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Align create_deep_agent model parameters with create_react_agent #73

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Align create_deep_agent model parameters with create_react_agent #73

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5f4c54c
align create_deep_agent model parameters with create_react_agent
Major-wagh Aug 25, 2025
89cdcfd
Merge branch 'master' into siddharth/deep-agent-flexible-models
Major-wagh Sep 1, 2025
e55cf17
Merge branch 'master' into siddharth/deep-agent-flexible-models
Major-wagh Sep 3, 2025
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73 changes: 66 additions & 7 deletions src/deepagents/graph.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,11 +2,16 @@
from deepagents.model import get_default_model
from deepagents.tools import write_todos, write_file, read_file, ls, edit_file
from deepagents.state import DeepAgentState
from typing import Sequence, Union, Callable, Any, TypeVar, Type, Optional
from typing import Sequence, Union, Callable, Any, TypeVar, Type, Optional, Dict, Awaitable
from langchain_core.tools import BaseTool, tool
from langchain_core.language_models import LanguageModelLike
from langchain_core.language_models import LanguageModelLike,LanguageModelInput, BaseMessage
from langchain_core.chat_models import BaseChatModel
from langchain_core.runnables import Runnable

from deepagents.interrupt import create_interrupt_hook, ToolInterruptConfig
from langgraph.types import Checkpointer
from langgraph.runtime import Runtime
from langgraph.typing import ContextT
from langgraph.prebuilt import create_react_agent

StateSchema = TypeVar("StateSchema", bound=DeepAgentState)
Expand All @@ -28,7 +33,19 @@
def _agent_builder(
tools: Sequence[Union[BaseTool, Callable, dict[str, Any]]],
instructions: str,
model: Optional[Union[str, LanguageModelLike]] = None,
model: Optional[Union[
str,
LanguageModelLike,
Callable[[StateSchema, Runtime[ContextT]], BaseChatModel],
Callable[[StateSchema, Runtime[ContextT]], Awaitable[BaseChatModel]],
Callable[
[StateSchema, Runtime[ContextT]], Runnable[LanguageModelInput, BaseMessage]
],
Callable[
[StateSchema, Runtime[ContextT]],
Awaitable[Runnable[LanguageModelInput, BaseMessage]],
],
]] = None,
subagents: list[SubAgent] = None,
state_schema: Optional[StateSchemaType] = None,
builtin_tools: Optional[list[str]] = None,
Expand Down Expand Up @@ -104,7 +121,19 @@ def _agent_builder(
def create_deep_agent(
tools: Sequence[Union[BaseTool, Callable, dict[str, Any]]],
instructions: str,
model: Optional[Union[str, LanguageModelLike]] = None,
model: Optional[Union[
str,
LanguageModelLike,
Callable[[StateSchema, Runtime[ContextT]], BaseChatModel],
Callable[[StateSchema, Runtime[ContextT]], Awaitable[BaseChatModel]],
Callable[
[StateSchema, Runtime[ContextT]], Runnable[LanguageModelInput, BaseMessage]
],
Callable[
[StateSchema, Runtime[ContextT]],
Awaitable[Runnable[LanguageModelInput, BaseMessage]],
],
]] = None,
subagents: list[SubAgent] = None,
state_schema: Optional[StateSchemaType] = None,
builtin_tools: Optional[list[str]] = None,
Expand All @@ -122,7 +151,16 @@ def create_deep_agent(
tools: The additional tools the agent should have access to.
instructions: The additional instructions the agent should have. Will go in
the system prompt.
model: The model to use.
model: The language model for the agent. Supports static and dynamic
model selection.
- **Static model**: A chat model instance (e.g., `ChatOpenAI()`) or
string identifier (e.g., `"openai:gpt-4"`)
- **Dynamic model**: A callable with signature
`(state, runtime) -> BaseChatModel` that returns different models
based on runtime context
If the model has tools bound via `.bind_tools()` or other configurations,
the return type should be a Runnable[LanguageModelInput, BaseMessage]
Coroutines are also supported, allowing for asynchronous model selection.
subagents: The subagents to use. Each subagent should be a dictionary with the
following keys:
- `name`
Expand Down Expand Up @@ -156,7 +194,19 @@ def create_deep_agent(
def async_create_deep_agent(
tools: Sequence[Union[BaseTool, Callable, dict[str, Any]]],
instructions: str,
model: Optional[Union[str, LanguageModelLike]] = None,
model: Optional[Union[
str,
LanguageModelLike,
Callable[[StateSchema, Runtime[ContextT]], BaseChatModel],
Callable[[StateSchema, Runtime[ContextT]], Awaitable[BaseChatModel]],
Callable[
[StateSchema, Runtime[ContextT]], Runnable[LanguageModelInput, BaseMessage]
],
Callable[
[StateSchema, Runtime[ContextT]],
Awaitable[Runnable[LanguageModelInput, BaseMessage]],
],
]] = None,
subagents: list[SubAgent] = None,
state_schema: Optional[StateSchemaType] = None,
builtin_tools: Optional[list[str]] = None,
Expand All @@ -174,7 +224,16 @@ def async_create_deep_agent(
tools: The additional tools the agent should have access to.
instructions: The additional instructions the agent should have. Will go in
the system prompt.
model: The model to use.
model: The language model for the agent. Supports static and dynamic
model selection.
- **Static model**: A chat model instance (e.g., `ChatOpenAI()`) or
string identifier (e.g., `"openai:gpt-4"`)
- **Dynamic model**: A callable with signature
`(state, runtime) -> BaseChatModel` that returns different models
based on runtime context
If the model has tools bound via `.bind_tools()` or other configurations,
the return type should be a Runnable[LanguageModelInput, BaseMessage]
Coroutines are also supported, allowing for asynchronous model selection.
subagents: The subagents to use. Each subagent should be a dictionary with the
following keys:
- `name`
Expand Down