Add LCAgentComponent and XMLAgentComponent

src/backend/langflow/base/agents/agent.py

@@ -0,0 +1,70 @@
+from typing import List, Union
+
+from langchain.agents import AgentExecutor, BaseMultiActionAgent, BaseSingleActionAgent
+
+from langflow import CustomComponent
+from langflow.field_typing import BaseMemory, Text, Tool
+
+
+class LCAgentComponent(CustomComponent):
+    def build_config(self):
+        return {
+            "lc": {
+                "display_name": "LangChain",
+                "info": "The LangChain to interact with.",
+            },
+            "handle_parsing_errors": {
+                "display_name": "Handle Parsing Errors",
+                "info": "If True, the agent will handle parsing errors. If False, the agent will raise an error.",
+                "advanced": True,
+            },
+            "output_key": {
+                "display_name": "Output Key",
+                "info": "The key to use to get the output from the agent.",
+                "advanced": True,
+            },
+            "memory": {
+                "display_name": "Memory",
+                "info": "Memory to use for the agent.",
+            },
+            "tools": {
+                "display_name": "Tools",
+                "info": "Tools the agent can use.",
+            },
+            "input_value": {
+                "display_name": "Input",
+                "info": "Input text to pass to the agent.",
+            },
+        }
+
+    async def run_agent(
+        self,
+        agent: Union[BaseSingleActionAgent, BaseMultiActionAgent, AgentExecutor],
+        inputs: str,
+        input_variables: list[str],
+        tools: List[Tool],
+        memory: BaseMemory = None,
+        handle_parsing_errors: bool = True,
+        output_key: str = "output",
+    ) -> Text:
+        if isinstance(agent, AgentExecutor):
+            runnable = agent
+        else:
+            runnable = AgentExecutor.from_agent_and_tools(
+                agent=agent, tools=tools, verbose=True, memory=memory, handle_parsing_errors=handle_parsing_errors
+            )
+        input_dict = {"input": inputs}
+        for var in input_variables:
+            if var not in ["agent_scratchpad", "input"]:
+                input_dict[var] = ""
+        result = await runnable.ainvoke(input_dict)
+        self.status = result
+        if output_key in result:
+            return result.get(output_key)
+        elif "output" not in result:
+            if output_key != "output":
+                raise ValueError(f"Output key not found in result. Tried '{output_key}' and 'output'.")
+            else:
+                raise ValueError("Output key not found in result. Tried 'output'.")
+
+        return result.get("output")

src/backend/langflow/components/agents/XMLAgent.py

@@ -1,13 +1,13 @@
 from typing import List
 
-from langchain.agents import AgentExecutor, create_xml_agent
+from langchain.agents import create_xml_agent
 from langchain_core.prompts import PromptTemplate
 
-from langflow import CustomComponent
+from langflow.base.agents.agent import LCAgentComponent
 from langflow.field_typing import BaseLLM, BaseMemory, Text, Tool
 
 
-class XMLAgentComponent(CustomComponent):
+class XMLAgentComponent(LCAgentComponent):
     display_name = "XMLAgent"
     description = "Construct an XML agent from an LLM and tools."
 
@@ -57,7 +57,7 @@ class XMLAgentComponent(CustomComponent):
                 "display_name": "Memory",
                 "info": "Memory to use for the agent.",
             },
-            "inputs": {
+            "input_value": {
                 "display_name": "Inputs",
                 "info": "Input text to pass to the agent.",
             },
@@ -65,7 +65,7 @@ class XMLAgentComponent(CustomComponent):
 
     async def build(
         self,
-        inputs: str,
+        input_value: str,
         llm: BaseLLM,
         tools: List[Tool],
         prompt: str,
@@ -84,13 +84,6 @@ class XMLAgentComponent(CustomComponent):
         prompt_template = PromptTemplate.from_template(prompt)
         input_variables = prompt_template.input_variables
         agent = create_xml_agent(llm, tools, prompt_template, tools_renderer=render_tool_description)
-        runnable = AgentExecutor.from_agent_and_tools(
-            agent=agent, tools=tools, verbose=True, memory=memory, handle_parsing_errors=handle_parsing_errors
-        )
-        input_dict = {"input": inputs}
-        for var in input_variables:
-            if var not in ["agent_scratchpad", "input"]:
-                input_dict[var] = ""
-        result = await runnable.ainvoke(input_dict)
+        result = await self.run_agent(agent, input_value, input_variables, tools, memory, handle_parsing_errors)
         self.status = result
-        return result.get("output")
+        return result