ActionKit is designed to give AI agents the ability to call out to integration logic as a part of a prompt or ongoing conversation with a user. The API exposes JSON Schema specs to easily provide your agent with capabilities including:
  • Creating tasks in Jira based on action items an agent recognizes from a meeting transcript
  • Querying real-time sales reports in Shopify when an agent is asked questions about online sales
  • Creating a Google Docs draft to start a project based on a user prompt

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Implementation Examples

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Vercel AI SDK

Implementing ActionKit in AI SDK
import { generateText, jsonSchema, tool } from "ai";
import { openai } from "@ai-sdk/openai";

const response = await fetch(
  "https://actionkit.useparagon.com/projects/<Project ID>/actions",
  {
    method: "GET",
    headers: {
      Authorization: `Bearer ${paragonUserToken}`,
    },
  }
);

const { actions, errors } = await response.json();

if (errors.length === 0) {
  await generateText({
    model: openai("gpt-4o"),
    tools: Object.fromEntries(
      actions.map((action) => [
        action.function.name,
        tool({
          description: tool.function.description,
          parameters: jsonSchema(tool.function.parameters),
          execute: async (params: any, { toolCallId }) => {
            try {
              const response = await fetch(
                `https://actionkit.useparagon.com/projects/<Project ID>/actions`,
                {
                  method: "POST",
                  body: JSON.stringify({
                    action: tool.function.name,
                    parameters: params,
                  }),
                  headers: {
                    Authorization: `Bearer ${session.paragonUserToken}`,
                    "Content-Type": "application/json",
                  },
                }
              );
              const output = await response.json();
              if (!response.ok) {
                throw new Error(JSON.stringify(output, null, 2));
              }
              return output;
            } catch (err) {
              if (err instanceof Error) {
                return { error: { message: err.message } };
              }
              return err;
            }
          },
        }),
      ])
    ),
    toolChoice: "auto",
    temperature: 0,
    system: "You are a helpful assistant. Be as concise as possible.",
    prompt: "Help me create a new task in Jira.",
  });
}

​
LangGraph / LangChain

Implementing ActionKit in LangChain
import json
import requests
from typing import Annotated, Any, TypedDict
from langchain.tools import BaseTool
from langchain.schema import HumanMessage
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode, tools_condition
from langchain_openai import ChatOpenAI

PARAGON_PROJECT_ID = ""
PARAGON_USER_TOKEN = ""
OPENAI_API_KEY = ""

class ActionKitTool(BaseTool):
    name: str
    description: str
    action_name: str
    paragon_token: str

    def _run(self, tool_input: str) -> str:
        try:
            params = json.loads(tool_input)
            response = requests.post(
                url=f"https://actionkit.useparagon.com/projects/{PARAGON_PROJECT_ID}/actions",
                headers={
                    "Authorization": f"Bearer {self.paragon_token}",
                    "Content-Type": "application/json",
                },
                json={
                    "action": self.action_name,
                    "parameters": params,
                },
                timeout=30
            )
            data = response.json()

            if not response.ok:
                raise ValueError(json.dumps(data, indent=2))

            return json.dumps(data)
        except Exception as e:
            return json.dumps({"error": {"message": str(e)}})

class State(TypedDict):
    messages: Annotated[list, add_messages]

def main():
    graph_builder = StateGraph(State)
    url = f"https://actionkit.useparagon.com/projects/{PARAGON_PROJECT_ID}/actions"
    headers = {"Authorization": f"Bearer {PARAGON_USER_TOKEN}"}
    
    resp = requests.get(url, headers=headers)
    json_resp = resp.json()
    actions = json_resp.get("actions", [])
    errors = json_resp.get("errors", [])

    if not actions:
        print("Failed to fetch Paragon actions or encountered errors:")
        print(errors)
        return

    tools = []
    for integration in actions:
        integration_actions = actions.get(integration)
        for action in integration_actions:
            func_def = action["function"]
            tool_name = func_def["name"]
            tool_description = func_def["description"]

            paragon_tool = ActionKitTool(
                name=tool_name,
                description=tool_description,
                action_name=tool_name,
                paragon_token=PARAGON_USER_TOKEN
            )
            tools.append(paragon_tool)

    llm = ChatOpenAI(
        openai_api_key=OPENAI_API_KEY,
        model_name="o1"
    )
    
    def chatbot(state: State):
        return {"messages": [llm.bind_tools(tools).invoke(state["messages"])]}
    
    graph_builder.add_node("chatbot", chatbot)
    tools_node = ToolNode(tools=tools)
    graph_builder.add_node("tools", tools_node)
    graph_builder.add_conditional_edges(
        "chatbot",
        tools_condition,
    )
    graph_builder.add_edge("tools", "chatbot")
    graph_builder.add_edge(START, "chatbot")

    graph = graph_builder.compile()

    def stream_graph_updates(user_input: str):
        for event in graph.stream({"messages": [{"role": "user", "content": user_input}]}):
            for value in event.values():
                print("Assistant:", value["messages"][-1].content)

    while True:
        try:
            user_input = input("User: ")
            if user_input.lower() in ["quit", "exit", "q"]:
                print("Goodbye!")
                break

            stream_graph_updates(user_input)
        except:
            # fallback if input() is not available
            user_input = "What do you know about LangGraph?"
            print("User: " + user_input)
            stream_graph_updates(user_input)
            break

if __name__ == "__main__":
    main()

​
Other implementations

If you’re not using TypeScript, you can pass the JSON Schema specs from the ActionKit to the request to your LLM. Here is an example in Python with OpenAI’s library:
Loading ActionKit tools for OpenAI client library
import requests
from openai import OpenAI
client = OpenAI()

actions_url = f"https://actionkit.useparagon.com/projects/{project_id}/actions"
actions_auth_header = {
	"Authorization": f"Bearer {user_token}"
}
get_actions_params = {
    "format": "json_schema",
    "categories": "crm,project_management"
}

response = requests.get(actions_url, params=params, headers=actions_auth_header)
paragon_tools = response.json()

messages = [{"role": "user", "content": "Help me create a Jira ticket"}]
completion = client.chat.completions.create(
  model="gpt-4o",
  messages=messages,
  tools=paragon_tools["actions"],
  tool_choice="auto"
)
When passing the specs directly, you will also need to respond to the agent’s request to use a tool and route it to the ActionKit:
Calling ActionKit tools with OpenAI client library
message = completion["choices"][0]["message"]

if message.get("function_call"):
    function_name = message["function_call"]["name"]
    arguments = json.loads(message["function_call"]["arguments"])
    
    # Check if this tool uses ActionKit
    if any(tool["name"] == function_name for tool in paragon_tools["actions"]):
        run_actions_body = {
            "action": function_name,
            "parameters": arguments
        }

        # Run Action
        response = requests.post(actions_url, body=run_actions_body, headers=actions_auth_header)
        result = response.json()
        messages.append({
            "role": "function",
            "name": function_name,
            "content": json.dumps(result)
        })

        # Return to chat with tool result
        completion = client.chat.completions.create(
            model="gpt-4o",
            messages=messages,
            tools=paragon_tools,
            tool_choice="auto"
        )