LangChain with Caddey

Caddey exposes app features as tools, and LangChain agents can dynamically choose and call these tools. This guide shows two ways to integrate Caddey tools—preloading them into your agent or loading them dynamically at runtime.

Prerequisites

• Caddey agent & API key (copy once when you create the agent)
• Python 3.x and a virtual environment
• LangChain & helpers

pip install langchain openai requests python-dotenv

• LLM API key (e.g. OPENAI_API_KEY for ChatOpenAI)

Approach A: Preload Tools into LangChain

Fetch tool definitions and convert them into LangChain Tool objects.

import os
import requests
import json
from dotenv import load_dotenv
from langchain.agents import Tool

load_dotenv()
CADDEY_KEY = os.getenv("CADDEY_API_KEY")

# Fetch tool definitions
response = requests.get(
    "https://api.caddey.ai/tools/query",
    headers={"X-API-KEY": CADDEY_KEY}
)
tools_data = response.json().get("content", [])

# Convert to LangChain Tool objects
preloaded_tools = []
for tool in tools_data:
    def make_func(tool_id):
        def run_tool(input_str: str):
            payload = json.loads(input_str) if input_str else {}
            res = requests.post(
                f"https://api.caddey.ai/tools/{tool_id}/execute",
                headers={"X-API-KEY": CADDEY_KEY},
                json=payload
            )
            return json.dumps(res.json(), indent=2)
        return run_tool

    preloaded_tools.append(
        Tool(
            name=tool.get("name", ""),
            description=tool.get("description", ""),
            func=make_func(tool.get("id", ""))
        )
    )

Approach B: Dynamic Tool Loading

Provide two generic tools that let the model fetch and execute tools on the fly.

import os
import requests
import json
from dotenv import load_dotenv
from langchain.agents import Tool

load_dotenv()
CADDEY_KEY = os.getenv("CADDEY_API_KEY")

def list_caddey_tools(input_json: str) -> str:
    """
    input_json (optional): JSON string with keys:
      - pageNumber (int)
      - pageSize (int)
      - searchTerm (str)  # free-text filter
    """
    params = {"pageNumber": 0, "pageSize": 20}
    if input_json:
        opts = json.loads(input_json)
        params["pageNumber"] = opts.get("pageNumber", params["pageNumber"])
        params["pageSize"]   = opts.get("pageSize", params["pageSize"])
        if "searchTerm" in opts:
            params["search"] = opts["searchTerm"]

    response = requests.get("https://api.caddey.ai/tools/query", headers={"X-API-KEY": CADDEY_KEY}, params=params)
    response.raise_for_status()
    return json.dumps(response.json(), indent=2)

def execute_caddey_tool(input_json: str) -> str:
    """
    input_json: JSON string with keys:
      - tool_id (str, UUID)
      - input (object)  # the payload to pass to the tool
    """
    payload = json.loads(input_json)
    tool_id = payload["tool_id"]
    params  = payload.get("input", {})
    response = requests.post(
        f"https://api.caddey.ai/tools/{tool_id}/execute",
        headers={"X-API-KEY": CADDEY_KEY},
        json={"input": params},
    )
    response.raise_for_status()
    return json.dumps(response.json(), indent=2)

list_tools_tool = Tool(
    name="List Caddey Tools",
    description=(
        "Fetch a paginated list of Caddey tools. "
        "Input JSON may specify pageNumber, pageSize, and searchTerm (free-text)."
    ),
    func=list_caddey_tools,
)

execute_tool = Tool(
    name="Execute Caddey Tool",
    description=(
        "Execute any Caddey tool by ID. "
        "Input JSON must include tool_id and an 'input' object for its parameters."
    ),
    func=execute_caddey_tool,
)

Initialize the Agent

After setting up your tool list (either preloaded_tools from Approach A or [list_tools_tool, execute_tool] from Approach B), initialize your LangChain agent.

from langchain.chat_models import ChatOpenAI
from langchain.agents import initialize_agent, AgentType
import os

llm = ChatOpenAI(
    temperature=0,
    openai_api_key=os.getenv("OPENAI_API_KEY")
)

# Use preloaded_tools OR dynamic tools
tools_list = preloaded_tools  # or [list_tools_tool, execute_tool]

agent = initialize_agent(
    tools=tools_list,
    llm=llm,
    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
    verbose=True
)

Using Your Agent

Simply prompt the agent in natural language, and it will pick the appropriate tool under the hood.

Examples:

• Create a reminder to call Bob tomorrow at 3 PM.
• Fetch the list of available tools.
• Send an email to [email protected] with subject “Project Update” and body “The report is ready.”

The agent will automatically invoke the listing or execution of tools as needed.