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## Summary Fixes #33709. When `AnthropicPromptCachingMiddleware` runs before `ModelFallbackMiddleware`, Anthropic-specific `cache_control` markers can leak into fallback attempts targeting non-Anthropic models. This change makes fallback retries sanitize those markers only on non-primary attempts, preserving primary-call behavior and avoiding API changes. Related: langchain-ai/deepagentsjs#551. ## Changes ### libs/langchain_v1 - Added a private fallback sanitizer in `model_fallback.py` to remove Anthropic `cache_control` markers from fallback requests. - Sanitization covers `model_settings`, system/content message blocks, and tool payloads (`BaseTool.extras` plus dict-style tools/extras). - Applied sanitization in both sync and async fallback retry paths (`wrap_model_call` and `awrap_model_call`) while leaving the primary attempt unchanged. ### libs/langchain_v1 tests - Added sync and async regression tests in `test_model_fallback.py` that simulate primary failure and assert fallback calls only succeed when cache markers are stripped. - Verified non-cache settings (for example `temperature` and `top_p`) are preserved on fallback. - Preserved existing fallback behavior coverage (ordering, exhaustion, and error propagation). ## Compatibility with `BedrockPromptCachingMiddleware` The sanitizer also covers `BedrockPromptCachingMiddleware`, which injects `cache_control` into `model_settings` only. Bedrock-specific markers (`cachePoint` blocks, content-block `cache_control`) are applied by the chat model classes at API-call time and never appear in the `ModelRequest`, so no additional handling is needed. --------- Co-authored-by: Mason Daugherty <mason@langchain.dev> Co-authored-by: Mason Daugherty <github@mdrxy.com>
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
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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
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