LLMClient

Overview

The LLMClient lets you connect to the ATP Agent Server, retrieve toolkit context, and execute tools or workflows using JSON payloads—perfect for LLM-based agents. It supports:

OpenAI (GPT-4, GPT-3.5, etc.)
Anthropic (Claude 3 Opus, Sonnet, Haiku)
Mistral AI (Mistral Large, Medium, Small)

Constructor

from atp_sdk.clients import LLMClient

llm_client = LLMClient(
    api_key: str,
    protocol: str = "ws",
    base_url: str = "https://api.chat-atp.com/ws/v1/atp/llm-client/"
)

Parameters

string

required

Your ATP API key. Get it from the ChatATP Developer Platform.

string

default:"ws"

Protocol to use (“ws” for WebSocket or “http” for HTTP).

string

default:"https://api.chat-atp.com/ws/v1/atp/llm-client/"

ATP server URL. Use the default unless you’re running a custom ATP server.

Methods

get_toolkit_context

Retrieves the toolkit context and system instructions for a given toolkit and user prompt.

context = llm_client.get_toolkit_context(
    toolkit_id: str,
    provider: str,
    user_prompt: str
)

Parameters

string

required

Unique ID of the toolkit you want to use.

string

required

The LLM provider: "openai", "anthropic", or "mistralai".

string

required

The user’s prompt or task description.

Returns

A dictionary containing the toolkit context, including provider-specific tool schemas. Example response:

{
  "toolkit_id": "your_toolkit_id",
  "toolkit_name": "Example Toolkit",
  "caption": "Example Caption",
  "provider": "openai",
  "tools": [
    {
      "type": "function",
      "name": "hello_world",
      "description": "Returns a greeting.",
      "parameters": {
        "type": "object",
        "properties": {
          "name": {
            "type": "string",
            "description": "Name to greet"
          }
        },
        "required": ["name"]
      }
    }
  ],
  "user_prompt": "What do you want to achieve?"
}

call_tool

Executes a tool or workflow on the ATP server.

response = llm_client.call_tool(
    toolkit_id: str,
    tool_calls: str | dict,
    provider: str,
    user_prompt: str
)

Parameters

string

required

Unique ID of the toolkit.

string | dict

required

JSON payload from an LLM containing the tool call. Can be a string or dict.Example:

{
  "function": "hello_world",
  "parameters": {"name": "Alice"}
}

string

required

The LLM provider: "openai", "anthropic", or "mistralai".

string

required

Additional user input to include in the execution.

Returns

The result of the tool execution. Example response:

{
  "result": {
    "message": "Hello, Alice!"
  }
}

OAuth2 Methods

initiate_oauth_connection

Starts the OAuth flow and returns an authorization URL for the user.

connection = llm_client.initiate_oauth_connection(
    platform_id: str,
    external_user_id: str,
    developer_redirect_url: str
)

Parameters

string

required

The platform ID (e.g., “hubspot”, “google”, “salesforce”).

string

required

Your user’s unique identifier (e.g., email address).

string

required

The URL to redirect the user to after OAuth authorization.

Returns

{
  "authorization_url": "https://oauth-provider.com/authorize?..."
}

wait_for_connection

Polls for OAuth connection completion and retrieves the integration ID.

account = llm_client.wait_for_connection(
    platform_id: str,
    external_user_id: str
)

Parameters

string

required

The platform ID (e.g., “hubspot”, “google”, “salesforce”).

string

required

Your user’s unique identifier (e.g., email address).

Returns

{
  "integration_id": "uuid"
}

get_user_tokens

Fetches the user’s access and refresh tokens for use in tool calls.

tokens = llm_client.get_user_tokens(
    platform_id: str,
    external_user_id: str
)

Parameters

string

required

The platform ID (e.g., “hubspot”, “google”, “salesforce”).

string

required

Your user’s unique identifier (e.g., email address).

Returns

{
  "access_token": "ACCESS_TOKEN",
  "refresh_token": "REFRESH_TOKEN"
}

Integration Examples

OpenAI

import openai
from atp_sdk.clients import LLMClient

openai_client = openai.OpenAI(api_key="YOUR_OPENAI_API_KEY")
llm_client = LLMClient(api_key="YOUR_ATP_LLM_CLIENT_API_KEY")

# Get toolkit context
context = llm_client.get_toolkit_context(
    toolkit_id="your_toolkit_id",
    provider="openai",
    user_prompt="Create a company and then list contacts."
)

# Use OpenAI to generate tool calls
response = openai_client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Create a company and then list contacts."}
    ],
    tools=context["tools"],
    tool_choice="auto"
)

# Extract and execute tool calls
tool_calls = response.choices[0].message.tool_calls

if tool_calls:

    result = llm_client.call_tool(
        toolkit_id="your_toolkit_id",
        tool_calls=tool_calls,
        provider="openai",
        user_prompt="Create a company and then list contacts."
    )

    print(f"Tool call result: {result}")

