AutoGen integration

examples/math_crewai.py

@@ -1,14 +1,14 @@
 from dotenv import load_dotenv
 
-from motleycrew import MotleyCrew, Task
+from motleycrew import MotleyCrew
 from motleycrew.agents.crewai import CrewAIMotleyAgent
-from motleycrew.tools.python_repl import create_repl_tool
+from motleycrew.tools import PythonREPLTool
 from motleycrew.common.utils import configure_logging
 
 
 def main():
     """Main function of running the example."""
-    repl_tool = create_repl_tool()
+    repl_tool = PythonREPLTool()
 
     # Define your agents with roles and goals
     solver1 = CrewAIMotleyAgent(

motleycrew/common/defaults.py

@@ -10,8 +10,8 @@ class Defaults:
 
     DEFAULT_GRAPH_STORE_TYPE = GraphStoreType.KUZU
 
-
-defaults_module_install_commands = {
-    "crewai": "pip install crewai",
-    "llama_index": "pip install llama-index"
-}
+    MODULE_INSTALL_COMMANDS = {
+        "crewai": "pip install crewai",
+        "llama_index": "pip install llama-index",
+        "autogen": "pip install autogen",
+    }

motleycrew/common/exceptions.py

@@ -1,4 +1,4 @@
-from motleycrew.common.defaults import defaults_module_install_commands
+from motleycrew.common import Defaults
 
 
 class LLMFamilyNotSupported(Exception):
@@ -55,7 +55,7 @@ class ModuleNotInstalledException(Exception):
 
     def __init__(self, module_name: str, install_command: str = None):
         self.module_name = module_name
-        self.install_command = install_command or defaults_module_install_commands.get(
+        self.install_command = install_command or Defaults.MODULE_INSTALL_COMMANDS.get(
             module_name, None
         )
 

motleycrew/tools/__init__.py

@@ -1,3 +1,7 @@
 from .tool import MotleyTool
-from .llm_tool import LLMTool
+
+from .autogen_chat_tool import AutoGenChatTool
 from .image_generation import DallEImageGeneratorTool
+from .llm_tool import LLMTool
+from .mermaid_evaluator_tool import MermaidEvaluatorTool
+from .python_repl import PythonREPLTool

motleycrew/tools/autogen_chat_tool.py

@@ -0,0 +1,79 @@
+from typing import Optional, Type, Callable, Any
+
+from langchain_core.tools import StructuredTool
+from langchain_core.prompts import PromptTemplate
+from langchain_core.prompts.base import BasePromptTemplate
+from langchain_core.pydantic_v1 import BaseModel, Field, create_model
+
+try:
+    from autogen import ConversableAgent, ChatResult
+except ImportError:
+    ConversableAgent = None
+    ChatResult = None
+
+from motleycrew.tools import MotleyTool
+from motleycrew.common.utils import ensure_module_is_installed
+
+
+def get_last_message(chat_result: ChatResult) -> str:
+    for message in reversed(chat_result.chat_history):
+        if message.get("content") and "TERMINATE" not in message["content"]:
+            return message["content"]
+
+
+class AutoGenChatTool(MotleyTool):
+    def __init__(
+        self,
+        name: str,
+        description: str,
+        prompt: str | BasePromptTemplate,
+        initiator: ConversableAgent,
+        recipient: ConversableAgent,
+        result_extractor: Callable[[ChatResult], Any] = get_last_message,
+        input_schema: Optional[Type[BaseModel]] = None,
+    ):
+        ensure_module_is_installed("autogen")
+        langchain_tool = create_autogen_chat_tool(
+            name=name,
+            description=description,
+            prompt=prompt,
+            initiator=initiator,
+            recipient=recipient,
+            result_extractor=result_extractor,
+            input_schema=input_schema,
+        )
+        super().__init__(langchain_tool)
+
+
+def create_autogen_chat_tool(
+    name: str,
+    description: str,
+    prompt: str | BasePromptTemplate,
+    initiator: ConversableAgent,
+    recipient: ConversableAgent,
+    result_extractor: Callable[[ChatResult], Any],
+    input_schema: Optional[Type[BaseModel]] = None,
+):
+    if not isinstance(prompt, BasePromptTemplate):
+        prompt = PromptTemplate.from_template(prompt)
+
+    if input_schema is None:
+        fields = {
+            var: (str, Field(description=f"Input {var} for the tool."))
+            for var in prompt.input_variables
+        }
+
+        # Create the AutoGenChatToolInput class dynamically
+        input_schema = create_model("AutoGenChatToolInput", **fields)
+
+    def run_autogen_chat(**kwargs) -> Any:
+        message = prompt.format(**kwargs)
+        chat_result = initiator.initiate_chat(recipient, message=message)
+        return result_extractor(chat_result)
+
+    return StructuredTool.from_function(
+        func=run_autogen_chat,
+        name=name,
+        description=description,
+        args_schema=input_schema,
+    )

motleycrew/tools/python_repl.py

@@ -5,6 +5,12 @@
 from .tool import MotleyTool
 
 
+class PythonREPLTool(MotleyTool):
+    def __init__(self):
+        langchain_tool = create_repl_tool()
+        super().__init__(langchain_tool)
+
+
 class REPLToolInput(BaseModel):
     """Input for the REPL tool."""
 
@@ -13,12 +19,10 @@ class REPLToolInput(BaseModel):
 
 # You can create the tool to pass to an agent
 def create_repl_tool():
-    return MotleyTool.from_langchain_tool(
-        Tool.from_function(
-            func=PythonREPL().run,
-            name="python_repl",
-            description="A Python shell. Use this to execute python commands. Input should be a valid python command. "
-            "MAKE SURE TO PRINT OUT THE RESULTS YOU CARE ABOUT USING `print(...)`.",
-            args_schema=REPLToolInput,
-        )
+    return Tool.from_function(
+        func=PythonREPL().run,
+        name="python_repl",
+        description="A Python shell. Use this to execute python commands. Input should be a valid python command. "
+        "MAKE SURE TO PRINT OUT THE RESULTS YOU CARE ABOUT USING `print(...)`.",
+        args_schema=REPLToolInput,
     )

motleycrew/tools/tool.py

@@ -1,4 +1,4 @@
-from typing import Union
+from typing import Union, Annotated
 
 from langchain.tools import BaseTool
 from langchain_core.runnables import Runnable
@@ -76,3 +76,16 @@ def to_llama_index_tool(self) -> LlamaIndex__BaseTool:
             fn_schema=self.tool.args_schema,
         )
         return llama_index_tool
+
+    def to_autogen_tool(self):
+        fields = list(self.tool.args_schema.__fields__.values())
+        if len(fields) != 1:
+            raise Exception("Multiple input fields are not supported in to_autogen_tool")
+
+        field_name = fields[0].name
+        field_type = fields[0].annotation
+
+        def autogen_tool_fn(input: field_type) -> str:
+            return self.invoke({field_name: input})
+
+        return autogen_tool_fn