5a9b1ec2dc
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".
🦜️🔗 LangChain
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
🤔 What is this?
LangChain is the easiest way to start building agents and applications powered by LLMs. With under 10 lines of code, you can connect to OpenAI, Anthropic, Google, and more. LangChain provides a pre-built agent architecture and model integrations to help you get started quickly and seamlessly incorporate LLMs into your agents and applications.
We recommend you use LangChain if you want to quickly build agents and autonomous applications. Use LangGraph, our low-level agent orchestration framework and runtime, when you have more advanced needs that require a combination of deterministic and agentic workflows, heavy customization, and carefully controlled latency.
LangChain agents are built on top of LangGraph in order to provide durable execution, streaming, human-in-the-loop, persistence, and more. (You do not need to know LangGraph for basic LangChain agent usage.)
📖 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.