Create PromptTemplates typed dict (#547)

docs/source/en/reference/agents.md

@@ -57,3 +57,11 @@ _This class is deprecated since 1.8.0: now you simply need to pass attributes `n
 > You must have `gradio` installed to use the UI. Please run `pip install smolagents[gradio]` if it's not the case.
 
 [[autodoc]] GradioUI
+
+## Prompts
+
+[[autodoc]] smolagents.agents.PromptTemplates
+
+[[autodoc]] smolagents.agents.PlanningPromptTemplate
+
+[[autodoc]] smolagents.agents.ManagedAgentPromptTemplate

docs/source/hi/reference/agents.md

@@ -155,3 +155,11 @@ model = OpenAIServerModel(
     api_key=os.environ["OPENAI_API_KEY"],
 )
 ```
+
+## Prompts
+
+[[autodoc]] smolagents.agents.PromptTemplates
+
+[[autodoc]] smolagents.agents.PlanningPromptTemplate
+
+[[autodoc]] smolagents.agents.ManagedAgentPromptTemplate

docs/source/zh/reference/agents.md

@@ -147,3 +147,11 @@ print(model(messages))
 ```
 
 [[autodoc]] LiteLLMModel
+
+## Prompts
+
+[[autodoc]] smolagents.agents.PromptTemplates
+
+[[autodoc]] smolagents.agents.PlanningPromptTemplate
+
+[[autodoc]] smolagents.agents.ManagedAgentPromptTemplate

src/smolagents/agents.py

@@ -14,6 +14,9 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+
+__all__ = ["AgentMemory", "CodeAgent", "MultiStepAgent", "ToolCallingAgent"]
+
 import importlib.resources
 import inspect
 import re
@@ -21,7 +24,7 @@ import textwrap
 import time
 from collections import deque
 from logging import getLogger
-from typing import Any, Callable, Dict, Generator, List, Optional, Set, Tuple, Union
+from typing import Any, Callable, Dict, Generator, List, Optional, Set, Tuple, TypedDict, Union
 
 import yaml
 from jinja2 import StrictUndefined, Template
@@ -80,6 +83,69 @@ def populate_template(template: str, variables: Dict[str, Any]) -> str:
         raise Exception(f"Error during jinja template rendering: {type(e).__name__}: {e}")
 
