#1206

autogen/agentchat/conversable_agent.py

@@ -1655,6 +1655,7 @@ def register_for_llm(
         *,
         name: Optional[str] = None,
         description: Optional[str] = None,
+        api_style: Optional[str] = None,
     ) -> Callable[[F], F]:
         """Decorator factory for registering a function to be used by an agent.
 
@@ -1667,6 +1668,9 @@ def register_for_llm(
             name (optional(str)): name of the function. If None, the function name will be used (default: None).
             description (optional(str)): description of the function (default: None). It is mandatory
                 for the initial decorator, but the following ones can omit it.
+            api_style: (optional(str)): the API style for function call.
+                For Azure OpenAI API as of 2023-09-01-preview, you should set this to
+                `"function"`. By default, it uses the OpenAI API's tool calling style.
 
         Returns:
             The decorator for registering a function to be used by an agent.
@@ -1680,6 +1684,13 @@ def my_function(a: Annotated[str, "description of a parameter"] = "a", b: int, c
                  return a + str(b * c)
             ```
 
+            For Azure OpenAI API as of 2023-09-01-preview, you should set `api_style` to `"function"`:
+            ```
+            @agent2.register_for_llm(api_style="function")
+            def my_function(a: Annotated[str, "description of a parameter"] = "a", b: int, c=3.14) -> str:
+                 return a + str(b * c)
+            ```
+
         """
 
         def _decorator(func: F) -> F:
@@ -1716,7 +1727,11 @@ def _decorator(func: F) -> F:
             if self.llm_config is None:
                 raise RuntimeError("LLM config must be setup before registering a function for LLM.")
 
-            self.update_tool_signature(f, is_remove=False)
+            if api_style == "function":
+                f = f["function"]
+                self.update_function_signature(f, is_remove=False)
+            else:
+                self.update_tool_signature(f, is_remove=False)
 
             return func
 

test/agentchat/test_conversable_agent.py

@@ -569,6 +569,77 @@ async def exec_sh(script: Annotated[str, "Valid shell script to execute."]) -> s
         assert agent3.llm_config["tools"] == expected3
 
 
+def test_register_for_llm_api_style_function():
+    with pytest.MonkeyPatch.context() as mp:
+        mp.setenv("OPENAI_API_KEY", "mock")
+        agent3 = ConversableAgent(name="agent3", llm_config={"config_list": []})
+        agent2 = ConversableAgent(name="agent2", llm_config={"config_list": []})
+        agent1 = ConversableAgent(name="agent1", llm_config={"config_list": []})
+
+        @agent3.register_for_llm(api_style="function")
+        @agent2.register_for_llm(name="python", api_style="function")
+        @agent1.register_for_llm(
+            description="run cell in ipython and return the execution result.", api_style="function"
+        )
+        def exec_python(cell: Annotated[str, "Valid Python cell to execute."]) -> str:
+            pass
+
+        expected1 = [
+            {
+                "description": "run cell in ipython and return the execution result.",
+                "name": "exec_python",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "cell": {
+                            "type": "string",
+                            "description": "Valid Python cell to execute.",
+                        }
+                    },
+                    "required": ["cell"],
+                },
+            }
+        ]
+        expected2 = copy.deepcopy(expected1)
+        expected2[0]["name"] = "python"
+        expected3 = expected2
+
+        assert agent1.llm_config["functions"] == expected1
+        assert agent2.llm_config["functions"] == expected2
+        assert agent3.llm_config["functions"] == expected3
+
+        @agent3.register_for_llm(api_style="function")
+        @agent2.register_for_llm(api_style="function")
+        @agent1.register_for_llm(
+            name="sh", description="run a shell script and return the execution result.", api_style="function"
+        )
+        async def exec_sh(script: Annotated[str, "Valid shell script to execute."]) -> str:
+            pass
+
+        expected1 = expected1 + [
+            {
+                "name": "sh",
+                "description": "run a shell script and return the execution result.",
+                "parameters": {
+                    "type": "object",
+                    "properties": {
+                        "script": {
+                            "type": "string",
+                            "description": "Valid shell script to execute.",
+                        }
+                    },
+                    "required": ["script"],
+                },
+            }
+        ]
+        expected2 = expected2 + [expected1[1]]
+        expected3 = expected3 + [expected1[1]]
+
+        assert agent1.llm_config["functions"] == expected1
+        assert agent2.llm_config["functions"] == expected2
+        assert agent3.llm_config["functions"] == expected3
+
+
 def test_register_for_llm_without_description():
     with pytest.MonkeyPatch.context() as mp:
         mp.setenv("OPENAI_API_KEY", "mock")