The old `before_model_jump_to` classvar approach was quite clunky, this
is nicer imo and easier to document. Also moving from `jump_to` to
`can_jump_to` which is more idiomatic.
Before:
```py
class MyMiddleware(AgentMiddleware):
before_model_jump_to: ClassVar[list[JumpTo]] = ["end"]
def before_model(state, runtime) -> dict[str, Any]:
return {"jump_to": "end"}
```
After
```py
class MyMiddleware(AgentMiddleware):
@hook_config(can_jump_to=["end"])
def before_model(state, runtime) -> dict[str, Any]:
return {"jump_to": "end"}
```
This makes branching **much** more simple internally and helps greatly
w/ type safety for users. It just allows for one signature on hooks
instead of multiple.
Opened after https://github.com/langchain-ai/langchain/pull/33164
ballooned more than expected, w/ branching for:
* sync vs async
* runtime vs no runtime (this is self imposed)
**This also removes support for nodes w/o `runtime` in the signature.**
We can always go back and add support for nodes w/o `runtime`.
I think @christian-bromann's idea to re-export `runtime` from
langchain's agents might make sense due to the abundance of imports
here.
Check out the value of the change based on this diff:
https://github.com/langchain-ai/langchain/pull/33176
The async embed function does not properly handle HTTP errors.
For instance with large batches, Mistral AI returns `Too many inputs in
request, split into more batches.` in a 400 error.
This leads to a KeyError in `response.json()["data"]` l.288
This PR fixes the issue by:
- calling `response.raise_for_status()` before returning
- adding a retry similarly to what is done in the synchronous
counterpart `embed_documents`
I also added an integration test, but willing to move it to unit tests
if more relevant.
- **Description:** Changing the key from `response` to
`structured_response` for middleware agent to keep it sync with agent
without middleware. This a breaking change.
- **Issue:** #33154
Porting the [planning
middleware](39c0138d0f/src/deepagents/middleware.py (L21))
over from deepagents.
Also adding the ability to configure:
* System prompt
* Tool description
```py
from langchain.agents.middleware.planning import PlanningMiddleware
from langchain.agents import create_agent
agent = create_agent("openai:gpt-4o", middleware=[PlanningMiddleware()])
result = await agent.invoke({"messages": [HumanMessage("Help me refactor my codebase")]})
print(result["todos"]) # Array of todo items with status tracking
```
