integry 0.0.5

The official Python library for the Integry API

Integry Python API Library

The Python API library allows access to Integry REST API from Python programs.

Installation

# install from PyPI
pip install integry

Usage with Agent Frameworks

1. LangChain/LangGraph

import os
from integry import Integry
from langchain_core.messages import SystemMessage, HumanMessage
from langchain_openai import ChatOpenAI
from langchain_core.tools import StructuredTool
from langgraph.prebuilt import create_react_agent

user_id = "your user's ID"

# Initialize the client
integry = Integry(
    app_key=os.environ.get("INTEGRY_APP_SECRET"),
    app_secret=os.environ.get("INTEGRY_APP_KEY"),
)

slack_post_message = await integry.functions.get("slack-post-message", user_id)

llm = ChatOpenAI(
    model="gpt-4o",
    api_key=os.environ.get("OPENAI_API_KEY"),
)

tool = slack_post_message.as_langchain_tool(StructuredTool.from_function, user_id)

agent = create_react_agent(
    tools=[tool],
    model=llm,
)

await agent.ainvoke({
    "messages": [
        SystemMessage(content="You are a helpful assistant"),
        HumanMessage(content="Say hello to my team on slack"),
    ]
})

2. CrewAI

import os
from integry import Integry
from langchain_openai import ChatOpenAI

user_id = "your user's ID"

# Initialize the client
integry = Integry(
    app_key=os.environ.get("INTEGRY_APP_SECRET"),
    app_secret=os.environ.get("INTEGRY_APP_KEY"),
)

slack_post_message = await integry.functions.get("slack-post-message", user_id)

tool = slack_post_message.as_langchain_tool(StructuredTool.from_function, user_id)

llm = ChatOpenAI(
    model="gpt-4o",
    api_key=SecretStr(os.environ.get("OPENAI_API_KEY")),
)

crewai_agent = Agent(
    role="Integration Assistant",
    goal="You are a helpful assistant",
    backstory="You help users make actions on various apps",
    verbose=True,
    tools=[tool],
    llm=llm,
)

task = Task(
    description="Say hello to my team on slack",
    agent=crewai_agent,
    expected_output="Status of the operation"
)

crew = Crew(
    agents = [crewai_agent],
    tasks = [task]
)

result = crew.kickoff()

3. Letta

import os
from integry import Integry

user_id = "your user's ID"

# Initialize the client
integry = Integry(
    app_key=os.environ.get("INTEGRY_APP_SECRET"),
    app_secret=os.environ.get("INTEGRY_APP_KEY"),
)

slack_post_message = await integry.functions.get("slack-post-message", user_id)

client = create_client()

agent_state = client.create_agent(
    name="Integration Assistant", 
)

tool = slack_post_message.as_langchain_tool(StructuredTool.from_function, user_id)
client.add_tool(tool)

response = client.send_message(agent_id=agent_state.id, role="user", message="say hello to my team on Slack")

4. AutoGen (WIP)

import os
from integry import Integry

from autogen import AssistantAgent, UserProxyAgent

user_id = "your user's ID"

# Initialize the client
integry = Integry(
    app_key=os.environ.get("INTEGRY_APP_SECRET"),
    app_secret=os.environ.get("INTEGRY_APP_KEY"),
)

llm_config = {
    "config_list": [
        {
            "model": "gpt-4o-mini",
            "api_key": "<your-api-key>",
        }
    ]
}

chatbot = AssistantAgent(
    "chatbot",
    system_message="Reply TERMINATE when the task is done or when user's content is empty",
    llm_config=llm_config,
)

user_proxy = UserProxyAgent(
    name="User",
    is_termination_msg=lambda x: x.get("content", "")
    and "TERMINATE" in x.get("content", ""),
    human_input_mode="NEVER",
    code_execution_config={"use_docker": False},
)

toolset.register_tools(apps=[App.GITHUB], caller=chatbot, executor=user_proxy)

response = user_proxy.initiate_chat(chatbot, message="say hello to my team on Slack")

Prediction

import os
from integry import Integry

user_id = "your user's ID"

# Initialize the client
integry = Integry(
    app_secret=os.environ.get("INTEGRY_APP_KEY"),
    app_key=os.environ.get("INTEGRY_APP_SECRET"),
)

# Get the most relevant function
predictions = await integry.functions.predict(
    prompt="say hello to my team on Slack", user_id=user_id, predict_arguments=True
)

if predictions:
    function = predictions[0]
    # Call the function
    await function(user_id, function.arguments)

Pagination

List methods are paginated and allow you to iterate over data without handling pagination manually.

from integry import Integry

user_id = "your user's ID"

integry = Integry(
    app_secret=os.environ.get("INTEGRY_APP_KEY"),
    app_key=os.environ.get("INTEGRY_APP_SECRET"),
)

async for function in integry.functions.list(user_id):
    # do something with function
    print(function.name)

If you want to control pagination, you can fetch individual pages by using the cursor returned by the previous page.

from integry import Integry

user_id = "your user's ID"

integry = Integry(
    app_secret=os.environ.get("INTEGRY_APP_KEY"),
    app_key=os.environ.get("INTEGRY_APP_SECRET"),
)

first_page = await integry.apps.list(user_id)

second_page = await integry.apps.list(user_id, cursor=first_page.cursor)