Add tool calling agent example

examples/tool_calling_agent.py

@@ -0,0 +1,22 @@
+from agents.agents import ToolCallingAgent
+from agents import tool, HfApiEngine, OpenAIEngine, AnthropicEngine
+
+# Choose which LLM engine to use!
+llm_engine = OpenAIEngine("gpt-4o")
+llm_engine = AnthropicEngine()
+llm_engine = HfApiEngine("meta-llama/Llama-3.3-70B-Instruct")
+
+@tool
+def get_weather(location: str) -> str:
+    """
+    Get weather in the next days at given location.
+    Secretly this tool does not care about the location, it hates the weather everywhere.
+
+    Args:
+        location: the location
+    """
+    return "The weather is UNGODLY with torrential rains and temperatures below -10°C"
+
+agent = ToolCallingAgent(tools=[get_weather], llm_engine=llm_engine)
+
+print(agent.run("What's the weather like in Paris?"))

src/agents/docker_alternative.py

@@ -40,7 +40,7 @@ class DockerPythonInterpreter:
         Execute Python code in the container and return stdout and stderr
         """
 
-        if tools != None:
+        if tools is not None:
             tool_instance = tools[0]()
 
             import_code = f"""

src/agents/llm_engines.py

@@ -50,7 +50,7 @@ class MessageRole(str, Enum):
         return [r.value for r in cls]
 
 
-llama_role_conversions = {
+tool_role_conversions = {
     MessageRole.TOOL_CALL: MessageRole.ASSISTANT,
     MessageRole.TOOL_RESPONSE: MessageRole.USER,
 }
@@ -232,7 +232,7 @@ class HfApiEngine(HfEngine):
     ) -> str:
         """Generates a text completion for the given message list"""
         messages = get_clean_message_list(
-            messages, role_conversions=llama_role_conversions
+            messages, role_conversions=tool_role_conversions
         )
 
         # Send messages to the Hugging Face Inference API
@@ -260,7 +260,7 @@ class HfApiEngine(HfEngine):
     ):
         """Generates a tool call for the given message list"""
         messages = get_clean_message_list(
-            messages, role_conversions=llama_role_conversions
+            messages, role_conversions=tool_role_conversions
         )
         response = self.client.chat.completions.create(
             messages=messages,
@@ -302,7 +302,7 @@ class TransformersEngine(HfEngine):
         max_tokens: int = 1500,
     ) -> str:
         messages = get_clean_message_list(
-            messages, role_conversions=llama_role_conversions
+            messages, role_conversions=tool_role_conversions
         )
 
         # Get LLM output
@@ -360,7 +360,7 @@ class OpenAIEngine:
         max_tokens: int = 1500,
     ) -> str:
         messages = get_clean_message_list(
-            messages, role_conversions=openai_role_conversions
+            messages, role_conversions=tool_role_conversions
         )
 
         response = self.client.chat.completions.create(
@@ -381,7 +381,7 @@ class OpenAIEngine:
     ):
         """Generates a tool call for the given message list"""
         messages = get_clean_message_list(
-            messages, role_conversions=llama_role_conversions
+            messages, role_conversions=tool_role_conversions
         )
         response = self.client.chat.completions.create(
             model=self.model_name,
@@ -448,7 +448,7 @@ class AnthropicEngine:
         max_tokens: int = 1500,
     ) -> str:
         messages = get_clean_message_list(
-            messages, role_conversions=llama_role_conversions
+            messages, role_conversions=tool_role_conversions
         )
         filtered_messages, system_prompt = self.separate_messages_system_prompt(
             messages
@@ -475,7 +475,7 @@ class AnthropicEngine:
     ):
         """Generates a tool call for the given message list"""
         messages = get_clean_message_list(
-            messages, role_conversions=llama_role_conversions
+            messages, role_conversions=tool_role_conversions
         )
         filtered_messages, system_prompt = self.separate_messages_system_prompt(
             messages
@@ -496,7 +496,7 @@ class AnthropicEngine:
 
 __all__ = [
     "MessageRole",
-    "llama_role_conversions",
+    "tool_role_conversions",
     "get_clean_message_list",
     "HfEngine",
     "TransformersEngine",

tests/test_agents.py

@@ -232,7 +232,7 @@ Action:
 
     def test_additional_args_added_to_task(self):
         agent = CodeAgent(tools=[], llm_engine=fake_code_llm)
-        output = agent.run(
+        agent.run(
             "What is 2 multiplied by 3.6452?", additional_instruction="Remember this."
         )
         assert "Remember this" in agent.task