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refactor(langchain-classic): retarget deprecations to create_agent, other chores (#37164)

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Sweep classic deprecations so every removal lands on `2.0.0`, runtime
warnings carry the auto-generated since/removal/alternative line, and
replacements steer at `langchain.agents.create_agent` and
`with_structured_output(...)` instead of pre-v1 LangGraph +
`python.langchain.com` links.

## Changes

- **Bump removal targets from `1.0` / `1.0.0` to `2.0.0`** across
agents, chains, memory, retrievers, structured-output, vectorstore
toolkits, and the `langchain_classic._api.module_import` shim — gives
users a real runway now that v1 has shipped.
- **Move bespoke `message=` strings onto `addendum=`** (or split into
`alternative=` + `addendum=`). `warn_deprecated` skips the
auto-generated since/removal/alternative line whenever `message=` is
set, so the prior pattern silently dropped that info from the runtime
`LangChainDeprecationWarning`. Matches the pattern already used in
`HTMLHeaderTextSplitter.split_text_from_url`, which is updated for
consistency.
- **Repoint `alternative=` at v1 replacements**: chains/memory/agent
toolkits → `langchain.agents.create_agent` (with checkpointer or
retrieval-tool guidance in the addendum); `openai_functions` and
`chains/structured_output` → `ChatModel.with_structured_output(...)`;
`openapi` chains → `ChatModel.bind_tools(...)` + HTTP client.
`ConversationChain` no longer points at `RunnableWithMessageHistory`.
- **Refresh `AGENT_DEPRECATION_WARNING`** in
`langchain_classic._api.deprecation` — drop stale LangGraph and
`python.langchain.com` links in favor of `langchain.agents.create_agent`
and the `docs.langchain.com/oss/python/migrate/langchain-v1` guide.
Propagates to all 13 caller sites in `agents/`.
- **Newly deprecate `langchain_classic.chat_models.init_chat_model` and
`langchain_classic.embeddings.init_embeddings`** with the framing
*"maintained in `langchain`; `langchain-classic` retains this entry
point for import-compatibility only"*. The classic docstring examples
and the warning admonition both point at `langchain.chat_models`.
- **Improve `init_chat_model` docstrings** in both `langchain_v1` and
the classic copy: clarify `provider:model` prefix vs. `model_provider=`,
recommend pinned IDs over moving aliases, add the `upstage` provider
row, and refresh examples to GA models (`gpt-5.5`, `claude-opus-4-7`).
- **Standardize partner Anthropic deprecations**: replace
`AnthropicLLM`'s `model_validator(raise_warning)` with
`@deprecated(since="0.1.0", removal="2.0.0",
alternative="ChatAnthropic")`, and pin the `ChatAnthropic`
`output_format` runtime warning at `langchain-anthropic 2.0.0` instead
of "a future version".
This commit is contained in:
Mason Daugherty
2026-05-03 13:15:59 -04:00
committed by GitHub
parent 255f227541
commit 5a9b1ec2dc
68 changed files with 550 additions and 620 deletions
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128
libs/langchain_v1/langchain/chat_models/base.py
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@@ -219,14 +219,16 @@ def init_chat_model(
**Two main use cases:**
1. **Fixed model** – specify the model upfront and get a ready-to-use chat model.
2. **Configurable model** – choose to specify parameters (including model name) at
runtime via `config`. Makes it easy to switch between models/providers without
changing your code
1. **Fixed model** – specify the model upfront and get a
ready-to-use chat model.
2. **Configurable model** – choose to specify parameters
(including model name) at runtime via `config`. Makes it easy to
switch between models/providers without changing your code
!!! note "Installation requirements"
Requires the integration package for the chosen model provider to be installed.
Requires the integration package for the chosen model provider to
be installed.
See the `model_provider` parameter below for specific package names
(e.g., `pip install langchain-openai`).
@@ -235,31 +237,49 @@ def init_chat_model(
for supported model parameters to use as `**kwargs`.
