lmsystems 1.0.8

SDK for integrating purchased graphs from the lmsystems marketplace.

LMSystems SDK

The LMSystems SDK provides flexible interfaces for integrating and executing purchased graphs from the LMSystems marketplace in your Python applications. The SDK offers two main approaches:

1. PurchasedGraph Class: For seamless integration with LangGraph workflows
2. LmsystemsClient: For direct, low-level interaction with LMSystems graphs, offering more flexibility and control

Try it in Colab

Get started quickly with our interactive Colab notebook:

This notebook provides a hands-on introduction to the LMSystems SDK with ready-to-run examples.

Installation

Install the package using pip:

pip install lmsystems==1.0.8

Quick Start

Using the Client SDK

The client SDK provides direct interaction with one LMSystems graphs (e.g. Deep Research Agent):

from lmsystems import (
    SyncLmsystemsClient,
    APIError
)
import os


def main():

    # Check for required environment variables
    api_key = os.environ.get("LMSYSTEMS_API_KEY")

    # Initialize client
    client = SyncLmsystemsClient(
        graph_name="groq-deep-research-agent-51",
        api_key=api_key
    )

    try:
        # Create a new thread
        thread = client.threads.create()
        print(f"Created thread with status: {client.get_thread_status(thread)}")

        # Example 1: Using default environment variables
        for chunk in client.stream_run(
            thread=thread,
            input = {
            "research_topic":"what are the best agent frameworks for building apps with llms?"
            },

            config =  {
                "configurable": {
                    "llm": "",
                    "tavily_api_key": "",
                    "groq_api_key": ""
                }
            },
            stream_mode=["messages", "updates"]
        ):
            print(f"Received chunk: {chunk}")
        # Example: Check final thread status
        final_status = client.get_thread_status(thread)
        print(f"Final thread status: {final_status}")

    except APIError as e:
        print(f"API Error: {str(e)}")
    except Exception as e:
        print(f"Unexpected error: {str(e)}")

if __name__ == "__main__":
    main()

Using PurchasedGraph with LangGraph

For integration with other Langgraph apps, you can plug Purchased Graphs in as a single node:

from lmsystems.purchased_graph import PurchasedGraph
from langgraph.graph import StateGraph, START, MessagesState
import os
from dataclasses import dataclass


@dataclass
class ResearchState:
    research_topic: str


api_key = os.environ.get("LMSYSTEMS_API_KEY")

def main():

    # Initialize our purchased graph (which wraps RemoteGraph)
    purchased_graph = PurchasedGraph(
        graph_name="groq-deep-research-agent-51",
        api_key=api_key,
        default_state_values = {
        "research_topic":""
        },

        config =  {
            "configurable": {
                "llm": "llama-3.1-8b-instant",
                "tavily_api_key": "",
                "groq_api_key": ""
            }
        },
    )

    # Create parent graph and add our purchased graph as a node
    builder = StateGraph(ResearchState)
    builder.add_node("purchased_node", purchased_graph)
    builder.add_edge(START, "purchased_node")
    graph = builder.compile()

    # Use the parent graph - invoke
    result = graph.invoke({
        "research_topic": "what are the best agent frameworks for building apps with llms?"
    })
    print("Parent graph result:", result)

    # Use the parent graph - stream
    for chunk in graph.stream({
        "research_topic":"what are the best agent frameworks for building apps with llms?"
    }, subgraphs=True):  # Include outputs from our purchased graph
        print("Stream chunk:", chunk)

if __name__ == "__main__":
    main()

Configuration

API Keys and Configuration

The SDK now automatically handles configuration through your LMSystems account. To set up:

1. Create an account at LMSystems
2. Navigate to your account settings
3. Configure your API keys (OpenAI, Anthropic, etc.)
4. Generate your LMSystems API key

Your configured API keys and settings will be automatically used when running graphs - no need to include them in your code!

Note: While configuration is handled automatically, you can still override settings programmatically if needed:

# Optional: Override stored config
config = {
    "configurable": {
        "model": "gpt-4",
        "openai_api_key": "your-custom-key"
    }
}
purchased_graph = PurchasedGraph(
    graph_name="github-agent-6",
    api_key=os.environ.get("LMSYSTEMS_API_KEY"),
    config=config  # Optional override
)

Store your LMSystems API key securely using environment variables:

export LMSYSTEMS_API_KEY="your-api-key"

API Reference

LmsystemsClient Class

LmsystemsClient.create(
    graph_name: str,
    api_key: str
)

Parameters:

• graph_name: Name of the graph to interact with
• api_key: Your LMSystems API key

Methods:

• create_thread(): Create a new thread for graph execution
• create_run(thread, input): Create a new run within a thread
• stream_run(thread, run): Stream the output of a run
• get_run(thread, run): Get the status and result of a run
• list_runs(thread): List all runs in a thread

PurchasedGraph Class

PurchasedGraph(
    graph_name: str,
    api_key: str,
    config: Optional[RunnableConfig] = None,
    default_state_values: Optional[dict[str, Any]] = None
)

Parameters:

• graph_name: Name of the purchased graph
• api_key: Your LMSystems API key
• config: Optional configuration for the graph
• default_state_values: Default values for required state parameters

Methods:

• invoke(): Execute the graph synchronously
• ainvoke(): Execute the graph asynchronously
• stream(): Stream graph outputs synchronously
• astream(): Stream graph outputs asynchronously

Error Handling

The SDK provides specific exceptions for different error cases:

• AuthenticationError: API key or authentication issues
• GraphError: Graph execution or configuration issues
• InputError: Invalid input parameters
• APIError: Backend communication issues

Example error handling:

from lmsystems.exceptions import (
    LmsystemsError,
    AuthenticationError,
    GraphError,
    InputError,
    APIError,
    GraphNotFoundError,
    GraphNotPurchasedError
)

try:
    result = graph.invoke(input_data)
except AuthenticationError as e:
    print(f"Authentication failed: {e}")
except GraphNotFoundError as e:
    print(f"Graph not found: {e}")
except GraphNotPurchasedError as e:
    print(f"Graph not purchased: {e}")
except GraphError as e:
    print(f"Graph execution failed: {e}")
except InputError as e:
    print(f"Invalid input: {e}")
except APIError as e:
    print(f"API communication error: {e}")
except LmsystemsError as e:
    print(f"General error: {e}")

Stream Modes

The SDK supports different streaming modes through the StreamMode enum:

from lmsystems import StreamMode

# Stream run with specific modes
async for chunk in client.stream_run(
    thread=thread,
    input=input_data,
    stream_mode=[
        StreamMode.MESSAGES,  # Stream message updates
        StreamMode.VALUES,    # Stream value updates from nodes
        StreamMode.UPDATES,   # Stream general state updates
        StreamMode.CUSTOM     # Stream custom-defined updates
    ]
):
    print(chunk)

Available stream modes:

• StreamMode.MESSAGES: Stream message updates from the graph
• StreamMode.VALUES: Stream value updates from graph nodes
• StreamMode.UPDATES: Stream general state updates
• StreamMode.CUSTOM: Stream custom-defined updates

Support

For support, feature requests, or bug reports:

• Contact me at [e-mail]