- Sort model profiles alphabetically by model ID (the top-level
`_PROFILES` dictionary keys, e.g. `claude-3-5-haiku-20241022`,
`gpt-4o-mini`) before writing `_profiles.py`, so that regenerating
profiles only shows actual data changes in diffs — not random reordering
from the models.dev API response order
- Regenerate all 10 partner profile files with the new sorted ordering
- Add `text_inputs` and `text_outputs` fields to `ModelProfile`
- Regenerate `_profiles.py` for all providers
## Why
models.dev data includes `'text'` as both an input and output modality,
but we didn't capture it.
models.dev broadly contains models without text input (Whisper/ASR) and
without text output (image generators, TTS).
Without this, downstream consumers can't filter on model text support
(e.g. preventing users from passing text input to an audio-only model).
---
We'd need to also run for Google, AWS and cut releases for all to
propagate
* `create_agent`'s `system_prompt` allows `str | SystemMessage`
* added `system_message: SystemMessage` on `ModelRequest`
* `ModelRequest.system_prompt` is a function of `system_message.text`,
now deprecated
* disallow setting `system_prompt` and `system_message`
* `ModelRequest.system_prompt` can still be set (w/ custom setattr) for
custom backwards compat, but the updates just get propogated to the
`ModelRequest.system_message`
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
* use `override` instead of directly patching things on `ModelRequest`
* rely on `ToolNode` for execution of tools related to said middleware,
using `wrap_model_call` to inject the relevant claude tool specs +
allowing tool node to forward them along to corresponding langchain tool
implementations
* making the same change for the native shell tool middleware
* allowing shell tool middleware to specify a name for the shell tool
(negative diff then for claude bash middleware)
long term I think the solution might be to attach metadata to a tool to
map the provider spec to a langchain implementation, which we could also
take some lessons from on the MCP front.
- use latest models in examples to highlight support
- standardize on using IDs in examples - no more aliases to improve
determinism in future tests
- bump lock
- in integration tests, fix stale casettes and use `MODEL_NAME`
uniformly where possible
- add case for default max tokens for sonnet-4-5 (was missing)
Moving all `ToolNode` related improvements back to LangGraph and
importing them in LC!
pairing w/ https://github.com/langchain-ai/langgraph/pull/6321
this fixes a couple of things:
1. `InjectedState`, store etc will continue to work as expected no
matter where the import is from
2. `ToolRuntime` is now usable w/in langgraph, woohoo!