Args:
model: The model name, optionally prefixed with provider (e.g., `'openai:gpt-4o'`).
model: Name of the model to use, with provider prefix — e.g.,
`'openai:gpt-5.5'`.
Prefer exact model IDs from provider docs over aliases for reliable behavior
(e.g., dated versions like `'...-20250514'` instead of `'...-latest'`).
A bare model name (e.g., `'claude-opus-4-7'`) is also accepted; we
will attempt to infer the provider from the prefix using the mapping
below. Inference is best-effort and not guaranteed, so prefer
the prefixed form when possible.
Will attempt to infer `model_provider` from model if not specified.
Prefer pinned model IDs over moving aliases (e.g.,
`'claude-haiku-4-5-20251001'` rather than `'claude-haiku-4-5'`)
so behavior does not drift if the alias is repointed upstream.
The following providers will be inferred based on these model prefixes:
Inferred providers by prefix (case-insensitive):
- `gpt-...` | `o1...` | `o3...` -> `openai`
- `claude...` -> `anthropic`
- `amazon...` -> `bedrock`
- `gemini...` -> `google_vertexai`
- `command...` -> `cohere`
- `accounts/fireworks...` -> `fireworks`
- `mistral...` -> `mistralai`
- `deepseek...` -> `deepseek`
- `grok...` -> `xai`
- `sonar...` -> `perplexity`
- `solar...` -> `upstage`
model_provider: The model provider if not specified as part of the model arg
(see above).
- `gpt-...` | `o1...` | `o3...` -> `openai`
- `claude...` -> `anthropic`
- `amazon....` | `anthropic....` | `meta....` -> `bedrock`
- `gemini...` -> `google_vertexai` (default changes in next major; pass `model_provider` to lock in)
- `command...` -> `cohere`
- `accounts/fireworks...` -> `fireworks`
- `mistral...` | `mixtral...` -> `mistralai`
- `deepseek...` -> `deepseek`
- `grok...` -> `xai`
- `sonar...` -> `perplexity`
- `solar...` -> `upstage`
- `chatgpt...` | `text-davinci...` -> `openai` (legacy)
model_provider: Provider of the model, passed separately instead of
as a prefix on `model`.
Supported `model_provider` values and the corresponding integration package
are:
Equivalent to the prefix form — e.g.,
`model='claude-sonnet-4-5', model_provider='anthropic'` behaves
the same as `model='anthropic:claude-sonnet-4-5'`.
Prefer the prefix form on `model` for most usage. Reach for this
kwarg when:
- The provider is dynamic (read from config or an env var) and
you'd otherwise concatenate strings.
- You want `model` and `model_provider` to be independently
swappable at runtime via `configurable_fields` (e.g., to route
the same model name to a different host).
Supported values and the integration package each requires:
- `openai` -> [`langchain-openai`](https://docs.langchain.com/oss/python/integrations/providers/openai)
- `anthropic` -> [`langchain-anthropic`](https://docs.langchain.com/oss/python/integrations/providers/anthropic)
@@ -338,9 +358,9 @@ def init_chat_model(
Returns:
A `BaseChatModel` corresponding to the `model_name` and `model_provider`
specified if configurability is inferred to be `False`. If configurable, a
chat model emulator that initializes the underlying model at runtime once a
config is passed in.
specified if configurability is inferred to be `False`.
If configurable, a chat model emulator that initializes the
underlying model at runtime once a config is passed in.
Raises:
ValueError: If `model_provider` cannot be inferred or isn't supported.
