages,
+            timeout=self.timeout,
+        )
+
+        usage = _make_usage(sdk_result)
+
+        generations: list[ChatGeneration] = []
+        for choice in sdk_result.choices:
+            tool_calls = _parse_tool_calls(choice)
+
+            ai_message = AIMessage(
+                content=choice.text or "",
+                tool_calls=tool_calls or [],
+                usage_metadata=usage,
+                response_metadata={
+                    "finish_reason": choice.finish_reason.value,
+                    "model": sdk_result.model,
+                    "status": choice.status.name,
+                },
+            )
+            generations.append(ChatGeneration(message=ai_message))
+
+        return ChatResult(
+            generations=generations,
+            llm_output={"model": sdk_result.model, "id": sdk_result.id},
+        )
+
+    # -----------------------------------------------------------------
+    # Core: stream
+    # -----------------------------------------------------------------
+
+    async def _astream(
+        self,
+        messages: list[BaseMessage],
+        stop: list[str] | None = None,
+        run_manager: AsyncCallbackManagerForLLMRun | None = None,
+        **kwargs: Any,
+    ) -> AsyncIterator[ChatGenerationChunk]:
+        chat_messages = _transform_messages(messages)
+
+        async for sdk_result in self.ycmlsdk_model._run_stream(
+            messages=chat_messages,
+            timeout=self.timeout,
+        ):
+            choice = sdk_result.choices[0]
+            usage = _make_usage(sdk_result)
+
+            delta = choice.delta if isinstance(choice, DeltaChatChoice) else choice.text
+            tool_calls = _parse_tool_calls(choice)
+
+            # tool_call_chunks: args as JSON string (LangChain convention)
+            tool_call_chunks = [
+                {
+                    "id": tc["id"],
+                    "name": tc["name"],
+                    "args": json.dumps(tc["args"], ensure_ascii=False),
+                    "index": i,
+                    "type": "tool_call_chunk",
+                }
+                for i, tc in enumerate(tool_calls)
+            ] if tool_calls else []
+
+            chunk = AIMessageChunk(
+                content=delta or "",
+                tool_call_chunks=tool_call_chunks,
+                usage_metadata=usage,
+                response_metadata={
+                    "finish_reason": choice.finish_reason.value,
+                    "status": choice.status.name,
+                },
+            )
+            yield ChatGenerationChunk(message=chunk)
+
+
+ChatYandexGPT.model_rebuild()
diff --git a/src/yandex_ai_studio_sdk/_chat/completions/model.py b/src/yandex_ai_studio_sdk/_chat/completions/model.py
index 2e9d40ba..e1940dc4 100644
--- a/src/yandex_ai_studio_sdk/_chat/completions/model.py
+++ b/src/yandex_ai_studio_sdk/_chat/completions/model.py
@@ -79,6 +79,17 @@ def configure(  # type: ignore[override]
             extra_query=extra_query,
         )
 
+    def langchain(self, timeout: int = 60):
+        """Initialize a LangChain chat model adapter.
+
+        Requires ``langchain_core`` to be installed.
+
+        :param timeout: Default timeout in seconds. Defaults to 60.
+        """
+        from .langchain import ChatYandexGPT  # pylint: disable=import-outside-toplevel
+
+        return ChatYandexGPT(ycmlsdk_model=self, timeout=timeout)
+
     def _build_request_json(self, messages: ChatMessageInputType, stream: bool) -> dict[str, Any]:
         result = {
             'model': self._uri,
diff --git a/tests/langchain_/cassettes/test_chat_api/test_ainvoke.yaml b/tests/langchain_/cassettes/test_chat_api/test_ainvoke.yaml
new file mode 100644
index 00000000..59fef63a
--- /dev/null
+++ b/tests/langchain_/cassettes/test_chat_api/test_ainvoke.yaml
@@ -0,0 +1,49 @@
+interactions:
+- request:
+    body: '{"model":"gpt://b1gr2p0etomug2vas4ie/yandexgpt/latest","messages":[{"content":"hello!","role":"user"},{"content":"Hi
+      there human!","role":"assistant"},{"content":"Meow!","role":"user"}],"stream":false}'
+    headers:
+      Accept:
+      - '*/*'
+      Accept-Encoding:
+      - gzip, deflate, zstd
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '201'
+      Content-Type:
+      - application/json
+      Host:
+      - llm.api.cloud.yandex.net
+      User-Agent:
+      - yandex-ai-studio-sdk/0.20.0 python/3.13
+      x-client-request-id:
+      - 1b3c1788-a8b0-499f-92bb-3fe7523a4389
+    method: POST
+    uri: https://llm.api.cloud.yandex.net/v1/chat/completions
+  response:
+    body:
+      string: '{"id":"f2bb6ba4-8f88-4ac9-87c0-6b41db01c635","object":"chat.completion","created":1774968384,"model":"gpt://b1gr2p0etomug2vas4ie/yandexgpt/latest","choices":[{"index":0,"message":{"role":"assistant","content":"Are
