feat: first init_agent node

src/backend/langflow/custom/customs.py

@@ -3,7 +3,11 @@ from langflow.template import nodes
 CUSTOM_NODES = {
     "prompts": {**nodes.ZeroShotPromptNode().to_dict()},
     "tools": {**nodes.PythonFunctionNode().to_dict(), **nodes.ToolNode().to_dict()},
-    "agents": {**nodes.JsonAgentNode().to_dict(), **nodes.CSVAgentNode().to_dict()},
+    "agents": {
+        **nodes.JsonAgentNode().to_dict(),
+        **nodes.CSVAgentNode().to_dict(),
+        **nodes.InitializeAgentNode().to_dict(),
+    },
 }
 
 

src/backend/langflow/interface/agents/custom.py

@@ -1,4 +1,4 @@
-from typing import Any, Optional
+from typing import Any, List, Optional
 
 from langchain import LLMChain
 from langchain.agents import AgentExecutor, ZeroShotAgent
@@ -8,7 +8,9 @@ from langchain.agents.agent_toolkits.pandas.prompt import PREFIX as PANDAS_PREFI
 from langchain.agents.agent_toolkits.pandas.prompt import SUFFIX as PANDAS_SUFFIX
 from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
 from langchain.schema import BaseLanguageModel
+from langchain.llms.base import BaseLLM
 from langchain.tools.python.tool import PythonAstREPLTool
+from langchain.agents import initialize_agent, Tool
 
 
 class JsonAgent(AgentExecutor):
@@ -87,7 +89,26 @@ class CSVAgent(AgentExecutor):
         return super().run(*args, **kwargs)
 
 
+class InitializeAgent(AgentExecutor):
+    """Initialize agent"""
+
+    @classmethod
+    def initialize(cls, llm: BaseLLM, tools: List[Tool], agent: str):
+        return initialize_agent(
+            tools=tools,
+            llm=llm,
+            agent=agent,
+        )
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def run(self, *args, **kwargs):
+        return super().run(*args, **kwargs)
+
+
 CUSTOM_AGENTS = {
     "JsonAgent": JsonAgent,
     "CSVAgent": CSVAgent,
+    "InitializeAgent": InitializeAgent,
 }

src/backend/langflow/interface/agents/prebuilt.py

@@ -0,0 +1,48 @@
+from typing import Optional
+
+from langchain import LLMChain
+from langchain.agents import AgentExecutor, ZeroShotAgent
+from langchain.agents.agent_toolkits.json.prompt import JSON_PREFIX, JSON_SUFFIX
+from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit
+from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
+from langchain.schema import BaseLanguageModel
+from pydantic import BaseModel
+
+
+class MalfoyAgent(AgentExecutor):
+    """Json agent"""
+
+    prefix = "Malfoy: "
+
+    @classmethod
+    def initialize(cls, *args, **kwargs):
+        return cls.from_toolkit_and_llm(*args, **kwargs)
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    @classmethod
+    def from_toolkit_and_llm(cls, toolkit: JsonToolkit, llm: BaseLanguageModel):
+        tools = toolkit.get_tools()
+        tool_names = [tool.name for tool in tools]
+        prompt = ZeroShotAgent.create_prompt(
+            tools,
+            prefix=JSON_PREFIX,
+            suffix=JSON_SUFFIX,
+            format_instructions=FORMAT_INSTRUCTIONS,
+            input_variables=None,
+        )
+        llm_chain = LLMChain(
+            llm=llm,
+            prompt=prompt,
+        )
+        agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names)
+        return cls.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
+
+    def run(self, *args, **kwargs):
+        return super().run(*args, **kwargs)
+
+
+PREBUILT_AGENTS = {
+    "MalfoyAgent": MalfoyAgent,
+}

src/backend/langflow/interface/prompts/custom.py

@@ -0,0 +1,63 @@
+from typing import Any, List, Optional
+
+from langchain.prompts import PromptTemplate
+from langflow.graph.utils import extract_input_variables_from_prompt
+from langflow.template.base import Template, TemplateField
+from langflow.template.nodes import PromptTemplateNode
+from pydantic import root_validator
+
+
+CHARACTER_PROMPT = """I want you to act like {character} from {series}.
+I want you to respond and answer like {character}. do not write any explanations. only answer like {character}.
+You must know all of the knowledge of {character}."""
+
+
+class BaseCustomPrompt(PromptTemplate):
+    template: Optional[str] = None
+    description: str
+    human_text: str = "\n {input}"
+
+    @root_validator(pre=False)
+    def build_template(cls, values):
+        format_dict = {}
+        for key in values.get("input_variables", []):
+            new_value = values[key]
+            format_dict[key] = new_value
+
+        values["template"] = values["template"].format(**format_dict)
+
+        values["template"] = values["template"] + values["human_text"]
+        values["input_variables"] = extract_input_variables_from_prompt(
+            values["template"]
+        )
+        return values
+
+    def build_frontend_node(self) -> PromptTemplateNode:
+        return PromptTemplateNode(
+            template=Template(
+                type_name="test",
+                fields=[
+                    TemplateField(name=field, field_type="str", required=True)
+                    for field in self.input_variables
+                ],
+            ),
+            description=self.description,
+        )
+
+
+class SeriesCharacterPrompt(BaseCustomPrompt):
+    # Add a very descriptive description for the prompt generator
+    description = "A prompt that asks the AI to act like a character from a series."
+    character: str
+    series: str
+    human_text: str = "\n {input}"
+    template: Optional[str] = CHARACTER_PROMPT
+
+    input_variables: List[str] = ["character", "series"]
+
+
+if __name__ == "__main__":
+    prompt = SeriesCharacterPrompt(character="Walter White", series="Breaking Bad")
+    user_input = "I am the one who knocks"
+    full_prompt = prompt.format(input=user_input)
+    print(full_prompt)

src/backend/langflow/template/constants.py

@@ -0,0 +1,11 @@
+FORCE_SHOW_FIELDS = [
+    "allowed_tools",
+    "memory",
+    "prefix",
+    "examples",
+    "temperature",
+    "model_name",
+    "headers",
+    "max_value_length",
+    "max_tokens",
+]

src/backend/langflow/template/nodes.py

@@ -2,6 +2,7 @@ from langchain.agents.mrkl import prompt
 
 from langflow.template.base import FrontendNode, Template, TemplateField
 from langflow.utils.constants import DEFAULT_PYTHON_FUNCTION
+from langchain.agents import loading
 
 
 class ZeroShotPromptNode(FrontendNode):
@@ -141,6 +142,41 @@ class JsonAgentNode(FrontendNode):
         return super().to_dict()
 
 
+class InitializeAgentNode(FrontendNode):
+    name: str = "InializeAgent"
+    template: Template = Template(
+        type_name="initailize_agent",
+        fields=[
+            TemplateField(
+                field_type="Tool",
+                required=True,
+                show=True,
+                name="tools",
+            ),
+            TemplateField(
+                field_type="BaseLanguageModel",
+                required=True,
+                show=True,
+                name="llm",
+            ),
+            TemplateField(
+                field_type="str",
+                required=True,
+                is_list=True,
+                show=True,
+                multiline=False,
+                options=list(loading.AGENT_TO_CLASS.keys()),
+                name="agent",
+            ),
+        ],
+    )
+    description: str = """Construct a json agent from an LLM and tools."""
+    base_classes: list[str] = ["AgentExecutor"]
+
+    def to_dict(self):
+        return super().to_dict()
+
+
 class CSVAgentNode(FrontendNode):
     name: str = "CSVAgent"
     template: Template = Template(