Anthropic

import anthropic
from atp_sdk.clients import LLMClient

anthropic_client = anthropic.Anthropic(api_key="YOUR_ANTHROPIC_API_KEY")
llm_client = LLMClient(api_key="YOUR_ATP_LLM_CLIENT_API_KEY")

# Get toolkit context
context = llm_client.get_toolkit_context(
    toolkit_id="your_toolkit_id",
    provider="anthropic",
    user_prompt="Create a company and then list contacts."
)

# Use Anthropic to generate tool calls
response = anthropic_client.messages.create(
    model="claude-3-opus-20240229",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Create a company and then list contacts."}
    ],
    tools=context["tools"]
)

# Extract and execute tool calls
tool_calls = response.content

if tool_calls[-1].type == "tool_calls":

    result = llm_client.call_tool(
        toolkit_id="your_toolkit_id",
        tool_calls=tool_calls[-1].content,
        provider="anthropic",
        user_prompt="Create a company and then list contacts."
    )

    print(f"Tool call result: {result}")

Mistral AI

from mistralai.client import MistralClient
from atp_sdk.clients import LLMClient

mistral_client = MistralClient(api_key="YOUR_MISTRAL_API_KEY")
llm_client = LLMClient(api_key="YOUR_ATP_LLM_CLIENT_API_KEY")

# Get toolkit context
context = llm_client.get_toolkit_context(
    toolkit_id="your_toolkit_id",
    provider="mistralai",
    user_prompt="Create a company and then list contacts."
)

# Use Mistral to generate tool calls
response = mistral_client.chat(
    model="mistral-large-latest",
    messages=[{"role": "user", "content": "Create a company and then list contacts."}],
    tools=context["tools"]
)

# Extract and execute tool calls
tool_calls = response.choices[0].message.tool_calls

if tool_calls:

    result = llm_client.call_tool(
        toolkit_id="your_toolkit_id",
        tool_calls=tool_calls,
        provider="mistralai",
        user_prompt="Create a company and then list contacts."
    )

    print(f"Tool call result: {result}")

OAuth2 Flow Example

from atp_sdk.clients import LLMClient

llm_client = LLMClient(api_key="YOUR_ATP_LLM_CLIENT_API_KEY")

# Step 1: Initiate OAuth connection
connection = llm_client.initiate_oauth_connection(
    platform_id="hubspot",
    external_user_id="[email protected]",
    developer_redirect_url="https://your-app.com/oauth/callback"
)
print("Authorize at:", connection["authorization_url"])

# Step 2: Wait for connection
account = llm_client.wait_for_connection(
    platform_id="hubspot",
    external_user_id="[email protected]"
)
print("Integration ID:", account["integration_id"])

# Step 3: Fetch tokens
tokens = llm_client.get_user_tokens(
    platform_id="hubspot",
    external_user_id="[email protected]"
)
print("Access token:", tokens["access_token"])

The ATP SDK will automatically inject tokens into tool calls as needed. You only need to handle OAuth and fetch tokens once.

Next Steps

ToolKitClient

Learn how to register and serve tools

OAuth2 Guide

Set up OAuth2 authentication

Examples

Explore more examples

Framework Integration

Use ATP with Django, FastAPI, or Flask

Getting Started

For Users

For Developers

Overview

Constructor

Parameters

Methods

get_toolkit_context

Parameters

Returns

call_tool

Parameters

Returns

OAuth2 Methods

initiate_oauth_connection

Parameters

Returns

wait_for_connection

Parameters

Returns

get_user_tokens

Parameters

Returns

Integration Examples

OpenAI

Anthropic

Mistral AI

OAuth2 Flow Example

Next Steps

ToolKitClient

OAuth2 Guide

Examples

Framework Integration

​Overview

​Constructor

​Parameters

​Methods

​get_toolkit_context

​Parameters

​Returns

​call_tool

​Parameters

​Returns

​OAuth2 Methods

​initiate_oauth_connection

​Parameters

​Returns

​wait_for_connection

​Parameters

​Returns

​get_user_tokens

​Parameters

​Returns

​Integration Examples

​OpenAI

​Anthropic

​Mistral AI

​OAuth2 Flow Example

​Next Steps

ToolKitClient

OAuth2 Guide

Examples

Framework Integration

Overview

Constructor

Parameters

Methods

get_toolkit_context

Parameters

Returns

call_tool

Parameters

Returns

OAuth2 Methods

initiate_oauth_connection

Parameters

Returns

wait_for_connection

Parameters

Returns

get_user_tokens

Parameters

Returns

Integration Examples

OpenAI

Anthropic

Mistral AI

OAuth2 Flow Example

Next Steps