Checked other resources

• This is a bug, not a usage question.
• I added a clear and descriptive title that summarizes this issue.
• I used the GitHub search to find a similar question and didn't find it.
• I am sure that this is a bug in LangChain rather than my code.
• The bug is not resolved by updating to the latest stable version of LangChain (or the specific integration package).
• This is not related to the langchain-community package.
• I read what a minimal reproducible example is (https://stackoverflow.com/help/minimal-reproducible-example).
• I posted a self-contained, minimal, reproducible example. A maintainer can copy it and run it AS IS.

Example Code

from langchain_anthropic import ChatAnthropic
from langchain_openai import ChatOpenAI
from langchain_anthropic.middleware.prompt_caching import AnthropicPromptCachingMiddleware
from langchain.agents.middleware import ModelFallbackMiddleware
from langchain.agents import create_agent

# Setup: Main model (Anthropic) with fallback to OpenAI
main_model = ChatAnthropic(
    model="claude-sonnet-4-latest",
    anthropic_api_key="YOUR_ANTHROPIC_KEY"
)

fallback_model = ChatOpenAI(
    model="gpt-4o-mini",
    openai_api_key="YOUR_OPENAI_KEY"
)

# Create middleware list
middleware = [
    AnthropicPromptCachingMiddleware(
        ttl="5m",
        unsupported_model_behavior="ignore"  # ← This doesn't prevent the bug
    ),
    ModelFallbackMiddleware(
        fallback_models=[fallback_model]
    )
]

# Create agent with middleware
agent = create_agent(
    model=main_model,
    system_prompt="You are a helpful assistant.",
    tools=[],
    middleware=middleware
)

# Trigger the bug:
# 1. Force Anthropic API to fail (e.g., network issue, rate limit)
# 2. ModelFallbackMiddleware kicks in → switches to OpenAI
# 3. OpenAI receives messages with cache_control parameter
# 4. Error: "AsyncCompletions.create() got an unexpected keyword argument 'cache_control'"

# Reproduce by invoking when Anthropic API is unstable
result = agent.invoke({"messages": [{"role": "user", "content": "Hello"}]})

Error Message and Stack Trace (if applicable)

2025-10-29 09:07:14 [INFO] Chat model invocation: ChatAnthropic
2025-10-29 09:07:20 [ERROR] LLM error: peer closed connection without sending complete message body (incomplete chunked read)

# Fallback triggered
2025-10-29 09:07:20 [INFO] Fallback activated: ChatAnthropic -> ChatOpenAI
2025-10-29 09:07:20 [INFO] Chat model invocation: ChatOpenAI

# Fallback fails with cache_control error
2025-10-29 09:07:20 [ERROR] LLM error: AsyncCompletions.create() got an unexpected keyword argument 'cache_control'

Traceback (most recent call last):
  File "langchain/agents/react/graph.py", line 284, in invoke
    result = await fallback_model.ainvoke(messages, config)
  File "langchain_openai/chat_models/base.py", line 412, in ainvoke
    response = await self.async_client.chat.completions.create(**params)
TypeError: AsyncCompletions.create() got an unexpected keyword argument 'cache_control'

Description

Problem

When using AnthropicPromptCachingMiddleware together with ModelFallbackMiddleware, the middleware adds Anthropic-specific cache_control parameters to messages. If the Anthropic model fails and triggers a fallback to a non-Anthropic provider (e.g., OpenAI, Google), the fallback model receives messages with the cache_control parameter, causing a TypeError.

This completely breaks the fallback mechanism, forcing production systems to choose between prompt caching optimization (AnthropicPromptCachingMiddleware) or reliability (ModelFallbackMiddleware), but not both.

Expected Behavior

1. Anthropic model fails with "peer closed connection"
2. ModelFallbackMiddleware activates and switches to OpenAI
3. AnthropicPromptCachingMiddleware should not apply cache_control to non-Anthropic models
4. OpenAI model succeeds and returns normal response

Note: The unsupported_model_behavior="ignore" parameter does not prevent this issue.

Actual Behavior

1. Anthropic model fails with "peer closed connection"
2. AnthropicPromptCachingMiddleware.before_model() adds cache_control to messages
3. ModelFallbackMiddleware activates and switches to OpenAI
4. The same message objects (with cache_control still present) are reused
5. OpenAI model fails with TypeError: unexpected keyword argument 'cache_control'
6. Both models fail, resulting in complete service failure

Root Cause

The AnthropicPromptCachingMiddleware modifies messages before the model invocation, but the unsupported_model_behavior parameter only controls whether to warn/raise/ignore when applied to non-Anthropic models. It does not remove cache_control parameters from messages.

When ModelFallbackMiddleware switches to a fallback model:

• The middleware pipeline does not re-run
• Messages already contain cache_control parameters
• Non-Anthropic providers reject these parameters

Why unsupported_model_behavior="ignore" Does Not Help

AnthropicPromptCachingMiddleware(
    ttl="5m",
    unsupported_model_behavior="ignore"  # Only affects warnings, not cleanup
)

This parameter:

• Prevents warnings/errors when middleware runs on non-Anthropic models
• Does not remove cache_control from messages after fallback
• Does not re-run middleware after fallback switches models

Execution Flow

Step 1: AnthropicPromptCachingMiddleware.before_model()
  - Adds cache_control to messages (Anthropic-specific)
  - Modified messages: [{"role": "user", "content": "...", "cache_control": {"type": "ephemeral"}}]

Step 2: ChatAnthropic.invoke(messages)
  - Fails: "peer closed connection" (network/API issue)

Step 3: ModelFallbackMiddleware activates
  - Switches to ChatOpenAI
  - Reuses the SAME message objects (with cache_control still present)

Step 4: ChatOpenAI.invoke(messages)
  - Receives messages with cache_control parameter
  - TypeError: AsyncCompletions.create() got an unexpected keyword argument 'cache_control'

System Info

System Information
------------------
OS: Linux
Python Version: 3.12.8

Package Information
-------------------
langchain-core: 1.0.1
langchain: 1.0.2
langchain-anthropic: 1.0.0
langchain-openai: 1.0.1
langgraph: 1.0.1
langgraph-checkpoint-postgres: 3.0.0
deepagents: 0.1.4
langmem: 0.0.28

Full package list from python -m langchain_core.sys_info:

langchain                      1.0.2
langchain-anthropic            1.0.0
langchain-aws                  1.0.0
langchain-community            1.0.0a1
langchain-core                 1.0.1
langchain-google-genai         3.0.0
langchain-google-vertexai      3.0.1
langchain-huggingface          1.0.0
langchain-openai               1.0.1
langgraph                      1.0.1
langgraph-checkpoint           3.0.0
langgraph-checkpoint-postgres  3.0.0