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Fixes #37533 --- `langchain-core` defines `ContextOverflowError` so that application code can catch an over-long prompt the same way regardless of which provider raised it. The Anthropic, OpenAI, and Fireworks integrations already promote their provider-specific context-length errors to a subclass of it, but `langchain-groq` did not: a context overflow there surfaced as a plain `groq.BadRequestError`, so anyone relying on the shared exception had to special-case Groq. This closes that gap for Groq. It adds a `GroqContextOverflowError` (a subclass of both `groq.BadRequestError` and `ContextOverflowError`) and a small promoter, `_handle_groq_invalid_request`, wired into the sync and async `generate` and `stream` paths. Because Groq's SDK mirrors OpenAI's, the implementation follows the same shape as the existing partners, and the promoted error keeps the original `response` and `body` so existing catchers that inspect `.response.status_code` keep working. Anything that already catches `groq.BadRequestError` is unaffected, since the new class is still a `BadRequestError`. One detail worth a reviewer's eye: Groq returns the overflow as a 400 whose JSON body carries `"code": "context_length_exceeded"`, but the SDK's `BadRequestError` does not expose that code as an attribute. The SDK does fold the full JSON body into the error message, so detection primarily matches `context_length_exceeded` against the stringified error, with `reduce the length` from the message as a secondary signal and an attribute check kept as defensive cover in case a future SDK adds `.code`. The unit tests construct the error exactly as the SDK does for a 4xx response and assert promotion across all four call paths, that an unrelated `BadRequestError` is left untouched, and that `response`/`body` are preserved. I scoped this to Groq and left Mistral as a follow-up: Mistral surfaces errors as raw `httpx.HTTPStatusError` rather than a typed SDK error, and I could not verify its exact context-overflow signal (status code plus body `code`/`message`) against an authoritative source well enough to assert it in a unit test without live API access, so I would rather not guess at the shape. --------- Co-authored-by: Mason Daugherty <github@mdrxy.com> Co-authored-by: Mason Daugherty <mason@langchain.dev>
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
uv add langchain
from langchain.chat_models import init_chat_model
model = init_chat_model("openai:gpt-5.5")
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.
Tip
For developing, debugging, and deploying AI agents and LLM applications, see LangSmith.
LangChain ecosystem
While the LangChain framework can be used standalone, it also integrates seamlessly with any LangChain product, giving developers a full suite of tools when building LLM applications.
- Deep Agents — Build agents that can plan, use subagents, and leverage file systems for complex tasks
- LangGraph — Build agents that can reliably handle complex tasks with our low-level agent orchestration framework
- Integrations — Chat & embedding models, tools & toolkits, and more
- LangSmith — Agent evals, observability, and debugging for LLM apps
- LangSmith Deployment — Deploy and scale agents with a purpose-built platform for long-running, stateful workflows
Why use LangChain?
LangChain helps developers build applications powered by LLMs through a standard interface for models, embeddings, vector stores, and more.
- Real-time data augmentation — Easily connect LLMs to diverse data sources and external/internal systems, drawing from LangChain's vast library of integrations with model providers, tools, vector stores, retrievers, and more
- Model interoperability — Swap models in and out as your engineering team experiments to find the best choice for your application's needs. As the industry frontier evolves, adapt quickly — LangChain's abstractions keep you moving without losing momentum
- Rapid prototyping — Quickly build and iterate on LLM applications with LangChain's modular, component-based architecture. Test different approaches and workflows without rebuilding from scratch, accelerating your development cycle
- Production-ready features — Deploy reliable applications with built-in support for monitoring, evaluation, and debugging through integrations like LangSmith. Scale with confidence using battle-tested patterns and best practices
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Resources
- Documentation — conceptual overviews and guides
- LangChain ecosystem overview — how LangChain, LangGraph, and Deep Agents fit together
- API reference — complete reference for all public classes, functions, and types
- Discussions — community forum for technical questions, ideas, and feedback
- LangChain Academy — comprehensive, free courses on LangChain libraries and products, made by the LangChain team
- Contributing Guide — how to contribute and find good first issues
- Code of Conduct — community guidelines and standards