kagraphx 0.1.1

LangGraph-compatible agent orchestration with message-native state, checkpointing, and KaTrace Studio.

KaGraph

KaGraph is a LangGraph-compatible agent orchestration framework built on top of kaggle_benchmarks conversation primitives. It helps you build stateful, multi-node agent workflows with traceable execution, message-aware state updates, checkpointing, and a built-in web studio for inspecting graph behavior.

If you find this project helpful and would like to support its development, consider buying me a coffee!

Features

• Native kaggle_benchmarks Integration: Preserves message roles and objects directly without flattening context.
• Stateful Workflows: Define your state schema and update it incrementally through independent graph nodes.
• Advanced Control Flow: Supports conditional routing, fan-out/fan-in, timeouts, and retry policies.
• Built-in Checkpointing: Pause, inspect, and resume graph execution for human-in-the-loop workflows.
• KaTrace Studio: Visualize graph paths, inspect trace events, review LLM calls, compare messages, and replay past runs.

Prerequisites

KaGraph relies on the kaggle_benchmarks library. In Kaggle notebooks, it is already available. In a local environment, install and configure it before running KaGraph.

For local development, configure your model proxy credentials:

cp .env.example .env

Then fill in MODEL_PROXY_API_KEY and MODEL_PROXY_URL.

Installation

pip install kagraphx

For local development from source:

git clone https://github.com/anpc849/ka-graph.git
cd ka-graph
pip install -e ".[dev]"

Quick Start

Start KaTrace Studio first so traces from your graph run are captured and visible:

# Local machine
kagraph-studio --mode local

# Kaggle notebook or remote environment
!kagraph-studio --mode localtunnel

Open http://127.0.0.1:3000 in local mode, or use the printed LocalTunnel URL in tunnel mode.

Once Studio is running, execute a traced graph:

from kagraph import START, END, StateGraph, MessagesState
from kagraph.llms import load_llm
from kagraph.prompts import invoke_llm
from kagraph.messages import HumanMessage
from kagraph.tracing import trace

llm = load_llm("qwen/qwen3-235b-a22b-instruct-2507")

def agent(state: MessagesState):
    response = invoke_llm(llm, messages=state["messages"], prompt="Answer the user.")
    return {"messages": [response]}

graph = StateGraph(MessagesState)
graph.add_node("agent", agent)
graph.add_edge(START, "agent")
graph.add_edge("agent", END)
app = graph.compile()

with trace("MyFirstAgent"):
    result = app.invoke({"messages": [HumanMessage("Hello!")]})

print(result["messages"][-1].content)

Learn More

Use the docs for API details and the tutorial notebooks for runnable workflows:

• Overview & Getting Started
• Building Graphs (StateGraph)
• Running Graphs (CompiledStateGraph)
• Prebuilt Nodes & Utilities
• KaTrace Studio & Tracing
• Web Application Guide
• Tutorial notebooks

License

This project is licensed under the MIT License. See the LICENSE file for details.