+        you trying to tell me that you''re a cat? Or are you just in the mood for
+        some feline fun? In any case, meow right back at you! Meow!"},"finish_reason":"stop"}],"usage":{"prompt_tokens":29,"total_tokens":69,"completion_tokens":40,"prompt_tokens_details":{"cached_tokens":0}}}
+
+        '
+    headers:
+      content-length:
+      - '489'
+      content-type:
+      - application/json
+      date:
+      - Tue, 31 Mar 2026 14:46:24 GMT
+      server:
+      - ycalb
+      x-client-request-id:
+      - 1b3c1788-a8b0-499f-92bb-3fe7523a4389
+      x-request-id:
+      - 1ff57598-fabc-40bf-89ca-ace621e49c22
+      x-server-trace-id:
+      - 85cfd10b60cdef2f:a32c621e24a87b57:55f05b16925bf389:1
+    status:
+      code: 200
+      message: OK
+version: 1
diff --git a/tests/langchain_/cassettes/test_chat_api/test_astream.yaml b/tests/langchain_/cassettes/test_chat_api/test_astream.yaml
new file mode 100644
index 00000000..9ef93a30
--- /dev/null
+++ b/tests/langchain_/cassettes/test_chat_api/test_astream.yaml
@@ -0,0 +1,61 @@
+interactions:
+- request:
+    body: '{"model":"gpt://b1gr2p0etomug2vas4ie/yandexgpt/latest","messages":[{"content":"hello!","role":"user"},{"content":"Hi
+      there human!","role":"assistant"},{"content":"Meow!","role":"user"}],"stream":true}'
+    headers:
+      Accept:
+      - '*/*'
+      Accept-Encoding:
+      - gzip, deflate, zstd
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '200'
+      Content-Type:
+      - application/json
+      Host:
+      - llm.api.cloud.yandex.net
+      User-Agent:
+      - yandex-ai-studio-sdk/0.20.0 python/3.13
+      x-client-request-id:
+      - 97ca42c1-d8ce-4966-88e9-879d60847857
+    method: POST
+    uri: https://llm.api.cloud.yandex.net/v1/chat/completions
+  response:
+    body:
+      string: 'data: {"id":"4ae6d707-cd09-4e3b-9622-9c79f5ae29a1","object":"chat.completion.chunk","created":1774968385,"model":"gpt://b1gr2p0etomug2vas4ie/yandexgpt/latest","choices":[{"index":0,"delta":{"role":"assistant","content":"Are"}}]}
+
+
+        data: {"id":"4ae6d707-cd09-4e3b-9622-9c79f5ae29a1","object":"chat.completion.chunk","created":1774968385,"model":"gpt://b1gr2p0etomug2vas4ie/yandexgpt/latest","choices":[{"index":0,"delta":{"content":"
+        you trying to tell me that you''re a cat? Or are you just in a playful mood?
+        Either way, meow back at you! Meow!"},"finish_reason":"stop"}]}
+
+
+        data: {"id":"4ae6d707-cd09-4e3b-9622-9c79f5ae29a1","object":"chat.completion.chunk","created":1774968385,"model":"gpt://b1gr2p0etomug2vas4ie/yandexgpt/latest","choices":[],"usage":{"prompt_tokens":29,"total_tokens":63,"completion_tokens":34,"prompt_tokens_details":{"cached_tokens":0}}}
+
+
+        data: [DONE]
+
+
+        '
+    headers:
+      cache-control:
+      - no-cache
+      content-type:
+      - text/event-stream
+      date:
+      - Tue, 31 Mar 2026 14:46:25 GMT
+      server:
+      - ycalb
+      transfer-encoding:
+      - chunked
+      x-client-request-id:
+      - 97ca42c1-d8ce-4966-88e9-879d60847857
+      x-request-id:
+      - 8f06983f-f3e7-480b-82b4-78dc0d5a2dfb
+      x-server-trace-id:
+      - 68a6526367f1c05b:94e2370f3b41800d:272414d44e47efff:1
+    status:
+      code: 200
+      message: OK
+version: 1
diff --git a/tests/langchain_/test_chat_api.py b/tests/langchain_/test_chat_api.py
new file mode 100644
index 00000000..6d2caf03
--- /dev/null
+++ b/tests/langchain_/test_chat_api.py
@@ -0,0 +1,40 @@
+"""Tests for LangChain integration via Chat API."""
+
+from __future__ import annotations
+
+import pytest
+
+pytestmark = [pytest.mark.asyncio, pytest.mark.vcr, pytest.mark.require_env('langchain_core')]
+
+
+@pytest.fixture(name='model')
+def fixture_model(async_sdk):
+    return async_sdk.chat.completions('yandexgpt').langchain()
+
+
+@pytest.fixture(name='chat_history')
+def fixture_chat_history():
+    from langchain_core.messages import AIMessage, HumanMessage  # pylint: disable=import-outside-toplevel,import-error
+
+    return [
+        HumanMessage(content="hello!"),
+        AIMessage(content="Hi there human!"),
+        HumanMessage(content="Meow!"),
+    ]
+
+
+async def test_ainvoke(model, chat_history):
+    result = await model.ainvoke(chat_history)
+
+    assert result.content
+    assert result.usage_metadata is not None
+    assert result.usage_metadata["total_tokens"] > 0
+    assert "finish_reason" in result.response_metadata
+    assert "model" in result.response_metadata
+
+
+async def test_astream(model, chat_history):
+    chunks = [chunk async for chunk in model.astream(chat_history)]
+
+    assert len(chunks) > 0
+    assert any(c.content for c in chunks)