@@ -349,35 +369,28 @@ def init_chat_model(
???+ example "Initialize a non-configurable model"
```python
# pip install langchain langchain-openai langchain-anthropic langchain-google-vertexai
# pip install langchain langchain-openai
from langchain.chat_models import init_chat_model
o3_mini = init_chat_model("openai:o3-mini", temperature=0)
claude_sonnet = init_chat_model("anthropic:claude-sonnet-4-5-20250929", temperature=0)
gemini_2-5_flash = init_chat_model("google_vertexai:gemini-2.5-flash", temperature=0)
o3_mini.invoke("what's your name")
claude_sonnet.invoke("what's your name")
gemini_2-5_flash.invoke("what's your name")
gpt_5 = init_chat_model("openai:gpt-5.5", temperature=0)
gpt_5.invoke("what's your name")
```
??? example "Partially configurable model with no default"
```python
# pip install langchain langchain-openai langchain-anthropic
# pip install langchain langchain-openai
from langchain.chat_models import init_chat_model
# (We don't need to specify configurable=True if a model isn't specified.)
configurable_model = init_chat_model(temperature=0)
configurable_model.invoke("what's your name", config={"configurable": {"model": "gpt-4o"}})
# Use GPT-4o to generate the response
# Use GPT-5.5 to generate the response
configurable_model.invoke(
"what's your name",
config={"configurable": {"model": "claude-sonnet-4-5-20250929"}},
config={"configurable": {"model": "gpt-5.5"}},
)
```
@@ -389,31 +402,33 @@ def init_chat_model(
from langchain.chat_models import init_chat_model
configurable_model_with_default = init_chat_model(
"openai:gpt-4o",
"openai:gpt-5.5",
configurable_fields="any", # This allows us to configure other params like temperature, max_tokens, etc at runtime.
config_prefix="foo",
temperature=0,
)
configurable_model_with_default.invoke("what's your name")
# GPT-4o response with temperature 0 (as set in default)
# GPT-5.5 response with temperature 0 (as set in default)
# Invoke overriding model and temperature at runtime via config.
# Note the use of the "foo_" prefix on the config keys, which matches
# the config_prefix we set when initializing the model.
configurable_model_with_default.invoke(
"what's your name",
config={
"configurable": {
"foo_model": "anthropic:claude-sonnet-4-5-20250929",
"foo_model": "anthropic:claude-opus-4-7",
"foo_temperature": 0.6,
}
},
)
# Override default to use Sonnet 4.5 with temperature 0.6 to generate response
```
??? example "Bind tools to a configurable model"
You can call any chat model declarative methods on a configurable model in the
same way that you would with a normal model:
You can call any chat model declarative methods on a configurable model
in the same way that you would with a normal model:
```python
# pip install langchain langchain-openai langchain-anthropic
@@ -435,7 +450,7 @@ def init_chat_model(
configurable_model = init_chat_model(
"gpt-4o", configurable_fields=("model", "model_provider"), temperature=0
"gpt-5.5", configurable_fields=("model", "model_provider"), temperature=0
)
configurable_model_with_tools = configurable_model.bind_tools(
@@ -447,13 +462,13 @@ def init_chat_model(
configurable_model_with_tools.invoke(
"Which city is hotter today and which is bigger: LA or NY?"
)
# Use GPT-4o
# Use GPT-5.5
configurable_model_with_tools.invoke(
"Which city is hotter today and which is bigger: LA or NY?",
config={"configurable": {"model": "claude-sonnet-4-5-20250929"}},
config={"configurable": {"model": "claude-opus-4-7"}},
)
# Use Sonnet 4.5
# Use Opus 4.7
```
""" # noqa: E501
@@ -539,8 +554,17 @@ def _attempt_infer_model_provider(model_name: str) -> str | None:
if model_lower.startswith("accounts/fireworks"):
return "fireworks"
# Google models
# Google models — prefix is ambiguous (Vertex AI vs the GenAI/AI Studio API).
if model_lower.startswith("gemini"):
warnings.warn(
f"Inferred `model_provider='google_vertexai'` from {model_name!r}. "
"This default will change to 'google_genai' in the next major release."
"To keep current behavior, pass `model_provider='google_vertexai'` "
f"(or use the prefix form, e.g. 'google_vertexai:{model_name}'); "
"for AI Studio / Gemini API, use 'google_genai' instead.",
DeprecationWarning,
stacklevel=5,
)
return "google_vertexai"
# AWS Bedrock models