AgentRuntime gains model_name and tools fields populated by create_agent at
wire time. A private _build_runtime hook on AgentMiddleware lets subpackages
(e.g. deepagents) return an enriched runtime subclass from their middleware
without exposing those fields in LangChain. All four middleware hook nodes
(before_agent, before_model, after_model, after_agent) now receive AgentRuntime
instead of the bare LangGraph Runtime; existing Runtime annotations remain
valid via Liskov substitution.
if "url" in annotation: in Line 15 , already ensures "url" is key in
annotation , so no need to check again to set "url" key in out object
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
The LLM shouldn't be seeing parameters it cannot control in the
ToolMessage error it gets when it invokes a tool with incorrect args.
This fixes the behavior within langchain to address immediate issue.
We may want to change the behavior in langchain_core as well to prevent
validation of injected arguments. But this would be done in a separate
change
added some noqas, this is a quick patch to support a bug uncovered in
the quickstart, will resolve fully depending on where we centralize
ToolNode stuff.
* The dependency is not used.
* It takes a long time to build in Python 3.14 as there are no prebuilt
binaries yet. This slows down CI a lot.
Co-authored-by: Mason Daugherty <mason@langchain.dev>
the fact that this was broken showcases that we need significantly
better test coverage, this is literally the most minimalistic usage of
this middleware there could be 😿
will document these two gotchas better for custom middleware
```py
from langchain.agents.middleware.shell_tool import ShellToolMiddleware
from langchain.agents import create_agent
agent = create_agent(model="openai:gpt-4",middleware = [ShellToolMiddleware()])
agent.invoke({"messages":[{"role": "user", "content": "hi"}]})
```
