5d20596d73
Standardizes inline code markup in Python docstrings and comments by replacing Sphinx-style double backticks with single-backtick Markdown. The cleanup keeps existing code fences intact while aligning inline references with the repo's docstring convention. ## Changes - Converted inline code references in core prompt-loading docs and LangSmith tracer comments, including `..`, `allow_dangerous_paths`, and inheritable metadata keys. - Normalized agent-related docstrings and comments around `wrap_model_call`, `ExtendedModelResponse`, `Command`, `create_structured_chat_agent`, and `DockerExecutionPolicy`. - Updated partner package docstrings for inline references such as `json_schema`, `ToolCall`, `apply_patch_call_output`, OpenRouter content block keys, and Perplexity tool-call serialization. - Cleaned test and helper docstrings that referenced command separators, fake `resource` modules, stream event names, and xdist rate-limit environment variables.
🦜🍎️ LangChain Core
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-core
🤔 What is this?
LangChain Core contains the base abstractions that power the LangChain ecosystem.
These abstractions are designed to be as modular and simple as possible.
The benefit of having these abstractions is that any provider can implement the required interface and then easily be used in the rest of the LangChain ecosystem.
⛰️ Why build on top of LangChain Core?
The LangChain ecosystem is built on top of langchain-core. Some of the benefits:
- Modularity: We've designed Core around abstractions that are independent of each other, and not tied to any specific model provider.
- Stability: We are committed to a stable versioning scheme, and will communicate any breaking changes with advance notice and version bumps.
- Battle-tested: Core components have the largest install base in the LLM ecosystem, and are used in production by many companies.
📖 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.