 
+class PlanningPromptTemplate(TypedDict):
+    """
+    Prompt templates for the planning step.
+
+    Args:
+        initial_facts (`str`): Initial facts prompt.
+        initial_plan (`str`): Initial plan prompt.
+        update_facts_pre_messages (`str`): Update facts pre-messages prompt.
+        update_facts_post_messages (`str`): Update facts post-messages prompt.
+        update_plan_pre_messages (`str`): Update plan pre-messages prompt.
+        update_plan_post_messages (`str`): Update plan post-messages prompt.
+    """
+
+    initial_facts: str
+    initial_plan: str
+    update_facts_pre_messages: str
+    update_facts_post_messages: str
+    update_plan_pre_messages: str
+    update_plan_post_messages: str
+
+
+class ManagedAgentPromptTemplate(TypedDict):
+    """
+    Prompt templates for the managed agent.
+
+    Args:
+        task (`str`): Task prompt.
+        report (`str`): Report prompt.
+    """
+
+    task: str
+    report: str
+
+
+class PromptTemplates(TypedDict):
+    """
+    Prompt templates for the agent.
+
+    Args:
+        system_prompt (`str`): System prompt.
+        planning ([`~agents.PlanningPromptTemplate`]): Planning prompt template.
+        managed_agent ([`~agents.ManagedAgentPromptTemplate`]): Managed agent prompt template.
+    """
+
+    system_prompt: str
+    planning: PlanningPromptTemplate
+    managed_agent: ManagedAgentPromptTemplate
+
+
+EMPTY_PROMPT_TEMPLATES = PromptTemplates(
+    system_prompt="",
+    planning=PlanningPromptTemplate(
+        initial_facts="",
+        initial_plan="",
+        update_facts_pre_messages="",
+        update_facts_post_messages="",
+        update_plan_pre_messages="",
+        update_plan_post_messages="",
+    ),
+    managed_agent=ManagedAgentPromptTemplate(task="", report=""),
+)
+
+
 class MultiStepAgent:
     """
     Agent class that solves the given task step by step, using the ReAct framework:
@@ -88,7 +154,7 @@ class MultiStepAgent:
     Args:
         tools (`list[Tool]`): [`Tool`]s that the agent can use.
         model (`Callable[[list[dict[str, str]]], ChatMessage]`): Model that will generate the agent's actions.
-        prompt_templates (`dict`, *optional*): Prompt templates.
+        prompt_templates ([`~agents.PromptTemplates`], *optional*): Prompt templates.
         max_steps (`int`, default `6`): Maximum number of steps the agent can take to solve the task.
         tool_parser (`Callable`, *optional*): Function used to parse the tool calls from the LLM output.
         add_base_tools (`bool`, default `False`): Whether to add the base tools to the agent's tools.
@@ -107,7 +173,7 @@ class MultiStepAgent:
         self,
         tools: List[Tool],
         model: Callable[[List[Dict[str, str]]], ChatMessage],
-        prompt_templates: Optional[dict] = None,
+        prompt_templates: Optional[PromptTemplates] = None,
         max_steps: int = 6,
         tool_parser: Optional[Callable] = None,
         add_base_tools: bool = False,
@@ -125,7 +191,7 @@ class MultiStepAgent:
             tool_parser = parse_json_tool_call
         self.agent_name = self.__class__.__name__
         self.model = model
-        self.prompt_templates = prompt_templates or {}
+        self.prompt_templates = prompt_templates or EMPTY_PROMPT_TEMPLATES
         self.max_steps = max_steps
         self.step_number: int = 0
         self.tool_parser = tool_parser
@@ -653,7 +719,7 @@ class ToolCallingAgent(MultiStepAgent):
     Args:
         tools (`list[Tool]`): [`Tool`]s that the agent can use.
         model (`Callable[[list[dict[str, str]]], ChatMessage]`): Model that will generate the agent's actions.
-        prompt_templates (`dict`, *optional*): Prompt templates.
+        prompt_templates ([`~agents.PromptTemplates`], *optional*): Prompt templates.
         planning_interval (`int`, *optional*): Interval at which the agent will run a planning step.
         **kwargs: Additional keyword arguments.
     """
@@ -662,7 +728,7 @@ class ToolCallingAgent(MultiStepAgent):
         self,
         tools: List[Tool],
         model: Callable[[List[Dict[str, str]]], ChatMessage],
-        prompt_templates: Optional[dict] = None,
+        prompt_templates: Optional[PromptTemplates] = None,
         planning_interval: Optional[int] = None,
         **kwargs,
     ):
@@ -775,7 +841,7 @@ class CodeAgent(MultiStepAgent):
     Args:
         tools (`list[Tool]`): [`Tool`]s that the agent can use.
         model (`Callable[[list[dict[str, str]]], ChatMessage]`): Model that will generate the agent's actions.
-        prompt_templates (`dict`, *optional*): Prompt templates.
+        prompt_templates ([`~agents.PromptTemplates`], *optional*): Prompt templates.
         grammar (`dict[str, str]`, *optional*): Grammar used to parse the LLM output.
         additional_authorized_imports (`list[str]`, *optional*): Additional authorized imports for the agent.
         planning_interval (`int`, *optional*): Interval at which the agent will run a planning step.
@@ -789,7 +855,7 @@ class CodeAgent(MultiStepAgent):
         self,
         tools: List[Tool],
         model: Callable[[List[Dict[str, str]]], ChatMessage],
-        prompt_templates: Optional[dict] = None,
+        prompt_templates: Optional[PromptTemplates] = None,
         grammar: Optional[Dict[str, str]] = None,
         additional_authorized_imports: Optional[List[str]] = None,
         planning_interval: Optional[int] = None,
@@ -941,6 +1007,3 @@ class CodeAgent(MultiStepAgent):
         self.logger.log(Group(*execution_outputs_console), level=LogLevel.INFO)
         memory_step.action_output = output
         return output if is_final_answer else None
-
-
-__all__ = ["MultiStepAgent", "CodeAgent", "ToolCallingAgent", "AgentMemory"]