lmsystems 0.1.1

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

Quick Start

Using the Client SDK

The client SDK provides direct interaction with LMSystems graphs:

from lmsystems.client import LmsystemsClient
import os
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

# Async usage
async def main():
    # Simple initialization with just graph name and API key
    client = await LmsystemsClient.create(
        graph_name="graph-name-id",
        api_key=os.environ["LMSYSTEMS_API_KEY"]
    )

    # Create thread and run with error handling
    try:
        thread = await client.create_thread()

        # No need to provide config - it's handled through your account settings
        run = await client.create_run(
            thread,
            input={"messages": [{"role": "user", "content": "What's this repo about?"}],
                  "repo_url": "",
                  "repo_path": "",
                  "github_token": ""}
        )

        # Stream response
        async for chunk in client.stream_run(thread, run):
            print(chunk)

    except Exception as e:
        print(f"Error: {str(e)}")

if __name__ == "__main__":
    import asyncio
    asyncio.run(main())

Using PurchasedGraph with LangGraph

For integration with LangGraph workflows:

from lmsystems.purchased_graph import PurchasedGraph
from langgraph.graph import StateGraph, START, MessagesState
import os
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

# Set required state values
state_values = {
    "repo_url": "https://github.com/yourusername/yourrepo",
    "github_token": "your-github-token",
    "repo_path": "/path/to/1234322"
}

# Initialize the purchased graph - no config needed!
purchased_graph = PurchasedGraph(
    graph_name="github-agent-6",
    api_key=os.environ.get("LMSYSTEMS_API_KEY"),
    default_state_values=state_values
)

# Create a parent graph with MessagesState schema
builder = StateGraph(MessagesState)
builder.add_node("purchased_node", purchased_graph)
builder.add_edge(START, "purchased_node")
graph = builder.compile()

# Invoke the graph
result = graph.invoke({
    "messages": [{"role": "user", "content": "what's this repo about?"}]
})

# Stream outputs (optional)
for chunk in graph.stream({
    "messages": [{"role": "user", "content": "what's this repo about?"}]
}, subgraphs=True):
    print(chunk)

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

try:
    result = graph.invoke(input_data)
except AuthenticationError as e:
    print(f"Authentication failed: {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}")

Support

For support, feature requests, or bug reports:

• Contact me at [e-mail]